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"
    },
    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: () => ao,
  Acos: () => nl,
  Acosh: () => sl,
  AdadeltaOptimizer: () => pb,
  AdagradOptimizer: () => hb,
  AdamOptimizer: () => fb,
  AdamaxOptimizer: () => mb,
  Add: () => kr,
  AddN: () => xa,
  All: () => rl,
  Any: () => al,
  ArgMax: () => wa,
  ArgMin: () => il,
  Asin: () => ol,
  Asinh: () => ul,
  Atan: () => ll,
  Atan2: () => dl,
  Atanh: () => cl,
  AvgPool: () => ka,
  AvgPool3D: () => Hd,
  AvgPool3DGrad: () => ag,
  AvgPoolGrad: () => rg,
  BackendWasm: () => ide,
  BatchMatMul: () => Ia,
  BatchToSpaceND: () => io,
  Bincount: () => ig,
  BroadcastArgs: () => og,
  BroadcastTo: () => w$,
  Callback: () => mW,
  CallbackList: () => TL,
  Cast: () => Sa,
  Ceil: () => Ca,
  ClipByValue: () => Ir,
  Complex: () => qd,
  ComplexAbs: () => jd,
  Concat: () => oo,
  Conv2D: () => Na,
  Conv2DBackpropFilter: () => ug,
  Conv2DBackpropInput: () => Ta,
  Conv3D: () => Kd,
  Conv3DBackpropFilterV2: () => lg,
  Conv3DBackpropInputV2: () => cg,
  Cos: () => $a,
  Cosh: () => _a,
  CropAndResize: () => lo,
  Cumsum: () => uo,
  CustomCallback: () => AL,
  DataStorage: () => Wd,
  DenseBincount: () => dg,
  DepthToSpace: () => co,
  DepthwiseConv2dNative: () => Aa,
  DepthwiseConv2dNativeBackpropFilter: () => pg,
  DepthwiseConv2dNativeBackpropInput: () => hg,
  Diag: () => fg,
  Dilation2D: () => Xd,
  Dilation2DBackpropFilter: () => Xf,
  Dilation2DBackpropInput: () => Kf,
  ENV: () => jw,
  EarlyStopping: () => gW,
  Einsum: () => Yd,
  Elu: () => Ra,
  EluGrad: () => mg,
  Environment: () => m$,
  Equal: () => po,
  Erf: () => pl,
  Exp: () => Da,
  ExpandDims: () => ho,
  Expm1: () => fo,
  FFT: () => gg,
  Fill: () => hl,
  FlipLeftRight: () => mo,
  Floor: () => Fa,
  FloorDiv: () => Oa,
  FromPixels: () => hd,
  FusedBatchNorm: () => Pa,
  FusedConv2D: () => sa,
  FusedDepthwiseConv2D: () => ra,
  GPGPUContext: () => qf,
  GatherNd: () => bo,
  GatherV2: () => go,
  GraphModel: () => w4,
  Greater: () => yo,
  GreaterEqual: () => za,
  History: () => _L,
  IFFT: () => bg,
  Identity: () => Ma,
  Imag: () => Qd,
  InputSpec: () => Dt,
  IsFinite: () => fl,
  IsInf: () => ml,
  IsNan: () => gl,
  KernelBackend: () => tl,
  LRN: () => Jd,
  LRNGrad: () => vg,
  LayerVariable: () => xL,
  LayersModel: () => pr,
  LeakyRelu: () => La,
  Less: () => vo,
  LessEqual: () => xo,
  LinSpace: () => yg,
  Log: () => Ba,
  Log1p: () => bl,
  LogSoftmax: () => k$,
  LogicalAnd: () => wo,
  LogicalNot: () => yl,
  LogicalOr: () => Zd,
  MathBackendCPU: () => RS,
  MathBackendWebGL: () => A1,
  Max: () => Va,
  MaxPool: () => Ua,
  MaxPool3D: () => ep,
  MaxPool3DGrad: () => wg,
  MaxPoolGrad: () => xg,
  MaxPoolWithArgmax: () => kg,
  Maximum: () => Wa,
  Mean: () => Ga,
  Min: () => Ha,
  Minimum: () => qa,
  MirrorPad: () => ja,
  Mod: () => vl,
  MomentumOptimizer: () => gb,
  Multinomial: () => Ig,
  Multiply: () => Ka,
  Neg: () => ko,
  NonMaxSuppressionV3: () => So,
  NonMaxSuppressionV4: () => xl,
  NonMaxSuppressionV5: () => Co,
  NotEqual: () => Io,
  OP_SCOPE_SUFFIX: () => e_,
  OneHot: () => To,
  OnesLike: () => No,
  Optimizer: () => $r,
  OptimizerConstructors: () => Vr,
  Pack: () => $o,
  PadV2: () => Xa,
  Pool: () => pde,
  Pow: () => Ya,
  Prelu: () => Qa,
  Prod: () => _o,
  RMSPropOptimizer: () => bb,
  RNN: () => _r,
  Range: () => wl,
  Rank: () => H$,
  Real: () => tp,
  RealDiv: () => Ea,
  Reciprocal: () => kl,
  Reduction: () => YF,
  Relu: () => Za,
  Relu6: () => ei,
  Reshape: () => Ao,
  ResizeBilinear: () => Ja,
  ResizeBilinearGrad: () => Cg,
  ResizeNearestNeighbor: () => Il,
  ResizeNearestNeighborGrad: () => Sg,
  Reverse: () => Eo,
  RotateWithOffset: () => Ho,
  Round: () => Ro,
  Rsqrt: () => ti,
  SGDOptimizer: () => kp,
  ScatterNd: () => Do,
  Select: () => Fo,
  Selu: () => Sl,
  Sequential: () => Vb,
  Sigmoid: () => si,
  Sign: () => Cl,
  Sin: () => ni,
  Sinh: () => Po,
  Slice: () => Oo,
  Softmax: () => ii,
  Softplus: () => Nl,
  SpaceToBatchND: () => zo,
  SparseFillEmptyRows: () => np,
  SparseReshape: () => Tl,
  SparseSegmentMean: () => sp,
  SparseSegmentSum: () => rp,
  SparseToDense: () => ap,
  SplitV: () => Mo,
  Sqrt: () => ri,
  Square: () => $l,
  SquaredDifference: () => oi,
  Step: () => di,
  StridedSlice: () => Lo,
  StringNGrams: () => ip,
  StringSplit: () => Ng,
  StringToHashBucketFast: () => Tg,
  Sub: () => ui,
  Sum: () => ai,
  SymbolicTensor: () => $s,
  Tan: () => Bo,
  Tanh: () => li,
  Tensor: () => et,
  TensorBuffer: () => Vt,
  Tile: () => Sr,
  TopK: () => Vo,
  Transform: () => Wo,
  Transpose: () => ci,
  Unique: () => $g,
  Unpack: () => Uo,
  UnsortedSegmentSum: () => op,
  Variable: () => md,
  ZerosLike: () => Go,
  _FusedMatMul: () => na,
  abs: () => Mt,
  acos: () => OA,
  acosh: () => zA,
  add: () => ie,
  addN: () => LA,
  all: () => Bk,
  any: () => cm,
  argMax: () => Gu,
  argMin: () => GA,
  asin: () => qA,
  asinh: () => KA,
  atan: () => YA,
  atan2: () => ZA,
  atanh: () => eE,
  avgPool: () => Wg,
  avgPool3d: () => Gk,
  backend: () => $A,
  backend_util: () => N,
  basicLSTMCell: () => Ade,
  batchNorm: () => qu,
  batchNorm2d: () => vE,
  batchNorm3d: () => wE,
  batchNorm4d: () => IE,
  batchToSpaceND: () => Ug,
  bincount: () => Hk,
  booleanMaskAsync: () => rpe,
  broadcastArgs: () => NE,
  broadcastTo: () => td,
  broadcast_util: () => qo,
  browser: () => xk,
  buffer: () => De,
  callbacks: () => fpe,
  cast: () => ce,
  ceil: () => _E,
  clipByValue: () => Bn,
  clone: () => lr,
  complex: () => aa,
  concat: () => Ft,
  concat1d: () => RE,
  concat2d: () => FE,
  concat3d: () => PE,
  concat4d: () => ME,
  constraints: () => $M,
  conv1d: () => qk,
  conv2d: () => ua,
  conv2dTranspose: () => jk,
  conv3d: () => Kk,
  conv3dTranspose: () => qE,
  copyRegisteredKernels: () => mde,
  cos: () => Hg,
  cosh: () => Yk,
  cosineWindow: () => wI,
  cumsum: () => Qk,
  customGrad: () => js,
  data: () => k4,
  denseBincount: () => QE,
  deprecationWarn: () => Mk,
  depthToSpace: () => JE,
  depthwiseConv2d: () => pp,
  deregisterOp: () => gpe,
  device_util: () => cp,
  diag: () => Ede,
  dilation2d: () => sR,
  disableDeprecationWarnings: () => vde,
  dispose: () => Re,
  disposeVariables: () => xde,
  div: () => xe,
  divNoNan: () => uR,
  dot: () => Rde,
  dropout: () => H3,
  einsum: () => dR,
  elu: () => hp,
  enableDebugMode: () => yde,
  enableProdMode: () => bde,
  enclosingPowerOfTwo: () => q3,
  engine: () => Ss,
  env: () => X,
  equal: () => qn,
  erf: () => fR,
  exp: () => jn,
  expandDims: () => On,
  expm1: () => yR,
  eye: () => Zk,
  fft: () => ob,
  fill: () => Fl,
  findBackend: () => Tde,
  findBackendFactory: () => $de,
  floor: () => fp,
  floorDiv: () => Lk,
  forceHalfFloat: () => DX,
  fused: () => da,
  gather: () => ju,
  gatherND: () => W3,
  gather_util: () => kk,
  getBackend: () => Cde,
  getGradient: () => Jv,
  getKernel: () => Yf,
  getKernelsForBackend: () => Qf,
  getThreadsCount: () => Npe,
  gpgpu_util: () => wK,
  grad: () => Ode,
  grads: () => Pde,
  greater: () => Wn,
  greaterEqual: () => jo,
  ifft: () => kd,
  imag: () => qg,
  image: () => ds,
  inTopKAsync: () => ipe,
  initializers: () => PM,
  input: () => CB,
  io: () => _n,
  irfft: () => fI,
  isFinite: () => Dde,
  isInf: () => Fde,
  isNaN: () => _R,
  keep: () => Ht,
  kernel_impls: () => xs,
  layers: () => yL,
  leakyRelu: () => jg,
  less: () => Jk,
  lessEqual: () => Ko,
  linalg: () => AO,
  linspace: () => DR,
  loadGraphModel: () => bpe,
  loadLayersModel: () => ppe,
  localResponseNormalization: () => OR,
  log: () => Kn,
  log1p: () => Kg,
  logSigmoid: () => Lde,
  logSoftmax: () => eI,
  logSumExp: () => XR,
  logicalAnd: () => Ds,
  logicalNot: () => Qg,
  logicalOr: () => rI,
  logicalXor: () => Bde,
  losses: () => lpe,
  matMul: () => We,
  math: () => Q_,
  max: () => As,
  maxPool: () => Zg,
  maxPool3d: () => aI,
  maxPoolWithArgmax: () => sD,
  maximum: () => Tr,
  mean: () => It,
  memory: () => lm,
  meshgrid: () => Vde,
  metrics: () => KV,
  min: () => pm,
  minimum: () => gp,
  mirrorPad: () => lD,
  mod: () => dD,
  model: () => cpe,
  models: () => cW,
  moments: () => Jg,
  movingAverage: () => ape,
  mul: () => V,
  multiRNNCell: () => Wde,
  multinomial: () => gD,
  neg: () => kt,
  nextFrame: () => RO,
  norm: () => vI,
  notEqual: () => Ku,
  oneHot: () => yd,
  ones: () => zn,
  onesLike: () => Xn,
  op: () => L,
  outerProduct: () => Ude,
  pad: () => pi,
  pad1d: () => Gde,
  pad2d: () => Hde,
  pad3d: () => qde,
  pad4d: () => jde,
  pool: () => Kde,
  pow: () => ca,
  prelu: () => tb,
  print: () => F_,
  prod: () => iI,
  profile: () => wde,
  rand: () => Xde,
  randomGamma: () => Yde,
  randomNormal: () => zD,
  randomUniform: () => Pl,
  range: () => Xu,
  ready: () => Sde,
  real: () => xd,
  reciprocal: () => VD,
  registerBackend: () => dp,
  registerCallbackConstructor: () => hpe,
  registerGradient: () => S$,
  registerKernel: () => _l,
  registerOp: () => mpe,
  regularizers: () => dW,
  relu: () => Xs,
  relu6: () => oI,
  removeBackend: () => Nde,
  reshape: () => G,
  reverse: () => Yn,
  reverse1d: () => Qde,
  reverse2d: () => Zde,
  reverse3d: () => Jde,
  reverse4d: () => epe,
  rfft: () => ub,
  round: () => uI,
  rsqrt: () => lI,
  scalar: () => Ie,
  scatterND: () => M3,
  scatter_util: () => Sk,
  selu: () => cI,
  separableConv2d: () => JD,
  sequential: () => dpe,
  serialization: () => ae,
  setBackend: () => Ide,
  setPlatform: () => _de,
  setThreadsCount: () => Cpe,
  setWasmPath: () => Ipe,
  setWasmPaths: () => Spe,
  setWebGLContext: () => p5,
  setdiff1dAsync: () => t3,
  shared: () => Yy,
  sigmoid: () => qs,
  sign: () => s3,
  signal: () => upe,
  sin: () => dI,
  sinh: () => pI,
  slice: () => He,
  slice1d: () => rb,
  slice2d: () => hI,
  slice3d: () => ab,
  slice4d: () => wd,
  slice_util: () => wt,
  softmax: () => ib,
  softplus: () => Ol,
  spaceToBatchND: () => eb,
  sparse: () => Vc,
  sparseToDense: () => xI,
  spectral: () => ope,
  split: () => Ln,
  sqrt: () => ln,
  square: () => ct,
  squaredDifference: () => mI,
  squeeze: () => mr,
  stack: () => Qn,
  step: () => bp,
  stridedSlice: () => k3,
  string: () => Pf,
  sub: () => ge,
  sum: () => ye,
  sumOutType: () => lp,
  tan: () => S3,
  tanh: () => Hu,
  tensor: () => hs,
  tensor1d: () => Qt,
  tensor2d: () => ji,
  tensor3d: () => sA,
  tensor4d: () => tpe,
  tensor5d: () => npe,
  tensor6d: () => spe,
  tensor_util: () => _s,
  test_util: () => xA,
  tidy: () => j,
  tile: () => cs,
  time: () => kde,
  topk: () => N3,
  train: () => Fi,
  transpose: () => qe,
  truncatedNormal: () => lb,
  unique: () => cx,
  unregisterGradient: () => fde,
  unregisterKernel: () => hde,
  unsortedSegmentSum: () => A3,
  unstack: () => Fs,
  upcastType: () => yn,
  util: () => w,
  valueAndGrad: () => zde,
  valueAndGrads: () => Mde,
  variable: () => R3,
  variableGrads: () => MR,
  version: () => $pe,
  version_converter: () => ype,
  version_core: () => gde,
  version_cpu: () => vpe,
  version_layers: () => t0,
  version_wasm: () => Tpe,
  version_webgl: () => xpe,
  webgl: () => wpe,
  webgl_util: () => d5,
  webgpu: () => Hie,
  where: () => vn,
  whereAsync: () => bI,
  zeros: () => $t,
  zerosLike: () => je
});
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var ya = (e, t) => OT(FT(Bd(e != null ? AT(RT(e)) : {}, "default", !t && e && e.__esModule ? { get: () => e.default, enumerable: true } : { value: e, enumerable: true })), e);
var PT = zt({ "src/node_modules/long/src/long.js"(e, t) {
  t.exports = s;
  var n = null;
  try {
    n = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 13, 2, 96, 0, 1, 127, 96, 4, 127, 127, 127, 127, 1, 127, 3, 7, 6, 0, 1, 1, 1, 1, 1, 6, 6, 1, 127, 1, 65, 0, 11, 7, 50, 6, 3, 109, 117, 108, 0, 1, 5, 100, 105, 118, 95, 115, 0, 2, 5, 100, 105, 118, 95, 117, 0, 3, 5, 114, 101, 109, 95, 115, 0, 4, 5, 114, 101, 109, 95, 117, 0, 5, 8, 103, 101, 116, 95, 104, 105, 103, 104, 0, 0, 10, 191, 1, 6, 4, 0, 35, 0, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 126, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 127, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 128, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 129, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 130, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11])), {}).exports;
  } catch (F) {
  }
  function s(F, $, z) {
    this.low = F | 0, this.high = $ | 0, this.unsigned = !!z;
  }
  s.prototype.__isLong__, Object.defineProperty(s.prototype, "__isLong__", { value: true });
  function r(F) {
    return (F && F.__isLong__) === true;
  }
  s.isLong = r;
  var a = {}, i = {};
  function o(F, $) {
    var z, W, q;
    return $ ? (F >>>= 0, (q = 0 <= F && F < 256) && (W = i[F], W) ? W : (z = l(F, (F | 0) < 0 ? -1 : 0, true), q && (i[F] = z), z)) : (F |= 0, (q = -128 <= F && F < 128) && (W = a[F], W) ? W : (z = l(F, F < 0 ? -1 : 0, false), q && (a[F] = z), z));
  }
  s.fromInt = o;
  function u(F, $) {
    if (isNaN(F))
      return $ ? x : v;
    if ($) {
      if (F < 0)
        return x;
      if (F >= g)
        return A;
    } else {
      if (F <= -b)
        return P;
      if (F + 1 >= b)
        return E;
    }
    return F < 0 ? u(-F, $).neg() : l(F % m | 0, F / m | 0, $);
  }
  s.fromNumber = u;
  function l(F, $, z) {
    return new s(F, $, z);
  }
  s.fromBits = l;
  var c = Math.pow;
  function p(F, $, z) {
    if (F.length === 0)
      throw Error("empty string");
    if (F === "NaN" || F === "Infinity" || F === "+Infinity" || F === "-Infinity")
      return v;
    if (typeof $ == "number" ? (z = $, $ = false) : $ = !!$, z = z || 10, z < 2 || 36 < z)
      throw RangeError("radix");
    var W;
    if ((W = F.indexOf("-")) > 0)
      throw Error("interior hyphen");
    if (W === 0)
      return p(F.substring(1), $, z).neg();
    for (var q = u(c(z, 8)), K = v, Y = 0; Y < F.length; Y += 8) {
      var Z = Math.min(8, F.length - Y), te = parseInt(F.substring(Y, Y + Z), z);
      if (Z < 8) {
        var ee = u(c(z, Z));
        K = K.mul(ee).add(u(te));
      } else
        K = K.mul(q), K = K.add(u(te));
    }
    return K.unsigned = $, K;
  }
  s.fromString = p;
  function d(F, $) {
    return typeof F == "number" ? u(F, $) : typeof F == "string" ? p(F, $) : l(F.low, F.high, typeof $ == "boolean" ? $ : F.unsigned);
  }
  s.fromValue = d;
  var h = 1 << 16, f = 1 << 24, m = h * h, g = m * m, b = g / 2, y = o(f), v = o(0);
  s.ZERO = v;
  var x = o(0, true);
  s.UZERO = x;
  var k = o(1);
  s.ONE = k;
  var C = o(1, true);
  s.UONE = C;
  var T = o(-1);
  s.NEG_ONE = T;
  var E = l(-1, 2147483647, false);
  s.MAX_VALUE = E;
  var A = l(-1, -1, true);
  s.MAX_UNSIGNED_VALUE = A;
  var P = l(0, -2147483648, false);
  s.MIN_VALUE = P;
  var R = s.prototype;
  R.toInt = function() {
    return this.unsigned ? this.low >>> 0 : this.low;
  }, R.toNumber = function() {
    return this.unsigned ? (this.high >>> 0) * m + (this.low >>> 0) : this.high * m + (this.low >>> 0);
  }, R.toString = function($) {
    if ($ = $ || 10, $ < 2 || 36 < $)
      throw RangeError("radix");
    if (this.isZero())
      return "0";
    if (this.isNegative())
      if (this.eq(P)) {
        var z = u($), W = this.div(z), q = W.mul(z).sub(this);
        return W.toString($) + q.toInt().toString($);
      } else
        return "-" + this.neg().toString($);
    for (var K = u(c($, 6), this.unsigned), Y = this, Z = ""; ; ) {
      var te = Y.div(K), ee = Y.sub(te.mul(K)).toInt() >>> 0, se = ee.toString($);
      if (Y = te, Y.isZero())
        return se + Z;
      for (; se.length < 6; )
        se = "0" + se;
      Z = "" + se + Z;
    }
  }, R.getHighBits = function() {
    return this.high;
  }, R.getHighBitsUnsigned = function() {
    return this.high >>> 0;
  }, R.getLowBits = function() {
    return this.low;
  }, R.getLowBitsUnsigned = function() {
    return this.low >>> 0;
  }, R.getNumBitsAbs = function() {
    if (this.isNegative())
      return this.eq(P) ? 64 : this.neg().getNumBitsAbs();
    for (var $ = this.high != 0 ? this.high : this.low, z = 31; z > 0 && ($ & 1 << z) == 0; z--)
      ;
    return this.high != 0 ? z + 33 : z + 1;
  }, R.isZero = function() {
    return this.high === 0 && this.low === 0;
  }, R.eqz = R.isZero, R.isNegative = function() {
    return !this.unsigned && this.high < 0;
  }, R.isPositive = function() {
    return this.unsigned || this.high >= 0;
  }, R.isOdd = function() {
    return (this.low & 1) === 1;
  }, R.isEven = function() {
    return (this.low & 1) === 0;
  }, R.equals = function($) {
    return r($) || ($ = d($)), this.unsigned !== $.unsigned && this.high >>> 31 === 1 && $.high >>> 31 === 1 ? false : this.high === $.high && this.low === $.low;
  }, R.eq = R.equals, R.notEquals = function($) {
    return !this.eq($);
  }, R.neq = R.notEquals, R.ne = R.notEquals, R.lessThan = function($) {
    return this.comp($) < 0;
  }, R.lt = R.lessThan, R.lessThanOrEqual = function($) {
    return this.comp($) <= 0;
  }, R.lte = R.lessThanOrEqual, R.le = R.lessThanOrEqual, R.greaterThan = function($) {
    return this.comp($) > 0;
  }, R.gt = R.greaterThan, R.greaterThanOrEqual = function($) {
    return this.comp($) >= 0;
  }, R.gte = R.greaterThanOrEqual, R.ge = R.greaterThanOrEqual, R.compare = function($) {
    if (r($) || ($ = d($)), this.eq($))
      return 0;
    var z = this.isNegative(), W = $.isNegative();
    return z && !W ? -1 : !z && W ? 1 : this.unsigned ? $.high >>> 0 > this.high >>> 0 || $.high === this.high && $.low >>> 0 > this.low >>> 0 ? -1 : 1 : this.sub($).isNegative() ? -1 : 1;
  }, R.comp = R.compare, R.negate = function() {
    return !this.unsigned && this.eq(P) ? P : this.not().add(k);
  }, R.neg = R.negate, R.add = function($) {
    r($) || ($ = d($));
    var z = this.high >>> 16, W = this.high & 65535, q = this.low >>> 16, K = this.low & 65535, Y = $.high >>> 16, Z = $.high & 65535, te = $.low >>> 16, ee = $.low & 65535, se = 0, ne = 0, oe = 0, re = 0;
    return re += K + ee, oe += re >>> 16, re &= 65535, oe += q + te, ne += oe >>> 16, oe &= 65535, ne += W + Z, se += ne >>> 16, ne &= 65535, se += z + Y, se &= 65535, l(oe << 16 | re, se << 16 | ne, this.unsigned);
  }, R.subtract = function($) {
    return r($) || ($ = d($)), this.add($.neg());
  }, R.sub = R.subtract, R.multiply = function($) {
    if (this.isZero())
      return v;
    if (r($) || ($ = d($)), n) {
      var z = n.mul(this.low, this.high, $.low, $.high);
      return l(z, n.get_high(), this.unsigned);
    }
    if ($.isZero())
      return v;
    if (this.eq(P))
      return $.isOdd() ? P : v;
    if ($.eq(P))
      return this.isOdd() ? P : v;
    if (this.isNegative())
      return $.isNegative() ? this.neg().mul($.neg()) : this.neg().mul($).neg();
    if ($.isNegative())
      return this.mul($.neg()).neg();
    if (this.lt(y) && $.lt(y))
      return u(this.toNumber() * $.toNumber(), this.unsigned);
    var W = this.high >>> 16, q = this.high & 65535, K = this.low >>> 16, Y = this.low & 65535, Z = $.high >>> 16, te = $.high & 65535, ee = $.low >>> 16, se = $.low & 65535, ne = 0, oe = 0, re = 0, le = 0;
    return le += Y * se, re += le >>> 16, le &= 65535, re += K * se, oe += re >>> 16, re &= 65535, re += Y * ee, oe += re >>> 16, re &= 65535, oe += q * se, ne += oe >>> 16, oe &= 65535, oe += K * ee, ne += oe >>> 16, oe &= 65535, oe += Y * te, ne += oe >>> 16, oe &= 65535, ne += W * se + q * ee + K * te + Y * Z, ne &= 65535, l(re << 16 | le, ne << 16 | oe, this.unsigned);
  }, R.mul = R.multiply, R.divide = function($) {
    if (r($) || ($ = d($)), $.isZero())
      throw Error("division by zero");
    if (n) {
      if (!this.unsigned && this.high === -2147483648 && $.low === -1 && $.high === -1)
        return this;
      var z = (this.unsigned ? n.div_u : n.div_s)(this.low, this.high, $.low, $.high);
      return l(z, n.get_high(), this.unsigned);
    }
    if (this.isZero())
      return this.unsigned ? x : v;
    var W, q, K;
    if (this.unsigned) {
      if ($.unsigned || ($ = $.toUnsigned()), $.gt(this))
        return x;
      if ($.gt(this.shru(1)))
        return C;
      K = x;
    } else {
      if (this.eq(P)) {
        if ($.eq(k) || $.eq(T))
          return P;
        if ($.eq(P))
          return k;
        var Y = this.shr(1);
        return W = Y.div($).shl(1), W.eq(v) ? $.isNegative() ? k : T : (q = this.sub($.mul(W)), K = W.add(q.div($)), K);
      } else if ($.eq(P))
        return this.unsigned ? x : v;
      if (this.isNegative())
        return $.isNegative() ? this.neg().div($.neg()) : this.neg().div($).neg();
      if ($.isNegative())
        return this.div($.neg()).neg();
      K = v;
    }
    for (q = this; q.gte($); ) {
      W = Math.max(1, Math.floor(q.toNumber() / $.toNumber()));
      for (var Z = Math.ceil(Math.log(W) / Math.LN2), te = Z <= 48 ? 1 : c(2, Z - 48), ee = u(W), se = ee.mul($); se.isNegative() || se.gt(q); )
        W -= te, ee = u(W, this.unsigned), se = ee.mul($);
      ee.isZero() && (ee = k), K = K.add(ee), q = q.sub(se);
    }
    return K;
  }, R.div = R.divide, R.modulo = function($) {
    if (r($) || ($ = d($)), n) {
      var z = (this.unsigned ? n.rem_u : n.rem_s)(this.low, this.high, $.low, $.high);
      return l(z, n.get_high(), this.unsigned);
    }
    return this.sub(this.div($).mul($));
  }, R.mod = R.modulo, R.rem = R.modulo, R.not = function() {
    return l(~this.low, ~this.high, this.unsigned);
  }, R.and = function($) {
    return r($) || ($ = d($)), l(this.low & $.low, this.high & $.high, this.unsigned);
  }, R.or = function($) {
    return r($) || ($ = d($)), l(this.low | $.low, this.high | $.high, this.unsigned);
  }, R.xor = function($) {
    return r($) || ($ = d($)), l(this.low ^ $.low, this.high ^ $.high, this.unsigned);
  }, R.shiftLeft = function($) {
    return r($) && ($ = $.toInt()), ($ &= 63) === 0 ? this : $ < 32 ? l(this.low << $, this.high << $ | this.low >>> 32 - $, this.unsigned) : l(0, this.low << $ - 32, this.unsigned);
  }, R.shl = R.shiftLeft, R.shiftRight = function($) {
    return r($) && ($ = $.toInt()), ($ &= 63) === 0 ? this : $ < 32 ? l(this.low >>> $ | this.high << 32 - $, this.high >> $, this.unsigned) : l(this.high >> $ - 32, this.high >= 0 ? 0 : -1, this.unsigned);
  }, R.shr = R.shiftRight, R.shiftRightUnsigned = function($) {
    if (r($) && ($ = $.toInt()), $ &= 63, $ === 0)
      return this;
    var z = this.high;
    if ($ < 32) {
      var W = this.low;
      return l(W >>> $ | z << 32 - $, z >>> $, this.unsigned);
    } else
      return $ === 32 ? l(z, 0, this.unsigned) : l(z >>> $ - 32, 0, this.unsigned);
  }, R.shru = R.shiftRightUnsigned, R.shr_u = R.shiftRightUnsigned, R.toSigned = function() {
    return this.unsigned ? l(this.low, this.high, false) : this;
  }, R.toUnsigned = function() {
    return this.unsigned ? this : l(this.low, this.high, true);
  }, R.toBytes = function($) {
    return $ ? this.toBytesLE() : this.toBytesBE();
  }, R.toBytesLE = function() {
    var $ = this.high, z = this.low;
    return [z & 255, z >>> 8 & 255, z >>> 16 & 255, z >>> 24, $ & 255, $ >>> 8 & 255, $ >>> 16 & 255, $ >>> 24];
  }, R.toBytesBE = function() {
    var $ = this.high, z = this.low;
    return [$ >>> 24, $ >>> 16 & 255, $ >>> 8 & 255, $ & 255, z >>> 24, z >>> 16 & 255, z >>> 8 & 255, z & 255];
  }, s.fromBytes = function($, z, W) {
    return W ? s.fromBytesLE($, z) : s.fromBytesBE($, z);
  }, s.fromBytesLE = function($, z) {
    return new s($[0] | $[1] << 8 | $[2] << 16 | $[3] << 24, $[4] | $[5] << 8 | $[6] << 16 | $[7] << 24, z);
  }, s.fromBytesBE = function($, z) {
    return new s($[4] << 24 | $[5] << 16 | $[6] << 8 | $[7], $[0] << 24 | $[1] << 16 | $[2] << 8 | $[3], z);
  };
} });
var zT = zt({ "(disabled):src/node_modules/node-fetch/browser.js"() {
} });
var MT = zt({ "(disabled):util"() {
} });
var LT = zt({ "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 BT = zt({ "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 VT = zt({ "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 WT = zt({ "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 UT = zt({ "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 GT = zt({ "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;
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      }, 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 HT = zt({ "(disabled):crypto"() {
} });
var qT = zt({ "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, C, T) {
      var E = [];
      C = C == true ? { entropy: true } : C || {};
      var A = y(b(C.entropy ? [k, x(s)] : k == null ? v() : k, 3), E), P = new m(E), R = function() {
        for (var F = P.g(i), $ = l, z = 0; F < c; )
          F = (F + z) * a, $ *= a, z = P.g(1);
        for (; F >= p; )
          F /= 2, $ /= 2, z >>>= 1;
        return (F + z) / $;
      };
      return R.int32 = function() {
        return P.g(4) | 0;
      }, R.quick = function() {
        return P.g(4) / 4294967296;
      }, R.double = R, y(x(P.S), s), (C.pass || T || function(F, $, z, W) {
        return W && (W.S && g(W, P), F.state = function() {
          return g(P, {});
        }), z ? (r[u] = F, $) : F;
      })(R, A, "global" in C ? C.global : this == r, C.state);
    }
    function m(k) {
      var C, T = k.length, E = this, A = 0, P = E.i = E.j = 0, R = E.S = [];
      for (T || (k = [T++]); A < a; )
        R[A] = A++;
      for (A = 0; A < a; A++)
        R[A] = R[P = d & P + k[A % T] + (C = R[A])], R[P] = C;
      (E.g = function(F) {
        for (var $, z = 0, W = E.i, q = E.j, K = E.S; F--; )
          $ = K[W = d & W + 1], z = z * a + K[d & (K[W] = K[q = d & q + $]) + (K[q] = $)];
        return E.i = W, E.j = q, z;
      })(a);
    }
    function g(k, C) {
      return C.i = k.i, C.j = k.j, C.S = k.S.slice(), C;
    }
    function b(k, C) {
      var T = [], E = typeof k, A;
      if (C && E == "object")
        for (A in k)
          try {
            T.push(b(k[A], C - 1));
          } catch (P) {
          }
      return T.length ? T : E == "string" ? k : k + "\0";
    }
    function y(k, C) {
      for (var T = k + "", E, A = 0; A < T.length; )
        C[d & A] = d & (E ^= C[d & A] * 19) + T.charCodeAt(A++);
      return x(C);
    }
    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 (E) {
        var C = n.navigator, T = C && C.plugins;
        return [+new Date(), n, T, 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 = HT();
      } catch (k) {
      }
    } else
      typeof define == "function" && define.amd ? define(function() {
        return f;
      }) : r["seed" + u] = f;
  })(typeof self != "undefined" ? self : e, [], Math);
} });
var Vd = zt({ "src/node_modules/seedrandom/index.js"(e, t) {
  var n = LT(), s = BT(), r = VT(), a = WT(), i = UT(), o = GT(), u = qT();
  u.alea = n, u.xor128 = s, u.xorwow = r, u.xorshift7 = a, u.xor4096 = i, u.tychei = o, t.exports = u;
} });
var Pw = zt({ "(disabled):src/node_modules/string_decoder/index.js"() {
} });
var Jm = zt({ "(disabled):fs"() {
} });
var cd = zt({ "(disabled):path"() {
} });
var jT = zt({ "(disabled):worker_threads"() {
} });
var KT = zt({ "(disabled):perf_hooks"() {
} });
var XT = zt({ "(disabled):os"() {
} });
var YT = zt({ "src/tfjs-backend-wasm/wasm-out/tfjs-backend-wasm-threaded-simd.js"(e, t) {
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        (x || k) && (k ? E = self.location.href : typeof document != "undefined" && document.currentScript && (E = document.currentScript.src), typeof s != "undefined" && s && (E = s), E.indexOf("blob:") !== 0 ? E = E.substr(0, E.replace(/[?#].*/, "").lastIndexOf("/") + 1) : E = "", C || (P = (S) => {
          var D = new XMLHttpRequest();
          return D.open("GET", S, false), D.send(null), D.responseText;
        }, k && (F = (S) => {
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        }), R = (S, D, B) => {
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              D(Q.response);
              return;
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      }, Ai = d._Less = function() {
        return (Ai = d._Less = d.asm.Less).apply(null, arguments);
      }, Sf = d._LessEqual = function() {
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} });
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          if (ke.status == 200 || ke.status == 0 && ke.response) {
            J(ke.response);
            return;
          }
          de();
        }, ke.onerror = de, ke.send(null);
      }, k = (H) => document.title = H);
      var P = a.print || console.log.bind(console), R = a.printErr || console.warn.bind(console);
      Object.assign(a, l), l = null, a.arguments && (c = a.arguments), a.thisProgram && (p = a.thisProgram), a.quit && (d = a.quit);
      var F = 4;
      function $(H) {
        $.shown || ($.shown = {}), $.shown[H] || ($.shown[H] = 1, R(H));
      }
      function z(H, J) {
        if (typeof WebAssembly.Function == "function") {
          for (var de = { i: "i32", j: "i64", f: "f32", d: "f64" }, ke = { parameters: [], results: J[0] == "v" ? [] : [de[J[0]]] }, Qe = 1; Qe < J.length; ++Qe)
            ke.parameters.push(de[J[Qe]]);
          return new WebAssembly.Function(ke, H);
        }
        var Ze = [1, 0, 1, 96], Ve = J.slice(0, 1), ze = J.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]]);
        Ve == "v" ? Ze.push(0) : Ze = Ze.concat([1, Tt[Ve]]), Ze[1] = Ze.length - 2;
        var rs = 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])), as = new WebAssembly.Module(rs), Ei = new WebAssembly.Instance(as, { e: { f: H } }), Lr = Ei.exports.f;
        return Lr;
      }
      var W = [], q;
      function K() {
        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, J) {
        for (var de = H; de < H + J; de++) {
          var ke = mu(de);
          ke && q.set(ke, de);
        }
      }
      var Z = 0, te = (H) => {
        Z = H;
      }, ee;
      a.wasmBinary && (ee = a.wasmBinary);
      var se = a.noExitRuntime || true;
      typeof WebAssembly != "object" && Fr("no native wasm support detected");
      var ne, oe = false, re;
      function le(H, J) {
        H || Fr(J);
      }
      function me(H) {
        var J = a["_" + H];
        return J;
      }
      function we(H, J, de, ke, Qe) {
        var Ze = { string: function(rn) {
          var nr = 0;
          if (rn != null && rn !== 0) {
            var Dc = (rn.length << 2) + 1;
            nr = yu(Dc), tt(rn, nr, Dc);
          }
          return nr;
        }, array: function(rn) {
          var nr = yu(rn.length);
          return rt(rn, nr), nr;
        } };
        function Ve(rn) {
          return J === "string" ? Je(rn) : J === "boolean" ? Boolean(rn) : rn;
        }
        var ze = me(H), Tt = [], rs = 0;
        if (ke)
          for (var as = 0; as < ke.length; as++) {
            var Ei = Ze[de[as]];
            Ei ? (rs === 0 && (rs = Ec()), Tt[as] = Ei(ke[as])) : Tt[as] = ke[as];
          }
        var Lr = ze.apply(null, Tt);
        function Cf(rn) {
          return rs !== 0 && Rc(rs), Ve(rn);
        }
        return Lr = Cf(Lr), Lr;
      }
      function Se(H, J, de, ke) {
        de = de || [];
        var Qe = de.every(function(Ve) {
          return Ve === "number";
        }), Ze = J !== "string";
        return Ze && Qe && !ke ? me(H) : function() {
          return we(H, J, de, arguments, ke);
        };
      }
      var Ee = 1, Pe = typeof TextDecoder != "undefined" ? new TextDecoder("utf8") : void 0;
      function Xe(H, J, de) {
        for (var ke = J + de, Qe = J; H[Qe] && !(Qe >= ke); )
          ++Qe;
        if (Qe - J > 16 && H.subarray && Pe)
          return Pe.decode(H.subarray(J, Qe));
        for (var Ze = ""; J < Qe; ) {
          var Ve = H[J++];
          if (!(Ve & 128)) {
            Ze += String.fromCharCode(Ve);
            continue;
          }
          var ze = H[J++] & 63;
          if ((Ve & 224) == 192) {
            Ze += String.fromCharCode((Ve & 31) << 6 | ze);
            continue;
          }
          var Tt = H[J++] & 63;
          if ((Ve & 240) == 224 ? Ve = (Ve & 15) << 12 | ze << 6 | Tt : Ve = (Ve & 7) << 18 | ze << 12 | Tt << 6 | H[J++] & 63, Ve < 65536)
            Ze += String.fromCharCode(Ve);
          else {
            var rs = Ve - 65536;
            Ze += String.fromCharCode(55296 | rs >> 10, 56320 | rs & 1023);
          }
        }
        return Ze;
      }
      function Je(H, J) {
        return H ? Xe(Jt, H, J) : "";
      }
      function Ye(H, J, de, ke) {
        if (!(ke > 0))
          return 0;
        for (var Qe = de, Ze = de + ke - 1, Ve = 0; Ve < H.length; ++Ve) {
          var ze = H.charCodeAt(Ve);
          if (ze >= 55296 && ze <= 57343) {
            var Tt = H.charCodeAt(++Ve);
            ze = 65536 + ((ze & 1023) << 10) | Tt & 1023;
          }
          if (ze <= 127) {
            if (de >= Ze)
              break;
            J[de++] = ze;
          } else if (ze <= 2047) {
            if (de + 1 >= Ze)
              break;
            J[de++] = 192 | ze >> 6, J[de++] = 128 | ze & 63;
          } else if (ze <= 65535) {
            if (de + 2 >= Ze)
              break;
            J[de++] = 224 | ze >> 12, J[de++] = 128 | ze >> 6 & 63, J[de++] = 128 | ze & 63;
          } else {
            if (de + 3 >= Ze)
              break;
            J[de++] = 240 | ze >> 18, J[de++] = 128 | ze >> 12 & 63, J[de++] = 128 | ze >> 6 & 63, J[de++] = 128 | ze & 63;
          }
        }
        return J[de] = 0, de - Qe;
      }
      function tt(H, J, de) {
        return Ye(H, Jt, J, de);
      }
      function Ce(H) {
        for (var J = 0, de = 0; de < H.length; ++de) {
          var ke = H.charCodeAt(de);
          ke >= 55296 && ke <= 57343 && (ke = 65536 + ((ke & 1023) << 10) | H.charCodeAt(++de) & 1023), ke <= 127 ? ++J : ke <= 2047 ? J += 2 : ke <= 65535 ? J += 3 : J += 4;
        }
        return J;
      }
      var ut = typeof TextDecoder != "undefined" ? new TextDecoder("utf-16le") : void 0;
      function rt(H, J) {
        Et.set(H, J);
      }
      function Zt(H, J, de) {
        for (var ke = 0; ke < H.length; ++ke)
          Et[J++ >> 0] = H.charCodeAt(ke);
        de || (Et[J >> 0] = 0);
      }
      function Nt(H, J) {
        return H % J > 0 && (H += J - H % J), H;
      }
      var In, Et, Jt, Sn, Cn, Xt, Rn, en, Ms;
      function Ls(H) {
        In = H, a.HEAP8 = Et = new Int8Array(H), a.HEAP16 = Sn = new Int16Array(H), a.HEAP32 = Xt = new Int32Array(H), a.HEAPU8 = Jt = new Uint8Array(H), a.HEAPU16 = Cn = new Uint16Array(H), a.HEAPU32 = Rn = new Uint32Array(H), a.HEAPF32 = en = new Float32Array(H), a.HEAPF64 = Ms = new Float64Array(H);
      }
      var xi = a.INITIAL_MEMORY || 16777216, Js, Bs = [], du = [], wi = [], tn = false, ac = false, ic = 0;
      function pu() {
        return se || ic > 0;
      }
      function oc() {
        if (a.preRun)
          for (typeof a.preRun == "function" && (a.preRun = [a.preRun]); a.preRun.length; )
            cc(a.preRun.shift());
        fu(Bs);
      }
      function uc() {
        tn = true, fu(du);
      }
      function Ev() {
        ac = true;
      }
      function lc() {
        if (a.postRun)
          for (typeof a.postRun == "function" && (a.postRun = [a.postRun]); a.postRun.length; )
            dc(a.postRun.shift());
        fu(wi);
      }
      function cc(H) {
        Bs.unshift(H);
      }
      function ns(H) {
        du.unshift(H);
      }
      function dc(H) {
        wi.unshift(H);
      }
      var Dn = 0, ki = null, er = null;
      function eh(H) {
        Dn++, a.monitorRunDependencies && a.monitorRunDependencies(Dn);
      }
      function pc(H) {
        if (Dn--, a.monitorRunDependencies && a.monitorRunDependencies(Dn), Dn == 0 && (ki !== null && (clearInterval(ki), ki = null), er)) {
          var J = er;
          er = null, J();
        }
      }
      a.preloadedImages = {}, a.preloadedAudios = {};
      function Fr(H) {
        a.onAbort && a.onAbort(H), H = "Aborted(" + H + ")", R(H), oe = true, re = 1, H += ". Build with -s ASSERTIONS=1 for more info.";
        var J = new WebAssembly.RuntimeError(H);
        throw o(J), J;
      }
      var th = "data:application/octet-stream;base64,";
      function hc(H) {
        return H.startsWith(th);
      }
      function Or(H) {
        return H.startsWith("file://");
      }
      var nn;
      nn = "tfjs-backend-wasm.wasm", hc(nn) || (nn = b(nn));
      function hu(H) {
        try {
          if (H == nn && ee)
            return new Uint8Array(ee);
          if (x)
            return x(H);
          throw "both async and sync fetching of the wasm failed";
        } catch (J) {
          Fr(J);
        }
      }
      function nh() {
        if (!ee && (h || f)) {
          if (typeof fetch == "function" && !Or(nn))
            return fetch(nn, { credentials: "same-origin" }).then(function(H) {
              if (!H.ok)
                throw "failed to load wasm binary file at '" + nn + "'";
              return H.arrayBuffer();
            }).catch(function() {
              return hu(nn);
            });
          if (v)
            return new Promise(function(H, J) {
              v(nn, function(de) {
                H(new Uint8Array(de));
              }, J);
            });
        }
        return Promise.resolve().then(function() {
          return hu(nn);
        });
      }
      function sh() {
        var H = { env: Ci, wasi_snapshot_preview1: Ci };
        function J(Ve, ze) {
          var Tt = Ve.exports;
          a.asm = Tt, ne = a.asm.memory, Ls(ne.buffer), Js = a.asm.__indirect_function_table, ns(a.asm.__wasm_call_ctors), pc("wasm-instantiate");
        }
        eh("wasm-instantiate");
        function de(Ve) {
          J(Ve.instance);
        }
        function ke(Ve) {
          return nh().then(function(ze) {
            return WebAssembly.instantiate(ze, H);
          }).then(function(ze) {
            return ze;
          }).then(Ve, function(ze) {
            R("failed to asynchronously prepare wasm: " + ze), Fr(ze);
          });
        }
        function Qe() {
          return !ee && typeof WebAssembly.instantiateStreaming == "function" && !hc(nn) && !Or(nn) && typeof fetch == "function" ? fetch(nn, { credentials: "same-origin" }).then(function(Ve) {
            var ze = WebAssembly.instantiateStreaming(Ve, H);
            return ze.then(de, function(Tt) {
              return R("wasm streaming compile failed: " + Tt), R("falling back to ArrayBuffer instantiation"), ke(de);
            });
          }) : ke(de);
        }
        if (a.instantiateWasm)
          try {
            var Ze = a.instantiateWasm(H, J);
            return Ze;
          } catch (Ve) {
            return R("Module.instantiateWasm callback failed with error: " + Ve), false;
          }
        return Qe().catch(o), {};
      }
      var Rv, Dv;
      function fu(H) {
        for (; H.length > 0; ) {
          var J = H.shift();
          if (typeof J == "function") {
            J(a);
            continue;
          }
          var de = J.func;
          typeof de == "number" ? J.arg === void 0 ? mu(de)() : mu(de)(J.arg) : de(J.arg === void 0 ? null : J.arg);
        }
      }
      function tr(H) {
        return H;
      }
      function fc(H) {
        var J = /\b_Z[\w\d_]+/g;
        return H.replace(J, function(de) {
          var ke = de;
          return de === ke ? de : ke + " [" + de + "]";
        });
      }
      var ss = [];
      function mu(H) {
        var J = ss[H];
        return J || (H >= ss.length && (ss.length = H + 1), ss[H] = J = Js.get(H)), J;
      }
      function Fv() {
        var H = new Error();
        if (!H.stack) {
          try {
            throw new Error();
          } catch (J) {
            H = J;
          }
          if (!H.stack)
            return "(no stack trace available)";
        }
        return H.stack.toString();
      }
      function Ii(H, J) {
        Js.set(H, J), ss[H] = J;
      }
      function rh() {
        Fr("");
      }
      function mc(H, J, de) {
        Jt.copyWithin(H, J, J + de);
      }
      function gc() {
        return 2147483648;
      }
      function sn(H) {
        try {
          return ne.grow(H - In.byteLength + 65535 >>> 16), Ls(ne.buffer), 1;
        } catch (J) {
        }
      }
      function bc(H) {
        var J = Jt.length;
        H = H >>> 0;
        var de = gc();
        if (H > de)
          return false;
        for (var ke = 1; ke <= 4; ke *= 2) {
          var Qe = J * (1 + 0.2 / ke);
          Qe = Math.min(Qe, H + 100663296);
          var Ze = Math.min(de, Nt(Math.max(H, Qe), 65536)), Ve = sn(Ze);
          if (Ve)
            return true;
        }
        return false;
      }
      var Si = { mappings: {}, buffers: [null, [], []], printChar: function(H, J) {
        var de = Si.buffers[H];
        J === 0 || J === 10 ? ((H === 1 ? P : R)(Xe(de, 0)), de.length = 0) : de.push(J);
      }, varargs: void 0, get: function() {
        Si.varargs += 4;
        var H = Xt[Si.varargs - 4 >> 2];
        return H;
      }, getStr: function(H) {
        var J = Je(H);
        return J;
      }, get64: function(H, J) {
        return H;
      } };
      function ah(H) {
        return 0;
      }
      function Ov(H, J, de, ke, Qe) {
      }
      function Pv(H, J, de, ke) {
        for (var Qe = 0, Ze = 0; Ze < de; Ze++) {
          var Ve = Xt[J >> 2], ze = Xt[J + 4 >> 2];
          J += 8;
          for (var Tt = 0; Tt < ze; Tt++)
            Si.printChar(H, Jt[Ve + Tt]);
          Qe += ze;
        }
        return Xt[ke >> 2] = Qe, 0;
      }
      function ih(H) {
        te(H);
      }
      var yc = false, Ci = { abort: rh, emscripten_memcpy_big: mc, emscripten_resize_heap: bc, fd_close: ah, fd_seek: Ov, fd_write: Pv, setTempRet0: ih }, zN = sh(), zv = a.___wasm_call_ctors = function() {
        return (zv = a.___wasm_call_ctors = a.asm.__wasm_call_ctors).apply(null, arguments);
      }, oh = a._init = function() {
        return (oh = a._init = a.asm.init).apply(null, arguments);
      }, uh = a._init_with_threads_count = function() {
        return (uh = a._init_with_threads_count = a.asm.init_with_threads_count).apply(null, arguments);
      }, vc = a._get_threads_count = function() {
        return (vc = a._get_threads_count = a.asm.get_threads_count).apply(null, arguments);
      }, xc = a._register_tensor = function() {
        return (xc = a._register_tensor = a.asm.register_tensor).apply(null, arguments);
      }, lh = a._dispose_data = function() {
        return (lh = a._dispose_data = a.asm.dispose_data).apply(null, arguments);
      }, $e = a._dispose = function() {
        return ($e = a._dispose = a.asm.dispose).apply(null, arguments);
      }, ch = a._Abs = function() {
        return (ch = a._Abs = a.asm.Abs).apply(null, arguments);
      }, wc = a._Add = function() {
        return (wc = a._Add = a.asm.Add).apply(null, arguments);
      }, Pr = a._AddN = function() {
        return (Pr = a._AddN = a.asm.AddN).apply(null, arguments);
      }, Ni = a._All = function() {
        return (Ni = a._All = a.asm.All).apply(null, arguments);
      }, dh = a._Any = function() {
        return (dh = a._Any = a.asm.Any).apply(null, arguments);
      }, Mv = a._ArgMax = function() {
        return (Mv = a._ArgMax = a.asm.ArgMax).apply(null, arguments);
      }, ph = a._AvgPool = function() {
        return (ph = a._AvgPool = a.asm.AvgPool).apply(null, arguments);
      }, Lv = a._BatchMatMul = function() {
        return (Lv = a._BatchMatMul = a.asm.BatchMatMul).apply(null, arguments);
      }, zr = a._Ceil = function() {
        return (zr = a._Ceil = a.asm.Ceil).apply(null, arguments);
      }, hh = a._ClipByValue = function() {
        return (hh = a._ClipByValue = a.asm.ClipByValue).apply(null, arguments);
      }, fh = a._Conv2D = function() {
        return (fh = a._Conv2D = a.asm.Conv2D).apply(null, arguments);
      }, mh = a._Conv2DBackpropInput = function() {
        return (mh = a._Conv2DBackpropInput = a.asm.Conv2DBackpropInput).apply(null, arguments);
      }, gh = a._Cos = function() {
        return (gh = a._Cos = a.asm.Cos).apply(null, arguments);
      }, bh = a._Cosh = function() {
        return (bh = a._Cosh = a.asm.Cosh).apply(null, arguments);
      }, yh = a._CropAndResize = function() {
        return (yh = a._CropAndResize = a.asm.CropAndResize).apply(null, arguments);
      }, kc = a._Cumsum = function() {
        return (kc = a._Cumsum = a.asm.Cumsum).apply(null, arguments);
      }, vh = a._DepthToSpace = function() {
        return (vh = a._DepthToSpace = a.asm.DepthToSpace).apply(null, arguments);
      }, xh = a._DepthwiseConv2dNative = function() {
        return (xh = a._DepthwiseConv2dNative = a.asm.DepthwiseConv2dNative).apply(null, arguments);
      }, wh = a._Elu = function() {
        return (wh = a._Elu = a.asm.Elu).apply(null, arguments);
      }, kh = a._Equal = function() {
        return (kh = a._Equal = a.asm.Equal).apply(null, arguments);
      }, Ih = a._Exp = function() {
        return (Ih = a._Exp = a.asm.Exp).apply(null, arguments);
      }, Ic = a._FlipLeftRight = function() {
        return (Ic = a._FlipLeftRight = a.asm.FlipLeftRight).apply(null, arguments);
      }, Sh = a._Floor = function() {
        return (Sh = a._Floor = a.asm.Floor).apply(null, arguments);
      }, Ch = a._FloorDiv = function() {
        return (Ch = a._FloorDiv = a.asm.FloorDiv).apply(null, arguments);
      }, Mr = a._FusedBatchNorm = function() {
        return (Mr = a._FusedBatchNorm = a.asm.FusedBatchNorm).apply(null, arguments);
      }, gu = a._FusedConv2D = function() {
        return (gu = a._FusedConv2D = a.asm.FusedConv2D).apply(null, arguments);
      }, Nh = a._FusedDepthwiseConv2D = function() {
        return (Nh = a._FusedDepthwiseConv2D = a.asm.FusedDepthwiseConv2D).apply(null, arguments);
      }, Th = a._Gather = function() {
        return (Th = a._Gather = a.asm.Gather).apply(null, arguments);
      }, $h = a._GatherNd = function() {
        return ($h = a._GatherNd = a.asm.GatherNd).apply(null, arguments);
      }, Le = a._Greater = function() {
        return (Le = a._Greater = a.asm.Greater).apply(null, arguments);
      }, _h = a._GreaterEqual = function() {
        return (_h = a._GreaterEqual = a.asm.GreaterEqual).apply(null, arguments);
      }, Ah = a._LeakyRelu = function() {
        return (Ah = a._LeakyRelu = a.asm.LeakyRelu).apply(null, arguments);
      }, Eh = a._Less = function() {
        return (Eh = a._Less = a.asm.Less).apply(null, arguments);
      }, Rh = a._LessEqual = function() {
        return (Rh = a._LessEqual = a.asm.LessEqual).apply(null, arguments);
      }, Dh = a._Log = function() {
        return (Dh = a._Log = a.asm.Log).apply(null, arguments);
      }, Fh = a._LogicalAnd = function() {
        return (Fh = a._LogicalAnd = a.asm.LogicalAnd).apply(null, arguments);
      }, bu = a._Max = function() {
        return (bu = a._Max = a.asm.Max).apply(null, arguments);
      }, Sc = a._MaxPool = function() {
        return (Sc = a._MaxPool = a.asm.MaxPool).apply(null, arguments);
      }, Cc = a._Maximum = function() {
        return (Cc = a._Maximum = a.asm.Maximum).apply(null, arguments);
      }, Oh = a._Mean = function() {
        return (Oh = a._Mean = a.asm.Mean).apply(null, arguments);
      }, Ph = a._Min = function() {
        return (Ph = a._Min = a.asm.Min).apply(null, arguments);
      }, zh = a._Minimum = function() {
        return (zh = a._Minimum = a.asm.Minimum).apply(null, arguments);
      }, Mh = a._MirrorPad = function() {
        return (Mh = a._MirrorPad = a.asm.MirrorPad).apply(null, arguments);
      }, Lh = a._Multiply = function() {
        return (Lh = a._Multiply = a.asm.Multiply).apply(null, arguments);
      }, Bh = a._Neg = function() {
        return (Bh = a._Neg = a.asm.Neg).apply(null, arguments);
      }, gt = a._NonMaxSuppressionV3 = function() {
        return (gt = a._NonMaxSuppressionV3 = a.asm.NonMaxSuppressionV3).apply(null, arguments);
      }, Vh = a._NonMaxSuppressionV4 = function() {
        return (Vh = a._NonMaxSuppressionV4 = a.asm.NonMaxSuppressionV4).apply(null, arguments);
      }, Wh = a._NonMaxSuppressionV5 = function() {
        return (Wh = a._NonMaxSuppressionV5 = a.asm.NonMaxSuppressionV5).apply(null, arguments);
      }, Uh = a._NotEqual = function() {
        return (Uh = a._NotEqual = a.asm.NotEqual).apply(null, arguments);
      }, Ti = a._OneHot = function() {
        return (Ti = a._OneHot = a.asm.OneHot).apply(null, arguments);
      }, Nc = a._PadV2 = function() {
        return (Nc = a._PadV2 = a.asm.PadV2).apply(null, arguments);
      }, Tc = a._Pow = function() {
        return (Tc = a._Pow = a.asm.Pow).apply(null, arguments);
      }, $c = a._Prelu = function() {
        return ($c = a._Prelu = a.asm.Prelu).apply(null, arguments);
      }, Gh = a._Prod = function() {
        return (Gh = a._Prod = a.asm.Prod).apply(null, arguments);
      }, _c = a._RealDiv = function() {
        return (_c = a._RealDiv = a.asm.RealDiv).apply(null, arguments);
      }, Hh = a._Relu = function() {
        return (Hh = a._Relu = a.asm.Relu).apply(null, arguments);
      }, Bv = a._Relu6 = function() {
        return (Bv = a._Relu6 = a.asm.Relu6).apply(null, arguments);
      }, Ac = a._ResizeBilinear = function() {
        return (Ac = a._ResizeBilinear = a.asm.ResizeBilinear).apply(null, arguments);
      }, Vv = a._Reverse = function() {
        return (Vv = a._Reverse = a.asm.Reverse).apply(null, arguments);
      }, qh = a._RotateWithOffset = function() {
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var ao = "Abs";
var nl = "Acos";
var sl = "Acosh";
var kr = "Add";
var xa = "AddN";
var rl = "All";
var al = "Any";
var wa = "ArgMax";
var il = "ArgMin";
var ol = "Asin";
var ul = "Asinh";
var ll = "Atan";
var cl = "Atanh";
var dl = "Atan2";
var ka = "AvgPool";
var rg = "AvgPoolGrad";
var Hd = "AvgPool3D";
var ag = "AvgPool3DGrad";
var Ia = "BatchMatMul";
var io = "BatchToSpaceND";
var ig = "Bincount";
var w$ = "BroadcastTo";
var og = "BroadcastArgs";
var Sa = "Cast";
var Ca = "Ceil";
var Ir = "ClipByValue";
var qd = "Complex";
var jd = "ComplexAbs";
var oo = "Concat";
var Na = "Conv2D";
var ug = "Conv2DBackpropFilter";
var Ta = "Conv2DBackpropInput";
var Kd = "Conv3D";
var lg = "Conv3DBackpropFilterV2";
var cg = "Conv3DBackpropInputV2";
var $a = "Cos";
var _a = "Cosh";
var uo = "Cumsum";
var lo = "CropAndResize";
var dg = "DenseBincount";
var co = "DepthToSpace";
var Aa = "DepthwiseConv2dNative";
var pg = "DepthwiseConv2dNativeBackpropFilter";
var hg = "DepthwiseConv2dNativeBackpropInput";
var fg = "Diag";
var Xd = "Dilation2D";
var Kf = "Dilation2DBackpropInput";
var Xf = "Dilation2DBackpropFilter";
var Ea = "RealDiv";
var Yd = "Einsum";
var Ra = "Elu";
var mg = "EluGrad";
var pl = "Erf";
var po = "Equal";
var Da = "Exp";
var ho = "ExpandDims";
var fo = "Expm1";
var gg = "FFT";
var hl = "Fill";
var mo = "FlipLeftRight";
var Fa = "Floor";
var Oa = "FloorDiv";
var Pa = "FusedBatchNorm";
var go = "GatherV2";
var bo = "GatherNd";
var yo = "Greater";
var za = "GreaterEqual";
var Ma = "Identity";
var bg = "IFFT";
var Qd = "Imag";
var fl = "IsFinite";
var ml = "IsInf";
var gl = "IsNan";
var La = "LeakyRelu";
var vo = "Less";
var xo = "LessEqual";
var yg = "LinSpace";
var Ba = "Log";
var bl = "Log1p";
var wo = "LogicalAnd";
var yl = "LogicalNot";
var Zd = "LogicalOr";
var k$ = "LogSoftmax";
var Jd = "LRN";
var vg = "LRNGrad";
var Va = "Max";
var Wa = "Maximum";
var Ua = "MaxPool";
var xg = "MaxPoolGrad";
var ep = "MaxPool3D";
var wg = "MaxPool3DGrad";
var kg = "MaxPoolWithArgmax";
var Ga = "Mean";
var Ha = "Min";
var qa = "Minimum";
var ja = "MirrorPad";
var vl = "Mod";
var Ig = "Multinomial";
var Ka = "Multiply";
var ko = "Neg";
var Io = "NotEqual";
var So = "NonMaxSuppressionV3";
var xl = "NonMaxSuppressionV4";
var Co = "NonMaxSuppressionV5";
var No = "OnesLike";
var To = "OneHot";
var $o = "Pack";
var Xa = "PadV2";
var pde = "Pool";
var Ya = "Pow";
var Qa = "Prelu";
var _o = "Prod";
var wl = "Range";
var tp = "Real";
var kl = "Reciprocal";
var Za = "Relu";
var Ao = "Reshape";
var Il = "ResizeNearestNeighbor";
var Sg = "ResizeNearestNeighborGrad";
var Ja = "ResizeBilinear";
var Cg = "ResizeBilinearGrad";
var ei = "Relu6";
var Eo = "Reverse";
var Ro = "Round";
var ti = "Rsqrt";
var Do = "ScatterNd";
var Fo = "Select";
var Sl = "Selu";
var Oo = "Slice";
var ni = "Sin";
var Po = "Sinh";
var Cl = "Sign";
var si = "Sigmoid";
var Nl = "Softplus";
var ri = "Sqrt";
var ai = "Sum";
var zo = "SpaceToBatchND";
var Mo = "SplitV";
var ii = "Softmax";
var np = "SparseFillEmptyRows";
var Tl = "SparseReshape";
var sp = "SparseSegmentMean";
var rp = "SparseSegmentSum";
var ap = "SparseToDense";
var oi = "SquaredDifference";
var $l = "Square";
var Lo = "StridedSlice";
var ip = "StringNGrams";
var Ng = "StringSplit";
var Tg = "StringToHashBucketFast";
var ui = "Sub";
var Bo = "Tan";
var li = "Tanh";
var Sr = "Tile";
var Vo = "TopK";
var Wo = "Transform";
var ci = "Transpose";
var $g = "Unique";
var Uo = "Unpack";
var op = "UnsortedSegmentSum";
var Go = "ZerosLike";
var di = "Step";
var hd = "FromPixels";
var Ho = "RotateWithOffset";
var na = "_FusedMatMul";
var sa = "FusedConv2D";
var ra = "FusedDepthwiseConv2D";
function ar(...e) {
  X().getBool("IS_TEST") || X().getBool("PROD") || console.warn(...e);
}
function I$(...e) {
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}
var Yi = sg("kernelRegistry", () => /* @__PURE__ */ new Map());
var Vu = sg("gradRegistry", () => /* @__PURE__ */ new Map());
function Yf(e, t) {
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}
function Jv(e) {
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}
function Qf(e) {
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  for (; ; ) {
    let { done: s, value: r } = t.next();
    if (s)
      break;
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  return n;
}
function _l(e) {
  let { kernelName: t, backendName: n } = e, s = _g(t, n);
  Yi.has(s) && ar(`The kernel '${t}' for backend '${n}' is already registered`), Yi.set(s, e);
}
function S$(e) {
  let { kernelName: t } = e;
  Vu.has(t) && X().getBool("DEBUG") && ar(`Overriding the gradient for '${t}'`), Vu.set(t, e);
}
function hde(e, t) {
  let n = _g(e, t);
  if (!Yi.has(n))
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}
function fde(e) {
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    throw new Error(`The gradient '${e}' for backend is not registered`);
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}
function mde(e, t) {
  Qf(e).forEach((s) => {
    let r = Object.assign({}, s, { backendName: t });
    _l(r);
  });
}
function _g(e, t) {
  return `${t}_${e}`;
}
var w = {};
Ae(w, { arraysEqual: () => wr, assert: () => O, assertNonNegativeIntegerDimensions: () => tg, assertNonNull: () => va, assertShapesMatch: () => dn, bytesFromStringArray: () => Uw, bytesPerElement: () => jf, checkConversionForErrors: () => Vw, clamp: () => Bu, computeStrides: () => ro, createScalarValue: () => A$, createShuffledIndices: () => u$, decodeString: () => fd, distSquared: () => r$, encodeString: () => El, fetch: () => R$, fingerPrint64: () => _$, flatten: () => ta, getArrayFromDType: () => Bw, getTypedArrayFromDType: () => Lw, hasEncodingLoss: () => d$, hexToLong: () => Al, indexToLoc: () => f$, inferDtype: () => Ud, inferFromImplicitShape: () => c$, isBoolean: () => Gw, isFunction: () => hr, isInt: () => Xi, isNumber: () => Hw, isPromise: () => ng, isScalarShape: () => a$, isString: () => ir, isTypedArray: () => Yt, isValidDtype: () => Ww, locToIndex: () => h$, makeOnesTypedArray: () => eg, makeZerosNestedTypedArray: () => p$, makeZerosTypedArray: () => Gd, nearestDivisor: () => pd, nearestLargerEven: () => t$, now: () => Wu, parseAxisParam: () => Jn, randUniform: () => s$, repeatedTry: () => l$, rightPad: () => Pu, shuffle: () => zw, shuffleCombo: () => e$, sizeFromShape: () => pt, sizeToSquarishShape: () => o$, squeezeShape: () => Mw, sum: () => n$, swap: () => dd, tanh: () => i$, toNestedArray: () => Gi, toTypedArray: () => up });
var ex = ya(PT());
var Gr = ex.default || ex;
function Al(e) {
  return Gr.fromString(e, true, 16);
}
var Xw = Al("c3a5c85c97cb3127");
var Ur = Al("b492b66fbe98f273");
var an = Al("9ae16a3b2f90404f");
function Zf(e) {
  return e.xor(e.shru(47));
}
function Yw(e, t, n) {
  let s = e.slice(t, t + n);
  return Gr.fromBytes(Array.from(s), true, true);
}
function lt(e, t) {
  return Yw(e, t, 8);
}
function tx(e, t) {
  return Yw(e, t, 4);
}
function Lt(e, t) {
  return t === 0 ? e : e.shru(t).or(e.shl(64 - t));
}
function ur(e, t, n = Al("9ddfea08eb382d69")) {
  let s = e.xor(t).mul(n);
  s = s.xor(s.shru(47));
  let r = t.xor(s).mul(n);
  return r = r.xor(r.shru(47)), r = r.mul(n), r;
}
function C$(e, t, n, s, r, a) {
  r = r.add(e), a = Lt(a.add(r).add(s), 21);
  let i = r;
  return r = r.add(t), r = r.add(n), a = a.add(Lt(r, 44)), [r.add(s), a.add(i)];
}
function Bc(e, t, n, s) {
  return C$(lt(e, t), lt(e, t + 8), lt(e, t + 16), lt(e, t + 24), n, s);
}
function N$(e, t = e.length) {
  if (t >= 8) {
    let n = an.add(t * 2), s = lt(e, 0).add(an), r = lt(e, t - 8), a = Lt(r, 37).mul(n).add(s), i = Lt(s, 25).add(r).mul(n);
    return ur(a, i, n);
  }
  if (t >= 4) {
    let n = an.add(t * 2), s = tx(e, 0);
    return ur(s.shl(3).add(t), tx(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 Zf(an.mul(a).xor(Xw.mul(i))).mul(an);
  }
  return an;
}
function T$(e, t = e.length) {
  let n = an.add(t * 2), s = lt(e, 0).mul(Ur), r = lt(e, 8), a = lt(e, t - 8).mul(n), i = lt(e, t - 16).mul(an);
  return ur(Lt(s.add(r), 43).add(Lt(a, 30)).add(i), s.add(Lt(r.add(an), 18)).add(a), n);
}
function $$(e, t = e.length) {
  let n = an.add(t * 2), s = lt(e, 0).mul(an), r = lt(e, 8), a = lt(e, t - 8).mul(n), i = lt(e, t - 16).mul(an), o = Lt(s.add(r), 43).add(Lt(a, 30)).add(i), u = ur(o, s.add(Lt(r.add(an), 18)).add(a), n), l = lt(e, 16).mul(n), c = lt(e, 24), p = o.add(lt(e, t - 32)).mul(n), d = u.add(lt(e, t - 24)).mul(n);
  return ur(Lt(l.add(c), 43).add(Lt(p, 30)).add(d), l.add(Lt(c.add(s), 18)).add(p), n);
}
function _$(e, t = e.length) {
  let n = Gr.fromNumber(81, true);
  if (t <= 32)
    return t <= 16 ? N$(e, t) : T$(e, t);
  if (t <= 64)
    return $$(e, t);
  let s = n, r = n.mul(Ur).add(113), a = Zf(r.mul(an).add(113)).mul(an), i = [Gr.UZERO, Gr.UZERO], o = [Gr.UZERO, Gr.UZERO];
  s = s.mul(an).add(lt(e, 0));
  let u = 0, l = (t - 1 >> 6) * 64, c = l + (t - 1 & 63) - 63;
  do
    s = Lt(s.add(r).add(i[0]).add(lt(e, u + 8)), 37).mul(Ur), r = Lt(r.add(i[1]).add(lt(e, u + 48)), 42).mul(Ur), s = s.xor(o[1]), r = r.add(i[0]).add(lt(e, u + 40)), a = Lt(a.add(o[0]), 33).mul(Ur), i = Bc(e, u, i[1].mul(Ur), s.add(o[0])), o = Bc(e, u + 32, a.add(o[1]), r.add(lt(e, u + 16))), [a, s] = [s, a], u += 64;
  while (u !== l);
  let p = Ur.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 = Lt(s.add(r).add(i[0]).add(lt(e, u + 8)), 37).mul(p), r = Lt(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 = Lt(a.add(o[0]), 33).mul(p), i = Bc(e, u, i[1].mul(p), s.add(o[0])), o = Bc(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(Zf(r).mul(Xw)).add(a), ur(i[1], o[1], p).add(s), p);
}
function A$(e, t) {
  return t === "string" ? El(e) : up([e], t);
}
function E$(e, t) {
  return e instanceof Float32Array && t === "float32" || e instanceof Int32Array && t === "int32" || e instanceof Uint8Array && t === "bool";
}
function up(e, t) {
  if (t === "string")
    throw new Error("Cannot convert a string[] to a TypedArray");
  if (Array.isArray(e) && (e = ta(e)), X().getBool("DEBUG") && Vw(e, t), E$(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 ${t}`);
}
function Wu() {
  return X().platform.now();
}
function R$(e, t) {
  return X().platform.fetch(e, t);
}
function El(e, t = "utf-8") {
  return t = t || "utf-8", X().platform.encode(e, t);
}
function fd(e, t = "utf-8") {
  return t = t || "utf-8", X().platform.decode(e, t);
}
var D$ = class {
  constructor(e, t) {
    this.backendTimer = e, this.logger = t, t == null && (this.logger = new O$());
  }
  profileKernel(e, t, n) {
    let s, r = () => {
      s = n();
    }, a, i = Wu();
    if (this.backendTimer.timerAvailable())
      a = this.backendTimer.time(r);
    else {
      r();
      for (let u of s)
        u.dataSync();
      a = Promise.resolve({ kernelMs: Wu() - i });
    }
    if (X().getBool("CHECK_COMPUTATION_FOR_ERRORS"))
      for (let u = 0; u < s.length; u++) {
        let l = s[u];
        l.data().then((c) => {
          F$(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 F$(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 O$ = class {
  logKernelProfile(e, t, n, s, r, a) {
    let i = typeof s == "number" ? Pu(`${s}ms`, 9) : s.error, o = Pu(e, 25), u = t.rank, l = t.size, c = Pu(t.shape.toString(), 14), p = "";
    for (let d in r) {
      let h = r[d];
      if (h != null) {
        let f = h.shape || t.shape, m = f.length;
        p += `${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 P$(e, t, n) {
  let s = {}, r = {};
  for (let u = 0; u < t.length; u++)
    s[t[u].id] = true;
  for (let u = 0; u < e.length; u++) {
    let l = e[u], c = l.inputs;
    for (let p in c) {
      let d = c[p], h = false;
      for (let f = 0; f < t.length; f++)
        if (s[d.id]) {
          l.outputs.forEach((m) => s[m.id] = true), h = true, r[l.id] = true;
          break;
        }
      if (h)
        break;
    }
  }
  let a = {};
  a[n.id] = true;
  let i = {};
  for (let u = e.length - 1; u >= 0; u--) {
    let l = e[u], c = l.inputs;
    for (let p = 0; p < l.outputs.length; p++)
      if (a[l.outputs[p].id]) {
        for (let d in c)
          a[c[d].id] = true, i[l.id] = true;
        break;
      }
  }
  let o = [];
  for (let u = 0; u < e.length; u++) {
    let l = e[u];
    if (r[l.id] && i[l.id]) {
      let c = {};
      for (let d in l.inputs) {
        let h = l.inputs[d];
        s[h.id] && (c[d] = h);
      }
      let p = Object.assign({}, l);
      p.inputs = c, p.outputs = l.outputs, o.push(p);
    }
  }
  return o;
}
function z$(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 (!wr(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 nx = 20;
var Iu = 3;
var Rf = 7;
function M$(e, t, n, s) {
  let r = ro(t), a = L$(e, t, n, r), i = t.length, o = Jc(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 L$(e, t, n, s) {
  let r = pt(t), a = s[s.length - 1], i = new Array(a).fill(0), o = t.length, u = n === "complex64" ? $u(e) : e;
  if (o > 1)
    for (let l = 0; l < r / a; l++) {
      let c = l * a;
      for (let p = 0; p < a; p++)
        i[p] = Math.max(i[p], Tu(u[c + p], 0, n).length);
    }
  return i;
}
function Tu(e, t, n) {
  let s;
  return Array.isArray(e) ? s = `${parseFloat(e[0].toFixed(Rf))} + ${parseFloat(e[1].toFixed(Rf))}j` : ir(e) ? s = `'${e}'` : n === "bool" ? s = Qw(e) : s = parseFloat(e.toFixed(Rf)).toString(), Pu(s, t);
}
function Qw(e) {
  return e === 0 ? "false" : "true";
}
function Jc(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 = $u(e);
      return [Tu(m[0], 0, n)];
    }
    return n === "bool" ? [Qw(e[0])] : [e[0].toString()];
  }
  if (u === 1) {
    if (o > nx) {
      let g = Iu * i, b = Array.from(e.slice(0, g)), y = Array.from(e.slice((o - Iu) * i, o * i));
      return n === "complex64" && (b = $u(b), y = $u(y)), ["[" + b.map((v, x) => Tu(v, r[x], n)).join(", ") + ", ..., " + y.map((v, x) => Tu(v, r[o - Iu + x], n)).join(", ") + "]"];
    }
    let m = n === "complex64" ? $u(e) : Array.from(e);
    return ["[" + m.map((g, b) => Tu(g, r[b], n)).join(", ") + "]"];
  }
  let l = t.slice(1), c = s.slice(1), p = s[0] * i, d = [];
  if (o > nx) {
    for (let m = 0; m < Iu; m++) {
      let g = m * p, b = g + p;
      d.push(...Jc(e.slice(g, b), l, n, c, r, false));
    }
    d.push("...");
    for (let m = o - Iu; m < o; m++) {
      let g = m * p, b = g + p;
      d.push(...Jc(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(...Jc(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 $u(e) {
  let t = [];
  for (let n = 0; n < e.length; n += 2)
    t.push([e[n], e[n + 1]]);
  return t;
}
var Vt = class {
  constructor(e, t, n) {
    if (this.dtype = t, this.shape = e.slice(), this.size = pt(e), n != null) {
      let s = n.length;
      O(s === this.size, () => `Length of values '${s}' does not match the size inferred by the shape '${this.size}'.`);
    }
    if (t === "complex64")
      throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");
    this.values = n || Bw(t, this.size), this.strides = ro(e);
  }
  set(e, ...t) {
    t.length === 0 && (t = [0]), O(t.length === this.rank, () => `The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);
    let n = this.locToIndex(t);
    this.values[n] = e;
  }
  get(...e) {
    e.length === 0 && (e = [0]);
    let t = 0;
    for (let s of e) {
      if (s < 0 || s >= this.shape[t]) {
        let r = `Requested out of range element at ${e}.   Buffer shape=${this.shape}`;
        throw new Error(r);
      }
      t++;
    }
    let n = e[e.length - 1];
    for (let s = 0; s < e.length - 1; ++s)
      n += this.strides[s] * e[s];
    return this.values[n];
  }
  locToIndex(e) {
    if (this.rank === 0)
      return 0;
    if (this.rank === 1)
      return e[0];
    let t = e[e.length - 1];
    for (let n = 0; n < e.length - 1; ++n)
      t += this.strides[n] * e[n];
    return t;
  }
  indexToLoc(e) {
    if (this.rank === 0)
      return [];
    if (this.rank === 1)
      return [e];
    let t = new Array(this.shape.length);
    for (let n = 0; n < t.length - 1; ++n)
      t[n] = Math.floor(e / this.strides[n]), e -= t[n] * this.strides[n];
    return t[t.length - 1] = e, t;
  }
  get rank() {
    return this.shape.length;
  }
  toTensor() {
    return us().makeTensor(this.values, this.shape, this.dtype);
  }
};
var us = null;
var Bi = null;
var B$ = null;
function V$(e) {
  us = e;
}
function W$(e) {
  Bi = e;
}
function U$(e) {
  B$ = 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 = pt(e), this.strides = ro(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 Bi.buffer(this.shape, this.dtype, e);
  }
  bufferSync() {
    return Bi.buffer(this.shape, this.dtype, this.dataSync());
  }
  async array() {
    let e = await this.data();
    return Gi(this.shape, e, this.dtype === "complex64");
  }
  arraySync() {
    return Gi(this.shape, this.dataSync(), this.dtype === "complex64");
  }
  async data() {
    this.throwIfDisposed();
    let e = us().read(this.dataId);
    if (this.dtype === "string") {
      let t = await e;
      try {
        return t.map((n) => fd(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(), us().readToGPU(this.dataId, e);
  }
  dataSync() {
    this.throwIfDisposed();
    let e = us().readSync(this.dataId);
    if (this.dtype === "string")
      try {
        return e.map((t) => fd(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 us().read(this.dataId);
    return this.dtype === "string" ? e : new Uint8Array(e.buffer);
  }
  dispose() {
    this.isDisposed || (us().disposeTensor(this), this.isDisposedInternal = true);
  }
  get isDisposed() {
    return this.isDisposedInternal;
  }
  throwIfDisposed() {
    if (this.isDisposed)
      throw new Error("Tensor is disposed.");
  }
  print(e = false) {
    return Bi.print(this, e);
  }
  clone() {
    return this.throwIfDisposed(), Bi.clone(this);
  }
  toString(e = false) {
    let t = this.dataSync();
    return M$(t, this.shape, this.dtype, e);
  }
  cast(e) {
    return this.throwIfDisposed(), Bi.cast(this, e);
  }
  variable(e = true, t, n) {
    return this.throwIfDisposed(), us().makeVariable(this, e, t, n);
  }
};
Object.defineProperty(et, Symbol.hasInstance, { value: (e) => !!e && e.data != null && e.dataSync != null && e.throwIfDisposed != null });
function G$() {
  return sg("Tensor", () => et);
}
G$();
var md = class extends et {
  constructor(e, t, n, s) {
    super(e.shape, e.dtype, e.dataId, s);
    this.trainable = t, this.name = n;
  }
  assign(e) {
    if (e.dtype !== this.dtype)
      throw new Error(`dtype of the new value (${e.dtype}) and previous value (${this.dtype}) must match`);
    if (!wr(e.shape, this.shape))
      throw new Error(`shape of the new value (${e.shape}) and previous value (${this.shape}) must match`);
    us().disposeTensor(this), this.dataId = e.dataId, us().incRef(this, null);
  }
  dispose() {
    us().disposeVariable(this), this.isDisposedInternal = true;
  }
};
Object.defineProperty(md, Symbol.hasInstance, { value: (e) => e instanceof et && e.assign != null && e.assign instanceof Function });
var _s = {};
Ae(_s, { assertTypesMatch: () => nk, getTensorsInContainer: () => Ag, isTensorInList: () => j$, makeTypesMatch: () => vt });
var H$ = ((e) => (e.R0 = "R0", e.R1 = "R1", e.R2 = "R2", e.R3 = "R3", e.R4 = "R4", e.R5 = "R5", e.R6 = "R6", e))(H$ || {});
var Zw = ((e) => (e.float32 = "float32", e.int32 = "int32", e.bool = "int32", e.complex64 = "complex64", e))(Zw || {});
var Jw = ((e) => (e.float32 = "float32", e.int32 = "int32", e.bool = "bool", e.complex64 = "complex64", e))(Jw || {});
var ek = ((e) => (e.float32 = "float32", e.int32 = "float32", e.bool = "float32", e.complex64 = "complex64", e))(ek || {});
var tk = ((e) => (e.float32 = "complex64", e.int32 = "complex64", e.bool = "complex64", e.complex64 = "complex64", e))(tk || {});
var q$ = { float32: ek, int32: Zw, bool: Jw, complex64: tk };
function yn(e, t) {
  if (e === "string" || t === "string") {
    if (e === "string" && t === "string")
      return "string";
    throw new Error(`Can not upcast ${e} with ${t}`);
  }
  return q$[e][t];
}
function lp(e) {
  return yn(e, "int32");
}
function vt(e, t) {
  if (e.dtype === t.dtype)
    return [e, t];
  let n = yn(e.dtype, t.dtype);
  return [e.cast(n), t.cast(n)];
}
function nk(e, t) {
  O(e.dtype === t.dtype, () => `The dtypes of the first(${e.dtype}) and second(${t.dtype}) input must match`);
}
function j$(e, t) {
  return t.some((n) => n.id === e.id);
}
function Ag(e) {
  let t = [];
  return sk(e, t, /* @__PURE__ */ new Set()), t;
}
function sk(e, t, n) {
  if (e == null)
    return;
  if (e instanceof et) {
    t.push(e);
    return;
  }
  if (!K$(e))
    return;
  let s = e;
  for (let r in s) {
    let a = s[r];
    n.has(a) || (n.add(a), sk(a, t, n));
  }
}
function K$(e) {
  return Array.isArray(e) || typeof e == "object";
}
function Df(e) {
  return e.kernelName != null;
}
var sx = class {
  constructor() {
    this.registeredVariables = {}, this.nextTapeNodeId = 0, this.numBytes = 0, this.numTensors = 0, this.numStringTensors = 0, this.numDataBuffers = 0, this.gradientDepth = 0, this.kernelDepth = 0, this.scopeStack = [], this.numDataMovesStack = [], this.nextScopeId = 0, this.tensorInfo = /* @__PURE__ */ new WeakMap(), this.profiling = false, this.activeProfile = { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null, get kernelNames() {
      return Array.from(new Set(this.kernels.map((e) => e.name)));
    } };
  }
  dispose() {
    for (let e in this.registeredVariables)
      this.registeredVariables[e].dispose();
  }
};
var Jf = class {
  constructor(e) {
    this.ENV = e, this.registry = {}, this.registryFactory = {}, this.pendingBackendInitId = 0, this.state = new sx();
  }
  async ready() {
    if (this.pendingBackendInit != null)
      return this.pendingBackendInit.then(() => {
      });
    if (this.backendInstance != null)
      return;
    let e = this.getSortedBackends();
    for (let t = 0; t < e.length; t++) {
      let n = e[t];
      if (await this.initializeBackend(n).success) {
        await this.setBackend(n);
        return;
      }
    }
    throw new Error("Could not initialize any backends, all backend initializations failed.");
  }
  get backend() {
    if (this.pendingBackendInit != null)
      throw new Error(`Backend '${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 '${e}' not found in registry`);
    if (this.backendName = e, this.registry[e] == null) {
      this.backendInstance = null;
      let { success: t, asyncInit: n } = this.initializeBackend(e);
      if (!(n ? await t : t))
        return false;
    }
    return this.backendInstance = this.registry[e], this.setupRegisteredKernels(), this.profiler = new D$(this.backendInstance), true;
  }
  setupRegisteredKernels() {
    Qf(this.backendName).forEach((t) => {
      t.setupFunc != null && t.setupFunc(this.backendInstance);
    });
  }
  disposeRegisteredKernels(e) {
    Qf(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 tl) && 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 Jf.nextTensorId++;
  }
  nextVariableId() {
    return Jf.nextVariableId++;
  }
  clone(e) {
    let t = M.runKernel(Ma, { x: e }), n = { x: e }, s = (a) => ({ x: () => {
      let i = "float32", o = { x: a }, u = { dtype: i };
      return M.runKernel(Sa, 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, !(Yf(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 '${e}'`);
  }
  runKernelFunc(e) {
    let t, n = [], s = this.isTapeOn(), r = this.state.numBytes, a = this.state.numTensors;
    this.shouldCheckForMemLeaks() && this.state.numDataMovesStack.push(0);
    let i;
    this.backendName == null && this.backend;
    let o, u = Df(e) ? e.kernelName : this.state.activeScope != null ? this.state.activeScope.name : "";
    if (Df(e)) {
      let { kernelName: h, inputs: f, attrs: m } = e;
      this.backendName == null && this.backend;
      let g = Yf(h, this.backendName);
      O(g != null, () => `Cannot find registered kernel '${h}' for backend '${this.backendName}'`), i = () => {
        let b = this.backend.numDataIds();
        o = g.kernelFunc({ inputs: f, attrs: m, backend: this.backend });
        let y = Array.isArray(o) ? o : [o];
        this.shouldCheckForMemLeaks() && this.checkKernelForMemLeak(h, b, y);
        let v = y.map((x) => {
          if (x.rank != null)
            return x;
          let { dataId: k, shape: C, dtype: T } = x;
          return this.makeTensorFromDataId(k, C, T);
        });
        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 = Df(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 = Jv(e);
    if (s != null) {
      let r = s.inputsToSave || [], a = s.outputsToSave || [], i;
      s.saveAllInputs ? (O(Array.isArray(t), () => "saveAllInputs is true, expected inputs to be an array."), i = Object.keys(t).map((u) => t[u])) : i = r.map((u) => t[u]);
      let o = n.filter((u, l) => a[l]);
      return i.concat(o);
    }
    return [];
  }
  makeTensor(e, t, n, s) {
    if (e == null)
      throw new Error("Values passed to engine.makeTensor() are null");
    n = n || "float32", s = s || this.backend;
    let r = e;
    n === "string" && ir(e[0]) && (r = e.map((o) => El(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 = Uw(r);
      this.state.numBytes += u - o.bytes, o.bytes = u;
    }
    return i;
  }
  makeTensorFromDataId(e, t, n, s) {
    n = n || "float32";
    let r = new et(t, n, e, this.nextTensorId());
    return this.trackTensor(r, s), r;
  }
  makeVariable(e, t = true, n, s) {
    n = n || this.nextVariableId().toString(), s != null && s !== e.dtype && (e = e.cast(s));
    let r = new md(e, t, n, this.nextTensorId());
    if (this.state.registeredVariables[r.name] != null)
      throw new Error(`Variable with name ${r.name} was already registered`);
    return this.state.registeredVariables[r.name] = r, this.incRef(r, this.backend), r;
  }
  trackTensor(e, t) {
    this.state.numTensors++, e.dtype === "string" && this.state.numStringTensors++;
    let n = 0;
    e.dtype !== "complex64" && e.dtype !== "string" && (n = e.size * jf(e.dtype)), this.state.numBytes += n, this.state.tensorInfo.has(e.dataId) || (this.state.numDataBuffers++, this.state.tensorInfo.set(e.dataId, { backend: t || this.backend, dtype: e.dtype, shape: e.shape, bytes: n })), e instanceof md || this.track(e);
  }
  incRef(e, t) {
    this.trackTensor(e, t), this.backend.incRef(e.dataId);
  }
  removeDataId(e, t) {
    this.state.tensorInfo.has(e) && this.state.tensorInfo.get(e).backend === t && (this.state.tensorInfo.delete(e), this.state.numDataBuffers--);
  }
  disposeTensor(e) {
    if (!this.state.tensorInfo.has(e.dataId))
      return;
    let t = this.state.tensorInfo.get(e.dataId);
    if (this.state.numTensors--, e.dtype === "string" && (this.state.numStringTensors--, this.state.numBytes -= t.bytes), e.dtype !== "complex64" && e.dtype !== "string") {
      let n = e.size * jf(e.dtype);
      this.state.numBytes -= n;
    }
    t.backend.disposeData(e.dataId) && this.removeDataId(e.dataId, t.backend);
  }
  disposeVariables() {
    for (let e in this.state.registeredVariables) {
      let t = this.state.registeredVariables[e];
      this.disposeVariable(t);
    }
  }
  disposeVariable(e) {
    this.disposeTensor(e), this.state.registeredVariables[e.name] != null && delete this.state.registeredVariables[e.name];
  }
  memory() {
    let e = this.backend.memory();
    return e.numTensors = this.state.numTensors, e.numDataBuffers = this.state.numDataBuffers, e.numBytes = this.state.numBytes, this.state.numStringTensors > 0 && (e.unreliable = true, e.reasons == null && (e.reasons = []), e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")), e;
  }
  async profile(e) {
    this.state.profiling = true;
    let t = this.state.numBytes, n = this.state.numTensors;
    this.state.activeProfile.kernels = [], this.state.activeProfile.result = await e(), this.state.profiling = false, this.state.activeProfile.peakBytes = Math.max(...this.state.activeProfile.kernels.map((s) => s.totalBytesSnapshot)), this.state.activeProfile.newBytes = this.state.numBytes - t, this.state.activeProfile.newTensors = this.state.numTensors - n;
    for (let s of this.state.activeProfile.kernels)
      s.kernelTimeMs = await s.kernelTimeMs, s.extraInfo = await s.extraInfo;
    return this.state.activeProfile;
  }
  isTapeOn() {
    return this.state.gradientDepth > 0 && this.state.kernelDepth === 0;
  }
  addTapeNode(e, t, n, s, r, a) {
    let i = { id: this.state.nextTapeNodeId++, kernelName: e, inputs: t, outputs: n, saved: r }, o = Jv(e);
    o != null && (s = o.gradFunc), s != null && (i.gradient = (u) => (u = u.map((l, c) => {
      if (l == null) {
        let p = n[c], d = Gd(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 = Ag(e), n = new Set(t.map((r) => r.id));
    for (let r = 0; r < this.state.activeScope.track.length; r++) {
      let a = this.state.activeScope.track[r];
      !a.kept && !n.has(a.id) && a.dispose();
    }
    let s = this.state.scopeStack.pop();
    this.state.activeScope = this.state.scopeStack.length === 0 ? null : this.state.scopeStack[this.state.scopeStack.length - 1], t.forEach((r) => {
      !r.kept && r.scopeId === s.id && this.track(r);
    });
  }
  gradients(e, t, n, s = false) {
    if (O(t.length > 0, () => "gradients() received an empty list of xs."), n != null && n.dtype !== "float32")
      throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);
    let r = this.scopedRun(() => this.startTape(), () => this.endTape(), () => this.tidy("forward", e));
    O(r instanceof et, () => "The result y returned by f() must be a tensor.");
    let a = P$(this.state.activeTape, t, r);
    if (!s && a.length === 0 && t.length > 0)
      throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");
    return this.tidy("backward", () => {
      let i = {};
      i[r.id] = n == null ? X$(r.shape) : n, z$(i, a, (u) => this.tidy(u), Y$);
      let o = t.map((u) => i[u.id]);
      return this.state.gradientDepth === 0 && (this.state.activeTape.forEach((u) => {
        for (let l of u.saved)
          l.dispose();
      }), this.state.activeTape = null), { value: r, grads: o };
    });
  }
  customGrad(e) {
    return O(hr(e), () => "The f passed in customGrad(f) must be a function."), (...t) => {
      O(t.every((i) => i instanceof et), () => "The args passed in customGrad(f)(x1, x2,...) must all be tensors");
      let n, s = {};
      t.forEach((i, o) => {
        s[o] = i;
      });
      let r = (i, o) => (n = e(...t, o), O(n.value instanceof et, () => "The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"), O(hr(n.gradFunc), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."), n.value), a = (i, o) => {
        let u = n.gradFunc(i, o), l = Array.isArray(u) ? u : [u];
        O(l.length === t.length, () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."), O(l.every((p) => p instanceof et), () => "The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.");
        let c = {};
        return l.forEach((p, d) => {
          c[d] = () => p;
        }), c;
      };
      return this.runKernelFunc({ forwardFunc: r, backwardsFunc: a, inputs: s });
    };
  }
  readSync(e) {
    return this.state.tensorInfo.get(e).backend.readSync(e);
  }
  read(e) {
    return this.state.tensorInfo.get(e).backend.read(e);
  }
  readToGPU(e, t) {
    return this.state.tensorInfo.get(e).backend.readToGPU(e, t);
  }
  async time(e) {
    let t = Wu(), n = await this.backend.time(e);
    return n.wallMs = Wu() - 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 sx();
    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 Eg = Jf;
Eg.nextTensorId = 0;
Eg.nextVariableId = 0;
function X$(e) {
  let t = eg(pt(e), "float32");
  return M.makeTensor(t, e, "float32");
}
function rk() {
  let e = Kw();
  if (e._tfengine == null) {
    let t = new m$(e);
    e._tfengine = new Eg(t);
  }
  return v$(e._tfengine.ENV), V$(() => e._tfengine), e._tfengine;
}
var M = rk();
function Y$(e, t) {
  let n = { a: e, b: t };
  return M.runKernel(kr, n);
}
var cp = {};
Ae(cp, { isBrowser: () => ak, isMobile: () => J$, mockIsMobile: () => Z$ });
function Q$() {
  return typeof navigator != "undefined" && navigator != null;
}
var em;
function Z$(e) {
  em = e;
}
function J$(e) {
  if (em !== void 0)
    return em;
  if (e || Q$()) {
    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 ak() {
  return typeof window != "undefined" && window.document != null || typeof WorkerGlobalScope != "undefined";
}
var gs = X();
gs.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.");
});
gs.registerFlag("IS_BROWSER", () => ak());
gs.registerFlag("IS_NODE", () => typeof process != "undefined" && typeof process.versions != "undefined" && typeof process.versions.node != "undefined");
gs.registerFlag("IS_CHROME", () => typeof navigator != "undefined" && navigator != null && navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && /Google Inc/.test(navigator.vendor));
gs.registerFlag("PROD", () => false);
gs.registerFlag("TENSORLIKE_CHECK_SHAPE_CONSISTENCY", () => gs.getBool("DEBUG"));
gs.registerFlag("DEPRECATION_WARNINGS_ENABLED", () => true);
gs.registerFlag("IS_TEST", () => false);
gs.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => true);
gs.registerFlag("WRAP_TO_IMAGEBITMAP", () => false);
function Rs(e, t) {
  let n = e;
  if (Yt(e))
    return t === "string" ? [] : [e.length];
  if (!Array.isArray(e))
    return [];
  let s = [];
  for (; Array.isArray(n) || Yt(n) && t !== "string"; )
    s.push(n.length), n = n[0];
  return Array.isArray(e) && X().getBool("TENSORLIKE_CHECK_SHAPE_CONSISTENCY") && ik(e, s, []), s;
}
function ik(e, t, n) {
  if (n = n || [], !Array.isArray(e) && !Yt(e)) {
    O(t.length === 0, () => `Element arr[${n.join("][")}] is a primitive, but should be an array/TypedArray of ${t[0]} elements`);
    return;
  }
  O(t.length > 0, () => `Element arr[${n.join("][")}] should be a primitive, but is an array of ${e.length} elements`), O(e.length === t[0], () => `Element arr[${n.join("][")}] should have ${t[0]} elements, but has ${e.length} elements`);
  let s = t.slice(1);
  for (let r = 0; r < e.length; ++r)
    ik(e[r], s, n.concat(r));
}
function rx(e, t, n, s) {
  if (e !== "string_or_numeric") {
    if (e == null)
      throw new Error("Expected dtype cannot be null.");
    if (e !== "numeric" && e !== t || e === "numeric" && t === "string")
      throw new Error(`Argument '${n}' passed to '${s}' must be ${e} tensor, but got ${t} tensor`);
  }
}
function _(e, t, n, s = "numeric") {
  if (e instanceof et)
    return rx(s, e.dtype, t, n), e;
  let r = Ud(e);
  if (r !== "string" && ["bool", "int32", "float32"].indexOf(s) >= 0 && (r = s), rx(s, r, t, n), e == null || !Yt(e) && !Array.isArray(e) && typeof e != "number" && typeof e != "boolean" && typeof e != "string") {
    let u = e == null ? "null" : e.constructor.name;
    throw new Error(`Argument '${t}' passed to '${n}' must be a Tensor or TensorLike, but got '${u}'`);
  }
  let a = Rs(e, r);
  !Yt(e) && !Array.isArray(e) && (e = [e]);
  let o = r !== "string" ? up(e, r) : ta(e, [], true);
  return M.makeTensor(o, a, r);
}
function Uu(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 e_ = "__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 + e_;
  let r = (...a) => {
    M.startScope(n);
    try {
      let i = s(...a);
      return ng(i) && console.error("Cannot return a Promise inside of tidy."), M.endScope(i), i;
    } catch (i) {
      throw M.endScope(null), i;
    }
  };
  return Object.defineProperty(r, "name", { value: n, configurable: true }), r;
}
function t_(e, t) {
  let n = _(e, "real", "complex"), s = _(t, "imag", "complex");
  dn(n.shape, s.shape, `real and imag shapes, ${n.shape} and ${s.shape}, must match in call to tf.complex().`);
  let r = { real: n, imag: s };
  return M.runKernel(qd, r);
}
var aa = L({ complex_: t_ });
function Cr(e, t, n, s) {
  if (s == null && (s = Ud(e)), s === "complex64")
    throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");
  if (!Yt(e) && !Array.isArray(e) && typeof e != "number" && typeof e != "boolean" && typeof e != "string")
    throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");
  if (t != null) {
    tg(t);
    let r = pt(t), a = pt(n);
    O(r === a, () => `Based on the provided shape, [${t}], the tensor should have ${r} values but has ${a}`);
    for (let i = 0; i < n.length; ++i) {
      let o = n[i], u = i === n.length - 1 ? o !== pt(t.slice(i)) : true;
      O(n[i] === t[i] || !u, () => `Error creating a new Tensor. Inferred shape (${n}) does not match the provided shape (${t}). `);
    }
  }
  return !Yt(e) && !Array.isArray(e) && (e = [e]), t = t || n, e = s !== "string" ? up(e, s) : ta(e, [], true), M.makeTensor(e, t, s);
}
function hs(e, t, n) {
  let s = Rs(e, n);
  return Cr(e, t, s, n);
}
var tm = { float32: 4, float16: 2, int32: 4, uint16: 2, uint8: 1, bool: 1, complex64: 8 };
var gd = 4;
async function n_(e, t) {
  let n = [], s = [], r = Array.isArray(e) ? e.map((i) => i.name) : Object.keys(e);
  for (let i = 0; i < r.length; ++i) {
    let o = r[i], u = Array.isArray(e) ? e[i].tensor : e[o];
    if (u.dtype !== "float32" && u.dtype !== "int32" && u.dtype !== "bool" && u.dtype !== "string" && u.dtype !== "complex64")
      throw new Error(`Unsupported dtype in weight '${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) + gd * 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 += gd, 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 ok(e, t) {
  let n = {}, s, r = 0;
  for (let a of t) {
    let i = a.name, o = a.dtype, u = a.shape, l = pt(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 = tm[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 = l_()), 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}': ${o}`);
      r += l * d;
    } else if (o === "string") {
      let p = pt(a.shape);
      c = [];
      for (let d = 0; d < p; d++) {
        let h = new Uint32Array(e.slice(r, r + gd))[0];
        r += gd;
        let f = new Uint8Array(e.slice(r, r + h));
        c.push(f), r += h;
      }
    } else {
      let p = tm[o], d = e.slice(r, r + l * p);
      if (o === "float32")
        c = new Float32Array(d);
      else if (o === "int32")
        c = new Int32Array(d);
      else if (o === "bool")
        c = new Uint8Array(d);
      else if (o === "complex64") {
        c = new Float32Array(d);
        let h = new Float32Array(c.length / 2), f = new Float32Array(c.length / 2);
        for (let b = 0; b < h.length; b++)
          h[b] = c[b * 2], f[b] = c[b * 2 + 1];
        let m = hs(h, u, "float32"), g = hs(f, u, "float32");
        n[i] = aa(m, g), m.dispose(), g.dispose();
      } else
        throw new Error(`Unsupported dtype in weight '${i}': ${o}`);
      r += l * p;
    }
    o !== "complex64" && (n[i] = hs(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 Rg = typeof Buffer != "undefined" && (typeof Blob == "undefined" || typeof atob == "undefined" || typeof btoa == "undefined");
function ax(e) {
  return Rg ? Buffer.byteLength(e) : new Blob([e]).size;
}
function r_(e) {
  if (Rg)
    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 a_(e) {
  if (Rg) {
    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 Dg(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 ix(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 uk(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 Fg(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 Rl(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 : ax(JSON.stringify(e.modelTopology)), weightSpecsBytes: e.weightSpecs == null ? 0 : ax(JSON.stringify(e.weightSpecs)), weightDataBytes: e.weightData == null ? 0 : e.weightData.byteLength };
}
function i_() {
  let e = (n) => {
    let s = n << 13, r = 0;
    for (; (s & 8388608) === 0; )
      r -= 8388608, s <<= 1;
    return s &= -8388609, r += 947912704, s | r;
  }, t = new Uint32Array(2048);
  t[0] = 0;
  for (let n = 1; n < 1024; n++)
    t[n] = e(n);
  for (let n = 1024; n < 2048; n++)
    t[n] = 939524096 + (n - 1024 << 13);
  return t;
}
function o_() {
  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 u_() {
  let e = new Uint32Array(64);
  for (let t = 0; t < 64; t++)
    e[t] = 1024;
  return e[0] = e[32] = 0, e;
}
function l_() {
  let e = i_(), t = o_(), n = u_();
  return (s) => {
    let r = new ArrayBuffer(4 * s.length), a = new Uint32Array(r);
    for (let i = 0; i < s.length; i++) {
      let o = s[i], u = e[n[o >> 10] + (o & 1023)] + t[o >> 10];
      a[i] = u;
    }
    return new Float32Array(r);
  };
}
var xt = class {
  constructor() {
    this.saveRouters = [], this.loadRouters = [];
  }
  static getInstance() {
    return xt.instance == null && (xt.instance = new xt()), xt.instance;
  }
  static registerSaveRouter(e) {
    xt.getInstance().saveRouters.push(e);
  }
  static registerLoadRouter(e) {
    xt.getInstance().loadRouters.push(e);
  }
  static getSaveHandlers(e) {
    return xt.getHandlers(e, "save");
  }
  static getLoadHandlers(e, t) {
    return xt.getHandlers(e, "load", t);
  }
  static getHandlers(e, t, n) {
    let s = [];
    return (t === "load" ? xt.getInstance().loadRouters : xt.getInstance().saveRouters).forEach((a) => {
      let i = a(e, n);
      i !== null && s.push(i);
    }), s;
  }
};
var c_ = (e) => xt.registerSaveRouter(e);
var d_ = (e) => xt.registerLoadRouter(e);
var p_ = (e) => xt.getSaveHandlers(e);
var h_ = (e, t) => xt.getLoadHandlers(e, t);
var nm = "tensorflowjs";
var sm = 1;
var Kr = "models_store";
var or = "model_info_store";
function lk() {
  if (!X().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 rm(e) {
  let t = e.result;
  t.createObjectStore(Kr, { keyPath: "modelPath" }), t.createObjectStore(or, { keyPath: "modelPath" });
}
var ia = class {
  constructor(e) {
    if (this.indexedDB = lk(), 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(nm, sm);
      r.onupgradeneeded = () => rm(r), r.onsuccess = () => {
        let a = r.result;
        if (t == null) {
          let i = a.transaction(Kr, "readonly"), u = i.objectStore(Kr).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 = Rl(t), o = a.transaction(or, "readwrite"), u = o.objectStore(or), l = u.put({ modelPath: this.modelPath, modelArtifactsInfo: i }), c;
          l.onsuccess = () => {
            c = a.transaction(Kr, "readwrite");
            let d = c.objectStore(Kr).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);
    });
  }
};
ia.URL_SCHEME = "indexeddb://";
var ck = (e) => X().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(ia.URL_SCHEME) ? f_(e.slice(ia.URL_SCHEME.length)) : null;
xt.registerSaveRouter(ck);
xt.registerLoadRouter(ck);
function f_(e) {
  return new ia(e);
}
function m_(e) {
  return e.startsWith(ia.URL_SCHEME) ? e.slice(ia.URL_SCHEME.length) : e;
}
var g_ = class {
  constructor() {
    this.indexedDB = lk();
  }
  async listModels() {
    return new Promise((e, t) => {
      let n = this.indexedDB.open(nm, sm);
      n.onupgradeneeded = () => rm(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 = m_(e), new Promise((t, n) => {
      let s = this.indexedDB.open(nm, sm);
      s.onupgradeneeded = () => rm(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(Kr, "readwrite");
              let d = u.objectStore(Kr).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 Gs = "/";
var Vi = "tensorflowjs_models";
var dk = "info";
var b_ = "model_topology";
var y_ = "weight_specs";
var v_ = "weight_data";
var x_ = "model_metadata";
function pk(e) {
  return { info: [Vi, e, dk].join(Gs), topology: [Vi, e, b_].join(Gs), weightSpecs: [Vi, e, y_].join(Gs), weightData: [Vi, e, v_].join(Gs), modelMetadata: [Vi, e, x_].join(Gs) };
}
function hk(e) {
  for (let t of Object.values(e))
    window.localStorage.removeItem(t);
}
function w_(e) {
  let t = e.split(Gs);
  if (t.length < 3)
    throw new Error(`Invalid key format: ${e}`);
  return t.slice(1, t.length - 1).join(Gs);
}
function k_(e) {
  return e.startsWith(oa.URL_SCHEME) ? e.slice(oa.URL_SCHEME.length) : e;
}
var oa = class {
  constructor(e) {
    if (!X().getBool("IS_BROWSER") || typeof window == "undefined" || typeof window.localStorage == "undefined")
      throw new Error("The current environment does not support local storage.");
    if (this.LS = window.localStorage, e == null || !e)
      throw new Error("For local storage, modelPath must not be null, undefined or empty.");
    this.modelPath = e, this.keys = pk(this.modelPath);
  }
  async save(e) {
    if (e.modelTopology instanceof ArrayBuffer)
      throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");
    {
      let t = JSON.stringify(e.modelTopology), n = JSON.stringify(e.weightSpecs), s = Rl(e);
      try {
        this.LS.setItem(this.keys.info, JSON.stringify(s)), this.LS.setItem(this.keys.topology, t), this.LS.setItem(this.keys.weightSpecs, n), this.LS.setItem(this.keys.weightData, r_(e.weightData));
        let r = { format: e.format, generatedBy: e.generatedBy, convertedBy: e.convertedBy, signature: e.signature != null ? e.signature : void 0, userDefinedMetadata: e.userDefinedMetadata != null ? e.userDefinedMetadata : void 0, modelInitializer: e.modelInitializer != null ? e.modelInitializer : void 0, trainingConfig: e.trainingConfig != null ? e.trainingConfig : void 0 };
        return this.LS.setItem(this.keys.modelMetadata, JSON.stringify(r)), { modelArtifactsInfo: s };
      } catch (r) {
        throw hk(this.keys), new Error(`Failed to save model '${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 '${this.modelPath}' are missing.`);
    return t.weightData = a_(a), t;
  }
};
oa.URL_SCHEME = "localstorage://";
var fk = (e) => X().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(oa.URL_SCHEME) ? I_(e.slice(oa.URL_SCHEME.length)) : null;
xt.registerSaveRouter(fk);
xt.registerLoadRouter(fk);
function I_(e) {
  return new oa(e);
}
var S_ = class {
  constructor() {
    O(X().getBool("IS_BROWSER"), () => "Current environment is not a web browser"), O(typeof window == "undefined" || typeof window.localStorage != "undefined", () => "Current browser does not appear to support localStorage"), this.LS = window.localStorage;
  }
  async listModels() {
    let e = {}, t = Vi + Gs, n = Gs + dk;
    for (let s = 0; s < this.LS.length; ++s) {
      let r = this.LS.key(s);
      if (r.startsWith(t) && r.endsWith(n)) {
        let a = w_(r);
        e[a] = JSON.parse(this.LS.getItem(r));
      }
    }
    return e;
  }
  async removeModel(e) {
    e = k_(e);
    let t = pk(e);
    if (this.LS.getItem(t.info) == null)
      throw new Error(`Cannot find model at path '${e}'`);
    let n = JSON.parse(this.LS.getItem(t.info));
    return hk(t), n;
  }
};
var Hi = "://";
var Pn = class {
  constructor() {
    this.managers = {};
  }
  static getInstance() {
    return Pn.instance == null && (Pn.instance = new Pn()), Pn.instance;
  }
  static registerManager(e, t) {
    O(e != null, () => "scheme must not be undefined or null."), e.endsWith(Hi) && (e = e.slice(0, e.indexOf(Hi))), O(e.length > 0, () => "scheme must not be an empty string.");
    let n = Pn.getInstance();
    O(n.managers[e] == null, () => `A model store manager is already registered for scheme '${e}'.`), n.managers[e] = t;
  }
  static getManager(e) {
    let t = this.getInstance().managers[e];
    if (t == null)
      throw new Error(`Cannot find model manager for scheme '${e}'`);
    return t;
  }
  static getSchemes() {
    return Object.keys(this.getInstance().managers);
  }
};
function ed(e) {
  if (e.indexOf(Hi) === -1)
    throw new Error(`The url string provided does not contain a scheme. Supported schemes are: ${Pn.getSchemes().join(",")}`);
  return { scheme: e.split(Hi)[0], path: e.split(Hi)[1] };
}
async function mk(e, t, n = false) {
  O(e !== t, () => `Old path and new path are the same: '${e}'`);
  let s = xt.getLoadHandlers(e);
  O(s.length > 0, () => `Copying failed because no load handler is found for source URL ${e}.`), O(s.length < 2, () => `Copying failed because more than one (${s.length}) load handlers for source URL ${e}.`);
  let r = s[0], a = xt.getSaveHandlers(t);
  O(a.length > 0, () => `Copying failed because no save handler is found for destination URL ${t}.`), O(a.length < 2, () => `Copying failed because more than one (${s.length}) save handlers for destination URL ${t}.`);
  let i = a[0], o = ed(e).scheme, u = ed(e).path, l = o === ed(e).scheme, c = await r.load();
  n && l && await Pn.getManager(o).removeModel(u);
  let p = await i.save(c);
  return n && !l && await Pn.getManager(o).removeModel(u), p.modelArtifactsInfo;
}
async function C_() {
  let e = Pn.getSchemes(), t = {};
  for (let n of e) {
    let s = await Pn.getManager(n).listModels();
    for (let r in s) {
      let a = n + Hi + r;
      t[a] = s[r];
    }
  }
  return t;
}
async function N_(e) {
  let t = ed(e);
  return Pn.getManager(t.scheme).removeModel(t.path);
}
async function T_(e, t) {
  return mk(e, t, false);
}
async function $_(e, t) {
  return mk(e, t, true);
}
var __ = 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 (X().get("IS_BROWSER")) {
  X().setPlatform("browser", new __());
  try {
    Pn.registerManager(oa.URL_SCHEME, new S_());
  } catch (e) {
  }
  try {
    Pn.registerManager(ia.URL_SCHEME, new g_());
  } catch (e) {
  }
}
var A_ = { importFetch: () => zT() };
var Ff;
var E_ = class {
  constructor() {
    this.util = MT(), this.textEncoder = new this.util.TextEncoder();
  }
  fetch(e, t) {
    return X().global.fetch != null ? X().global.fetch(e, t) : (Ff == null && (Ff = A_.importFetch()), Ff(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);
  }
};
X().get("IS_NODE") && !X().get("IS_BROWSER") && X().setPlatform("node", new E_());
function De(e, t = "float32", n) {
  return t = t || "float32", tg(e), new Vt(e, t, n);
}
function R_(e, t) {
  let n = _(e, "x", "cast");
  if (!Ww(t))
    throw new Error(`Failed to cast to unknown dtype ${t}`);
  if (t === "string" && n.dtype !== "string" || t !== "string" && n.dtype === "string")
    throw new Error("Only strings can be casted to strings");
  let s = { x: n }, r = { dtype: t };
  return M.runKernel(Sa, s, r);
}
var ce = L({ cast_: R_ });
function D_(e) {
  let n = { x: _(e, "x", "clone", "string_or_numeric") };
  return M.runKernel(Ma, n);
}
var lr = L({ clone_: D_ });
function F_(e, t = false) {
  console.log(e.toString(t));
}
rk();
var O_ = { buffer: De, cast: ce, clone: lr, print: F_ };
W$(O_);
var _n = {};
Ae(_n, { browserFiles: () => W_, browserHTTPRequest: () => j_, concatenateArrayBuffers: () => Dg, copyModel: () => T_, decodeWeights: () => ok, encodeWeights: () => n_, fromMemory: () => X_, getLoadHandlers: () => h_, getModelArtifactsForJSON: () => Fg, getModelArtifactsInfoForJSON: () => Rl, getSaveHandlers: () => p_, http: () => Pg, isHTTPScheme: () => im, listModels: () => C_, loadWeights: () => U_, moveModel: () => $_, registerLoadRouter: () => d_, registerSaveRouter: () => c_, removeModel: () => N_, weightsLoaderFactory: () => bk, withSaveHandler: () => Y_ });
var P_ = "model";
var z_ = ".json";
var M_ = ".weights.bin";
function ox(e) {
  return new Promise((t) => setTimeout(t)).then(e);
}
var am = class {
  constructor(e) {
    if (!X().getBool("IS_BROWSER"))
      throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");
    e.startsWith(am.URL_SCHEME) && (e = e.slice(am.URL_SCHEME.length)), (e == null || e.length === 0) && (e = P_), this.modelJsonFileName = e + z_, this.weightDataFileName = e + M_;
  }
  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 = uk(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 ox(() => 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 ox(() => i.dispatchEvent(new MouseEvent("click")));
      }
      return { modelArtifactsInfo: Rl(e) };
    }
  }
};
var bd = am;
bd.URL_SCHEME = "downloads://";
var L_ = 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 ${this.jsonFile.name}`));
          return;
        }
        if (this.weightsFiles.length === 0) {
          e({ modelTopology: a });
          return;
        }
        let o = Fg(r, (u) => this.loadWeights(u));
        e(o);
      }, n.onerror = (s) => t(`Failed to read model topology and weights manifest JSON from file '${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, Dg(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 '${e}'.`), r.readAsArrayBuffer(t);
    });
  }
  checkManifestAndWeightFiles(e) {
    let t = [], n = this.weightsFiles.map((r) => ix(r.name)), s = {};
    for (let r of e)
      r.paths.forEach((a) => {
        let i = ix(a);
        if (t.indexOf(i) !== -1)
          throw new Error(`Duplicate file basename found in weights manifest: '${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 B_ = (e) => X().getBool("IS_BROWSER") && !Array.isArray(e) && e.startsWith(bd.URL_SCHEME) ? V_(e.slice(bd.URL_SCHEME.length)) : null;
xt.registerSaveRouter(B_);
function V_(e = "model") {
  return new bd(e);
}
function W_(e) {
  return new L_(e);
}
function ux(e, t, n, s) {
  i(e), n = n == null ? 0 : n, s = s == null ? 1 : s, o(n, s);
  let r = 0, a = (u) => (u.then((l) => {
    let c = n + ++r / e.length * (s - n);
    return t(c), l;
  }), u);
  function i(u) {
    O(u != null && Array.isArray(u) && u.length > 0, () => "promises must be a none empty array");
  }
  function o(u, l) {
    O(u >= 0 && u <= 1, () => `Progress fraction must be in range [0, 1], but got startFraction ${u}`), O(l >= 0 && l <= 1, () => `Progress fraction must be in range [0, 1], but got endFraction ${l}`), O(l >= u, () => `startFraction must be no more than endFraction, but got startFraction ${u} and endFraction ${l}`);
  }
  return Promise.all(e.map(a));
}
async function gk(e, t) {
  t == null && (t = {});
  let n = t.fetchFunc == null ? X().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 ux(s, t.onProgress, r, a)).map((p) => p.arrayBuffer()), u = 0.5, l = 1;
  return t.onProgress == null ? await Promise.all(o) : await ux(o, t.onProgress, u, l);
}
async function U_(e, t = "", n, s) {
  return bk((i) => gk(i, { requestInit: s }))(e, t, n);
}
function bk(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 = tm[b] * pt(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), C = ok(k, [x.manifestEntry]);
        for (let T in C)
          p[T] = C[T];
      }), d += f;
    }), p;
  };
}
var G_ = "application/octet-stream";
var H_ = "application/json";
var Og = class {
  constructor(e, t) {
    if (this.DEFAULT_METHOD = "POST", t == null && (t = {}), this.weightPathPrefix = t.weightPathPrefix, this.onProgress = t.onProgress, this.weightUrlConverter = t.weightUrlConverter, t.fetchFunc != null ? (O(typeof t.fetchFunc == "function", () => "Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"), this.fetch = t.fetchFunc) : this.fetch = X().platform.fetch, O(e != null && e.length > 0, () => "URL path for http must not be null, undefined or empty."), Array.isArray(e) && O(e.length === 2, () => `URL paths for http must have a length of 2, (actual length is ${e.length}).`), this.path = e, t.requestInit != null && t.requestInit.body != null)
      throw new Error("requestInit is expected to have no pre-existing body, but has one.");
    this.requestInit = t.requestInit || {};
  }
  async save(e) {
    if (e.modelTopology instanceof ArrayBuffer)
      throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");
    let t = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);
    t.body = new FormData();
    let n = [{ paths: ["./model.weights.bin"], weights: e.weightSpecs }], s = uk(e, n);
    t.body.append("model.json", new Blob([JSON.stringify(s)], { type: H_ }), "model.json"), e.weightData != null && t.body.append("model.weights.bin", new Blob([e.weightData], { type: G_ }), "model.weights.bin");
    let r = await this.fetch(this.path, t);
    if (r.ok)
      return { modelArtifactsInfo: Rl(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 Fg(t, (r) => this.loadWeights(r));
  }
  async loadWeights(e) {
    let t = Array.isArray(this.path) ? this.path[1] : this.path, [n, s] = q_(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 gk(i, { requestInit: this.requestInit, fetchFunc: this.fetch, onProgress: this.onProgress });
    return [a, Dg(u)];
  }
};
Og.URL_SCHEME_REGEX = /^https?:\/\//;
function q_(e) {
  let t = e.lastIndexOf("/"), n = e.lastIndexOf("?"), s = e.substring(0, t), r = n > t ? e.substring(n) : "";
  return [s + "/", r];
}
function im(e) {
  return e.match(Og.URL_SCHEME_REGEX) != null;
}
var yk = (e, t) => {
  if (typeof fetch == "undefined" && (t == null || t.fetchFunc == null))
    return null;
  {
    let n = true;
    if (Array.isArray(e) ? n = e.every((s) => im(s)) : n = im(e), n)
      return Pg(e, t);
  }
  return null;
};
xt.registerSaveRouter(yk);
xt.registerLoadRouter(yk);
function Pg(e, t) {
  return new Og(e, t);
}
function j_(e, t) {
  return Pg(e, t);
}
var Of = class {
  constructor(e) {
    this.modelArtifacts = e;
  }
  async load() {
    return this.modelArtifacts;
  }
};
var K_ = class {
  constructor(e) {
    this.saveHandler = e;
  }
  async save(e) {
    return this.saveHandler(e);
  }
};
function X_(e, t, n, s) {
  return arguments.length === 1 ? e.modelTopology != null || e.weightSpecs != null ? new Of(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 Of({ 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 Of({ modelTopology: e, weightSpecs: t, weightData: n, trainingConfig: s }));
}
function Y_(e) {
  return new K_(e);
}
var Q_ = {};
Ae(Q_, { confusionMatrix: () => nA });
function Z_(e, t, n = false, s = false) {
  let r = _(e, "a", "matMul"), a = _(t, "b", "matMul");
  [r, a] = vt(r, a);
  let i = { a: r, b: a }, o = { transposeA: n, transposeB: s };
  return M.runKernel(Ia, i, o);
}
var We = L({ matMul_: Z_ });
function J_(e, t, n = 1, s = 0) {
  if (t < 2)
    throw new Error(`Error in oneHot: depth must be >=2, but it is ${t}`);
  let a = { indices: _(e, "indices", "oneHot", "int32") }, i = { depth: t, onValue: n, offValue: s };
  return M.runKernel(To, a, i);
}
var yd = L({ oneHot_: J_ });
function eA(e, t) {
  let n = _(e, "x", "transpose");
  if (t == null && (t = n.shape.map((a, i) => i).reverse()), O(n.rank === t.length, () => `Error in transpose: rank of input ${n.rank} must match length of perm ${t}.`), t.forEach((a) => {
    O(a >= 0 && a < n.rank, () => `All entries in 'perm' must be between 0 and ${n.rank - 1} but got ${t}`);
  }), n.rank <= 1)
    return n.clone();
  let s = { x: n }, r = { perm: t };
  return M.runKernel(ci, s, r);
}
var qe = L({ transpose_: eA });
function tA(e, t, n) {
  let s = _(e, "labels", "confusionMatrix"), r = _(t, "predictions", "confusionMatrix");
  O(n == null || n > 0 && Number.isInteger(n), () => `If provided, numClasses must be a positive integer, but got ${n}`), O(s.rank === 1, () => `Expected the rank of labels to be 1, but got ${s.rank}`), O(r.rank === 1, () => `Expected the rank of predictions to be 1, but got ${r.rank}`), O(s.shape[0] === r.shape[0], () => `Mismatch in the number of examples: ${s.shape[0]} vs. ${r.shape[0]}. Labels and predictions should have the same number of elements.`), O(n > 0 && Number.isInteger(n), () => `numClasses is required to be a positive integer, but got ${n}`);
  let a = yd(ce(s, "int32"), n), i = yd(ce(r, "int32"), n), o = qe(a), u = We(o, i);
  return ce(u, "int32");
}
var nA = L({ confusionMatrix_: tA });
var qo = {};
Ae(qo, { assertAndGetBroadcastShape: () => it, getBroadcastDims: () => vk, getReductionAxes: () => _t });
function vk(e, t) {
  let n = e.length, s = [];
  for (let r = 0; r < n; r++) {
    let a = n - 1 - r, i = e[a] || 1;
    (t[t.length - 1 - r] || 1) > 1 && i === 1 && s.unshift(a);
  }
  return s;
}
function _t(e, t) {
  let n = [];
  for (let s = 0; s < t.length; s++) {
    let r = e[e.length - s - 1], a = t.length - s - 1, i = t[a];
    (r == null || r === 1 && i > 1) && n.unshift(a);
  }
  return n;
}
function it(e, t) {
  let n = [], s = Math.max(e.length, t.length);
  for (let r = 0; r < s; r++) {
    let a = e[e.length - r - 1];
    a == null && (a = 1);
    let i = t[t.length - r - 1];
    if (i == null && (i = 1), a === 1)
      n.unshift(i);
    else if (i === 1)
      n.unshift(a);
    else if (a !== i) {
      let o = `Operands could not be broadcast together with shapes ${e} and ${t}.`;
      throw Error(o);
    } else
      n.unshift(a);
  }
  return n;
}
var xk = {};
Ae(xk, { fromPixels: () => cA, fromPixelsAsync: () => uA, toPixels: () => lA });
function sA(e, t, n) {
  if (va(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 Cr(e, t, s, n);
}
var Br;
function wk(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 (Yf(hd, M.backendName) != null) {
    let f = { pixels: e }, m = { numChannels: t };
    return M.runKernel(hd, 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 (Br == null)
      if (typeof document == "undefined")
        if (typeof OffscreenCanvas != "undefined" && typeof OffscreenCanvasRenderingContext2D != "undefined")
          Br = new OffscreenCanvas(1, 1).getContext("2d");
        else
          throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");
      else
        Br = document.createElement("canvas").getContext("2d");
    Br.canvas.width = l, Br.canvas.height = c, Br.drawImage(e, 0, 0, l, c), p = Br.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 sA(d, [c, l, t], "int32");
}
function rA(e) {
  return e != null && e.data instanceof Uint8Array;
}
function aA() {
  return typeof window != "undefined" && typeof ImageBitmap != "undefined" && window.hasOwnProperty("createImageBitmap");
}
function iA(e) {
  return e != null && e.width !== 0 && e.height !== 0;
}
function oA(e) {
  return aA() && !(e instanceof ImageBitmap) && iA(e) && !rA(e);
}
async function uA(e, t = 3) {
  let n = null;
  if (X().getBool("WRAP_TO_IMAGEBITMAP") && oA(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 wk(n, t);
}
async function lA(e, t) {
  let n = _(e, "img", "toPixels");
  if (!(e instanceof et)) {
    let l = n;
    n = ce(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 cA = L({ fromPixels_: wk });
var kk = {};
Ae(kk, { prepareAndValidate: () => Ik });
function Ik(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 (pt(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 = [...ro(e.shape).map((p) => p / l), 1].slice(0, a);
  return [u, i, l, c];
}
var Sk = {};
Ae(Sk, { calculateShapes: () => Ck, validateInput: () => Mg, validateUpdateShape: () => zg });
function zg(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 Mg(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: ${e.shape}`);
  }
  zg(n, t, e);
}
function Ck(e, t, n) {
  let s = t.shape.length, r = s > 1 ? t.shape[s - 1] : 1, a = n.length, i = 1;
  for (let p = r; p < a; ++p)
    i *= n[p];
  let o = r < 1 ? 1 : r, u = pt(t.shape) / o, l = [...ro(n.slice(0, r)), 1], c = pt(n);
  return { sliceRank: r, numUpdates: u, sliceSize: i, strides: l, outputSize: c };
}
var wt = {};
Ae(wt, { assertParamsValid: () => pA, computeFlatOffset: () => bA, computeOutShape: () => fA, getNormalizedAxes: () => mA, isSliceContinous: () => gA, maskToAxes: () => hA, parseSliceParams: () => Fk, sliceInfo: () => yA, startForAxis: () => Rk, startIndicesWithElidedDims: () => _k, stopForAxis: () => Dk, stopIndicesWithElidedDims: () => Ak, stridesForAxis: () => Ek, stridesWithElidedDims: () => Nk });
var om = -2;
var dA = -1;
function pA(e, t, n) {
  let s = e.shape.length;
  O(s === t.length, () => `Error in slice${s}D: Length of begin ${t} must match the rank of the array (${s}).`), O(s === n.length, () => `Error in slice${s}D: Length of size ${n} must match the rank of the array (${s}).`);
  for (let r = 0; r < s; ++r)
    O(t[r] + n[r] <= e.shape[r], () => `Error in slice${s}D: begin[${r}] + size[${r}] (${t[r] + n[r]}) would overflow input.shape[${r}] (${e.shape[r]})`);
}
function hA(e) {
  let t = [], n = 0;
  for (; e > 0; )
    e & 1 && t.push(n), e /= 2, n++;
  return t;
}
function fA(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 Nk(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 Tk(e, t, n) {
  return n <= e ? n : n - (t - 1);
}
function $k(e, t) {
  let n = [];
  for (let s = 0; s < e; s++)
    n.push(t + s);
  return n;
}
function mA(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 = _k(i, h, f, s, e), p = Ak(o, h, f, r, e), d = Nk(a, h, f, e);
  } else
    for (let h = 0; h < l; h++)
      c[h] = Rk(i, s, a, e, h, u), p[h] = Dk(o, r, a, e, h, u), d[h] = Ek(a, h, u);
  return { begin: c, end: p, strides: d };
}
function _k(e, t, n, s, r) {
  let a = [...r], i = $k(n, t);
  for (let o = 0; o < a.length; o++)
    if (i.indexOf(o) > -1)
      a[o] = 0;
    else {
      let u = Tk(t, n, o), l = s[u];
      e & 1 << u && (l = 0), a[o] = l;
    }
  return a;
}
function Ak(e, t, n, s, r) {
  let a = [...r], i = $k(n, t);
  for (let o = 0; o < a.length; o++)
    if (i.indexOf(o) > -1)
      a[o] = Number.MAX_SAFE_INTEGER;
    else {
      let u = Tk(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] = Bu(0, a[o], r[o]);
  }
  return a;
}
function Ek(e, t, n) {
  let s = e[t];
  return (n & 1 << t || s == null) && (s = 1), s;
}
function Rk(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 = Bu(0, i, u - 1), i;
}
function Dk(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 = Bu(0, i, u) : i = Bu(-1, i, u - 1), i;
}
function gA(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 bA(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 Fk(e, t, n) {
  let s, r = e.shape.length;
  typeof t == "number" ? s = [t, ...new Array(r - 1).fill(0)] : t.length < r ? s = t.concat(new Array(r - t.length).fill(0)) : s = t.slice(), s.forEach((i) => {
    O(i !== -1, () => "slice() does not support negative begin indexing.");
  });
  let a;
  return n == null ? a = new Array(r).fill(-1) : typeof n == "number" ? a = [n, ...new Array(r - 1).fill(-1)] : n.length < r ? a = n.concat(new Array(r - n.length).fill(-1)) : a = n, a = a.map((i, o) => i >= 0 ? i : (O(i === -1, () => `Negative size values should be exactly -1 but got ${i} for the slice() size at index ${o}.`), e.shape[o] - s[o])), [s, a];
}
function yA(e, t, n, s, r, a, i, o, u) {
  let l;
  if (s == null ? (l = new Array(t.length), l.fill(1)) : l = s, i != null && (i & i - 1) !== 0)
    throw new Error("Multiple ellipses in slice is not allowed.");
  let c = false, p = { dims: l.length, numAddAxisAfterEllipsis: 0, begin: t.slice(), end: n.slice(), strides: l.slice(), beginMask: r, endMask: a, ellipsisMask: i, newAxisMask: o, shrinkAxisMask: u };
  for (let v = 0; v < p.dims; v++)
    c && (1 << v & o) !== 0 && p.numAddAxisAfterEllipsis++, 1 << v & i && (c = true);
  c || (p.ellipsisMask |= 1 << p.dims, p.dims++);
  let d = { dims: e.length, beginMask: 0, endMask: 0, beginValid: false, endValid: false };
  vA(p, d);
  let h = true, f = true, m = true, g = [], b = [];
  for (let v = 0; v < e.length; ++v) {
    if (d.strides[v] === 0)
      throw Error(`strides[${v}] must be non-zero`);
    let x = !!(d.shrinkAxisMask & 1 << v), k = e[v];
    if (k === -1) {
      g.push(x ? 1 : -1);
      continue;
    }
    let C = [d.beginMask & 1 << v, d.endMask & 1 << v], T = [d.strides[v] > 0 ? 0 : -1, d.strides[v] > 0 ? k : k - 1];
    if (x && d.strides[v] <= 0)
      throw Error("only stride 1 allowed on non-range indexing.");
    m = m && d.strides[v] === 1;
    let E = !!(d.beginMask & 1 << v && d.endMask & 1 << v);
    if (d.beginValid && d.endValid) {
      if (x) {
        let F = d.begin[v] < 0 ? k + d.begin[v] : d.begin[v];
        if (d.begin[v] = F, d.end[v] = d.begin[v] + 1, F < 0 || F >= k)
          throw Error(`slice index ${d.begin[v]} of dimension ${v} out of bounds.`);
      } else
        d.begin[v] = lx(d.begin[v], 0, d.strides[v], k, C, T), d.end[v] = lx(d.end[v], 1, d.strides[v], k, C, T);
      let R = d.strides[v] === 1 && d.begin[v] === 0 && d.end[v] === k;
      h = h && R, f = f && (v === 0 && d.strides[v] === 1 || R);
    } else
      h = h && d.strides[v] === 1 && E, f = f && (v === 0 && d.strides[v] === 1 || E);
    let A, P = false;
    if (d.beginValid && d.endValid ? (A = d.end[v] - d.begin[v], P = true) : x ? (A = 1, P = true) : E && k >= 0 && (d.strides[v] < 0 ? A = -k : A = k, P = true), P) {
      let R;
      A === 0 || A < 0 != d.strides[v] < 0 ? R = 0 : R = Math.trunc(A / d.strides[v]) + (A % d.strides[v] !== 0 ? 1 : 0), g.push(R);
    } else
      g.push(-1);
  }
  for (let v = 0; v < d.finalShapeGatherIndices.length; ++v) {
    let x = d.finalShapeGatherIndices[v];
    x >= 0 ? b.push(g[x]) : x === om && b.push(1);
  }
  return { finalShapeSparse: b.filter((v, x) => d.finalShapeGatherIndices[x] !== om), finalShape: b, isIdentity: h, sliceDim0: f, isSimpleSlice: m, begin: d.begin, end: d.end, strides: d.strides };
}
function vA(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(om), 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(dA), t.finalShapeGatherIndicesSparse.push(-1), t.shrinkAxisMask |= 1 << n) : (t.finalShapeGatherIndices.push(n), t.finalShapeGatherIndicesSparse.push(s)), t.inputShapeGatherIndicesSparse[n] = s, n++;
    }
}
function lx(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 ae = {};
Ae(ae, { Serializable: () => Ok, SerializationMap: () => Hr, registerClass: () => Nr });
var Ok = class {
  getClassName() {
    return this.constructor.className;
  }
  static fromConfig(e, t) {
    return new e(t);
  }
};
var Hr = class {
  constructor() {
    this.classNameMap = {};
  }
  static getMap() {
    return Hr.instance == null && (Hr.instance = new Hr()), Hr.instance;
  }
  static register(e) {
    Hr.getMap().classNameMap[e.className] = [e, e.fromConfig];
  }
};
function Nr(e) {
  O(e.className != null, () => "Class being registered does not have the static className property defined."), O(typeof e.className == "string", () => "className is required to be a string, but got type " + typeof e.className), O(e.className.length > 0, () => "Class being registered has an empty-string as its className, which is disallowed."), Hr.register(e);
}
var xA = {};
Ae(xA, { TEST_EPSILON_FLOAT16: () => Pk, encodeStrings: () => zk, expectArrayBuffersEqual: () => TA, expectArraysClose: () => kA, expectArraysEqual: () => SA, expectNumbersClose: () => CA, expectPromiseToFail: () => IA, expectValuesInRange: () => NA, testEpsilon: () => Lg });
var wA = 1e-3;
var Pk = 0.1;
function kA(e, t, n) {
  return n == null && (n = Lg()), um(e, t, (s, r) => Bg(s, r, n));
}
function Lg() {
  return M.backend.floatPrecision() === 32 ? wA : Pk;
}
function um(e, t, n) {
  let s = true;
  if ((Yt(e) || Yt(t)) && (s = false), Yt(e) && Yt(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: ${o}`);
  }
  if (Array.isArray(e) && Array.isArray(t)) {
    let i = Rs(e), o = Rs(t);
    if (!wr(i, o))
      throw new Error(`Arrays have different shapes. Actual: [${i}]. Expected: [${o}]`);
  }
  let r = Yt(e) ? e : ta(e), a = Yt(t) ? t : ta(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 IA(e, t) {
  e().then(() => t.fail(), () => t());
}
function SA(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]) ? um(e, n, (s, r) => s == r) : um(e, t, (s, r) => Bg(s, r, 0));
}
function CA(e, t, n) {
  if (n == null && (n = Lg()), !Bg(e, t, n))
    throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`);
}
function Bg(e, t, n) {
  return !isFinite(e) && !isFinite(t) ? true : !(isNaN(e) || isNaN(t) || Math.abs(e - t) > n);
}
function NA(e, t, n) {
  for (let s = 0; s < e.length; s++)
    if (e[s] < t || e[s] > n)
      throw new Error(`Value out of range:${e[s]} low: ${t}, high: ${n}`);
}
function TA(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 ${n[r]} instead`);
}
function zk(e) {
  for (let t = 0; t < e.length; t++) {
    let n = e[t];
    Array.isArray(n) ? zk(n) : e[t] = El(n);
  }
  return e;
}
var gde = "0.0.0";
function bde() {
  X().set("PROD", true);
}
function yde() {
  X().set("DEBUG", true);
}
function vde() {
  X().set("DEPRECATION_WARNINGS_ENABLED", false), console.warn("TensorFlow.js deprecation warnings have been disabled.");
}
function Mk(e) {
  X().getBool("DEPRECATION_WARNINGS_ENABLED") && console.warn(e + " You can disable deprecation warnings with tf.disableDeprecationWarnings().");
}
U$(Mk);
function xde() {
  M.disposeVariables();
}
function Ss() {
  return M;
}
function lm() {
  return M.memory();
}
function wde(e) {
  return M.profile(e);
}
function j(e, t) {
  return M.tidy(e, t);
}
function Re(e) {
  Ag(e).forEach((n) => n.dispose());
}
function Ht(e) {
  return M.keep(e);
}
function kde(e) {
  return M.time(e);
}
function Ide(e) {
  return M.setBackend(e);
}
function Sde() {
  return M.ready();
}
function Cde() {
  return M.backendName;
}
function Nde(e) {
  M.removeBackend(e);
}
function Tde(e) {
  return M.findBackend(e);
}
function $de(e) {
  return M.findBackendFactory(e);
}
function dp(e, t, n = 1) {
  return M.registerBackend(e, t, n);
}
function $A() {
  return M.backend;
}
function _de(e, t) {
  X().setPlatform(e, t);
}
function _A(e, t) {
  let n = _(e, "a", "add"), s = _(t, "b", "add");
  [n, s] = vt(n, s);
  let r = { a: n, b: s };
  return M.runKernel(kr, r);
}
var ie = L({ add_: _A });
function AA(e, t) {
  let n = _(e, "a", "floorDiv"), s = _(t, "b", "floorDiv");
  [n, s] = vt(n, s);
  let r = { a: n, b: s };
  return M.runKernel(Oa, r);
}
var Lk = L({ floorDiv_: AA });
function EA(e, t) {
  let n = _(e, "a", "div"), s = _(t, "b", "div");
  if ([n, s] = vt(n, s), n.dtype === "int32" && s.dtype === "int32")
    return Lk(n, s);
  let r = { a: n, b: s }, a = {};
  return M.runKernel(Ea, r, a);
}
var xe = L({ div_: EA });
function RA(e, t) {
  let n = _(e, "a", "mul"), s = _(t, "b", "mul");
  [n, s] = vt(n, s);
  let r = { a: n, b: s };
  return M.runKernel(Ka, r);
}
var V = L({ mul_: RA });
function DA(e) {
  let t = _(e, "x", "abs");
  if (t.dtype === "complex64") {
    let n = { x: t };
    return M.runKernel(jd, n);
  } else {
    let n = { x: t };
    return M.runKernel(ao, n);
  }
}
var Mt = L({ abs_: DA });
function FA(e) {
  let n = { x: _(e, "x", "acos") };
  return M.runKernel(nl, n);
}
var OA = L({ acos_: FA });
function PA(e) {
  let n = { x: _(e, "x", "acosh") };
  return M.runKernel(sl, n);
}
var zA = L({ acosh_: PA });
function MA(e) {
  O(Array.isArray(e), () => "The argument passed to tf.addN() must be a list of tensors"), O(e.length >= 1, () => `Must pass at least one tensor to tf.addN(), but got ${e.length}`);
  let t = e.map((r, a) => _(r, `tensors${a}`, "addN")), n = t[0];
  t.forEach((r) => {
    if (r.dtype !== n.dtype)
      throw new Error("All tensors passed to tf.addN() must have the same dtype");
  }), t.forEach((r) => {
    if (!wr(r.shape, n.shape))
      throw new Error("All tensors passed to tf.addN() must have the same shape");
  });
  let s = t;
  return M.runKernel(xa, s);
}
var LA = L({ addN_: MA });
function BA(e, t = null, n = false) {
  let r = { x: _(e, "x", "all", "bool") }, a = { axis: t, keepDims: n };
  return M.runKernel(rl, r, a);
}
var Bk = L({ all_: BA });
function VA(e, t = null, n = false) {
  let r = { x: _(e, "x", "any", "bool") }, a = { axis: t, keepDims: n };
  return M.runKernel(al, r, a);
}
var cm = L({ any_: VA });
function WA(e, t = 0) {
  let s = { x: _(e, "x", "argMax") }, r = { axis: t };
  return M.runKernel(wa, s, r);
}
var Gu = L({ argMax_: WA });
function UA(e, t = 0) {
  let s = { x: _(e, "x", "argMin") }, r = { axis: t };
  return M.runKernel(il, s, r);
}
var GA = L({ argMin_: UA });
function HA(e) {
  let n = { x: _(e, "x", "asin") };
  return M.runKernel(ol, n);
}
var qA = L({ asin_: HA });
function jA(e) {
  let n = { x: _(e, "x", "asinh") };
  return M.runKernel(ul, n);
}
var KA = L({ asinh_: jA });
function XA(e) {
  let n = { x: _(e, "x", "atan") };
  return M.runKernel(ll, n);
}
var YA = L({ atan_: XA });
function QA(e, t) {
  let n = _(e, "a", "atan2"), s = _(t, "b", "atan2");
  [n, s] = vt(n, s);
  let r = { a: n, b: s };
  return M.runKernel(dl, r);
}
var ZA = L({ atan2_: QA });
function JA(e) {
  let n = { x: _(e, "x", "atanh") };
  return M.runKernel(cl, n);
}
var eE = L({ atanh_: JA });
function tE(e, t, n, s, r = "NHWC", a) {
  let i = e[3], o = [...t, i], u = Uk(r);
  return Dl(e, o, n, a, s, null, null, u);
}
function Vk(e, t, n, s, r, a, i = "channelsLast") {
  let [o, u] = vd(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 Dl(e, l, n, s, r, a, false, i);
}
function nE(e, t, n, s, r, a, i = "NDHWC") {
  let [o, u, l] = dm(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 Wk(e, c, n, s, r, false, p, a);
}
function Dl(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] = vd(n), [b, y] = vd(s), v = qi(d, b), x = qi(h, y), { padInfo: k, outHeight: C, outWidth: T } = aE(r, l, c, m, g, v, x, a, o), E = i ? f * p : f, A;
  return o === "channelsFirst" ? A = [u, E, C, T] : o === "channelsLast" && (A = [u, C, T, E]), { batchSize: u, dataFormat: o, inHeight: l, inWidth: c, inChannels: p, outHeight: C, outWidth: T, outChannels: E, padInfo: k, strideHeight: m, strideWidth: g, filterHeight: d, filterWidth: h, effectiveFilterHeight: v, effectiveFilterWidth: x, dilationHeight: b, dilationWidth: y, inShape: e, outShape: A, filterShape: t };
}
function Wk(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] = dm(n), [x, k, C] = dm(s), T = qi(h, x), E = qi(f, k), A = qi(m, C), { padInfo: P, outDepth: R, outHeight: F, outWidth: $ } = iE(r, l, c, p, b, y, v, T, E, A, o), z = a ? g * d : g, W;
  return i === "channelsFirst" ? W = [u, z, R, F, $] : i === "channelsLast" && (W = [u, R, F, $, z]), { batchSize: u, dataFormat: i, inDepth: l, inHeight: c, inWidth: p, inChannels: d, outDepth: R, outHeight: F, outWidth: $, outChannels: z, padInfo: P, strideDepth: b, strideHeight: y, strideWidth: v, filterDepth: h, filterHeight: f, filterWidth: m, effectiveFilterDepth: T, effectiveFilterHeight: E, effectiveFilterWidth: A, dilationDepth: x, dilationHeight: k, dilationWidth: C, inShape: e, outShape: W, filterShape: t };
}
function sE(e, t, n, s, r) {
  s == null && (s = Vg(e, t, n));
  let a = e[0], i = e[1], o = Qr((a - t + 2 * s) / n + 1, r), u = Qr((i - t + 2 * s) / n + 1, r);
  return [o, u];
}
function rE(e, t, n, s, r, a) {
  r == null && (r = Vg(e, t, s));
  let i = e[0], o = e[1], u = e[2], l = Qr((i - t + 2 * r) / s + 1, a), c = Qr((o - t + 2 * r) / s + 1, a), p = Qr((u - t + 2 * r) / s + 1, a);
  return [l, c, p, n];
}
function Vg(e, t, n, s = 1) {
  let r = qi(t, s);
  return Math.floor((e[0] * (n - 1) - n + r) / 2);
}
function vd(e) {
  return typeof e == "number" ? [e, e, e] : e.length === 2 ? [e[0], e[1], 1] : e;
}
function dm(e) {
  return typeof e == "number" ? [e, e, e] : e;
}
function qi(e, t) {
  return t <= 1 ? e : e + (e - 1) * (t - 1);
}
function aE(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 = sE([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 = Qr((t - a + d + h) / s + 1, o), p = Qr((n - i + f + m) / r + 1, o);
  } else
    throw Error(`Unknown padding parameter: ${e}`);
  return { padInfo: l, outHeight: c, outWidth: p };
}
function iE(e, t, n, s, r, a, i, o, u, l, c) {
  let p, d, h, f;
  if (typeof e == "number") {
    p = { top: e, bottom: e, left: e, right: e, front: e, back: e, type: e === 0 ? "VALID" : "NUMBER" };
    let g = rE([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, C = Math.floor(b / 2), T = b - C;
    p = { top: x, bottom: k, left: C, right: T, 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 Qr(e, t) {
  if (!t)
    return Math.trunc(e);
  switch (t) {
    case "round":
      return Math.round(e);
    case "ceil":
      return Math.ceil(e);
    case "floor":
      return Math.floor(e);
    default:
      throw new Error(`Unknown roundingMode ${t}`);
  }
}
function fr(e) {
  let [t, n, s] = vd(e);
  return t === 1 && n === 1 && s === 1;
}
function Ps(e, t) {
  return fr(e) || fr(t);
}
function Uk(e) {
  if (e === "NHWC")
    return "channelsLast";
  if (e === "NCHW")
    return "channelsFirst";
  throw new Error(`Unknown dataFormat ${e}`);
}
function pn(e, t, n) {
  if (n != null) {
    if (typeof t == "string")
      throw Error(`Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${t}.`);
    if (typeof t == "number")
      O(Xi(t), () => `Error in ${e}: pad must be an integer when using dimRoundingMode ${n} but got pad ${t}.`);
    else if (typeof t == "object")
      t.forEach((s) => {
        s.forEach((r) => {
          O(Xi(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 oE(e, t) {
  let s = { x: _(e, "x", "reshape", "string_or_numeric") }, r = { shape: t };
  return M.runKernel(Ao, s, r);
}
var G = L({ reshape_: oE });
function uE(e, t, n, s, r) {
  let a = _(e, "x", "avgPool", "float32"), i = 1;
  O(Ps(n, i), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`);
  let o = a, u = false;
  a.rank === 3 && (u = true, o = G(a, [1, a.shape[0], a.shape[1], a.shape[2]])), O(o.rank === 4, () => `Error in avgPool: x must be rank 4 but got rank ${o.rank}.`), pn("avgPool", s, r);
  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r }, p = M.runKernel(ka, l, c);
  return p = ce(p, a.dtype), u ? G(p, [p.shape[1], p.shape[2], p.shape[3]]) : p;
}
var Wg = L({ avgPool_: uE });
function lE(e, t, n, s, r, a = "NDHWC") {
  let i = _(e, "x", "avgPool3d", "float32"), o = i, u = false;
  i.rank === 4 && (u = true, o = G(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]])), O(o.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${o.rank}.`), O(a === "NDHWC", () => `Error in avgPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`), pn("avgPool3d", s, r);
  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r, dataFormat: a }, p = M.runKernel(Hd, l, c);
  return p = ce(p, o.dtype), u ? G(p, [p.shape[1], p.shape[2], p.shape[3], p.shape[4]]) : p;
}
var Gk = L({ avgPool3d_: lE });
function cE(e, t = 0) {
  O(e.length >= 1, () => "Pass at least one tensor to concat");
  let n = Uu(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 M.runKernel(oo, s, r);
}
var Ft = L({ concat_: cE });
function dE(e) {
  let n = { x: _(e, "x", "sigmoid", "float32") };
  return M.runKernel(si, n);
}
var qs = L({ sigmoid_: dE });
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 M.runKernel(Oo, r, a);
}
var He = L({ slice_: pE });
function hE(e) {
  let n = { x: _(e, "x", "tanh", "float32") };
  return M.runKernel(li, n);
}
var Hu = L({ tanh_: hE });
function fE(e, t, n, s, r, a) {
  let i = _(e, "forgetBias", "basicLSTMCell"), o = _(t, "lstmKernel", "basicLSTMCell"), u = _(n, "lstmBias", "basicLSTMCell"), l = _(s, "data", "basicLSTMCell"), c = _(r, "c", "basicLSTMCell"), p = _(a, "h", "basicLSTMCell"), d = Ft([l, p], 1), h = We(d, o), f = ie(h, u), m = f.shape[0], g = f.shape[1] / 4, b = [m, g], y = He(f, [0, 0], b), v = He(f, [0, g], b), x = He(f, [0, g * 2], b), k = He(f, [0, g * 3], b), C = ie(V(qs(y), Hu(v)), V(c, qs(ie(i, x)))), T = V(Hu(C), qs(k));
  return [C, T];
}
var Ade = L({ basicLSTMCell_: fE });
function mE(e, t, n) {
  let s = _(e, "x", "batchToSpaceND"), r = t.reduce((o, u) => o * u);
  O(s.rank >= 1 + t.length, () => `input rank is ${s.rank} but should be > than blockShape.length ${t.length}`), O(n.length === t.length, () => `crops.length is ${n.length} but should be equal to blockShape.length  ${t.length}`), O(s.shape[0] % r === 0, () => `input tensor batch is ${s.shape[0]} but is not divisible by the product of the elements of blockShape ${t.join(" * ")} === ${r}`);
  let a = { x: s }, i = { blockShape: t, crops: n };
  return M.runKernel(io, a, i);
}
var Ug = L({ batchToSpaceND_: mE });
function gE(e) {
  let t;
  return e.rank === 0 || e.rank === 1 ? t = G(e, [1, 1, 1, e.size]) : e.rank === 2 ? t = G(e, [1, 1, e.shape[0], e.shape[1]]) : e.rank === 3 ? t = G(e, [1, e.shape[0], e.shape[1], e.shape[2]]) : t = e, t;
}
function bE(e, t, n, s, r, a) {
  a == null && (a = 1e-3);
  let i = _(e, "x", "batchNorm"), o = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
  r != null && (l = _(r, "scale", "batchNorm"));
  let c;
  s != null && (c = _(s, "offset", "batchNorm")), O(o.rank === u.rank, () => "Batch normalization gradient requires mean and variance to have equal ranks."), O(c == null || o.rank === c.rank, () => "Batch normalization gradient requires mean and offset to have equal ranks."), O(l == null || o.rank === l.rank, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
  let d = { x: gE(i), scale: l, offset: c, mean: o, variance: u }, h = { varianceEpsilon: a }, f = M.runKernel(Pa, d, h);
  return G(f, i.shape);
}
var qu = L({ batchNorm_: bE });
function yE(e, t, n, s, r, a) {
  let i = _(e, "x", "batchNorm"), o = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
  r != null && (l = _(r, "scale", "batchNorm"));
  let c;
  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 2, () => `Error in batchNorm2D: x must be rank 2 but got rank ${i.rank}.`), O(o.rank === 2 || o.rank === 1, () => `Error in batchNorm2D: mean must be rank 2 or rank 1 but got rank ${o.rank}.`), O(u.rank === 2 || u.rank === 1, () => `Error in batchNorm2D: variance must be rank 2 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 2 || l.rank === 1, () => `Error in batchNorm2D: scale must be rank 2 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 2 || c.rank === 1, () => `Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${c.rank}.`), qu(i, o, u, c, l, a);
}
var vE = L({ batchNorm2d_: yE });
function xE(e, t, n, s, r, a) {
  let i = _(e, "x", "batchNorm"), o = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
  r != null && (l = _(r, "scale", "batchNorm"));
  let c;
  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 3, () => `Error in batchNorm3D: x must be rank 3 but got rank ${i.rank}.`), O(o.rank === 3 || o.rank === 1, () => `Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${o.rank}.`), O(u.rank === 3 || u.rank === 1, () => `Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 3 || l.rank === 1, () => `Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 3 || c.rank === 1, () => `Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${c.rank}.`), qu(i, o, u, c, l, a);
}
var wE = L({ batchNorm3d_: xE });
function kE(e, t, n, s, r, a) {
  let i = _(e, "x", "batchNorm"), o = _(t, "mean", "batchNorm"), u = _(n, "variance", "batchNorm"), l;
  r != null && (l = _(r, "scale", "batchNorm"));
  let c;
  return s != null && (c = _(s, "offset", "batchNorm")), O(i.rank === 4, () => `Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`), O(o.rank === 4 || o.rank === 1, () => `Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`), O(u.rank === 4 || u.rank === 1, () => `Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`), l != null && O(l.rank === 4 || l.rank === 1, () => `Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`), c != null && O(c.rank === 4 || c.rank === 1, () => `Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${c.rank}.`), qu(i, o, u, c, l, a);
}
var IE = L({ batchNorm4d_: kE });
function SE(e, t, n) {
  let s = _(e, "x", "bincount"), r = _(t, "weights", "bincount");
  O(s.dtype === "int32", () => `Error in bincount: input dtype must be int32, but got ${s.dtype}`), O(n >= 0, () => `size must be non-negative, but got ${n}.`), O(r.size === s.size || r.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${s.shape}, weights shape: ${r.shape}.`);
  let a = { x: s, weights: r }, i = { size: n };
  return M.runKernel(ig, a, i);
}
var Hk = L({ bincount_: SE });
function CE(e, t) {
  let n = _(e, "s0", "broadcastArgs", "int32"), s = _(t, "s1", "broadcastArgs", "int32");
  if (n.rank !== 1)
    throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${n.rank}`);
  if (s.rank !== 1)
    throw new Error(`broadcastArgs(): second input must be a vector (rank=1). Has rank ${s.rank}`);
  let r = { s0: n, s1: s };
  return M.runKernel(og, r);
}
var NE = L({ broadcastArgs_: CE });
function TE(e, t) {
  let n = _(e, "broadcastTo", "x"), s = n.shape;
  if (t.some((l) => !(l > 0) || l % 1 !== 0))
    throw new Error(`broadcastTo(): Invalid broadcast shape [${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 = G(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 M.runKernel(Sr, o, u);
}
var td = L({ broadcastTo_: TE });
function $E(e) {
  let n = { x: _(e, "x", "ceil", "float32") };
  return M.runKernel(Ca, n);
}
var _E = L({ ceil_: $E });
function AE(e, t, n) {
  let s = _(e, "x", "clipByValue");
  O(t <= n, () => `Error in clip: min (${t}) must be less than or equal to max (${n}).`);
  let r = { x: s }, a = { clipValueMin: t, clipValueMax: n };
  return M.runKernel(Ir, r, a);
}
var Bn = L({ clipByValue_: AE });
function EE(e) {
  return Ft(e, 0);
}
var RE = L({ concat1d_: EE });
function DE(e, t) {
  return Ft(e, t);
}
var FE = L({ concat2d_: DE });
function OE(e, t) {
  return Ft(e, t);
}
var PE = L({ concat3d_: OE });
function zE(e, t) {
  return Ft(e, t);
}
var ME = L({ concat4d_: zE });
function LE(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 = G(o, [1, o.shape[0], o.shape[1], o.shape[2]])), O(l.rank === 4, () => `Error in conv2d: input must be rank 4, but got rank ${l.rank}.`), O(u.rank === 4, () => `Error in conv2d: filter must be rank 4, but got rank ${u.rank}.`), pn("conv2d", s, i);
  let p = r === "NHWC" ? l.shape[3] : l.shape[1];
  O(p === u.shape[2], () => `Error in conv2d: depth of input (${p}) must match input depth for filter ${u.shape[2]}.`), O(Ps(n, a), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`);
  let d = { x: l, filter: u }, h = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: i }, f = M.runKernel(Na, d, h);
  return c ? G(f, [f.shape[1], f.shape[2], f.shape[3]]) : f;
}
var ua = L({ conv2d_: LE });
function BE(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 = G(o, [1, o.shape[0], o.shape[1]])), O(l.rank === 3, () => `Error in conv1d: input must be rank 3, but got rank ${l.rank}.`), O(u.rank === 3, () => `Error in conv1d: filter must be rank 3, but got rank ${u.rank}.`), pn("conv1d", s, i), O(l.shape[2] === u.shape[1], () => `Error in conv1d: depth of input (${l.shape[2]}) must match input depth for filter ${u.shape[1]}.`), O(Ps(n, a), () => `Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${a}'`), O(r === "NWC", () => `Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);
  let p = G(u, [1, u.shape[0], u.shape[1], u.shape[2]]), d = G(l, [l.shape[0], 1, l.shape[1], l.shape[2]]), g = ua(d, p, [1, n], s, "NHWC", [1, a], i);
  return c ? G(g, [g.shape[2], g.shape[3]]) : G(g, [g.shape[0], g.shape[2], g.shape[3]]);
}
var qk = L({ conv1d_: BE });
function VE(e, t, n, s, r, a = "NHWC", i) {
  O(e.length === t.rank, () => `Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);
  let o = e, u = t, l = false;
  t.rank === 3 && (l = true, u = G(t, [1, t.shape[0], t.shape[1], t.shape[2]]), o = [1, e[0], e[1], e[2]]), O(o.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ${o.length}.`), O(u.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got rank ${u.rank}`), O(n.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);
  let c = a === "NHWC" ? o[3] : o[1], p = a === "NHWC" ? u.shape[3] : u.shape[1];
  O(c === n.shape[2], () => `Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${n.shape[2]}.`), O(p === n.shape[3], () => `Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${n.shape[3]}.`), pn("conv2dDerInput", r, i);
  let d = { dy: u, filter: n }, h = { strides: s, pad: r, dataFormat: a, dimRoundingMode: i, inputShape: o }, f = M.runKernel(Ta, d, h);
  return l ? G(f, [f.shape[1], f.shape[2], f.shape[3]]) : f;
}
var Gg = L({ conv2DBackpropInput_: VE });
function WE(e, t, n, s, r, a) {
  let i = _(e, "x", "conv2dTranspose"), o = _(t, "filter", "conv2dTranspose");
  return Gg(n, i, o, s, r, "NHWC", a);
}
var jk = L({ conv2dTranspose_: WE });
function UE(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 = G(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]])), O(u.rank === 5, () => `Error in conv3d: input must be rank 5, but got rank ${u.rank}.`), O(o.rank === 5, () => `Error in conv3d: filter must be rank 5, but got rank ${o.rank}.`), O(u.shape[4] === o.shape[3], () => `Error in conv3d: depth of input (${u.shape[4]}) must match input depth for filter ${o.shape[3]}.`), O(Ps(n, a), () => `Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`), O(r === "NDHWC", () => `Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);
  let c = { x: u, filter: o }, p = { strides: n, pad: s, dataFormat: r, dilations: a }, d = M.runKernel(Kd, c, p);
  return l ? G(d, [d.shape[1], d.shape[2], d.shape[3], d.shape[4]]) : d;
}
var Kk = L({ conv3d_: UE });
function GE(e, t, n, s, r) {
  O(e.length === t.rank, () => `Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);
  let a = e, i = t, o = false;
  t.rank === 4 && (o = true, i = G(t, [1, t.shape[0], t.shape[1], t.shape[2], t.shape[3]]), a = [1, e[0], e[1], e[2], e[3]]);
  let u = a[4], l = i.shape[4];
  O(a.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ${a.length}.`), O(i.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got rank ${i.rank}`), O(n.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`), O(u === n.shape[3], () => `Error in conv3dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[3]}.`), O(l === n.shape[4], () => `Error in conv3dDerInput: depth of output (${l}) must match output depth for filter ${n.shape[4]}.`);
  let c = { dy: i, filter: n }, p = { pad: r, strides: s, inputShape: a }, d = M.runKernel(cg, c, p);
  return o ? G(d, [d.shape[1], d.shape[2], d.shape[3], d.shape[4]]) : d;
}
var Xk = L({ conv3DBackpropInput_: GE });
function HE(e, t, n, s, r) {
  let a = _(e, "x", "conv3dTranspose"), i = _(t, "filter", "conv3dTranspose");
  return Xk(n, a, i, s, r);
}
var qE = L({ conv3dTranspose_: HE });
function jE(e) {
  let n = { x: _(e, "x", "cos", "float32") };
  return M.runKernel($a, n);
}
var Hg = L({ cos_: jE });
function KE(e) {
  let n = { x: _(e, "x", "cosh", "float32") };
  return M.runKernel(_a, n);
}
var Yk = L({ cosh_: KE });
function XE(e, t = 0, n = false, s = false) {
  let a = { x: _(e, "x", "cumsum") }, i = { axis: t, exclusive: n, reverse: s };
  return M.runKernel(uo, a, i);
}
var Qk = L({ cumsum_: XE });
function YE(e, t, n, s = false) {
  let r = _(e, "x", "denseBincount"), a = _(t, "weights", "denseBincount");
  O(r.dtype === "int32", () => `Error in denseBincount: input dtype must be int32, but got ${r.dtype}`), O(r.rank <= 2, () => `Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`), O(n >= 0, () => `size must be non-negative, but got ${n}.`), O(a.size === r.size || a.size === 0, () => `Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${a.shape}.`);
  let i = { x: r, weights: a }, o = { size: n, binaryOutput: s };
  return M.runKernel(dg, i, o);
}
var QE = L({ denseBincount_: YE });
function ZE(e, t, n = "NHWC") {
  let s = _(e, "x", "depthToSpace", "float32"), r = n === "NHWC" ? s.shape[1] : s.shape[2], a = n === "NHWC" ? s.shape[2] : s.shape[3], i = n === "NHWC" ? s.shape[3] : s.shape[1];
  O(t > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${t}`), O(r * t >= 0, () => `Negative dimension size caused by overflow when multiplying
    ${r} and ${t}  for depthToSpace with input shape
    ${s.shape}`), O(a * t >= 0, () => `Negative dimension size caused by overflow when multiplying
    ${a} and ${t} for depthToSpace with input shape
        ${s.shape}`), O(i % (t * t) === 0, () => `Dimension size must be evenly divisible by ${t * t} but is ${i} for depthToSpace with input shape ${s.shape}`);
  let o = { x: s }, u = { blockSize: t, dataFormat: n };
  return M.runKernel(co, o, u);
}
var JE = L({ depthToSpace_: ZE });
function eR(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 = G(o, [1, o.shape[0], o.shape[1], o.shape[2]])), O(l.rank === 4, () => `Error in depthwiseConv2d: input must be rank 4, but got rank ${l.rank}.`), O(u.rank === 4, () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ${u.rank}.`), O(l.shape[3] === u.shape[2], () => `Error in depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${u.shape[2]}.`), pn("depthwiseConv2d", s, i);
  let p = { x: l, filter: u }, d = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: i }, h = M.runKernel(Aa, p, d);
  return c ? G(h, [h.shape[1], h.shape[2], h.shape[3]]) : h;
}
var pp = L({ depthwiseConv2d_: eR });
function tR(e) {
  let n = { x: _(e, "x", "diag") };
  return M.runKernel(fg, n);
}
var Ede = L({ diag_: tR });
function nR(e, t, n, s, r = [1, 1], a = "NHWC") {
  let i = _(e, "x", "dilation2d"), o = _(t, "filter", "dilation2d");
  O(i.rank === 3 || i.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`), O(o.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`), O(a === "NHWC", () => `Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${a}`);
  let u = i, l = false;
  i.rank === 3 && (u = G(i, [1, i.shape[0], i.shape[1], i.shape[2]]), l = true);
  let c = { x: u, filter: o }, p = { strides: n, pad: s, dilations: r }, d = M.runKernel(Xd, c, p);
  return l ? G(d, [d.shape[1], d.shape[2], d.shape[3]]) : d;
}
var sR = L({ dilation2d_: nR });
function rR(e, t) {
  let n = _(e, "a", "equal", "string_or_numeric"), s = _(t, "b", "equal", "string_or_numeric");
  [n, s] = vt(n, s), it(n.shape, s.shape);
  let r = { a: n, b: s };
  return M.runKernel(po, r);
}
var qn = L({ equal_: rR });
function aR(e, t, n) {
  let s = _(t, "a", "where"), r = _(n, "b", "where"), a = _(e, "condition", "where", "bool"), i = it(it(a.shape, s.shape), r.shape), o = td(a, i), u = td(s, i), l = td(r, i), c = { condition: o, t: u, e: l };
  return M.runKernel(Fo, c);
}
var vn = L({ where_: aR });
function iR(e) {
  let n = { x: _(e, "x", "zerosLike") };
  return M.runKernel(Go, n);
}
var je = L({ zerosLike_: iR });
function oR(e, t) {
  let n = _(e, "a", "div"), s = _(t, "b", "div");
  [n, s] = vt(n, s);
  let r = xe(n, s), a = je(r), i = qn(s, a);
  return vn(i, a, r);
}
var uR = L({ divNoNan_: oR });
function lR(e, t) {
  let n = _(e, "t1", "dot"), s = _(t, "t2", "dot");
  O((n.rank === 1 || n.rank === 2) && (s.rank === 1 || s.rank === 2), () => `Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${s.rank}.`);
  let r = n.rank === 1 ? n.size : n.shape[1], a = s.rank === 1 ? s.size : s.shape[0];
  if (O(r === a, () => `Error in dot: inner dimensions of inputs must match, but got ${r} and ${a}.`), n.rank === 1 && s.rank === 1) {
    let i = G(n, [1, -1]), o = G(s, [-1, 1]), u = We(i, o);
    return G(u, []);
  } else if (n.rank === 1 && s.rank === 2) {
    let i = G(n, [1, -1]), o = G(s, [s.shape[0], s.shape[1]]), u = We(i, o);
    return G(u, [u.size]);
  } else if (n.rank === 2 && s.rank === 1) {
    let i = G(s, [-1, 1]), o = We(n, i);
    return G(o, [o.size]);
  } else {
    let i = G(s, [s.shape[0], s.shape[1]]);
    return We(n, i);
  }
}
var Rde = L({ dot_: lR });
function cR(e, ...t) {
  let n = t.map((r, a) => _(r, `tensors${a}`, "einsum")), s = { equation: e };
  return M.runKernel(Yd, n, s);
}
var dR = L({ einsum_: cR });
function pR(e) {
  let n = { x: _(e, "x", "elu", "float32") };
  return M.runKernel(Ra, n);
}
var hp = L({ elu_: pR });
function hR(e) {
  let t = _(e, "x", "erf");
  O(t.dtype === "int32" || t.dtype === "float32", () => "Input dtype must be `int32` or `float32`."), t.dtype === "int32" && (t = ce(t, "float32"));
  let n = { x: t };
  return M.runKernel(pl, n);
}
var fR = L({ erf_: hR });
function mR(e) {
  let n = { x: _(e, "x", "exp") };
  return M.runKernel(Da, n);
}
var jn = L({ exp_: mR });
function gR(e, t = 0) {
  let n = _(e, "x", "expandDims", "string_or_numeric");
  O(t <= n.rank, () => "Axis must be <= rank of the tensor");
  let s = { input: n }, r = { dim: t };
  return M.runKernel(ho, s, r);
}
var On = L({ expandDims_: gR });
function bR(e) {
  let n = { x: _(e, "x", "expm1") };
  return M.runKernel(fo, n);
}
var yR = L({ expm1_: bR });
function vR(e, t) {
  let n = _(e, "x", "tile", "string_or_numeric");
  O(n.rank === t.length, () => `Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);
  let s = { x: n }, r = { reps: t };
  return M.runKernel(Sr, s, r);
}
var cs = L({ tile_: vR });
function xR(e, t, n, s = "float32") {
  t == null && (t = e);
  let r = De([e, t], s), a = e <= t ? e : t;
  for (let o = 0; o < a; ++o)
    r.set(1, o, o);
  let i = G(r.toTensor(), [e, t]);
  if (n == null)
    return i;
  if (n.length === 1)
    return cs(On(i, 0), [n[0], 1, 1]);
  if (n.length === 2)
    return cs(On(On(i, 0), 0), [n[0], n[1], 1, 1]);
  if (n.length === 3)
    return cs(On(On(On(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 Zk = L({ eye_: xR });
function Fl(e, t, n) {
  let s = { shape: e, value: t, dtype: n };
  return M.runKernel(hl, {}, s);
}
function wR(e) {
  let n = { x: _(e, "x", "floor", "float32") };
  return M.runKernel(Fa, n);
}
var fp = L({ floor_: wR });
function kR(e, t, n = 0, s = 0) {
  let r = _(e, "x", "gather"), a = _(t, "indices", "gather", "int32"), i = { x: r, indices: a }, o = { axis: n, batchDims: s };
  return M.runKernel(go, i, o);
}
var ju = L({ gather_: kR });
function IR(e, t) {
  let n = _(e, "a", "greater", "string_or_numeric"), s = _(t, "b", "greater", "string_or_numeric");
  [n, s] = vt(n, s), it(n.shape, s.shape);
  let r = { a: n, b: s };
  return M.runKernel(yo, r);
}
var Wn = L({ greater_: IR });
function SR(e, t) {
  let n = _(e, "a", "greaterEqual", "string_or_numeric"), s = _(t, "b", "greaterEqual", "string_or_numeric");
  [n, s] = vt(n, s), it(n.shape, s.shape);
  let r = { a: n, b: s };
  return M.runKernel(za, r);
}
var jo = L({ greaterEqual_: SR });
function CR(e) {
  let n = { input: _(e, "input", "imag") };
  return M.runKernel(Qd, n);
}
var qg = L({ imag_: CR });
function NR(e) {
  let n = { x: _(e, "x", "isFinite") };
  return M.runKernel(fl, n);
}
var Dde = L({ isFinite_: NR });
function TR(e) {
  let n = { x: _(e, "x", "isInf") };
  return M.runKernel(ml, n);
}
var Fde = L({ isInf_: TR });
function $R(e) {
  let n = { x: _(e, "x", "isNaN") };
  return M.runKernel(gl, n);
}
var _R = L({ isNaN_: $R });
function AR(e, t = 0.2) {
  let s = { x: _(e, "x", "leakyRelu") }, r = { alpha: t };
  return M.runKernel(La, s, r);
}
var jg = L({ leakyRelu_: AR });
function ER(e, t) {
  let n = _(e, "a", "less", "string_or_numeric"), s = _(t, "b", "less", "string_or_numeric");
  [n, s] = vt(n, s), it(n.shape, s.shape);
  let r = { a: n, b: s };
  return M.runKernel(vo, r);
}
var Jk = L({ less_: ER });
function RR(e, t) {
  let n = _(e, "a", "lessEqual", "string_or_numeric"), s = _(t, "b", "lessEqual", "string_or_numeric");
  [n, s] = vt(n, s), it(n.shape, s.shape);
  let r = { a: n, b: s };
  return M.runKernel(xo, r);
}
var Ko = L({ lessEqual_: RR });
function DR(e, t, n) {
  if (n <= 0)
    throw new Error("The number of values should be positive.");
  let s = { start: e, stop: t, num: n };
  return M.runKernel(yg, {}, s);
}
function FR(e, t = 5, n = 1, s = 1, r = 0.5) {
  let a = _(e, "x", "localResponseNormalization");
  O(a.rank === 4 || a.rank === 3, () => `Error in localResponseNormalization: x must be rank 3 or 4 but got
               rank ${a.rank}.`), O(Xi(t), () => `Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);
  let i = a, o = false;
  a.rank === 3 && (o = true, i = G(a, [1, a.shape[0], a.shape[1], a.shape[2]]));
  let u = { x: i }, l = { depthRadius: t, bias: n, alpha: s, beta: r }, c = M.runKernel(Jd, u, l);
  return o ? G(c, [c.shape[1], c.shape[2], c.shape[3]]) : c;
}
var OR = L({ localResponseNormalization_: FR });
function PR(e) {
  let n = { x: _(e, "x", "log", "float32") };
  return M.runKernel(Ba, n);
}
var Kn = L({ log_: PR });
function zR(e) {
  let n = { x: _(e, "x", "log1p") };
  return M.runKernel(bl, n);
}
var Kg = L({ log1p_: zR });
function Ode(e) {
  return O(hr(e), () => "The f passed in grad(f) must be a function"), (t, n) => {
    let s = _(t, "x", "tf.grad", "string_or_numeric"), r = n != null ? _(n, "dy", "tf.grad") : null;
    return M.tidy(() => {
      let { value: a, grads: i } = M.gradients(() => e(s), [s], r);
      return r != null && dn(a.shape, r.shape, "The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"), mp(i), i[0];
    });
  };
}
function Pde(e) {
  return O(hr(e), () => "The f passed in grads(f) must be a function"), (t, n) => {
    O(Array.isArray(t), () => "The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");
    let s = Uu(t, "args", "tf.grads", "string_or_numeric"), r = n != null ? _(n, "dy", "tf.grads") : null;
    return M.tidy(() => {
      let { value: a, grads: i } = M.gradients(() => e(...s), s, r);
      return r != null && dn(a.shape, r.shape, "The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), mp(i), i;
    });
  };
}
function zde(e) {
  return O(hr(e), () => "The f passed in valueAndGrad(f) must be a function"), (t, n) => {
    O(t instanceof et, () => "The x passed in valueAndGrad(f)(x) must be a tensor"), O(n == null || n instanceof et, () => "The dy passed in valueAndGrad(f)(x, dy) must be a tensor");
    let { grads: s, value: r } = M.gradients(() => e(t), [t], n);
    return mp(s), { grad: s[0], value: r };
  };
}
function Mde(e) {
  return O(hr(e), () => "The f passed in valueAndGrads(f) must be a function"), (t, n) => {
    O(Array.isArray(t) && t.every((r) => r instanceof et), () => "The args passed in valueAndGrads(f)(args) must be array of tensors"), O(n == null || n instanceof et, () => "The dy passed in valueAndGrads(f)(args, dy) must be a tensor");
    let s = M.gradients(() => e(...t), t, n);
    return n != null && dn(s.value.shape, n.shape, "The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"), mp(s.grads), s;
  };
}
function MR(e, t) {
  O(hr(e), () => "The f passed in variableGrads(f) must be a function"), O(t == null || Array.isArray(t) && t.every((l) => l instanceof md), () => "The varList passed in variableGrads(f, varList) must be an array of variables");
  let n = t != null;
  if (!n) {
    t = [];
    for (let l in M.registeredVariables)
      t.push(M.registeredVariables[l]);
  }
  let s = n ? t.filter((l) => !l.trainable) : null, r = t.length;
  t = t.filter((l) => l.trainable), O(t.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${r} variables is trainable.`);
  let a = true, { value: i, grads: o } = M.gradients(e, t, null, a);
  O(o.some((l) => l != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."), O(i.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${i.rank} tensor`);
  let u = {};
  return t.forEach((l, c) => {
    o[c] != null && (u[l.name] = o[c]);
  }), s != null && s.forEach((l) => u[l.name] = null), { value: i, grads: u };
}
function js(e) {
  return M.customGrad(e);
}
function mp(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 LR(e) {
  let n = { x: _(e, "x", "neg") };
  return M.runKernel(ko, n);
}
var kt = L({ neg_: LR });
function BR(e) {
  let n = { x: _(e, "x", "softplus") };
  return M.runKernel(Nl, n);
}
var Ol = L({ softplus_: BR });
function VR(e) {
  let t = _(e, "x", "logSigmoid");
  return js((s) => ({ value: kt(Ol(kt(s))), gradFunc: (i) => V(i, qs(kt(s))) }))(t);
}
var Lde = L({ logSigmoid_: VR });
function WR(e, t = null, n = false) {
  let r = { x: _(e, "x", "max") }, a = { reductionIndices: t, keepDims: n };
  return M.runKernel(Va, r, a);
}
var As = L({ max_: WR });
function UR(e, t) {
  let n = _(e, "a", "sub"), s = _(t, "b", "sub");
  [n, s] = vt(n, s);
  let r = { a: n, b: s };
  return M.runKernel(ui, r);
}
var ge = L({ sub_: UR });
function GR(e, t = null, n = false) {
  let s = _(e, "x", "sum");
  s.dtype === "bool" && (s = ce(s, "int32"));
  let r = { x: s }, a = { axis: t, keepDims: n };
  return M.runKernel(ai, r, a);
}
var ye = L({ sum_: GR });
function HR(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 ${t}`);
  return js((r, a) => {
    let o = As(r, t, true), u = ge(r, o), l = ge(ce(u, "float32"), Kn(ye(jn(u), t, true)));
    return a([l]), { value: l, gradFunc: (p, d) => {
      let [h] = d, f = true, m = jn(h);
      return ge(p, V(ye(p, t, f), m));
    } };
  })(n);
}
var eI = L({ logSoftmax_: HR });
function Xg(e, t) {
  for (let n = 0; n < e.length; ++n)
    if (e[e.length - n - 1] !== t - 1 - n)
      return false;
  return true;
}
function tI(e, t, n) {
  let s = e.length + t.length, r = [], a = 0, i = 0;
  for (let o = 0; o < s; o++)
    n.indexOf(o) === -1 ? r.push(e[a++]) : r.push(t[i++]);
  return r;
}
function nI(e, t) {
  let n = [], s = e.length;
  for (let a = 0; a < s; a++)
    t.indexOf(a) === -1 && n.push(e[a]);
  let r = t.map((a) => e[a]);
  return [n, r];
}
function la(e, t) {
  let n = t.map((s) => 1);
  return tI(e, n, t);
}
function qR(e, t, n) {
  O(Xg(t, n), () => `${e} supports only inner-most axes for now. Got axes ${t} and rank-${n} input.`);
}
function sI(e, t) {
  if (Xg(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 Yg(e) {
  return e.map((t, n) => [n, t]).sort((t, n) => t[1] - n[1]).map((t) => t[0]);
}
function jR(e, t) {
  let n = [];
  for (let s = t - e; s < t; ++s)
    n.push(s);
  return n;
}
function KR(e, t = null, n = false) {
  let s = _(e, "x", "logSumExp"), r = Jn(t, s.shape), a = As(s, r, true), i = ge(s, a), o = jn(i), u = ye(o, r), l = Kn(u), c = ie(G(a, l.shape), l);
  if (n) {
    let p = la(c.shape, r);
    return G(c, p);
  }
  return c;
}
var XR = L({ logSumExp_: KR });
function YR(e, t) {
  let n = _(e, "a", "logicalAnd", "bool"), s = _(t, "b", "logicalAnd", "bool");
  it(n.shape, s.shape);
  let r = { a: n, b: s };
  return M.runKernel(wo, r);
}
var Ds = L({ logicalAnd_: YR });
function QR(e) {
  let n = { x: _(e, "x", "logicalNot", "bool") };
  return M.runKernel(yl, n);
}
var Qg = L({ logicalNot_: QR });
function ZR(e, t) {
  let n = _(e, "a", "logicalOr", "bool"), s = _(t, "b", "logicalOr", "bool");
  it(n.shape, s.shape);
  let r = { a: n, b: s };
  return M.runKernel(Zd, r);
}
var rI = L({ logicalOr_: ZR });
function JR(e, t) {
  let n = _(e, "a", "logicalXor", "bool"), s = _(t, "b", "logicalXor", "bool");
  return it(n.shape, s.shape), Ds(rI(e, t), Qg(Ds(e, t)));
}
var Bde = L({ logicalXor_: JR });
function eD(e, t, n, s, r) {
  let a = _(e, "x", "maxPool"), i = 1, o = a, u = false;
  a.rank === 3 && (u = true, o = G(a, [1, a.shape[0], a.shape[1], a.shape[2]])), O(o.rank === 4, () => `Error in maxPool: input must be rank 4 but got rank ${o.rank}.`), O(Ps(n, i), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${n} and dilations '${i}'`), pn("maxPool", s, r);
  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r }, p = M.runKernel(Ua, l, c);
  return u ? G(p, [p.shape[1], p.shape[2], p.shape[3]]) : p;
}
var Zg = L({ maxPool_: eD });
function tD(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 = G(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]])), O(o.rank === 5, () => `Error in maxPool3d: x must be rank 5 but got rank ${o.rank}.`), O(a === "NDHWC", () => `Error in maxPool3d: Only NDHWC is currently supported, but got dataFormat of ${a}`), pn("maxPool3d", s, r);
  let l = { x: o }, c = { filterSize: t, strides: n, pad: s, dimRoundingMode: r, dataFormat: a }, p = M.runKernel(ep, l, c);
  return u ? G(p, [p.shape[1], p.shape[2], p.shape[3], p.shape[4]]) : p;
}
var aI = L({ maxPool3d_: tD });
function nD(e, t, n, s, r = false) {
  let i = { x: _(e, "x", "maxPoolWithArgmax") }, o = { filterSize: t, strides: n, pad: s, includeBatchInIndex: r }, u = M.runKernel(kg, i, o);
  return { result: u[0], indexes: u[1] };
}
var sD = L({ maxPoolWithArgmax_: nD });
function rD(e, t) {
  let n = _(e, "a", "maximum"), s = _(t, "b", "maximum");
  [n, s] = vt(n, s), n.dtype === "bool" && (n = ce(n, "int32"), s = ce(s, "int32")), it(n.shape, s.shape);
  let r = { a: n, b: s };
  return M.runKernel(Wa, r);
}
var Tr = L({ maximum_: rD });
function aD(e, t = null, n = false) {
  let r = { x: _(e, "x", "mean") }, a = { axis: t, keepDims: n };
  return M.runKernel(Ga, r, a);
}
var It = L({ mean_: aD });
function $t(e, t = "float32") {
  if (t === "complex64") {
    let s = $t(e, "float32"), r = $t(e, "float32");
    return aa(s, r);
  }
  let n = Gd(pt(e), t);
  return M.makeTensor(n, e, t);
}
function zn(e, t = "float32") {
  if (t === "complex64") {
    let s = zn(e, "float32"), r = $t(e, "float32");
    return aa(s, r);
  }
  let n = eg(pt(e), t);
  return M.makeTensor(n, e, t);
}
function Vde(e, t, { indexing: n = "xy" } = {}) {
  if (n !== "xy" && n !== "ij")
    throw new TypeError(`${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 = pt(s.shape), i = pt(r.shape);
  return n === "xy" ? (s = G(s, [1, -1]), r = G(r, [-1, 1]), [We(zn([i, 1], s.dtype), s), We(r, zn([1, a], r.dtype))]) : (s = G(s, [-1, 1]), r = G(r, [1, -1]), [We(s, zn([1, i], s.dtype)), We(zn([a, 1], r.dtype), r)]);
}
function iD(e, t = null, n = false) {
  let r = { x: _(e, "x", "min") }, a = { axis: t, keepDims: n };
  return M.runKernel(Ha, r, a);
}
var pm = L({ min_: iD });
function oD(e, t) {
  let n = _(e, "a", "minimum"), s = _(t, "b", "minimum");
  [n, s] = vt(n, s), n.dtype === "bool" && (n = ce(n, "int32"), s = ce(s, "int32")), it(n.shape, s.shape);
  let r = { a: n, b: s };
  return M.runKernel(qa, r);
}
var gp = L({ minimum_: oD });
function uD(e, t, n) {
  O(n === "reflect" || n === "symmetric", () => `Invalid mode. Mode must be either reflect or symmetric. Got ${n}.`);
  let s = _(e, "x", "mirrorPad");
  if (s.rank === 0)
    throw new Error("mirrorPad(scalar) is not defined. Pass non-scalar to mirrorPad");
  O(t.length === s.rank, () => `Padding doesn't match input. Must be ${s.rank}. Got ${t.length}.`);
  let r = n === "reflect" ? 1 : 0;
  for (let o = 0; o < s.rank; o++)
    O(t[o].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), O(t[o][0] >= 0 && t[o][0] <= s.shape[o] - r && t[o][1] >= 0 && t[o][1] <= s.shape[o] - r, () => `Padding in dimension ${o} cannot be greater than or equal to ${s.shape[o] - r} or less than 0 for input of shape ${s.shape}`);
  let a = { paddings: t, mode: n }, i = { x: s };
  return M.runKernel(ja, i, a);
}
var lD = L({ mirrorPad_: uD });
function cD(e, t) {
  let n = _(e, "a", "mod"), s = _(t, "b", "mod");
  [n, s] = vt(n, s);
  let r = { a: n, b: s };
  return M.runKernel(vl, r);
}
var dD = L({ mod_: cD });
function pD(e) {
  let t = _(e, "x", "square"), n = {};
  return M.runKernel("Square", { x: t }, n);
}
var ct = L({ square_: pD });
function hD(e, t = null, n = false) {
  e = _(e, "x", "moments");
  let s = Jn(t, e.shape), r = It(e, s, n), a = r.shape;
  n || (a = la(r.shape, s));
  let i = ct(ge(ce(e, "float32"), G(r, a))), o = It(i, s, n);
  return { mean: r, variance: o };
}
var Jg = L({ moments_: hD });
function fD(e, t, n, s) {
  let r = _(t, "data", "multiRNNCell"), a = Uu(n, "c", "multiRNNCell"), i = Uu(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 Wde = L({ multiRNNCell_: fD });
function mD(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 ? G(r, [1, -1]) : r }, l = { numSamples: t, seed: n, normalized: s }, c = M.runKernel(Ig, u, l);
  return i === 1 ? G(c, [c.size]) : c;
}
var gD = L({ multinomial_: mD });
function bD(e, t) {
  let n = _(e, "a", "notEqual", "string_or_numeric"), s = _(t, "b", "notEqual", "string_or_numeric");
  [n, s] = vt(n, s), it(n.shape, s.shape);
  let r = { a: n, b: s };
  return M.runKernel(Io, r);
}
var Ku = L({ notEqual_: bD });
function yD(e) {
  let n = { x: _(e, "x", "onesLike") };
  return M.runKernel(No, n);
}
var Xn = L({ onesLike_: yD });
function vD(e, t) {
  let n = _(e, "v1", "outerProduct"), s = _(t, "v2", "outerProduct");
  O(n.rank === 1 && s.rank === 1, () => `Error in outerProduct: inputs must be rank 1, but got ranks ${n.rank} and ${s.rank}.`);
  let r = G(n, [-1, 1]), a = G(s, [1, -1]);
  return We(r, a);
}
var Ude = L({ outerProduct_: vD });
function xD(e, t, n = 0) {
  let s = _(e, "x", "pad");
  if (s.rank === 0)
    throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");
  let r = { paddings: t, constantValue: n }, a = { x: s };
  return M.runKernel(Xa, a, r);
}
var pi = L({ pad_: xD });
function wD(e, t, n = 0) {
  return O(t.length === 2, () => "Invalid number of paddings. Must be length of 2."), pi(e, [t], n);
}
var Gde = L({ pad1d_: wD });
function kD(e, t, n = 0) {
  return O(t.length === 2 && t[0].length === 2 && t[1].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), pi(e, t, n);
}
var Hde = L({ pad2d_: kD });
function ID(e, t, n = 0) {
  return O(t.length === 3 && t[0].length === 2 && t[1].length === 2 && t[2].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), pi(e, t, n);
}
var qde = L({ pad3d_: ID });
function SD(e, t, n = 0) {
  return O(t.length === 4 && t[0].length === 2 && t[1].length === 2 && t[2].length === 2 && t[3].length === 2, () => "Invalid number of paddings. Must be length of 2 each."), pi(e, t, n);
}
var jde = L({ pad4d_: SD });
function CD(e, t, n) {
  let s = _(e, "x", "spaceToBatchND");
  O(s.rank >= 1 + t.length, () => `input rank ${s.rank} should be > than [blockShape] ${t.length}`), O(n.length === t.length, () => `paddings.shape[0] ${n.length} must be equal to [blockShape] ${t.length}`), O(s.shape.reduce((i, o, u) => u > 0 && u <= t.length ? i && (o + n[u - 1][0] + n[u - 1][1]) % t[u - 1] === 0 : i, true), () => `input spatial dimensions ${s.shape.slice(1)} with paddings ${n.toString()} must be divisible by blockShapes ${t.toString()}`);
  let r = { x: s }, a = { blockShape: t, paddings: n };
  return M.runKernel(zo, r, a);
}
var eb = L({ spaceToBatchND_: CD });
function ND(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 = G(o, [1, o.shape[0], o.shape[1], o.shape[2]])), O(Ps(a, r), () => `Error in pool: Either strides or dilations must be 1. Got strides ${a} and dilations '${r}'`);
  let c = Vk(u.shape, t, a, r, s), p = [c.dilationHeight, c.dilationWidth], d;
  s === "same" ? d = $D([c.filterHeight, c.filterWidth], p) : d = [[0, 0], [0, 0]];
  let h = p[0] === 1 && p[1] === 1, [f, m] = TD([c.inHeight, c.inWidth], p, d), g = h ? s : "valid", b = h ? u : eb(u, p, f), v = (n === "avg" ? () => Wg(b, t, a, g, i) : () => Zg(b, t, a, g, i))(), x = h ? v : Ug(v, p, m);
  return l ? G(x, [x.shape[1], x.shape[2], x.shape[3]]) : x;
}
function TD(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 $D(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 Kde = L({ pool_: ND });
function _D(e, t) {
  let n = _(e, "base", "pow"), s = _(t, "exp", "pow");
  [n, s] = vt(n, s);
  let r = { a: n, b: s };
  return M.runKernel(Ya, r);
}
var ca = L({ pow_: _D });
function AD(e, t) {
  let n = _(e, "x", "prelu"), s = _(t, "alpha", "prelu"), r = { x: n, alpha: s };
  return M.runKernel(Qa, r);
}
var tb = L({ prelu_: AD });
function ED(e, t = null, n = false) {
  let s = _(e, "x", "prod");
  s.dtype === "bool" && (s = ce(s, "int32"));
  let r = { x: s }, a = { axis: t, keepDims: n };
  return M.runKernel(_o, r, a);
}
var iI = L({ prod_: ED });
function RD(e, t, n) {
  let s = pt(e), r = null;
  if (n == null || n === "float32")
    r = new Float32Array(s);
  else if (n === "int32")
    r = new Int32Array(s);
  else if (n === "bool")
    r = new Uint8Array(s);
  else
    throw new Error(`Unknown data type ${n}`);
  for (let a = 0; a < s; a++)
    r[a] = t();
  return M.makeTensor(r, e, n);
}
var Xde = L({ rand_: RD });
var nb = ya(Vd());
var sb = 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 = nb.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 DD = class {
  constructor(e, t, n, s) {
    this.alpha = e, this.beta = 1 / t, this.dtype = n;
    let r = s || Math.random();
    this.randu = nb.alea(r.toString()), this.randn = new sb(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 FD = class {
  constructor(e = 0, t = 1, n, s) {
    if (this.canReturnFloat = () => this.dtype == null || this.dtype === "float32", this.min = e, this.range = t - e, this.dtype = n, s == null && (s = Math.random()), typeof s == "number" && (s = s.toString()), !this.canReturnFloat() && this.range <= 1)
      throw new Error(`The difference between ${e} - ${t} <= 1 and dtype is not float`);
    this.random = nb.alea(s);
  }
  convertValue(e) {
    return this.canReturnFloat() ? e : Math.round(e);
  }
  nextValue() {
    return this.convertValue(this.min + this.range * this.random());
  }
};
function OD(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 DD(t, n, s, r), i = De(e, s);
  for (let o = 0; o < i.values.length; o++)
    i.values[o] = a.nextValue();
  return i.toTensor();
}
var Yde = L({ randomGamma_: OD });
function PD(e, t = 0, n = 1, s, r) {
  if (s != null && s === "bool")
    throw new Error(`Unsupported data type ${s}`);
  let a = new sb(t, n, s, false, r), i = De(e, s);
  for (let o = 0; o < i.values.length; o++)
    i.values[o] = a.nextValue();
  return i.toTensor();
}
var zD = L({ randomNormal_: PD });
function MD(e, t = 0, n = 1, s = "float32", r) {
  let a = De(e, s), i = new FD(t, n, null, r);
  for (let o = 0; o < a.values.length; o++)
    a.values[o] = i.nextValue();
  return a.toTensor();
}
var Pl = L({ randomUniform_: MD });
function Xu(e, t, n = 1, s = "float32") {
  if (n === 0)
    throw new Error("Cannot have a step of zero");
  let r = { start: e, stop: t, step: n, dtype: s };
  return M.runKernel(wl, {}, r);
}
function LD(e) {
  let n = { input: _(e, "input", "real") };
  return M.runKernel(tp, n);
}
var xd = L({ real_: LD });
function BD(e) {
  let n = { x: _(e, "x", "reciprocal") };
  return M.runKernel(kl, n);
}
var VD = L({ reciprocal_: BD });
function WD(e) {
  let n = { x: _(e, "x", "relu") };
  return M.runKernel(Za, n);
}
var Xs = L({ relu_: WD });
function UD(e) {
  let n = { x: _(e, "x", "relu6") };
  return M.runKernel(ei, n);
}
var oI = L({ relu6_: UD });
function GD(e, t) {
  let s = { x: _(e, "x", "reverse") }, r = { dims: t };
  return M.runKernel(Eo, s, r);
}
var Yn = L({ reverse_: GD });
function HD(e) {
  let t = _(e, "x", "reverse");
  return O(t.rank === 1, () => `Error in reverse1D: x must be rank 1 but got rank ${t.rank}.`), Yn(t, 0);
}
var Qde = L({ reverse1d_: HD });
function qD(e, t) {
  let n = _(e, "x", "reverse");
  return O(n.rank === 2, () => `Error in reverse2D: x must be rank 2 but got rank ${n.rank}.`), Yn(n, t);
}
var Zde = L({ reverse2d_: qD });
function jD(e, t) {
  let n = _(e, "x", "reverse");
  return O(n.rank === 3, () => `Error in reverse3D: x must be rank 3 but got rank ${n.rank}.`), Yn(n, t);
}
var Jde = L({ reverse3d_: jD });
function KD(e, t) {
  let n = _(e, "x", "reverse");
  return O(n.rank === 4, () => `Error in reverse4D: x must be rank 4 but got rank ${n.rank}.`), Yn(n, t);
}
var epe = L({ reverse4d_: KD });
function XD(e) {
  let n = { x: _(e, "x", "round") };
  return M.runKernel(Ro, n);
}
var uI = L({ round_: XD });
function YD(e) {
  let n = { x: _(e, "x", "rsqrt", "float32") };
  return M.runKernel(ti, n);
}
var lI = L({ rsqrt_: YD });
function Ie(e, t) {
  if ((Yt(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" && Yt(e) && !(e instanceof Uint8Array))
    throw new Error("When making a scalar from encoded string, the value must be `Uint8Array`.");
  return Cr(e, [], [], t);
}
function QD(e) {
  let n = { x: _(e, "x", "selu") };
  return M.runKernel(Sl, n);
}
var cI = L({ selu_: QD });
function ZD(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 = G(o, [1, o.shape[0], o.shape[1], o.shape[2]])), i === "NCHW")
    throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");
  O(c.rank === 4, () => `Error in separableConv2d: input must be rank 4, but got rank ${c.rank}.`), O(u.rank === 4, () => `Error in separableConv2d: depthwise filter must be rank 4, but got rank ${u.rank}.`), O(l.rank === 4, () => `Error in separableConv2d: pointwise filter must be rank 4, but got rank ${u.rank}.`), O(l.shape[0] === 1, () => `Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got ${l.shape[0]}.`), O(l.shape[1] === 1, () => `Error in separableConv2d: the second dimension of pointwise filter must be 1, but got ${l.shape[1]}.`);
  let d = u.shape[2], h = u.shape[3];
  O(l.shape[2] === d * h, () => `Error in separableConv2d: the third dimension of pointwise filter must be ${d * h}, but got ${l.shape[2]}.`);
  let f = pp(c, u, s, r, i, a), g = ua(f, l, 1, "valid", i);
  return p ? G(g, [g.shape[1], g.shape[2], g.shape[3]]) : g;
}
var JD = L({ separableConv2d_: ZD });
async function e3(e, t) {
  let n = _(e, "x", "setdiff1d"), s = _(t, "y", "setdiff1d");
  O(n.dtype === s.dtype, () => `x and y should have the same dtype, but got x (${n.dtype}) and y (${s.dtype}).`), O(n.rank === 1, () => `x should be 1D tensor, but got x (${n.shape}).`), O(s.rank === 1, () => `y should be 1D tensor, but got y (${s.shape}).`);
  let r = await n.data(), a = await s.data(), i = new Set(a), o = 0;
  for (let c = 0; c < r.length; c++)
    i.has(r[c]) || o++;
  let u = new Vt([o], n.dtype), l = new Vt([o], "int32");
  for (let c = 0, p = 0; c < r.length; c++)
    i.has(r[c]) || (u.values[p] = r[c], l.values[p] = c, p++);
  return [u.toTensor(), l.toTensor()];
}
var t3 = e3;
function n3(e) {
  let n = { x: _(e, "x", "sign") };
  return M.runKernel(Cl, n);
}
var s3 = L({ sign_: n3 });
function r3(e) {
  let n = { x: _(e, "x", "sin", "float32") };
  return M.runKernel(ni, n);
}
var dI = L({ sin_: r3 });
function a3(e) {
  let n = { x: _(e, "x", "sinh") };
  return M.runKernel(Po, n);
}
var pI = L({ sinh_: a3 });
function i3(e, t, n) {
  let s = _(e, "x", "slice1d");
  return O(s.rank === 1, () => `slice1d expects a rank-1 tensor, but got a rank-${s.rank} tensor`), He(s, [t], [n]);
}
var rb = L({ slice1d_: i3 });
function o3(e, t, n) {
  let s = _(e, "x", "slice2d");
  return O(s.rank === 2, () => `slice2d expects a rank-2 tensor, but got a rank-${s.rank} tensor`), He(s, t, n);
}
var hI = L({ slice2d_: o3 });
function u3(e, t, n) {
  let s = _(e, "x", "slice3d");
  return O(s.rank === 3, () => `slice3d expects a rank-3 tensor, but got a rank-${s.rank} tensor`), He(s, t, n);
}
var ab = L({ slice3d_: u3 });
function l3(e, t, n) {
  let s = _(e, "x", "slice4d");
  return O(s.rank === 4, () => `slice4d expects a rank-4 tensor, but got a rank-${s.rank} tensor`), He(s, t, n);
}
var wd = L({ slice4d_: l3 });
function c3(e, t = -1) {
  let n = _(e, "logits", "softmax", "float32");
  if (t === -1 && (t = n.rank - 1), t !== n.rank - 1)
    throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${n.rank} and dim was ${t}`);
  let s = { logits: n }, r = { dim: t };
  return M.runKernel(ii, s, r);
}
var ib = L({ softmax_: c3 });
function d3(e) {
  O(e.dtype === "complex64", () => `The dtype for tf.spectral.fft() must be complex64 but got ${e.dtype}.`);
  let t = { input: e };
  return M.runKernel(gg, t);
}
var ob = L({ fft_: d3 });
function p3(e) {
  O(e.dtype === "complex64", () => `The dtype for tf.spectral.ifft() must be complex64 but got ${e.dtype}.`);
  let t = { input: e };
  return M.runKernel(bg, t);
}
var kd = L({ ifft_: p3 });
function h3(e) {
  let t = e.shape[e.shape.length - 1], n = e.size / t, s;
  if (t <= 2) {
    let r = G(e, [n, t]);
    s = kd(r);
  } else {
    let r = [n, 2 * (t - 1)], a = G(xd(e), [n, t]), i = G(qg(e), [n, t]), o = Yn(He(a, [0, 1], [n, t - 2]), 1), u = V(Yn(He(i, [0, 1], [n, t - 2]), 1), Ie(-1)), l = Ft([a, o], 1), c = Ft([i, u], 1), p = G(aa(l, c), [r[0], r[1]]);
    s = kd(p);
  }
  if (s = xd(s), e.rank === 3 && e.shape[0] !== 0) {
    let r = s, a = e.shape[0];
    s = G(s, [a, s.shape[0] / a, s.shape[1]]), r.dispose();
  }
  return s;
}
var fI = L({ irfft_: h3 });
function f3(e, t, n = 0) {
  let r = { x: _(e, "x", "split") }, a = { numOrSizeSplits: t, axis: n };
  return M.runKernel(Mo, r, a);
}
var Ln = L({ split_: f3 });
function m3(e, t) {
  O(e.dtype === "float32", () => `The dtype for rfft() must be real value but got ${e.dtype}`);
  let n = e.shape[e.shape.length - 1], s = e.size / n, r;
  if (t != null && t < n) {
    let f = e.shape.map((g) => 0), m = e.shape.map((g) => g);
    m[e.shape.length - 1] = t, r = He(e, f, m), n = t;
  } else if (t != null && t > n) {
    let f = e.shape.map((m) => m);
    f[e.shape.length - 1] = t - n, r = Ft([e, $t(f)], e.shape.length - 1), n = t;
  } else
    r = e;
  let a = je(r), i = G(aa(r, a), [s, n]), o = ob(i), u = Math.floor(n / 2) + 1, l = xd(o), c = qg(o), p = Ln(l, [u, n - u], l.shape.length - 1), d = Ln(c, [u, n - u], c.shape.length - 1), h = r.shape.slice();
  return h[r.shape.length - 1] = u, G(aa(p[0], d[0]), h);
}
var ub = L({ rfft_: m3 });
function g3(e) {
  let n = { x: _(e, "x", "sqrt", "float32") };
  return M.runKernel(ri, n);
}
var ln = L({ sqrt_: g3 });
function b3(e, t) {
  let n = _(e, "a", "squaredDifference"), s = _(t, "b", "squaredDifference");
  [n, s] = vt(n, s), it(n.shape, s.shape);
  let r = { a: n, b: s }, a = {};
  return M.runKernel(oi, r, a);
}
var mI = L({ squaredDifference_: b3 });
function y3(e, t) {
  let n = _(e, "x", "squeeze");
  return G(n, Mw(n.shape, t).newShape);
}
var mr = L({ squeeze_: y3 });
function v3(e, t = 0) {
  let n = Uu(e, "tensors", "stack", "string_or_numeric");
  O(n.length >= 1, () => "Pass at least one tensor to tf.stack"), n.length > 0 && O(t <= n[0].rank, () => "Axis must be <= rank of the tensor");
  let s = n, r = { axis: t };
  return M.runKernel($o, s, r);
}
var Qn = L({ stack_: v3 });
function x3(e, t = 0) {
  let s = { x: _(e, "x", "step") }, r = { alpha: t };
  return M.runKernel(di, s, r);
}
var bp = L({ step_: x3 });
function w3(e, t, n, s, r = 0, a = 0, i = 0, o = 0, u = 0) {
  let c = { x: _(e, "x", "stridedSlice", "string_or_numeric") }, p = { begin: t, end: n, strides: s, beginMask: r, endMask: a, ellipsisMask: i, newAxisMask: o, shrinkAxisMask: u };
  return M.runKernel(Lo, c, p);
}
var k3 = L({ stridedSlice_: w3 });
function I3(e) {
  let n = { x: _(e, "x", "tan", "float32") };
  return M.runKernel(Bo, n);
}
var S3 = L({ tan_: I3 });
function Qt(e, t) {
  va(e);
  let n = Rs(e, t);
  if (n.length !== 1)
    throw new Error("tensor1d() requires values to be a flat/TypedArray");
  return Cr(e, null, n, t);
}
function ji(e, t, n) {
  if (va(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 Cr(e, t, s, n);
}
function tpe(e, t, n) {
  if (va(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 Cr(e, t, s, n);
}
function npe(e, t, n) {
  if (va(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 Cr(e, t, s, n);
}
function spe(e, t, n) {
  if (va(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, Cr(e, t, s, n);
}
function C3(e, t = 1, n = true) {
  let s = _(e, "x", "topk");
  if (s.rank === 0)
    throw new Error("topk() expects the input to be of rank 1 or higher");
  let r = s.shape[s.shape.length - 1];
  if (t < 0)
    throw new Error(`'k' passed to topk() must be >= 0 but got ${t}`);
  if (t > r)
    throw new Error(`'k' passed to topk() must be <= the last dimension (${r}) but got ${t}`);
  let a = { x: s }, i = { k: t, sorted: n }, [o, u] = M.runKernel(Vo, a, i);
  return { values: o, indices: u };
}
var N3 = L({ topk_: C3 });
function T3(e, t = 0, n = 1, s, r) {
  if (s != null && s === "bool")
    throw new Error("Unsupported data type $ { dtype }");
  let a = new sb(t, n, s, true, r), i = De(e, s);
  for (let o = 0; o < i.values.length; o++)
    i.values[o] = a.nextValue();
  return i.toTensor();
}
var lb = L({ truncatedNormal_: T3 });
function $3(e, t = 0) {
  let n = _(e, "x", "unique", "string_or_numeric");
  O(n.rank > 0, () => "The input tensor must be at least 1D");
  let s = { x: n }, r = { axis: t }, [a, i] = M.runKernel($g, s, r);
  return { values: a, indices: i };
}
var cx = L({ unique_: $3 });
function _3(e, t, n) {
  let s = _(e, "x", "unsortedSegmentSum"), r = _(t, "segmentIds", "unsortedSegmentSum", "int32");
  O(Xi(n), () => "numSegments must be of dtype int");
  let a = { x: s, segmentIds: r }, i = { numSegments: n };
  return M.runKernel(op, a, i);
}
var A3 = L({ unsortedSegmentSum_: _3 });
function E3(e, t = 0) {
  let n = _(e, "x", "unstack", "string_or_numeric");
  O(t >= -n.shape.length && t < n.shape.length, () => `Axis = ${t} is not in [-${n.shape.length}, ${n.shape.length})`);
  let s = { value: n }, r = { axis: t };
  return M.runKernel(Uo, s, r);
}
var Fs = L({ unstack_: E3 });
function R3(e, t = true, n, s) {
  return M.makeVariable(e, t, n, s);
}
function gI(e, t) {
  let n = [];
  for (let a = 0; a < t.length; a++)
    t[a] && n.push(a);
  let s = De(e, "int32"), r = De([n.length, e.length], "int32");
  for (let a = 0; a < n.length; a++) {
    let i = s.indexToLoc(n[a]), o = a * e.length;
    r.values.set(i, o);
  }
  return r.toTensor();
}
async function D3(e) {
  let t = _(e, "condition", "whereAsync", "bool"), n = await t.data(), s = gI(t.shape, n);
  return e !== t && t.dispose(), s;
}
var bI = D3;
async function F3(e, t, n) {
  let s = _(e, "tensor", "boolMask"), r = _(t, "mask", "boolMask", "bool"), a = n == null ? 0 : n, i = r.rank, o = s.shape;
  O(i > 0, () => "mask cannot be scalar"), dn(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 = G(s, l), p = G(r, [-1]), d = await bI(p), h = mr(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 rpe = F3;
function O3(e, t = "euclidean", n = null, s = false) {
  e = _(e, "x", "norm");
  let r = yI(e, t, n), a = r.shape;
  if (s) {
    let i = Jn(n, e.shape);
    a = la(r.shape, i);
  }
  return G(r, a);
}
function yI(e, t, n = null) {
  if (e.rank === 0)
    return Mt(e);
  if (e.rank !== 1 && n === null)
    return yI(G(e, [-1]), t, n);
  if (e.rank === 1 || typeof n == "number" || Array.isArray(n) && n.length === 1) {
    if (t === 1)
      return ye(Mt(e), n);
    if (t === 1 / 0)
      return As(Mt(e), n);
    if (t === -1 / 0)
      return pm(Mt(e), n);
    if (t === "euclidean" || t === 2)
      return ln(ye(ca(Mt(e), Ie(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(ye(Mt(e), n[0]), n[1] - 1);
    if (t === 1 / 0)
      return As(ye(Mt(e), n[1]), n[0]);
    if (t === -1 / 0)
      return pm(ye(Mt(e), n[1]), n[0]);
    if (t === "fro" || t === "euclidean")
      return ln(ye(ct(e), n));
    throw new Error(`Error in norm: invalid ord value: ${t}`);
  }
  throw new Error(`Error in norm: invalid axis: ${n}`);
}
var vI = L({ norm_: O3 });
function P3(e, t, n, s, r = true) {
  let a = _(e, "v", "movingAverage"), i = _(t, "x", "movingAverage"), o = _(n, "decay", "movingAverage");
  nk(a, i), O(wr(a.shape, i.shape), () => "Shape mismatch in v and x");
  let u = Ie(1), l = ge(u, o), c = V(ge(i, a), l);
  if (r) {
    O(s != null, () => "When using zeroDebias: true, step is required.");
    let p = _(s, "step", "movingAverage");
    c = xe(c, ge(u, ca(o, p)));
  }
  return ie(a, c);
}
var ape = L({ movingAverage_: P3 });
function z3(e, t, n) {
  let s = _(e, "indices", "scatterND", "int32"), r = _(t, "updates", "scatterND");
  Mg(r, s, n);
  let a = { indices: s, updates: r }, i = { shape: n };
  return M.runKernel(Do, a, i);
}
var M3 = L({ scatterND_: z3 });
function L3(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 B3(e, t, n, s = 0) {
  let r = _(e, "sparseIndices", "sparseToDense", "int32"), a = _(t, "sparseValues", "sparseToDense"), i = _(s, "defaultValue", "sparseToDense", a.dtype);
  L3(r, a, n, i);
  let o = { sparseIndices: r, sparseValues: a, defaultValue: i }, u = { outputShape: n };
  return M.runKernel(ap, o, u);
}
var xI = L({ sparseToDense_: B3 });
function V3(e, t) {
  let n = _(t, "indices", "gatherND", "int32"), r = { params: _(e, "x", "gatherND", "string_or_numeric"), indices: n };
  return M.runKernel(bo, r);
}
var W3 = L({ gatherND_: V3 });
function U3(e, t) {
  if (t == null)
    return e.shape.slice();
  if (wr(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 G3(e, t, n, s) {
  let r = _(e, "x", "dropout");
  if (O(r.dtype === "float32", () => `x has to be a floating point tensor since it's going to be scaled, but got a ${r.dtype} tensor instead.`), O(t >= 0 && t < 1, () => `rate must be a float in the range [0, 1), but got ${t}.`), t === 0)
    return e instanceof et ? r.clone() : r;
  let a = U3(r, n), i = 1 - t, o = xe(fp(ie(Pl(a, 0, 1, "float32", s), i)), i);
  return V(r, o);
}
var H3 = L({ dropout_: G3 });
function q3(e) {
  return Math.floor(Math.pow(2, Math.ceil(Math.log(e) / Math.log(2))));
}
function wI(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 Qt(r, "float32");
}
async function j3(e, t, n = 1) {
  let s = _(e, "predictions", "inTopK"), r = _(t, "targets", "inTopK");
  O(s.rank > 1, () => `inTopK() expects the predictions to be of rank 2 or higher, but got ${s.rank}`), O(s.rank - 1 === r.rank, () => `predictions rank should be 1 larger than targets rank, but got predictions rank ${s.rank} and targets rank ${r.rank}`), dn(s.shape.slice(0, s.shape.length - 1), r.shape, "predictions's shape should be align with the targets' shape, except the last dimension.");
  let a = s.shape[s.shape.length - 1];
  O(n > 0 && n <= a, () => `'k' passed to inTopK() must be > 0 && <= the predictions last dimension (${a}), but got ${n}`);
  let i = await s.data(), o = await r.data(), [u, l] = [i.length / a, a], c = Lw("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(), hs(c, r.shape, "bool");
}
var ipe = j3;
var da = {};
Ae(da, { conv2d: () => Y3, depthwiseConv2d: () => eF, matMul: () => nF });
function K3(e, t, n, s, r, a = "NHWC", i) {
  let o = e;
  e.rank === 3 && (o = G(e, [1, e.shape[0], e.shape[1], e.shape[2]]));
  let u = t;
  u.rank === 3 && (u = G(t, [1, t.shape[0], t.shape[1], t.shape[2]])), O(o.rank === 4, () => `Error in conv2dDerFilter: input must be rank 4, but got shape ${o.shape}.`), O(u.rank === 4, () => `Error in conv2dDerFilter: dy must be rank 4, but got shape ${u.shape}.`), O(n.length === 4, () => `Error in conv2dDerFilter: filterShape must be length 4, but got ${n}.`);
  let l = a === "NHWC" ? o.shape[3] : o.shape[1], c = a === "NHWC" ? u.shape[3] : u.shape[1];
  O(l === n[2], () => `Error in conv2dDerFilter: depth of input ${l}) must match input depth in filter (${n[2]}.`), O(c === n[3], () => `Error in conv2dDerFilter: depth of dy (${c}) must match output depth for filter (${n[3]}).`), pn("conv2dDerFilter", r, i);
  let p = { x: o, dy: u }, d = { strides: s, pad: r, dataFormat: a, dimRoundingMode: i, filterShape: n };
  return M.runKernel(ug, p, d);
}
var cb = L({ conv2DBackpropFilter_: K3 });
function yp(e, t, n) {
  if (n == null || n === "linear")
    return e;
  if (n === "relu")
    return V(e, bp(t));
  throw new Error(`Cannot compute gradient for fused activation ${n}.`);
}
function vp(e, t) {
  let n = t, s = _t(e.shape, t.shape);
  return s.length > 0 && (n = ye(n, s)), G(n, e.shape);
}
function xp(e, t, n, s) {
  if (t === "linear")
    return e;
  if (t === "relu")
    return Xs(e);
  if (t === "elu")
    return hp(e);
  if (t === "relu6")
    return oI(e);
  if (t === "prelu")
    return tb(e, n);
  if (t === "leakyrelu")
    return jg(e, s);
  if (t === "sigmoid")
    return qs(e);
  throw new Error(`Unknown fused activation ${t}.`);
}
var wp = (e, t) => !(e > 0) || t === "linear";
function X3({ 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", wp(M.state.gradientDepth, u) === false) {
    let k = ua(e, t, n, s, r, a, i);
    return o != null && (k = ie(k, o)), xp(k, u, l, c);
  }
  let p = _(e, "x", "conv2d", "float32"), d = _(t, "filter", "conv2d", "float32"), h = p, f = false;
  p.rank === 3 && (f = true, h = G(p, [1, p.shape[0], p.shape[1], p.shape[2]])), O(h.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ${h.rank}.`), O(d.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ${d.rank}.`), pn("fused conv2d", s, i), O(h.shape[3] === d.shape[2], () => `Error in conv2d: depth of input (${h.shape[3]}) must match input depth for filter ${d.shape[2]}.`), O(Ps(n, a), () => `Error in conv2D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`), O(r === "NHWC", () => `Error in conv2d: got dataFormat of ${r} but only NHWC is currently supported.`);
  let m = Dl(h.shape, d.shape, n, a, s, i), g;
  o != null && (g = _(o, "bias", "fused conv2d"), [g] = vt(g, p), it(m.outShape, g.shape));
  let b;
  l != null && (b = _(l, "prelu weights", "fused conv2d"));
  let y = (k, C) => {
    let [T, E, A, P] = C, R = yp(k, A, u);
    O(fr(a), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`);
    let F = Gg(E.shape, R, T, n, s), $ = cb(E, R, T.shape, n, s), z = [F, $];
    if (P != null) {
      let W = vp(P, R);
      z.push(W);
    }
    return z;
  }, v = { x: h, filter: d, bias: g, preluActivationWeights: b }, x = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: i, activation: u, leakyreluAlpha: c };
  return o == null ? js((C, T, E) => {
    let A = M.runKernel(sa, v, x);
    return E([T, C, A]), f && (A = G(A, [A.shape[1], A.shape[2], A.shape[3]])), { value: A, gradFunc: y };
  })(h, d) : js((C, T, E, A) => {
    let P = M.runKernel(sa, v, x);
    return A([T, C, P, E]), f && (P = G(P, [P.shape[1], P.shape[2], P.shape[3]])), { value: P, gradFunc: y };
  })(h, d, g);
}
var Y3 = L({ fusedConv2d_: X3 });
function Q3(e, t, n, s, r, a = [1, 1], i) {
  let o = e;
  e.rank === 3 && (o = G(e, [1, e.shape[0], e.shape[1], e.shape[2]]));
  let u = t;
  u.rank === 3 && (u = G(t, [1, t.shape[0], t.shape[1], t.shape[2]]));
  let l = { x: o, dy: u }, c = { strides: s, pad: r, dimRoundingMode: i, dilations: a, filterShape: n };
  return M.runKernel(pg, l, c);
}
var kI = L({ depthwiseConv2dNativeBackpropFilter_: Q3 });
function Z3(e, t, n, s, r, a = [1, 1], i) {
  let o = t, u = false;
  t.rank === 3 && (u = true, o = G(t, [1, t.shape[0], t.shape[1], t.shape[2]]));
  let l = { dy: o, filter: n }, c = { strides: s, pad: r, dimRoundingMode: i, dilations: a, inputShape: e }, p = M.runKernel(hg, l, c);
  return u ? G(p, [p.shape[1], p.shape[2], p.shape[3]]) : p;
}
var II = L({ depthwiseConv2dNativeBackpropInput_: Z3 });
function J3({ 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 (wp(M.state.gradientDepth, u) === false) {
    let k = pp(e, t, n, s, r, a, i);
    return o != null && (k = ie(k, o)), xp(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 = G(p, [1, p.shape[0], p.shape[1], p.shape[2]])), O(h.rank === 4, () => `Error in fused depthwiseConv2d: input must be rank 4, but got rank ${h.rank}.`), O(d.rank === 4, () => `Error in fused depthwiseConv2d: filter must be rank 4, but got rank ${d.rank}.`), O(h.shape[3] === d.shape[2], () => `Error in fused depthwiseConv2d: number of input channels (${h.shape[3]}) must match the inChannels dimension in filter ${d.shape[2]}.`), a == null && (a = [1, 1]), O(Ps(n, a), () => `Error in fused depthwiseConv2d: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`), pn("fused depthwiseConv2d", s, i);
  let m = Dl(h.shape, d.shape, n, a, s, i, true), g;
  o != null && (g = _(o, "bias", "fused conv2d"), [g] = vt(g, p), it(m.outShape, g.shape));
  let b;
  l != null && (b = _(l, "prelu weights", "fused depthwiseConv2d"));
  let y = (k, C) => {
    O(fr(a), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${a}'`);
    let [T, E, A, P] = C, R = yp(k, A, u), F = II(E.shape, R, T, n, s, a, i), $ = kI(E, R, T.shape, n, s, a, i);
    if (P != null) {
      let z = vp(g, R);
      return [F, $, z];
    }
    return [F, $];
  }, v = { x: h, filter: d, bias: g, preluActivationWeights: b }, x = { strides: n, pad: s, dataFormat: r, dilations: a, dimRoundingMode: i, activation: u, leakyreluAlpha: c };
  return o == null ? js((C, T, E) => {
    let A = M.runKernel(ra, v, x);
    return E([T, C, A]), f && (A = G(A, [A.shape[1], A.shape[2], A.shape[3]])), { value: A, gradFunc: y };
  })(h, d) : js((C, T, E, A) => {
    let P = M.runKernel(ra, v, x);
    return A([T, C, P, E]), f && (P = G(P, [P.shape[1], P.shape[2], P.shape[3]])), { value: P, gradFunc: y };
  })(h, d, g);
}
var eF = L({ fusedDepthwiseConv2d_: J3 });
function tF({ a: e, b: t, transposeA: n = false, transposeB: s = false, bias: r, activation: a = "linear", preluActivationWeights: i, leakyreluAlpha: o }) {
  if (wp(M.state.gradientDepth, a) === false) {
    let R = We(e, t, n, s);
    return r != null && (R = ie(R, r)), xp(R, a, i, o);
  }
  let u = _(e, "a", "fused matMul"), l = _(t, "b", "fused matMul");
  [u, l] = vt(u, l);
  let c = n ? u.shape[u.rank - 2] : u.shape[u.rank - 1], p = s ? l.shape[l.rank - 1] : l.shape[l.rank - 2], d = n ? u.shape[u.rank - 1] : u.shape[u.rank - 2], h = s ? l.shape[l.rank - 2] : l.shape[l.rank - 1], f = u.shape.slice(0, -2), m = l.shape.slice(0, -2), g = pt(f), b = pt(m);
  O(c === p, () => `Error in fused matMul: inner shapes (${c}) and (${p}) of Tensors with shapes ${u.shape} and ${l.shape} and transposeA=${n} and transposeB=${s} must match.`);
  let v = it(u.shape.slice(0, -2), l.shape.slice(0, -2)).concat([d, h]), x = n ? G(u, [g, c, d]) : G(u, [g, d, c]), k = s ? G(l, [b, h, p]) : G(l, [b, p, h]), C;
  r != null && (C = _(r, "bias", "fused matMul"), [C] = vt(C, u), it(v, C.shape));
  let T;
  i != null && (T = _(i, "prelu weights", "fused matMul"));
  let E = (R, F) => {
    let [$, z, W, q] = F, K = yp(G(R, W.shape), W, a), Y, Z;
    if (!n && !s ? (Y = We(K, z, false, true), Z = We($, K, true, false)) : !n && s ? (Y = We(K, z, false, false), Z = We(K, $, true, false)) : n && !s ? (Y = We(z, K, false, true), Z = We($, K, false, false)) : (Y = We(z, K, true, true), Z = We(K, $, true, true)), r != null) {
      let te = vp(q, K);
      return [Y, Z, te];
    } else
      return [Y, Z];
  }, A = { a: x, b: k, bias: C, preluActivationWeights: T }, P = { transposeA: n, transposeB: s, activation: a, leakyreluAlpha: o };
  return r == null ? js((F, $, z) => {
    let W = M.runKernel(na, A, P);
    return z([F, $, W]), { value: G(W, v), gradFunc: E };
  })(x, k) : js((F, $, z, W) => {
    let q = M.runKernel(na, A, P);
    return W([F, $, q, z]), { value: G(q, v), gradFunc: E };
  })(x, k, C);
}
var nF = L({ fusedMatMul_: tF });
function sF(e) {
  return wI(e, 0.54, 0.46);
}
var rF = L({ hammingWindow_: sF });
function aF(e) {
  return wI(e, 0.5, 0.5);
}
var SI = L({ hannWindow_: aF });
function iF(e, t, n, s = false, r = 0) {
  let a = 0, i = [];
  for (; a + t <= e.size; )
    i.push(He(e, a, t)), a += n;
  if (s)
    for (; a < e.size; ) {
      let o = a + t - e.size, u = Ft([He(e, a, t - o), Fl([o], r)]);
      i.push(u), a += n;
    }
  return i.length === 0 ? ji([], [0, t]) : G(Ft(i), [i.length, t]);
}
var CI = L({ frame_: iF });
function oF(e, t, n, s, r = SI) {
  s == null && (s = q3(t));
  let a = CI(e, t, n), i = V(a, r(t));
  return ub(i, s);
}
var uF = L({ stft_: oF });
function lF(e, t, n, s, r = "bilinear", a = 0) {
  let i = _(e, "image", "cropAndResize"), o = _(t, "boxes", "cropAndResize", "float32"), u = _(n, "boxInd", "cropAndResize", "int32"), l = o.shape[0];
  O(i.rank === 4, () => `Error in cropAndResize: image must be rank 4,but got rank ${i.rank}.`), O(o.rank === 2 && o.shape[1] === 4, () => `Error in cropAndResize: boxes must be have size [${l},4] but had shape ${o.shape}.`), O(u.rank === 1 && u.shape[0] === l, () => `Error in cropAndResize: boxInd must be have size [${l}] but had shape ${o.shape}.`), O(s.length === 2, () => `Error in cropAndResize: cropSize must be of length 2, but got length ${s.length}.`), O(s[0] >= 1 && s[1] >= 1, () => `cropSize must be atleast [1,1], but was ${s}`), O(r === "bilinear" || r === "nearest", () => `method must be bilinear or nearest, but was ${r}`);
  let c = { image: i, boxes: o, boxInd: u }, p = { method: r, extrapolationValue: a, cropSize: s };
  return M.runKernel(lo, c, p);
}
var cF = L({ cropAndResize_: lF });
function dF(e) {
  let t = _(e, "image", "flipLeftRight", "float32");
  O(t.rank === 4, () => `Error in flipLeftRight: image must be rank 4,but got rank ${t.rank}.`);
  let n = { image: t };
  return M.runKernel(mo, n, {});
}
var pF = L({ flipLeftRight_: dF });
function hF(e) {
  let t = _(e, "image", "grayscaleToRGB"), n = t.rank - 1, s = t.shape[n];
  O(t.rank >= 2, () => `Error in grayscaleToRGB: images must be at least rank 2, but got rank ${t.rank}.`), O(s === 1, () => `Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${s}.`);
  let r = new Array(t.rank);
  return r.fill(1, 0, n), r[n] = 3, cs(t, r);
}
var fF = L({ grayscaleToRGB_: hF });
function mF(e, t, n = 0, s = 0.5) {
  let r = _(e, "image", "rotateWithOffset", "float32");
  O(r.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${r.rank}.`);
  let a = { image: r }, i = { radians: t, fillValue: n, center: s };
  return M.runKernel(Ho, a, i);
}
var gF = L({ rotateWithOffset_: mF });
function Xo(e, t, n, s, r, a) {
  s == null && (s = 0.5), r == null && (r = Number.NEGATIVE_INFINITY), a == null && (a = 0);
  let i = e.shape[0];
  return n = Math.min(n, i), O(0 <= s && s <= 1, () => `iouThreshold must be in [0, 1], but was '${s}'`), O(e.rank === 2, () => `boxes must be a 2D tensor, but was of rank '${e.rank}'`), O(e.shape[1] === 4, () => `boxes must have 4 columns, but 2nd dimension was ${e.shape[1]}`), O(t.rank === 1, () => "scores must be a 1D tensor"), O(t.shape[0] === i, () => `scores has incompatible shape with boxes. Expected ${i}, but was ${t.shape[0]}`), O(0 <= a && a <= 1, () => `softNmsSigma must be in [0, 1], but was '${a}'`), { maxOutputSize: n, iouThreshold: s, scoreThreshold: r, softNmsSigma: a };
}
function bF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY) {
  let a = _(e, "boxes", "nonMaxSuppression", "float32"), i = _(t, "scores", "nonMaxSuppression", "float32"), o = Xo(a, i, n, s, r);
  n = o.maxOutputSize, s = o.iouThreshold, r = o.scoreThreshold;
  let u = { maxOutputSize: n, iouThreshold: s, scoreThreshold: r };
  return M.runKernel(So, { boxes: a, scores: i }, u);
}
var yF = L({ nonMaxSuppression_: bF });
function vF(e, t, n) {
  let s = xF(e, t, n), r = s < 0 ? -(s + 1) : s;
  e.splice(r, 0, t);
}
function xF(e, t, n) {
  return kF(e, t, n || wF);
}
function wF(e, t) {
  return e > t ? 1 : e < t ? -1 : 0;
}
function kF(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 NI(e, t, n, s, r) {
  return db(e, t, n, s, r, 0);
}
function TI(e, t, n, s, r, a) {
  return db(e, t, n, s, r, 0, false, a, true);
}
function $I(e, t, n, s, r, a) {
  return db(e, t, n, s, r, a, true);
}
function db(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(dx);
  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 C = IF(e, y, p[k]);
      if (C >= s) {
        x = true;
        break;
      }
      if (g.score = g.score * SF(s, c, C), g.score <= r)
        break;
    }
    g.suppressBeginIndex = p.length, x || (g.score === b ? (p.push(y), d.push(g.score)) : g.score > r && vF(l, g, dx));
  }
  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 IF(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 SF(e, t, n) {
  let s = Math.exp(t * n * n);
  return n <= e ? s : 0;
}
function dx(e, t) {
  return e.score - t.score || e.score === t.score && t.boxIndex - e.boxIndex;
}
async function CF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY) {
  let a = _(e, "boxes", "nonMaxSuppressionAsync"), i = _(t, "scores", "nonMaxSuppressionAsync"), o = Xo(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 } = NI(l, c, n, s, r);
  return a !== e && a.dispose(), i !== t && i.dispose(), Qt(p, "int32");
}
var NF = CF;
function TF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = 0) {
  let i = _(e, "boxes", "nonMaxSuppression"), o = _(t, "scores", "nonMaxSuppression"), u = Xo(i, o, n, s, r, a);
  n = u.maxOutputSize, s = u.iouThreshold, r = u.scoreThreshold, a = u.softNmsSigma;
  let l = { boxes: i, scores: o }, c = { maxOutputSize: n, iouThreshold: s, scoreThreshold: r, softNmsSigma: a }, p = M.runKernel(Co, l, c);
  return { selectedIndices: p[0], selectedScores: p[1] };
}
var $F = L({ nonMaxSuppressionWithScore_: TF });
async function _F(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = 0) {
  let i = _(e, "boxes", "nonMaxSuppressionAsync"), o = _(t, "scores", "nonMaxSuppressionAsync"), u = Xo(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 } = $I(c, p, n, s, r, a);
  return i !== e && i.dispose(), o !== t && o.dispose(), { selectedIndices: Qt(d, "int32"), selectedScores: Qt(h) };
}
var AF = _F;
function EF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = false) {
  let i = _(e, "boxes", "nonMaxSuppression"), o = _(t, "scores", "nonMaxSuppression"), u = Xo(i, o, n, s, r, null), l = u.maxOutputSize, c = u.iouThreshold, p = u.scoreThreshold, d = { boxes: i, scores: o }, h = { maxOutputSize: l, iouThreshold: c, scoreThreshold: p, padToMaxOutputSize: a }, f = M.runKernel(xl, d, h);
  return { selectedIndices: f[0], validOutputs: f[1] };
}
var RF = L({ nonMaxSuppressionPadded_: EF });
async function DF(e, t, n, s = 0.5, r = Number.NEGATIVE_INFINITY, a = false) {
  let i = _(e, "boxes", "nonMaxSuppressionAsync"), o = _(t, "scores", "nonMaxSuppressionAsync"), u = Xo(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 } = TI(d, h, l, c, p, a);
  return i !== e && i.dispose(), o !== t && o.dispose(), { selectedIndices: Qt(f, "int32"), validOutputs: Ie(m, "int32") };
}
var FF = DF;
function OF(e, t, n = false, s = false) {
  let r = _(e, "images", "resizeBilinear");
  O(r.rank === 3 || r.rank === 4, () => `Error in resizeBilinear: x must be rank 3 or 4, but got rank ${r.rank}.`), O(t.length === 2, () => `Error in resizeBilinear: new shape must 2D, but got shape ${t}.`), O(s === false || n === false, () => "Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.");
  let a = r, i = false;
  r.rank === 3 && (i = true, a = G(r, [1, r.shape[0], r.shape[1], r.shape[2]]));
  let [] = t, o = { images: a }, u = { alignCorners: n, halfPixelCenters: s, size: t }, l = M.runKernel(Ja, o, u);
  return i ? G(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
}
var PF = L({ resizeBilinear_: OF });
function zF(e, t, n = false, s = false) {
  let r = _(e, "images", "resizeNearestNeighbor");
  O(r.rank === 3 || r.rank === 4, () => `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank ${r.rank}.`), O(t.length === 2, () => `Error in resizeNearestNeighbor: new shape must 2D, but got shape ${t}.`), O(r.dtype === "float32" || r.dtype === "int32", () => "`images` must have `int32` or `float32` as dtype"), O(s === false || n === false, () => "Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.");
  let a = r, i = false;
  r.rank === 3 && (i = true, a = G(r, [1, r.shape[0], r.shape[1], r.shape[2]]));
  let [] = t, o = { images: a }, u = { alignCorners: n, halfPixelCenters: s, size: t }, l = M.runKernel(Il, o, u);
  return i ? G(l, [l.shape[1], l.shape[2], l.shape[3]]) : l;
}
var MF = L({ resizeNearestNeighbor_: zF });
function LF(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(Qt([s]), 255), c, p, d, h;
  if (O(r.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${r.rank}.`), O(r.shape[2] === 3 || r.shape[2] === 1, () => `Error in threshold: image color channel must be equal to 3 or 1but got ${r.shape[2]}.`), O(r.dtype === "int32" || r.dtype === "float32", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${r.dtype}.`), O(t === "otsu" || t === "binary", () => `Method must be binary or otsu, but was ${t}`), r.shape[2] === 3) {
    [c, p, d] = Ln(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 = Hk(ce(uI(h), "int32"), hs([]), 256);
    l = BF(g, u);
  }
  let f = n ? Ko(h, l) : Wn(h, l);
  return ce(V(f, 255), "int32");
}
function BF(e, t) {
  let n = Qt([-1]), s = Qt([0]), r = Qt([0]), a, i, o, u, l, c;
  for (let p = 0; p < e.size - 1; p++) {
    a = He(e, 0, p + 1), i = He(e, p + 1), l = xe(ye(a), t), c = xe(ye(i), t);
    let d = ye(V(a, Xu(0, a.size)));
    o = xe(d, ye(a));
    let h = Fl(i.shape, a.size), f = ie(Xu(0, i.size), h), m = V(i, f);
    u = xe(ye(m), ye(i));
    let g = ge(o, u), b = ge(o, u), y = V(l, c);
    r = V(V(y, g), b);
    let v = Wn(r, s);
    s = vn(v, r, s), n = vn(v, Qt([p]), n);
  }
  return n;
}
var VF = L({ threshold_: LF });
function WF(e, t, n = "nearest", s = "constant", r = 0, a) {
  let i = _(e, "image", "transform", "float32"), o = _(t, "transforms", "transform", "float32");
  O(i.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${i.rank}.`), O(o.rank === 2 && (o.shape[0] === i.shape[0] || o.shape[0] === 1) && o.shape[1] === 8, () => "Error in transform: Input transform should be batch x 8 or 1 x 8"), O(a == null || a.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${a}.`);
  let u = { image: i, transforms: o }, l = { interpolation: n, fillMode: s, fillValue: r, outputShape: a };
  return M.runKernel(Wo, u, l);
}
var UF = L({ transform_: WF });
function GF(e, t, n) {
  O(t % 1 === 0, () => `bandPart(): numLower must be an integer, got ${t}.`), O(n % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${n}.`);
  let s = _(e, "a", "bandPart");
  O(s.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${s.rank}.`);
  let r = s.shape, [a, i] = s.shape.slice(-2);
  if (!(t <= a))
    throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${a}).`);
  if (!(n <= i))
    throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${i}).`);
  t < 0 && (t = a), n < 0 && (n = i);
  let o = G(Xu(0, a, 1, "int32"), [-1, 1]), u = Xu(0, i, 1, "int32"), l = ge(o, u), c = Ds(Ko(l, Ie(+t, "int32")), jo(l, Ie(-n, "int32"))), p = $t([a, i], s.dtype);
  return G(Qn(Fs(G(s, [-1, a, i])).map((d) => vn(c, d, p))), r);
}
var HF = L({ bandPart_: GF });
function qF(e) {
  let t;
  if (Array.isArray(e)) {
    t = false, O(e != null && e.length > 0, () => "Gram-Schmidt process: input must not be null, undefined, or empty");
    let r = e[0].shape[0];
    for (let a = 1; a < e.length; ++a)
      O(e[a].shape[0] === r, () => `Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[a].shape[0]} vs. ${r})`);
  } else
    t = true, e = Ln(e, e.shape[0], 0).map((r) => mr(r, [0]));
  O(e.length <= e[0].shape[0], () => `Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);
  let n = [], s = e;
  for (let r = 0; r < e.length; ++r)
    n.push(M.tidy(() => {
      let a = s[r];
      if (r > 0)
        for (let i = 0; i < r; ++i) {
          let o = V(ye(V(n[i], a)), n[i]);
          a = ge(a, o);
        }
      return xe(a, vI(a, "euclidean"));
    }));
  return t ? Qn(n, 0) : n;
}
var jF = L({ gramSchmidt_: qF });
function KF(e, t = false) {
  if (O(e.rank >= 2, () => `qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`), e.rank === 2)
    return px(e, t);
  {
    let n = e.shape.slice(0, e.shape.length - 2).reduce((u, l) => u * l), s = Fs(G(e, [n, e.shape[e.shape.length - 2], e.shape[e.shape.length - 1]]), 0), r = [], a = [];
    s.forEach((u) => {
      let [l, c] = px(u, t);
      r.push(l), a.push(c);
    });
    let i = G(Qn(r, 0), e.shape), o = G(Qn(a, 0), e.shape);
    return [i, o];
  }
}
function px(e, t = false) {
  return M.tidy(() => {
    O(e.shape.length === 2, () => `qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);
    let n = e.shape[0], s = e.shape[1], r = Zk(n), a = lr(e), i = ji([[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] = M.tidy(() => {
        let h = He(a, [l, l], [n - l, 1]), f = vI(h), m = He(a, [l, l], [1, 1]), g = vn(Wn(m, 0), ji([[-1]]), ji([[1]])), b = ge(m, V(g, f)), y = xe(h, b);
        y.shape[0] === 1 ? o = lr(i) : o = Ft([i, He(y, [1, 0], [y.shape[0] - 1, y.shape[1]])], 0);
        let v = kt(xe(We(g, b), f)), x = He(a, [l, 0], [n - l, s]), k = V(v, o), C = qe(o);
        if (l === 0)
          a = ge(x, We(k, We(C, x)));
        else {
          let A = ge(x, We(k, We(C, x)));
          a = Ft([He(a, [0, 0], [l, s]), A], 0);
        }
        let T = qe(k), E = He(r, [0, l], [n, r.shape[1] - l]);
        if (l === 0)
          r = ge(E, We(We(E, o), T));
        else {
          let A = ge(E, We(We(E, o), T));
          r = Ft([He(r, [0, 0], [n, l]), A], 1);
        }
        return [o, a, r];
      }), Re([c, p, d]);
    }
    return !t && n > s && (r = He(r, [0, 0], [n, s]), a = He(a, [0, 0], [s, s])), [r, a];
  });
}
var XF = L({ qr_: KF });
var YF = ((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))(YF || {});
function QF(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 ye(a);
  if (n === 1) {
    if (r == null)
      return It(a);
    {
      let i = s.size / r.size, o = xe(ye(a), ye(r));
      return i > 1 ? xe(o, Ie(i)) : o;
    }
  }
  if (n === 3) {
    if (r == null)
      return xe(ye(a), Ie(s.size));
    {
      let i = V(r, zn(s.shape)), o = ce(ye(Ku(i, Ie(0))), "float32");
      return xe(ye(a), o);
    }
  }
  throw Error(`Unknown reduction: ${n}`);
}
var Ys = L({ computeWeightedLoss_: QF });
function ZF(e, t, n, s = 3) {
  let r = _(e, "labels", "absoluteDifference"), a = _(t, "predictions", "absoluteDifference"), i = null;
  n != null && (i = _(n, "weights", "absoluteDifference")), dn(r.shape, a.shape, "Error in absoluteDifference: ");
  let o = Mt(ge(r, a));
  return Ys(o, i, s);
}
var JF = L({ absoluteDifference_: ZF });
function eO(e, t, n, s, r = 3) {
  let a = _(e, "labels", "cosineDistance"), i = _(t, "predictions", "cosineDistance"), o = null;
  s != null && (o = _(s, "weights", "cosineDistance")), dn(a.shape, i.shape, "Error in cosineDistance: ");
  let u = Ie(1), l = ge(u, ye(V(a, i), n, true));
  return Ys(l, o, r);
}
var tO = L({ cosineDistance_: eO });
function nO(e, t, n, s = 3) {
  let r = _(e, "labels", "hingeLoss"), a = _(t, "predictions", "hingeLoss"), i = null;
  n != null && (i = _(n, "weights", "hingeLoss")), dn(r.shape, a.shape, "Error in hingeLoss: ");
  let o = Ie(1);
  r = ge(V(Ie(2), r), o);
  let u = Xs(ge(o, V(r, a)));
  return Ys(u, i, s);
}
var sO = L({ hingeLoss_: nO });
function rO(e, t, n, s = 1, r = 3) {
  let a = _(e, "labels", "huberLoss"), i = _(t, "predictions", "huberLoss"), o = null;
  n != null && (o = _(n, "weights", "huberLoss")), dn(a.shape, i.shape, "Error in huberLoss: ");
  let u = Ie(s), l = Mt(ge(i, a)), c = gp(l, u), p = ge(l, c), d = ie(V(Ie(0.5), ct(c)), V(u, p));
  return Ys(d, o, r);
}
var aO = L({ huberLoss_: rO });
function iO(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")), dn(a.shape, i.shape, "Error in logLoss: ");
  let u = Ie(1), l = Ie(s), c = kt(V(a, Kn(ie(i, l)))), p = V(ge(u, a), Kn(ie(ge(u, i), l))), d = ge(c, p);
  return Ys(d, o, r);
}
var oO = L({ logLoss_: iO });
function uO(e, t, n, s = 3) {
  let r = _(e, "labels", "meanSquaredError"), a = _(t, "predictions", "meanSquaredError"), i = null;
  n != null && (i = _(n, "weights", "meanSquaredError")), dn(r.shape, a.shape, "Error in meanSquaredError: ");
  let o = mI(r, a);
  return Ys(o, i, s);
}
var lO = L({ meanSquaredError_: uO });
function cO(e, t) {
  let n = _(e, "labels", "sigmoidCrossEntropyWithLogits"), s = _(t, "logits", "sigmoidCrossEntropyWithLogits");
  dn(n.shape, s.shape, "Error in sigmoidCrossEntropyWithLogits: ");
  let r = Xs(s), a = V(s, n), i = Kg(jn(kt(Mt(s))));
  return ie(ge(r, a), i);
}
function dO(e, t, n, s = 0, r = 3) {
  let a = _(e, "multiClassLabels", "sigmoidCrossEntropy"), i = _(t, "logits", "sigmoidCrossEntropy"), o = null;
  if (n != null && (o = _(n, "weights", "sigmoidCrossEntropy")), dn(a.shape, i.shape, "Error in sigmoidCrossEntropy: "), s > 0) {
    let l = Ie(s), c = Ie(1), p = Ie(0.5);
    a = ie(V(a, ge(c, l)), V(p, l));
  }
  let u = cO(a, i);
  return Ys(u, o, r);
}
var pO = L({ sigmoidCrossEntropy_: dO });
function hO(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 = XR(a, [n], true), l = ge(ce(a, "float32"), u);
    i([r, l]);
    let c = kt(V(l, r));
    return { value: ye(c, [n]), gradFunc: (h, f) => {
      let [m, g] = f, b = la(h.shape, [n]);
      return [V(G(h, b), ge(ce(m, "float32"), jn(g))), V(G(h, b), ge(jn(g), ce(m, "float32")))];
    } };
  })(e, t);
}
function fO(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")), dn(a.shape, i.shape, "Error in softmaxCrossEntropy: "), s > 0) {
    let l = Ie(s), c = Ie(1), p = Ie(a.shape[1]);
    a = ie(V(a, ge(c, l)), xe(l, p));
  }
  let u = hO(a, i);
  return Ys(u, o, r);
}
var mO = L({ softmaxCrossEntropy_: fO });
function gO(e, t, n, s) {
  let r = _(e, "indices", "sparseFillEmptyRows", "int32"), a = _(t, "values", "sparseFillEmptyRows"), i = _(n, "denseShape", "sparseFillEmptyRows", "int32"), o = _(s, "defaultValue", "sparseFillEmptyRows", a.dtype);
  if (r.rank !== 2)
    throw new Error(`Indices should be Tensor2D but received shape
        ${r.shape}`);
  if (a.rank !== 1)
    throw new Error(`Values should be Tensor1D but received shape ${a.shape}`);
  if (i.rank !== 1)
    throw new Error(`Dense shape should be Tensor1D but received shape ${i.shape}`);
  if (o.rank !== 0)
    throw new Error(`Default value should be a scalar but received shape ${o.shape}`);
  let u = { indices: r, values: a, denseShape: i, defaultValue: o }, l = M.runKernel(np, u);
  return { outputIndices: l[0], outputValues: l[1], emptyRowIndicator: l[2], reverseIndexMap: l[3] };
}
var bO = L({ sparseFillEmptyRows_: gO });
function yO(e, t, n) {
  let s = _(e, "inputIndices", "sparseReshape", "int32"), r = _(t, "inputShape", "sparseReshape", "int32"), a = _(n, "newShape", "sparseReshape", "int32");
  if (s.rank !== 2)
    throw new Error(`Input indices should be Tensor2D but received shape
        ${s.shape}`);
  if (r.rank !== 1)
    throw new Error(`Input shape should be Tensor1D but received shape ${r.shape}`);
  if (a.rank !== 1)
    throw new Error(`New shape should be Tensor1D but received shape ${a.shape}`);
  let i = { inputIndices: s, inputShape: r, newShape: a }, o = M.runKernel(Tl, i);
  return { outputIndices: o[0], outputShape: o[1] };
}
var vO = L({ sparseReshape_: yO });
function xO(e, t, n) {
  let s = _(e, "data", "sparseSegmentMean"), r = _(t, "indices", "sparseSegmentMean", "int32"), a = _(n, "segmentIds", "sparseSegmentMean", "int32");
  if (s.rank < 1)
    throw new Error("Data should be at least 1 dimensional but received scalar");
  if (r.rank !== 1)
    throw new Error(`Indices should be Tensor1D but received shape
          ${r.shape}`);
  if (a.rank !== 1)
    throw new Error(`Segment ids should be Tensor1D but received shape
          ${a.shape}`);
  let i = { data: s, indices: r, segmentIds: a };
  return M.runKernel(sp, i);
}
var wO = L({ sparseSegmentMean_: xO });
function kO(e, t, n) {
  let s = _(e, "data", "sparseSegmentSum"), r = _(t, "indices", "sparseSegmentSum", "int32"), a = _(n, "segmentIds", "sparseSegmentSum", "int32");
  if (s.rank < 1)
    throw new Error("Data should be at least 1 dimensional but received scalar");
  if (r.rank !== 1)
    throw new Error(`Indices should be Tensor1D but received shape
         ${r.shape}`);
  if (a.rank !== 1)
    throw new Error(`Segment ids should be Tensor1D but received shape
         ${a.shape}`);
  let i = { data: s, indices: r, segmentIds: a };
  return M.runKernel(rp, i);
}
var IO = L({ sparseSegmentSum_: kO });
function SO(e, t, n, s, r, a, i, o) {
  let u = _(e, "data", "stringNGrams", "string");
  if (u.dtype !== "string")
    throw new Error("Data must be of datatype string");
  if (u.shape.length !== 1)
    throw new Error(`Data must be a vector, saw: ${u.shape}`);
  let l = _(t, "dataSplits", "stringNGrams");
  if (l.dtype !== "int32")
    throw new Error("Data splits must be of datatype int32");
  let c = { separator: n, nGramWidths: s, leftPad: r, rightPad: a, padWidth: i, preserveShortSequences: o }, p = { data: u, dataSplits: l }, d = M.runKernel(ip, p, c);
  return { nGrams: d[0], nGramsSplits: d[1] };
}
var CO = L({ stringNGrams_: SO });
function NO(e, t, n = true) {
  let s = _(e, "input", "stringSplit", "string"), r = _(t, "delimiter", "stringSplit", "string");
  if (s.rank !== 1)
    throw new Error(`Input should be Tensor1D but received shape ${s.shape}`);
  if (r.rank !== 0)
    throw new Error(`Delimiter should be a scalar but received shape ${r.shape}`);
  let a = { skipEmpty: n }, i = { input: s, delimiter: r }, o = M.runKernel(Ng, i, a);
  return { indices: o[0], values: o[1], shape: o[2] };
}
var TO = L({ stringSplit_: NO });
function $O(e, t) {
  let n = _(e, "input", "stringToHashBucketFast", "string"), s = { numBuckets: t };
  if (t <= 0)
    throw new Error("Number of buckets must be at least 1");
  let r = { input: n };
  return M.runKernel(Tg, r, s);
}
var _O = L({ stringToHashBucketFast_: $O });
var ope = { fft: ob, ifft: kd, rfft: ub, irfft: fI };
var upe = { hammingWindow: rF, hannWindow: SI, frame: CI, stft: uF };
var ds = { flipLeftRight: pF, grayscaleToRGB: fF, resizeNearestNeighbor: MF, resizeBilinear: PF, rotateWithOffset: gF, cropAndResize: cF, nonMaxSuppression: yF, nonMaxSuppressionAsync: NF, nonMaxSuppressionWithScore: $F, nonMaxSuppressionWithScoreAsync: AF, nonMaxSuppressionPadded: RF, nonMaxSuppressionPaddedAsync: FF, threshold: VF, transform: UF };
var AO = { bandPart: HF, gramSchmidt: jF, qr: XF };
var lpe = { absoluteDifference: JF, computeWeightedLoss: Ys, cosineDistance: tO, hingeLoss: sO, huberLoss: aO, logLoss: oO, meanSquaredError: lO, sigmoidCrossEntropy: pO, softmaxCrossEntropy: mO };
var Vc = { sparseFillEmptyRows: bO, sparseReshape: vO, sparseSegmentMean: wO, sparseSegmentSum: IO };
var Pf = { stringNGrams: CO, stringSplit: TO, stringToHashBucketFast: _O };
var $r = class extends Ok {
  minimize(e, t = false, n) {
    let { value: s, grads: r } = this.computeGradients(e, n);
    if (n != null) {
      let a = n.map((i) => ({ name: i.name, tensor: r[i.name] }));
      this.applyGradients(a);
    } else
      this.applyGradients(r);
    return Re(r), t ? s : (s.dispose(), null);
  }
  get iterations() {
    return this.iterations_ == null && (this.iterations_ = 0), this.iterations_;
  }
  incrementIterations() {
    this.iterations_ = this.iterations + 1;
  }
  computeGradients(e, t) {
    return MR(e, t);
  }
  dispose() {
    this.iterations_ != null && Re(this.iterations_);
  }
  async saveIterations() {
    return this.iterations_ == null && (this.iterations_ = 0), { name: "iter", tensor: Ie(this.iterations_, "int32") };
  }
  async getWeights() {
    throw new Error("getWeights() is not implemented for this optimizer yet.");
  }
  async setWeights(e) {
    throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`);
  }
  async extractIterations(e) {
    return this.iterations_ = (await e[0].tensor.data())[0], e.slice(1);
  }
};
Object.defineProperty($r, Symbol.hasInstance, { value: (e) => e.minimize != null && e.computeGradients != null && e.applyGradients != null });
var pb = class extends $r {
  constructor(e, t, n = null) {
    super();
    this.learningRate = e, this.rho = t, this.epsilon = n, this.accumulatedGrads = [], this.accumulatedUpdates = [], n == null && (this.epsilon = M.backend.epsilon());
  }
  applyGradients(e) {
    (Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e)).forEach((n, s) => {
      let r = M.registeredVariables[n], a = false;
      this.accumulatedGrads[s] == null && (this.accumulatedGrads[s] = { originalName: `${n}/accum_grad`, variable: j(() => je(r).variable(a)) }), this.accumulatedUpdates[s] == null && (this.accumulatedUpdates[s] = { originalName: `${n}/accum_var`, variable: j(() => 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;
      j(() => {
        let l = ie(V(o, this.rho), V(ct(i), 1 - this.rho)), c = V(xe(ln(ie(u, this.epsilon)), ln(ie(o, this.epsilon))), i), p = ie(V(u, this.rho), V(ct(c), 1 - this.rho));
        o.assign(l), u.assign(p);
        let d = ie(V(c, -this.learningRate), r);
        r.assign(d);
      });
    }), this.incrementIterations();
  }
  dispose() {
    this.accumulatedUpdates != null && (Re(this.accumulatedGrads.map((e) => e.variable)), Re(this.accumulatedUpdates.map((e) => e.variable)));
  }
  async getWeights() {
    let e = [...this.accumulatedGrads, ...this.accumulatedUpdates];
    return [await this.saveIterations()].concat(e.map((t) => ({ name: t.originalName, tensor: t.variable })));
  }
  async setWeights(e) {
    e = await this.extractIterations(e);
    let t = e.length / 2, n = false;
    this.accumulatedGrads = e.slice(0, t).map((s) => ({ originalName: s.name, variable: s.tensor.variable(n) })), this.accumulatedUpdates = e.slice(t, t * 2).map((s) => ({ originalName: s.name, variable: s.tensor.variable(n) }));
  }
  getConfig() {
    return { learningRate: this.learningRate, rho: this.rho, epsilon: this.epsilon };
  }
  static fromConfig(e, t) {
    return new e(t.learningRate, t.rho, t.epsilon);
  }
};
pb.className = "Adadelta";
Nr(pb);
var hb = class extends $r {
  constructor(e, t = 0.1) {
    super();
    this.learningRate = e, this.initialAccumulatorValue = t, this.accumulatedGrads = [];
  }
  applyGradients(e) {
    (Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e)).forEach((n, s) => {
      let r = M.registeredVariables[n];
      this.accumulatedGrads[s] == null && (this.accumulatedGrads[s] = { originalName: `${n}/accumulator`, variable: j(() => Fl(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;
      j(() => {
        let o = ie(i, ct(a));
        i.assign(o);
        let u = ie(V(xe(a, ln(ie(o, M.backend.epsilon()))), -this.learningRate), r);
        r.assign(u);
      });
    }), this.incrementIterations();
  }
  dispose() {
    this.accumulatedGrads != null && Re(this.accumulatedGrads.map((e) => e.variable));
  }
  async getWeights() {
    return [await this.saveIterations()].concat(this.accumulatedGrads.map((e) => ({ name: e.originalName, tensor: e.variable })));
  }
  async setWeights(e) {
    e = await this.extractIterations(e);
    let t = false;
    this.accumulatedGrads = e.map((n) => ({ originalName: n.name, variable: n.tensor.variable(t) }));
  }
  getConfig() {
    return { learningRate: this.learningRate, initialAccumulatorValue: this.initialAccumulatorValue };
  }
  static fromConfig(e, t) {
    return new e(t.learningRate, t.initialAccumulatorValue);
  }
};
hb.className = "Adagrad";
Nr(hb);
var fb = class extends $r {
  constructor(e, t, n, s = null) {
    super();
    this.learningRate = e, this.beta1 = t, this.beta2 = n, this.epsilon = s, this.accumulatedFirstMoment = [], this.accumulatedSecondMoment = [], j(() => {
      this.accBeta1 = Ie(t).variable(), this.accBeta2 = Ie(n).variable();
    }), s == null && (this.epsilon = M.backend.epsilon());
  }
  applyGradients(e) {
    let t = Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e);
    j(() => {
      let n = ge(1, this.accBeta1), s = ge(1, this.accBeta2);
      t.forEach((r, a) => {
        let i = M.registeredVariables[r], o = false;
        this.accumulatedFirstMoment[a] == null && (this.accumulatedFirstMoment[a] = { originalName: `${r}/m`, variable: j(() => je(i).variable(o)) }), this.accumulatedSecondMoment[a] == null && (this.accumulatedSecondMoment[a] = { originalName: `${r}/v`, variable: j(() => 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(ln(f), this.epsilon)), -this.learningRate), i);
        i.assign(m);
      }), this.accBeta1.assign(V(this.accBeta1, this.beta1)), this.accBeta2.assign(V(this.accBeta2, this.beta2));
    }), this.incrementIterations();
  }
  dispose() {
    this.accBeta1.dispose(), this.accBeta2.dispose(), this.accumulatedFirstMoment != null && Re(this.accumulatedFirstMoment.map((e) => e.variable)), this.accumulatedSecondMoment != null && Re(this.accumulatedSecondMoment.map((e) => e.variable));
  }
  async getWeights() {
    let e = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];
    return [await this.saveIterations()].concat(e.map((t) => ({ name: t.originalName, tensor: t.variable })));
  }
  async setWeights(e) {
    e = await this.extractIterations(e), j(() => {
      this.accBeta1.assign(ca(this.beta1, this.iterations_ + 1)), this.accBeta2.assign(ca(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);
  }
};
fb.className = "Adam";
Nr(fb);
var mb = class extends $r {
  constructor(e, t, n, s = null, r = 0) {
    super();
    this.learningRate = e, this.beta1 = t, this.beta2 = n, this.epsilon = s, this.decay = r, this.accumulatedFirstMoment = [], this.accumulatedWeightedInfNorm = [], j(() => {
      this.iteration = Ie(0).variable(), this.accBeta1 = Ie(t).variable();
    }), s == null && (this.epsilon = M.backend.epsilon());
  }
  applyGradients(e) {
    let t = Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e);
    j(() => {
      let n = ge(1, this.accBeta1), s = xe(-this.learningRate, ie(V(this.iteration, this.decay), 1));
      t.forEach((r, a) => {
        let i = M.registeredVariables[r], o = false;
        this.accumulatedFirstMoment[a] == null && (this.accumulatedFirstMoment[a] = { originalName: `${r}/m`, variable: je(i).variable(o) }), this.accumulatedWeightedInfNorm[a] == null && (this.accumulatedWeightedInfNorm[a] = { originalName: `${r}/v`, variable: je(i).variable(o) });
        let u = Array.isArray(e) ? e[a].tensor : e[r];
        if (u == null)
          return;
        let l = this.accumulatedFirstMoment[a].variable, c = this.accumulatedWeightedInfNorm[a].variable, p = ie(V(l, this.beta1), V(u, 1 - this.beta1)), d = V(c, this.beta2), h = Mt(u), f = Tr(d, h);
        l.assign(p), c.assign(f);
        let m = ie(V(xe(s, n), xe(p, ie(f, this.epsilon))), i);
        i.assign(m);
      }), this.iteration.assign(ie(this.iteration, 1)), this.accBeta1.assign(V(this.accBeta1, this.beta1));
    }), this.incrementIterations();
  }
  dispose() {
    this.accBeta1.dispose(), this.iteration.dispose(), this.accumulatedFirstMoment != null && Re(this.accumulatedFirstMoment.map((e) => e.variable)), this.accumulatedWeightedInfNorm != null && Re(this.accumulatedWeightedInfNorm.map((e) => e.variable));
  }
  async getWeights() {
    throw new Error("getWeights() is not implemented for Adamax yet.");
  }
  async setWeights(e) {
    throw new Error("setWeights() is not implemented for Adamax yet.");
  }
  getConfig() {
    return { learningRate: this.learningRate, beta1: this.beta1, beta2: this.beta2, epsilon: this.epsilon, decay: this.decay };
  }
  static fromConfig(e, t) {
    return new e(t.learningRate, t.beta1, t.beta2, t.epsilon, t.decay);
  }
};
mb.className = "Adamax";
Nr(mb);
var kp = class extends $r {
  constructor(e) {
    super();
    this.learningRate = e, this.setLearningRate(e);
  }
  applyGradients(e) {
    (Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e)).forEach((n, s) => {
      let r = Array.isArray(e) ? e[s].tensor : e[n];
      if (r == null)
        return;
      let a = M.registeredVariables[n];
      j(() => {
        let i = ie(V(this.c, r), a);
        a.assign(i);
      });
    }), this.incrementIterations();
  }
  setLearningRate(e) {
    this.learningRate = e, this.c != null && this.c.dispose(), this.c = Ht(Ie(-e));
  }
  dispose() {
    this.c.dispose();
  }
  async getWeights() {
    return [await this.saveIterations()];
  }
  async setWeights(e) {
    if (e = await this.extractIterations(e), e.length !== 0)
      throw new Error("SGD optimizer does not have settable weights.");
  }
  getConfig() {
    return { learningRate: this.learningRate };
  }
  static fromConfig(e, t) {
    return new e(t.learningRate);
  }
};
kp.className = "SGD";
Nr(kp);
var gb = class extends kp {
  constructor(e, t, n = false) {
    super(e);
    this.learningRate = e, this.momentum = t, this.useNesterov = n, this.accumulations = [], this.m = Ie(this.momentum);
  }
  applyGradients(e) {
    (Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e)).forEach((n, s) => {
      let r = M.registeredVariables[n];
      this.accumulations[s] == null && (this.accumulations[s] = { originalName: `${n}/momentum`, variable: j(() => je(r).variable(false)) });
      let a = this.accumulations[s].variable, i = Array.isArray(e) ? e[s].tensor : e[n];
      i != null && j(() => {
        let o, u = ie(V(this.m, a), i);
        this.useNesterov ? o = ie(V(this.c, ie(i, V(u, this.m))), r) : o = ie(V(this.c, u), r), a.assign(u), r.assign(o);
      });
    }), this.incrementIterations();
  }
  dispose() {
    this.m.dispose(), this.accumulations != null && Re(this.accumulations.map((e) => e.variable));
  }
  setMomentum(e) {
    this.momentum = e;
  }
  async getWeights() {
    return [await this.saveIterations()].concat(this.accumulations.map((e) => ({ name: e.originalName, tensor: e.variable })));
  }
  async setWeights(e) {
    e = await this.extractIterations(e);
    let t = false;
    this.accumulations = e.map((n) => ({ originalName: n.name, variable: n.tensor.variable(t) }));
  }
  getConfig() {
    return { learningRate: this.learningRate, momentum: this.momentum, useNesterov: this.useNesterov };
  }
  static fromConfig(e, t) {
    return new e(t.learningRate, t.momentum, t.useNesterov);
  }
};
gb.className = "Momentum";
Nr(gb);
var bb = class extends $r {
  constructor(e, t = 0.9, n = 0, s = null, r = false) {
    super();
    if (this.learningRate = e, this.decay = t, this.momentum = n, this.epsilon = s, this.accumulatedMeanSquares = [], this.accumulatedMoments = [], this.accumulatedMeanGrads = [], this.centered = r, s == null && (this.epsilon = M.backend.epsilon()), e == null)
      throw new Error("learningRate for RMSPropOptimizer must be defined.");
  }
  applyGradients(e) {
    (Array.isArray(e) ? e.map((n) => n.name) : Object.keys(e)).forEach((n, s) => {
      let r = M.registeredVariables[n], a = false;
      this.accumulatedMeanSquares[s] == null && (this.accumulatedMeanSquares[s] = { originalName: `${n}/rms`, variable: j(() => je(r).variable(a)) }), this.accumulatedMoments[s] == null && (this.accumulatedMoments[s] = { originalName: `${n}/momentum`, variable: j(() => je(r).variable(a)) }), this.accumulatedMeanGrads[s] == null && this.centered && (this.accumulatedMeanGrads[s] = { originalName: `${n}/mg`, variable: j(() => 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;
      j(() => {
        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), ln(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), ln(ie(c, this.epsilon))));
          o.assign(c), u.assign(p);
          let d = ge(r, p);
          r.assign(d);
        }
      });
    }), this.incrementIterations();
  }
  dispose() {
    this.accumulatedMeanSquares != null && Re(this.accumulatedMeanSquares.map((e) => e.variable)), this.accumulatedMeanGrads != null && this.centered && Re(this.accumulatedMeanGrads.map((e) => e.variable)), this.accumulatedMoments != null && Re(this.accumulatedMoments.map((e) => e.variable));
  }
  async getWeights() {
    let e = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];
    return this.centered && e.push(...this.accumulatedMeanGrads), [await this.saveIterations()].concat(e.map((t) => ({ name: t.originalName, tensor: t.variable })));
  }
  async setWeights(e) {
    e = await this.extractIterations(e);
    let t = this.centered ? e.length / 3 : e.length / 2, n = false;
    this.accumulatedMeanSquares = e.slice(0, t).map((s) => ({ originalName: s.name, variable: s.tensor.variable(n) })), this.accumulatedMoments = e.slice(t, t * 2).map((s) => ({ originalName: s.name, variable: s.tensor.variable(n) })), this.centered && (this.accumulatedMeanGrads = e.slice(t * 2, t * 3).map((s) => ({ originalName: s.name, variable: s.tensor.variable(n) })));
  }
  getConfig() {
    return { learningRate: this.learningRate, decay: this.decay, momentum: this.momentum, epsilon: this.epsilon, centered: this.centered };
  }
  static fromConfig(e, t) {
    return new e(t.learningRate, t.decay, t.momentum, t.epsilon, t.centered);
  }
};
bb.className = "RMSProp";
Nr(bb);
var Vr = class {
  static sgd(e) {
    return new kp(e);
  }
  static momentum(e, t, n = false) {
    return new gb(e, t, n);
  }
  static rmsprop(e, t = 0.9, n = 0, s = null, r = false) {
    return new bb(e, t, n, s, r);
  }
  static adam(e = 1e-3, t = 0.9, n = 0.999, s = null) {
    return new fb(e, t, n, s);
  }
  static adadelta(e = 1e-3, t = 0.95, n = null) {
    return new pb(e, t, n);
  }
  static adamax(e = 2e-3, t = 0.9, n = 0.999, s = null, r = 0) {
    return new mb(e, t, n, s, r);
  }
  static adagrad(e, t = 0.1) {
    return new hb(e, t);
  }
};
var Fi = { sgd: Vr.sgd, momentum: Vr.momentum, adadelta: Vr.adadelta, adagrad: Vr.adagrad, rmsprop: Vr.rmsprop, adamax: Vr.adamax, adam: Vr.adam };
var EO = (() => typeof requestAnimationFrame != "undefined" ? requestAnimationFrame : typeof setImmediate != "undefined" ? setImmediate : (e) => e())();
function RO() {
  return new Promise((e) => EO(() => e()));
}
var N = {};
Ae(N, { ERF_A1: () => UO, ERF_A2: () => GO, ERF_A3: () => HO, ERF_A4: () => qO, ERF_A5: () => jO, ERF_P: () => WO, PARALLELIZE_THRESHOLD: () => yb, SELU_SCALE: () => AI, SELU_SCALEALPHA: () => _I, applyActivation: () => xp, assertAndGetBroadcastShape: () => it, assertAxesAreInnerMostDims: () => qR, assertParamsConsistent: () => DO, assignToTypedArray: () => JO, axesAreInnerMostDims: () => Xg, calculateShapes: () => Ck, checkEinsumDimSizes: () => aP, checkPadOnDimRoundingMode: () => pn, combineLocations: () => tI, complexWithEvenIndex: () => YO, complexWithOddIndex: () => QO, computeConv2DInfo: () => Dl, computeConv3DInfo: () => Wk, computeDefaultPad: () => Vg, computeDilation2DInfo: () => tE, computeOptimalWindowSize: () => OO, computeOutAndReduceShapes: () => nI, computeOutShape: () => FO, computePool2DInfo: () => Vk, computePool3DInfo: () => nE, convertConv2DDataFormat: () => Uk, decodeEinsumEquation: () => sP, eitherStridesOrDilationsAreOne: () => Ps, expandShapeToKeepDim: () => la, exponent: () => tP, exponents: () => eP, fromStringArrayToUint8: () => NP, fromUint8ToStringArray: () => CP, getAxesPermutation: () => sI, getBroadcastDims: () => vk, getComplexWithIndex: () => ZO, getEinsumComputePath: () => iP, getEinsumPermutation: () => rP, getFusedBiasGradient: () => vp, getFusedDyActivation: () => yp, getImageCenter: () => PO, getInnerMostAxes: () => jR, getPermuted: () => MO, getReductionAxes: () => _t, getReshaped: () => zO, getReshapedPermuted: () => LO, getSliceBeginCoords: () => BO, getSliceSize: () => VO, getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => cP, getSparseFillEmptyRowsNegativeIndexErrorMessage: () => dP, getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => pP, getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => mP, getSparseReshapeInputOutputMismatchErrorMessage: () => bP, getSparseReshapeInputOutputMultipleErrorMessage: () => gP, getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => hP, getSparseReshapeNegativeOutputDimErrorMessage: () => fP, getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => wP, getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => yP, getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => vP, getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => xP, getUndoAxesPermutation: () => Yg, isIdentityPermutation: () => oP, log: () => I$, mergeRealAndImagArrays: () => KO, prepareAndValidate: () => Ik, prepareSplitSize: () => lP, segment_util: () => EI, shouldFuse: () => wp, slice_util: () => wt, splitRealAndImagArrays: () => XO, tupleValuesAreOne: () => fr, upcastType: () => yn, validateInput: () => Mg, validateUpdateShape: () => zg, warn: () => ar });
function DO(e, t) {
  let n = e[0].length;
  e.forEach((r, a) => {
    O(r.length === n, () => `Error in concat${n}D: rank of tensors[${a}] must be the same as the rank of the rest (${n})`);
  }), O(t >= 0 && t < n, () => `Error in concat${n}D: axis must be between 0 and ${n - 1}.`);
  let s = e[0];
  e.forEach((r, a) => {
    for (let i = 0; i < n; i++)
      O(i === t || r[i] === s[i], () => `Error in concat${n}D: Shape of tensors[${a}] (${r}) does not match the shape of the rest (${s}) along the non-concatenated axis ${a}.`);
  });
}
function FO(e, t) {
  let n = e[0].slice();
  for (let s = 1; s < e.length; s++)
    n[t] += e[s][t];
  return n;
}
var yb = 30;
function OO(e) {
  return e <= yb ? e : pd(e, Math.floor(Math.sqrt(e)));
}
function PO(e, t, n) {
  let s = n * (typeof e == "number" ? e : e[0]), r = t * (typeof e == "number" ? e : e[1]);
  return [s, r];
}
function zO(e, t, n, s = true) {
  let r = [];
  if (s)
    r = r.concat(t.slice(0)), r.push(e[0] / n), r = r.concat(e.slice(1));
  else {
    r = r.concat(e[0]);
    let a = t.length;
    for (let i = 0; i < a; ++i)
      r = r.concat([e[i + 1] / t[i], t[i]]);
    r = r.concat(e.slice(a + 1));
  }
  return r;
}
function MO(e, t, n = true) {
  let s = [];
  if (n) {
    s.push(t);
    for (let r = t + 1; r < e; ++r)
      r <= 2 * t ? (s.push(r), s.push(r - (t + 1))) : s.push(r);
  } else {
    let r = [], a = [];
    for (let i = 1; i < e; ++i)
      i >= t * 2 + 1 || i % 2 === 1 ? a.push(i) : r.push(i);
    s.push(...r), s.push(0), s.push(...a);
  }
  return s;
}
function LO(e, t, n, s = true) {
  let r = [];
  s ? r.push(e[0] / n) : r.push(e[0] * n);
  for (let a = 1; a < e.length; ++a)
    a <= t.length ? s ? r.push(t[a - 1] * e[a]) : r.push(e[a] / t[a - 1]) : r.push(e[a]);
  return r;
}
function BO(e, t) {
  let n = [0];
  for (let s = 0; s < t; ++s)
    n.push(e[s][0]);
  return n;
}
function VO(e, t, n) {
  let s = e.slice(0, 1);
  for (let r = 0; r < n; ++r)
    s.push(e[r + 1] - t[r][0] - t[r][1]);
  return s;
}
var _I = 1.7580993408473768;
var AI = 1.0507009873554805;
var WO = 0.3275911;
var UO = 0.254829592;
var GO = -0.284496736;
var HO = 1.421413741;
var qO = -1.453152027;
var jO = 1.061405429;
function KO(e, t) {
  if (e.length !== t.length)
    throw new Error(`Cannot merge real and imag arrays of different lengths. real:${e.length}, imag: ${t.length}.`);
  let n = new Float32Array(e.length * 2);
  for (let s = 0; s < n.length; s += 2)
    n[s] = e[s / 2], n[s + 1] = t[s / 2];
  return n;
}
function XO(e) {
  let t = new Float32Array(e.length / 2), n = new Float32Array(e.length / 2);
  for (let s = 0; s < e.length; s += 2)
    t[s / 2] = e[s], n[s / 2] = e[s + 1];
  return { real: t, imag: n };
}
function YO(e) {
  let t = Math.ceil(e.length / 4), n = new Float32Array(t), s = new Float32Array(t);
  for (let r = 0; r < e.length; r += 4)
    n[Math.floor(r / 4)] = e[r], s[Math.floor(r / 4)] = e[r + 1];
  return { real: n, imag: s };
}
function QO(e) {
  let t = Math.floor(e.length / 4), n = new Float32Array(t), s = new Float32Array(t);
  for (let r = 2; r < e.length; r += 4)
    n[Math.floor(r / 4)] = e[r], s[Math.floor(r / 4)] = e[r + 1];
  return { real: n, imag: s };
}
function ZO(e, t) {
  let n = e[t * 2], s = e[t * 2 + 1];
  return { real: n, imag: s };
}
function JO(e, t, n, s) {
  e[s * 2] = t, e[s * 2 + 1] = n;
}
function eP(e, t) {
  let n = new Float32Array(e / 2), s = new Float32Array(e / 2);
  for (let r = 0; r < Math.ceil(e / 2); r++) {
    let a = (t ? 2 : -2) * Math.PI * (r / e);
    n[r] = Math.cos(a), s[r] = Math.sin(a);
  }
  return { real: n, imag: s };
}
function tP(e, t, n) {
  let s = (n ? 2 : -2) * Math.PI * (e / t), r = Math.cos(s), a = Math.sin(s);
  return { real: r, imag: a };
}
var zf = "->";
var nP = /->/g;
var hx = ",";
var fx = "...";
function sP(e, t) {
  e = e.replace(/\s/g, "");
  let n = (e.length - e.replace(nP, "").length) / zf.length;
  if (n < 1)
    throw new Error("Equations without an arrow are not supported.");
  if (n > 1)
    throw new Error(`Equation must contain exactly one arrow ("${zf}").`);
  let [s, r] = e.split(zf);
  O(s.indexOf(fx) === -1, () => `The ellipsis notation ("${fx}") is not supported yet.`);
  let a = s.split(hx), 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 !== hx && 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 rP(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 aP(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] : O(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 iP(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 = uP(t, o);
    for (let l of u)
      a.indexOf(l) === -1 && (s[i].push(l), a.push(l));
  }
  return { path: n, steps: s };
}
function oP(e) {
  return e.every((t, n) => t === n);
}
function uP(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 lP(e, t, n = 0) {
  let s = [];
  if (typeof t == "number")
    O(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);
    O(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;
    }
    O(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 cP(e) {
  return `Received SparseTensor with denseShape[0] = 0 but
  indices.shape[0] = ${e}`;
}
function dP(e, t) {
  return `indices(${e}, 0) is invalid: ${t} < 0`;
}
function pP(e, t, n) {
  return `indices(${e}, 0) is invalid: ${t} >= ${n}`;
}
function hP(e, t) {
  return `only one output dimension may be -1, not both ${e} and ${t}`;
}
function fP(e, t) {
  return `size ${e} must be non-negative, not ${t}`;
}
function mP() {
  return "reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero";
}
function gP(e, t) {
  let n = pt(e), s = pt(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 bP(e, t) {
  let n = pt(e), s = pt(t);
  return `Input to reshape is a tensor with ${n} dense values, but the requested shape has ${s}. inputShape=${e} outputShape=${t}`;
}
function yP() {
  return "segment ids must be >= 0";
}
function vP() {
  return "segment ids are not increasing";
}
function xP(e, t) {
  return `Segment id ${e} out of range [0, ${t}), possibly because segmentIds input is not sorted.`;
}
function wP(e, t, n) {
  return `Bad: indices[${e}] == ${t} out of range [0, ${n})`;
}
var EI = {};
Ae(EI, { collectGatherOpShapeInfo: () => SP, computeOutShape: () => IP, segOpComputeOptimalWindowSize: () => kP });
function kP(e, t) {
  let n = false, s;
  for (e <= yb ? (s = e, n = true) : s = pd(e, Math.floor(Math.sqrt(e))); !n; )
    s > t || s === e ? n = true : s = pd(e, s + 1);
  return s;
}
function IP(e, t, n) {
  let s = [], r = e.length;
  for (let a = 0; a < r; a++)
    a !== t ? s.push(e[a]) : s.push(n);
  return s;
}
function SP(e, t, n, s) {
  let r = t.shape.length, a = e.shape.length;
  if (s !== 0 && (s < -r || s > r))
    throw new Error(`Expect batchDims in the range of [-${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 CP(e) {
  try {
    return e.map((t) => fd(t));
  } catch (t) {
    throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`);
  }
}
function NP(e) {
  return e.map((t) => El(t));
}
var xs = {};
Ae(xs, { nonMaxSuppressionV3Impl: () => NI, nonMaxSuppressionV4Impl: () => TI, nonMaxSuppressionV5Impl: () => $I, whereImpl: () => gI });
var RI = { kernelName: ao, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(e, bp(ce(n, "float32"), -1)) };
} };
var TP = { kernelName: nl, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => {
    let s = ct(ce(n, "float32")), r = ln(ge(Ie(1), s));
    return kt(xe(e, r));
  } };
} };
var $P = { kernelName: sl, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => {
    let s = ln(ge(ct(ce(n, "float32")), 1));
    return xe(e, s);
  } };
} };
var _P = { kernelName: kr, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = it(n.shape, s.shape);
  return { a: () => {
    let o = e, u = _t(n.shape, r);
    return u.length > 0 && (o = ye(o, u)), G(o, n.shape);
  }, b: () => {
    let o = e, u = _t(s.shape, r);
    return u.length > 0 && (o = ye(o, u)), G(o, s.shape);
  } };
} };
var AP = { kernelName: xa, saveAllInputs: true, gradFunc: (e, t) => {
  let n = {};
  return t.forEach((s, r) => {
    n[r] = () => e.clone();
  }), n;
} };
var EP = { kernelName: wa, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => je(n) };
} };
var RP = { kernelName: il, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => je(n) };
} };
var DP = { kernelName: ol, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, ln(ge(Ie(1), ct(ce(n, "float32"))))) };
} };
var FP = { kernelName: ul, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => {
    let s = ln(ie(Ie(1), ct(ce(n, "float32"))));
    return xe(e, s);
  } };
} };
var OP = { kernelName: dl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = it(n.shape, s.shape);
  return { a: () => {
    let o = ie(ct(n), ct(s)), u = V(e, xe(s, o)), l = _t(n.shape, r);
    return l.length > 0 && (u = ye(u, l)), G(u, n.shape);
  }, b: () => {
    let o = ie(ct(n), ct(s)), u = kt(V(e, xe(n, o))), l = _t(s.shape, r);
    return l.length > 0 && (u = ye(u, l)), G(u, s.shape);
  } };
} };
var PP = { kernelName: ll, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, ie(ct(ce(n, "float32")), 1)) };
} };
var zP = { kernelName: cl, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, ge(Ie(1), ct(ce(n, "float32")))) };
} };
function MP(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 = G(i, [1, i.shape[0], i.shape[1], i.shape[2], i.shape[3]]), l = G(o, [1, o.shape[0], o.shape[1], o.shape[2], o.shape[3]])), O(u.rank === 5, () => `Error in avgPool3dGrad: dy must be rank 5 but got rank ${u.rank}.`), O(l.rank === 5, () => `Error in avgPool3dGrad: input must be rank 5 but got rank ${l.rank}.`), pn("avgPool3dGrad", r, a);
  let p = { dy: u, input: l }, d = { filterSize: n, strides: s, pad: r, dimRoundingMode: a }, h = M.runKernel(ag, p, d);
  return c ? G(h, [h.shape[1], h.shape[2], h.shape[3], h.shape[4]]) : h;
}
var LP = L({ avgPool3dGrad_: MP });
var BP = { kernelName: Hd, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { filterSize: r, strides: a, pad: i, dimRoundingMode: o } = n;
  return { x: () => LP(e, s, r, a, i, o) };
} };
function VP(e, t, n, s, r) {
  let a = _(e, "dy", "avgPoolGrad"), i = _(t, "input", "avgPoolGrad");
  O(i.rank === a.rank, () => `Rank of input (${i.rank}) does not match rank of dy (${a.rank})`);
  let o = i, u = a, l = false;
  i.rank === 3 && (l = true, o = G(i, [1, i.shape[0], i.shape[1], i.shape[2]]), u = G(a, [1, a.shape[0], a.shape[1], a.shape[2]])), O(u.rank === 4, () => `Error in avgPoolGrad: dy must be rank 4 but got rank ${u.rank}.`), O(o.rank === 4, () => `Error in avgPoolGrad: input must be rank 4 but got rank ${o.rank}.`);
  let c = { dy: u, input: o }, p = { filterSize: n, strides: s, pad: r }, d = M.runKernel(rg, c, p);
  return l ? G(d, [d.shape[1], d.shape[2], d.shape[3]]) : d;
}
var WP = L({ avgPoolGrad_: VP });
var UP = { kernelName: ka, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { filterSize: r, strides: a, pad: i } = n;
  return { x: () => WP(e, s, r, a, i) };
} };
var GP = { kernelName: Ia, inputsToSave: ["a", "b"], gradFunc: (e, t, n) => {
  let [s, r] = t, { transposeA: a, transposeB: i } = n;
  return !a && !i ? { a: () => We(e, r, false, true), b: () => We(s, e, true, false) } : !a && i ? { a: () => We(e, r, false, false), b: () => We(e, s, true, false) } : a && !i ? { a: () => We(r, e, false, true), b: () => We(s, e, false, false) } : { a: () => We(r, e, true, true), b: () => We(e, s, true, true) };
} };
var HP = { kernelName: io, gradFunc: (e, t, n) => {
  let { blockShape: s, crops: r } = n;
  return { x: () => eb(e, s, r) };
} };
var qP = { kernelName: w$, 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: () => ye(e, o, true) };
} };
var jP = { kernelName: Sa, gradFunc: (e) => ({ x: () => e.clone() }) };
var KP = { kernelName: Ca, gradFunc: (e) => ({ x: () => je(e) }) };
var XP = { kernelName: Ir, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { clipValueMin: r, clipValueMax: a } = n;
  return { x: () => vn(Ds(jo(s, r), Ko(s, a)), e, je(e)) };
} };
var YP = { kernelName: jd, inputsToSave: ["x"], gradFunc: RI.gradFunc };
var QP = { kernelName: oo, saveAllInputs: true, gradFunc: (e, t, n) => {
  let s = t.map((u) => u.shape), { axis: r } = n, a = Jn(r, t[0].shape)[0], i = s.map((u) => u[a]);
  return Ln(e, i, a).map((u) => () => u);
} };
var ZP = { kernelName: Na, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
  let [s, r] = t, { dilations: a, strides: i, pad: o, dataFormat: u } = n;
  return O(fr(a), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${a}'`), { x: () => Gg(s.shape, e, r, i, o, u), filter: () => cb(s, e, r.shape, i, o, u) };
} };
var JP = { kernelName: Ta, inputsToSave: ["dy", "filter"], gradFunc: (e, t, n) => {
  let [s, r] = t, { strides: a, pad: i, dataFormat: o, dimRoundingMode: u } = n;
  return { dy: () => ua(e, r, a, i, o, 1, u), filter: () => cb(e, s, r.shape, a, i, o, u) };
} };
function ez(e, t, n, s, r) {
  let a = e;
  e.rank === 4 && (a = G(e, [1, e.shape[0], e.shape[1], e.shape[2], e.shape[3]]));
  let i = t;
  i.rank === 4 && (i = G(t, [1, t.shape[0], t.shape[1], t.shape[2], t.shape[3]])), O(a.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ${a.shape}.`), O(i.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ${i.shape}.`), O(n.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ${n}.`), O(a.shape[4] === n[3], () => `Error in conv3dDerFilter: depth of input ${a.shape[4]}) must match input depth in filter (${n[3]}.`), O(i.shape[4] === n[4], () => `Error in conv3dDerFilter: depth of dy (${i.shape[4]}) must match output depth for filter (${n[4]}).`);
  let o = { x: a, dy: i }, u = { strides: s, pad: r, filterShape: n };
  return M.runKernel(lg, o, u);
}
var tz = L({ conv3DBackpropFilter_: ez });
var nz = { kernelName: Kd, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
  let { dilations: s, strides: r, pad: a } = n;
  O(fr(s), () => `Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${s}'`);
  let [i, o] = t;
  return { x: () => Xk(i.shape, e, o, r, a), filter: () => tz(i, e, o.shape, r, a) };
} };
var sz = { kernelName: $a, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(kt(dI(ce(n, "float32"))), e) };
} };
var rz = { kernelName: _a, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(pI(ce(n, "float32")), e) };
} };
var az = { kernelName: uo, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { axis: r, exclusive: a, reverse: i } = n;
  return { x: () => {
    let o = sI([r], s.rank), u = Qk(e, r, a, !i);
    return o != null && (u = qe(u, o)), u;
  } };
} };
var iz = { kernelName: Aa, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
  let { dilations: s, strides: r, pad: a, dimRoundingMode: i } = n, o = s == null ? [1, 1] : s;
  O(fr(o), () => `Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${o}'`);
  let [u, l] = t;
  return O(u.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${u.rank}.`), O(l.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${l.rank}.`), O(u.shape[3] === l.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`), O(Ps(r, o), () => `Error in gradient of depthwiseConv2d: Either strides or dilations must be  1. Got strides ${r} and dilations '${o}'.`), pn("depthwiseConv2d", a, i), { x: () => II(u.shape, e, l, r, a, o, i), filter: () => kI(u, e, l.shape, r, a, o, i) };
} };
var oz = { kernelName: Xd, inputsToSave: ["x", "filter"], gradFunc: (e, t, n) => {
  let [s, r] = t, a = { x: s, filter: r, dy: e }, i = { x: s, filter: r, dy: e };
  return { x: () => M.runKernel(Kf, a, n), filter: () => M.runKernel(Xf, i, n) };
} };
var uz = { kernelName: Ra, outputsToSave: [true], gradFunc: (e, t) => {
  let [n] = t, s = { dy: e, y: n };
  return { x: () => M.runKernel(mg, s) };
} };
var lz = { kernelName: pl, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t, s = V(jn(kt(ct(n))), 2 / Math.sqrt(Math.PI));
  return { x: () => V(e, s) };
} };
var cz = { kernelName: Da, outputsToSave: [true], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(e, n) };
} };
var dz = { kernelName: ho, inputsToSave: ["input"], gradFunc: (e, t) => {
  let [n] = t;
  return { input: () => G(e, n.shape) };
} };
var pz = { kernelName: fo, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(e, jn(n)) };
} };
var hz = { kernelName: Fa, gradFunc: (e) => ({ x: () => je(e) }) };
var fz = { kernelName: Oa, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = it(n.shape, s.shape);
  return { a: () => {
    let o = xe(e, ce(s, "float32")), u = _t(n.shape, r);
    return u.length > 0 ? G(ye(o, u), n.shape) : o;
  }, b: () => {
    let o = V(e, ce(n, "float32")), u = _t(s.shape, r);
    u.length > 0 && (o = G(ye(o, u), s.shape));
    let l = ct(s);
    return kt(xe(o, ce(l, "float32")));
  } };
} };
var mz = { kernelName: Pa, inputsToSave: ["x", "mean", "variance", "scale"], gradFunc: (e, t, n) => {
  let { varianceEpsilon: s } = n, [r, a, i, o] = t, u = o == null ? Ie(1) : o, l = _t(a.shape, r.shape), c = [];
  if (a.rank === 1) {
    for (let x = 0; x < r.shape.length - 1; ++x)
      c.push(r.shape[x]);
    c.push(1);
  }
  let p = ge(r, a), d = V(e, u), h = lI(ie(i, Ie(s))), f = V(V(V(h, h), h), Ie(-0.5));
  return { x: () => a.rank === 1 ? G(V(V(e, cs(G(h, [1, 1, 1, a.shape[0]]), c)), u), r.shape) : G(V(V(e, h), u), r.shape), mean: () => {
    let x = V(V(h, Ie(-1)), d);
    return a.rank === 1 && (x = ye(x, l)), G(x, a.shape);
  }, variance: () => {
    let x = V(V(f, p), d);
    return a.rank === 1 && (x = ye(x, l)), G(x, a.shape);
  }, scale: () => {
    let x = V(p, h), k = V(e, x);
    return a.rank === 1 && (k = ye(k, l)), G(k, a.shape);
  }, offset: () => {
    let x = e;
    return a.rank === 1 && (x = ye(x, l)), G(x, a.shape);
  } };
} };
var gz = { kernelName: go, inputsToSave: ["x", "indices"], gradFunc: (e, t, n) => {
  let [s, r] = t, { axis: a } = n, i = Jn(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 = mx(0, p), m = mx(p + 1, p + 1 + h), g = gx([c, [l], d]), b = G(e, g), y = G(r, [l]), v = gx([[p], f, m]), x = qe(b, v), k = A3(x, y, s.shape[i]), C = Yg(v);
    return k = qe(k, C), k;
  }, indices: () => r };
} };
function mx(e, t) {
  let n = [];
  for (let s = e; s < t; ++s)
    n.push(s);
  return n;
}
function gx(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 bz = { kernelName: za, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t;
  return { a: () => je(n), b: () => je(s) };
} };
var yz = { kernelName: Ma, gradFunc: (e) => ({ x: () => ce(e, "float32") }) };
var vz = { kernelName: fl, gradFunc: (e) => ({ x: () => je(e) }) };
var xz = { kernelName: ml, gradFunc: (e) => ({ x: () => je(e) }) };
var wz = { kernelName: gl, gradFunc: (e) => ({ x: () => je(e) }) };
var kz = { kernelName: La, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { alpha: r } = n, a = Wn(s, 0);
  return { x: () => vn(a, e, V(e, r)) };
} };
var Iz = { kernelName: bl, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, ie(n, 1)) };
} };
var Sz = { kernelName: Ba, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, ce(n, "float32")) };
} };
var Cz = { kernelName: k$, inputsToSave: [], outputsToSave: [true], gradFunc: (e, t, n) => {
  let [s] = t, { axis: r } = n;
  return { logits: () => {
    let i = jn(s);
    return ge(e, V(ye(e, r, true), i));
  } };
} };
function Nz(e, t, n, s = 5, r = 1, a = 1, i = 0.5) {
  let o = { x: e, y: t, dy: n }, u = { depthRadius: s, bias: r, alpha: a, beta: i };
  return M.runKernel(vg, o, u);
}
var Tz = L({ localResponseNormalizationBackprop_: Nz });
var $z = { kernelName: Jd, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
  let [s, r] = t, { depthRadius: a, bias: i, alpha: o, beta: u } = n;
  return { x: () => Tz(s, r, e, a, i, o, u) };
} };
function DI(e, t, n, s) {
  return t.rank < n.rank && (t = G(t, la(t.shape, s))), e.rank < n.rank && (e = G(e, la(e.shape, s))), { x: () => V(e, ce(qn(n, t), e.dtype)) };
}
var bx = { kernelName: Va, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
  let s = n, { reductionIndices: r } = s, a = t[0], i = t[1], o = Jn(r, a.shape), u = DI(e, i, a, o);
  return { x: () => u.x() };
} };
var _z = { kernelName: Wa, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t;
  return { a: () => V(e, ce(jo(n, s), "float32")), b: () => V(e, ce(Jk(n, s), "float32")) };
} };
function Az(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 = G(o, [1, o.shape[0], o.shape[1], o.shape[2], o.shape[3]]), p = G(u, [1, u.shape[0], u.shape[1], u.shape[2], u.shape[3]]), d = G(l, [1, l.shape[0], l.shape[1], l.shape[2], l.shape[3]])), O(c.rank === 5, () => `Error in maxPool3dGrad: dy must be rank 5 but got rank ${c.rank}.`), O(p.rank === 5, () => `Error in maxPool3dGrad: input must be rank 5 but got rank ${p.rank}.`), O(d.rank === 5, () => `Error in maxPool3dGrad: output must be rank 5 but got rank ${d.rank}.`), pn("maxPool3dGrad", a, i);
  let f = { dy: c, input: p, output: d }, m = { filterSize: s, strides: r, pad: a, dimRoundingMode: i }, g = M.runKernel(wg, f, m);
  return h ? G(g, [g.shape[1], g.shape[2], g.shape[3], g.shape[4]]) : g;
}
var Ez = L({ maxPool3dGrad_: Az });
var Rz = { kernelName: ep, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
  let [s, r] = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = n;
  return { x: () => Ez(e, s, r, a, i, o, u) };
} };
function Dz(e, t, n, s, r, a, i) {
  let o = _(e, "dy", "maxPoolGrad"), u = _(t, "input", "maxPoolGrad"), l = _(n, "output", "maxPoolGrad");
  O(u.rank === o.rank, () => `Rank of input (${u.rank}) does not match rank of dy (${o.rank})`), O(o.rank === 4, () => `Error in maxPoolGrad: dy must be rank 4 but got rank ${o.rank}.`), O(u.rank === 4, () => `Error in maxPoolGrad: input must be rank 4 but got rank ${u.rank}.`), pn("maxPoolGrad", a, i);
  let c = { dy: o, input: u, output: l }, p = { filterSize: s, strides: r, pad: a, dimRoundingMode: i };
  return M.runKernel(xg, c, p);
}
var Fz = L({ maxPoolGrad_: Dz });
var Oz = { kernelName: Ua, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
  let [s, r] = t, { filterSize: a, strides: i, pad: o } = n;
  return { x: () => Fz(e, s, r, a, i, o) };
} };
var Pz = { kernelName: Ga, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { axis: r } = n, a = Jn(r, s.shape), o = nI(s.shape, a)[1], u = pt(o);
  return { x: () => {
    let c = s.shape.slice();
    a.forEach((h) => {
      c[h] = 1;
    });
    let p = G(e, c);
    return xe(V(p, zn(s.shape, "float32")), u);
  } };
} };
var zz = { kernelName: Ha, inputsToSave: ["x"], outputsToSave: [true], gradFunc: (e, t, n) => {
  let s = n, { axis: r } = s, [a, i] = t, o = Jn(r, a.shape), u = DI(e, i, a, o);
  return { x: () => u.x() };
} };
var Mz = { kernelName: qa, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t;
  return { a: () => V(e, ce(Ko(n, s), "float32")), b: () => V(e, ce(Wn(n, s), "float32")) };
} };
var Lz = { kernelName: ja, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let s = t[0], { paddings: r } = n, a = r.map((i) => i[0]);
  return { x: () => He(e, a, s.shape) };
} };
var Bz = { kernelName: vl, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = it(n.shape, s.shape);
  return { a: () => {
    let o = _t(n.shape, r);
    return o.length > 0 ? G(ye(e, o), n.shape) : e;
  }, b: () => {
    let o = V(e, kt(fp(xe(n, s)))), u = _t(s.shape, r);
    return u.length > 0 ? G(ye(o, u), s.shape) : o;
  } };
} };
var Vz = { kernelName: Ka, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = it(n.shape, s.shape);
  return { a: () => {
    let o = V(e, ce(s, "float32")), u = _t(n.shape, r);
    return u.length > 0 ? G(ye(o, u), n.shape) : o;
  }, b: () => {
    let o = V(e, ce(n, "float32")), u = _t(s.shape, r);
    return u.length > 0 ? G(ye(o, u), s.shape) : o;
  } };
} };
var Wz = { kernelName: ko, gradFunc: (e) => ({ x: () => kt(e) }) };
var Uz = { kernelName: To, inputsToSave: ["indices"], gradFunc: (e, t) => {
  let n = t[0];
  return { indices: () => $t(n.shape, "float32") };
} };
var Gz = { kernelName: No, gradFunc: (e) => ({ x: () => je(e) }) };
var Hz = { kernelName: $o, saveAllInputs: true, gradFunc: (e, t, n) => {
  let { axis: s } = n;
  return Fs(e, s).map((a) => () => a);
} };
var yx = { kernelName: Xa, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let s = t[0], { paddings: r } = n, a = r.map((i) => i[0]);
  return { x: () => He(e, a, s.shape) };
} };
var qz = { kernelName: Ya, inputsToSave: ["a", "b"], outputsToSave: [true], gradFunc: (e, t) => {
  let [n, s, r] = t, a = n, i = s, o = it(a.shape, i.shape);
  return { a: () => {
    let c = ce(i, "float32"), p = V(e, V(c, ca(a, ge(c, Ie(1))))), d = _t(a.shape, o);
    return d.length > 0 && (p = ye(p, d)), G(p, a.shape);
  }, b: () => {
    let c = Wn(a, 0), p = vn(c, Kn(a), je(a)), d = V(e, V(r, p)), h = _t(i.shape, o);
    return h.length > 0 && (d = ye(d, h)), G(d, i.shape);
  } };
} };
var jz = { kernelName: Qa, inputsToSave: ["x", "alpha"], gradFunc: (e, t) => {
  let [n, s] = t, r = Wn(n, 0);
  return { x: () => vn(r, e, V(e, s)), alpha: () => {
    let a = vn(r, je(e), V(e, n)), i = _t(s.shape, e.shape);
    return i.length > 0 && (a = ye(a, i)), G(a, s.shape);
  } };
} };
var Kz = { kernelName: Ea, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = it(n.shape, s.shape);
  return { a: () => {
    let o = xe(e, ce(s, "float32")), u = _t(n.shape, r);
    return u.length > 0 ? G(ye(o, u), n.shape) : o;
  }, b: () => {
    let o = V(e, ce(n, "float32")), u = _t(s.shape, r);
    u.length > 0 && (o = G(ye(o, u), s.shape));
    let l = ct(s);
    return kt(xe(o, ce(l, "float32")));
  } };
} };
var Xz = { kernelName: kl, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, kt(ct(n))) };
} };
var Yz = { kernelName: ei, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t, s = V(Ko(n, 6), bp(n));
  return { x: () => V(e, ce(s, "float32")) };
} };
var Qz = { kernelName: Za, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(e, ce(bp(n), "float32")) };
} };
var Zz = { kernelName: Ao, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => G(e, n.shape) };
} };
var Jz = { kernelName: Ja, inputsToSave: ["images"], gradFunc: (e, t, n) => {
  let [s] = t, r = { dy: e, images: s };
  return { images: () => M.runKernel(Cg, r, n) };
} };
var eM = { kernelName: Il, inputsToSave: ["images"], gradFunc: (e, t, n) => {
  let [s] = t, r = { dy: e, images: s };
  return { images: () => M.runKernel(Sg, r, n) };
} };
var tM = { kernelName: Eo, gradFunc: (e, t, n) => {
  let { dims: s } = n, r = Jn(s, e.shape);
  return { x: () => Yn(e, r) };
} };
var nM = { kernelName: Ro, gradFunc: (e) => ({ x: () => je(e) }) };
var sM = { kernelName: ti, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => kt(xe(e, V(ca(n, 1.5), 2))) };
} };
var rM = { kernelName: Fo, inputsToSave: ["condition"], gradFunc: (e, t) => {
  let [n] = t;
  return { condition: () => ce(je(n), "float32"), t: () => V(e, ce(n, e.dtype)), e: () => V(e, ce(Qg(n), e.dtype)) };
} };
var aM = { kernelName: Sl, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => {
    let s = Wn(n, Ie(0)), r = Ie(_I), a = Ie(AI), i = V(e, a), o = V(V(e, r), jn(ce(n, "float32")));
    return vn(s, i, o);
  } };
} };
var iM = { kernelName: si, outputsToSave: [true], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(e, V(n, ge(Ie(1), n))) };
} };
var oM = { kernelName: Cl, gradFunc: (e) => ({ x: () => je(e) }) };
var uM = { kernelName: ni, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(Hg(ce(n, "float32")), e) };
} };
var lM = { kernelName: Po, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(Yk(ce(n, "float32")), e) };
} };
var cM = { kernelName: Oo, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, { begin: r, size: a } = n, i = s.shape, [o, u] = Fk(s, r, a), l = [];
  for (let c = 0; c < e.rank; c++)
    l.push([o[c], i[c] - o[c] - u[c]]);
  return { x: () => pi(e, l) };
} };
var dM = { kernelName: ii, outputsToSave: [true], gradFunc: (e, t, n) => {
  let [s] = t, { dim: r } = n, a = true, i = V(e, s);
  return { logits: () => ge(i, V(ye(i, [r], a), s)) };
} };
var pM = { kernelName: Nl, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(e, qs(n)) };
} };
var vx = { kernelName: zo, gradFunc: (e, t, n) => {
  let { blockShape: s, paddings: r } = n;
  return { x: () => Ug(e, s, r) };
} };
var xx = { kernelName: Mo, gradFunc: (e, t, n) => {
  let { axis: s } = n;
  return { x: () => Ft(e, s) };
} };
var hM = { kernelName: ri, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, V(ln(ce(n, "float32")), 2)) };
} };
var fM = { kernelName: $l, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(e, V(ce(n, "float32"), 2)) };
} };
var mM = { kernelName: oi, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = Ie(2);
  return { a: () => V(e, V(r, ge(n, s))), b: () => V(e, V(r, ge(s, n))) };
} };
var gM = { kernelName: di, gradFunc: (e) => ({ x: () => je(e) }) };
var bM = { kernelName: ui, inputsToSave: ["a", "b"], gradFunc: (e, t) => {
  let [n, s] = t, r = it(n.shape, s.shape);
  return { a: () => {
    let o = e, u = _t(n.shape, r);
    return u.length > 0 && (o = ye(o, u)), G(o, n.shape);
  }, b: () => {
    let o = e, u = _t(s.shape, r);
    return u.length > 0 && (o = ye(o, u)), G(kt(o), s.shape);
  } };
} };
var yM = { kernelName: ai, inputsToSave: ["x"], gradFunc: (e, t, n) => {
  let [s] = t, r = s.shape.slice(), { axis: a } = n;
  Jn(a, s.shape).forEach((l) => {
    r[l] = 1;
  });
  let o = G(e, r), u = V(o, zn(s.shape, "float32"));
  return { x: () => u };
} };
var vM = { kernelName: Bo, inputsToSave: ["x"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => xe(e, ct(Hg(n))) };
} };
var xM = { kernelName: li, outputsToSave: [true], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => V(ge(Ie(1), ct(n)), e) };
} };
var wM = { kernelName: Sr, 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, He(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, He(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, He(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, He(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 kM = { kernelName: ci, gradFunc: (e, t, n) => {
  let s = n, { perm: r } = s, a = Yg(r);
  return { x: () => qe(e, a) };
} };
var IM = { kernelName: Uo, gradFunc: (e, t, n) => {
  let s = n, { axis: r } = s;
  return { value: () => Qn(e, r) };
} };
var SM = { kernelName: op, inputsToSave: ["segmentIds"], gradFunc: (e, t) => {
  let [n] = t;
  return { x: () => CM(e, n) };
} };
function CM(e, t) {
  let n = Tr(t, je(t)), s = ju(e, n), r = jo(t, Ie(0, "int32")), a = s.rank - r.rank;
  for (let o = 0; o < a; ++o)
    r = On(r, o + 1);
  r = Ds(r, zn(s.shape, "bool"));
  let i = je(s);
  return vn(r, s, i);
}
var NM = { kernelName: Go, gradFunc: (e) => ({ x: () => je(e) }) };
var TM = [RI, TP, $P, _P, AP, EP, RP, DP, FP, OP, PP, zP, BP, UP, GP, HP, qP, jP, KP, XP, YP, QP, JP, ZP, nz, sz, rz, az, iz, oz, Kz, uz, lz, cz, dz, pz, fz, hz, mz, gz, bz, yz, vz, xz, wz, kz, Iz, Sz, Cz, $z, bx, bx, _z, Rz, Oz, Pz, zz, Mz, Lz, Bz, Vz, Wz, Uz, Gz, Hz, yx, yx, qz, jz, Xz, Yz, Qz, Zz, Jz, eM, tM, nM, sM, rM, aM, iM, oM, uM, lM, cM, dM, pM, vx, vx, xx, xx, hM, mM, fM, gM, bM, yM, vM, xM, wM, kM, IM, SM, NM];
for (let e of TM)
  S$(e);
var $M = {};
Ae($M, { maxNorm: () => RM, minMaxNorm: () => OM, nonNeg: () => FM, unitNorm: () => DM });
var Mf;
function Rt() {
  return Mf == null && (Mf = $A().epsilon()), Mf;
}
function bs() {
  return "channelsLast";
}
var Vs = class extends Error {
  constructor(e) {
    super(e);
    Object.setPrototypeOf(this, Vs.prototype);
  }
};
var ps = class extends Error {
  constructor(e) {
    super(e);
    Object.setPrototypeOf(this, ps.prototype);
  }
};
var U = class extends Error {
  constructor(e) {
    super(e);
    Object.setPrototypeOf(this, U.prototype);
  }
};
var Fe = class extends Error {
  constructor(e) {
    super(e);
    Object.setPrototypeOf(this, Fe.prototype);
  }
};
var FI = class extends Error {
  constructor(e) {
    super(e);
    Object.setPrototypeOf(this, FI.prototype);
  }
};
function pa(e, t) {
  if (Array.isArray(e)) {
    let n = [];
    for (let s = 0; s < t; s++)
      n = n.concat(e);
    return n;
  } else {
    let n = new Array(t);
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kb.className = "MaxNorm";
ae.registerClass(kb);
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Sb.className = "NonNeg";
ae.registerClass(Sb);
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function RM(e) {
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function DM(e) {
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function OM(e) {
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function zI(e) {
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function MI(e) {
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var GM = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);
function BI(e) {
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function HM(e) {
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function Ip(e, t) {
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function Ll(e, t = -1) {
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function Nb(e, t = -1) {
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function Cx(e, t) {
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}
function fm(e, t) {
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}
function Sp(e, t = 0, n = 1, s, r) {
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}
function Es(e, t, n, s) {
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    throw new Fe(`dot requires both inputs to be rank >= 2 but got x shape = ${e.shape} and y shape = ${t.shape}`);
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    let r = e.shape.slice(), a = r.pop();
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function VI(e, t, n) {
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function Bl(e) {
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function mm(e, t, n) {
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    if (n === "channelsLast")
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    if (n === "channelsLast")
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}
function ws(e, t, n) {
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}
function XM(e, t = 1) {
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    throw new Fe(`Support for alpha values other than 1 (${t}) is not implemented yet.`);
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}
function YM(e) {
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}
function WI(e, t, n, s) {
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function QM(e) {
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}
function Vl(e, t, n = false) {
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var ZM = ["fanIn", "fanOut", "fanAvg"];
var JM = ["normal", "uniform", "truncatedNormal"];
function eL(e) {
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function tL(e) {
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var es = class extends ae.Serializable {
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  getConfig() {
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};
var Tb = class extends es {
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};
Tb.className = "Zeros";
ae.registerClass(Tb);
var Cp = class extends es {
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};
Cp.className = "Ones";
ae.registerClass(Cp);
var $b = class extends es {
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    this.value = e.value;
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  apply(e, t) {
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  getConfig() {
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};
$b.className = "Constant";
ae.registerClass($b);
var _b = class extends es {
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  apply(e, t) {
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  getConfig() {
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};
_b.className = "RandomUniform";
ae.registerClass(_b);
var Ab = class extends es {
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  apply(e, t) {
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};
Ab.className = "RandomNormal";
ae.registerClass(Ab);
var Eb = class extends es {
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  apply(e, t) {
    if (t = t || "float32", t !== "float32" && t !== "int32")
      throw new Fe(`truncatedNormal does not support dType ${t}.`);
    return lb(e, this.mean, this.stddev, t, this.seed);
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};
Eb.className = "TruncatedNormal";
ae.registerClass(Eb);
var Rb = class extends es {
  constructor(e) {
    super();
    this.gain = e.gain != null ? e.gain : 1;
  }
  apply(e, t) {
    return j(() => {
      if (e.length !== 2 || e[0] !== e[1])
        throw new U("Identity matrix initializer can only be used for 2D square matrices.");
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  getConfig() {
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};
Rb.className = "Identity";
ae.registerClass(Rb);
function nL(e, t = "channelsLast") {
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  if (St(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 es {
  constructor(e) {
    super();
    if (e.scale < 0)
      throw new U(`scale must be a positive float. Got: ${e.scale}`);
    this.scale = e.scale == null ? 1 : e.scale, this.mode = e.mode == null ? "fanIn" : e.mode, eL(this.mode), this.distribution = e.distribution == null ? "normal" : e.distribution, tL(this.distribution), this.seed = e.seed;
  }
  apply(e, t) {
    let n = nL(e), s = n[0], r = n[1], a = this.scale;
    if (this.mode === "fanIn" ? a /= Math.max(1, s) : this.mode === "fanOut" ? a /= Math.max(1, r) : a /= Math.max(1, (s + r) / 2), this.distribution === "normal") {
      let i = Math.sqrt(a);
      if (t = t || "float32", t !== "float32" && t !== "int32")
        throw new Fe(`${this.getClassName()} does not support dType ${t}.`);
      return lb(e, 0, i, t, this.seed);
    } else {
      let i = Math.sqrt(3 * a);
      return Pl(e, -i, i, t);
    }
  }
  getConfig() {
    return { scale: this.scale, mode: this.mode, distribution: this.distribution, seed: this.seed };
  }
};
xn.className = "VarianceScaling";
ae.registerClass(xn);
var Np = class extends xn {
  constructor(e) {
    super({ scale: 1, mode: "fanAvg", distribution: "uniform", seed: e == null ? null : e.seed });
  }
  getClassName() {
    return xn.className;
  }
};
Np.className = "GlorotUniform";
ae.registerClass(Np);
var Tp = class extends xn {
  constructor(e) {
    super({ scale: 1, mode: "fanAvg", distribution: "normal", seed: e == null ? null : e.seed });
  }
  getClassName() {
    return xn.className;
  }
};
Tp.className = "GlorotNormal";
ae.registerClass(Tp);
var $p = class extends xn {
  constructor(e) {
    super({ scale: 2, mode: "fanIn", distribution: "normal", seed: e == null ? null : e.seed });
  }
  getClassName() {
    return xn.className;
  }
};
$p.className = "HeNormal";
ae.registerClass($p);
var _p = class extends xn {
  constructor(e) {
    super({ scale: 2, mode: "fanIn", distribution: "uniform", seed: e == null ? null : e.seed });
  }
  getClassName() {
    return xn.className;
  }
};
_p.className = "HeUniform";
ae.registerClass(_p);
var Ap = class extends xn {
  constructor(e) {
    super({ scale: 1, mode: "fanIn", distribution: "normal", seed: e == null ? null : e.seed });
  }
  getClassName() {
    return xn.className;
  }
};
Ap.className = "LeCunNormal";
ae.registerClass(Ap);
var Ep = class extends xn {
  constructor(e) {
    super({ scale: 1, mode: "fanIn", distribution: "uniform", seed: e == null ? null : e.seed });
  }
  getClassName() {
    return xn.className;
  }
};
Ep.className = "LeCunNormal";
ae.registerClass(Ep);
var Db = class extends es {
  constructor(e) {
    super();
    if (this.DEFAULT_GAIN = 1, this.gain = e.gain == null ? this.DEFAULT_GAIN : e.gain, this.seed = e.seed, this.seed != null)
      throw new Fe("Random seed is not implemented for Orthogonal Initializer yet.");
  }
  apply(e, t) {
    return j(() => {
      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 = Sp(n, 0, 1, "float32"), r = AO.gramSchmidt(s);
      return e[0] > e[1] && (r = qe(r)), V(this.gain, r);
    });
  }
  getConfig() {
    return { gain: this.gain, seed: this.seed };
  }
};
Db.className = "Orthogonal";
ae.registerClass(Db);
var Nx = { constant: "Constant", glorotNormal: "GlorotNormal", glorotUniform: "GlorotUniform", heNormal: "HeNormal", heUniform: "HeUniform", identity: "Identity", leCunNormal: "LeCunNormal", leCunUniform: "LeCunUniform", ones: "Ones", orthogonal: "Orthogonal", randomNormal: "RandomNormal", randomUniform: "RandomUniform", truncatedNormal: "TruncatedNormal", varianceScaling: "VarianceScaling", zeros: "Zeros" };
function Tx(e, t = {}) {
  return zl(e, ae.SerializationMap.getMap().classNameMap, t, "initializer");
}
function yt(e) {
  return vb(e);
}
function ht(e) {
  if (typeof e == "string") {
    let t = e in Nx ? Nx[e] : e;
    if (t === "GlorotNormal")
      return new Tp();
    if (t === "GlorotUniform")
      return new Np();
    if (t === "HeNormal")
      return new $p();
    if (t === "HeUniform")
      return new _p();
    if (t === "LeCunNormal")
      return new Ap();
    if (t === "LeCunUniform")
      return new Ep();
    {
      let n = {};
      return n.className = t, n.config = {}, Tx(n);
    }
  } else
    return e instanceof es ? e : Tx(e);
}
function sL() {
  return new Tb();
}
function rL() {
  return new Cp();
}
function aL(e) {
  return new $b(e);
}
function iL(e) {
  return new _b(e);
}
function oL(e) {
  return new Ab(e);
}
function uL(e) {
  return new Eb(e);
}
function lL(e) {
  return new Rb(e);
}
function cL(e) {
  return new xn(e);
}
function dL(e) {
  return new Np(e);
}
function pL(e) {
  return new Tp(e);
}
function hL(e) {
  return new $p(e);
}
function fL(e) {
  return new _p(e);
}
function mL(e) {
  return new Ap(e);
}
function gL(e) {
  return new Ep(e);
}
function bL(e) {
  return new Db(e);
}
var yL = {};
Ae(yL, { Layer: () => Ge, RNN: () => _r, RNNCell: () => Gl, activation: () => eV, add: () => lV, alphaDropout: () => qV, average: () => cV, averagePooling1d: () => Uy, averagePooling2d: () => Gy, averagePooling3d: () => Hy, avgPool1d: () => vV, avgPool2d: () => wV, avgPool3d: () => IV, avgPooling1d: () => xV, avgPooling2d: () => kV, avgPooling3d: () => SV, batchNormalization: () => gV, bidirectional: () => MV, concatenate: () => dV, conv1d: () => HB, conv2d: () => qB, conv2dTranspose: () => jB, conv3d: () => KB, conv3dTranspose: () => XB, convLstm2d: () => FV, convLstm2dCell: () => OV, cropping2D: () => QB, dense: () => tV, depthwiseConv2d: () => JB, dot: () => mV, dropout: () => nV, elu: () => LB, embedding: () => uV, flatten: () => rV, gaussianDropout: () => HV, gaussianNoise: () => GV, globalAveragePooling1d: () => CV, globalAveragePooling2d: () => NV, globalMaxPool1d: () => BV, globalMaxPool2d: () => VV, globalMaxPooling1d: () => z0, globalMaxPooling2d: () => M0, gru: () => $V, gruCell: () => _V, input: () => CB, inputLayer: () => MB, layerNormalization: () => bV, leakyReLU: () => VB, lstm: () => AV, lstmCell: () => EV, masking: () => jV, maxPool1d: () => WV, maxPool2d: () => UV, maxPooling1d: () => L0, maxPooling2d: () => B0, maxPooling3d: () => TV, maximum: () => pV, minimum: () => hV, multiply: () => fV, permute: () => oV, prelu: () => WB, reLU: () => BB, repeatVector: () => aV, reshape: () => iV, rnn: () => PV, separableConv2d: () => YB, simpleRNN: () => RV, simpleRNNCell: () => DV, softmax: () => UB, spatialDropout1d: () => sV, stackedRNNCells: () => zV, thresholdedReLU: () => GB, timeDistributed: () => LV, upSampling2d: () => ZB, zeroPadding2d: () => yV });
var vL = 0;
function UI() {
  return vL++;
}
var Gc = {};
function Rp(e = "") {
  return e in Gc || (Gc[e] = 0), Gc[e] += 1, e + Gc[e].toString();
}
function gm(e) {
  return Array.isArray(e) && Array.isArray(e[0]);
}
function Id(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 U(`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 U(`Expected exactly 1 Shape; got ${e.length}`);
  } else
    return e;
}
function Sd(e) {
  let t = 0;
  for (let n of e)
    n.shape.length === 0 ? t += 1 : t += n.shape.reduce((s, r) => s * r);
  return t;
}
var $x = "Variable";
var xL = class {
  constructor(e, t = "float32", n = $x, s = true, r = null) {
    this.dtype = t == null ? "float32" : t, this.shape = e.shape, this.id = UI(), n = n == null ? $x : n, this.originalName = MI(n), this.name = LI(this.originalName), this.trainable_ = s, this.constraint = r, this.val = R3(e, this.trainable_, this.name, this.dtype);
  }
  read() {
    return this.assertNotDisposed(), this.val;
  }
  write(e) {
    return this.assertNotDisposed(), wL(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 wL(e, t) {
  if (e.shape.toString() !== t.shape.toString())
    throw new Error("Shape mismatch: " + JSON.stringify(e.shape) + " vs. " + JSON.stringify(t.shape));
}
function bm(e) {
  return e.map((t) => t.read());
}
function Fb(e) {
  e.forEach((t) => {
    t[0].write(t[1]);
  });
}
var Dt = class {
  constructor(e) {
    this.dtype = e.dtype, this.shape = e.shape, e.shape != null ? this.ndim = e.shape.length : this.ndim = e.ndim, this.maxNDim = e.maxNDim, this.minNDim = e.minNDim, this.axes = e.axes || {};
  }
};
var $s = class {
  constructor(e, t, n, s, r, a, i) {
    this.dtype = e, this.shape = t, this.sourceLayer = n, this.inputs = s, this.callArgs = r, this.outputTensorIndex = i, this.id = UI(), a != null && (this.originalName = MI(a), this.name = LI(this.originalName)), this.rank = t.length;
  }
};
var kL = 0;
var Dp = class {
  constructor(e, t) {
    this.callArgs = t, this.id = kL++, 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 IL = 0;
var Ge = class extends ae.Serializable {
  constructor(e = {}) {
    super();
    this._callHook = null, this._addedWeightNames = [], this._stateful = false, this.id = IL++, 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 = Ws(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 ps(`The layer has never been called and thus has no defined ${t}.`);
    if (this.inboundNodes.length <= e)
      throw new U(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);
    return this.inboundNodes[e];
  }
  getInputAt(e) {
    return gn(this.getNodeAtIndex(e, "input").inputTensors);
  }
  getOutputAt(e) {
    return gn(this.getNodeAtIndex(e, "output").outputTensors);
  }
  get input() {
    if (this.inboundNodes.length > 1)
      throw new Vs(`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 Vs(`Layer ${this.name} is not connected, no input to return.`);
    return gn(this.getNodeAtIndex(0, "input").inputTensors);
  }
  get output() {
    if (this.inboundNodes.length === 0)
      throw new Vs(`Layer ${this.name} has no inbound nodes.`);
    if (this.inboundNodes.length > 1)
      throw new Vs(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);
    return gn(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 = dt(e), this.inputSpec == null || this.inputSpec.length === 0)
      return;
    let t = dt(this.inputSpec);
    if (e.length !== t.length)
      throw new U(`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 U(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);
      if (r.maxNDim != null && a > r.maxNDim)
        throw new U(`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 U(`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 U(`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 U(`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 U(`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 = dt(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 U("Arguments to apply() must be all SymbolicTensors or all Tensors");
    return Zr(this.name, () => {
      if (!this.built) {
        this.assertInputCompatibility(e);
        let a = [];
        for (let i of dt(e))
          a.push(i.shape);
        this.build(gn(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 = dt(a), o = [];
        for (let u of i)
          n.indexOf(u) !== -1 && (u = u.clone()), o.push(u);
        if (a = gn(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 = SL(e), i = this.computeOutputShape(a), o, u = CL(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, dt(e), t, this.name, c)) : o = new $s(u, i, this, dt(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 Vs(`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 Vs(`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 ps(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);
    return Sd(this.weights);
  }
  build(e) {
    this.built = true;
  }
  getWeights(e = false) {
    return bm(e ? this.trainableWeights : this.weights);
  }
  setWeights(e) {
    j(() => {
      let t = this.weights;
      if (t.length !== e.length)
        throw new U(`You called setWeights(weights) on layer "${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 = bm(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 U(`Layer weight shape ${a.shape} not compatible with provided weight shape ${o.shape}`);
        n.push([i, o]);
      }
      Fb(n);
    });
  }
  addWeight(e, t, n, s, r, a, i, o) {
    if (this._addedWeightNames.indexOf(e) !== -1)
      throw new U(`Duplicate weight name ${e} for layer ${this.name}`);
    this._addedWeightNames.push(e), n == null && (n = "float32"), this.fastWeightInitDuringBuild && (s = o != null ? o() : ht("zeros"));
    let u = s.apply(t, n), l = new xL(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 = dt(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 ${this.name} does not support masking, but was passed an inputMask.`);
      return null;
    }
    return t;
  }
  addInboundNode(e, t, n, s, r, a, i = null) {
    let o = dt(e);
    t = dt(t), n = dt(n), s = dt(s), r = Id(r), a = Id(a);
    let u = [], l = [], c = [];
    for (let p of o)
      u.push(p.sourceLayer), l.push(p.nodeIndex), c.push(p.tensorIndex);
    new Dp({ outboundLayer: this, inboundLayers: u, nodeIndices: l, tensorIndices: c, inputTensors: o, outputTensors: t, inputMasks: n, outputMasks: s, inputShapes: r, outputShapes: a }, i);
    for (let p = 0; p < t.length; p++)
      t[p].sourceLayer = this, t[p].nodeIndex = this.inboundNodes.length - 1, t[p].tensorIndex = p;
  }
  getConfig() {
    let e = { name: this.name, trainable: this.trainable };
    return this.batchInputShape != null && (e.batchInputShape = this.batchInputShape), this.dtype != null && (e.dtype = this.dtype), e;
  }
  disposeWeights() {
    return this.weights.forEach((e) => e.dispose()), this.weights.length;
  }
  assertNotDisposed() {
    if (this._refCount === 0)
      throw new Error(`Layer '${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 SL(e) {
  e = dt(e);
  let t = [];
  for (let n of e)
    t.push(n.shape);
  return gn(t);
}
function CL(e) {
  return "float32";
}
function GI(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 = GI(i, o, u);
        for (let c of l)
          r.indexOf(c) === -1 && r.push(c);
      }
      return r;
    }
  }
}
var Yo = class extends Ge {
  constructor(e) {
    super({ dtype: e.dtype, name: e.name != null ? e.name : Rp("input").toString() });
    if (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 U("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 U("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");
      t = [e.batchSize].concat(e.inputShape);
    } else if (e.batchSize != null)
      throw new U("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 Dp({ outboundLayer: this, inboundLayers: [], nodeIndices: [], tensorIndices: [], inputTensors: [s], outputTensors: [s], inputMasks: [null], outputMasks: [null], inputShapes: [t], outputShapes: [t] });
  }
  apply(e, t) {
    throw new U(`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 };
  }
};
Yo.className = "InputLayer";
ae.registerClass(Yo);
function HI(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 U("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 Yo({ batchInputShape: t, name: e.name, dtype: n, sparse: e.sparse }).inboundNodes[0].outputTensors[0];
}
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];
    Re(s);
  }
}
function qI(e) {
  if (e != null)
    for (let t in e) {
      let n = e[t];
      typeof n != "number" && n.dispose();
    }
}
var NL = 125;
var Zi = 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 TL = 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 $L = class extends Zi {
  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 = j(() => 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 : j(() => {
          let s = V(xe(1, this.seen), this.totals[n]);
          t[n] = s, this.totals[n].dispose(), Ht(t[n]);
        }));
  }
};
var _L = class extends Zi {
  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 AL = class extends Zi {
  constructor(e, t) {
    super();
    if (this.currentEpoch = 0, this.nowFunc = e.nowFunc, this.nextFrameFunc = e.nextFrameFunc || RO, this.yieldEvery = t || "auto", this.yieldEvery === "auto" && (this.yieldEvery = NL), 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 = EM(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 jI(e, t) {
  return e == null && (e = {}), e instanceof Zi ? [e] : Array.isArray(e) && e[0] instanceof Zi ? e : dt(e).map((s) => new AL(s, t));
}
var ks = 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}`), ks.checkForDuplicate(t), ks.constructors[e] == null && (ks.constructors[e] = []), ks.constructors[e].push(t);
  }
  static checkForDuplicate(e) {
    for (let t in ks.constructors)
      ks.constructors[+t].forEach((s) => {
        if (s === e)
          throw new U("Duplicate callback constructor.");
      });
  }
  static clear() {
    ks.constructors = {};
  }
  static createCallbacks(e) {
    let t = [];
    for (let n in ks.constructors) {
      let s = +n;
      e >= s && t.push(...ks.constructors[s]);
    }
    return t.map((n) => new n());
  }
};
var Ob = ks;
Ob.constructors = {};
function KI(e, t, n, s, r, a, i, o, u) {
  let l = new _L(), c = [new $L(), ...Ob.createCallbacks(t)];
  e != null && c.push(...e), c.push(l);
  let p = new TL(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 fs(e, t = {}, n = false) {
  return zl(e, ae.SerializationMap.getMap().classNameMap, t, "layer", n);
}
function Cd(e, t) {
  return j(() => {
    e.dtype !== "float32" && (e = ce(e, "float32"));
    let n = ye(Bl(e), t, true), s = Fl(n.shape, Rt()), r = ln(Tr(n, s));
    return xe(e, r);
  });
}
function fi(e, t) {
  return j(() => It(Bl(ge(t, e)), -1));
}
function Fp(e, t) {
  return j(() => It(Mt(ge(t, e)), -1));
}
function Qo(e, t) {
  return j(() => {
    let n = ge(e, t), s = Bn(Mt(e), Rt(), Number.MAX_VALUE), r = Mt(xe(n, s));
    return V(100, It(r, -1));
  });
}
function EL(e, t) {
  return j(() => {
    let n = Bn(t, Rt(), Number.MAX_VALUE), s = Kn(ie(1, n)), r = Bn(e, Rt(), Number.MAX_VALUE), a = Kn(ie(1, r));
    return It(Bl(ge(s, a)), -1);
  });
}
function RL(e, t) {
  return j(() => {
    let n = Tr(0, ge(1, V(e, t)));
    return It(Bl(n), -1);
  });
}
function DL(e, t) {
  return j(() => {
    let n = Tr(0, ge(1, V(e, t)));
    return It(n, -1);
  });
}
function FL(e, t) {
  return j(() => {
    let n = ye(V(e, t), -1), s = As(V(ge(1, e), t), -1);
    return Tr(0, ie(1, ge(s, n)));
  });
}
function OL(e, t) {
  return j(() => {
    let n = Math.log(2), s = ge(t, e), r = ge(ie(s, Ol(V(-2, s))), n);
    return It(r, -1);
  });
}
function Yu(e, t, n = false) {
  return j(() => {
    if (n)
      t = ib(t);
    else {
      let s = ye(t, t.shape.length - 1, true);
      t = xe(t, s);
    }
    return t = Bn(t, Rt(), 1 - Rt()), kt(ye(V(ce(e, "float32"), Kn(t)), t.shape.length - 1));
  });
}
function Nd(e, t, n = false) {
  return j(() => {
    let s = ce(fp(jM(e)), "int32");
    t = Bn(t, Rt(), 1 - Rt());
    let r = t.shape, a = G(yd(s, r[r.length - 1]), r);
    return Yu(a, t, n);
  });
}
function PL(e, t) {
  if (!w.arraysEqual(e.shape, t.shape))
    throw new U(`logits and labels must have the same shape, but got shapes ${JSON.stringify(e.shape)} and ${JSON.stringify(t.shape)}`);
  return j(() => {
    let n = Xs(t), s = kt(Mt(t));
    return ie(ge(n, V(t, e)), Kg(jn(s)));
  });
}
function Op(e, t) {
  return j(() => {
    let n;
    return n = Bn(t, Rt(), 1 - Rt()), n = Kn(xe(n, ge(1, n))), It(PL(e, n), -1);
  });
}
function zL(e, t) {
  return j(() => {
    let n = Bn(e, Rt(), 1), s = Bn(t, Rt(), 1);
    return ye(V(e, Kn(xe(n, s))), -1);
  });
}
function ML(e, t) {
  return j(() => {
    let n = Kn(ie(Rt(), t));
    return It(ge(t, V(e, n)), -1);
  });
}
function Pb(e, t) {
  return j(() => {
    let n = Cd(e, -1), s = Cd(t, -1), r = V(n, s);
    return kt(ye(r, -1));
  });
}
var Td = { meanSquaredError: fi, meanAbsoluteError: Fp, meanAbsolutePercentageError: Qo, meanSquaredLogarithmicError: EL, squaredHinge: RL, hinge: DL, categoricalHinge: FL, logcosh: OL, categoricalCrossentropy: Yu, sparseCategoricalCrossentropy: Nd, binaryCrossentropy: Op, kullbackLeiblerDivergence: zL, poisson: ML, cosineProximity: Pb };
function Bf(e) {
  if (typeof e == "string") {
    if (e in Td)
      return Td[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 U(t);
  } else
    return e;
}
function zb(e, t) {
  return j(() => {
    let n = V(0.5, Xn(t)), s = Ip(Wn(t, n), e.dtype);
    return It(qn(e, s), -1);
  });
}
function Mb(e, t) {
  return j(() => Ip(qn(Gu(e, -1), Gu(t, -1)), "float32"));
}
function XI(e, t) {
  return j(() => ce(ye(Ds(qn(e, 1), qn(t, 1))), "float32"));
}
function LL(e, t) {
  return j(() => ce(ye(Ds(qn(e, 1), qn(t, 0))), "float32"));
}
function BL(e, t) {
  return j(() => ce(ye(Ds(qn(e, 0), qn(t, 1))), "float32"));
}
function YI(e, t) {
  return j(() => {
    let n = XI(e, t), s = BL(e, t), r = ie(n, s);
    return ce(vn(Wn(r, 0), xe(n, r), 0), "float32");
  });
}
function VL(e, t) {
  return j(() => {
    let n = XI(e, t), s = LL(e, t), r = ie(n, s);
    return ce(vn(Wn(r, 0), xe(n, r), 0), "float32");
  });
}
function QI(e, t) {
  return Op(e, t);
}
function ZI(e, t) {
  return e.rank === t.rank && (e = mr(e, [e.rank - 1])), t = Gu(t, -1), t.dtype !== e.dtype && (t = ce(t, e.dtype)), ce(qn(e, t), "float32");
}
var WL = fi;
var UL = fi;
var GL = Fp;
var HL = Fp;
var qL = Qo;
var jL = Qo;
var Lb = Yu;
var KL = Pb;
var JI = Nd;
var $d = { binaryAccuracy: zb, categoricalAccuracy: Mb, precision: YI, categoricalCrossentropy: Lb, sparseCategoricalCrossentropy: JI, mse: WL, MSE: UL, mae: GL, MAE: HL, mape: qL, MAPE: jL, cosine: KL };
function XL(e) {
  if (typeof e == "string" && e in $d)
    return $d[e];
  if (typeof e != "string" && e != null)
    return e;
  throw new U(`Unknown metric ${e}`);
}
function Hc(e) {
  if (Cs(e !== null, `Unknown LossOrMetricFn ${e}`), typeof e == "string")
    return e;
  {
    let t;
    for (let n of Object.keys(Td))
      if (Td[n] === e) {
        t = n;
        break;
      }
    if (t !== void 0)
      return t;
    for (let n of Object.keys($d))
      if ($d[n] === e) {
        t = n;
        break;
      }
    return t !== void 0 ? t : e.name;
  }
}
function YL(e) {
  let t = { Adagrad: () => Fi.adagrad(0.01), Adadelta: () => Fi.adadelta(1, 0.95, Rt()), Adam: () => Fi.adam(1e-3, 0.9, 0.999, Rt()), Adamax: () => Fi.adamax(2e-3, 0.9, 0.999, Rt(), 0), RMSProp: () => Fi.rmsprop(1e-3, 0.9, 0, Rt()), SGD: () => Fi.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 U(`Unknown Optimizer ${e}`);
}
var _x = 1 * 1024 * 1024;
function Ax(e, t, n = false) {
  if (e == null || typeof e != "object" || Object.getPrototypeOf(e) !== Object.prototype || !ym(e))
    throw new Error("User-defined metadata is expected to be a JSON object, but is not.");
  if (n) {
    let s = JSON.stringify(e);
    s.length > _x && console.warn(`User-defined metadata of model "${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 <= ${_x}.`);
  }
}
function ym(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" || !ym(e[n]))
          return false;
      return true;
    } else if (Array.isArray(e)) {
      for (let t of e)
        if (!ym(t))
          return false;
      return true;
    } else
      return false;
  else {
    let t = typeof e;
    return t === "string" || t === "number" || t === "boolean";
  }
}
function QL(e, t, n, s = console.log) {
  let r = JL(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)), _d(a, n, s), s("=".repeat(t));
  let o = e.layers;
  for (let c = 0; c < o.length; ++c)
    r ? eB(o[c], n, s) : tB(o[c], n, i, s), s((c === o.length - 1 ? "=" : "_").repeat(t));
  e.checkTrainableWeightsConsistency();
  let u = ZL(e), l = Sd(e.nonTrainableWeights);
  s(`Total params: ${u + l}`), s(`Trainable params: ${u}`), s(`Non-trainable params: ${l}`), s("_".repeat(t));
}
function ZL(e) {
  let t;
  return e.collectedTrainableWeights != null ? t = Sd(e.collectedTrainableWeights) : t = Sd(e.trainableWeights), t;
}
function JL(e) {
  let t = true, n = [], s = [];
  for (let r in e.nodesByDepth)
    n.push(e.nodesByDepth[r]);
  for (let r of n) {
    if (r.length > 1 || r.length === 1 && r[0].inboundLayers.length > 1) {
      t = false;
      break;
    }
    s.push(...r);
  }
  if (t)
    for (let r of e.layers) {
      let a = false;
      for (let i of r.inboundNodes)
        if (s.indexOf(i) !== -1)
          if (a) {
            t = false;
            break;
          } else
            a = true;
      if (!t)
        break;
    }
  return t;
}
function _d(e, t, n = console.log) {
  let s = "";
  for (let r = 0; r < e.length; ++r)
    r > 0 && (s = s.slice(0, s.length - 1) + " "), s += e[r], s = s.slice(0, t[r]), s += " ".repeat(t[r] - s.length);
  n(s);
}
function eB(e, t, n) {
  let s, r;
  try {
    r = e.inboundNodes.map((u) => JSON.stringify(u.inputShapes)).join(",");
  } catch (u) {
    r = "multiple";
  }
  try {
    s = JSON.stringify(e.outputShape);
  } catch (u) {
    s = "multiple";
  }
  let a = e.name, i = e.getClassName(), o = [`${a} (${i})`, r, s, e.countParams().toString()];
  _d(o, t, n);
}
function tB(e, t, n, s) {
  let r, a;
  try {
    a = e.inboundNodes.map((p) => JSON.stringify(p.inputShapes)).join(",");
  } catch (p) {
    a = "multiple";
  }
  try {
    r = JSON.stringify(e.outputShape);
  } catch (p) {
    r = "multiple";
  }
  let i = [];
  for (let p of e.inboundNodes)
    if (!(n != null && n.length > 0 && n.indexOf(p) === -1))
      for (let d = 0; d < p.inboundLayers.length; ++d) {
        let h = p.inboundLayers[d].name, f = p.nodeIndices[d], m = p.tensorIndices[d];
        i.push(`${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];
  _d(c, t, s);
  for (let p = 1; p < i.length; ++p)
    _d(["", "", "", "", i[p]], t, s);
}
function e0(e, t, n) {
  return (e === "inboundNodes" || e === "outputLayers" || e === "inputLayers") && t === 0 && typeof n == "string";
}
function Qu(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];
      e0(t, r, a) ? n.push(a) : n.push(Qu(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] = Qu(r, a);
      }
    }
    return n;
  }
}
function vm(e, t) {
  if (e == null)
    return null;
  if (typeof e == "string")
    return Ws(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];
      e0(t, r, a) ? n.push(a) : n.push(vm(a, t));
    }
    return n;
  } else {
    let n = {};
    for (let s of Object.keys(e)) {
      let r = e[s], a = Ws(s);
      (s === "name" || s === "className") && typeof r == "string" ? n[a] = r : n[a] = vm(r, s);
    }
    return n;
  }
}
var t0 = "0.0.0";
function nB(e, t) {
  if (e.dtype == null || e.dtype === t.dtype)
    return t;
  try {
    return ce(t, e.dtype);
  } catch (n) {
    throw new U(`The dtype of the feed (${t.dtype}) can not be cast to the dtype of the key '${e.name}' (${e.dtype}).`);
  }
}
var Xr = class {
  constructor(e) {
    if (this.id2Value = {}, this.id2Mask = {}, this.name2Id = {}, e instanceof Xr)
      for (let t in e.id2Value)
        this.id2Value[t] = e.id2Value[t], t in e.id2Mask && (this.id2Mask[t] = e.id2Mask[t]);
    else {
      if (e == null)
        return;
      for (let t of e)
        this.add(t.key, t.value);
    }
  }
  add(e, t, n) {
    if (this.id2Value[e.id] == null)
      this.id2Value[e.id] = nB(e, t), this.name2Id[e.name] = e.id, n != null && (this.id2Mask[e.id] = n);
    else
      throw new U(`Duplicate key: name=${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 U(`Nonexistent key: ${e.name}`);
      return this.id2Value[e.id];
    } else {
      let t = this.name2Id[e];
      if (t == null)
        throw new U(`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 U(`Nonexistent key: ${e.name}`);
      return this.id2Mask[e.id];
    } else {
      let t = this.name2Id[e];
      if (t == null)
        throw new U(`Feed dict has no SymbolicTensor name: ${e}`);
      return this.id2Mask[t];
    }
  }
  disposeMasks() {
    this.id2Mask != null && Re(this.id2Mask);
  }
};
var Vf = {};
var Ex = {};
function _u(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().join(","), p, d;
  if (Vf[c] == null) {
    let f = sB(i, t);
    p = f.sorted, d = f.recipientCounts, Vf[c] = p, Ex[c] = d;
  }
  p = Vf[c], d = {}, r || Object.assign(d, Ex[c]);
  let h = new Xr(t);
  for (let f = 0; f < p.length; ++f) {
    if (s != null) {
      let A = lm().numTensors;
      A > s.maxNumTensors && (s.maxNumTensors = A), A < s.minNumTensors && (s.minNumTensors = A);
    }
    let m = p[f], g = m.sourceLayer;
    if (g instanceof Yo)
      continue;
    let b = [], y = [], v = [], x = false;
    for (let A of m.inputs) {
      let P = h.getValue(A), R = h.getMask(A);
      b.push(P), y.push(R), R != null && (x = true), r || (d[A.name]--, d[A.name] === 0 && !t.hasKey(A) && o.indexOf(A.name) === -1 && !P.isDisposed && A.sourceLayer.stateful !== true && v.push(P));
    }
    x && (n = n || {}, n.mask = y[0]);
    let k = dt(g.apply(b, n)), C = null;
    g.supportsMasking && (C = g.computeMask(b, y));
    let T = aB(m), E = Array.isArray(T) ? T : [T];
    for (let A = 0; A < E.length; ++A) {
      h.hasKey(E[A]) || h.add(E[A], k[A], Array.isArray(C) ? C[0] : C);
      let P = o.indexOf(E[A].name);
      P !== -1 && (u[P] = k[A]);
    }
    r || Re(v);
  }
  return h.disposeMasks(), a ? u : u[0];
}
function sB(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 = Rx(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 } = Rx(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: rB(s) };
}
function rB(e) {
  let t = {};
  for (let n in e)
    t[n] = e[n].size;
  return t;
}
function Rx(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 aB(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 Is = class extends Ge {
  constructor(e) {
    super({});
    if (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 U(`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 Yo))
        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, C) => {
      (x == null || k == null || C == null) && (x = b.sourceLayer, k = b.nodeIndex, C = b.tensorIndex);
      let T = x.inboundNodes[k];
      if (v.indexOf(T) !== -1)
        throw new ps(`The tensor ${b.name} at layer "${x.name}" is part of a cycle.`);
      if (y.indexOf(T) !== -1)
        return;
      this.containerNodes.add(Is.nodeKey(x, k)), x.id in a || (a[x.id] = Object.keys(a).length), v.indexOf(T) === -1 && v.push(T);
      let E = T.inboundLayers.length;
      for (let A = 0; A < E; A++) {
        let P = T.inputTensors[A], R = T.inboundLayers[A], F = T.nodeIndices[A], $ = T.tensorIndices[A];
        o(P, y, v, R, F, $);
      }
      for (y.push(T); v.indexOf(T) >= 0; )
        v.splice(v.indexOf(T), 1);
      i.push(T);
    }, 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], C = b.nodeIndices[x], T = k.inboundNodes[C], E = t[T.id] == null ? 0 : t[T.id];
        t[T.id] = Math.max(y + 1, E), n[T.id] = T;
      }
    }
    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(Wc);
    this.layers = [];
    for (let b of h) {
      let y = d[b];
      y.sort((v, x) => {
        let k = a[v.id], C = a[x.id];
        return k < C ? -1 : k > C ? 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(Wc);
    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 ps(`Graph disconnected: cannot obtain value for tensor ${x} at layer "${v.name}". The following previous layers were accessed without issue: ${m}`);
          for (let x of y.outputTensors)
            f.push(x);
          m.push(v.name);
        }
      }
    this.nodesByDepth = p;
    let g = this.layers.map((b) => b.name);
    for (let b of g) {
      let y = g.filter((v) => v === b).length;
      if (y !== 1)
        throw new ps(`The name "${b}" is used ${y} times in the model. All layer names should be unique. Layer names: ` + JSON.stringify(g));
    }
    this.outboundNodes = [], this.inboundNodes = [], new Dp({ outboundLayer: this, inboundLayers: [], nodeIndices: [], tensorIndices: [], inputTensors: this.inputs, outputTensors: this.outputs, inputMasks: this.inputs.map((b) => null), outputMasks: this.outputs.map((b) => null), inputShapes: this.inputs.map((b) => b.shape), outputShapes: this.outputs.map((b) => b.shape) }), this.built = true, this._refCount = 1;
  }
  assertNotDisposed() {
    if (this._refCount === 0)
      throw new Error(`Container '${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 U("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 U(`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 U(`Provided weight data has no target variable: ${a}`);
      delete n[i];
    }
    if (t) {
      let a = [];
      for (let i in n)
        a.push(i);
      if (a.length > 0)
        throw new U(`${a.length} of ${s} weights are not set: ${a}`);
    }
    Fb(r);
  }
  updatedConfig() {
    let e = this.getConfig(), t = {};
    return t.className = this.getClassName(), t.config = e, t.kerasVersion = `tfjs-layers ${t0}`, t.backend = "TensorFlow.js", t;
  }
  toJSON(e, t = true) {
    let n = vm(this.updatedConfig());
    return t ? JSON.stringify(n) : n;
  }
  call(e, t) {
    return j(() => {
      e = dt(e);
      let n = new Xr();
      for (let s = 0; s < this.inputs.length; ++s)
        n.add(this.inputs[s], e[s]);
      return _u(this.outputs, n, t);
    });
  }
  computeMask(e, t) {
    return j(() => {
      e = dt(e);
      let n;
      return t == null ? n = pa(null, e.length) : n = dt(t), this.runInternalGraph(e, n)[1];
    });
  }
  computeOutputShape(e) {
    let t = Id(e);
    if (t.length !== this.inputLayers.length)
      throw new U(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);
    let n = {};
    for (let i = 0; i < t.length; i++) {
      let o = this.inputLayers[i], u = t[i], l = o.name + "_0_0";
      n[l] = u;
    }
    let s = Object.keys(this.nodesByDepth).map((i) => parseInt(i, 10)).sort(Wc);
    if (s.length > 1)
      for (let i of s) {
        let o = this.nodesByDepth[i];
        for (let u of o) {
          let l = u.outboundLayer;
          if (this.inputLayers.map((f) => f.id).indexOf(l.id) !== -1)
            continue;
          let c = [];
          for (let f = 0; f < u.inboundLayers.length; f++) {
            let m = u.inboundLayers[f], g = u.nodeIndices[f], b = u.tensorIndices[f], y = `${m.name}_${g}_${b}`, v = n[y];
            c.push(v);
          }
          let p = l.computeOutputShape(gn(c)), d = Id(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 gn(r);
  }
  runInternalGraph(e, t) {
    t == null && (t = pa(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(Wc);
    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 = dt(c.call(v, f)), y = dt(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 = dt(c.call(m, f)), y = dt(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], C = y[v];
            n[x.id] = [k, C];
          }
        }
      }
    }
    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 U(`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 U("Provide either a layer name or layer index");
    for (let n of this.layers)
      if (n.name === e)
        return n;
    throw new U(`No such layer: ${e}`);
  }
  calculateLosses() {
    return j(() => {
      let e = [];
      for (let t of this.layers)
        for (let n = 0; n < t.inboundNodes.length; ++n) {
          let s = 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], C = v[2];
        if (y = v[3] == null ? {} : v[3], !(x in r)) {
          i(m, g);
          return;
        }
        let T = r[x];
        if (T.inboundNodes.length <= k) {
          i(m, g);
          return;
        }
        let E = T.inboundNodes[k];
        b.push(E.outputTensors[C]);
      }
      b.length > 0 && m.apply(gn(b), y);
    }
    function u(m) {
      let g = m.name, b = fs(m, t.customObjects != null ? t.customObjects : {});
      b.setFastWeightInitDuringBuild(s), r[g] = b, m.inboundNodes.forEach((v) => {
        if (!(v instanceof Array))
          throw new U(`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 (; !AM(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 U("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() {
    j(() => {
      this.layers.forEach((e) => {
        e.stateful && e.resetStates();
      });
    });
  }
};
function iB(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: ${JSON.stringify(e)}`);
}
function n0(e, t) {
  return iB(e, t, "classWeight");
}
async function s0(e, t, n, s) {
  if (t != null || s != null)
    throw new Error("Support sampleWeight is not implemented yet");
  if (n != null) {
    let r = j(() => {
      if (e.shape.length === 1)
        return lr(e);
      if (e.shape.length === 2) {
        if (e.shape[1] > 1)
          return Gu(e, 1);
        if (e.shape[1] === 1)
          return G(e, [e.shape[0]]);
        throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`);
      } else
        throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`);
    }), a = Array.from(await r.data());
    Re(r);
    let i = [];
    return a.forEach((o) => {
      if (n[o] == null)
        throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);
      i.push(n[o]);
    }), Qt(i, "float32");
  } else
    return null;
}
function oB(e, t) {
  return V(e, t);
}
var uB = 32;
function r0(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 = Dx("input", e.inputNames, n), i = Dx("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 Dx(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 U(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);
      s.push(n[r]);
    }
    return s;
  }
}
function lB(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 cB(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 (Fx(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 = lB(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 = jI(n.callbacks, n.yieldEvery), p = n.verbose == null ? 1 : n.verbose, { callbackList: d, history: h } = KI(c, p, n.epochs, null, null, dB(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, ${n.batchesPerEpoch * n.epochs} batches). You may need to use the repeat() function when building your dataset.`);
          break;
        }
        if (v.value != null) {
          let { xs: x, ys: k } = r0(e, v.value), C = {};
          C.batch = y, C.size = x[0].shape[0], await d.onBatchBegin(y, C);
          let T = [];
          if (n.classWeight != null) {
            let P = n0(n.classWeight, e.outputNames);
            for (let R = 0; R < P.length; ++R)
              T.push(await s0(k[R], null, P[R]));
          }
          let E = x.concat(k).concat(T), A = o(E);
          Re(E);
          for (let P = 0; P < u.length; ++P) {
            let R = u[P], F = A[P];
            C[R] = F, Ht(F);
          }
          await d.onBatchEnd(y, C), qI(C), y++, b++;
        }
        if (s ? b >= n.batchesPerEpoch : v.done) {
          if (r) {
            let x;
            Fx(n.validationData) ? x = dt(await e.evaluateDataset(n.validationData, { batches: n.validationBatches })) : x = dt(e.evaluate(a, i, { batchSize: n.validationBatchSize == null ? uB : n.validationBatchSize, verbose: 0 }));
            for (let k = 0; k < e.metricsNames.length; ++k)
              g[`val_${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 dB(e, t) {
  let n = null;
  return t.batchesPerEpoch != null ? n = t.batchesPerEpoch : Number.isFinite(e.size) && (n = e.size), n;
}
function Fx(e) {
  return typeof e.iterator == "function";
}
function pB(e) {
  return typeof e.next == "function";
}
async function hB(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 = pB(t) ? t : await t.iterator(), o = 0, u = 0;
  for (; !s || u < n.batches; ) {
    let l = await i.next();
    if (a = j(() => {
      if (l.value) {
        let { xs: c, ys: p } = r0(e, l.value), d = c.concat(p), h = j(() => r(d));
        if (Re(d), u === 0)
          for (let m = 0; m < h.length; ++m)
            a.push(Ie(0));
        let f = d[0].shape[0];
        for (let m = 0; m < h.length; ++m) {
          let g = h[m], b = a[m];
          a[m] = j(() => ie(a[m], V(f, g))), u > 0 && Re(b);
        }
        Re(h), o += f, ++u;
      }
      return a;
    }), l.done) {
      s && console.warn(`Your dataset iterator ran out of data during evaluateDataset(). Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${n.batches} batches). You may need to use the repeat() function when building your dataset.`);
      break;
    }
  }
  for (let l = 0; l < a.length; ++l) {
    let c = a[l];
    a[l] = xe(a[l], o), Re(c);
  }
  return gn(a);
}
function xm(e) {
  w.assert(e > 0 && Number.isInteger(e), () => `batchSize is required to be a positive integer, but got ${e}`);
}
function Au(e, t, n) {
  return e == null ? [null] : Array.isArray(e) ? e.map((s) => Jr(s, t, n - t)) : Jr(e, t, n - t);
}
function Bb(e, t) {
  return j(() => e == null ? null : Array.isArray(e) ? e.map((n) => Bb(n, t)) : VI(e, t.dtype === "int32" ? t : ce(t, "int32")));
}
function wm(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 fB(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 U("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 } = KI(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 C = Qt(b), T = wm(g, r);
      for (let E = 0; E < T.length; ++E) {
        let A = {};
        if (await y.onBatchBegin(E, A), j(() => {
          let P = T[E][0], R = T[E][1], F = Jr(C, P, R - P);
          A.batch = E, A.size = R - P;
          let $ = Bb(n, F), z = t($);
          for (let W = 0; W < s.length; ++W) {
            let q = s[W], K = z[W];
            A[q] = K, Ht(K);
          }
          if (E === T.length - 1 && m) {
            let W = e.testLoop(u, l, r);
            for (let q = 0; q < s.length; ++q) {
              let K = s[q], Y = W[q];
              Ht(Y), k["val_" + K] = Y;
            }
          }
        }), await y.onBatchEnd(E, A), qI(A), e.stopTraining_)
          break;
      }
      C.dispose();
    }
    if (await y.onEpochEnd(x, k), e.stopTraining_)
      break;
  }
  return await y.onTrainEnd(), await e.history.syncData(), e.history;
}
async function mB(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;
    xm(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 U(`When passing validation data, it must contain 2 (valX, valY) or 3 (valX, valY, valSampleWeight) items; ${s.validationData} is invalid.`);
      let A = true, P = await e.standardizeUserData(u, l, null, null, A, h);
      c = P[0], p = P[1], b = c.concat(p);
    } else if (s.validationSplit != null && s.validationSplit > 0 && s.validationSplit < 1) {
      g = true;
      let A = Math.floor(r[0].shape[0] * (1 - s.validationSplit)), P = r[0].shape[0];
      c = Au(r, A, P), i = r, r = Au(r, 0, A), p = Au(a, A, P), o = a, a = Au(a, 0, A), b = c.concat(p);
    } else
      s.validationSteps != null && (g = true);
    let y = r.concat(a).concat(d);
    e.checkTrainableWeightsConsistency();
    let v = e.makeTrainFunction(), x = e.getDedupedMetricsNames(), k, C;
    g ? (e.makeTestFunction(), k = e.testFunction, C = x.slice().concat(x.map((A) => "val_" + A))) : (k = null, b = [], C = x.slice());
    let T = jI(s.callbacks, s.yieldEvery);
    return await fB(e, v, y, x, h, s.epochs, s.verbose, T, k, b, s.shuffle, C, s.initialEpoch, null, null);
  } finally {
    e.isTraining = false, ls(r, t), ls(a, n), ls(i, t), ls(o, n), ls(c, u), ls(p, l), d != null && Re(d);
  }
}
function a0(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(Ll(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 ls(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 gB(e) {
  return e instanceof et;
}
function km(e) {
  return Array.isArray(e);
}
function Ox(e) {
  return !gB(e) && !km(e);
}
function Px(e, t, n, s = true, r = "") {
  if (t == null || t.length === 0) {
    if (e != null) {
      let i = false;
      if (km(e) && e.length > 0)
        i = true;
      else if (Ox(e)) {
        for (let o in e)
          if (e.hasOwnProperty(o)) {
            i = true;
            break;
          }
      } else
        i = true;
      if (i)
        throw new U(`Error when checking model ${r} expected no data, but got ${e}`);
    }
    return [];
  }
  if (e == null)
    return t.map((i) => null);
  let a;
  if (Ox(e)) {
    e = e, a = [];
    for (let i of t) {
      if (e[i] == null)
        throw new U(`No data provided for "${i}". Need data for each key in: ${t}`);
      a.push(e[i]);
    }
  } else if (km(e)) {
    if (e = e, e.length !== t.length)
      throw new U(`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 U(`The model ${r} expects ${t.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${e.shape}`);
    a = [e];
  }
  if (a = a0(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 U(`Error when checking ${r}: expected ${t[i]} to have ${n[i].length} dimension(s). but got array with shape ${o.shape}`);
      for (let u = 0; u < n[i].length; ++u) {
        if (u === 0 && !s)
          continue;
        let l = o.shape[u], c = n[i][u];
        if (c != null && c >= 0 && l !== c)
          throw new U(`${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 bB(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 U(`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 U(`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 U(`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 yB(e, t, n) {
  let s = [fi, Op, Yu];
  for (let r = 0; r < e.length; ++r) {
    let a = e[r], i = t[r], o = n[r];
    if (i != null) {
      if (i === Yu && a.shape[a.shape.length - 1] === 1)
        throw new U(`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 U(`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 zx(e, t, n, s = true, r = "") {
  let a;
  if (Array.isArray(e)) {
    if (e.length !== t.length)
      throw new U(`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 U(`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 U(`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 U(`Error when checking ${r}: expected ${t[i]} to have shape ${JSON.stringify(n[i])} but got array with shape ${JSON.stringify(o.shape)}.`);
      }
    }
}
function vB(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 xB = "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 U("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).");
    QL(this, e, t, n);
  }
  compile(e) {
    if (e.loss == null && (e.loss = []), this.loss = e.loss, typeof e.optimizer == "string")
      this.optimizer_ = YL(e.optimizer), this.isOptimizerOwned = true;
    else {
      if (!(e.optimizer instanceof $r))
        throw new U("User-defined optimizer must be an instance of tf.Optimizer.");
      this.optimizer_ = e.optimizer, this.isOptimizerOwned = false;
    }
    let t = [];
    if (!Array.isArray(e.loss) && typeof e.loss != "string" && typeof e.loss != "function") {
      e.loss = e.loss;
      for (let a in e.loss)
        if (this.outputNames.indexOf(a) === -1)
          throw new U(`Unknown entry in loss dictionary: "${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(Bf(e.loss[a]));
    } else if (Array.isArray(e.loss)) {
      if (e.loss.length !== this.outputs.length)
        throw new U(`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) => Bf(i));
    } else {
      let a = Bf(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 = [], Zr("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 = vB(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]);
    };
    Zr("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] === Op ? ["accuracy", "acc"].indexOf(h) !== -1 ? p = zb : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = QI) : this.lossFunctions[a] === Nd ? ["accuracy", "acc"].indexOf(h) !== -1 ? p = ZI : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = JI) : ["accuracy", "acc"].indexOf(h) !== -1 ? p = Mb : ["crossentropy", "ce"].indexOf(h) !== -1 && (p = Lb);
              let g;
              ["accuracy", "acc"].indexOf(h) !== -1 ? g = "acc" : ["crossentropy", "ce"].indexOf(h) !== -1 && (g = "ce"), d = p, c = l + g;
            } else
              d = XL(h), c = l + Hc(h);
            let f;
            Zr(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;
    xm(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 gn(u);
    } finally {
      ls(a[0], e), ls(a[1], t);
    }
  }
  async evaluateDataset(e, t) {
    return this.makeTestFunction(), hB(this, e, t);
  }
  checkNumSamples(e, t, n, s = "steps") {
    let r;
    if (n != null) {
      if (r = null, t != null)
        throw new U(`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 U(`Either the input data should have a defined shape, or ${s} shoud be specified.`);
    return r;
  }
  execute(e, t) {
    if (Array.isArray(t) && t.length === 0)
      throw new U("`outputs` is an empty Array, which is not allowed.");
    let n = Array.isArray(t), s = n ? t : [t], r = this.retrieveSymbolicTensors(s), a = new Xr();
    if (e instanceof et && (e = [e]), Array.isArray(e)) {
      if (e.length !== this.inputs.length)
        throw new U(`The number of inputs provided (${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 U(`No value is provided for the model's input ${o.name}`);
        a.add(o, u);
      }
    let i = _u(r, a);
    return n ? i : i[0];
  }
  retrieveSymbolicTensors(e) {
    let t = pa(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 U(`Cannot find SymbolicTensors for output name(s): ${JSON.stringify(s)}`);
    }
    return t;
  }
  predictLoop(e, t = 32, n = false) {
    return j(() => {
      let s = this.checkNumSamples(e);
      if (n)
        throw new Fe("Verbose predictLoop() is not implemented yet.");
      let r = wm(s, t), a = this.outputs.map((i) => []);
      for (let i = 0; i < r.length; ++i)
        j(() => {
          let u = r[i][0], l = r[i][1], c = Au(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 Xr(p);
          return _u(this.outputs, d);
        }).forEach((u, l) => a[l].push(u));
      return gn(a.map((i) => Ft(i, 0)));
    });
  }
  predict(e, t = {}) {
    let n = a0(e);
    zx(n, this.inputNames, this.feedInputShapes, false);
    try {
      let s = t.batchSize == null ? 32 : t.batchSize;
      return xm(s), this.predictLoop(n, s);
    } finally {
      ls(n, e);
    }
  }
  predictOnBatch(e) {
    zx(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 ps("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] === Nd ? r.push(i.slice(0, i.length - 1).concat([1])) : r.push(i);
    }
    if (e = Px(e, this.feedInputNames, this.feedInputShapes, false, "input"), t = Px(t, this.feedOutputNames, r, false, "target"), bB(e, t, null), yB(t, this.feedLossFns, this.feedOutputShapes), this.stateful && s != null && s > 0 && e[0].shape[0] % s !== 0)
      throw new U(`In a stateful network, you should only pass inputs with a number of samples that is divisible by the batch size ${s}. Found: ${e[0].shape[0]} sample(s).`);
    return [e, t];
  }
  async standardizeUserData(e, t, n, s, r = true, a) {
    let [i, o] = this.standardizeUserDataXY(e, t, r, a);
    if (n != null)
      throw new Error("sample weight is not supported yet.");
    let u = null;
    if (s != null) {
      let l = n0(s, this.outputNames);
      u = [];
      for (let c = 0; c < l.length; ++c)
        u.push(await s0(o[c], null, l[c]));
    }
    return [i, o, u];
  }
  testLoop(e, t, n, s = 0, r) {
    return j(() => {
      let a = this.checkNumSamples(t, n, r, "steps"), i = [];
      if (s > 0)
        throw new Fe("Verbose mode is not implemented yet.");
      if (r != null)
        throw new Fe("steps mode in testLoop() is not implemented yet");
      {
        let o = wm(a, n), u = Qt(ys(0, a));
        for (let l = 0; l < o.length; ++l) {
          let c = o[l][0], p = o[l][1], d = Jr(u, c, p - c), h = Bb(t, d), f = e(h);
          if (l === 0)
            for (let m = 0; m < f.length; ++m)
              i.push(Ie(0));
          for (let m = 0; m < f.length; ++m) {
            let g = f[m];
            i[m] = ie(i[m], V(p - c, g));
          }
        }
        for (let l = 0; l < i.length; ++l)
          i[l] = xe(i[l], a);
      }
      return i;
    });
  }
  getDedupedMetricsNames() {
    let e = this.metricsNames, t = [];
    for (let n = 0; n < e.length; ++n) {
      let s = e[n], r = s;
      wx(e, s) > 1 && (r += `_${wx(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 Xr(c), d = _u(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 = oB(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]));
          }
          Ht(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) => j(() => {
      let t = [], n, s = e.slice(0, this.inputs.length), r = e.slice(this.inputs.length, this.inputs.length + this.outputs.length), a = [];
      for (let u = 0; u < this.inputs.length; ++u)
        a.push({ key: this.inputs[u], value: s[u] });
      let i = new Xr(a), o = _u(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 mB(this, e, t, n);
  }
  async fitDataset(e, t) {
    return cB(this, e, t);
  }
  async trainOnBatch(e, t) {
    let n = await this.standardizeUserData(e, t), s = n[0], r = n[1], i = this.makeTrainFunction()(s.concat(r)), o = [];
    for (let u of i) {
      let l = await u.data();
      o.push(l[0]);
    }
    return Re(i), ls(n[0], e), ls(n[1], t), gn(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 = lm().numTensors;
      this.optimizer_.dispose(), e.numDisposedVariables += t - lm().numTensors;
    }
    return e;
  }
  getLossIdentifiers() {
    let e;
    if (typeof this.loss == "string")
      e = Ws(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) => Ws(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] = Ws(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 [Ws(Hc(this.metrics))];
    if (Array.isArray(this.metrics))
      return this.metrics.map((e) => Ws(Hc(e)));
    {
      let e = {};
      for (let t in this.metrics)
        e[t] = Ws(Hc(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 = Qu(e.optimizer_config), n = fs(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 = _n.getSaveHandlers(e);
      if (u.length === 0)
        throw new U(`Cannot find any save handlers for URL '${e}'`);
      if (u.length > 1)
        throw new U(`Found more than one (${u.length}) save handlers for URL '${e}'`);
      e = u[0];
    }
    if (e.save == null)
      throw new U("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");
    let n = await _n.encodeWeights(this.getNamedWeights(t)), s = false, r = null, i = { modelTopology: this.toJSON(r, s), format: xB, generatedBy: `TensorFlow.js tfjs-layers v${t0}`, convertedBy: null };
    if ((t == null ? false : t.includeOptimizer) && this.optimizer != null) {
      i.trainingConfig = this.getTrainingConfig();
      let u = "optimizer", { data: l, specs: c } = await _n.encodeWeights(await this.optimizer.getWeights(), u);
      n.specs.push(...c), n.data = _n.concatenateArrayBuffers([n.data, l]);
    }
    return this.userDefinedMetadata != null && (Ax(this.userDefinedMetadata, this.name, true), i.userDefinedMetadata = this.userDefinedMetadata), i.weightData = n.data, i.weightSpecs = n.specs, e.save(i);
  }
  setUserDefinedMetadata(e) {
    Ax(e, this.name), this.userDefinedMetadata = e;
  }
  getUserDefinedMetadata() {
    return this.userDefinedMetadata;
  }
};
pr.className = "Model";
ae.registerClass(pr);
var i0 = class extends pr {
};
i0.className = "Functional";
ae.registerClass(i0);
async function wB(e, t) {
  "modelTopology" in e || (e = { modelTopology: e }), e = e;
  let n = e.modelTopology;
  n.model_config != null && (n = n.model_config);
  let s = Qu(n), r = fs(s, t);
  if (e.weightsManifest != null) {
    let a = await _n.loadWeights(e.weightsManifest, e.pathPrefix, r.weights.map((o) => o.originalName)), i = {};
    for (let o of r.weights)
      i[o.originalName] = a[o.originalName];
    r.loadWeights(i), Re(a);
  }
  return r;
}
async function kB(e, t) {
  if (t == null && (t = {}), typeof e == "string") {
    let n = _n.getLoadHandlers(e, t);
    if (n.length === 0)
      n.push(_n.browserHTTPRequest(e, t));
    else if (n.length > 1)
      throw new U(`Found more than one (${n.length}) load handlers for URL '${e}'`);
    e = n[0];
  }
  return IB(e, void 0, t);
}
async function IB(e, t, n) {
  if (n == null && (n = {}), e.load == null)
    throw new U("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 = fs(Qu(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 U("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");
    let { modelWeights: l, optimizerWeights: c } = SB(s.weightData, s.weightSpecs);
    o.loadWeights(l, a), o.optimizer != null && c.length > 0 && await o.optimizer.setWeights(c), Re(l), Re(c.map((p) => p.tensor));
  }
  return o;
}
function SB(e, t) {
  let n = _n.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 Im = class extends pr {
  constructor(e) {
    super({ inputs: [], outputs: [] });
    if (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 U(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`);
  }
  add(e) {
    let t = e instanceof Im || e instanceof pr, n;
    if (t) {
      if (n = e, n.outputs.length !== 1)
        throw new U("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 U("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 U("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");
        let s = HI({ 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 U(`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 U("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 = GI(this.outputs[0]);
      }
      this.inboundNodes = [], new Dp({ outboundLayer: this, inboundLayers: [], nodeIndices: [], tensorIndices: [], inputTensors: this.inputs, outputTensors: this.outputs, inputMasks: pa(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 ps("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 ps("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 ps("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 ps("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 U("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 Im))
      throw new Fe(`Sequential.fromConfig called on non-Sequential input: ${i}`);
    for (let o of r) {
      let l = fs(o, void 0, s);
      s && l.setFastWeightInitDuringBuild(true), i.add(l);
    }
    return i;
  }
  set stopTraining(e) {
    if (this.model == null)
      throw new U("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 U("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 Vb = Im;
Vb.className = "Sequential";
ae.registerClass(Vb);
function cpe(e) {
  return new pr(e);
}
function dpe(e) {
  return new Vb(e);
}
function ppe(e, t) {
  return t == null && (t = {}), kB(e, t);
}
function CB(e) {
  return HI(e);
}
function hpe(e, t) {
  Ob.registerCallbackConstructor(e, t);
}
var wn = class extends ae.Serializable {
  getConfig() {
    return {};
  }
};
var o0 = class extends wn {
  apply(e, t = 1) {
    return XM(e, t);
  }
};
o0.className = "elu";
ae.registerClass(o0);
var u0 = class extends wn {
  apply(e) {
    return cI(e);
  }
};
u0.className = "selu";
ae.registerClass(u0);
var l0 = class extends wn {
  apply(e) {
    return Xs(e);
  }
};
l0.className = "relu";
ae.registerClass(l0);
var c0 = class extends wn {
  apply(e) {
    return j(() => gp(6, Xs(e)));
  }
};
c0.className = "relu6";
ae.registerClass(c0);
var d0 = class extends wn {
  apply(e) {
    return e;
  }
};
d0.className = "linear";
ae.registerClass(d0);
var p0 = class extends wn {
  apply(e) {
    return qs(e);
  }
};
p0.className = "sigmoid";
ae.registerClass(p0);
var h0 = class extends wn {
  apply(e) {
    return QM(e);
  }
};
h0.className = "hardSigmoid";
ae.registerClass(h0);
var f0 = class extends wn {
  apply(e) {
    return Ol(e);
  }
};
f0.className = "softplus";
ae.registerClass(f0);
var m0 = class extends wn {
  apply(e) {
    return YM(e);
  }
};
m0.className = "softsign";
ae.registerClass(m0);
var g0 = class extends wn {
  apply(e) {
    return Hu(e);
  }
};
g0.className = "tanh";
ae.registerClass(g0);
var Wb = class extends wn {
  apply(e, t = -1) {
    return ib(e, t);
  }
};
Wb.className = "softmax";
ae.registerClass(Wb);
var b0 = class extends wn {
  apply(e, t = -1) {
    return eI(e, t);
  }
};
b0.className = "logSoftmax";
ae.registerClass(b0);
var y0 = class extends wn {
  apply(e, t = 1) {
    return j(() => V(qs(V(e, t)), e));
  }
};
y0.className = "swish";
ae.registerClass(y0);
var v0 = class extends wn {
  apply(e) {
    return j(() => V(e, Hu(Ol(e))));
  }
};
v0.className = "mish";
ae.registerClass(v0);
function br(e) {
  return e.getClassName();
}
function Wf(e, t = {}) {
  return zl(e, ae.SerializationMap.getMap().classNameMap, t, "activation");
}
function yr(e) {
  if (e == null) {
    let t = {};
    return t.className = "linear", t.config = {}, Wf(t);
  }
  if (typeof e == "string") {
    let t = {};
    return t.className = e, t.config = {}, Wf(t);
  } else
    return e instanceof wn ? e : Wf(e);
}
function Ub(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 x0 = class extends ae.Serializable {
};
var Wl = class extends x0 {
  constructor(e) {
    super();
    Ub(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 j(() => {
      let t = $t([1]);
      return this.hasL1 && (t = ie(t, ye(V(this.l1, Mt(e))))), this.hasL2 && (t = ie(t, ye(V(this.l2, Bl(e))))), G(t, []);
    });
  }
  getConfig() {
    return { l1: this.l1, l2: this.l2 };
  }
  static fromConfig(e, t) {
    return new e({ l1: t.l1, l2: t.l2 });
  }
};
Wl.className = "L1L2";
ae.registerClass(Wl);
function NB(e) {
  return Ub(e), new Wl({ l1: e != null ? e.l1 : null, l2: 0 });
}
function TB(e) {
  return Ub(e), new Wl({ l2: e != null ? e.l2 : null, l1: 0 });
}
var Mx = { l1l2: "L1L2" };
function at(e) {
  return vb(e);
}
function Lx(e, t = {}) {
  return zl(e, ae.SerializationMap.getMap().classNameMap, t, "regularizer");
}
function ft(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 x0 ? e : Lx(e);
}
var Gb = class extends Ge {
  constructor(e) {
    super(e == null ? {} : e);
    this.supportsMasking = true, e != null && (this.maxValue = e.maxValue);
  }
  call(e, t) {
    e = Oe(e);
    let n = Xs(e);
    return this.maxValue != null && (n = Bn(n, 0, this.maxValue)), n;
  }
  computeOutputShape(e) {
    return e;
  }
  getConfig() {
    let e = { maxValue: this.maxValue }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
Gb.className = "ReLU";
ae.registerClass(Gb);
var Hb = class extends Ge {
  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 jg(n, this.alpha);
  }
  computeOutputShape(e) {
    return e;
  }
  getConfig() {
    let e = { alpha: this.alpha }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
Hb.className = "LeakyReLU";
ae.registerClass(Hb);
var qb = class extends Ge {
  constructor(e) {
    super(e == null ? {} : e);
    if (this.DEFAULT_ALPHA_INITIALIZER = "zeros", e == null && (e = {}), this.supportsMasking = true, this.alphaInitializer = ht(e.alphaInitializer || this.DEFAULT_ALPHA_INITIALIZER), this.alphaRegularizer = ft(e.alphaRegularizer), this.alphaConstraint = Pt(e.alphaConstraint), e.sharedAxes == null)
      this.sharedAxes = null;
    else if (Array.isArray(e.sharedAxes))
      this.sharedAxes = e.sharedAxes;
    else if (typeof e.sharedAxes == "number")
      this.sharedAxes = [e.sharedAxes];
    else
      throw new U(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`);
  }
  build(e) {
    e = nt(e);
    let t = e.slice(1);
    if (this.sharedAxes != null)
      for (let s of this.sharedAxes)
        t[s - 1] = 1;
    this.alpha = this.addWeight("alpha", t, "float32", this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);
    let n = {};
    if (this.sharedAxes != null)
      for (let s = 1; s < e.length; ++s)
        n[s] = e[s];
    this.inputSpec = [new Dt({ ndim: e.length, axes: n })], this.built = true;
  }
  call(e, t) {
    return e = Oe(e), tb(e, this.alpha.read());
  }
  getConfig() {
    let e = { alphaInitializer: yt(this.alphaInitializer), alphaRegularizer: at(this.alphaRegularizer), alphaConstraint: Ot(this.alphaConstraint), sharedAxes: this.sharedAxes }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
qb.className = "PReLU";
ae.registerClass(qb);
var jb = class extends Ge {
  constructor(e) {
    super(e == null ? {} : e);
    if (this.DEFAULT_ALPHA = 1, e == null && (e = {}), e.alpha != null && e.alpha !== this.DEFAULT_ALPHA)
      throw new Fe(`Non-default alpha value (${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 hp(n);
  }
  computeOutputShape(e) {
    return e;
  }
  getConfig() {
    let e = { alpha: this.alpha }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
jb.className = "ELU";
ae.registerClass(jb);
var Kb = class extends Ge {
  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, ce(Wn(n, this.theta), "float32"));
  }
  computeOutputShape(e) {
    return e;
  }
  getConfig() {
    let e = { theta: this.theta }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
Kb.className = "ThresholdedReLU";
ae.registerClass(Kb);
var Xb = class extends Ge {
  constructor(e) {
    super(e == null ? {} : e);
    this.DEFAULT_AXIS = 1, e == null && (e = {}), this.softmax = new Wb().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;
  }
};
Xb.className = "Softmax";
ae.registerClass(Xb);
function Ki(e, t, n) {
  if (typeof e == "number")
    return pa(e, t);
  if (e.length !== t)
    throw new U(`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 (!HM(r))
      throw new U(`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 ms(e, t, n, s, r = 1) {
  if (e == null)
    return e;
  let a = t + (t - 1) * (r - 1), i;
  return n === "same" ? i = e : i = e - a + 1, Math.floor((i + s - 1) / s);
}
function Ns(e, t, n, s) {
  if (e == null)
    return null;
  if (s === "valid")
    e = e * t + gr([n - t, 0]);
  else if (s === "same")
    e = e * t;
  else
    throw new U(`Unsupport padding mode: ${s}.`);
  return e;
}
function Yb(e, t) {
  return j(() => (St(t), t === "channelsFirst" ? qe(e, [0, 2, 3, 1]) : e));
}
function w0(e, t) {
  return j(() => (St(t), t === "channelsFirst" ? qe(e, [0, 2, 3, 4, 1]) : e));
}
function $B(e, t, n, s = 1, r = "valid", a, i = 1) {
  return j(() => {
    if (a == null && (a = bs()), St(a), e.shape.length !== 3)
      throw new U(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);
    if (t.shape.length !== 3)
      throw new U(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);
    if (n != null && n.shape.length !== 1)
      throw new U(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);
    if (a === "channelsFirst" && (e = qe(e, [0, 2, 1])), r === "causal")
      throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");
    let o = qk(e, t, s, r === "same" ? "same" : "valid", "NWC", i);
    return n != null && (o = ws(o, n)), o;
  });
}
function Bx(e, t, n, s = [1, 1], r = "valid", a, i, o = null) {
  return j(() => {
    if (a == null && (a = bs()), St(a), e.rank !== 3 && e.rank !== 4)
      throw new U(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);
    if (t.rank !== 3 && t.rank !== 4)
      throw new U(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);
    let u = Yb(e, a);
    if (r === "causal")
      throw new Fe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");
    return u = da.conv2d({ x: u, filter: t, strides: s, pad: r === "same" ? "same" : "valid", dilations: i, dataFormat: "NHWC", bias: n, activation: o }), a === "channelsFirst" && (u = qe(u, [0, 3, 1, 2])), u;
  });
}
function _B(e, t, n, s = [1, 1, 1], r = "valid", a, i) {
  return j(() => {
    if (a == null && (a = bs()), St(a), e.rank !== 4 && e.rank !== 5)
      throw new U(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);
    if (t.rank !== 4 && t.rank !== 5)
      throw new U(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);
    let o = w0(e, a);
    if (r === "causal")
      throw new Fe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");
    return o = Kk(o, t, s, r === "same" ? "same" : "valid", "NDHWC", i), n != null && (o = ws(o, n)), a === "channelsFirst" && (o = qe(o, [0, 4, 1, 2, 3])), o;
  });
}
var Qb = class extends Ge {
  constructor(e, t) {
    super(t);
    if (this.bias = null, this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_BIAS_INITIALIZER = "zeros", Qb.verifyArgs(t), this.rank = e, Bt(this.rank, "rank"), this.rank !== 1 && this.rank !== 2 && this.rank !== 3)
      throw new Fe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);
    if (this.kernelSize = Ki(t.kernelSize, e, "kernelSize"), this.strides = Ki(t.strides == null ? 1 : t.strides, e, "strides"), this.padding = t.padding == null ? "valid" : t.padding, Un(this.padding), this.dataFormat = t.dataFormat == null ? "channelsLast" : t.dataFormat, St(this.dataFormat), this.activation = yr(t.activation), this.useBias = t.useBias == null ? true : t.useBias, this.biasInitializer = ht(t.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.biasConstraint = Pt(t.biasConstraint), this.biasRegularizer = ft(t.biasRegularizer), this.activityRegularizer = ft(t.activityRegularizer), this.dilationRate = Ki(t.dilationRate == null ? 1 : t.dilationRate, e, "dilationRate"), this.rank === 1 && Array.isArray(this.dilationRate) && this.dilationRate.length !== 1)
      throw new U(`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 U(`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 U(`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" && !xb(e.kernelSize, "number", 1, 3))
      throw new U(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`);
  }
  getConfig() {
    let e = { kernelSize: this.kernelSize, strides: this.strides, padding: this.padding, dataFormat: this.dataFormat, dilationRate: this.dilationRate, activation: br(this.activation), useBias: this.useBias, biasInitializer: yt(this.biasInitializer), biasRegularizer: at(this.biasRegularizer), activityRegularizer: at(this.activityRegularizer), biasConstraint: Ot(this.biasConstraint) }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
var Ul = class extends Qb {
  constructor(e, t) {
    super(e, t);
    this.kernel = null, Ul.verifyArgs(t), this.filters = t.filters, Bt(this.filters, "filters"), this.kernelInitializer = ht(t.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.kernelConstraint = Pt(t.kernelConstraint), this.kernelRegularizer = ft(t.kernelRegularizer);
  }
  build(e) {
    e = nt(e);
    let t = this.dataFormat === "channelsFirst" ? 1 : e.length - 1;
    if (e[t] == null)
      throw new U(`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 j(() => {
      e = Oe(e);
      let n, s = this.bias == null ? null : this.bias.read(), r = PI(this.activation.getClassName());
      if (r != null && this.rank === 2)
        n = Bx(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate, r);
      else {
        if (this.rank === 1)
          n = $B(e, this.kernel.read(), s, this.strides[0], this.padding, this.dataFormat, this.dilationRate[0]);
        else if (this.rank === 2)
          n = Bx(e, this.kernel.read(), s, this.strides, this.padding, this.dataFormat, this.dilationRate);
        else if (this.rank === 3)
          n = _B(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 = ms(n[r], this.kernelSize[r], this.padding, this.strides[r], typeof this.dilationRate == "number" ? this.dilationRate : this.dilationRate[r]);
      t.push(a);
    }
    let s = [e[0]];
    return this.dataFormat === "channelsLast" ? (s = s.concat(t), s.push(this.filters)) : (s.push(this.filters), s = s.concat(t)), s;
  }
  getConfig() {
    let e = { filters: this.filters, kernelInitializer: yt(this.kernelInitializer), kernelRegularizer: at(this.kernelRegularizer), kernelConstraint: Ot(this.kernelConstraint) }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
  static verifyArgs(e) {
    if (!("filters" in e) || typeof e.filters != "number" || e.filters < 1)
      throw new U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`);
  }
};
var k0 = class extends Ul {
  constructor(e) {
    super(2, e);
    k0.verifyArgs(e);
  }
  getConfig() {
    let e = super.getConfig();
    return delete e.rank, e;
  }
  static verifyArgs(e) {
    if (typeof e.kernelSize != "number" && !xb(e.kernelSize, "number", 1, 2))
      throw new U(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`);
  }
};
var Pp = k0;
Pp.className = "Conv2D";
ae.registerClass(Pp);
var I0 = class extends Ul {
  constructor(e) {
    super(3, e);
    I0.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 U(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`);
  }
};
var zp = I0;
zp.className = "Conv3D";
ae.registerClass(zp);
var Zb = class extends Pp {
  constructor(e) {
    super(e);
    if (this.inputSpec = [new Dt({ ndim: 4 })], this.padding !== "same" && this.padding !== "valid")
      throw new U(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);
  }
  build(e) {
    if (e = nt(e), e.length !== 4)
      throw new U("Input should have rank 4; Received input shape: " + JSON.stringify(e));
    let t = this.dataFormat === "channelsFirst" ? 1 : e.length - 1;
    if (e[t] == null)
      throw new U("The channel dimension of the inputs should be defined. Found `None`.");
    let n = e[t], s = this.kernelSize.concat([this.filters, n]);
    this.kernel = this.addWeight("kernel", s, "float32", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint), this.useBias && (this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint)), this.inputSpec = [new Dt({ ndim: 4, axes: { [t]: n } })], this.built = true;
  }
  call(e, t) {
    return j(() => {
      let n = Oe(e);
      if (n.shape.length !== 4)
        throw new U(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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 = qe(n, [0, 2, 3, 1]));
      let g = jk(n, this.kernel.read(), m, this.strides, this.padding);
      return this.dataFormat !== "channelsLast" && (g = qe(g, [0, 3, 1, 2])), this.bias != null && (g = ws(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;
  }
};
Zb.className = "Conv2DTranspose";
ae.registerClass(Zb);
var Jb = class extends zp {
  constructor(e) {
    super(e);
    if (this.inputSpec = [new Dt({ ndim: 5 })], this.padding !== "same" && this.padding !== "valid")
      throw new U(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);
  }
  build(e) {
    if (e = nt(e), e.length !== 5)
      throw new U("Input should have rank 5; Received input shape: " + JSON.stringify(e));
    let t = this.dataFormat === "channelsFirst" ? 1 : e.length - 1;
    if (e[t] == null)
      throw new U("The channel dimension of the inputs should be defined. Found `None`.");
    let n = e[t], s = this.kernelSize.concat([this.filters, n]);
    this.kernel = this.addWeight("kernel", s, "float32", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint), this.useBias && (this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint)), this.inputSpec = [new Dt({ ndim: 5, axes: { [t]: n } })], this.built = true;
  }
  call(e, t) {
    return j(() => {
      let n = Oe(e);
      if (n.shape.length !== 5)
        throw new U(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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 = qe(n, [0, 2, 3, 4, 1]));
      let k = qE(n, this.kernel.read(), x, this.strides, this.padding);
      return this.dataFormat !== "channelsLast" && (k = qe(k, [0, 4, 1, 2, 3])), this.bias !== null && (k = ws(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;
  }
};
Jb.className = "Conv3DTranspose";
ae.registerClass(Jb);
var S0 = class extends Ul {
  constructor(e, t) {
    super(e, t);
    if (this.DEFAULT_DEPTHWISE_INITIALIZER = "glorotUniform", this.DEFAULT_POINTWISE_INITIALIZER = "glorotUniform", this.depthwiseKernel = null, this.pointwiseKernel = null, t.filters == null)
      throw new U("The `filters` configuration field is required by SeparableConv, but is unspecified.");
    if (t.kernelInitializer != null || t.kernelRegularizer != null || t.kernelConstraint != null)
      throw new U("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 U(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);
    this.depthMultiplier = t.depthMultiplier == null ? 1 : t.depthMultiplier, this.depthwiseInitializer = ht(t.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER), this.depthwiseRegularizer = ft(t.depthwiseRegularizer), this.depthwiseConstraint = Pt(t.depthwiseConstraint), this.pointwiseInitializer = ht(t.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER), this.pointwiseRegularizer = ft(t.pointwiseRegularizer), this.pointwiseConstraint = Pt(t.pointwiseConstraint);
  }
  build(e) {
    if (e = nt(e), e.length < this.rank + 2)
      throw new U(`Inputs to SeparableConv${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 U(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);
    let n = e[t], s = this.kernelSize.concat([n, this.depthMultiplier]), r = [];
    for (let i = 0; i < this.rank; ++i)
      r.push(1);
    r.push(n * this.depthMultiplier, this.filters);
    let a = true;
    this.depthwiseKernel = this.addWeight("depthwise_kernel", s, "float32", this.depthwiseInitializer, this.depthwiseRegularizer, a, this.depthwiseConstraint), this.pointwiseKernel = this.addWeight("pointwise_kernel", r, "float32", this.pointwiseInitializer, this.pointwiseRegularizer, a, this.pointwiseConstraint), this.useBias ? this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, a, this.biasConstraint) : this.bias = null, this.inputSpec = [new Dt({ ndim: this.rank + 2, axes: { [t]: n } })], this.built = true;
  }
  call(e, t) {
    return j(() => {
      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 = qe(e, [0, 2, 3, 1])), n = JD(e, this.depthwiseKernel.read(), this.pointwiseKernel.read(), this.strides, this.padding, this.dilationRate, "NHWC")), this.useBias && (n = ws(n, this.bias.read(), this.dataFormat)), this.activation != null && (n = this.activation.apply(n)), this.dataFormat === "channelsFirst" && (n = qe(n, [0, 3, 1, 2])), n;
    });
  }
  getConfig() {
    let e = super.getConfig();
    return delete e.rank, delete e.kernelInitializer, delete e.kernelRegularizer, delete e.kernelConstraint, e.depthwiseInitializer = yt(this.depthwiseInitializer), e.pointwiseInitializer = yt(this.pointwiseInitializer), e.depthwiseRegularizer = at(this.depthwiseRegularizer), e.pointwiseRegularizer = at(this.pointwiseRegularizer), e.depthwiseConstraint = Ot(this.depthwiseConstraint), e.pointwiseConstraint = Ot(this.pointwiseConstraint), e;
  }
};
S0.className = "SeparableConv";
var ey = class extends S0 {
  constructor(e) {
    super(2, e);
  }
};
ey.className = "SeparableConv2D";
ae.registerClass(ey);
var C0 = class extends Ul {
  constructor(e) {
    super(1, e);
    C0.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" && !xb(e.kernelSize, "number", 1, 1))
      throw new U(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`);
  }
};
var ty = C0;
ty.className = "Conv1D";
ae.registerClass(ty);
var ny = class extends Ge {
  constructor(e) {
    super(e);
    typeof e.cropping == "number" ? this.cropping = [[e.cropping, e.cropping], [e.cropping, e.cropping]] : typeof e.cropping[0] == "number" ? this.cropping = [[e.cropping[0], e.cropping[0]], [e.cropping[1], e.cropping[1]]] : this.cropping = e.cropping, this.dataFormat = e.dataFormat === void 0 ? "channelsLast" : e.dataFormat, this.inputSpec = [{ ndim: 4 }];
  }
  computeOutputShape(e) {
    return this.dataFormat === "channelsFirst" ? [e[0], e[1], e[2] - this.cropping[0][0] - this.cropping[0][1], e[3] - this.cropping[1][0] - this.cropping[1][1]] : [e[0], e[1] - this.cropping[0][0] - this.cropping[0][1], e[2] - this.cropping[1][0] - this.cropping[1][1], e[3]];
  }
  call(e, t) {
    return j(() => {
      if (e = Oe(e), this.dataFormat === "channelsLast") {
        let n = Uc(e, this.cropping[0][0], e.shape[1] - this.cropping[0][0] - this.cropping[0][1], 2);
        return Uc(n, this.cropping[1][0], e.shape[2] - this.cropping[1][1] - this.cropping[1][0], 3);
      } else {
        let n = Uc(e, this.cropping[0][0], e.shape[2] - this.cropping[0][0] - this.cropping[0][1], 3);
        return Uc(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;
  }
};
ny.className = "Cropping2D";
ae.registerClass(ny);
var sy = class extends Ge {
  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, St(this.dataFormat), this.interpolation = e.interpolation == null ? "nearest" : e.interpolation, WM(this.interpolation);
  }
  computeOutputShape(e) {
    if (this.dataFormat === "channelsFirst") {
      let t = e[2] == null ? null : this.size[0] * e[2], n = e[3] == null ? null : this.size[1] * e[3];
      return [e[0], e[1], t, n];
    } else {
      let t = e[1] == null ? null : this.size[0] * e[1], n = e[2] == null ? null : this.size[1] * e[2];
      return [e[0], t, n, e[3]];
    }
  }
  call(e, t) {
    return j(() => {
      let n = Oe(e), s = n.shape;
      if (this.dataFormat === "channelsFirst") {
        n = qe(n, [0, 2, 3, 1]);
        let r = this.size[0] * s[2], a = this.size[1] * s[3], i = this.interpolation === "nearest" ? ds.resizeNearestNeighbor(n, [r, a]) : ds.resizeBilinear(n, [r, a]);
        return qe(i, [0, 3, 1, 2]);
      } else {
        let r = this.size[0] * s[1], a = this.size[1] * s[2];
        return this.interpolation === "nearest" ? ds.resizeNearestNeighbor(n, [r, a]) : ds.resizeBilinear(n, [r, a]);
      }
    });
  }
  getConfig() {
    let e = { size: this.size, dataFormat: this.dataFormat }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
sy.className = "UpSampling2D";
ae.registerClass(sy);
function AB(e, t, n = [1, 1], s = "valid", r, a) {
  return j(() => {
    r == null && (r = bs()), St(r);
    let i = Yb(e, r);
    if (e.rank !== 4)
      throw new U(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);
    if (t.rank !== 4)
      throw new U(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);
    return i = pp(i, t, n, s === "same" ? "same" : "valid", "NHWC", a), r === "channelsFirst" && (i = qe(i, [0, 3, 1, 2])), i;
  });
}
var ry = class extends Qb {
  constructor(e) {
    super(2, e);
    this.depthwiseKernel = null, this.depthMultiplier = e.depthMultiplier == null ? 1 : e.depthMultiplier, this.depthwiseInitializer = ht(e.depthwiseInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.depthwiseConstraint = Pt(e.depthwiseConstraint), this.depthwiseRegularizer = ft(e.depthwiseRegularizer);
  }
  build(e) {
    if (e = nt(e), e.length < 4)
      throw new U(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);
    let t = this.dataFormat === "channelsFirst" ? 1 : 3;
    if (e[t] == null || e[t] < 0)
      throw new U(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${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 j(() => {
      e = Oe(e);
      let n = AB(e, this.depthwiseKernel.read(), this.strides, this.padding, this.dataFormat, null);
      return this.useBias && (n = ws(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 = ms(t, this.kernelSize[0], this.padding, this.strides[0]), a = ms(n, this.kernelSize[1], this.padding, this.strides[1]);
    return this.dataFormat === "channelsFirst" ? [e[0], s, r, a] : [e[0], r, a, s];
  }
  getConfig() {
    let e = super.getConfig();
    return e.depthMultiplier = this.depthMultiplier, e.depthwiseInitializer = yt(this.depthwiseInitializer), e.depthwiseRegularizer = at(this.depthwiseRegularizer), e.depthwiseConstraint = Ot(this.depthwiseRegularizer), e;
  }
};
ry.className = "DepthwiseConv2D";
ae.registerClass(ry);
function N0(e, t, n, s) {
  if (Array.isArray(e)) {
    if (t != null || n != null)
      throw new U("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 T0(e, t, n, s = false, r, a, i = false, o = false) {
  return j(() => {
    let u = t.shape.length;
    if (u < 3)
      throw new U(`Input should be at least 3D, but is ${u}D.`);
    let l = [1, 0].concat(ys(2, u));
    if (t = qe(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 = ce(ce(r, "bool"), "float32"), r.rank === u - 1 && (r = On(r, -1)), r = qe(r, l)), s && (t = Yn(t, 0), r != null && (r = Yn(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 = j(() => e(y, d));
      if (r == null)
        p = v[0], d = v[1];
      else {
        let x = j(() => {
          let k = m[b], C = ge(Xn(k), k), T = ie(V(v[0], k), V(d[0], C)), E = d.map((A, P) => ie(V(v[1][P], k), V(A, C)));
          return { output: T, newStates: E };
        });
        p = x.output, d = x.newStates;
      }
      o && c.push(p);
    }
    let g;
    return o && (g = Qn(c, 1)), [p, g, d];
  });
}
var $0 = class extends Ge {
  constructor(e) {
    super(e);
    let t;
    if (e.cell == null)
      throw new U("cell property is missing for the constructor of RNN.");
    if (Array.isArray(e.cell) ? t = new Bp({ cells: e.cell }) : t = e.cell, t.stateSize == null)
      throw new U("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");
    this.cell = t, this.returnSequences = e.returnSequences == null ? false : e.returnSequences, this.returnState = e.returnState == null ? false : e.returnState, this.goBackwards = e.goBackwards == null ? false : e.goBackwards, this._stateful = e.stateful == null ? false : e.stateful, this.unroll = e.unroll == null ? false : e.unroll, this.supportsMasking = true, this.inputSpec = [new Dt({ ndim: 3 })], this.stateSpec = null, this.states_ = null, this.numConstants = null, this.keptStates = [];
  }
  getStates() {
    if (this.states_ == null) {
      let e = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
      return ys(0, e).map((t) => null);
    } else
      return this.states_;
  }
  setStates(e) {
    this.states_ = e;
  }
  computeOutputShape(e) {
    gm(e) && (e = e[0]), e = e;
    let t = this.cell.stateSize;
    Array.isArray(t) || (t = [t]);
    let n = t[0], s;
    if (this.returnSequences ? s = [e[0], e[1], n] : s = [e[0], n], this.returnState) {
      let r = [];
      for (let a of t)
        r.push([e[0], a]);
      return [s].concat(r);
    } else
      return s;
  }
  computeMask(e, t) {
    return j(() => {
      Array.isArray(t) && (t = t[0]);
      let n = this.returnSequences ? t : null;
      if (this.returnState) {
        let s = this.states.map((r) => null);
        return [n].concat(s);
      } else
        return n;
    });
  }
  get states() {
    if (this.states_ == null) {
      let e = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1, t = [];
      for (let n = 0; n < e; ++n)
        t.push(null);
      return t;
    } else
      return this.states_;
  }
  set states(e) {
    this.states_ = e;
  }
  build(e) {
    if (this.numConstants != null)
      throw new Fe("Constants support is not implemented in RNN yet.");
    gm(e) && (e = e[0]), e = e;
    let n = this.stateful ? e[0] : null, s = e.slice(2);
    this.inputSpec[0] = new Dt({ shape: [n, null, ...s] });
    let r = [e[0]].concat(e.slice(2));
    this.cell.build(r);
    let a;
    if (Array.isArray(this.cell.stateSize) ? a = this.cell.stateSize : a = [this.cell.stateSize], this.stateSpec != null) {
      if (!w.arraysEqual(this.stateSpec.map((i) => i.shape[i.shape.length - 1]), a))
        throw new U(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`);
    } else
      this.stateSpec = a.map((i) => new Dt({ shape: [null, i] }));
    this.stateful && this.resetStates();
  }
  resetStates(e, t = false) {
    j(() => {
      if (!this.stateful)
        throw new Vs("Cannot call resetStates() on an RNN Layer that is not stateful.");
      let n = this.inputSpec[0].shape[0];
      if (n == null)
        throw new U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");
      if (this.states_ == null)
        Array.isArray(this.cell.stateSize) ? this.states_ = this.cell.stateSize.map((s) => $t([n, s])) : this.states_ = [$t([n, this.cell.stateSize])];
      else if (e == null)
        Re(this.states_), this.keptStates != null && (Re(this.keptStates), this.keptStates = []), Array.isArray(this.cell.stateSize) ? this.states_ = this.cell.stateSize.map((s) => $t([n, s])) : this.states_[0] = $t([n, this.cell.stateSize]);
      else {
        if (Array.isArray(e) || (e = [e]), e.length !== this.states_.length)
          throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);
        t === true ? this.keptStates.push(this.states_.slice()) : Re(this.states_);
        for (let s = 0; s < this.states_.length; ++s) {
          let r = e[s], a = Array.isArray(this.cell.stateSize) ? this.cell.stateSize[s] : this.cell.stateSize, i = [n, a];
          if (!w.arraysEqual(r.shape, i))
            throw new U(`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) => Ht(s.clone()));
    });
  }
  apply(e, t) {
    let n = t == null ? null : t.initialState, s = t == null ? null : t.constants;
    t == null && (t = {});
    let r = N0(e, n, s, this.numConstants);
    e = r.inputs, n = r.initialState, s = r.constants;
    let a = [], i = [];
    if (n != null) {
      t.initialState = n, a = a.concat(n), this.stateSpec = [];
      for (let u of n)
        this.stateSpec.push(new Dt({ shape: u.shape }));
      i = i.concat(this.stateSpec);
    }
    if (s != null && (t.constants = s, a = a.concat(s), this.numConstants = s.length), a[0] instanceof $s) {
      let u = [e].concat(a), l = this.inputSpec.concat(i), c = this.inputSpec;
      this.inputSpec = l;
      let p = super.apply(u, t);
      return this.inputSpec = c, p;
    } else
      return super.apply(e, t);
  }
  call(e, t) {
    return j(() => {
      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 U(`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 = T0((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 j(() => {
      let t = $t(e.shape);
      return t = ye(t, [1, 2]), t = Ll(t), Array.isArray(this.cell.stateSize) ? this.cell.stateSize.map((n) => n > 1 ? fm(t, [1, n]) : t) : this.cell.stateSize > 1 ? [fm(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() === $0.className && (t.cell = { className: this.cell.getClassName(), config: n }), { ...n, ...e, ...t };
  }
  static fromConfig(e, t, n = {}) {
    let s = t.cell, r = fs(s, n);
    return new e(Object.assign(t, { cell: r }));
  }
};
var _r = $0;
_r.className = "RNN";
ae.registerClass(_r);
var Gl = class extends Ge {
};
var Mp = class extends Gl {
  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, Bt(this.units, "units"), this.activation = yr(e.activation == null ? this.DEFAULT_ACTIVATION : e.activation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = Qi([1, gr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = Qi([1, gr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.stateSize = this.units, this.dropoutMask = null, this.recurrentDropoutMask = null;
  }
  build(e) {
    e = nt(e), this.kernel = this.addWeight("kernel", [e[e.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint), this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint), this.useBias ? this.bias = this.addWeight("bias", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint) : this.bias = null, this.built = true;
  }
  call(e, t) {
    return j(() => {
      if (e = e, e.length !== 2)
        throw new U(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);
      let n = e[1];
      e = e[0];
      let s = t.training == null ? false : t.training;
      0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = vr({ ones: () => Xn(e), rate: this.dropout, training: s, dropoutFunc: this.dropoutFunc })), 0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = vr({ ones: () => Xn(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 = ws(r, this.bias.read())), i != null && (n = V(n, i));
      let o = ie(r, Es(n, this.recurrentKernel.read()));
      return this.activation != null && (o = this.activation.apply(o)), [o, o];
    });
  }
  getConfig() {
    let e = super.getConfig(), t = { units: this.units, activation: br(this.activation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), recurrentInitializer: yt(this.recurrentInitializer), biasInitializer: yt(this.biasInitializer), kernelRegularizer: at(this.kernelRegularizer), recurrentRegularizer: at(this.recurrentRegularizer), biasRegularizer: at(this.biasRegularizer), activityRegularizer: at(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), recurrentConstraint: Ot(this.recurrentConstraint), biasConstraint: Ot(this.biasConstraint), dropout: this.dropout, recurrentDropout: this.recurrentDropout };
    return { ...e, ...t };
  }
};
Mp.className = "SimpleRNNCell";
ae.registerClass(Mp);
var ay = class extends _r {
  constructor(e) {
    e.cell = new Mp(e);
    super(e);
  }
  call(e, t) {
    return j(() => {
      this.cell.dropoutMask != null && (Re(this.cell.dropoutMask), this.cell.dropoutMask = null), this.cell.recurrentDropoutMask != null && (Re(this.cell.recurrentDropoutMask), this.cell.recurrentDropoutMask = null);
      let n = t == null ? null : t.mask, s = t == null ? null : t.training, r = t == null ? null : t.initialState;
      return super.call(e, { mask: n, training: s, initialState: r });
    });
  }
  static fromConfig(e, t) {
    return new e(t);
  }
};
ay.className = "SimpleRNN";
ae.registerClass(ay);
var Lp = class extends Gl {
  constructor(e) {
    super(e);
    if (this.DEFAULT_ACTIVATION = "tanh", this.DEFAULT_RECURRENT_ACTIVATION = "hardSigmoid", this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal", this.DEFAULT_RECURRENT_INITIALIZER = "orthogonal", this.DEFAULT_BIAS_INITIALIZER = "zeros", e.resetAfter)
      throw new U("GRUCell does not support reset_after parameter set to true.");
    this.units = e.units, Bt(this.units, "units"), this.activation = yr(e.activation === void 0 ? this.DEFAULT_ACTIVATION : e.activation), this.recurrentActivation = yr(e.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : e.recurrentActivation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = Qi([1, gr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = Qi([1, gr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.implementation = e.implementation, this.stateSize = this.units, this.dropoutMask = null, this.recurrentDropoutMask = null;
  }
  build(e) {
    e = nt(e);
    let t = e[e.length - 1];
    this.kernel = this.addWeight("kernel", [t, this.units * 3], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint), this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units * 3], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint), this.useBias ? this.bias = this.addWeight("bias", [this.units * 3], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint) : this.bias = null, this.built = true;
  }
  call(e, t) {
    return j(() => {
      if (e = e, e.length !== 2)
        throw new U(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);
      let n = t.training == null ? false : t.training, s = e[1];
      e = e[0], 0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = vr({ ones: () => Xn(e), rate: this.dropout, training: n, count: 3, dropoutFunc: this.dropoutFunc })), 0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = vr({ ones: () => Xn(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 = ws(l, this.bias.read())), 0 < this.recurrentDropout && this.recurrentDropout < 1 && (s = V(s, a[0]));
      let c = this.recurrentKernel.read(), [p, d] = Ln(c, [2 * this.units, this.units], c.rank - 1), h = Es(s, p), [f, m, g] = Ln(l, 3, l.rank - 1), [b, y] = Ln(h, 2, h.rank - 1);
      i = this.recurrentActivation.apply(ie(f, b)), o = this.recurrentActivation.apply(ie(m, y));
      let v = Es(V(o, s), d);
      u = this.activation.apply(ie(g, v));
      let x = ie(V(i, s), V(ie(1, kt(i)), u));
      return [x, x];
    });
  }
  getConfig() {
    let e = super.getConfig(), t = { units: this.units, activation: br(this.activation), recurrentActivation: br(this.recurrentActivation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), recurrentInitializer: yt(this.recurrentInitializer), biasInitializer: yt(this.biasInitializer), kernelRegularizer: at(this.kernelRegularizer), recurrentRegularizer: at(this.recurrentRegularizer), biasRegularizer: at(this.biasRegularizer), activityRegularizer: at(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), recurrentConstraint: Ot(this.recurrentConstraint), biasConstraint: Ot(this.biasConstraint), dropout: this.dropout, recurrentDropout: this.recurrentDropout, implementation: this.implementation, resetAfter: false };
    return { ...e, ...t };
  }
};
Lp.className = "GRUCell";
ae.registerClass(Lp);
var iy = class extends _r {
  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 Lp(e);
    super(e);
  }
  call(e, t) {
    return j(() => {
      this.cell.dropoutMask != null && (Re(this.cell.dropoutMask), this.cell.dropoutMask = null), this.cell.recurrentDropoutMask != null && (Re(this.cell.recurrentDropoutMask), this.cell.recurrentDropoutMask = null);
      let n = t == null ? null : t.mask, s = t == null ? null : t.training, r = t == null ? null : t.initialState;
      return super.call(e, { mask: n, training: s, initialState: r });
    });
  }
  static fromConfig(e, t) {
    return t.implmentation === 0 && (t.implementation = 1), new e(t);
  }
};
iy.className = "GRU";
ae.registerClass(iy);
var Hl = class extends Gl {
  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, Bt(this.units, "units"), this.activation = yr(e.activation === void 0 ? this.DEFAULT_ACTIVATION : e.activation), this.recurrentActivation = yr(e.recurrentActivation === void 0 ? this.DEFAULT_RECURRENT_ACTIVATION : e.recurrentActivation), this.useBias = e.useBias == null ? true : e.useBias, this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.recurrentInitializer = ht(e.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.unitForgetBias = e.unitForgetBias, this.kernelRegularizer = ft(e.kernelRegularizer), this.recurrentRegularizer = ft(e.recurrentRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.kernelConstraint = Pt(e.kernelConstraint), this.recurrentConstraint = Pt(e.recurrentConstraint), this.biasConstraint = Pt(e.biasConstraint), this.dropout = Qi([1, gr([0, e.dropout == null ? 0 : e.dropout])]), this.recurrentDropout = Qi([1, gr([0, e.recurrentDropout == null ? 0 : e.recurrentDropout])]), this.dropoutFunc = e.dropoutFunc, this.implementation = e.implementation, this.stateSize = [this.units, this.units], this.dropoutMask = null, this.recurrentDropoutMask = null;
  }
  build(e) {
    var t;
    e = nt(e);
    let n = e[e.length - 1];
    this.kernel = this.addWeight("kernel", [n, this.units * 4], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint), this.recurrentKernel = this.addWeight("recurrent_kernel", [this.units, this.units * 4], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);
    let s;
    if (this.useBias) {
      if (this.unitForgetBias) {
        let r = this.biasInitializer, a = this.units;
        s = new (t = class extends es {
          apply(i, o) {
            let u = r.apply([a]), l = new Cp().apply([a]), c = r.apply([a * 2]);
            return Cx(Cx(u, l), c);
          }
        }, t.className = "CustomInit", t)();
      } else
        s = this.biasInitializer;
      this.bias = this.addWeight("bias", [this.units * 4], null, s, this.biasRegularizer, true, this.biasConstraint);
    } else
      this.bias = null;
    this.built = true;
  }
  call(e, t) {
    return j(() => {
      let n = t.training == null ? false : t.training;
      if (e = e, e.length !== 3)
        throw new U(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);
      let s = e[1], r = e[2];
      e = e[0], 0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = vr({ ones: () => Xn(e), rate: this.dropout, training: n, count: 4, dropoutFunc: this.dropoutFunc })), 0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = vr({ ones: () => Xn(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 = ws(p, this.bias.read()));
      let [d, h, f, m] = Ln(p, 4, p.rank - 1);
      o = this.recurrentActivation.apply(d), u = this.recurrentActivation.apply(h), l = ie(V(u, r), V(o, this.activation.apply(f))), c = this.recurrentActivation.apply(m);
      let g = V(c, this.activation.apply(l));
      return [g, g, l];
    });
  }
  getConfig() {
    let e = super.getConfig(), t = { units: this.units, activation: br(this.activation), recurrentActivation: br(this.recurrentActivation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), recurrentInitializer: yt(this.recurrentInitializer), biasInitializer: yt(this.biasInitializer), unitForgetBias: this.unitForgetBias, kernelRegularizer: at(this.kernelRegularizer), recurrentRegularizer: at(this.recurrentRegularizer), biasRegularizer: at(this.biasRegularizer), activityRegularizer: at(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), recurrentConstraint: Ot(this.recurrentConstraint), biasConstraint: Ot(this.biasConstraint), dropout: this.dropout, recurrentDropout: this.recurrentDropout, implementation: this.implementation };
    return { ...e, ...t };
  }
};
Hl.className = "LSTMCell";
ae.registerClass(Hl);
var oy = class extends _r {
  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 Hl(e);
    super(e);
  }
  call(e, t) {
    return j(() => {
      this.cell.dropoutMask != null && (Re(this.cell.dropoutMask), this.cell.dropoutMask = null), this.cell.recurrentDropoutMask != null && (Re(this.cell.recurrentDropoutMask), this.cell.recurrentDropoutMask = null);
      let n = t == null ? null : t.mask, s = t == null ? null : t.training, r = t == null ? null : t.initialState;
      return super.call(e, { mask: n, training: s, initialState: r });
    });
  }
  static fromConfig(e, t) {
    return t.implmentation === 0 && (t.implementation = 1), new e(t);
  }
};
oy.className = "LSTM";
ae.registerClass(oy);
var Bp = class extends Gl {
  constructor(e) {
    super(e);
    this.cells = e.cells;
  }
  get stateSize() {
    let e = [];
    for (let t of this.cells.slice().reverse())
      Array.isArray(t.stateSize) ? e.push(...t.stateSize) : e.push(t.stateSize);
    return e;
  }
  call(e, t) {
    return j(() => {
      e = e;
      let n = e.slice(1), s = [];
      for (let 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) {
    gm(e) && (e = e[0]), e = e;
    let t;
    this.cells.forEach((n, s) => {
      Zr(`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(fs(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 bm(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]]);
    }
    Fb(t);
  }
};
Bp.className = "StackedRNNCells";
ae.registerClass(Bp);
function vr(e) {
  let { ones: t, rate: n, training: s = false, count: r = 1, dropoutFunc: a } = e, i = () => a != null ? a(t(), n) : WI(t(), n), o = () => Vl(i, t, s);
  return !r || r <= 1 ? Ht(o().clone()) : Array(r).fill(void 0).map(o).map((l) => Ht(l.clone()));
}
var _0 = class extends _r {
  constructor(e) {
    if (e.unroll)
      throw new Fe("Unrolling is not possible with convolutional RNNs.");
    if (Array.isArray(e.cell))
      throw new Fe("It is not possible at the moment to stack convolutional cells.");
    super(e);
    this.inputSpec = [new Dt({ ndim: 5 })];
  }
  call(e, t) {
    return j(() => {
      if (this.cell.dropoutMask != null && (Re(this.cell.dropoutMask), this.cell.dropoutMask = null), this.cell.recurrentDropoutMask != null && (Re(this.cell.recurrentDropoutMask), this.cell.recurrentDropoutMask = null), t && t.constants)
        throw new U("ConvRNN2D cell does not support constants");
      let n = t == null ? null : t.mask, s = t == null ? null : t.training, r = t == null ? null : t.initialState;
      return super.call(e, { mask: n, training: s, initialState: r });
    });
  }
  computeOutputShape(e) {
    let t = this.computeSingleOutputShape(e);
    return this.returnSequences || (t = [t[0], ...t.slice(2)]), this.returnState && (t = [t, ...Array(2).fill([e[0], ...t.slice(-3)])]), t;
  }
  getInitialState(e) {
    return j(() => {
      let { stateSize: t } = this.cell, n = e.shape, s = this.computeSingleOutputShape(n), r = [s[0], ...s.slice(2)], a = $t(r);
      return Array.isArray(t) ? Array(t.length).fill(a) : [a];
    });
  }
  resetStates(e, t = false) {
    j(() => {
      if (!this.stateful)
        throw new Vs("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 U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");
      if (this.getStates() == null)
        Array.isArray(this.cell.stateSize) ? this.states_ = this.cell.stateSize.map(() => $t(r)) : this.states_ = [$t(r)];
      else if (e == null)
        Re(this.states_), this.keptStates != null && (Re(this.keptStates), this.keptStates = []), Array.isArray(this.cell.stateSize) ? this.states_ = this.cell.stateSize.map(() => $t(r)) : this.states_[0] = $t(r);
      else {
        if (Array.isArray(e) || (e = [e]), e.length !== this.states_.length)
          throw new U(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);
        t ? this.keptStates.push(this.states_.slice()) : Re(this.states_);
        for (let i = 0; i < this.states_.length; ++i) {
          let o = e[i], u = r;
          if (!w.arraysEqual(o.shape, u))
            throw new U(`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) => Ht(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 = ms(u, s[0], r, a[0], i[0]), p = ms(l, s[1], r, a[1], i[1]);
    return [...e.slice(0, 2), ...o ? [n, c, p] : [c, p, n]];
  }
};
_0.className = "ConvRNN2D";
var Vp = class extends Hl {
  constructor(e) {
    let { filters: t, kernelSize: n, strides: s, padding: r, dataFormat: a, dilationRate: i } = e;
    super({ ...e, units: t });
    this.filters = t, Bt(this.filters, "filters"), this.kernelSize = Ki(n, 2, "kernelSize"), this.kernelSize.forEach((o) => Bt(o, "kernelSize")), this.strides = Ki(s || 1, 2, "strides"), this.strides.forEach((o) => Bt(o, "strides")), this.padding = r || "valid", Un(this.padding), this.dataFormat = a || "channelsLast", St(this.dataFormat), this.dilationRate = Ki(i || 1, 2, "dilationRate"), this.dilationRate.forEach((o) => Bt(o, "dilationRate"));
  }
  build(e) {
    var t;
    e = nt(e);
    let n = this.dataFormat === "channelsFirst" ? 1 : e.length - 1;
    if (e[n] == null)
      throw new U(`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 es {
          apply(c, p) {
            let d = u.apply([l]), h = zn([l]), f = u.apply([l * 2]);
            return Nb([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 j(() => {
      if (e.length !== 3)
        throw new U(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);
      let n = t.training || false, s = e[0], r = e[1], a = e[2], i = 4;
      0 < this.dropout && this.dropout < 1 && this.dropoutMask == null && (this.dropoutMask = vr({ ones: () => Xn(s), rate: this.dropout, training: n, count: i, dropoutFunc: this.dropoutFunc }));
      let o = this.dropoutMask, u = (Z, te, ee) => !te || !te[ee] ? Z : V(te[ee], Z), l = u(s, o, 0), c = u(s, o, 1), p = u(s, o, 2), d = u(s, o, 3);
      0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null && (this.recurrentDropoutMask = vr({ ones: () => Xn(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, C] = Ln(this.kernel.read(), i, y), [T, E, A, P] = this.useBias ? Ln(this.bias.read(), i) : [null, null, null, null];
      l = this.inputConv(l, v, T, this.padding), c = this.inputConv(c, x, E, this.padding), p = this.inputConv(p, k, A, this.padding), d = this.inputConv(d, C, P, this.padding);
      let [R, F, $, z] = Ln(this.recurrentKernel.read(), i, y);
      f = this.recurrentConv(f, R), m = this.recurrentConv(m, F), g = this.recurrentConv(g, $), b = this.recurrentConv(b, z);
      let W = this.recurrentActivation.apply(ie(l, f)), q = this.recurrentActivation.apply(ie(c, m)), K = ie(V(q, a), V(W, this.activation.apply(ie(p, g)))), Y = V(this.recurrentActivation.apply(ie(d, b)), this.activation.apply(K));
      return [Y, Y, K];
    });
  }
  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 = ua(e, t, this.strides, s || "valid", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC", this.dilationRate);
    return n ? ws(r, n, this.dataFormat) : r;
  }
  recurrentConv(e, t) {
    return ua(e, t, 1, "same", this.dataFormat === "channelsFirst" ? "NCHW" : "NHWC");
  }
};
Vp.className = "ConvLSTM2DCell";
ae.registerClass(Vp);
var uy = class extends _0 {
  constructor(e) {
    let t = new Vp(e);
    super({ ...e, cell: t });
  }
  static fromConfig(e, t) {
    return new e(t);
  }
};
uy.className = "ConvLSTM2D";
ae.registerClass(uy);
var Wp = class extends Ge {
  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 j(() => {
      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 Vl(() => WI(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();
  }
};
Wp.className = "Dropout";
ae.registerClass(Wp);
var ly = class extends Wp {
  constructor(e) {
    super(e);
    this.inputSpec = [{ ndim: 3 }];
  }
  getNoiseShape(e) {
    let t = e.shape;
    return [t[0], 1, t[2]];
  }
};
ly.className = "SpatialDropout1D";
ae.registerClass(ly);
var cy = class extends Ge {
  constructor(e) {
    super(e);
    if (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, Bt(this.units, "units"), this.activation = yr(e.activation), e.useBias != null && (this.useBias = e.useBias), this.kernelInitializer = ht(e.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER), this.biasInitializer = ht(e.biasInitializer || this.DEFAULT_BIAS_INITIALIZER), this.kernelConstraint = Pt(e.kernelConstraint), this.biasConstraint = Pt(e.biasConstraint), this.kernelRegularizer = ft(e.kernelRegularizer), this.biasRegularizer = ft(e.biasRegularizer), this.activityRegularizer = ft(e.activityRegularizer), this.supportsMasking = true, this.inputSpec = [{ minNDim: 2 }];
  }
  build(e) {
    e = nt(e);
    let t = e[e.length - 1];
    this.kernel == null && (this.kernel = this.addWeight("kernel", [t, this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint), this.useBias && (this.bias = this.addWeight("bias", [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint))), this.inputSpec = [{ minNDim: 2, axes: { [-1]: t } }], this.built = true;
  }
  computeOutputShape(e) {
    e = nt(e);
    let t = e.slice();
    return t[t.length - 1] = this.units, t;
  }
  call(e, t) {
    return j(() => {
      this.invokeCallHook(e, t);
      let n = Oe(e), s = PI(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 = ws(r, this.bias.read())), this.activation != null && (r = this.activation.apply(r))), r;
    });
  }
  getConfig() {
    let e = { units: this.units, activation: br(this.activation), useBias: this.useBias, kernelInitializer: yt(this.kernelInitializer), biasInitializer: yt(this.biasInitializer), kernelRegularizer: at(this.kernelRegularizer), biasRegularizer: at(this.biasRegularizer), activityRegularizer: at(this.activityRegularizer), kernelConstraint: Ot(this.kernelConstraint), biasConstraint: Ot(this.biasConstraint) }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
cy.className = "Dense";
ae.registerClass(cy);
var dy = class extends Ge {
  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 U(`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 j(() => {
      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 = qe(n, s);
      }
      return KM(n);
    });
  }
  getConfig() {
    let e = {};
    this.dataFormat != null && (e.dataFormat = this.dataFormat);
    let t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
dy.className = "Flatten";
ae.registerClass(dy);
var py = class extends Ge {
  constructor(e) {
    super(e);
    this.supportsMasking = true, this.activation = yr(e.activation);
  }
  call(e, t) {
    return j(() => {
      this.invokeCallHook(e, t);
      let n = Oe(e);
      return this.activation.apply(n);
    });
  }
  getConfig() {
    let e = { activation: br(this.activation) }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
py.className = "Activation";
ae.registerClass(py);
var hy = class extends Ge {
  constructor(e) {
    super(e);
    this.n = e.n, this.inputSpec = [{ ndim: 2 }];
  }
  computeOutputShape(e) {
    return [e[0], this.n, e[1]];
  }
  call(e, t) {
    return j(() => (e = Oe(e), qM(e, this.n)));
  }
  getConfig() {
    let e = { n: this.n }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
hy.className = "RepeatVector";
ae.registerClass(hy);
var fy = class extends Ge {
  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 U("Can only specifiy one unknown dimension.");
      else
        r *= u;
    }
    let i = dr(e);
    if (a !== null) {
      if (r === 0 || i % r !== 0)
        throw new U(n);
      s[a] = i / r;
    } else if (i !== r)
      throw new U(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 j(() => {
      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 G(n, r);
    });
  }
  getConfig() {
    let e = { targetShape: this.targetShape }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
fy.className = "Reshape";
ae.registerClass(fy);
var my = class extends Ge {
  constructor(e) {
    super(e);
    if (e.dims == null)
      throw new Error("Required configuration field `dims` is missing during Permute constructor call.");
    if (!Array.isArray(e.dims))
      throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);
    let t = ys(1, e.dims.length + 1);
    if (!w.arraysEqual(e.dims.slice().sort(), t))
      throw new Error("Invalid permutation `dims`: " + JSON.stringify(e.dims) + " `dims` must contain consecutive integers starting from 1.");
    this.dims = e.dims, this.dimsIncludingBatch = [0].concat(this.dims), this.inputSpec = [new Dt({ ndim: this.dims.length + 1 })];
  }
  computeOutputShape(e) {
    e = nt(e);
    let t = e.slice();
    return this.dims.forEach((n, s) => {
      t[s + 1] = e[n];
    }), t;
  }
  call(e, t) {
    return qe(Oe(e), this.dimsIncludingBatch);
  }
  getConfig() {
    let e = { dims: this.dims }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
my.className = "Permute";
ae.registerClass(my);
var gy = class extends Ge {
  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 cm(Ku(n, this.maskValue), s);
  }
  call(e, t) {
    return j(() => {
      this.invokeCallHook(e, t);
      let n = Oe(e), s = -1, r = true, a = cm(Ku(n, this.maskValue), s, r);
      return V(n, ce(a, n.dtype));
    });
  }
};
gy.className = "Masking";
ae.registerClass(gy);
var by = class extends Ge {
  constructor(e) {
    super(e);
    if (this.embeddings = null, this.DEFAULT_EMBEDDINGS_INITIALIZER = "randomUniform", e.batchInputShape == null && e.inputShape == null) {
      let t = null;
      e.batchSize != null && (t = e.batchSize), e.inputLength == null ? this.batchInputShape = [t, null] : this.batchInputShape = [t].concat(dt(e.inputLength));
    }
    this.inputDim = e.inputDim, Bt(this.inputDim, "inputDim"), this.outputDim = e.outputDim, Bt(this.outputDim, "outputDim"), this.embeddingsInitializer = ht(e.embeddingsInitializer || this.DEFAULT_EMBEDDINGS_INITIALIZER), this.embeddingsRegularizer = ft(e.embeddingsRegularizer), this.activityRegularizer = ft(e.activityRegularizer), this.embeddingsConstraint = Pt(e.embeddingsConstraint), this.maskZero = e.maskZero, this.supportsMasking = e.maskZero, this.inputLength = e.inputLength;
  }
  build(e) {
    this.embeddings = this.addWeight("embeddings", [this.inputDim, this.outputDim], this.dtype, this.embeddingsInitializer, this.embeddingsRegularizer, true, this.embeddingsConstraint), this.built = true;
  }
  warnOnIncompatibleInputShape(e) {
  }
  computeMask(e, t) {
    return j(() => this.maskZero ? (e = Oe(e), Ku(e, je(e))) : null);
  }
  computeOutputShape(e) {
    if (e = nt(e), this.inputLength == null)
      return [...e, this.outputDim];
    let t = dt(this.inputLength);
    if (t.length !== e.length - 1)
      throw new U(`"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 U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);
        r == null && (t[n] = a), n++;
      }
    }
    return [e[0], ...t, this.outputDim];
  }
  call(e, t) {
    return j(() => {
      this.invokeCallHook(e, t);
      let n = Oe(e);
      n.dtype !== "int32" && (n = Ip(n, "int32"));
      let s = VI(this.embeddings.read(), G(n, [n.size]));
      return G(s, nt(this.computeOutputShape(n.shape)));
    });
  }
  getConfig() {
    let e = { inputDim: this.inputDim, outputDim: this.outputDim, embeddingsInitializer: yt(this.embeddingsInitializer), embeddingsRegularizer: at(this.embeddingsRegularizer), activityRegularizer: at(this.activityRegularizer), embeddingsConstraint: Ot(this.embeddingsConstraint), maskZero: this.maskZero, inputLength: this.inputLength }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
by.className = "Embedding";
ae.registerClass(by);
var mi = class extends Ge {
  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 U("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 U(`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 U(`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 j(() => {
      if (e = e, this.reshapeRequired) {
        let n = [], s = e.map((r) => r.rank);
        if (s.indexOf(null) === -1) {
          let r = gr(s);
          for (let a of e) {
            let i = a.rank;
            for (let o = 0; o < r - i; ++o)
              a = Ll(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 = G(o, [c].concat(dr(l.slice(1))));
              d = qe(d, [1, 0]), d = G(d, p), n.push(d), r = true;
            } else if (u > 1) {
              let l = ys(1, u).concat([0]);
              n.push(qe(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 = G(qe(G(a, [-1, l]), [1, 0]), c);
            } else if (i > 1) {
              let o = [i - 1].concat(ys(0, i - 1));
              a = qe(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 j(() => {
      if (t == null)
        return null;
      if (!Array.isArray(t))
        throw new U("`mask` should be an Array");
      if (!Array.isArray(e))
        throw new U("`inputs` should be an Array");
      if (t.length !== e.length)
        throw new U(`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 : On(s, 0));
      let n = t[0];
      for (let s = 1; s < t.length - 1; ++s)
        n = Ds(n, t[s]);
      return n;
    });
  }
};
var yy = class extends mi {
  constructor(e) {
    super(e);
  }
  mergeFunction(e) {
    return j(() => {
      let t = e[0].clone();
      for (let n = 1; n < e.length; ++n)
        t = ie(t, e[n]);
      return t;
    });
  }
};
yy.className = "Add";
ae.registerClass(yy);
var vy = class extends mi {
  constructor(e) {
    super(e);
  }
  mergeFunction(e) {
    return j(() => {
      let t = e[0].clone();
      for (let n = 1; n < e.length; ++n)
        t = V(t, e[n]);
      return t;
    });
  }
};
vy.className = "Multiply";
ae.registerClass(vy);
var xy = class extends mi {
  constructor(e) {
    super(e);
  }
  mergeFunction(e) {
    return j(() => {
      let t = e[0].clone();
      for (let n = 1; n < e.length; ++n)
        t = ie(t, e[n]);
      return V(1 / e.length, t);
    });
  }
};
xy.className = "Average";
ae.registerClass(xy);
var wy = class extends mi {
  constructor(e) {
    super(e);
  }
  mergeFunction(e) {
    return j(() => {
      let t = e[0];
      for (let n = 1; n < e.length; ++n)
        t = Tr(t, e[n]);
      return t;
    });
  }
};
wy.className = "Maximum";
ae.registerClass(wy);
var ky = class extends mi {
  constructor(e) {
    super(e);
  }
  mergeFunction(e) {
    return j(() => {
      let t = e[0];
      for (let n = 1; n < e.length; ++n)
        t = gp(t, e[n]);
      return t;
    });
  }
};
ky.className = "Minimum";
ae.registerClass(ky);
var Iy = class extends mi {
  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 U("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 U("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: " + JSON.stringify(e));
  }
  mergeFunction(e) {
    return j(() => Nb(e, this.axis));
  }
  computeOutputShape(e) {
    if (!(Array.isArray(e) && Array.isArray(e[0])))
      throw new U("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 U("`mask` should be an array for Concatenate");
    if (!Array.isArray(e))
      throw new U("`inputs` should be an array for Concatenate");
    if (t.length !== e.length)
      throw new U(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);
    return j(() => {
      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(ce(Xn(e[a]), "bool")) : t[a].rank < e[a].rank ? s.push(On(t[a], -1)) : s.push(t[a]);
      let r = Ft(s, this.axis);
      return Bk(r, -1, false);
    });
  }
  getConfig() {
    let e = { axis: this.axis }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
Iy.className = "Concatenate";
ae.registerClass(Iy);
function Su(e, t) {
  for (; e < 0; )
    e += t;
  return e;
}
function EB(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 j(() => {
    let i;
    if (s > r) {
      i = s - r;
      let u = [];
      for (let l = 0; l < i; ++l)
        u.push(1);
      t = G(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 = G(e, e.shape.concat(u));
    } else
      i = 0;
    let o;
    if (e.shape.length === 2 && t.shape.length === 2)
      a[0] === a[1] ? o = ye(V(e, t), a[0]) : o = ye(V(qe(e, [1, 0]), t), a[1]);
    else {
      let u = a[0] !== e.shape.length - 1, l = a[1] === t.shape.length - 1;
      o = We(e, t, u, l);
    }
    if (i > 0) {
      let u;
      s > r ? u = s + r - 3 : u = s - 1;
      let l = [];
      for (let c = u; c < u + i; ++c)
        l.push(c);
      o = mr(o, l);
    }
    return o.shape.length === 1 && (o = On(o, 1)), o;
  });
}
var Sy = class extends mi {
  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 U(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`);
  }
  mergeFunction(e) {
    if (e.length !== 2)
      throw new U(`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) => Su(r, e[a].shape.length)) : s = [Su(this.axes, t.shape.length), Su(this.axes, n.shape.length)], this.normalize && (t = Cd(t, s[0]), n = Cd(n, s[1])), EB(t, n, s);
  }
  interpretAxes(e, t) {
    let n;
    return Array.isArray(this.axes) ? n = this.axes : n = [Su(this.axes, e.length), Su(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;
  }
};
Sy.className = "Dot";
ae.registerClass(Sy);
var Cy = class extends Ge {
  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 j(() => {
      this.invokeCallHook(e, t);
      let n = Oe(e);
      return Vl(() => ie(Sp(n.shape, 0, this.stddev), n), () => n, t.training || false);
    });
  }
};
Cy.className = "GaussianNoise";
ae.registerClass(Cy);
var Ny = class extends Ge {
  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 j(() => {
      this.invokeCallHook(e, t);
      let n = Oe(e);
      return this.rate > 0 && this.rate < 1 ? Vl(() => {
        let r = Math.sqrt(this.rate / (1 - this.rate));
        return V(n, Sp(n.shape, 1, r));
      }, () => n, t.training || false) : n;
    });
  }
};
Ny.className = "GaussianDropout";
ae.registerClass(Ny);
var Ty = class extends Ge {
  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 j(() => {
      if (this.rate < 1 && this.rate > 0) {
        let n = this._getNoiseShape(e);
        return Vl(() => {
          let r = Oe(e), a = 1.6732632423543772, i = 1.0507009873554805, o = -a * i, u = jo(Pl(n), this.rate);
          u = Ip(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;
    });
  }
};
Ty.className = "AlphaDropout";
ae.registerClass(Ty);
function Zu(e, t, n, s, r, a = 1e-3) {
  let i;
  if (e.rank === 2)
    i = vE(e, t, n, s, r, a);
  else if (e.rank === 3)
    i = wE(e, t, n, s, r, a);
  else if (e.rank === 4)
    i = IE(e, t, n, s, r, a);
  else
    throw new Fe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);
  return i;
}
function RB(e, t, n, s, r = 1e-3) {
  return j(() => {
    let a = Jg(e, s), i = a.mean, o = a.variance;
    return [Zu(e, i, o, n, t, r), i, o];
  });
}
function DB(e, t, n, s, r = 1e-3) {
  return j(() => {
    let a = Jg(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 = G(i, u), c = G(o, u), p = t == null ? null : G(t, u), d = n == null ? null : G(n, u);
    return [Zu(e, l, c, d, p, r), i, o];
  });
}
function FB(e, t, n, s, r = 1e-3) {
  return w.arraysEqual(s.slice().sort(), ys(0, e.rank - 1)) ? RB(e, t, n, s, r) : DB(e, t, n, s, r);
}
var $y = class extends Ge {
  constructor(e) {
    e == null && (e = {});
    super(e);
    this.supportsMasking = true, this.axis = e.axis == null ? -1 : e.axis, this.momentum = e.momentum == null ? 0.99 : e.momentum, this.epsilon = e.epsilon == null ? 1e-3 : e.epsilon, this.center = e.center == null ? true : e.center, this.scale = e.scale == null ? true : e.scale, this.betaInitializer = ht(e.betaInitializer || "zeros"), this.gammaInitializer = ht(e.gammaInitializer || "ones"), this.movingMeanInitializer = ht(e.movingMeanInitializer || "zeros"), this.movingVarianceInitializer = ht(e.movingVarianceInitializer || "ones"), this.betaConstraint = Pt(e.betaConstraint), this.gammaConstraint = Pt(e.gammaConstraint), this.betaRegularizer = ft(e.betaRegularizer), this.gammaRegularizer = ft(e.gammaRegularizer);
  }
  build(e) {
    e = nt(e);
    let t = this.axis >= 0 ? this.axis : this.axis + e.length, n = e[t];
    if (n == null)
      throw new U(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);
    this.inputSpec = [new Dt({ ndim: e.length, axes: { [t]: n } })];
    let s = [n];
    this.scale && (this.gamma = this.addWeight("gamma", s, null, this.gammaInitializer, this.gammaRegularizer, true, this.gammaConstraint)), this.center && (this.beta = this.addWeight("beta", s, null, this.betaInitializer, this.betaRegularizer, true, this.betaConstraint)), this.movingMean = this.addWeight("moving_mean", s, null, this.movingMeanInitializer, null, false), this.movingVariance = this.addWeight("moving_variance", s, null, this.movingVarianceInitializer, null, false), this.built = true;
  }
  call(e, t) {
    return j(() => {
      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 = pa(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 = G(this.movingMean.read(), u), y = G(this.movingVariance.read(), u), v = this.center ? G(this.beta.read(), u) : null, x = this.scale ? G(this.gamma.read(), u) : null;
          return Zu(s, b, y, v, x, this.epsilon);
        } else
          return Zu(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] = FB(s, this.gamma.read(), this.beta.read(), i, this.epsilon), m = (b, y, v) => {
        j(() => {
          let x = 1 - v, k = b.read(), C = V(ge(k, y), x);
          b.write(ge(k, C));
        });
      };
      return (() => {
        m(this.movingMean, h, this.momentum), m(this.movingVariance, f, this.momentum);
      })(), d;
    });
  }
  getConfig() {
    let e = { axis: this.axis, momentum: this.momentum, epsilon: this.epsilon, center: this.center, scale: this.scale, betaInitializer: yt(this.betaInitializer), gammaInitializer: yt(this.gammaInitializer), movingMeanInitializer: yt(this.movingMeanInitializer), movingVarianceInitializer: yt(this.movingVarianceInitializer), betaRegularizer: at(this.betaRegularizer), gammaRegularizer: at(this.gammaRegularizer), betaConstraint: Ot(this.betaConstraint), gammaConstraint: Ot(this.gammaConstraint) }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
$y.className = "BatchNormalization";
ae.registerClass($y);
var _y = class extends Ge {
  constructor(e) {
    e == null && (e = {});
    super(e);
    if (this.axis = e.axis == null ? -1 : e.axis, typeof this.axis == "number") {
      if (!Number.isInteger(this.axis))
        throw new Error(`Expected axis to be an integer, but received ${this.axis}`);
    } else if (Array.isArray(this.axis)) {
      for (let t of this.axis)
        if (!Number.isInteger(t))
          throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`);
    } else
      throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);
    this.epsilon = e.epsilon == null ? 1e-3 : e.epsilon, this.center = e.center == null ? true : e.center, this.scale = e.scale == null ? true : e.scale, this.betaInitializer = ht(e.betaInitializer || "zeros"), this.gammaInitializer = ht(e.gammaInitializer || "ones"), this.betaRegularizer = ft(e.betaRegularizer), this.gammaRegularizer = ft(e.gammaRegularizer), this.supportsMasking = true;
  }
  build(e) {
    e = nt(e);
    let t = e.length;
    typeof this.axis == "number" && (this.axis = [this.axis]);
    for (let r = 0; r < this.axis.length; ++r)
      this.axis[r] < 0 && (this.axis[r] += t);
    for (let r of this.axis)
      if (r < 0 || r >= t)
        throw new Error(`Invalid axis: ${r}`);
    if (this.axis.length !== 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 j(() => {
      let { mean: i, variance: o } = Jg(n, this.axis, true), u = pa(1, r);
      for (let f of this.axis)
        u[f] = s[f];
      let l = (f) => f != null && f.shape.length !== r ? G(f, u) : f, c = l(this.gamma.read()), p = l(this.beta.read()), d = [], h = [];
      for (let f = 0; f < r; ++f)
        this.axis.indexOf(f) !== -1 ? (d.push(s[f]), h.push(1)) : (d.push(1), h.push(s[f]));
      return i = cs(i, d), o = cs(o, d), c = cs(c, h), p = cs(p, h), Zu(n, i, o, p, c, this.epsilon);
    });
  }
  getConfig() {
    let e = { axis: this.axis, epsilon: this.epsilon, center: this.center, scale: this.scale, betaInitializer: yt(this.betaInitializer), gammaInitializer: yt(this.gammaInitializer), betaRegularizer: at(this.betaRegularizer), gammaRegularizer: at(this.gammaRegularizer) }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
_y.className = "LayerNormalization";
ae.registerClass(_y);
function OB(e, t, n) {
  return j(() => {
    if (e.rank !== 4)
      throw new U(`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 U("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");
    if (n == null && (n = bs()), n !== "channelsLast" && n !== "channelsFirst")
      throw new U(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);
    let s;
    return n === "channelsFirst" ? s = [[0, 0], [0, 0], t[0], t[1]] : s = [[0, 0], t[0], t[1], [0, 0]], pi(e, s);
  });
}
var Ay = class extends Ge {
  constructor(e) {
    e == null && (e = {});
    super(e);
    if (this.dataFormat = e.dataFormat == null ? bs() : e.dataFormat, e.padding == null)
      this.padding = [[1, 1], [1, 1]];
    else if (typeof e.padding == "number")
      this.padding = [[e.padding, e.padding], [e.padding, e.padding]];
    else {
      if (e.padding = e.padding, e.padding.length !== 2)
        throw new U(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 U(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);
        if (t = e.padding[0], e.padding[1].length !== 2)
          throw new U(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);
        n = e.padding[1];
      }
      this.padding = [t, n];
    }
    this.inputSpec = [new Dt({ ndim: 4 })];
  }
  computeOutputShape(e) {
    e = nt(e);
    let t, n;
    return this.dataFormat === "channelsFirst" ? (e[2] != null && e[2] >= 0 ? t = e[2] + this.padding[0][0] + this.padding[0][1] : t = null, e[3] != null && e[3] >= 0 ? n = e[3] + this.padding[1][0] + this.padding[1][1] : n = null, [e[0], e[1], t, n]) : (e[1] != null && e[1] >= 0 ? t = e[1] + this.padding[0][0] + this.padding[0][1] : t = null, e[2] != null && e[2] >= 0 ? n = e[2] + this.padding[1][0] + this.padding[1][1] : n = null, [e[0], t, n, e[3]]);
  }
  call(e, t) {
    return j(() => OB(Oe(e), this.padding, this.dataFormat));
  }
  getConfig() {
    let e = { padding: this.padding, dataFormat: this.dataFormat }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
Ay.className = "ZeroPadding2D";
ae.registerClass(Ay);
function Up(e, t, n, s, r, a) {
  return j(() => {
    St(r), zI(a), Un(s), n == null && (n = [1, 1]), s == null && (s = "valid"), r == null && (r = bs()), a == null && (a = "max"), e = Yb(e, r);
    let i, o = s === "same" ? "same" : "valid";
    return a === "max" ? i = Zg(e, t, n, o) : i = Wg(e, t, n, o), r === "channelsFirst" && (i = qe(i, [0, 3, 1, 2])), i;
  });
}
function A0(e, t, n, s, r, a) {
  return j(() => {
    St(r), zI(a), Un(s), n == null && (n = [1, 1, 1]), s == null && (s = "valid"), r == null && (r = bs()), a == null && (a = "max"), e = w0(e, r);
    let i, o = s === "same" ? "same" : "valid";
    return a === "max" ? i = aI(e, t, n, o) : i = Gk(e, t, n, o), r === "channelsFirst" && (i = qe(i, [0, 4, 1, 2, 3])), i;
  });
}
var E0 = class extends Ge {
  constructor(e) {
    e.poolSize == null && (e.poolSize = 2);
    super(e);
    if (typeof e.poolSize == "number")
      this.poolSize = [e.poolSize];
    else if (Array.isArray(e.poolSize) && e.poolSize.length === 1 && typeof e.poolSize[0] == "number")
      this.poolSize = e.poolSize;
    else
      throw new U(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);
    if (Bt(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 U(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);
    Bt(this.strides, "strides"), this.padding = e.padding == null ? "valid" : e.padding, Un(this.padding), this.inputSpec = [new Dt({ ndim: 3 })];
  }
  computeOutputShape(e) {
    e = nt(e);
    let t = ms(e[1], this.poolSize[0], this.padding, this.strides[0]);
    return [e[0], t, e[2]];
  }
  call(e, t) {
    return j(() => {
      this.invokeCallHook(e, t), e = Ll(Oe(e), 2);
      let n = this.poolingFunction(Oe(e), [this.poolSize[0], 1], [this.strides[0], 1], this.padding, "channelsLast");
      return mr(n, [2]);
    });
  }
  getConfig() {
    let e = { poolSize: this.poolSize, padding: this.padding, strides: this.strides }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
var Ey = class extends E0 {
  constructor(e) {
    super(e);
  }
  poolingFunction(e, t, n, s, r) {
    return St(r), Un(s), Up(e, t, n, s, r, "max");
  }
};
Ey.className = "MaxPooling1D";
ae.registerClass(Ey);
var Ry = class extends E0 {
  constructor(e) {
    super(e);
  }
  poolingFunction(e, t, n, s, r) {
    return St(r), Un(s), Up(e, t, n, s, r, "avg");
  }
};
Ry.className = "AveragePooling1D";
ae.registerClass(Ry);
var R0 = class extends Ge {
  constructor(e) {
    e.poolSize == null && (e.poolSize = [2, 2]);
    super(e);
    if (this.poolSize = Array.isArray(e.poolSize) ? e.poolSize : [e.poolSize, e.poolSize], e.strides == null)
      this.strides = this.poolSize;
    else if (Array.isArray(e.strides)) {
      if (e.strides.length !== 2)
        throw new U(`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];
    Bt(this.poolSize, "poolSize"), Bt(this.strides, "strides"), this.padding = e.padding == null ? "valid" : e.padding, this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, St(this.dataFormat), Un(this.padding), this.inputSpec = [new Dt({ ndim: 4 })];
  }
  computeOutputShape(e) {
    e = nt(e);
    let t = this.dataFormat === "channelsFirst" ? e[2] : e[1], n = this.dataFormat === "channelsFirst" ? e[3] : e[2];
    return t = ms(t, this.poolSize[0], this.padding, this.strides[0]), n = ms(n, this.poolSize[1], this.padding, this.strides[1]), this.dataFormat === "channelsFirst" ? [e[0], e[1], t, n] : [e[0], t, n, e[3]];
  }
  call(e, t) {
    return j(() => (this.invokeCallHook(e, t), this.poolingFunction(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 Dy = class extends R0 {
  constructor(e) {
    super(e);
  }
  poolingFunction(e, t, n, s, r) {
    return St(r), Un(s), Up(e, t, n, s, r, "max");
  }
};
Dy.className = "MaxPooling2D";
ae.registerClass(Dy);
var Fy = class extends R0 {
  constructor(e) {
    super(e);
  }
  poolingFunction(e, t, n, s, r) {
    return St(r), Un(s), Up(e, t, n, s, r, "avg");
  }
};
Fy.className = "AveragePooling2D";
ae.registerClass(Fy);
var D0 = class extends Ge {
  constructor(e) {
    e.poolSize == null && (e.poolSize = [2, 2, 2]);
    super(e);
    if (this.poolSize = Array.isArray(e.poolSize) ? e.poolSize : [e.poolSize, e.poolSize, e.poolSize], e.strides == null)
      this.strides = this.poolSize;
    else if (Array.isArray(e.strides)) {
      if (e.strides.length !== 3)
        throw new U(`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];
    Bt(this.poolSize, "poolSize"), Bt(this.strides, "strides"), this.padding = e.padding == null ? "valid" : e.padding, this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, St(this.dataFormat), Un(this.padding), this.inputSpec = [new Dt({ ndim: 5 })];
  }
  computeOutputShape(e) {
    e = nt(e);
    let t = this.dataFormat === "channelsFirst" ? e[2] : e[1], n = this.dataFormat === "channelsFirst" ? e[3] : e[2], s = this.dataFormat === "channelsFirst" ? e[4] : e[3];
    return t = ms(t, this.poolSize[0], this.padding, this.strides[0]), n = ms(n, this.poolSize[1], this.padding, this.strides[1]), s = ms(s, this.poolSize[2], this.padding, this.strides[2]), this.dataFormat === "channelsFirst" ? [e[0], e[1], t, n, s] : [e[0], t, n, s, e[4]];
  }
  call(e, t) {
    return j(() => (this.invokeCallHook(e, t), this.poolingFunction(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 Oy = class extends D0 {
  constructor(e) {
    super(e);
  }
  poolingFunction(e, t, n, s, r) {
    return St(r), Un(s), A0(e, t, n, s, r, "max");
  }
};
Oy.className = "MaxPooling3D";
ae.registerClass(Oy);
var Py = class extends D0 {
  constructor(e) {
    super(e);
  }
  poolingFunction(e, t, n, s, r) {
    return St(r), Un(s), A0(e, t, n, s, r, "avg");
  }
};
Py.className = "AveragePooling3D";
ae.registerClass(Py);
var F0 = class extends Ge {
  constructor(e) {
    super(e);
    this.inputSpec = [new Dt({ ndim: 3 })];
  }
  computeOutputShape(e) {
    return [e[0], e[2]];
  }
  call(e, t) {
    throw new Fe();
  }
};
var zy = class extends F0 {
  constructor(e) {
    super(e || {});
  }
  call(e, t) {
    return j(() => {
      let n = Oe(e);
      return It(n, 1);
    });
  }
};
zy.className = "GlobalAveragePooling1D";
ae.registerClass(zy);
var My = class extends F0 {
  constructor(e) {
    super(e || {});
  }
  call(e, t) {
    return j(() => {
      let n = Oe(e);
      return As(n, 1);
    });
  }
};
My.className = "GlobalMaxPooling1D";
ae.registerClass(My);
var O0 = class extends Ge {
  constructor(e) {
    super(e);
    this.dataFormat = e.dataFormat == null ? "channelsLast" : e.dataFormat, St(this.dataFormat), this.inputSpec = [new Dt({ ndim: 4 })];
  }
  computeOutputShape(e) {
    return e = e, this.dataFormat === "channelsLast" ? [e[0], e[3]] : [e[0], e[1]];
  }
  call(e, t) {
    throw new Fe();
  }
  getConfig() {
    let e = { dataFormat: this.dataFormat }, t = super.getConfig();
    return Object.assign(e, t), e;
  }
};
var Ly = class extends O0 {
  call(e, t) {
    return j(() => {
      let n = Oe(e);
      return this.dataFormat === "channelsLast" ? It(n, [1, 2]) : It(n, [2, 3]);
    });
  }
};
Ly.className = "GlobalAveragePooling2D";
ae.registerClass(Ly);
var By = class extends O0 {
  call(e, t) {
    return j(() => {
      let n = Oe(e);
      return this.dataFormat === "channelsLast" ? As(n, [1, 2]) : As(n, [2, 3]);
    });
  }
};
By.className = "GlobalMaxPooling2D";
ae.registerClass(By);
var P0 = class extends Ge {
  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 = fs(s, n);
    delete t.layer;
    let a = { layer: r };
    return Object.assign(a, t), new e(a);
  }
};
var Vy = class extends P0 {
  constructor(e) {
    super(e);
    this.supportsMasking = true;
  }
  build(e) {
    if (e = nt(e), e.length < 3)
      throw new U(`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 j(() => (e = Oe(e), T0((a, i) => [Oe(this.layer.call(a, t)), []], e, [], false, null, null, false, true)[1]));
  }
};
Vy.className = "TimeDistributed";
ae.registerClass(Vy);
function PB(e) {
  hi(VM, "BidirectionalMergeMode", e);
}
var zB = "concat";
var Wy = class extends P0 {
  constructor(e) {
    super(e);
    let t = e.layer.getConfig(), n = {};
    n.className = e.layer.getClassName(), n.config = t, this.forwardLayer = fs(n), t.goBackwards = t.goBackwards !== true;
    let s = {};
    if (s.className = e.layer.getClassName(), s.config = t, this.backwardLayer = fs(s), this.forwardLayer.name = "forward_" + this.forwardLayer.name, this.backwardLayer.name = "backward_" + this.backwardLayer.name, this.mergeMode = e.mergeMode === void 0 ? zB : e.mergeMode, PB(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()) : gn(s);
  }
  apply(e, t) {
    let n = t == null ? null : t.initialState, s = t == null ? null : t.constants;
    t == null && (t = {});
    let r = N0(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 U("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");
      t.initialState = n, a.push(...n);
      let l = n.map((c) => new Dt({ shape: c.shape }));
      this.forwardLayer.stateSpec = l.slice(0, u / 2), this.backwardLayer.stateSpec = l.slice(u / 2), i.push(...l);
    }
    if (s != null)
      throw new Fe("Support for constants in Bidirectional layers is not implemented yet.");
    let o = a[0] instanceof $s;
    for (let u of a)
      if (u instanceof $s !== o)
        throw new U("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 j(() => {
      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 = Yn(r, 1));
      let i;
      return this.mergeMode === "concat" ? i = Nb([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) {
    Zr(this.forwardLayer.name, () => {
      this.forwardLayer.build(e);
    }), Zr(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 = fs(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);
  }
};
Wy.className = "Bidirectional";
ae.registerClass(Wy);
function MB(e) {
  return new Yo(e);
}
function LB(e) {
  return new jb(e);
}
function BB(e) {
  return new Gb(e);
}
function VB(e) {
  return new Hb(e);
}
function WB(e) {
  return new qb(e);
}
function UB(e) {
  return new Xb(e);
}
function GB(e) {
  return new Kb(e);
}
function HB(e) {
  return new ty(e);
}
function qB(e) {
  return new Pp(e);
}
function jB(e) {
  return new Zb(e);
}
function KB(e) {
  return new zp(e);
}
function XB(e) {
  return new Jb(e);
}
function YB(e) {
  return new ey(e);
}
function QB(e) {
  return new ny(e);
}
function ZB(e) {
  return new sy(e);
}
function JB(e) {
  return new ry(e);
}
function eV(e) {
  return new py(e);
}
function tV(e) {
  return new cy(e);
}
function nV(e) {
  return new Wp(e);
}
function sV(e) {
  return new ly(e);
}
function rV(e) {
  return new dy(e);
}
function aV(e) {
  return new hy(e);
}
function iV(e) {
  return new fy(e);
}
function oV(e) {
  return new my(e);
}
function uV(e) {
  return new by(e);
}
function lV(e) {
  return new yy(e);
}
function cV(e) {
  return new xy(e);
}
function dV(e) {
  return new Iy(e);
}
function pV(e) {
  return new wy(e);
}
function hV(e) {
  return new ky(e);
}
function fV(e) {
  return new vy(e);
}
function mV(e) {
  return new Sy(e);
}
function gV(e) {
  return new $y(e);
}
function bV(e) {
  return new _y(e);
}
function yV(e) {
  return new Ay(e);
}
function Uy(e) {
  return new Ry(e);
}
function vV(e) {
  return Uy(e);
}
function xV(e) {
  return Uy(e);
}
function Gy(e) {
  return new Fy(e);
}
function wV(e) {
  return Gy(e);
}
function kV(e) {
  return Gy(e);
}
function Hy(e) {
  return new Py(e);
}
function IV(e) {
  return Hy(e);
}
function SV(e) {
  return Hy(e);
}
function CV(e) {
  return new zy(e);
}
function NV(e) {
  return new Ly(e);
}
function z0(e) {
  return new My(e);
}
function M0(e) {
  return new By(e);
}
function L0(e) {
  return new Ey(e);
}
function B0(e) {
  return new Dy(e);
}
function TV(e) {
  return new Oy(e);
}
function $V(e) {
  return new iy(e);
}
function _V(e) {
  return new Lp(e);
}
function AV(e) {
  return new oy(e);
}
function EV(e) {
  return new Hl(e);
}
function RV(e) {
  return new ay(e);
}
function DV(e) {
  return new Mp(e);
}
function FV(e) {
  return new uy(e);
}
function OV(e) {
  return new Vp(e);
}
function PV(e) {
  return new _r(e);
}
function zV(e) {
  return new Bp(e);
}
function MV(e) {
  return new Wy(e);
}
function LV(e) {
  return new Vy(e);
}
var BV = z0;
var VV = M0;
var WV = L0;
var UV = B0;
function GV(e) {
  return new Cy(e);
}
function HV(e) {
  return new Ny(e);
}
function qV(e) {
  return new Ty(e);
}
function jV(e) {
  return new gy(e);
}
var KV = {};
Ae(KV, { MAPE: () => aW, MSE: () => uW, binaryAccuracy: () => XV, binaryCrossentropy: () => YV, categoricalAccuracy: () => ZV, categoricalCrossentropy: () => JV, cosineProximity: () => nW, mape: () => iW, meanAbsoluteError: () => sW, meanAbsolutePercentageError: () => rW, meanSquaredError: () => oW, mse: () => lW, precision: () => eW, recall: () => tW, sparseCategoricalAccuracy: () => QV });
function XV(e, t) {
  return zb(e, t);
}
function YV(e, t) {
  return QI(e, t);
}
function QV(e, t) {
  return ZI(e, t);
}
function ZV(e, t) {
  return Mb(e, t);
}
function JV(e, t) {
  return Lb(e, t);
}
function eW(e, t) {
  return YI(e, t);
}
function tW(e, t) {
  return VL(e, t);
}
function nW(e, t) {
  return Pb(e, t);
}
function sW(e, t) {
  return Fp(e, t);
}
function rW(e, t) {
  return Qo(e, t);
}
function aW(e, t) {
  return Qo(e, t);
}
function iW(e, t) {
  return Qo(e, t);
}
function oW(e, t) {
  return fi(e, t);
}
function uW(e, t) {
  return fi(e, t);
}
function lW(e, t) {
  return fi(e, t);
}
var cW = {};
Ae(cW, { modelFromJSON: () => wB });
var dW = {};
Ae(dW, { l1: () => hW, l1l2: () => pW, l2: () => fW });
function pW(e) {
  return new Wl(e);
}
function hW(e) {
  return NB(e);
}
function fW(e) {
  return TB(e);
}
var mW = class extends Zi {
  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 Vx(e, t) {
  return e > t;
}
var gW = class extends mW {
  constructor(e) {
    super();
    if (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 = Vx : this.monitor.indexOf("acc") !== -1 ? this.monitorFunc = Vx : 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 bW(e) {
  return new gW(e);
}
var fpe = { earlyStopping: bW };
var yW = X();
yW.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 V0 = ((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))(V0 || {});
var Wx;
((e) => {
  let t;
  ((n) => {
    n[n.LEGACY = 0] = "LEGACY", n[n.V1 = 1] = "V1", n[n.V2 = 2] = "V2";
  })(t = e.CheckpointFormatVersion || (e.CheckpointFormatVersion = {}));
})(Wx || (Wx = {}));
var qy = {};
function mpe(e, t) {
  let n = { tfOpName: e, category: "custom", inputs: [], attrs: [], customExecutor: t };
  qy[e] = n;
}
function W0(e) {
  return qy[e];
}
function gpe(e) {
  delete qy[e];
}
function I(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 on(t.inputNames[a.inputIndexStart], n, s, r);
    if (a.type === "tensors")
      return t.inputNames.slice(o, u).map((d) => on(d, n, s, r));
    let l = on(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 on(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[Ad(r, o)]);
  return i !== void 0 ? t[Ad(r, i)][a] : void 0;
}
function vW(e, t, n) {
  return t[Ad(e, n.currentContextId)];
}
function Ts(e, t) {
  let [n, s, r] = $n(e);
  return [Ad(n, t && t.currentContextId), s, r];
}
function Ad(e, t) {
  return t ? `${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 nd(e, t, n) {
  let s = I("pad", e, t, n);
  if (s === "explicit") {
    s = I("explicitPaddings", e, t, n);
    let r = [[0, 0], [0, 0], [0, 0], [0, 0]];
    for (let a = 0; a < 4; a++)
      r[a][0] = s[a * 2], r[a][1] = s[a * 2 + 1];
    return r;
  }
  return s;
}
function Us(e) {
  return e.kept ? e : lr(e);
}
var U0 = {};
Ae(U0, { json: () => xW });
var xW = [{ 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 G0 = {};
Ae(G0, { json: () => wW });
var wW = [{ 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 H0 = {};
Ae(H0, { json: () => kW });
var kW = [{ tfOpName: "EmptyTensorList", category: "control", inputs: [{ start: 0, name: "elementShape", type: "shape" }, { start: 1, name: "maxNumElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "LoopCond", category: "control", inputs: [{ start: 0, name: "pred", type: "tensor" }] }, { tfOpName: "Switch", category: "control", inputs: [{ start: 0, name: "data", type: "tensor" }, { start: 1, name: "pred", type: "tensor" }] }, { tfOpName: "Merge", category: "control", inputs: [{ start: 0, end: 0, name: "tensors", type: "tensors" }] }, { tfOpName: "Enter", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }, { tfName: "frame_name", name: "frameName", type: "string" }, { tfName: "is_constant", name: "isConstant", type: "bool" }] }, { tfOpName: "Exit", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "NextIteration", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "TensorArrayV3", category: "control", inputs: [{ start: 0, name: "size", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "element_shape", name: "elementShape", type: "shape" }, { tfName: "dynamic_size", name: "dynamicSize", type: "bool" }, { tfName: "clear_after_read", name: "clearAfterRead", type: "bool" }, { tfName: "identical_element_shapes", name: "identicalElementShapes", type: "bool" }, { tfName: "tensor_array_name", name: "name", type: "string" }] }, { tfOpName: "TensorArrayWriteV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "tensor", type: "tensor" }, { start: 3, name: "flowIn", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "TensorArrayReadV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "flowIn", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype", notSupported: true }] }, { tfOpName: "TensorArrayGatherV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "flowIn", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "element_shape", name: "elementShape", type: "shape" }] }, { tfOpName: "TensorArrayScatterV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "tensor", type: "tensor" }, { start: 3, name: "flowIn", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "TensorArrayConcatV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "flowIn", type: "number" }], attrs: [{ tfName: "dtype", name: "dtype", type: "dtype" }, { tfName: "element_shape_except0", name: "elementShapeExcept0", type: "shape", notSupported: true }] }, { tfOpName: "TensorArraySplitV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "tensor", type: "tensor" }, { start: 2, name: "lengths", type: "number[]" }, { start: 3, name: "flowIn", type: "number" }], attrs: [{ tfName: "T", name: "dtype", type: "dtype" }] }, { tfOpName: "TensorArraySizeV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }, { start: 1, name: "flowIn", type: "number" }] }, { tfOpName: "TensorArrayCloseV3", category: "control", inputs: [{ start: 0, name: "tensorArrayId", type: "tensor" }] }, { tfOpName: "StatelessIf", category: "control", inputs: [{ start: 0, name: "cond", type: "tensor" }, { start: 1, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "then_branch", name: "thenBranch", type: "func" }, { tfName: "else_branch", name: "elseBranch", type: "func" }] }, { tfOpName: "If", category: "control", inputs: [{ start: 0, name: "cond", type: "tensor" }, { start: 1, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "then_branch", name: "thenBranch", type: "func" }, { tfName: "else_branch", name: "elseBranch", type: "func" }] }, { tfOpName: "StatelessWhile", category: "control", inputs: [{ start: 0, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "cond", name: "cond", type: "func" }, { tfName: "body", name: "body", type: "func" }] }, { tfOpName: "While", category: "control", inputs: [{ start: 0, end: 0, name: "args", type: "tensors" }], attrs: [{ tfName: "cond", name: "cond", type: "func" }, { tfName: "body", name: "body", type: "func" }] }, { tfOpName: "TensorListScatter", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListScatterV2", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }, { start: 3, name: "numElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListGather", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "indices", type: "number[]" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListGetItem", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListSetItem", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "index", type: "number" }, { start: 2, name: "tensor", type: "tensor" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListReserve", category: "control", inputs: [{ start: 0, name: "elementShape", type: "shape" }, { start: 1, name: "numElements", type: "number" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListFromTensor", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListStack", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }, { tfName: "num_elements", name: "numElements", type: "dtype" }] }, { tfOpName: "TensorListSplit", category: "control", inputs: [{ start: 0, name: "tensor", type: "tensor" }, { start: 1, name: "elementShape", type: "shape" }, { start: 2, name: "lengths", type: "number[]" }], attrs: [{ tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "TensorListConcat", category: "control", inputs: [{ start: 0, name: "tensorListId", type: "tensor" }], attrs: [{ tfName: "element_shape", name: "elementShape", type: "shape" }, { tfName: "element_dtype", name: "elementDType", type: "dtype" }] }, { tfOpName: "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" }] }];
var q0 = {};
Ae(q0, { json: () => IW });
var IW = [{ 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 j0 = {};
Ae(j0, { json: () => SW });
var SW = [{ 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 K0 = {};
Ae(K0, { json: () => CW });
var CW = [{ 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 X0 = {};
Ae(X0, { json: () => NW });
var NW = [{ tfOpName: "TopKV2", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "k", type: "number" }], attrs: [{ tfName: "sorted", name: "sorted", type: "bool" }] }, { tfOpName: "Unique", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }] }, { tfOpName: "UniqueV2", category: "evaluation", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }];
var Y0 = {};
Ae(Y0, { json: () => TW });
var TW = [{ 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 Q0 = {};
Ae(Q0, { json: () => $W });
var $W = [{ 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 Z0 = {};
Ae(Z0, { json: () => _W });
var _W = [{ 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" }] }];
var J0 = {};
Ae(J0, { json: () => AW });
var AW = [{ 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 eS = {};
Ae(eS, { json: () => EW });
var EW = [{ 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 tS = {};
Ae(tS, { json: () => RW });
var RW = [{ 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 nS = {};
Ae(nS, { json: () => DW });
var DW = [{ tfOpName: "Bincount", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "size", type: "number" }, { start: 2, name: "weights", type: "tensor" }] }, { tfOpName: "DenseBincount", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "size", type: "number" }, { start: 2, name: "weights", type: "tensor" }], attrs: [{ tfName: "binary_output", name: "binaryOutput", type: "bool" }] }, { tfOpName: "Max", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Mean", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Min", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Sum", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "All", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Any", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "ArgMax", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "ArgMin", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }] }, { tfOpName: "Prod", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number[]" }], attrs: [{ tfName: "keep_dims", name: "keepDims", type: "bool" }] }, { tfOpName: "Cumsum", category: "reduction", inputs: [{ start: 0, name: "x", type: "tensor" }, { start: 1, name: "axis", type: "number" }], attrs: [{ tfName: "exclusive", name: "exclusive", type: "bool" }, { tfName: "reverse", name: "reverse", type: "bool" }] }];
var sS = {};
Ae(sS, { json: () => FW });
var FW = [{ 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 rS = {};
Ae(rS, { json: () => OW });
var OW = [{ 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 aS = {};
Ae(aS, { json: () => PW });
var PW = [{ 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 iS = {};
Ae(iS, { json: () => zW });
var zW = [{ 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 oS = {};
Ae(oS, { json: () => MW });
var MW = [{ 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 Ux = class {
  static get Instance() {
    return this._instance || (this._instance = new this());
  }
  constructor() {
    let e = [U0, G0, H0, q0, j0, K0, X0, Y0, Q0, Z0, J0, eS, tS, nS, sS, rS, aS, iS, oS], 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 C = `${y}:${k}`;
            m.inputNames[b] = C;
          }
        }
        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 = W0(e.op) || this.opMappers[e.op] || {};
    e.attr == null && (e.attr = {});
    let n = { name: e.name, op: e.op, category: t.category, inputNames: (e.input || []).map((s) => s.startsWith("^") ? s.substr(1) : s), inputs: [], children: [], inputParams: {}, attrParams: {}, rawAttrs: e.attr, outputs: t.outputs };
    return t.inputs != null && (n.inputParams = t.inputs.reduce((s, r) => (s[r.name] = { type: r.type, inputIndexStart: r.start, inputIndexEnd: r.end }, s), {})), t.attrs != null && (n.attrParams = t.attrs.reduce((s, r) => {
      let a = r.type, i;
      switch (r.type) {
        case "string":
          i = Sm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Sm(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "string[]":
          i = Em(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Em(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "number":
          i = Nm(e.attr, r.tfName, r.defaultValue || 0), i === void 0 && !!r.tfDeprecatedName && (i = Nm(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "number[]":
          i = Am(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Am(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "bool":
          i = Cm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Cm(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "bool[]":
          i = Dm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Dm(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "shape":
          i = _m(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = _m(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "shape[]":
          i = Rm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Rm(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "dtype":
          i = Tm(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Tm(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "dtype[]":
          i = $m(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = $m(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "func":
          i = Gx(e.attr, r.tfName, r.defaultValue), i === void 0 && !!r.tfDeprecatedName && (i = Gx(e.attr, r.tfDeprecatedName, r.defaultValue));
          break;
        case "tensor":
        case "tensors":
          break;
        default:
          throw new Error(`Unsupported param type: ${r.type} for op: ${e.op}`);
      }
      return s[r.name] = { value: i, type: a }, s;
    }, {})), n;
  }
  mapFunction(e) {
    let t = e.nodeDef, n = [], s = [], r = {};
    t != null && (r = t.reduce((c, p) => (c[p.name] = this.mapNode(p), p.op === "Const" && s.push(c[p.name]), c), {}));
    let a = [], i = [];
    e.signature.inputArg.forEach((c) => {
      let [p] = Ts(c.name), d = { name: p, op: "Placeholder", inputs: [], inputNames: [], category: "graph", inputParams: {}, attrParams: { dtype: { value: jy(c.type), type: "dtype" } }, children: [] };
      d.signatureKey = c.name, a.push(d), r[p] = d;
    }), Object.keys(r).forEach((c) => {
      let p = r[c];
      p.inputNames.forEach((d, h) => {
        let [f, , m] = Ts(d), g = r[f];
        if (g.outputs != null) {
          let b = g.outputs.indexOf(m);
          if (b !== -1) {
            let y = `${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 LW(e) {
  let t = X().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 uS(e, t) {
  let n = Array.isArray(e) ? String.fromCharCode.apply(null, e) : LW(e);
  return t ? n : n.toLowerCase();
}
function Sm(e, t, n, s = false) {
  let r = e[t];
  return r != null ? uS(r.s, s) : n;
}
function Cm(e, t, n) {
  let s = e[t];
  return s ? s.b : n;
}
function Nm(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 jy(e) {
  switch (typeof e == "string" && (e = V0[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 Gx(e, t, n) {
  let s = e[t];
  return s && s.func ? s.func.name : n;
}
function Tm(e, t, n) {
  let s = e[t];
  return s && s.type ? jy(s.type) : n;
}
function $m(e, t, n) {
  let s = e[t];
  return s && s.list && s.list.type ? s.list.type.map((r) => jy(r)) : n;
}
function lS(e) {
  if (!e.unknownRank)
    return e.dim != null ? e.dim.map((t) => typeof t.size == "number" ? t.size : parseInt(t.size, 10)) : [];
}
function _m(e, t, n) {
  let s = e[t];
  return s && s.shape ? lS(s.shape) : n;
}
function Am(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 Em(e, t, n, s = false) {
  let r = e[t];
  return r && r.list && r.list.s ? r.list.s.map((a) => uS(a, s)) : n;
}
function Rm(e, t, n) {
  let s = e[t];
  return s && s.list && s.list.shape ? s.list.shape.map((r) => lS(r)) : n;
}
function Dm(e, t, n) {
  let s = e[t];
  return s && s.list && s.list.b ? s.list.b : n;
}
var BW = 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 on(e, this.tensorMap, this.context);
  }
  getAttr(e, t) {
    let n = this.node.rawAttrs[e];
    if (n.tensor != null)
      return on(e, this.tensorMap, this.context);
    if (n.i != null || n.f != null)
      return Nm(this.node.rawAttrs, e, t);
    if (n.s != null)
      return Sm(this.node.rawAttrs, e, t);
    if (n.b != null)
      return Cm(this.node.rawAttrs, e, t);
    if (n.shape != null)
      return _m(this.node.rawAttrs, e, t);
    if (n.type != null)
      return Tm(this.node.rawAttrs, e, t);
    if (n.list != null) {
      if (n.list.i != null || n.list.f != null)
        return Am(this.node.rawAttrs, e, t);
      if (n.list.s != null)
        return Em(this.node.rawAttrs, e, t);
      if (n.list.shape != null)
        return Rm(this.node.rawAttrs, e, t);
      if (n.list.b != null)
        return Dm(this.node.rawAttrs, e, t);
      if (n.list.type != null)
        return $m(this.node.rawAttrs, e, t);
    }
    return t;
  }
};
var VW = (e, t, n) => {
  switch (e.op) {
    case "BiasAdd":
    case "AddV2":
    case "Add":
      return [ie(I("a", e, t, n), I("b", e, t, n))];
    case "AddN":
      return [LA(I("tensors", e, t, n))];
    case "FloorMod":
    case "Mod":
      return [dD(I("a", e, t, n), I("b", e, t, n))];
    case "Mul":
      return [V(I("a", e, t, n), I("b", e, t, n))];
    case "RealDiv":
    case "Div":
      return [xe(I("a", e, t, n), I("b", e, t, n))];
    case "DivNoNan":
      return [uR(I("a", e, t, n), I("b", e, t, n))];
    case "FloorDiv":
      return [Lk(I("a", e, t, n), I("b", e, t, n))];
    case "Sub":
      return [ge(I("a", e, t, n), I("b", e, t, n))];
    case "Minimum":
      return [gp(I("a", e, t, n), I("b", e, t, n))];
    case "Maximum":
      return [Tr(I("a", e, t, n), I("b", e, t, n))];
    case "Pow":
      return [ca(I("a", e, t, n), I("b", e, t, n))];
    case "SquaredDifference":
      return [mI(I("a", e, t, n), I("b", e, t, n))];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var WW = (e, t, n) => {
  switch (e.op) {
    case "Abs":
    case "ComplexAbs":
      return [Mt(I("x", e, t, n))];
    case "Acos":
      return [OA(I("x", e, t, n))];
    case "Acosh":
      return [zA(I("x", e, t, n))];
    case "Asin":
      return [qA(I("x", e, t, n))];
    case "Asinh":
      return [KA(I("x", e, t, n))];
    case "Atan":
      return [YA(I("x", e, t, n))];
    case "Atan2":
      return [ZA(I("x", e, t, n), I("y", e, t, n))];
    case "Atanh":
      return [eE(I("x", e, t, n))];
    case "Ceil":
      return [_E(I("x", e, t, n))];
    case "Complex":
      return [aa(I("real", e, t, n), I("imag", e, t, n))];
    case "Cos":
      return [Hg(I("x", e, t, n))];
    case "Cosh":
      return [Yk(I("x", e, t, n))];
    case "Elu":
      return [hp(I("x", e, t, n))];
    case "Erf":
      return [fR(I("x", e, t, n))];
    case "Exp":
      return [jn(I("x", e, t, n))];
    case "Expm1":
      return [yR(I("x", e, t, n))];
    case "Floor":
      return [fp(I("x", e, t, n))];
    case "Log":
      return [Kn(I("x", e, t, n))];
    case "Log1p":
      return [Kg(I("x", e, t, n))];
    case "Imag":
      return [qg(I("x", e, t, n))];
    case "Neg":
      return [kt(I("x", e, t, n))];
    case "Reciprocal":
      return [VD(I("x", e, t, n))];
    case "Real":
      return [xd(I("x", e, t, n))];
    case "Relu":
      return [Xs(I("x", e, t, n))];
    case "Round":
      return [uI(I("x", e, t, n))];
    case "Selu":
      return [cI(I("x", e, t, n))];
    case "Sigmoid":
      return [qs(I("x", e, t, n))];
    case "Sin":
      return [dI(I("x", e, t, n))];
    case "Sign":
      return [s3(I("x", e, t, n))];
    case "Sinh":
      return [pI(I("x", e, t, n))];
    case "Softplus":
      return [Ol(I("x", e, t, n))];
    case "Sqrt":
      return [ln(I("x", e, t, n))];
    case "Square":
      return [ct(I("x", e, t, n))];
    case "Tanh":
      return [Hu(I("x", e, t, n))];
    case "Tan":
      return [S3(I("x", e, t, n))];
    case "ClipByValue":
      return [Bn(I("x", e, t, n), I("clipValueMin", e, t, n), I("clipValueMax", e, t, n))];
    case "Relu6":
      return [oI(I("x", e, t, n))];
    case "Rsqrt":
      return [lI(on(e.inputNames[0], t, n))];
    case "Prod":
      return [iI(I("x", e, t, n), I("axes", e, t, n))];
    case "LeakyRelu":
      return [jg(I("x", e, t, n), I("alpha", e, t, n))];
    case "Prelu":
      return [tb(I("x", e, t, n), I("alpha", e, t, n))];
    case "IsNan":
      return [_R(on(e.inputNames[0], t, n))];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
function Hn(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 Hx(e) {
  return !(typeof e == "number" || e.some((t) => t < 0));
}
function Cu(e, t, n) {
  let s = Fm(e, n), r = !Hx(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 = Fm(a.shape, s);
  }), !Hx(s))
    throw new Error(`Non-fully-defined elementShape: ${s}`);
  return s;
}
function Fm(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 UW = 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 = Ie(0), Ht(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), Hn(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, Ht(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 hs([], [0].concat(this.elementShape));
    let n = this.readMany(e);
    return Hn(this.elementShape, n[0].shape, "TensorArray shape mismatch: "), Qn(n, 0);
  }
  concat(e) {
    if (!!e && e !== this.dtype)
      throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);
    if (this.size() === 0)
      return hs([], [0].concat(this.elementShape));
    let t = [];
    for (let s = 0; s < this.size(); s++)
      t.push(s);
    let n = this.readMany(t);
    return Hn(this.elementShape, n[0].shape, `TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`), Ft(n, 0);
  }
  scatter(e, t) {
    if (t.dtype !== this.dtype)
      throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);
    if (e.length !== t.shape[0])
      throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t.shape[0]}`);
    let n = Math.max(...e);
    if (!this.dynamicSize && n >= this.maxSize)
      throw new Error(`Max index must be < array size (${n}  vs. ${this.maxSize})`);
    this.writeMany(e, Fs(t, 0));
  }
  split(e, t) {
    if (t.dtype !== this.dtype)
      throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);
    let n = 0, s = e.map((o) => (n += o, n));
    if (n !== t.shape[0])
      throw new Error(`Expected sum of lengths to be equal to
          tensor.shape[0], but sum of lengths is
        ${n}, and tensor's shape is: ${t.shape}`);
    if (!this.dynamicSize && e.length !== this.maxSize)
      throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);
    let r = n === 0 ? 0 : t.size / n, a = [];
    j(() => {
      t = G(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] = G(He(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 ql = 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}`);
      Hn(t, r.shape, "TensorList shape mismatch: "), Ht(r);
    }), this.idTensor = Ie(0), this.maxNumElements = s, Ht(this.idTensor);
  }
  get id() {
    return this.idTensor.id;
  }
  copy() {
    return new ql([...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.`);
    Hn(e, this.elementShape, "TensorList shape mismatch: ");
    let s = Cu(this.elementShape, this.tensors, e);
    return j(() => {
      let r = this.tensors.map((a) => G(a, s));
      return Qn(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 = Cu(this.elementShape, this.tensors, e), s = this.tensors.pop();
    return Hn(s.shape, e, "TensorList shape mismatch: "), G(s, n);
  }
  pushBack(e) {
    if (e.dtype !== this.elementDtype)
      throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);
    if (Hn(e.shape, this.elementShape, "TensorList shape mismatch: "), this.maxNumElements === this.size())
      throw new Error("Trying to push element into a full list.");
    Ht(e), this.tensors.push(e);
  }
  resize(e) {
    if (e < 0)
      throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);
    if (this.maxNumElements !== -1 && e > this.maxNumElements)
      throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);
    this.tensors.length = e;
  }
  getItem(e, t, n) {
    if (n !== this.elementDtype)
      throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);
    if (e < 0 || e > this.tensors.length)
      throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);
    if (this.tensors[e] == null)
      throw new Error(`element at index ${e} is null.`);
    Hn(this.tensors[e].shape, t, "TensorList shape mismatch: ");
    let s = Cu(this.elementShape, this.tensors, t);
    return G(this.tensors[e], s);
  }
  setItem(e, t) {
    if (t.dtype !== this.elementDtype)
      throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);
    if (e < 0 || this.maxNumElements !== -1 && e >= this.maxNumElements)
      throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);
    Hn(this.elementShape, t.shape, "TensorList shape mismatch: "), Ht(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}`);
    Hn(this.elementShape, n, "TensorList shape mismatch: "), e = e.slice(0, this.size());
    let s = Cu(this.elementShape, this.tensors, n);
    return e.length === 0 ? hs([], [0].concat(s)) : j(() => {
      let r = e.map((a) => G(this.tensors[a], s));
      return Qn(r, 0);
    });
  }
  concat(e, t) {
    if (!!e && e !== this.elementDtype)
      throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);
    Hn(this.elementShape, t, "TensorList shape mismatch: ");
    let n = Cu(this.elementShape, this.tensors, t);
    return this.size() === 0 ? hs([], [0].concat(n)) : j(() => {
      let s = this.tensors.map((r) => G(r, n));
      return Ft(s, 0);
    });
  }
};
function GW(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);
  Hn(r, t, "TensorList shape mismatch: ");
  let a = Fs(e);
  return new ql(a, t, s);
}
function HW(e, t, n) {
  return new ql([], e, t, n);
}
function qW(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 ql([], n, e.dtype, s), i = Fs(e, 0);
  return t.forEach((o, u) => {
    a.setItem(o, i[u]);
  }), a;
}
function jW(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 = Fm(a, n), o = s === 0 ? 0 : e.size / s, u = j(() => {
    let c = [];
    e = G(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] = G(He(e, h, f), i);
    }
    return e.dispose(), c;
  }), l = new ql([], n, e.dtype, t.length);
  for (let c = 0; c < u.length; c++)
    l.setItem(c, u[c]);
  return l;
}
var KW = async (e, t, n) => {
  switch (e.op) {
    case "If":
    case "StatelessIf": {
      let s = I("thenBranch", e, t, n), r = I("elseBranch", e, t, n), a = I("cond", e, t, n), i = I("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 = I("body", e, t, n), r = I("cond", e, t, n), a = I("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 = I("pred", e, t, n);
      return [Us(s)];
    }
    case "Switch": {
      let s = I("pred", e, t, n), r = I("data", e, t, n);
      return r.kept || (r = Us(r)), (await s.data())[0] ? [void 0, r] : [r, void 0];
    }
    case "Merge": {
      let s = e.inputNames.find((r) => on(r, t, n) !== void 0);
      if (s) {
        let r = on(s, t, n);
        return [Us(r)];
      }
      return;
    }
    case "Enter": {
      let s = I("frameName", e, t, n), r = I("tensor", e, t, n);
      return n.enterFrame(s), [Us(r)];
    }
    case "Exit": {
      let s = I("tensor", e, t, n);
      return n.exitFrame(), [Us(s)];
    }
    case "NextIteration": {
      let s = I("tensor", e, t, n);
      return n.nextIteration(), [Us(s)];
    }
    case "TensorArrayV3": {
      let s = I("size", e, t, n), r = I("dtype", e, t, n), a = I("elementShape", e, t, n), i = I("dynamicSize", e, t, n), o = I("clearAfterRead", e, t, n), u = I("identicalElementShapes", e, t, n), l = I("name", e, t, n), c = new UW(l, r, s, a, u, i, o);
      return n.addTensorArray(c), [c.idTensor, Ie(1)];
    }
    case "TensorArrayWriteV3": {
      let s = I("tensorArrayId", e, t, n), r = I("index", e, t, n), a = I("tensor", e, t, n), i = n.getTensorArray(s.id);
      return i.write(r, a), [i.idTensor];
    }
    case "TensorArrayReadV3": {
      let s = I("tensorArrayId", e, t, n), r = I("index", e, t, n);
      return [n.getTensorArray(s.id).read(r)];
    }
    case "TensorArrayGatherV3": {
      let s = I("tensorArrayId", e, t, n), r = I("indices", e, t, n), a = I("dtype", e, t, n);
      return [n.getTensorArray(s.id).gather(r, a)];
    }
    case "TensorArrayScatterV3": {
      let s = I("tensorArrayId", e, t, n), r = I("indices", e, t, n), a = I("tensor", e, t, n), i = n.getTensorArray(s.id);
      return i.scatter(r, a), [i.idTensor];
    }
    case "TensorArrayConcatV3": {
      let s = I("tensorArrayId", e, t, n), r = n.getTensorArray(s.id), a = I("dtype", e, t, n);
      return [r.concat(a)];
    }
    case "TensorArraySplitV3": {
      let s = I("tensorArrayId", e, t, n), r = I("tensor", e, t, n), a = I("lengths", e, t, n), i = n.getTensorArray(s.id);
      return i.split(a, r), [i.idTensor];
    }
    case "TensorArraySizeV3": {
      let s = I("tensorArrayId", e, t, n), r = n.getTensorArray(s.id);
      return [Ie(r.size(), "int32")];
    }
    case "TensorArrayCloseV3": {
      let s = I("tensorArrayId", e, t, n), r = n.getTensorArray(s.id);
      return r.clearAndClose(), [r.idTensor];
    }
    case "TensorListSetItem": {
      let s = I("tensorListId", e, t, n), r = I("index", e, t, n), a = I("tensor", e, t, n), i = n.getTensorList(s.id);
      return i.setItem(r, a), [i.idTensor];
    }
    case "TensorListGetItem": {
      let s = I("tensorListId", e, t, n), r = I("index", e, t, n), a = I("elementShape", e, t, n), i = I("elementDType", e, t, n);
      return [n.getTensorList(s.id).getItem(r, a, i)];
    }
    case "TensorListScatterV2":
    case "TensorListScatter": {
      let s = I("indices", e, t, n), r = I("tensor", e, t, n), a = I("elementShape", e, t, n), i = I("numElements", e, t, n), o = qW(r, s, a, i);
      return n.addTensorList(o), [o.idTensor];
    }
    case "TensorListReserve":
    case "EmptyTensorList": {
      let s = I("elementShape", e, t, n), r = I("elementDType", e, t, n), a;
      e.op === "TensorListReserve" ? a = "numElements" : a = "maxNumElements";
      let i = I(a, e, t, n), o = HW(s, r, i);
      return n.addTensorList(o), [o.idTensor];
    }
    case "TensorListGather": {
      let s = I("tensorListId", e, t, n), r = I("indices", e, t, n), a = I("elementShape", e, t, n), i = I("elementDType", e, t, n);
      return [n.getTensorList(s.id).gather(r, i, a)];
    }
    case "TensorListStack": {
      let s = I("tensorListId", e, t, n), r = I("elementShape", e, t, n), a = I("elementDType", e, t, n), i = I("numElements", e, t, n);
      return [n.getTensorList(s.id).stack(r, a, i)];
    }
    case "TensorListFromTensor": {
      let s = I("tensor", e, t, n), r = I("elementShape", e, t, n), a = I("elementDType", e, t, n), i = GW(s, r, a);
      return n.addTensorList(i), [i.idTensor];
    }
    case "TensorListConcat": {
      let s = I("tensorListId", e, t, n), r = n.getTensorList(s.id), a = I("dtype", e, t, n), i = I("elementShape", e, t, n);
      return [r.concat(a, i)];
    }
    case "TensorListPushBack": {
      let s = I("tensorListId", e, t, n), r = I("tensor", e, t, n), a = n.getTensorList(s.id);
      return a.pushBack(r), [a.idTensor];
    }
    case "TensorListPopBack": {
      let s = I("tensorListId", e, t, n), r = I("elementShape", e, t, n), a = I("elementDType", e, t, n);
      return [n.getTensorList(s.id).popBack(r, a)];
    }
    case "TensorListSplit": {
      let s = I("tensor", e, t, n), r = I("elementShape", e, t, n), a = I("lengths", e, t, n), i = jW(s, a, r);
      return n.addTensorList(i), [i.idTensor];
    }
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
function qx(e, t, n) {
  let [s, r] = I("fusedOps", e, t, n), a = s === "biasadd", i = !a, o = r === "prelu", u = s === "fusedbatchnorm", l = I("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 = I("strides", e, t, n), p = nd(e, t, n), d = I("dataFormat", e, t, n).toUpperCase(), h = I("dilations", e, t, n), [f, m] = I("args", e, t, n);
  i && (m = f, f = void 0);
  let g = I("leakyreluAlpha", e, t, n);
  return { stride: c, pad: p, dataFormat: d, dilations: h, biasArg: f, preluArg: m, activationFunc: r, leakyreluAlpha: g };
}
var XW = (e, t, n) => {
  switch (e.op) {
    case "Conv1D": {
      let s = I("stride", e, t, n), r = I("pad", e, t, n), a = I("dataFormat", e, t, n).toUpperCase(), i = I("dilation", e, t, n);
      return [qk(I("x", e, t, n), I("filter", e, t, n), s, r, a, i)];
    }
    case "Conv2D": {
      let s = I("strides", e, t, n), r = nd(e, t, n), a = I("dataFormat", e, t, n).toUpperCase(), i = I("dilations", e, t, n);
      return [ua(I("x", e, t, n), I("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 } = qx(e, t, n);
      return [da.conv2d({ x: I("x", e, t, n), filter: I("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 } = qx(e, t, n);
      return [da.depthwiseConv2d({ x: I("x", e, t, n), filter: I("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 = I("outputShape", e, t, n), r = I("strides", e, t, n), a = nd(e, t, n);
      return [jk(I("x", e, t, n), I("filter", e, t, n), s, [r[1], r[2]], a)];
    }
    case "DepthwiseConv2dNative":
    case "DepthwiseConv2d": {
      let s = I("strides", e, t, n), r = nd(e, t, n), a = I("dilations", e, t, n), i = I("dataFormat", e, t, n).toUpperCase();
      return [pp(I("input", e, t, n), I("filter", e, t, n), [s[1], s[2]], r, i, [a[1], a[2]])];
    }
    case "Conv3D": {
      let s = I("strides", e, t, n), r = I("pad", e, t, n), a = I("dataFormat", e, t, n).toUpperCase(), i = I("dilations", e, t, n);
      return [Kk(I("x", e, t, n), I("filter", e, t, n), [s[1], s[2], s[3]], r, a, [i[1], i[2], i[3]])];
    }
    case "AvgPool": {
      let s = I("strides", e, t, n), r = I("pad", e, t, n), a = I("kernelSize", e, t, n);
      return [Wg(I("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r)];
    }
    case "MaxPool": {
      let s = I("strides", e, t, n), r = I("pad", e, t, n), a = I("kernelSize", e, t, n);
      return [Zg(I("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r)];
    }
    case "MaxPoolWithArgmax": {
      let s = I("strides", e, t, n), r = I("pad", e, t, n), a = I("kernelSize", e, t, n), i = I("includeBatchInIndex", e, t, n), { result: o, indexes: u } = sD(I("x", e, t, n), [a[1], a[2]], [s[1], s[2]], r, i);
      return [o, u];
    }
    case "AvgPool3D": {
      let s = I("strides", e, t, n), r = I("pad", e, t, n), a = I("kernelSize", e, t, n);
      return [Gk(I("x", e, t, n), [a[1], a[2], a[3]], [s[1], s[2], s[3]], r)];
    }
    case "MaxPool3D": {
      let s = I("strides", e, t, n), r = I("pad", e, t, n), a = I("kernelSize", e, t, n);
      return [aI(I("x", e, t, n), [a[1], a[2], a[3]], [s[1], s[2], s[3]], r)];
    }
    case "Dilation2D": {
      let s = I("strides", e, t, n), r = I("pad", e, t, n), a = I("dilations", e, t, n), i = s[1], o = s[2], u = a[1], l = a[2];
      return [sR(I("x", e, t, n), I("filter", e, t, n), [i, o], r, [u, l], "NHWC")];
    }
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var YW = (e, t, n) => {
  switch (e.op) {
    case "Fill": {
      let s = I("shape", e, t, n), r = I("dtype", e, t, n), a = I("value", e, t, n);
      return [Fl(s, a, r)];
    }
    case "LinSpace": {
      let s = I("start", e, t, n), r = I("stop", e, t, n), a = I("num", e, t, n);
      return [DR(s, r, a)];
    }
    case "Multinomial": {
      let s = I("logits", e, t, n), r = I("numSamples", e, t, n), a = I("seed", e, t, n);
      return [gD(s, r, a)];
    }
    case "OneHot": {
      let s = I("indices", e, t, n), r = I("depth", e, t, n), a = I("onValue", e, t, n), i = I("offValue", e, t, n);
      return [yd(s, r, a, i)];
    }
    case "Ones":
      return [zn(I("shape", e, t, n), I("dtype", e, t, n))];
    case "OnesLike":
      return [Xn(I("x", e, t, n))];
    case "RandomUniform":
      return [Pl(I("shape", e, t, n), I("minval", e, t, n), I("maxval", e, t, n), I("dtype", e, t, n))];
    case "Range": {
      let s = I("start", e, t, n), r = I("stop", e, t, n), a = I("step", e, t, n);
      return [Xu(s, r, a, I("dtype", e, t, n))];
    }
    case "TruncatedNormal": {
      let s = I("shape", e, t, n), r = I("mean", e, t, n), a = I("stdDev", e, t, n), i = I("seed", e, t, n);
      return [lb(s, r, a, I("dtype", e, t, n), i)];
    }
    case "Zeros":
      return [$t(I("shape", e, t, n), I("dtype", e, t, n))];
    case "ZerosLike":
      return [je(I("x", e, t, n))];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
function Uf(e, t, n) {
  let s = I("boxes", e, t, n), r = I("scores", e, t, n), a = I("maxOutputSize", e, t, n), i = I("iouThreshold", e, t, n), o = I("scoreThreshold", e, t, n), u = I("softNmsSigma", e, t, n);
  return { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o, softNmsSigma: u };
}
var QW = async (e, t, n) => {
  switch (e.op) {
    case "NonMaxSuppressionV5": {
      let { boxes: s, scores: r, maxOutputSize: a, iouThreshold: i, scoreThreshold: o, softNmsSigma: u } = Uf(e, t, n), l = await ds.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 } = Uf(e, t, n), u = I("padToMaxOutputSize", e, t, n), l = await ds.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 } = Uf(e, t, n);
      return [await ds.nonMaxSuppressionAsync(s, r, a, i, o)];
    }
    case "Where": {
      let s = ce(I("condition", e, t, n), "bool"), r = [await bI(s)];
      return s.dispose(), r;
    }
    case "ListDiff":
      return t3(I("x", e, t, n), I("y", e, t, n));
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var ZW = (e, t, n) => {
  switch (e.op) {
    case "TopKV2": {
      let s = I("x", e, t, n), r = I("k", e, t, n), a = I("sorted", e, t, n), i = N3(s, r, a);
      return [i.values, i.indices];
    }
    case "Unique": {
      let s = I("x", e, t, n), r = cx(s);
      return [r.values, r.indices];
    }
    case "UniqueV2": {
      let s = I("x", e, t, n), r = I("axis", e, t, n), a = cx(s, r);
      return [a.values, a.indices];
    }
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var JW = (e, t, n) => {
  switch (e.op) {
    case "Const":
      return t[e.name];
    case "PlaceholderWithDefault":
      let s = I("default", e, t, n);
      return [on(e.name, t, n) || s];
    case "Placeholder":
      return [on(e.name, t, n)];
    case "Identity":
    case "StopGradient":
    case "FakeQuantWithMinMaxVars": {
      let l = I("x", e, t, n);
      return [Us(l)];
    }
    case "IdentityN":
      return I("x", e, t, n).map((l) => Us(l));
    case "Snapshot":
      let r = I("x", e, t, n);
      return [Us(r)];
    case "Shape":
      return [Qt(I("x", e, t, n).shape, "int32")];
    case "ShapeN":
      return I("x", e, t, n).map((l) => Qt(l.shape));
    case "Size":
      return [Ie(I("x", e, t, n).size, "int32")];
    case "Rank":
      return [Ie(I("x", e, t, n).rank, "int32")];
    case "NoOp":
      return [Ie(1)];
    case "Print":
      let a = I("x", e, t, n), i = I("data", e, t, n), o = I("message", e, t, n), u = I("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 = Ie(0), this.tensorMap = /* @__PURE__ */ new Map(), Ht(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 Ie(this.size(), "int32");
  }
  async import(e, t) {
    this.checkKeyAndValueTensor(e, t);
    let n = await e.data();
    return this.tensorMap.forEach((s) => s.dispose()), this.tensorMap.clear(), j(() => {
      let s = 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];
        Ht(u), this.tensorMap.set(o, u);
      }
      return this.handle;
    });
  }
  async find(e, t) {
    this.checkKeyAndValueTensor(e, t);
    let n = await e.data();
    return j(() => {
      let s = [];
      for (let r = 0; r < n.length; r++) {
        let a = n[r], i = this.findWithDefault(a, t);
        s.push(i);
      }
      return Qn(s);
    });
  }
  findWithDefault(e, t) {
    let n = this.tensorMap.get(e);
    return n != null ? n : t;
  }
  checkKeyAndValueTensor(e, t) {
    if (e.dtype !== this.keyDType)
      throw new Error(`Expect key dtype ${this.keyDType}, but got ${e.dtype}`);
    if (t.dtype !== this.valueDType)
      throw new Error(`Expect value dtype ${this.valueDType}, but got ${t.dtype}`);
  }
};
var t4 = async (e, t, n, s) => {
  switch (e.op) {
    case "HashTable":
    case "HashTableV2": {
      let r = I("keyDType", e, t, n), a = I("valueDType", e, t, n), i = new e4(r, a);
      return s.addHashTable(e.name, i), [i.handle];
    }
    case "LookupTableImport":
    case "LookupTableImportV2": {
      let r = I("tableHandle", e, t, n, s), a = I("keys", e, t, n), i = I("values", e, t, n);
      return [await s.getHashTableById(r.id).import(a, i)];
    }
    case "LookupTableFind":
    case "LookupTableFindV2": {
      let r = I("tableHandle", e, t, n, s), a = I("keys", e, t, n), i = I("defaultValue", e, t, n);
      return [await s.getHashTableById(r.id).find(a, i)];
    }
    case "LookupTableSize":
    case "LookupTableSizeV2": {
      let r = I("tableHandle", e, t, n, s);
      return [s.getHashTableById(r.id).tensorSize()];
    }
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var n4 = (e, t, n) => {
  switch (e.op) {
    case "ResizeBilinear": {
      let s = I("images", e, t, n), r = I("size", e, t, n), a = I("alignCorners", e, t, n), i = I("halfPixelCenters", e, t, n);
      return [ds.resizeBilinear(s, [r[0], r[1]], a, i)];
    }
    case "ResizeNearestNeighbor": {
      let s = I("images", e, t, n), r = I("size", e, t, n), a = I("alignCorners", e, t, n), i = I("halfPixelCenters", e, t, n);
      return [ds.resizeNearestNeighbor(s, [r[0], r[1]], a, i)];
    }
    case "CropAndResize": {
      let s = I("image", e, t, n), r = I("boxes", e, t, n), a = I("boxInd", e, t, n), i = I("cropSize", e, t, n), o = I("method", e, t, n), u = I("extrapolationValue", e, t, n);
      return [ds.cropAndResize(s, r, a, i, o, u)];
    }
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var s4 = (e, t, n) => {
  switch (e.op) {
    case "Equal":
      return [qn(I("a", e, t, n), I("b", e, t, n))];
    case "NotEqual":
      return [Ku(I("a", e, t, n), I("b", e, t, n))];
    case "Greater":
      return [Wn(I("a", e, t, n), I("b", e, t, n))];
    case "GreaterEqual":
      return [jo(I("a", e, t, n), I("b", e, t, n))];
    case "Less":
      return [Jk(I("a", e, t, n), I("b", e, t, n))];
    case "LessEqual":
      return [Ko(I("a", e, t, n), I("b", e, t, n))];
    case "LogicalAnd":
      return [Ds(I("a", e, t, n), I("b", e, t, n))];
    case "LogicalNot":
      return [Qg(I("a", e, t, n))];
    case "LogicalOr":
      return [rI(I("a", e, t, n), I("b", e, t, n))];
    case "Select":
    case "SelectV2":
      return [vn(I("condition", e, t, n), I("a", e, t, n), I("b", e, t, n))];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var r4 = (e, t, n) => {
  switch (e.op) {
    case "BatchMatMul":
    case "BatchMatMulV2":
    case "MatMul":
      return [We(I("a", e, t, n), I("b", e, t, n), I("transposeA", e, t, n), I("transposeB", e, t, n))];
    case "Einsum":
      return [dR(I("equation", e, t, n), ...I("tensors", e, t, n))];
    case "Transpose":
      return [qe(I("x", e, t, n), I("perm", e, t, n))];
    case "_FusedMatMul":
      let [s, r] = I("fusedOps", e, t, n), a = s === "biasadd", i = r === "prelu", o = I("numArgs", e, t, n), u = I("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] = I("args", e, t, n);
      return [da.matMul({ a: I("a", e, t, n), b: I("b", e, t, n), transposeA: I("transposeA", e, t, n), transposeB: I("transposeB", e, t, n), bias: l, activation: r, preluActivationWeights: c, leakyreluAlpha: u })];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var a4 = (e, t, n) => {
  switch (e.op) {
    case "FusedBatchNorm":
    case "FusedBatchNormV2":
      return [qu(I("x", e, t, n), I("mean", e, t, n), I("variance", e, t, n), I("offset", e, t, n), I("scale", e, t, n), I("epsilon", e, t, n))];
    case "FusedBatchNormV3":
      return [qu(I("x", e, t, n), I("mean", e, t, n), I("variance", e, t, n), I("offset", e, t, n), I("scale", e, t, n), I("epsilon", e, t, n))];
    case "LRN":
      return [OR(I("x", e, t, n), I("radius", e, t, n), I("bias", e, t, n), I("alpha", e, t, n), I("beta", e, t, n))];
    case "Softmax":
      return [ib(I("x", e, t, n))];
    case "LogSoftmax":
      return [eI(I("x", e, t, n))];
    case "SparseToDense":
      return [xI(I("sparseIndices", e, t, n), I("outputShape", e, t, n), I("sparseValues", e, t, n), I("defaultValue", e, t, n))];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var i4 = (e, t, n) => {
  switch (e.op) {
    case "Max": {
      let i = I("axis", e, t, n), o = I("keepDims", e, t, n);
      return [As(I("x", e, t, n), i, o)];
    }
    case "Mean": {
      let i = I("axis", e, t, n), o = I("keepDims", e, t, n);
      return [It(I("x", e, t, n), i, o)];
    }
    case "Min": {
      let i = I("axis", e, t, n), o = I("keepDims", e, t, n);
      return [pm(I("x", e, t, n), i, o)];
    }
    case "Sum": {
      let i = I("axis", e, t, n), o = I("keepDims", e, t, n);
      return [ye(I("x", e, t, n), i, o)];
    }
    case "All": {
      let i = I("axis", e, t, n), o = I("keepDims", e, t, n);
      return [Bk(I("x", e, t, n), i, o)];
    }
    case "Any": {
      let i = I("axis", e, t, n), o = I("keepDims", e, t, n);
      return [cm(I("x", e, t, n), i, o)];
    }
    case "ArgMax": {
      let i = I("axis", e, t, n);
      return [Gu(I("x", e, t, n), i)];
    }
    case "ArgMin": {
      let i = I("axis", e, t, n);
      return [GA(I("x", e, t, n), i)];
    }
    case "Prod": {
      let i = I("axis", e, t, n), o = I("keepDims", e, t, n);
      return [iI(I("x", e, t, n), i, o)];
    }
    case "Cumsum": {
      let i = I("axis", e, t, n), o = I("exclusive", e, t, n), u = I("reverse", e, t, n);
      return [Qk(I("x", e, t, n), i, o, u)];
    }
    case "Bincount":
      let s = I("x", e, t, n), r = I("weights", e, t, n), a = I("size", e, t, n);
      return [Hk(s, r, a)];
    case "DenseBincount": {
      let i = I("x", e, t, n), o = I("weights", e, t, n), u = I("size", e, t, n), l = I("binaryOutput", e, t, n);
      return [QE(i, o, u, l)];
    }
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var o4 = (e, t, n) => {
  switch (e.op) {
    case "ConcatV2":
    case "Concat": {
      let s = I("n", e, t, n), r = I("axis", e, t, n), a = I("tensors", e, t, n);
      return a = a.slice(0, s), [Ft(a, r)];
    }
    case "Gather": {
      let s = I("x", e, t, n), r = I("indices", e, t, n);
      return [ju(s, ce(r, "int32"), 0)];
    }
    case "GatherV2": {
      let s = I("axis", e, t, n), r = I("batchDims", e, t, n), a = I("x", e, t, n), i = I("indices", e, t, n);
      return [ju(a, ce(i, "int32"), s, r)];
    }
    case "Reverse": {
      let s = I("dims", e, t, n), r = [];
      for (let i = 0; i < s.length; i++)
        s[i] && r.push(i);
      let a = I("x", e, t, n);
      return [Yn(a, r)];
    }
    case "ReverseV2": {
      let s = I("axis", e, t, n), r = I("x", e, t, n);
      return [Yn(r, s)];
    }
    case "Slice": {
      let s = I("begin", e, t, n), r = I("size", e, t, n);
      return [He(I("x", e, t, n), s, r)];
    }
    case "StridedSlice": {
      let s = I("begin", e, t, n), r = I("end", e, t, n), a = I("strides", e, t, n), i = I("beginMask", e, t, n), o = I("endMask", e, t, n), u = I("ellipsisMask", e, t, n), l = I("newAxisMask", e, t, n), c = I("shrinkAxisMask", e, t, n), p = I("x", e, t, n);
      return [k3(p, s, r, a, i, o, u, l, c)];
    }
    case "Pack":
      return j(() => {
        let s = I("axis", e, t, n), r = I("tensors", e, t, n), a = r[0].shape, i = mr(r[0]).shape, o = r.map((u) => {
          let l = w.arraysEqual(u.shape, a);
          if (!l && !w.arraysEqual(mr(u).shape, i))
            throw new Error("the input tensors shape does not match");
          return l ? u : G(u, a);
        });
        return [Qn(o, s)];
      });
    case "Unpack": {
      let s = I("axis", e, t, n), r = I("tensor", e, t, n);
      return Fs(r, s);
    }
    case "Tile": {
      let s = I("reps", e, t, n);
      return [cs(I("x", e, t, n), s)];
    }
    case "Split":
    case "SplitV": {
      let s = I("axis", e, t, n), r = I("numOrSizeSplits", e, t, n), a = I("x", e, t, n);
      return Ln(a, r, s);
    }
    case "ScatterNd": {
      let s = I("indices", e, t, n), r = I("values", e, t, n), a = I("shape", e, t, n);
      return [M3(s, r, a)];
    }
    case "GatherNd": {
      let s = I("x", e, t, n), r = I("indices", e, t, n);
      return [W3(s, r)];
    }
    case "SparseToDense": {
      let s = I("sparseIndices", e, t, n), r = I("outputShape", e, t, n), a = I("sparseValues", e, t, n), i = I("defaultValue", e, t, n);
      return [xI(s, a, r, a.dtype === i.dtype ? i : ce(i, a.dtype))];
    }
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var u4 = (e, t, n) => {
  switch (e.op) {
    case "SparseFillEmptyRows": {
      let { outputIndices: s, outputValues: r, emptyRowIndicator: a, reverseIndexMap: i } = Vc.sparseFillEmptyRows(I("indices", e, t, n), I("values", e, t, n), I("denseShape", e, t, n), I("defaultValue", e, t, n));
      return [s, r, a, i];
    }
    case "SparseReshape": {
      let { outputIndices: s, outputShape: r } = Vc.sparseReshape(I("inputIndices", e, t, n), I("inputShape", e, t, n), I("newShape", e, t, n));
      return [s, r];
    }
    case "SparseSegmentMean":
      return [Vc.sparseSegmentMean(I("data", e, t, n), I("indices", e, t, n), I("segmentIds", e, t, n))];
    case "SparseSegmentSum":
      return [Vc.sparseSegmentSum(I("data", e, t, n), I("indices", e, t, n), I("segmentIds", e, t, n))];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var l4 = (e, t, n) => {
  switch (e.op) {
    case "FFT":
      return [ob(I("x", e, t, n))];
    case "IFFT":
      return [kd(I("x", e, t, n))];
    case "RFFT":
      return [ub(I("x", e, t, n))];
    case "IRFFT":
      return [fI(I("x", e, t, n))];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var c4 = (e, t, n) => {
  switch (e.op) {
    case "StringNGrams": {
      let { nGrams: s, nGramsSplits: r } = Pf.stringNGrams(I("data", e, t, n), I("dataSplits", e, t, n), I("separator", e, t, n), I("nGramWidths", e, t, n), I("leftPad", e, t, n), I("rightPad", e, t, n), I("padWidth", e, t, n), I("preserveShortSequences", e, t, n));
      return [s, r];
    }
    case "StringSplit": {
      let { indices: s, values: r, shape: a } = Pf.stringSplit(I("input", e, t, n), I("delimiter", e, t, n), I("skipEmpty", e, t, n));
      return [s, r, a];
    }
    case "StringToHashBucketFast":
      return [Pf.stringToHashBucketFast(I("input", e, t, n), I("numBuckets", e, t, n))];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
var d4 = (e, t, n) => {
  switch (e.op) {
    case "Cast":
      return [ce(I("x", e, t, n), I("dtype", e, t, n))];
    case "ExpandDims": {
      let s = I("axis", e, t, n);
      return [On(I("x", e, t, n), s)];
    }
    case "Squeeze": {
      let s = I("axis", e, t, n);
      return [mr(I("x", e, t, n), s)];
    }
    case "Reshape":
      return [G(I("x", e, t, n), I("shape", e, t, n))];
    case "MirrorPad":
      return [lD(I("x", e, t, n), I("padding", e, t, n), I("mode", e, t, n))];
    case "PadV2":
    case "Pad":
      return [pi(I("x", e, t, n), I("padding", e, t, n), I("constantValue", e, t, n))];
    case "SpaceToBatchND": {
      let s = I("blockShape", e, t, n), r = I("paddings", e, t, n);
      return [eb(I("x", e, t, n), s, r)];
    }
    case "BatchToSpaceND": {
      let s = I("blockShape", e, t, n), r = I("crops", e, t, n);
      return [Ug(I("x", e, t, n), s, r)];
    }
    case "DepthToSpace": {
      let s = I("blockSize", e, t, n), r = I("dataFormat", e, t, n).toUpperCase();
      return [JE(I("x", e, t, n), s, r)];
    }
    case "BroadcastTo":
      return [td(I("x", e, t, n), I("shape", e, t, n))];
    case "BroadcastArgs":
      return [NE(I("s0", e, t, n), I("s1", e, t, n))];
    default:
      throw TypeError(`Node type ${e.op} is not implemented`);
  }
};
function jx(e, t, n, s) {
  let r = ((a, i, o) => {
    switch (a.category) {
      case "arithmetic":
        return j(() => VW(a, i, o));
      case "basic_math":
        return j(() => WW(a, i, o));
      case "control":
        return KW(a, i, o);
      case "convolution":
        return j(() => XW(a, i, o));
      case "creation":
        return j(() => YW(a, i, o));
      case "dynamic":
        return QW(a, i, o);
      case "evaluation":
        return j(() => ZW(a, i, o));
      case "image":
        return j(() => n4(a, i, o));
      case "graph":
        return j(() => JW(a, i, o));
      case "logical":
        return j(() => s4(a, i, o));
      case "matrices":
        return j(() => r4(a, i, o));
      case "normalization":
        return j(() => a4(a, i, o));
      case "reduction":
        return j(() => i4(a, i, o));
      case "slice_join":
        return j(() => o4(a, i, o));
      case "sparse":
        return j(() => u4(a, i, o));
      case "spectral":
        return j(() => l4(a, i, o));
      case "string":
        return j(() => c4(a, i, o));
      case "transformation":
        return j(() => d4(a, i, o));
      case "hash_table":
        return t4(a, i, o, s);
      case "custom":
        let u = W0(a.op);
        if (u && u.customExecutor)
          return u.customExecutor(new BW(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 Kx = 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 Xx(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 ((cS(d) || g4(d) || b4(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 p4(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 h4 = ["Switch", "Merge", "Enter", "Exit", "NextIteration", "StatelessIf", "StatelessWhile", "if", "While"];
var f4 = ["NonMaxSuppressionV2", "NonMaxSuppressionV3", "NonMaxSuppressionV5", "Where"];
var m4 = ["HashTable", "HashTableV2", "LookupTableImport", "LookupTableImportV2", "LookupTableFind", "LookupTableFindV2", "LookupTableSize", "LookupTableSizeV2"];
function cS(e) {
  return h4.indexOf(e.op) >= 0;
}
function g4(e) {
  return f4.indexOf(e.op) >= 0;
}
function b4(e) {
  return m4.indexOf(e.op) >= 0;
}
var Om = 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 Om(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}:${e.defaultOutput}` : t;
    });
  }
  get functions() {
    return Object.keys(this._functions).reduce((e, t) => (e[t] = this._functions[t].signature, e), {});
  }
  getCompilationKey(e, t) {
    let n = e.map((r) => r.name).sort(), s = t.map((r) => r.name).sort();
    return n.join(this.SEPERATOR) + "--" + s.join(this.SEPERATOR);
  }
  compile(e, t) {
    let n = Xx(e, t, this.weightMap, this._initNodes), { missingInputs: s, dynamicNode: r, syncInputs: a } = n;
    if (r != null)
      throw new Error(`This execution contains the node '${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: [${s}]`);
    }
    return p4(this.graph, this.weightMap, n);
  }
  execute(e, t) {
    e = this.mapInputs(e);
    let n = Object.keys(e).sort();
    this.checkInputs(e), this.checkInputShapeAndType(e), t = this.mapOutputs(t), this.checkOutputs(t);
    let s = n.map((c) => this.graph.nodes[$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 j(() => {
      let c = new Kx(this.weightMap, u, l, this.functionExecutorMap), p = { ...this.weightMap };
      Object.keys(e).forEach((f) => {
        let [m, g] = $n(f), b = [];
        b[g] = e[f], p[m] = b;
      });
      let d = this.getFrozenTensorIds(p), h = {};
      for (let f = 0; f < o.length; f++) {
        let m = o[f];
        if (!p[m.name]) {
          let g = jx(m, p, c, this._resourceManager);
          if (w.isPromise(g))
            throw new Error(`The execution of the op '${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) => on(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 = vW(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 = X().getBool("KEEP_INTERMEDIATE_TENSORS");
    } catch (l) {
      console.warn(l.message);
    }
    this.resetIntermediateTensors();
    let a = new Kx(this.weightMap, s, r, this.functionExecutorMap);
    this.tensorsMap = await this.executeWithControlFlow(e, a, t, n);
    let i = t.map((l) => on(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 } = Xx(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] = $n(y), k = [];
      k[x] = e[y], h[v] = k;
    });
    let f = {}, m = this.getFrozenTensorIds(h), g = {};
    for (; d.length > 0; ) {
      let y = this.processStack(a, d, t, h, g, m, i, f, u);
      await Promise.all(y);
    }
    c == null && !s && console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");
    let b = o.filter((y) => !cS(y) && !on(y.name, h, t)).map((y) => y.name);
    if (b.length > 0) {
      let y = "";
      throw c != null && (y = `Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${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" && I("isConstant", c.node, s, n) && ([p] = Ts(c.node.name, n)), s[c.node.name] == null) {
        let d = jx(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) => !!on(u, s, n)) && (r[o] = true, t.push({ contexts: n.currentContext, node: i })) : i.inputNames.every((u) => !!on(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 y4 = 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 v4 = "?tfjs-format=file";
var x4 = "model.json";
var w4 = class {
  constructor(e, t = {}) {
    this.modelUrl = e, this.loadOptions = t, this.version = "n/a", t == null && (this.loadOptions = {}), this.resourceManager = new y4();
  }
  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 = _n.browserHTTPRequest(e, this.loadOptions);
    else {
      let t = _n.getLoadHandlers(e, this.loadOptions);
      if (t.length === 0)
        t.push(_n.browserHTTPRequest(e, this.loadOptions));
      else if (t.length > 1)
        throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);
      this.handler = t[0];
    }
  }
  async load() {
    if (this.findIOHandler(), this.handler.load == null)
      throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");
    let e = await this.handler.load();
    return this.loadSync(e);
  }
  loadSync(e) {
    this.artifacts = e;
    let t = this.artifacts.modelTopology, n;
    this.artifacts.userDefinedMetadata != null && this.artifacts.userDefinedMetadata.signature != null ? n = this.artifacts.userDefinedMetadata.signature : n = this.artifacts.signature, this.signature = n, this.version = `${t.versions.producer}.${t.versions.minConsumer}`;
    let s = _n.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs);
    if (this.executor = new Om(Ux.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 = Ux.Instance.transformGraph(e.modelInitializer);
      this.initializer = new Om(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 = _n.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 ${e.length} input tensors.`);
    return this.inputNodes.reduce((t, n, s) => (t[n] = e[s], t), {});
  }
  normalizeOutputs(e) {
    return e = e || this.outputNodes, Array.isArray(e) ? e : [e];
  }
  execute(e, t) {
    e = this.normalizeInputs(e), t = this.normalizeOutputs(t);
    let n = this.executor.execute(e, t);
    return n.length > 1 ? n : n[0];
  }
  async executeAsync(e, t) {
    e = this.normalizeInputs(e), t = this.normalizeOutputs(t);
    let n = await this.executor.executeAsync(e, t);
    return n.length > 1 ? n : n[0];
  }
  getIntermediateTensors() {
    return this.executor.getIntermediateTensors();
  }
  disposeIntermediateTensors() {
    this.executor.disposeIntermediateTensors();
  }
  convertTensorMapToTensorsMap(e) {
    return Object.keys(e).reduce((t, n) => (t[n] = [e[n]], t), {});
  }
  dispose() {
    this.executor.dispose(), this.initializer && this.initializer.dispose(), this.resourceManager.dispose();
  }
};
async function bpe(e, t = {}) {
  if (e == null)
    throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");
  t == null && (t = {}), t.fromTFHub && e.load == null && (e.endsWith("/") || (e = e + "/"), e = `${e}${x4}${v4}`);
  let n = new w4(e, t);
  return await n.load(), n;
}
var ype = "0.0.0";
var k4 = {};
Ae(k4, { CSVDataset: () => kS, Dataset: () => Zo, FileDataSource: () => _S, TextLineDataset: () => wS, URLDataSource: () => AS, array: () => H4, csv: () => nU, func: () => sU, generator: () => rU, microphone: () => iU, version_data: () => oU, webcam: () => aU, zip: () => q4 });
var I4 = ya(Vd());
var S4 = ya(Vd());
function C4(e, t) {
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  if (n.has(e))
    return n.get(e);
  let r = t(e);
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        a[i] = u;
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      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 N4(e, t = pS) {
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}
function dS(e, t, n = /* @__PURE__ */ new Set()) {
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    throw new Error("Circular references are not supported.");
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    throw new Error("A deep zip function may not return both a value and recurse=true.");
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      return n.delete(s), a;
    } else
      throw new Error(`Can't recurse into non-iterable type: ${s}`);
  else
    return r.value;
}
function pS(e) {
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}
async function hS(e, t) {
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      n.set(r, i);
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  return Ed(e, t, n);
}
function Ji(e) {
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  if (X().get("IS_BROWSER"))
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    let { StringDecoder: n } = Pw();
    t = e instanceof n;
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  return e != null && !ArrayBuffer.isView(e) && (Array.isArray(e) || typeof e == "object" && !(e instanceof et) && !(e instanceof Promise) && !t);
}
function T4(e) {
  return e == null || $4(e) || Array.isArray(e) || typeof e == "object" && e instanceof et || w.isTypedArray(e);
}
function $4(e) {
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}
function _4(e) {
  return C4(e, A4);
}
function A4(e) {
  return e instanceof et ? { value: e.clone(), recurse: false } : Ji(e) ? { value: null, recurse: true } : { value: e, recurse: false };
}
var fS = 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;
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  wrap(e) {
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  get(e) {
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  set(e, t) {
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    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);
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  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);
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  shuffleExcise(e) {
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      throw new RangeError("Ring buffer is empty.");
    let t = this.wrap(this.begin + e), n = this.get(t);
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};
var mS = class extends fS {
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  }
  isFull() {
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  }
  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 gS = mS;
gS.INITIAL_CAPACITY = 32;
function bS(e) {
  return new D4(e);
}
function Ky(e) {
  return new F4(e);
}
function E4(e, t) {
  return new yS(e, t);
}
function R4(e, t = vS.FAIL) {
  return new U4(e, t);
}
var Ut = class {
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    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 V4(this, e);
  }
  filter(e) {
    return new L4(this, e);
  }
  map(e) {
    return new B4(this, e);
  }
  mapAsync(e) {
    return new Yx(this, e);
  }
  serialMapAsync(e) {
    return new Yx(this, e).serial();
  }
  flatmap(e) {
    return new W4(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 M4(this, e, t);
  }
  columnMajorBatch(e, t = true, n = pS) {
    return this.rowMajorBatch(e, t).map((r) => N4(r, n));
  }
  concatenate(e, t) {
    return new yS(bS([this, e]), t);
  }
  take(e) {
    return e < 0 || e == null ? this : new z4(this, e);
  }
  skip(e) {
    return e < 0 || e == null ? this : new P4(this, e);
  }
  prefetch(e) {
    return new xS(this, e);
  }
  shuffle(e, t) {
    return new G4(this, e, t);
  }
  serial() {
    return new O4(this);
  }
};
var D4 = class extends Ut {
  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: _4(e), done: false };
  }
};
var F4 = class extends Ut {
  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: ${e.message}`, e;
    }
  }
};
var O4 = class extends Ut {
  constructor(e) {
    super();
    this.upstream = e, this.lastRead = Promise.resolve({ value: null, done: false });
  }
  summary() {
    return `${this.upstream.summary()} -> Serial`;
  }
  async next() {
    return this.lastRead = this.lastRead.then(() => this.serialNext()), this.lastRead;
  }
  async serialNext() {
    return this.upstream.next();
  }
};
var P4 = class extends Ut {
  constructor(e, t) {
    super();
    this.upstream = e, this.maxCount = t, this.count = 0, this.lastRead = Promise.resolve({ value: null, done: false });
  }
  summary() {
    return `${this.upstream.summary()} -> Skip`;
  }
  async next() {
    return this.lastRead = this.lastRead.then(() => this.serialNext()), this.lastRead;
  }
  async serialNext() {
    for (; this.count++ < this.maxCount; ) {
      let e = await this.upstream.next();
      if (e.done)
        return e;
      Re(e.value);
    }
    return this.upstream.next();
  }
};
var z4 = class extends Ut {
  constructor(e, t) {
    super();
    this.upstream = e, this.maxCount = t, this.count = 0;
  }
  summary() {
    return `${this.upstream.summary()} -> Take`;
  }
  async next() {
    return this.count++ >= this.maxCount ? { value: null, done: true } : this.upstream.next();
  }
};
var M4 = class extends Ut {
  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 `${this.upstream.summary()} -> RowMajorBatch`;
  }
  async next() {
    return this.lastRead = this.lastRead.then(() => this.serialNext()), this.lastRead;
  }
  async serialNext() {
    let e = [];
    for (; e.length < this.batchSize; ) {
      let t = await this.upstream.next();
      if (t.done)
        return this.enableSmallLastBatch && e.length > 0 ? { value: e, done: false } : { value: null, done: true };
      e.push(t.value);
    }
    return { value: e, done: false };
  }
};
var L4 = class extends Ut {
  constructor(e, t) {
    super();
    this.upstream = e, this.predicate = t, this.lastRead = Promise.resolve({ value: null, done: false });
  }
  summary() {
    return `${this.upstream.summary()} -> Filter`;
  }
  async next() {
    return this.lastRead = this.lastRead.then(() => this.serialNext()), this.lastRead;
  }
  async serialNext() {
    for (; ; ) {
      let e = await this.upstream.next();
      if (e.done || this.predicate(e.value))
        return e;
      Re(e.value);
    }
  }
};
var B4 = class extends Ut {
  constructor(e, t) {
    super();
    this.upstream = e, this.transform = t;
  }
  summary() {
    return `${this.upstream.summary()} -> Map`;
  }
  async next() {
    let e = await this.upstream.next();
    if (e.done)
      return { value: null, done: true };
    let t = _s.getTensorsInContainer(e.value), n = this.transform(e.value), s = _s.getTensorsInContainer(n);
    for (let r of t)
      _s.isTensorInList(r, s) || r.dispose();
    return { value: n, done: false };
  }
};
var V4 = class extends Ut {
  constructor(e, t) {
    super();
    this.upstream = e, this.handler = t, this.count = 0, this.lastRead = Promise.resolve({ value: null, done: false });
  }
  summary() {
    return `${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 Yx = class extends Ut {
  constructor(e, t) {
    super();
    this.upstream = e, this.transform = t;
  }
  summary() {
    return `${this.upstream.summary()} -> AsyncMap`;
  }
  async next() {
    let e = await this.upstream.next();
    if (e.done)
      return { value: null, done: true };
    let t = _s.getTensorsInContainer(e.value), n = await this.transform(e.value), s = _s.getTensorsInContainer(n);
    for (let r of t)
      _s.isTensorInList(r, s) || r.dispose();
    return { value: n, done: false };
  }
};
var Xy = class extends Ut {
  constructor() {
    super();
    this.outputQueue = new gS(), this.lastRead = Promise.resolve({ value: null, done: false });
  }
  async next() {
    return this.lastRead = this.lastRead.then(() => this.serialNext()), this.lastRead;
  }
  async serialNext() {
    for (; this.outputQueue.length() === 0; )
      if (!await this.pump())
        return { value: null, done: true };
    return { value: this.outputQueue.shift(), done: false };
  }
};
var W4 = class extends Xy {
  constructor(e, t) {
    super();
    this.upstream = e, this.transform = t;
  }
  summary() {
    return `${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 yS = class extends Ut {
  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 vS = ((e) => (e[e.FAIL = 0] = "FAIL", e[e.SHORTEST = 1] = "SHORTEST", e[e.LONGEST = 2] = "LONGEST", e))(vS || {});
var U4 = class extends Ut {
  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 Ut ? { value: a.next().then((o) => (t++, o.done && n++, o.value)), recurse: false } : { value: null, recurse: true };
    }
    let r = await hS(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 ${this.count}.`);
        case 1:
          return { value: null, done: true };
        case 2:
        default:
      }
    return this.count++, { value: r, done: false };
  }
  async next() {
    return this.currentPromise = this.nextState(this.currentPromise), this.currentPromise;
  }
};
var xS = class extends Ut {
  constructor(e, t) {
    super();
    this.upstream = e, this.bufferSize = t, this.buffer = new fS(t);
  }
  summary() {
    return `${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 G4 = class extends xS {
  constructor(e, t, n) {
    super(e, t);
    this.upstream = e, this.windowSize = t, this.upstreamExhausted = false, this.random = S4.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 Zo = 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), Tn(async () => (await n.iterator()).columnMajorBatch(e, t, j4), 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, Tn(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, Tn(async () => (await t.iterator()).filter((s) => j(() => e(s))), n);
  }
  async forEachAsync(e) {
    return (await this.iterator()).forEachAsync(e);
  }
  map(e) {
    let t = this;
    return Tn(async () => (await t.iterator()).map((n) => j(() => e(n))), this.size);
  }
  mapAsync(e) {
    let t = this;
    return Tn(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 Tn(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, Tn(async () => {
      let s = Ky(async () => ({ value: await t.iterator(), done: false }));
      return E4(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, Tn(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 = I4.alea(t || w.now().toString());
    return Tn(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, Tn(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();
  }
};
Zo.MAX_BUFFER_SIZE = 1e4;
function Tn(e, t = null) {
  return new class extends Zo {
    constructor() {
      super(...arguments);
      this.size = t;
    }
    async iterator() {
      return e();
    }
  }();
}
function H4(e) {
  return Tn(async () => bS(e), e.length);
}
function q4(e) {
  if (!Ji(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 Tn(async () => {
    let n = await hS(e, (s) => {
      if (s instanceof Zo)
        return { value: s.iterator(), recurse: false };
      if (Ji(s))
        return { value: null, recurse: true };
      throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.");
    });
    return R4(n, 1);
  }, t);
}
function j4(e) {
  if (e === null)
    return null;
  let t = e[0];
  return T4(t) ? { value: K4(e), recurse: false } : { value: null, recurse: true };
}
function K4(e) {
  if (e.length === 0)
    throw new Error("Can't make a batch of zero elements.");
  return e[0] instanceof et ? Qn(e) : hs(e);
}
var wS = class extends Zo {
  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 jc = '"';
var Nu = Symbol("out");
var Qx = Symbol("field");
var Kc = Symbol("quote");
var Gf = Symbol("quoteafterquote");
var Zx = Symbol("quoteinquote");
var kS = class extends Zo {
  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 wS(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 = Nu;
    for (let i = 0; i < r; i++)
      switch (a) {
        case Nu:
          switch (e.charAt(i)) {
            case jc:
              s = i + 1, a = Kc;
              break;
            case this.delimiter:
              if (s = i + 1, this.delimiter === " " && this.delimWhitespace)
                break;
              n.push(""), a = Nu;
              break;
            default:
              a = Qx, s = i;
              break;
          }
          break;
        case Qx:
          switch (e.charAt(i)) {
            case this.delimiter:
              n.push(e.substring(s, i)), a = Nu, s = i + 1;
              break;
            default:
          }
          break;
        case Kc:
          switch (e.charAt(i)) {
            case jc:
              a = Gf;
              break;
            default:
          }
          break;
        case Gf:
          switch (e.charAt(i)) {
            case this.delimiter:
              n.push(e.substring(s, i - 1)), a = Nu, s = i + 1;
              break;
            case jc:
              a = Kc;
              break;
            default:
              a = Zx;
              break;
          }
          break;
        case Zx:
          switch (e.charAt(i)) {
            case jc:
              a = Kc;
              break;
            default:
          }
          break;
        default:
      }
    if (a === Gf ? 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 IS = class extends Ut {
  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 (X().get("IS_NODE"))
      throw new Error("microphone API is only supported in browser environment.");
    let t = new IS(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), hs(n, t);
  }
};
var SS = class extends Ut {
  constructor(e, t) {
    super();
    if (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 = Qt([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 = ji([a, r, o, i], [1, 4]);
      } else
        this.cropBox = ji([0, 0, 1, 1], [1, 4]);
  }
  summary() {
    return "webcam";
  }
  static async create(e, t = {}) {
    if (X().get("IS_NODE"))
      throw new Error("tf.data.webcam is only supported in browser environment.");
    if (!e) {
      if (e = document.createElement("video"), !t.resizeWidth || !t.resizeHeight)
        throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");
      e.width = t.resizeWidth, e.height = t.resizeHeight;
    }
    let n = new SS(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 = xk.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: ${t.message}`);
      } finally {
        e.dispose();
      }
    else
      return { value: e, done: false };
  }
  needToResize() {
    return !!(this.webcamConfig.resizeWidth && this.webcamConfig.resizeHeight && (this.webcamVideoElement.width !== this.webcamConfig.resizeWidth || this.webcamVideoElement.height !== this.webcamConfig.resizeHeight));
  }
  cropAndResizeFrame(e) {
    return j(() => {
      let t = On(ce(e, "float32"), 0), n;
      n = ds.cropAndResize(t, this.cropBox, this.cropBoxInd, this.cropSize, "bilinear");
      let s = n.shape;
      return G(n, s.slice(1));
    });
  }
  async capture() {
    return (await this.next()).value;
  }
  stop() {
    this.stream.getTracks().forEach((t) => t.stop());
    try {
      this.webcamVideoElement.srcObject = null;
    } catch (t) {
      console.log(t), this.webcamVideoElement.src = null;
    }
    this.isClosed = true;
  }
  toArray() {
    throw new Error("Can not convert infinite video stream to array.");
  }
};
var CS = class {
};
var NS = class extends Ut {
  split(e) {
    return new X4(this, e);
  }
};
var X4 = class extends NS {
  constructor(e, t) {
    super();
    this.upstream = e, this.impl = new Y4(e, t);
  }
  summary() {
    return this.impl.summary();
  }
  async next() {
    return this.impl.next();
  }
};
var Y4 = class extends Xy {
  constructor(e, t) {
    super();
    this.upstream = e, this.separator = t, this.carryover = "";
  }
  summary() {
    return `${this.upstream.summary()} -> Split('${this.separator}')`;
  }
  async pump() {
    let e = await this.upstream.next();
    if (e.done)
      return this.carryover === "" ? false : (this.outputQueue.push(this.carryover), this.carryover = "", true);
    let t = e.value.split(this.separator);
    t[0] = this.carryover + t[0];
    for (let n of t.slice(0, -1))
      this.outputQueue.push(n);
    return this.carryover = t[t.length - 1], true;
  }
};
var Q4 = class extends Ut {
  decodeUTF8() {
    return new Z4(this);
  }
};
var Z4 = class extends NS {
  constructor(e) {
    super();
    this.upstream = e, this.impl = new J4(e);
  }
  summary() {
    return this.impl.summary();
  }
  async next() {
    return this.impl.next();
  }
};
var J4 = class extends Xy {
  constructor(e) {
    super();
    if (this.upstream = e, X().get("IS_BROWSER"))
      this.decoder = new TextDecoder("utf-8");
    else {
      let { StringDecoder: t } = Pw();
      this.decoder = new t("utf8");
    }
  }
  summary() {
    return `${this.upstream.summary()} -> Utf8`;
  }
  async pump() {
    let e = await this.upstream.next(), t;
    if (e.done)
      return false;
    t = e.value;
    let n;
    return X().get("IS_BROWSER") ? n = this.decoder.decode(t, { stream: true }) : n = this.decoder.write(Buffer.from(t.buffer)), this.outputQueue.push(n), true;
  }
};
var TS = class extends Q4 {
  constructor(e, t = {}) {
    super();
    this.file = e, this.options = t, w.assert(e instanceof Uint8Array || (X().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 = tU(e));
  let a = await (n || w.fetch)(s, r);
  if (a.ok) {
    let i = new Uint8Array(await a.arrayBuffer());
    return new TS(i, t);
  } else
    throw new Error(a.statusText);
}
var tU = (e) => ({ method: e.method, headers: e.headers, body: e.body, mode: e.mode, credentials: e.credentials, cache: e.cache, redirect: e.redirect, referrer: e.referrer, integrity: e.integrity });
function $S(e) {
  return typeof e == "string" && e.substr(0, 7) === "file://";
}
var _S = class extends CS {
  constructor(e, t = {}) {
    super();
    this.input = e, this.options = t;
  }
  async iterator() {
    if ($S(this.input) && X().get("IS_NODE")) {
      let e = Jm();
      this.input = e.readFileSync(this.input.substr(7));
    }
    return new TS(this.input, this.options);
  }
};
var AS = class extends CS {
  constructor(e, t = {}) {
    super();
    this.url = e, this.fileOptions = t;
  }
  async iterator() {
    return $S(this.url) ? new _S(this.url, this.fileOptions).iterator() : eU(this.url, this.fileOptions);
  }
};
function nU(e, t = {}) {
  return new kS(new AS(e), t);
}
function sU(e) {
  let t = Ky(e);
  return Tn(async () => t);
}
function rU(e) {
  return Tn(async () => {
    let t = await e();
    return Ky(() => t.next());
  });
}
async function aU(e, t) {
  return SS.create(e, t);
}
async function iU(e) {
  return IS.create(e);
}
var oU = "0.0.0";
function ve(e, t) {
  Array.isArray(e) || (e = [e]), e.forEach((n) => {
    n != null && w.assert(n.dtype !== "complex64", () => `${t} does not support complex64 tensors in the CPU backend.`);
  });
}
var uU = xs.whereImpl;
var ES = class extends tl {
  constructor() {
    super();
    this.blockSize = 48, this.firstUse = true, this.data = new Wd(this, Ss());
  }
  nextDataId() {
    return ES.nextDataId++;
  }
  write(e, t, n) {
    this.firstUse && (this.firstUse = false, X().get("IS_NODE") && N.warn(`
============================
Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
============================`));
    let s = { id: this.nextDataId() };
    return this.data.set(s, { values: e, dtype: n, refCount: 1 }), s;
  }
  makeTensorInfo(e, t, n) {
    let s;
    if (t === "string" && n != null && n.length > 0 && w.isString(n[0])) {
      let r = n.map((a) => w.encodeString(a));
      s = this.write(r, e, t);
    } else
      s = this.write(n, e, t);
    return { dataId: s, shape: e, dtype: t };
  }
  refCount(e) {
    return this.data.has(e) ? this.data.get(e).refCount : 0;
  }
  incRef(e) {
    let t = this.data.get(e);
    t.refCount++;
  }
  decRef(e) {
    if (this.data.has(e)) {
      let t = this.data.get(e);
      t.refCount--;
    }
  }
  move(e, t, n, s, r) {
    this.data.set(e, { values: t, dtype: s, refCount: r });
  }
  numDataIds() {
    return this.data.numDataIds();
  }
  async read(e) {
    return this.readSync(e);
  }
  readSync(e) {
    let { dtype: t, complexTensorInfos: n } = this.data.get(e);
    if (t === "complex64") {
      let s = this.readSync(n.real.dataId), r = this.readSync(n.imag.dataId);
      return N.mergeRealAndImagArrays(s, r);
    }
    return this.data.get(e).values;
  }
  bufferSync(e) {
    let t = this.readSync(e.dataId), n = t;
    if (e.dtype === "string")
      try {
        n = t.map((s) => w.decodeString(s));
      } catch (s) {
        throw new Error("Failed to decode encoded string bytes into utf-8");
      }
    return De(e.shape, e.dtype, n);
  }
  makeOutput(e, t, n) {
    let s = this.write(e, t, n);
    return Ss().makeTensorFromDataId(s, t, n, 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) {
    ve([e], "where");
    let t = this.readSync(e.dataId);
    return uU(e.shape, t);
  }
  dispose() {
  }
  floatPrecision() {
    return 32;
  }
  epsilon() {
    return super.epsilon();
  }
};
var RS = ES;
RS.nextDataId = 0;
var Yy = {};
Ae(Yy, { addImpl: () => FS, bincountImpl: () => Zy, bincountReduceImpl: () => OS, ceilImpl: () => PS, concatImpl: () => Jy, equalImpl: () => zS, expImpl: () => LS, expm1Impl: () => VS, floorImpl: () => WS, gatherNdImpl: () => US, gatherV2Impl: () => GS, greaterEqualImpl: () => qS, greaterImpl: () => HS, lessEqualImpl: () => KS, lessImpl: () => jS, linSpaceImpl: () => XS, logImpl: () => YS, maxImpl: () => QS, maximumImpl: () => ZS, minimumImpl: () => JS, multiplyImpl: () => ev, negImpl: () => eC, notEqualImpl: () => tC, prodImpl: () => nC, rangeImpl: () => nv, rsqrtImpl: () => sC, sigmoidImpl: () => YU, simpleAbsImpl: () => DS, sliceImpl: () => Dd, sparseFillEmptyRowsImpl: () => aC, sparseReshapeImpl: () => iC, sparseSegmentReductionImpl: () => sv, sqrtImpl: () => JU, squaredDifferenceImpl: () => oC, stridedSliceImpl: () => uC, stringNGramsImpl: () => lC, stringSplitImpl: () => cC, stringToHashBucketFastImpl: () => dC, subImpl: () => pC, tileImpl: () => hC, topKImpl: () => mC, transposeImpl: () => tv, uniqueImpl: () => gC });
function DS(e) {
  let t = new Float32Array(e.length);
  for (let n = 0; n < e.length; ++n)
    t[n] = Math.abs(e[n]);
  return t;
}
var lU = (e) => {
  let { x: t } = e.inputs, n = e.backend;
  ve(t, "abs");
  let s = new Float32Array(w.sizeFromShape(t.shape)), r = n.data.get(t.dataId).values;
  return s = DS(r), n.makeOutput(s, t.shape, t.dtype);
};
var cU = { kernelName: ao, backendName: "cpu", kernelFunc: lU };
function At(e) {
  return (t, n, s, r, a) => {
    let i = N.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 = N.getBroadcastDims(t, i), g = N.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((T) => v[T] = 0);
        let x = w.locToIndex(v, p, h), k = y.slice(-d);
        g.forEach((T) => k[T] = 0);
        let C = w.locToIndex(k, d, f);
        c[b] = e(s[x], r[C]);
      }
    return [c, i];
  };
}
function An(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 dU = { kernelName: qd, backendName: "cpu", kernelFunc: An };
function Rd(e, t, n = "float32") {
  if (n === "complex64") {
    let r = Rd(e, t, "float32"), a = Rd(e, t, "float32");
    return An({ 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 pU = { kernelName: Ma, backendName: "cpu", kernelFunc: Os };
function ha(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 hU = { kernelName: tp, backendName: "cpu", kernelFunc: ha };
function xr(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dtype: a } = s;
  if (a === "complex64") {
    if (r.dtype === "complex64")
      return Os({ inputs: { x: r }, backend: n });
    let i = Rd(n, r.shape, r.dtype), o = xr({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = An({ inputs: { real: o, imag: i }, backend: n });
    return n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
  }
  if (r.dtype === "complex64") {
    let i = ha({ inputs: { input: r }, backend: n }), o = xr({ inputs: { x: i }, backend: n, attrs: { dtype: a } });
    return n.disposeIntermediateTensorInfo(i), o;
  }
  if (!w.hasEncodingLoss(r.dtype, a)) {
    let i = Os({ inputs: { x: r }, backend: n });
    return { dataId: i.dataId, shape: i.shape, dtype: a };
  }
  if (a === "int32") {
    let i = n.data.get(r.dataId).values, o = Int32Array.from(i);
    return n.makeTensorInfo(r.shape, "int32", o);
  }
  if (a === "bool") {
    let i = n.data.get(r.dataId).values, o = w.toTypedArray([0], r.dtype), [u, l] = At((c, p) => c !== p ? 1 : 0)(r.shape, [], i, o, "bool");
    return n.makeTensorInfo(l, "bool", u);
  }
  throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`);
}
var fU = { kernelName: Sa, backendName: "cpu", kernelFunc: xr };
function Gt(e, t, n, s) {
  return n == null ? ({ inputs: r, backend: a }) => {
    let { a: i, b: o } = r, u = a;
    ve([i, o], e);
    let l = u.data.get(i.dataId).values, c = u.data.get(o.dataId).values, p = i.dtype === "string" ? N.fromUint8ToStringArray(l) : l, d = i.dtype === "string" ? N.fromUint8ToStringArray(c) : c, h = s || i.dtype, [f, m] = t(i.shape, o.shape, p, d, h);
    return u.makeTensorInfo(m, h, f);
  } : ({ inputs: r, backend: a }) => {
    let { a: i, b: o } = r, u = a;
    if (i.dtype === "complex64" || o.dtype === "complex64") {
      let l = xr({ inputs: { x: i }, backend: u, attrs: { dtype: "complex64" } }), c = u.data.get(l.dataId), p = c.complexTensorInfos.real, d = c.complexTensorInfos.imag, h = u.data.get(p.dataId).values, f = u.data.get(d.dataId).values, m = xr({ inputs: { x: o }, backend: u, attrs: { dtype: "complex64" } }), g = u.data.get(m.dataId), b = g.complexTensorInfos.real, y = g.complexTensorInfos.imag, v = u.data.get(b.dataId).values, x = u.data.get(y.dataId).values, [k, C, T] = n(i.shape, o.shape, h, f, v, x), E = u.makeTensorInfo(T, "float32", k), A = u.makeTensorInfo(T, "float32", C), P = An({ inputs: { real: E, imag: A }, backend: u });
      return u.disposeIntermediateTensorInfo(l), u.disposeIntermediateTensorInfo(m), u.disposeIntermediateTensorInfo(E), u.disposeIntermediateTensorInfo(A), P;
    } else {
      let l = u.data.get(i.dataId).values, c = u.data.get(o.dataId).values, p = s || i.dtype, [d, h] = t(i.shape, o.shape, l, c, p);
      return u.makeTensorInfo(h, p, d);
    }
  };
}
function Qy(e) {
  return (t, n, s, r, a, i) => {
    let o = N.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 = N.getBroadcastDims(t, o), f = N.getBroadcastDims(n, o), m = N.mergeRealAndImagArrays(s, r), g = N.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 C = k % m.length, T = k % g.length, E = e(m[C * 2], m[C * 2 + 1], g[T * 2], g[T * 2 + 1]);
        p[k] = E.real, d[k] = E.imag;
      }
    else
      for (let k = 0; k < p.length; k++) {
        let C = w.indexToLoc(k, l, c), T = C.slice(-b);
        h.forEach((F) => T[F] = 0);
        let E = w.locToIndex(T, b, y), A = C.slice(-v);
        f.forEach((F) => A[F] = 0);
        let P = w.locToIndex(A, v, x), R = e(m[E * 2], m[E * 2 + 1], g[P * 2], g[P * 2 + 1]);
        p[k] = R.real, d[k] = R.imag;
      }
    return [p, d, o];
  };
}
var FS = At((e, t) => e + t);
var mU = Qy((e, t, n, s) => ({ real: e + n, imag: t + s }));
var jl = Gt(kr, FS, mU);
var gU = { kernelName: kr, backendName: "cpu", kernelFunc: jl };
function Zy(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 OS(e, t, n, s = false) {
  let r = e.shape[0], a = e.shape[1], i = De([r, n], t.dtype);
  for (let o = 0; o < r; o++)
    for (let u = 0; u < a; u++) {
      let l = e.get(o, u);
      if (l < 0)
        throw new Error("Input x must be non-negative!");
      l >= n || (s ? i.set(1, o, l) : t.size > 0 ? i.set(i.get(o, l) + t.get(o, u), o, l) : i.set(i.get(o, l) + 1, o, l));
    }
  return i;
}
function Ar(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 (ve(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 Jo(e, t, n) {
  return ({ inputs: s, attrs: r, backend: a }) => {
    let { x: i } = s;
    if (ve(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 PS = Ar((e) => Math.ceil(e));
var bU = Jo(Ca, PS);
var yU = { kernelName: Ca, backendName: "cpu", kernelFunc: bU };
function Jy(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" ? N.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 zS = At((e, t) => e === t ? 1 : 0);
var MS = Gt(po, zS, null, "bool");
var vU = { kernelName: po, backendName: "cpu", kernelFunc: MS };
var LS = Ar((e) => Math.exp(e));
var BS = Jo(Da, LS, "float32");
var xU = { kernelName: Da, backendName: "cpu", kernelFunc: BS };
var VS = Ar((e) => Math.expm1(e));
var wU = Jo(fo, VS);
var kU = { kernelName: fo, backendName: "cpu", kernelFunc: wU };
var WS = Ar((e) => Math.floor(e));
var IU = Jo(Fa, WS);
var SU = { kernelName: Fa, backendName: "cpu", kernelFunc: IU };
function US(e, t, n, s, r, a, i, o, u) {
  let l = De([s, a], n);
  for (let c = 0; c < s; c++) {
    let p = [], d = 0;
    for (let h = 0; h < r; h++) {
      let f = e[c * r + h];
      d += f * i[h], p.push(f);
    }
    if (d < 0 || d >= u / a)
      throw new Error(`Invalid indices: ${p} does not index into ${o}`);
    for (let h = 0; h < a; h++)
      l.values[c * a + h] = t.get(...t.indexToLoc(d * a + h));
  }
  return l;
}
function GS(e, t, n) {
  let s = De(n, e.dtype);
  for (let r = 0; r < s.size; ++r) {
    let i = s.indexToLoc(r).slice(), o = i[0], u = i[2], l = t.locToIndex([o, u]);
    i[2] = t.values[l];
    let c = e.locToIndex(i);
    0 <= c && c < e.values.length && (s.values[r] = e.values[c]);
  }
  return s;
}
var HS = At((e, t) => e > t ? 1 : 0);
var CU = Gt(yo, HS, null, "bool");
var NU = { kernelName: yo, backendName: "cpu", kernelFunc: CU };
var qS = At((e, t) => e >= t ? 1 : 0);
var TU = Gt(za, qS, null, "bool");
var $U = { kernelName: za, backendName: "cpu", kernelFunc: TU };
var jS = At((e, t) => e < t ? 1 : 0);
var _U = Gt(vo, jS, null, "bool");
var AU = { kernelName: vo, backendName: "cpu", kernelFunc: _U };
var KS = At((e, t) => e <= t ? 1 : 0);
var EU = Gt(xo, KS, null, "bool");
var RU = { kernelName: xo, backendName: "cpu", kernelFunc: EU };
function XS(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 YS = Ar((e) => Math.log(e));
var DU = Jo(Ba, YS);
var FU = { kernelName: Ba, backendName: "cpu", kernelFunc: DU };
function QS(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 ZS = At((e, t) => Math.max(e, t));
var OU = Gt(Wa, ZS);
var PU = { kernelName: Wa, backendName: "cpu", kernelFunc: OU };
var JS = At((e, t) => Math.min(e, t));
var zU = Gt(qa, JS);
var MU = { kernelName: qa, backendName: "cpu", kernelFunc: zU };
var ev = At((e, t) => e * t);
var LU = Qy((e, t, n, s) => ({ real: e * n - t * s, imag: e * s + t * n }));
var Gp = Gt(Ka, ev, LU);
var BU = { kernelName: Ka, backendName: "cpu", kernelFunc: Gp };
function eC(e, t, n) {
  let s = w.createScalarValue(-1, n);
  return ev([], t, s, e, n);
}
function VU(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  ve(s, "neg");
  let r = n.data.get(s.dataId).values, [a, i] = eC(r, s.shape, s.dtype);
  return n.makeTensorInfo(i, s.dtype, a);
}
var WU = { kernelName: ko, backendName: "cpu", kernelFunc: VU };
var tC = At((e, t) => e !== t ? 1 : 0);
var UU = Gt(Io, tC, null, "bool");
var GU = { kernelName: Io, backendName: "cpu", kernelFunc: UU };
function tv(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 Vn(e) {
  let { inputs: t, attrs: n, backend: s } = e, { x: r } = t, { perm: a } = n;
  ve(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 = tv(u, r.shape, r.dtype, a, o);
  return { dataId: s.write(l, o, r.dtype), shape: o, dtype: r.dtype };
}
var HU = { kernelName: ci, backendName: "cpu", kernelFunc: Vn };
function nC(e, t, n, s) {
  let [r, a] = N.computeOutAndReduceShapes(e, s), i = yn(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 qU(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
  ve(r, "prod");
  let o = r.shape.length, u = w.parseAxisParam(a, r.shape), l = N.getAxesPermutation(u, o), c = u, p = r, d = [];
  l != null && (p = Vn({ inputs: { x: r }, backend: n, attrs: { perm: l } }), d.push(p), c = N.getInnerMostAxes(c.length, o));
  let h = n.data.get(p.dataId).values, { outVals: f, outShape: m, outDtype: g } = nC(p.shape, p.dtype, h, c), b = m;
  return i && (b = N.expandShapeToKeepDim(m, u)), d.forEach((y) => n.disposeIntermediateTensorInfo(y)), n.makeTensorInfo(b, g, f);
}
var jU = { kernelName: _o, backendName: "cpu", kernelFunc: qU };
function nv(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 sC = Ar((e) => 1 / Math.sqrt(e));
var KU = Jo(ti, sC);
var XU = { kernelName: ti, backendName: "cpu", kernelFunc: KU };
var YU = Ar((e) => 1 / (1 + Math.exp(-e)));
var rC = st(si, (e) => 1 / (1 + Math.exp(-e)));
var QU = { kernelName: si, backendName: "cpu", kernelFunc: rC };
function Dd(e, t, n, s, r) {
  let a = wt.isSliceContinous(s, t, n), i = w.sizeFromShape(n), o = w.computeStrides(s);
  if (a) {
    let p = wt.computeFlatOffset(t, o);
    return r === "string" ? e.slice(p, p + i) : e.subarray(p, p + i);
  }
  let u = r === "string" ? N.fromUint8ToStringArray(e) : e, l = De(s, r, u), c = De(n, r);
  for (let p = 0; p < c.size; ++p) {
    let d = c.indexToLoc(p), h = d.map((f, m) => f + t[m]);
    c.set(l.get(...h), ...d);
  }
  return r === "string" ? N.fromStringArrayToUint8(c.values) : c.values;
}
function fa(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, size: i } = s;
  ve(r, "slice");
  let [o, u] = wt.parseSliceParams(r, a, i);
  wt.assertParamsValid(r, o, u);
  let l = n.data.get(r.dataId).values, c = Dd(l, o, u, r.shape, r.dtype);
  return n.makeTensorInfo(u, r.dtype, c);
}
var ZU = { kernelName: Oo, backendName: "cpu", kernelFunc: fa };
function aC(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(N.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(N.getSparseFillEmptyRowsNegativeIndexErrorMessage(g, b));
    if (b >= u)
      throw new Error(N.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], C = v[k], T = (k === 0 ? 0 : f[k - 1]) + C;
      v[k]++;
      for (let E = 0; E < p; ++E)
        b[T * p + E] = e[x * p + E];
      y[T] = s[x], c[x] = T;
    }
    for (let x = 0; x < u; ++x)
      if (v[x] === 0) {
        let C = x === 0 ? 0 : f[x - 1];
        b[C * p + 0] = x;
        for (let T = 1; T < p; ++T)
          b[C * p + T] = 0;
        y[C] = i;
      }
    return [b, [g, p], y, l, c];
  }
}
function iC(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(N.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(c, g));
      c = g, u.push(1);
    } else {
      if (b < 0)
        throw new Error(N.getSparseReshapeNegativeOutputDimErrorMessage(g, b));
      l *= b, u.push(b);
    }
  }
  if (c !== -1) {
    if (l <= 0)
      throw new Error(N.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());
    let g = Math.trunc(a / l);
    if (l * g !== a)
      throw new Error(N.getSparseReshapeInputOutputMultipleErrorMessage(s, u));
    u[c] = g;
  }
  if (w.sizeFromShape(u) !== a)
    throw new Error(N.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 sv(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(N.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(N.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(N.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage());
    }
    if (y < 0 || y >= p)
      throw new Error(N.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(N.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(x, s[x], u[0]));
      for (let C = 0; C < l; C++)
        f[y * l + C] += e[k * l + C];
    }
    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 JU = Ar((e) => Math.sqrt(e));
var eG = st(ri, (e) => Math.sqrt(e));
var tG = { kernelName: ri, backendName: "cpu", kernelFunc: eG };
var oC = At((e, t) => {
  let n = e - t;
  return n * n;
});
var nG = Gt(oi, oC);
var sG = { kernelName: oi, backendName: "cpu", kernelFunc: nG };
function uC(e, t, n, s) {
  let r = De(e, t.dtype);
  for (let a = 0; a < r.size; a++) {
    let i = r.indexToLoc(a), o = new Array(i.length);
    for (let u = 0; u < o.length; u++)
      o[u] = i[u] * n[u] + s[u];
    r.set(t.get(...o), ...i);
  }
  return r;
}
var rG = 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 lC(e, t, n, s, r, a, i, o) {
  return new rG(n, s, r, a, i, o).compute(e, t);
}
function aG(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 cC(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;
    aG(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 dC(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 pC = At((e, t) => e - t);
var iG = Qy((e, t, n, s) => ({ real: e - n, imag: t - s }));
var rv = Gt(ui, pC, iG);
var oG = { kernelName: ui, backendName: "cpu", kernelFunc: rv };
function hC(e, t) {
  let n = new Array(e.rank);
  for (let r = 0; r < n.length; r++)
    n[r] = e.shape[r] * t[r];
  let s = De(n, e.dtype);
  for (let r = 0; r < s.values.length; ++r) {
    let a = s.indexToLoc(r), i = new Array(e.rank);
    for (let u = 0; u < i.length; u++)
      i[u] = a[u] % e.shape[u];
    let o = e.locToIndex(i);
    s.values[r] = e.values[o];
  }
  return s;
}
var Eu = (e, t) => {
  let n = t.value - e.value;
  return n === 0 ? e.index - t.index : n;
};
function fC(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));
      fC(e, t, d, h);
    }
    let r = e[t], a = n, i = s;
    for (w.swap(e, n, t), Eu(e[s], r) > 0 && w.swap(e, n, s); a < i; ) {
      for (w.swap(e, a, i), a++, i--; Eu(e[a], r) < 0; )
        a = a + 1;
      for (; Eu(e[i], r) > 0; )
        i = i - 1;
    }
    Eu(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 mC(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 && (fC(f, s), f = f.slice(0, s)), r && f.sort(Eu);
    let m = p * s, g = u.subarray(m, m + s), b = l.subarray(m, m + s);
    for (let y = 0; y < s; y++)
      g[y] = f[y].value, b[y] = f[y].index;
  }
  let c = t.slice();
  return c[c.length - 1] = s, [De(c, n, u), De(c, "int32", l)];
}
function gC(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 Vt(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 Vt(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 vpe = "0.0.0";
dp("cpu", () => new RS(), 1);
var bC = st(Ra, (e) => e >= 0 ? e : Math.exp(e) - 1);
var uG = { kernelName: Ra, backendName: "cpu", kernelFunc: bC };
function yC(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s;
  ve([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 lG = { kernelName: La, backendName: "cpu", kernelFunc: yC };
var cG = At((e, t) => e < 0 ? t * e : e);
function vC(e) {
  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t;
  ve([s, r], "prelu");
  let a = n.data.get(s.dataId).values, i = n.data.get(r.dataId).values, [o, u] = cG(s.shape, r.shape, a, i, "float32");
  return n.makeTensorInfo(u, "float32", o);
}
var dG = { kernelName: Qa, backendName: "cpu", kernelFunc: vC };
var xC = st(Za, (e) => Math.max(0, e));
var pG = { kernelName: Za, backendName: "cpu", kernelFunc: xC };
var wC = st(ei, (e) => Math.min(Math.max(0, e), 6));
var hG = { kernelName: ei, backendName: "cpu", kernelFunc: wC };
function av(e, t, n, s, r) {
  if (n === "linear")
    return Os({ inputs: { x: t }, backend: e });
  if (n === "relu")
    return xC({ inputs: { x: t }, backend: e });
  if (n === "elu")
    return bC({ inputs: { x: t }, backend: e });
  if (n === "relu6")
    return wC({ inputs: { x: t }, backend: e });
  if (n === "prelu")
    return vC({ inputs: { x: t, alpha: s }, backend: e });
  if (n === "leakyrelu")
    return yC({ inputs: { x: t }, backend: e, attrs: { alpha: r } });
  if (n === "sigmoid")
    return rC({ inputs: { x: t }, backend: e });
  throw new Error(`Activation ${n} has not been implemented for the CPU backend.`);
}
function mt(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { shape: a } = s, i = w.sizeFromShape(r.shape), o = w.inferFromImplicitShape(a, i), u = w.sizeFromShape(o);
  w.assert(i === u, () => `The new shape (${o}) has ${u} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`), n.incRef(r.dataId);
  let l = n.data.get(r.dataId);
  if (l.complexTensorInfos != null) {
    let c = l.complexTensorInfos.real, p = l.complexTensorInfos.imag;
    c.shape = o, p.shape = o;
  }
  return { dataId: r.dataId, shape: o, dtype: r.dtype };
}
var fG = { kernelName: Ao, backendName: "cpu", kernelFunc: mt };
function kC(e) {
  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
  ve([r, a], "matMul");
  let u = r.shape.length, l = a.shape.length, c = i ? r.shape[u - 2] : r.shape[u - 1], p = o ? a.shape[l - 1] : a.shape[l - 2], d = i ? r.shape[u - 1] : r.shape[u - 2], h = o ? a.shape[l - 2] : a.shape[l - 1], f = r.shape.slice(0, -2), m = a.shape.slice(0, -2), g = w.sizeFromShape(f), b = w.sizeFromShape(m), v = qo.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)).concat([d, h]);
  w.assert(c === p, () => `Error in matMul: inner shapes (${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], C = mt({ inputs: { x: r }, backend: n, attrs: { shape: x } }), T = mt({ inputs: { x: a }, backend: n, attrs: { shape: k } }), E = i ? C.shape[1] : C.shape[2], A = i ? C.shape[2] : C.shape[1], P = o ? T.shape[1] : T.shape[2], R = Math.max(g, b), F = n.data.get(C.dataId).values, $ = n.data.get(T.dataId).values, z = w.computeStrides(C.shape), W = w.computeStrides(T.shape), [q, K, Y] = i ? [z[0], 1, z[1]] : [z[0], z[1], 1], [Z, te, ee] = o ? [1, W[1], W[0]] : [W[1], 1, W[0]], se = A * P, ne = De([R, A, P], C.dtype), oe = ne.values, re = n.blockSize;
  for (let le = 0; le < R; le++)
    for (let me = 0; me < A; me += re)
      for (let we = 0; we < P; we += re)
        for (let Se = 0; Se < E; Se += re) {
          let Ee = Math.min(me + re, A), Pe = Math.min(we + re, P), Xe = Math.min(Se + re, E);
          for (let Je = me; Je < Ee; Je++)
            for (let Ye = we; Ye < Pe; Ye++) {
              let tt = 0;
              for (let Ce = Se; Ce < Xe; Ce++) {
                let ut = Math.min(le, g - 1) * q, rt = Math.min(le, b - 1) * ee, Zt = F[ut + Je * K + Ce * Y], Nt = $[Ce * Z + Ye * te + rt];
                tt += Zt * Nt;
              }
              oe[le * se + (Je * P + Ye)] += tt;
            }
        }
  return n.disposeIntermediateTensorInfo(C), n.disposeIntermediateTensorInfo(T), n.makeTensorInfo(v, ne.dtype, ne.values);
}
var mG = { kernelName: Ia, backendName: "cpu", kernelFunc: kC };
function gG(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 = kC({ inputs: { a: r, b: a }, attrs: { transposeA: u, transposeB: l }, backend: n }), i && (h = jl({ inputs: { a: d, b: i }, backend: n }), m.push(d), d = h), c && (f = av(n, d, c, o, p), m.push(d), d = f);
  for (let b of m)
    n.disposeIntermediateTensorInfo(b);
  return d;
}
var bG = { kernelName: na, backendName: "cpu", kernelFunc: gG };
var yG = st(nl, (e) => Math.acos(e));
var vG = { kernelName: nl, backendName: "cpu", kernelFunc: yG };
var xG = st(sl, (e) => Math.acosh(e));
var wG = { kernelName: sl, backendName: "cpu", kernelFunc: xG };
function kG(e) {
  let { inputs: t, backend: n } = e, s = t;
  ve(t, "addN");
  let r = s.map((o) => n.data.get(o.dataId).values), a = De(s[0].shape, s[0].dtype), i = a.values;
  for (let o = 0; o < s.length; o++) {
    let u = r[o];
    for (let l = 0; l < i.length; l++)
      i[l] += u[l];
  }
  return n.makeTensorInfo(a.shape, a.dtype, a.values);
}
var IG = { kernelName: xa, backendName: "cpu", kernelFunc: kG };
function SG(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
  ve(r, "all");
  let o = w.parseAxisParam(a, r.shape), u = o, l = N.getAxesPermutation(u, r.shape.length), c = r;
  l != null && (c = Vn({ inputs: { x: r }, backend: n, attrs: { perm: l } }), u = N.getInnerMostAxes(u.length, r.shape.length)), N.assertAxesAreInnerMostDims("all", u, c.shape.length);
  let [p, d] = N.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 = N.expandShapeToKeepDim(p, o), y = mt({ inputs: { x: g }, backend: n, attrs: { shape: b } });
    return n.disposeIntermediateTensorInfo(g), y;
  }
  return g;
}
var CG = { kernelName: rl, backendName: "cpu", kernelFunc: SG };
function NG(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
  ve(r, "any");
  let o = w.parseAxisParam(a, r.shape), u = o, l = N.getAxesPermutation(u, r.shape.length), c = r;
  l != null && (c = Vn({ inputs: { x: r }, backend: n, attrs: { perm: l } }), u = N.getInnerMostAxes(u.length, r.shape.length)), N.assertAxesAreInnerMostDims("any", u, c.shape.length);
  let [p, d] = N.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 = N.expandShapeToKeepDim(p, o), y = mt({ inputs: { x: g }, backend: n, attrs: { shape: b } });
    return n.disposeIntermediateTensorInfo(g), y;
  }
  return g;
}
var TG = { kernelName: al, backendName: "cpu", kernelFunc: NG };
function $G(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s;
  ve(r, "argMax");
  let i = w.parseAxisParam(a, r.shape), o = N.getAxesPermutation(i, r.shape.length), u = r, l = [];
  o != null && (u = Vn({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = N.getInnerMostAxes(i.length, u.shape.length)), i = [i[0]], N.assertAxesAreInnerMostDims("argMax", i, u.shape.length);
  let [c, p] = N.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 _G = { kernelName: wa, backendName: "cpu", kernelFunc: $G };
function AG(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s;
  ve(r, "argMin");
  let i = w.parseAxisParam(a, r.shape), o = N.getAxesPermutation(i, r.shape.length), u = r, l = [];
  o != null && (u = Vn({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = N.getInnerMostAxes(i.length, u.shape.length)), i = [i[0]], N.assertAxesAreInnerMostDims("argMin", i, u.shape.length);
  let [c, p] = N.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 EG = { kernelName: il, backendName: "cpu", kernelFunc: AG };
var RG = st(ol, (e) => Math.asin(e));
var DG = { kernelName: ol, backendName: "cpu", kernelFunc: RG };
var FG = st(ul, (e) => Math.asinh(e));
var OG = { kernelName: ul, backendName: "cpu", kernelFunc: FG };
var PG = st(ll, (e) => Math.atan(e));
var zG = { kernelName: ll, backendName: "cpu", kernelFunc: PG };
var MG = At((e, t) => Math.atan2(e, t));
var LG = Gt(dl, MG);
var BG = { kernelName: dl, backendName: "cpu", kernelFunc: LG };
var VG = st(cl, (e) => Math.atanh(e));
var WG = { kernelName: cl, backendName: "cpu", kernelFunc: VG };
function iv(e, t, n, s, r, a) {
  let i = r.strideHeight, o = r.strideWidth, u = r.dilationHeight, l = r.dilationWidth, c = r.effectiveFilterHeight, p = r.effectiveFilterWidth, d = r.padInfo.top, h = r.padInfo.left, f = a === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, m = De(r.outShape, n), g = m.values, b = r.outShape[1] * r.outShape[2] * r.outShape[3], y = r.outShape[2] * r.outShape[3], v = r.outShape[3];
  for (let x = 0; x < r.batchSize; ++x) {
    let k = x * b, C = x * s[0];
    for (let T = 0; T < r.inChannels; ++T)
      for (let E = 0; E < r.outHeight; ++E) {
        let A = E * i - d, P = Math.max(0, A), R = Math.min(r.inHeight, c + A), F = k + E * y;
        for (let $ = 0; $ < r.outWidth; ++$) {
          let z = $ * o - h, W = Math.max(0, z), q = Math.min(r.inWidth, p + z), K = f, Y = 0, Z = 0;
          for (let ee = P; ee < R; ee += u) {
            let se = C + ee * s[1];
            for (let ne = W; ne < q; ne += l) {
              let oe = se + ne * s[2], re = e[oe + T];
              a === "max" && re > K ? K = re : a === "avg" && (Y += re, Z++);
            }
            if (isNaN(K))
              break;
          }
          let te = F + $ * v + T;
          g[te] = a === "avg" ? Y / Z : K;
        }
      }
  }
  return m;
}
function IC(e, t, n, s, r = false, a = false) {
  let i = De(s.outShape, "int32"), o = s.strideHeight, u = s.strideWidth, l = s.dilationHeight, c = s.dilationWidth, p = s.effectiveFilterHeight, d = s.effectiveFilterWidth, h = s.padInfo.top, f = s.padInfo.left, m = De(t, n, e);
  for (let g = 0; g < s.batchSize; ++g)
    for (let b = 0; b < s.inChannels; ++b)
      for (let y = 0; y < s.outHeight; ++y) {
        let v = y * o - h, x = v;
        for (; x < 0; )
          x += l;
        let k = Math.min(s.inHeight, p + v);
        for (let C = 0; C < s.outWidth; ++C) {
          let T = C * u - f, E = T;
          for (; E < 0; )
            E += c;
          let A = Math.min(s.inWidth, d + T), P = Number.NEGATIVE_INFINITY, R = -1;
          for (let F = x; F < k; F += l) {
            let $ = F - v;
            for (let z = E; z < A; z += c) {
              let W = z - T, q = m.get(g, F, z, b);
              q > P && (P = q, r ? R = a ? ((g * s.inHeight + F) * s.inWidth + z) * s.inChannels + b : (F * s.inWidth + z) * s.inChannels + b : R = $ * d + W);
            }
          }
          i.set(R, g, y, C, b);
        }
      }
  return i;
}
function SC(e, t, n, s, r, a) {
  let i = r.strideDepth, o = r.strideHeight, u = r.strideWidth, l = r.dilationDepth, c = r.dilationHeight, p = r.dilationWidth, d = r.effectiveFilterDepth, h = r.effectiveFilterHeight, f = r.effectiveFilterWidth, m = r.padInfo.front, g = r.padInfo.top, b = r.padInfo.left, y = a === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY, v = De(r.outShape, n), x = v.values, k = r.outShape[1] * r.outShape[2] * r.outShape[3] * r.outShape[4], C = r.outShape[2] * r.outShape[3] * r.outShape[4], T = r.outShape[3] * r.outShape[4], E = r.outShape[4];
  for (let A = 0; A < r.batchSize; ++A) {
    let P = A * k, R = A * s[0];
    for (let F = 0; F < r.inChannels; ++F)
      for (let $ = 0; $ < r.outDepth; ++$) {
        let z = $ * i - m, W = z;
        for (; W < 0; )
          W += l;
        let q = Math.min(r.inDepth, d + z), K = P + $ * C;
        for (let Y = 0; Y < r.outHeight; ++Y) {
          let Z = Y * o - g, te = Z;
          for (; te < 0; )
            te += c;
          let ee = Math.min(r.inHeight, h + Z), se = K + Y * T;
          for (let ne = 0; ne < r.outWidth; ++ne) {
            let oe = ne * u - b, re = oe;
            for (; re < 0; )
              re += p;
            let le = Math.min(r.inWidth, f + oe), me = se + ne * E, we = y, Se = 0, Ee = 0;
            for (let Xe = W; Xe < q; Xe += l) {
              let Je = R + Xe * s[1];
              for (let Ye = te; Ye < ee; Ye += c) {
                let tt = Je + Ye * s[2];
                for (let Ce = re; Ce < le; Ce += p) {
                  let ut = tt + Ce * s[3], rt = e[ut + F];
                  if (a === "max" && rt > we ? we = rt : a === "avg" && (Se += rt, Ee++), isNaN(we))
                    break;
                }
                if (isNaN(we))
                  break;
              }
              if (isNaN(we))
                break;
            }
            let Pe = me + F;
            x[Pe] = a === "avg" ? Se / Ee : we;
          }
        }
      }
  }
  return v;
}
function UG(e, t) {
  let n = De(t.outShape, "int32"), s = t.strideDepth, r = t.strideHeight, a = t.strideWidth, i = t.dilationDepth, o = t.dilationHeight, u = t.dilationWidth, l = t.effectiveFilterDepth, c = t.effectiveFilterHeight, p = t.effectiveFilterWidth, d = t.padInfo.front, h = t.padInfo.top, f = t.padInfo.left;
  for (let m = 0; m < t.batchSize; ++m)
    for (let g = 0; g < t.inChannels; ++g)
      for (let b = 0; b < t.outDepth; ++b) {
        let y = b * s - d, v = y;
        for (; v < 0; )
          v += i;
        let x = Math.min(t.inDepth, l + y);
        for (let k = 0; k < t.outHeight; ++k) {
          let C = k * r - h, T = C;
          for (; T < 0; )
            T += o;
          let E = Math.min(t.inHeight, c + C);
          for (let A = 0; A < t.outWidth; ++A) {
            let P = A * a - f, R = P;
            for (; R < 0; )
              R += u;
            let F = Math.min(t.inWidth, p + P), $ = Number.NEGATIVE_INFINITY, z = -1;
            for (let W = v; W < x; W += i) {
              let q = W - y;
              for (let K = T; K < E; K += o) {
                let Y = K - C;
                for (let Z = R; Z < F; Z += u) {
                  let te = Z - P, ee = e.get(m, W, K, Z, g);
                  ee >= $ && ($ = ee, z = q * c * p + Y * c + te);
                }
              }
            }
            n.set(z, m, b, k, A, g);
          }
        }
      }
  return n;
}
function GG(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t;
  ve(r, "avgPool");
  let { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1;
  w.assert(N.eitherStridesOrDilationsAreOne(i, l), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);
  let c = N.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 = iv(d, r.shape, r.dtype, h, c, "avg");
    p = n.makeTensorInfo(c.outShape, r.dtype, f.values);
  }
  return p;
}
var HG = { kernelName: ka, backendName: "cpu", kernelFunc: GG };
function qG(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u, dataFormat: l } = s;
  ve(r, "avgPool3d");
  let c = N.computePool3DInfo(r.shape, a, i, 1, o, u, l), p = n.data.get(r.dataId).values, d = SC(p, r.shape, r.dtype, w.computeStrides(r.shape), c, "avg");
  return n.makeTensorInfo(d.shape, "float32", d.values);
}
var jG = { kernelName: Hd, backendName: "cpu", kernelFunc: qG };
function KG(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, { filterSize: i, strides: o, pad: u, dimRoundingMode: l } = s;
  ve([r, a], "avgPool3DGrad");
  let c = N.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, C = c.effectiveFilterWidth, T = x - 1 - c.padInfo.front, E = C - 1 - c.padInfo.left, A = k - 1 - c.padInfo.top, P = De(a.shape, "float32"), R = 1 / (f * m * g), F = n.bufferSync(r);
  for (let $ = 0; $ < c.batchSize; ++$)
    for (let z = 0; z < c.inChannels; ++z)
      for (let W = 0; W < c.inDepth; ++W)
        for (let q = 0; q < c.inHeight; ++q)
          for (let K = 0; K < c.inWidth; ++K) {
            let Y = W - T, Z = q - A, te = K - E, ee = 0;
            for (let se = 0; se < x; se += b) {
              let ne = (Y + se) / p;
              if (!(ne < 0 || ne >= c.outDepth || Math.floor(ne) !== ne))
                for (let oe = 0; oe < k; oe += y) {
                  let re = (Z + oe) / d;
                  if (!(re < 0 || re >= c.outHeight || Math.floor(re) !== re))
                    for (let le = 0; le < C; le += v) {
                      let me = (te + le) / h;
                      if (me < 0 || me >= c.outWidth || Math.floor(me) !== me)
                        continue;
                      ee += F.get($, ne, re, me, z);
                    }
                }
            }
            P.set(ee * R, $, W, q, K, z);
          }
  return n.makeTensorInfo(P.shape, P.dtype, P.values);
}
var XG = { kernelName: ag, backendName: "cpu", kernelFunc: KG };
function YG(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a;
  ve([r, a], "avgPoolGrad");
  let { filterSize: o, strides: u, pad: l } = s, c = N.computePool2DInfo(i.shape, o, u, 1, l), p = c.strideHeight, d = c.strideWidth, h = c.filterHeight, f = c.filterWidth, m = c.dilationHeight, g = c.dilationWidth, b = c.effectiveFilterHeight, y = c.effectiveFilterWidth, v = y - 1 - c.padInfo.left, x = b - 1 - c.padInfo.top, k = De(i.shape, "float32"), C = 1 / (h * f), T = n.data.get(r.dataId).values, E = De(r.shape, "float32", T);
  for (let A = 0; A < c.batchSize; ++A)
    for (let P = 0; P < c.inChannels; ++P)
      for (let R = 0; R < c.inHeight; ++R)
        for (let F = 0; F < c.inWidth; ++F) {
          let $ = R - x, z = F - v, W = 0;
          for (let q = 0; q < b; q += m) {
            let K = ($ + q) / p;
            if (!(K < 0 || K >= c.outHeight || Math.floor(K) !== K))
              for (let Y = 0; Y < y; Y += g) {
                let Z = (z + Y) / d;
                if (Z < 0 || Z >= c.outWidth || Math.floor(Z) !== Z)
                  continue;
                W += E.get(A, K, Z, P);
              }
          }
          k.set(W * C, A, R, F, P);
        }
  return n.makeTensorInfo(k.shape, k.dtype, k.values);
}
var QG = { kernelName: rg, backendName: "cpu", kernelFunc: YG };
function ZG(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."), ve([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, C = 0, T = 0;
  for (let E = 0; E < c.length; ++E)
    m[E] = f[x++] + (c[E] - p[k++]) * h[C++] / Math.sqrt(d[T++] + l), x >= g && (x = 0), k >= v && (k = 0), C >= b && (C = 0), T >= y && (T = 0);
  return n.makeTensorInfo(r.shape, r.dtype, m);
}
var JG = { kernelName: Pa, backendName: "cpu", kernelFunc: ZG };
function eH(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, crops: i } = s;
  ve([r], "batchToSpaceND");
  let o = a.reduce((b, y) => b * y), u = N.getReshaped(r.shape, a, o), l = N.getPermuted(u.length, a.length), c = N.getReshapedPermuted(r.shape, a, o), p = N.getSliceBeginCoords(i, a.length), d = N.getSliceSize(c, i, a.length), h = mt({ inputs: { x: r }, backend: n, attrs: { shape: u } }), f = Vn({ inputs: { x: h }, backend: n, attrs: { perm: l } }), m = mt({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = fa({ inputs: { x: m }, backend: n, attrs: { begin: p, size: d } });
  return n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(m), g;
}
var tH = { kernelName: io, backendName: "cpu", kernelFunc: eH };
function nH(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 = Zy(o, u, a.dtype, a.shape, i);
  return n.makeTensorInfo([i], a.dtype, l);
}
var sH = { kernelName: ig, backendName: "cpu", kernelFunc: nH };
function rH(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 = N.assertAndGetBroadcastShape(Array.from(a), Array.from(i));
  return n.makeTensorInfo([o.length], "int32", Int32Array.from(o));
}
var aH = { kernelName: og, backendName: "cpu", kernelFunc: rH };
var iH = st(Ir, (e, t) => {
  let n = t;
  return e > n.clipValueMax ? n.clipValueMax : e < n.clipValueMin ? n.clipValueMin : e;
});
var oH = { kernelName: Ir, backendName: "cpu", kernelFunc: iH };
var uH = (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 lH = { kernelName: jd, backendName: "cpu", kernelFunc: uH };
function eo(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 cH = { kernelName: Qd, backendName: "cpu", kernelFunc: eo };
function to(e) {
  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s, a = w.parseAxisParam(r, t[0].shape)[0], i = N.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 (N.assertParamsConsistent(u, a), o[0].dtype === "complex64") {
    let m = o.map((x) => ha({ inputs: { input: x }, backend: n })), g = o.map((x) => eo({ inputs: { input: x }, backend: n })), b = to({ inputs: m, backend: n, attrs: { axis: a } }), y = to({ inputs: g, backend: n, attrs: { axis: a } }), v = An({ inputs: { real: b, imag: y }, backend: n });
    return m.forEach((x) => n.disposeIntermediateTensorInfo(x)), g.forEach((x) => n.disposeIntermediateTensorInfo(x)), n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(y), v;
  }
  let l = o.map((m) => {
    let g = w.sizeFromShape(m.shape.slice(a));
    return mt({ inputs: { x: m }, backend: n, attrs: { shape: [-1, g] } });
  }), c = l.map((m) => ({ vals: n.data.get(m.dataId).values, shape: m.shape }));
  i = N.computeOutShape(l.map((m) => m.shape), 1);
  let p = l[0].shape[0] === 1, d = Jy(c, i, t[0].dtype, p), h = N.computeOutShape(o.map((m) => m.shape), a), f = n.makeTensorInfo(h, t[0].dtype, d);
  return l.forEach((m) => n.disposeIntermediateTensorInfo(m)), f;
}
var dH = { kernelName: oo, backendName: "cpu", kernelFunc: to };
function CC(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;
  ve([r, a], "conv2d");
  let p = N.convertConv2DDataFormat(u), d = N.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 Vt(d.outShape, r.dtype), k = w.computeStrides(r.shape), C = w.computeStrides(a.shape), T = k[0], E = v ? k[1] : k[2], A = v ? k[2] : 1, P = v ? 1 : k[1], R = x.strides[0], F = v ? x.strides[1] : x.strides[2], $ = v ? x.strides[2] : 1, z = v ? 1 : x.strides[1], W = n.data.get(r.dataId).values, q = n.data.get(a.dataId).values, K = x.values;
  for (let Y = 0; Y < d.batchSize; ++Y) {
    let Z = Y * T, te = Y * R;
    for (let ee = 0; ee < d.outHeight; ++ee) {
      let se = te + ee * F, ne = ee * d.strideHeight - y;
      for (let oe = 0; oe < h; ++oe) {
        let re = ne + oe * m;
        if (re < 0 || re >= d.inHeight)
          continue;
        let le = oe * C[0], me = Z + re * E;
        for (let we = 0; we < d.outWidth; ++we) {
          let Se = se + we * $, Ee = we * d.strideWidth - b;
          for (let Pe = 0; Pe < f; ++Pe) {
            let Xe = Ee + Pe * g;
            if (Xe < 0 || Xe >= d.inWidth)
              continue;
            let Je = le + Pe * C[1], Ye = me + Xe * A, tt = Je;
            for (let Ce = 0; Ce < d.inChannels; ++Ce) {
              let ut = W[Ye + Ce * P];
              for (let rt = 0; rt < d.outChannels; ++rt)
                K[Se + rt * z] += ut * q[tt + rt];
              tt += d.outChannels;
            }
          }
        }
      }
    }
  }
  return n.makeTensorInfo(x.shape, x.dtype, K);
}
var pH = { kernelName: Na, backendName: "cpu", kernelFunc: CC };
function hH(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;
  ve([r, a], "conv2dBackpropFilter");
  let p = N.convertConv2DDataFormat(u), d = N.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 Vt(d.filterShape, "float32"), v = d.padInfo.left, x = d.padInfo.top, k = n.data.get(r.dataId).values, C = n.data.get(a.dataId).values, T = new Vt(r.shape, r.dtype, k), E = new Vt(a.shape, a.dtype, C);
  for (let A = 0; A < m; ++A) {
    let P = Math.max(0, Math.ceil((x - A) / h)), R = Math.min(d.outHeight, (d.inHeight + x - A) / h);
    for (let F = 0; F < g; ++F) {
      let $ = Math.max(0, Math.ceil((v - F) / f)), z = Math.min(d.outWidth, (d.inWidth + v - F) / f);
      for (let W = 0; W < d.inChannels; ++W)
        for (let q = 0; q < d.outChannels; ++q) {
          let K = 0;
          for (let Y = 0; Y < d.batchSize; ++Y)
            for (let Z = P; Z < R; ++Z) {
              let te = A + Z * h - x;
              for (let ee = $; ee < z; ++ee) {
                let se = F + ee * f - v;
                b ? K += T.get(Y, te, se, W) * E.get(Y, Z, ee, q) : K += T.get(Y, W, te, se) * E.get(Y, q, Z, ee);
              }
            }
          y.set(K, A, F, W, q);
        }
    }
  }
  return n.makeTensorInfo(y.shape, y.dtype, y.values);
}
var fH = { kernelName: ug, backendName: "cpu", kernelFunc: hH };
function mH(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;
  ve([r, a], "conv2dBackpropInput");
  let p = w.computeStrides(a.shape), d = w.computeStrides(r.shape), h = N.convertConv2DDataFormat(l), f = N.computeConv2DInfo(i, a.shape, o, 1, u, c, false, h), m = new Vt(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: C, filterHeight: T, filterWidth: E, inChannels: A, inHeight: P, inWidth: R, outChannels: F, outHeight: $, outWidth: z, strideHeight: W, strideWidth: q } = f;
  h = f.dataFormat;
  let K = T - 1 - f.padInfo.top, Y = E - 1 - f.padInfo.left, Z = h === "channelsLast", te = m.strides[0], ee = Z ? m.strides[1] : m.strides[2], se = Z ? m.strides[2] : 1, ne = Z ? 1 : m.strides[1], oe = d[0], re = Z ? d[1] : d[2], le = Z ? d[2] : 1, me = Z ? 1 : d[1];
  for (let we = 0; we < C; ++we)
    for (let Se = 0; Se < A; ++Se)
      for (let Ee = 0; Ee < P; ++Ee) {
        let Pe = Ee - K, Xe = Math.max(0, Math.ceil(Pe / W)), Je = Math.min($, (T + Pe) / W);
        for (let Ye = 0; Ye < R; ++Ye) {
          let tt = Ye - Y, Ce = Math.max(0, Math.ceil(tt / q)), ut = Math.min(z, (E + tt) / q), rt = 0;
          for (let Nt = Xe; Nt < Je; ++Nt) {
            let In = Nt * W - Pe;
            for (let Et = Ce; Et < ut; ++Et) {
              let Jt = Et * q - tt, Sn = oe * we + re * Nt + le * Et, Cn = v * (T - 1 - In) + x * (E - 1 - Jt) + k * Se;
              for (let Xt = 0; Xt < F; ++Xt) {
                let Rn = b[Sn + me * Xt], en = y[Cn + Xt];
                rt += Rn * en;
              }
            }
          }
          let Zt = te * we + ee * Ee + se * Ye + ne * Se;
          g[Zt] = rt;
        }
      }
  return n.makeTensorInfo(m.shape, m.dtype, m.values);
}
var gH = { kernelName: Ta, backendName: "cpu", kernelFunc: mH };
function bH(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u } = s;
  ve([r, a], "conv3d");
  let l = N.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 Vt(l.outShape, r.dtype), k = n.data.get(r.dataId).values, C = n.data.get(a.dataId).values, T = x.values, E = w.computeStrides(r.shape), A = w.computeStrides(a.shape);
  for (let P = 0; P < l.batchSize; ++P) {
    let R = P * E[0], F = P * x.strides[0];
    for (let $ = 0; $ < l.outDepth; ++$) {
      let z = F + $ * x.strides[1], W = $ * l.strideDepth - b;
      for (let q = 0; q < c; ++q) {
        let K = W + q * h;
        if (K < 0 || K >= l.inDepth)
          continue;
        let Y = q * A[0], Z = R + K * E[1];
        for (let te = 0; te < l.outHeight; ++te) {
          let ee = z + te * x.strides[2], se = te * l.strideHeight - v;
          for (let ne = 0; ne < p; ++ne) {
            let oe = se + ne * f;
            if (oe < 0 || oe >= l.inHeight)
              continue;
            let re = Y + ne * A[1], le = Z + oe * E[2];
            for (let me = 0; me < l.outWidth; ++me) {
              let we = ee + me * l.outChannels, Se = me * l.strideWidth - y;
              for (let Ee = 0; Ee < d; ++Ee) {
                let Pe = Se + Ee * m;
                if (Pe < 0 || Pe >= l.inWidth)
                  continue;
                let Xe = re + Ee * A[2], Je = le + 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)
                    T[we + ut] += Ce * C[Ye + ut];
                  Ye += l.outChannels;
                }
              }
            }
          }
        }
      }
    }
  }
  return n.makeTensorInfo(x.shape, x.dtype, x.values);
}
var yH = { kernelName: Kd, backendName: "cpu", kernelFunc: bH };
function vH(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, pad: o, filterShape: u } = s;
  ve([r, a], "conv3dBackpropFilterV2");
  let l = w.computeStrides(r.shape), c = w.computeStrides(a.shape), p = N.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 Vt(p.filterShape, "float32"), v = y.values, [x, k, C, T] = y.strides, E = n.data.get(a.dataId).values, [A, P, R, F] = c, $ = n.data.get(r.dataId).values, [z, W, q, K] = l, Y = p.padInfo.front, Z = p.padInfo.left, te = p.padInfo.top;
  for (let ee = 0; ee < m; ++ee) {
    let se = Math.max(0, Math.ceil((Y - ee) / d)), ne = Math.min(p.outDepth, (p.inDepth + Y - ee) / d), oe = ee * x;
    for (let re = 0; re < g; ++re) {
      let le = Math.max(0, Math.ceil((te - re) / h)), me = Math.min(p.outHeight, (p.inHeight + te - re) / h), we = re * k + oe;
      for (let Se = 0; Se < b; ++Se) {
        let Ee = Math.max(0, Math.ceil((Z - Se) / f)), Pe = Math.min(p.outWidth, (p.inWidth + Z - Se) / f), Xe = Se * C + we;
        for (let Je = 0; Je < p.inChannels; ++Je) {
          let Ye = Je * T + Xe;
          for (let tt = 0; tt < p.outChannels; ++tt) {
            let Ce = 0;
            for (let ut = 0; ut < p.batchSize; ++ut) {
              let rt = ut * z, Zt = ut * A;
              for (let Nt = se; Nt < ne; ++Nt) {
                let Et = (ee + Nt * d - Y) * W + rt, Jt = Nt * P + Zt;
                for (let Sn = le; Sn < me; ++Sn) {
                  let Xt = (re + Sn * h - te) * q + Et, Rn = Sn * R + Jt;
                  for (let en = Ee; en < Pe; ++en) {
                    let Ls = (Se + en * f - Z) * K + Xt, xi = en * F + Rn;
                    Ce += $[Ls + Je] * E[xi + tt];
                  }
                }
              }
            }
            v[Ye + tt] = Ce;
          }
        }
      }
    }
  }
  return n.makeTensorInfo(y.shape, y.dtype, y.values);
}
var xH = { kernelName: lg, backendName: "cpu", kernelFunc: vH };
function wH(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { pad: i, strides: o, inputShape: u } = s;
  ve([r], "conv3dBackpropInputV2");
  let l = w.computeStrides(r.shape), c = w.computeStrides(a.shape), p = N.computeConv3DInfo(u, a.shape, o, 1, i), d = new Vt(p.inShape, "float32"), h = d.values, [f, m, g, b] = d.strides, y = n.data.get(r.dataId).values, [v, x, k, C] = l, T = n.data.get(a.dataId).values, [E, A, P, R] = c, { batchSize: F, filterDepth: $, filterHeight: z, filterWidth: W, inChannels: q, inDepth: K, inHeight: Y, inWidth: Z, outChannels: te, outDepth: ee, outHeight: se, outWidth: ne, strideDepth: oe, strideHeight: re, strideWidth: le } = p, me = $ - 1 - p.padInfo.front, we = z - 1 - p.padInfo.top, Se = W - 1 - p.padInfo.left;
  for (let Ee = 0; Ee < F; ++Ee)
    for (let Pe = 0; Pe < q; ++Pe)
      for (let Xe = 0; Xe < K; ++Xe) {
        let Je = Xe - me, Ye = Math.max(0, Math.ceil(Je / oe)), tt = Math.min(ee, ($ + Je) / oe);
        for (let Ce = 0; Ce < Y; ++Ce) {
          let ut = Ce - we, rt = Math.max(0, Math.ceil(ut / re)), Zt = Math.min(se, (z + ut) / re);
          for (let Nt = 0; Nt < Z; ++Nt) {
            let In = Nt - Se, Et = Math.max(0, Math.ceil(In / le)), Jt = Math.min(ne, (W + In) / le), Sn = 0;
            for (let Cn = Ye; Cn < tt; ++Cn) {
              let Xt = Cn * oe - Je;
              for (let Rn = rt; Rn < Zt; ++Rn) {
                let en = Rn * re - ut;
                for (let Ms = Et; Ms < Jt; ++Ms) {
                  let Ls = Ms * le - In, xi = v * Ee + x * Cn + k * Rn + C * Ms, Js = E * ($ - 1 - Xt) + A * (z - 1 - en) + P * (W - 1 - Ls) + R * Pe;
                  for (let Bs = 0; Bs < te; ++Bs) {
                    let du = y[xi + Bs], wi = T[Js + Bs];
                    Sn += du * wi;
                  }
                }
              }
            }
            h[f * Ee + m * Xe + g * Ce + b * Nt + Pe] = Sn;
          }
        }
      }
  return n.makeTensorInfo(d.shape, d.dtype, d.values);
}
var kH = { kernelName: cg, backendName: "cpu", kernelFunc: wH };
var IH = st($a, (e) => Math.cos(e));
var SH = { kernelName: $a, backendName: "cpu", kernelFunc: IH };
var CH = st(_a, (e) => Math.cosh(e));
var NH = { kernelName: _a, backendName: "cpu", kernelFunc: CH };
function TH(e) {
  let { inputs: t, backend: n, attrs: s } = e, { image: r, boxes: a, boxInd: i } = t, { cropSize: o, method: u, extrapolationValue: l } = s, [c, p, d, h] = r.shape, f = a.shape[0], [m, g] = o, b = De([f, m, g, h], "float32"), y = n.data.get(a.dataId).values, v = n.data.get(i.dataId).values, x = n.data.get(r.dataId).values, k = w.computeStrides(r.shape), C = w.computeStrides(b.shape);
  for (let T = 0; T < f; T++) {
    let E = T * 4, A = y[E], P = y[E + 1], R = y[E + 2], F = y[E + 3], $ = v[T];
    if ($ >= c)
      continue;
    let z = m > 1 ? (R - A) * (p - 1) / (m - 1) : 0, W = g > 1 ? (F - P) * (d - 1) / (g - 1) : 0;
    for (let q = 0; q < m; q++) {
      let K = m > 1 ? A * (p - 1) + q * z : 0.5 * (A + R) * (p - 1);
      if (K < 0 || K > p - 1) {
        for (let Y = 0; Y < g; Y++)
          for (let Z = 0; Z < h; Z++) {
            let te = Z + Y * C[2] + q * C[1] + T * C[0];
            b.values[te] = l;
          }
        continue;
      }
      if (u === "bilinear") {
        let Y = Math.floor(K), Z = Math.ceil(K), te = K - Y;
        for (let ee = 0; ee < g; ee++) {
          let se = g > 1 ? P * (d - 1) + ee * W : 0.5 * (P + F) * (d - 1);
          if (se < 0 || se > d - 1) {
            for (let le = 0; le < h; le++) {
              let me = le + ee * C[2] + q * C[1] + T * C[0];
              b.values[me] = l;
            }
            continue;
          }
          let ne = Math.floor(se), oe = Math.ceil(se), re = se - ne;
          for (let le = 0; le < h; le++) {
            let me = le + ne * k[2] + Y * k[1] + $ * k[0], we = x[me];
            me = le + oe * k[2] + Y * k[1] + $ * k[0];
            let Se = x[me];
            me = le + ne * k[2] + Z * k[1] + $ * k[0];
            let Ee = x[me];
            me = le + oe * k[2] + Z * k[1] + $ * k[0];
            let Pe = x[me], Xe = we + (Se - we) * re, Je = Ee + (Pe - Ee) * re;
            me = le + ee * C[2] + q * C[1] + T * C[0], b.values[me] = Xe + (Je - Xe) * te;
          }
        }
      } else
        for (let Y = 0; Y < g; ++Y) {
          let Z = g > 1 ? P * (d - 1) + Y * W : 0.5 * (P + F) * (d - 1);
          if (Z < 0 || Z > d - 1) {
            for (let se = 0; se < h; se++) {
              let ne = se + Y * C[2] + q * C[1] + T * C[0];
              b.values[ne] = l;
            }
            continue;
          }
          let te = Math.round(Z), ee = Math.round(K);
          for (let se = 0; se < h; se++) {
            let ne = se + te * k[2] + ee * k[1] + $ * k[0], oe = se + Y * C[2] + q * C[1] + T * C[0];
            b.values[oe] = x[ne];
          }
        }
    }
  }
  return n.makeTensorInfo(b.shape, b.dtype, b.values);
}
var $H = { kernelName: lo, backendName: "cpu", kernelFunc: TH };
function _H(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s;
  ve(r, "cumsum");
  let u = N.getAxesPermutation([a], r.shape.length), l = r;
  u != null && (l = Vn({ inputs: { x: r }, backend: n, attrs: { perm: u } }));
  let c = N.getInnerMostAxes(1, r.shape.length)[0];
  if (c !== l.shape.length - 1)
    throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length - 1} but got axis=${c}`);
  let p = yn(l.dtype, "int32"), d = w.makeZerosTypedArray(w.sizeFromShape(l.shape), p), h = n.data.get(l.dataId).values, f = l.shape[l.shape.length - 1], m = o ? (b, y) => b + f - y - 1 : (b, y) => b + y;
  for (let b = 0; b < h.length; b += f)
    for (let y = 0; y < f; y++) {
      let v = m(b, y);
      if (y === 0)
        d[v] = i ? 0 : h[v];
      else {
        let x = m(b, y - 1);
        d[v] = i ? h[x] + d[x] : h[v] + d[x];
      }
    }
  let g = n.makeTensorInfo(l.shape, p, d);
  if (u != null) {
    let b = N.getUndoAxesPermutation(u), y = Vn({ inputs: { x: g }, backend: n, attrs: { perm: b } });
    return n.disposeIntermediateTensorInfo(g), n.disposeIntermediateTensorInfo(l), y;
  }
  return g;
}
var AH = { kernelName: uo, backendName: "cpu", kernelFunc: _H };
function EH(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i, binaryOutput: o } = s;
  if (r.shape.length === 1) {
    let u = n.data.get(r.dataId).values, l = n.data.get(a.dataId).values, c = Zy(u, l, a.dtype, a.shape, i);
    return n.makeTensorInfo([i], a.dtype, c);
  } else if (r.shape.length === 2) {
    let u = n.bufferSync(r), l = n.bufferSync(a), c = OS(u, l, i, o);
    return n.makeTensorInfo(c.shape, a.dtype, c.values);
  }
  throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`);
}
var RH = { kernelName: dg, backendName: "cpu", kernelFunc: EH };
function DH(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 C = Math.floor(k / a), T = k % a, E = (x * a + T) * h;
        for (let A = 0; A < h; ++A) {
          let R = A + E + c * (C + l * (v + u * b));
          m[g++] = f[R];
        }
      }
    }
  return n.makeTensorInfo([o, p, d, h], r.dtype, m);
}
var FH = { kernelName: co, backendName: "cpu", kernelFunc: DH };
function NC(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u, dimRoundingMode: l } = s;
  ve([r, a], "depthwiseConv2DNative");
  let c = w.computeStrides(r.shape), p = w.computeStrides(a.shape), d = u;
  d == null && (d = [1, 1]), w.assert(N.eitherStridesOrDilationsAreOne(i, d), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);
  let h = N.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, C = new Vt(h.outShape, r.dtype), T = n.data.get(r.dataId).values, E = n.data.get(a.dataId).values, A = C.values;
  for (let P = 0; P < h.batchSize; ++P) {
    let R = P * c[0], F = P * C.strides[0];
    for (let $ = 0; $ < h.outHeight; ++$) {
      let z = F + $ * C.strides[1], W = $ * h.strideHeight - x;
      for (let q = 0; q < f; ++q) {
        let K = W + q * g;
        if (K < 0 || K >= h.inHeight)
          continue;
        let Y = q * p[0], Z = R + K * c[1];
        for (let te = 0; te < h.outWidth; ++te) {
          let ee = z + te * C.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 re = Y + ne * p[1], le = Z + oe * h.inChannels, me = ee, we = re;
            for (let Se = 0; Se < h.inChannels; ++Se) {
              let Ee = T[le + Se];
              for (let Pe = 0; Pe < k; ++Pe)
                A[me + Pe] += Ee * E[we + Pe];
              me += k, we += k;
            }
          }
        }
      }
    }
  }
  return n.makeTensorInfo(C.shape, C.dtype, C.values);
}
var OH = { kernelName: Aa, backendName: "cpu", kernelFunc: NC };
function PH(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;
  ve([r, a], "depthwiseConv2dNativeBackpropFilter");
  let p = N.computeConv2DInfo(r.shape, c, i, o, u, l, true), { strideHeight: d, strideWidth: h, filterHeight: f, filterWidth: m } = p, g = new Vt(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 Vt(r.shape, r.dtype, x), C = n.data.get(a.dataId).values, T = new Vt(a.shape, a.dtype, C);
  for (let E = 0; E < f; ++E) {
    let A = Math.max(0, Math.ceil((y - E) / d)), P = Math.min(p.outHeight, (p.inHeight + y - E) / d);
    for (let R = 0; R < m; ++R) {
      let F = Math.max(0, Math.ceil((b - R) / h)), $ = Math.min(p.outWidth, (p.inWidth + b - R) / h);
      for (let z = 0; z < p.outChannels; ++z) {
        let W = Math.trunc(z / v), q = z % v, K = 0;
        for (let Y = 0; Y < p.batchSize; ++Y)
          for (let Z = A; Z < P; ++Z) {
            let te = E + Z * d - y;
            for (let ee = F; ee < $; ++ee) {
              let se = R + ee * h - b;
              K += k.get(Y, te, se, W) * T.get(Y, Z, ee, z);
            }
          }
        g.set(K, E, R, W, q);
      }
    }
  }
  return n.makeTensorInfo(g.shape, g.dtype, g.values);
}
var zH = { kernelName: pg, backendName: "cpu", kernelFunc: PH };
function MH(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;
  ve([r, a], "depthwiseConv2DNativeBackpropInput");
  let p = w.computeStrides(r.shape), d = w.computeStrides(a.shape), h = N.computeConv2DInfo(c, a.shape, i, o, u, l, true), f = new Vt(h.inShape, "float32"), m = f.values, [g, b, y] = f.strides, v = n.data.get(r.dataId).values, [x, k, C] = p, T = n.data.get(a.dataId).values, [E, A, P] = d, { batchSize: R, filterHeight: F, filterWidth: $, inChannels: z, inHeight: W, inWidth: q, outChannels: K, outHeight: Y, outWidth: Z, strideHeight: te, strideWidth: ee } = h, se = F - 1 - h.padInfo.top, ne = $ - 1 - h.padInfo.left, oe = K / z;
  for (let re = 0; re < R; ++re)
    for (let le = 0; le < z; ++le)
      for (let me = 0; me < W; ++me) {
        let we = me - se, Se = Math.max(0, Math.ceil(we / te)), Ee = Math.min(Y, (F + we) / te);
        for (let Pe = 0; Pe < q; ++Pe) {
          let Xe = Pe - ne, Je = Math.max(0, Math.ceil(Xe / ee)), Ye = Math.min(Z, ($ + Xe) / ee), tt = 0;
          for (let Ce = Se; Ce < Ee; ++Ce) {
            let ut = Ce * te - we;
            for (let rt = Je; rt < Ye; ++rt) {
              let Zt = rt * ee - Xe, Nt = x * re + k * Ce + C * rt, In = E * (F - 1 - ut) + A * ($ - 1 - Zt) + P * le;
              for (let Et = 0; Et < oe; ++Et) {
                let Jt = le * oe + Et, Sn = v[Nt + Jt], Cn = T[In + Et];
                tt += Sn * Cn;
              }
            }
          }
          m[g * re + b * me + y * Pe + le] = tt;
        }
      }
  return n.makeTensorInfo(f.shape, f.dtype, f.values);
}
var LH = { kernelName: hg, backendName: "cpu", kernelFunc: MH };
function BH(e) {
  let { inputs: t, backend: n } = e, { x: s } = t, r = w.sizeFromShape(s.shape), a = n.data.get(s.dataId).values, i = De([r, r], s.dtype), o = i.values;
  for (let l = 0; l < a.length; l++)
    o[l * r + l] = a[l];
  let u = [...s.shape, ...s.shape];
  return n.makeTensorInfo(u, i.dtype, i.values);
}
var VH = { kernelName: fg, backendName: "cpu", kernelFunc: BH };
var WH = { kernelName: Xd, 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: C, filterWidth: T, dilationHeight: E, dilationWidth: A, outShape: P } = N.computeDilation2DInfo(s.shape, r.shape, a, i, "NHWC", o), R = w.sizeFromShape(P), F = P.length, $ = w.getArrayFromDType(s.dtype, R);
  for (let W = 0; W < h; ++W)
    for (let q = 0; q < b; ++q) {
      let K = q * x - v.top;
      for (let Y = 0; Y < y; ++Y) {
        let Z = Y * k - v.left;
        for (let te = 0; te < g; ++te) {
          let ee = Number.MIN_SAFE_INTEGER;
          for (let ne = 0; ne < C; ++ne) {
            let oe = K + ne * E;
            if (oe >= 0 && oe < f)
              for (let re = 0; re < T; ++re) {
                let le = Z + re * A;
                if (le >= 0 && le < m) {
                  let me = w.locToIndex([W, oe, le, te], c, w.computeStrides(s.shape)), we = w.locToIndex([ne, re, te], d, w.computeStrides(r.shape)), Se = l[me] + p[we];
                  Se > ee && (ee = Se);
                }
              }
          }
          let se = w.locToIndex([W, q, Y, te], F, w.computeStrides(P));
          $[se] = ee;
        }
      }
    }
  return { dataId: u.write(w.toTypedArray($, s.dtype), P, s.dtype), shape: P, dtype: s.dtype };
} };
var UH = { kernelName: Xf, 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: C, dilationHeight: T, dilationWidth: E, outShape: A } = N.computeDilation2DInfo(s.shape, r.shape, i, o, "NHWC", u);
  w.assert(a.rank === A.length, () => `Error in ${Xf}, dy must have the same rank as output ${A.length}, but got ${a.rank}`);
  let P = w.toNestedArray(A, l.data.get(a.dataId).values), R = w.makeZerosNestedTypedArray(r.shape, r.dtype);
  for (let $ = 0; $ < d; ++$)
    for (let z = 0; z < g; ++z) {
      let W = z * v - y.top;
      for (let q = 0; q < b; ++q) {
        let K = q * x - y.left;
        for (let Y = 0; Y < m; ++Y) {
          let Z = Number.MIN_SAFE_INTEGER, te = 0, ee = 0;
          for (let se = 0; se < k; ++se) {
            let ne = W + se * T;
            if (ne >= 0 && ne < h)
              for (let oe = 0; oe < C; ++oe) {
                let re = K + oe * E;
                if (re >= 0 && re < f) {
                  let le = c[$][ne][re][Y] + p[se][oe][Y];
                  le > Z && (Z = le, te = se, ee = oe);
                }
              }
          }
          R[te][ee][Y] += P[$][z][q][Y];
        }
      }
    }
  return { dataId: l.write(w.toTypedArray(R, s.dtype), r.shape, r.dtype), shape: r.shape, dtype: r.dtype };
} };
var GH = { kernelName: Kf, 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: C, dilationHeight: T, dilationWidth: E, outShape: A } = N.computeDilation2DInfo(s.shape, r.shape, i, o, "NHWC", u);
  w.assert(a.rank === A.length, () => `Error in ${Kf}, dy must have the same rank as output ${A.length}, but got ${a.rank}`);
  let P = w.toNestedArray(A, l.data.get(a.dataId).values), R = w.makeZerosNestedTypedArray(s.shape, s.dtype);
  for (let $ = 0; $ < d; ++$)
    for (let z = 0; z < g; ++z) {
      let W = z * v - y.top;
      for (let q = 0; q < b; ++q) {
        let K = q * x - y.left;
        for (let Y = 0; Y < m; ++Y) {
          let Z = Number.MIN_SAFE_INTEGER, te = W < 0 ? 0 : W, ee = K < 0 ? 0 : K;
          for (let se = 0; se < k; ++se) {
            let ne = W + se * T;
            if (ne >= 0 && ne < h)
              for (let oe = 0; oe < C; ++oe) {
                let re = K + oe * E;
                if (re >= 0 && re < f) {
                  let le = c[$][ne][re][Y] + p[se][oe][Y];
                  le > Z && (Z = le, te = ne, ee = re);
                }
              }
          }
          R[$][te][ee][Y] += P[$][z][q][Y];
        }
      }
    }
  return { dataId: l.write(w.toTypedArray(R, s.dtype), s.shape, s.dtype), shape: s.shape, dtype: s.dtype };
} };
function Kl(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
  ve(r, "sum");
  let o;
  r.dtype === "bool" ? o = xr({ inputs: { x: r }, backend: n, attrs: { dtype: "int32" } }) : o = Os({ inputs: { x: r }, backend: n });
  let u = o.shape.length, l = w.parseAxisParam(a, o.shape), c = N.getAxesPermutation(l, u), p = l, d = o;
  c != null && (d = Vn({ inputs: { x: o }, backend: n, attrs: { perm: c } }), p = N.getInnerMostAxes(p.length, u)), N.assertAxesAreInnerMostDims("sum", p, d.shape.length);
  let [h, f] = N.computeOutAndReduceShapes(d.shape, p), m = N.upcastType(d.dtype, "int32"), g = Rd(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, C = 0;
    for (let T = 0; T < b; ++T)
      C += v[k + T];
    y[x] = C;
  }
  if (i) {
    let x = N.expandShapeToKeepDim(g.shape, l), k = g;
    g = mt({ inputs: { x: g }, backend: n, attrs: { shape: x } }), n.disposeIntermediateTensorInfo(k);
  }
  return n.disposeIntermediateTensorInfo(o), c != null && n.disposeIntermediateTensorInfo(d), g;
}
var HH = { kernelName: ai, backendName: "cpu", kernelFunc: Kl };
function qH(e) {
  let { inputs: t, backend: n, attrs: s } = e, { equation: r } = s, a = t, { allDims: i, summedDims: o, idDims: u } = N.decodeEinsumEquation(r, a.length);
  N.checkEinsumDimSizes(i.length, u, a);
  let { path: l, steps: c } = N.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 } = N.getEinsumPermutation(h, u[g]), v;
      N.isIdentityPermutation(b) ? v = a[g] : (v = Vn({ inputs: { x: a[g] }, backend: n, attrs: { perm: b } }), f.push(v));
      let x = v.shape.slice();
      for (let k = 0; k < y.length; ++k)
        x.splice(y[k], 0, 1);
      w.arraysEqual(v.shape, x) || (v = mt({ inputs: { x: v }, backend: n, attrs: { shape: x } }), f.push(v)), d === null ? d = v : (d = Gp({ inputs: { a: v, b: d }, backend: n }), f.push(d));
    }
    m < p - 1 && (l[m] >= 0 && (d = Kl({ 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 jH = { kernelName: Yd, backendName: "cpu", kernelFunc: qH };
function KH(e) {
  let { inputs: t, backend: n } = e, { dy: s, y: r } = t;
  ve([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 XH = { kernelName: mg, backendName: "cpu", kernelFunc: KH };
var YH = N.ERF_P;
var QH = N.ERF_A1;
var ZH = N.ERF_A2;
var JH = N.ERF_A3;
var e6 = N.ERF_A4;
var t6 = N.ERF_A5;
var n6 = st(pl, (e) => {
  let t = Math.sign(e), n = Math.abs(e), s = 1 / (1 + YH * n);
  return t * (1 - ((((t6 * s + e6) * s + JH) * s + ZH) * s + QH) * s * Math.exp(-n * n));
});
var s6 = { kernelName: pl, backendName: "cpu", kernelFunc: n6 };
function Fd(e) {
  let { inputs: t, backend: n, attrs: s } = e, { input: r } = t, { dim: a } = s, i = r.shape.length, o = r.shape.slice(), u = a;
  return a < 0 && (w.assert(-(i + 1) <= a, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + a + 1), o.splice(u, 0, 1), mt({ inputs: { x: r }, backend: n, attrs: { shape: o } });
}
var r6 = { kernelName: ho, backendName: "cpu", kernelFunc: Fd };
var a6 = At((e, t) => e / t);
var ov = Gt(Ea, a6);
var Pm = { kernelName: Ea, backendName: "cpu", kernelFunc: ov };
function TC(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 = fa({ inputs: { x: o }, backend: n, attrs: { begin: [g, 0], size: [1, a] } }), y = fa({ inputs: { x: u }, backend: n, attrs: { begin: [g, 0], size: [1, a] } }), v = An({ inputs: { real: b, imag: y }, backend: n }), { real: x, imag: k } = i6(v, t, n), C = N.mergeRealAndImagArrays(x, k);
    for (let T = 0; T < a; T++) {
      let E = N.getComplexWithIndex(C, T);
      p[g * a + T] = E.real, d[g * a + T] = E.imag;
    }
    n.disposeIntermediateTensorInfo(b), n.disposeIntermediateTensorInfo(y), n.disposeIntermediateTensorInfo(v);
  }
  let h = n.makeTensorInfo(l, "float32", p), f = n.makeTensorInfo(l, "float32", d), m = An({ inputs: { real: h, imag: f }, backend: n });
  return n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(f), m;
}
function i6(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 (o6(s)) {
    let o = zm(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 = Pm.kernelFunc({ inputs: { a: l, b: p }, backend: n }), f = Pm.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 = N.mergeRealAndImagArrays(a, i), u = u6(o, s, t);
    return N.splitRealAndImagArrays(u);
  }
}
function o6(e) {
  return (e & e - 1) === 0;
}
function zm(e, t, n, s, r) {
  if (n === 1)
    return { real: e, imag: t };
  let a = N.mergeRealAndImagArrays(e, t), i = n / 2, o = N.complexWithEvenIndex(a), u = o.real, l = o.imag, c = [u.length], p = r.makeTensorInfo(c, "float32", u), d = r.makeTensorInfo(c, "float32", l), h = An({ inputs: { real: p, imag: d }, backend: r }), f = N.complexWithOddIndex(a), m = f.real, g = f.imag, b = [m.length], y = r.makeTensorInfo(b, "float32", m), v = r.makeTensorInfo(b, "float32", g), x = An({ inputs: { real: y, imag: v }, backend: r }), k = zm(u, l, i, s, r), C = k.real, T = k.imag, E = [C.length], A = r.makeTensorInfo(E, "float32", C), P = r.makeTensorInfo(E, "float32", T), R = An({ inputs: { real: A, imag: P }, backend: r }), F = zm(m, g, i, s, r), $ = F.real, z = F.imag, W = [$.length], q = r.makeTensorInfo(W, "float32", $), K = r.makeTensorInfo(W, "float32", z), Y = An({ inputs: { real: q, imag: K }, backend: r }), Z = N.exponents(n, s), te = [Z.real.length], ee = r.makeTensorInfo(te, "float32", Z.real), se = r.makeTensorInfo(te, "float32", Z.imag), ne = An({ inputs: { real: ee, imag: se }, backend: r }), oe = Gp({ inputs: { a: ne, b: Y }, backend: r }), re = jl({ inputs: { a: R, b: oe }, backend: r }), le = rv({ inputs: { a: R, b: oe }, backend: r }), me = ha({ inputs: { input: re }, backend: r }), we = ha({ inputs: { input: le }, backend: r }), Se = eo({ inputs: { input: re }, backend: r }), Ee = eo({ inputs: { input: le }, backend: r }), Pe = to({ inputs: [me, we], backend: r, attrs: { axis: 0 } }), Xe = to({ inputs: [Se, Ee], backend: r, attrs: { axis: 0 } }), Je = r.data.get(Pe.dataId).values, Ye = r.data.get(Xe.dataId).values;
  return r.disposeIntermediateTensorInfo(p), r.disposeIntermediateTensorInfo(d), r.disposeIntermediateTensorInfo(h), r.disposeIntermediateTensorInfo(y), r.disposeIntermediateTensorInfo(v), r.disposeIntermediateTensorInfo(x), r.disposeIntermediateTensorInfo(A), r.disposeIntermediateTensorInfo(P), r.disposeIntermediateTensorInfo(R), r.disposeIntermediateTensorInfo(q), r.disposeIntermediateTensorInfo(K), r.disposeIntermediateTensorInfo(Y), r.disposeIntermediateTensorInfo(ee), r.disposeIntermediateTensorInfo(se), r.disposeIntermediateTensorInfo(ne), r.disposeIntermediateTensorInfo(oe), r.disposeIntermediateTensorInfo(re), r.disposeIntermediateTensorInfo(le), r.disposeIntermediateTensorInfo(me), r.disposeIntermediateTensorInfo(Se), r.disposeIntermediateTensorInfo(we), r.disposeIntermediateTensorInfo(Ee), r.disposeIntermediateTensorInfo(Pe), r.disposeIntermediateTensorInfo(Xe), { real: Je, imag: Ye };
}
function u6(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 = N.exponent(r * o, t, n), l = N.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), N.assignToTypedArray(s, a, i, r);
  }
  return s;
}
function l6(e) {
  let { inputs: t, backend: n } = e, { input: s } = t, r = w.sizeFromShape(s.shape), a = s.shape[s.shape.length - 1], i = r / a, o = mt({ inputs: { x: s }, backend: n, attrs: { shape: [i, a] } }), u = TC(o, false, n), l = mt({ inputs: { x: u }, backend: n, attrs: { shape: s.shape } });
  return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(u), l;
}
var c6 = { kernelName: gg, backendName: "cpu", kernelFunc: l6 };
function uv(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 p6(o, r, i), t.makeTensorInfo(s, i, o);
}
var d6 = { kernelName: hl, backendName: "cpu", kernelFunc: uv };
function p6(e, t, n) {
  e.fill(t);
}
var h6 = { kernelName: mo, 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 C = v * l, T = h + m + C + y;
            k = c[T];
          }
          a[x] = k;
        }
      }
    }
  }
  return { dataId: r.write(a, s.shape, s.dtype), shape: s.shape, dtype: s.dtype };
} };
var f6 = At((e, t) => Math.floor(e / t));
var m6 = Gt(Oa, f6, null, "int32");
var g6 = { kernelName: Oa, backendName: "cpu", kernelFunc: m6 };
function b6(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 = CC({ inputs: { x: r, filter: a }, backend: n, attrs: { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d } });
  if (i) {
    let g = m;
    m = jl({ inputs: { a: m, b: i }, backend: n }), n.disposeIntermediateTensorInfo(g);
  }
  if (h) {
    let g = m;
    m = av(n, m, h, o, f), n.disposeIntermediateTensorInfo(g);
  }
  return m;
}
var y6 = { kernelName: sa, backendName: "cpu", kernelFunc: b6 };
function v6(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 = NC({ inputs: { x: r, filter: a }, backend: n, attrs: { strides: u, pad: l, dataFormat: c, dilations: p, dimRoundingMode: d } });
  if (i) {
    let g = m;
    m = jl({ inputs: { a: m, b: i }, backend: n }), n.disposeIntermediateTensorInfo(g);
  }
  if (h) {
    let g = m;
    m = av(n, m, h, o, f), n.disposeIntermediateTensorInfo(g);
  }
  return m;
}
var x6 = { kernelName: ra, backendName: "cpu", kernelFunc: v6 };
function w6(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] = N.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 = US(d, h, s.dtype, l, o, c, p, s.shape, a);
  return n.makeTensorInfo(u, s.dtype, f.values);
}
var k6 = { kernelName: bo, backendName: "cpu", kernelFunc: w6 };
function I6(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, indices: a } = t, { axis: i, batchDims: o } = s;
  ve([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 = N.segment_util.collectGatherOpShapeInfo(r, a, u, p), f = mt({ inputs: { x: r }, backend: n, attrs: { shape: [h.batchSize, h.outerSize, h.dimSize, h.sliceSize] } }), m = mt({ inputs: { x: a }, backend: n, attrs: { shape: [h.batchSize, d / h.batchSize] } }), g = [h.batchSize, h.outerSize, d / h.batchSize, h.sliceSize], b = n.bufferSync(m), y = n.bufferSync(f), v = GS(y, b, g);
  return n.disposeIntermediateTensorInfo(f), n.disposeIntermediateTensorInfo(m), n.makeTensorInfo(h.outputShape, v.dtype, v.values);
}
var S6 = { kernelName: go, backendName: "cpu", kernelFunc: I6 };
function C6(e) {
  let { inputs: t, backend: n } = e, { input: s } = t, r = w.sizeFromShape(s.shape), a = s.shape[s.shape.length - 1], i = r / a, o = mt({ inputs: { x: s }, backend: n, attrs: { shape: [i, a] } }), u = TC(o, true, n), l = mt({ inputs: { x: u }, backend: n, attrs: { shape: s.shape } });
  return n.disposeIntermediateTensorInfo(o), n.disposeIntermediateTensorInfo(u), l;
}
var N6 = { kernelName: bg, backendName: "cpu", kernelFunc: C6 };
var T6 = st(fl, (e) => Number.isFinite(e) ? 1 : 0, "bool");
var $6 = { kernelName: fl, backendName: "cpu", kernelFunc: T6 };
var _6 = st(ml, (e) => Math.abs(e) === 1 / 0 ? 1 : 0, "bool");
var A6 = { kernelName: ml, backendName: "cpu", kernelFunc: _6 };
var E6 = st(gl, (e) => Number.isNaN(e) ? 1 : 0, "bool");
var R6 = { kernelName: gl, backendName: "cpu", kernelFunc: E6 };
function D6(e) {
  let { backend: t, attrs: n } = e, { start: s, stop: r, num: a } = n, i = XS(s, r, a);
  return t.makeTensorInfo([i.length], "float32", i);
}
var F6 = { kernelName: yg, backendName: "cpu", kernelFunc: D6 };
var O6 = st(bl, (e) => Math.log1p(e));
var P6 = { kernelName: bl, backendName: "cpu", kernelFunc: O6 };
var z6 = At((e, t) => e && t);
var M6 = Gt(wo, z6, null, "bool");
var L6 = { kernelName: wo, backendName: "cpu", kernelFunc: M6 };
var B6 = st(yl, (e) => e ? 0 : 1, "bool");
var V6 = { kernelName: yl, backendName: "cpu", kernelFunc: B6 };
var W6 = At((e, t) => e || t);
var U6 = Gt(Zd, W6, null, "bool");
var G6 = { kernelName: Zd, backendName: "cpu", kernelFunc: U6 };
function H6(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { depthRadius: a, bias: i, alpha: o, beta: u } = s;
  ve(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 q6 = { kernelName: Jd, backendName: "cpu", kernelFunc: H6 };
function j6(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;
  ve(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), C = 0;
    for (let T = x; T < k; T++)
      C += Math.pow(f[T], 2);
    C = l * C + u;
    for (let T = x; T < k; T++) {
      let E = -2 * l * c * f[T] * m[y] / C;
      y === T && (E += Math.pow(C, -c)), E *= h[y], g[T] += E;
    }
  }
  return n.makeTensorInfo(i.shape, r.dtype, g);
}
var K6 = { kernelName: vg, backendName: "cpu", kernelFunc: j6 };
function $C(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 = N.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 = tv(h, u, r.dtype, d, x), p = N.getInnerMostAxes(p.length, l), u = x;
  }
  ve(r, "max"), N.assertAxesAreInnerMostDims("max", p, l);
  let [f, m] = N.computeOutAndReduceShapes(u, p), g = w.sizeFromShape(m), b = QS(h, g, f, r.dtype), y = o.write(b, f, r.dtype), v = f;
  return i && (v = N.expandShapeToKeepDim(f, c)), { dataId: y, shape: v, dtype: r.dtype };
}
var X6 = { kernelName: Va, backendName: "cpu", kernelFunc: $C };
function Y6(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t;
  ve(r, "maxPool");
  let { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1;
  w.assert(N.eitherStridesOrDilationsAreOne(i, l), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);
  let c = N.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 = iv(d, r.shape, r.dtype, h, c, "max");
    p = n.makeTensorInfo(c.outShape, r.dtype, f.values);
  }
  return p;
}
var Q6 = { kernelName: Ua, backendName: "cpu", kernelFunc: Y6 };
function Z6(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u, dataFormat: l } = s;
  ve(r, "maxPool3d");
  let c = N.computePool3DInfo(r.shape, a, i, 1, o, u, l), p = n.data.get(r.dataId).values, d = SC(p, r.shape, r.dtype, w.computeStrides(r.shape), c, "max");
  return n.makeTensorInfo(d.shape, "float32", d.values);
}
var J6 = { kernelName: ep, backendName: "cpu", kernelFunc: Z6 };
function eq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, { filterSize: i, strides: o, pad: u, dimRoundingMode: l } = s;
  ve([r, a], "maxPool3DGrad");
  let c = N.computePool3DInfo(a.shape, i, o, 1, u, l), p = n.bufferSync(a), d = UG(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, C = v - 1 - c.padInfo.front, T = k - 1 - c.padInfo.left, E = x - 1 - c.padInfo.top, A = De(a.shape, "float32"), P = n.bufferSync(r);
  for (let R = 0; R < c.batchSize; ++R)
    for (let F = 0; F < c.inChannels; ++F)
      for (let $ = 0; $ < c.inDepth; ++$)
        for (let z = 0; z < c.inHeight; ++z)
          for (let W = 0; W < c.inWidth; ++W) {
            let q = $ - C, K = z - E, Y = W - T, Z = 0;
            for (let te = 0; te < v; te += g) {
              let ee = (q + te) / h;
              if (!(ee < 0 || ee >= c.outDepth || Math.floor(ee) !== ee))
                for (let se = 0; se < x; se += b) {
                  let ne = (K + se) / f;
                  if (!(ne < 0 || ne >= c.outHeight || Math.floor(ne) !== ne))
                    for (let oe = 0; oe < k; oe += y) {
                      let re = (Y + oe) / m;
                      if (re < 0 || re >= c.outWidth || Math.floor(re) !== re)
                        continue;
                      let le = v * x * k - 1 - d.get(R, ee, ne, re, F), me = te * x * k + se * k + oe, we = le === me ? 1 : 0;
                      if (we === 0)
                        continue;
                      Z += P.get(R, ee, ne, re, F) * we;
                    }
                }
            }
            A.set(Z, R, $, z, W, F);
          }
  return n.makeTensorInfo(A.shape, A.dtype, A.values);
}
var tq = { kernelName: wg, backendName: "cpu", kernelFunc: eq };
function nq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a, output: i } = t, o = a;
  ve([a, i], "maxPoolGrad");
  let { filterSize: u, strides: l, pad: c, dimRoundingMode: p } = s, d = N.computePool2DInfo(o.shape, u, l, 1, c, p), h = n.data.get(o.dataId).values, f = De(d.outShape, o.dtype, IC(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, C = v - 1 - d.padInfo.top, T = De(o.shape, "float32"), E = n.data.get(r.dataId).values, A = De(r.shape, "float32", E);
  for (let P = 0; P < d.batchSize; ++P)
    for (let R = 0; R < d.inChannels; ++R)
      for (let F = 0; F < d.inHeight; ++F)
        for (let $ = 0; $ < d.inWidth; ++$) {
          let z = F - C, W = $ - k, q = 0;
          for (let K = 0; K < v; K += b) {
            let Y = (z + K) / 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 ee = v * x - 1 - f.get(P, Y, te, R), se = K * x + Z, ne = ee === se ? 1 : 0;
                if (ne === 0)
                  continue;
                q += A.get(P, Y, te, R) * ne;
              }
          }
          T.set(q, P, F, $, R);
        }
  return n.makeTensorInfo(T.shape, T.dtype, T.values);
}
var sq = { kernelName: xg, backendName: "cpu", kernelFunc: nq };
function rq(e, t, n, s, r) {
  let a = w.computeStrides(t), i = iv(e, t, n, a, r, "max"), o = IC(e, t, n, r, true, s);
  return [i.values, o.values];
}
var aq = { kernelName: kg, backendName: "cpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
  let { x: s } = e, { filterSize: r, strides: a, pad: i, includeBatchInIndex: o } = t, u = n;
  ve(s, "MaxPoolWithArgmax");
  let l = u.data.get(s.dataId).values, c = N.computePool2DInfo(s.shape, r, a, [1, 1], i), [p, d] = rq(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 iq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s, o = w.parseAxisParam(a, r.shape), l = N.computeOutAndReduceShapes(r.shape, o)[1], c = w.sizeFromShape(l), p = [], d = n.makeTensorInfo([], "float32", new Float32Array([c]));
  p.push(d);
  let h = xr({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } });
  p.push(h);
  let f = ov({ inputs: { a: h, b: d }, backend: n });
  p.push(f);
  let m = Kl({ inputs: { x: f }, backend: n, attrs: { axis: a, keepDims: i } });
  return p.forEach((g) => n.disposeIntermediateTensorInfo(g)), m;
}
var oq = { kernelName: Ga, backendName: "cpu", kernelFunc: iq };
function uq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
  ve(r, "min");
  let o = w.parseAxisParam(a, r.shape), u = o, l = N.getAxesPermutation(u, r.shape.length), c = r;
  l != null && (c = Vn({ inputs: { x: r }, backend: n, attrs: { perm: l } }), u = N.getInnerMostAxes(u.length, r.shape.length)), N.assertAxesAreInnerMostDims("min", u, c.shape.length);
  let [p, d] = N.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 = N.expandShapeToKeepDim(p, o), y = mt({ inputs: { x: g }, backend: n, attrs: { shape: b } });
    return n.disposeIntermediateTensorInfo(g), y;
  }
  return g;
}
var lq = { kernelName: Ha, backendName: "cpu", kernelFunc: uq };
function cq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { paddings: a, mode: i } = s;
  ve(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 C = 0; C < m; C++)
      x[C] < u[C] ? x[C] = u[C] * 2 - x[C] - c : x[C] >= l[C] && (x[C] = (l[C] - 1) * 2 - x[C] + c);
    x = x.map((C, T) => C - u[T]);
    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 dq = { kernelName: ja, backendName: "cpu", kernelFunc: cq };
var pq = At((e, t) => {
  let n = e % t;
  return e < 0 && t < 0 || e >= 0 && t >= 0 ? n : (n + t) % t;
});
var hq = Gt(vl, pq);
var fq = { kernelName: vl, backendName: "cpu", kernelFunc: hq };
var mq = ya(Vd());
function _C(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 = $C({ inputs: { x: r }, backend: n, attrs: { reductionIndices: u, keepDims: false } }), c = N.expandShapeToKeepDim(l.shape, u), p = mt({ inputs: { x: l }, backend: n, attrs: { shape: c } }), d = rv({ inputs: { a: r, b: p }, backend: n }), h = BS({ inputs: { x: d }, backend: n }), f = Kl({ inputs: { x: h }, backend: n, attrs: { axis: u, keepDims: false } }), m = mt({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = ov({ 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 gq = { kernelName: ii, backendName: "cpu", kernelFunc: _C };
function bq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { numSamples: a, seed: i, normalized: o } = s;
  ve(r, "multinomial");
  let u = o ? r : _C({ 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 = mq.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 yq = { kernelName: Ig, backendName: "cpu", kernelFunc: bq };
var vq = xs.nonMaxSuppressionV3Impl;
function xq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u } = s;
  ve(r, "NonMaxSuppression");
  let l = n.data.get(r.dataId).values, c = n.data.get(a.dataId).values, { selectedIndices: p } = vq(l, c, i, o, u);
  return n.makeTensorInfo([p.length], "int32", new Int32Array(p));
}
var wq = { kernelName: So, backendName: "cpu", kernelFunc: xq };
var kq = xs.nonMaxSuppressionV4Impl;
function Iq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, padToMaxOutputSize: l } = s;
  ve(r, "NonMaxSuppressionPadded");
  let c = n.data.get(r.dataId).values, p = n.data.get(a.dataId).values, { selectedIndices: d, validOutputs: h } = kq(c, p, i, o, u, l);
  return [n.makeTensorInfo([d.length], "int32", new Int32Array(d)), n.makeTensorInfo([], "int32", new Int32Array([h]))];
}
var Sq = { kernelName: xl, backendName: "cpu", kernelFunc: Iq };
var Cq = xs.nonMaxSuppressionV5Impl;
function Nq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { boxes: r, scores: a } = t, { maxOutputSize: i, iouThreshold: o, scoreThreshold: u, softNmsSigma: l } = s;
  ve(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 } = Cq(c, p, d, h, f, m);
  return [n.makeTensorInfo([g.length], "int32", new Int32Array(g)), n.makeTensorInfo([b.length], "float32", new Float32Array(b))];
}
var Tq = { kernelName: Co, backendName: "cpu", kernelFunc: Nq };
function $q(e) {
  let { inputs: t, backend: n, attrs: s } = e, { indices: r } = t, { depth: a, onValue: i, offValue: o } = s;
  ve(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 _q = { kernelName: To, backendName: "cpu", kernelFunc: $q };
function Od(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 = ha({ inputs: { input: s }, backend: n }), a = Od({ inputs: { x: r }, backend: n }), i = eo({ inputs: { input: s }, backend: n }), o = Od({ inputs: { x: i }, backend: n }), u = An({ inputs: { real: a, imag: o }, backend: n });
    return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
  } else
    return uv({ backend: n, attrs: { shape: s.shape, value: 0, dtype: s.dtype } });
}
var Aq = { kernelName: Go, backendName: "cpu", kernelFunc: Od };
function AC(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 = ha({ inputs: { input: s }, backend: n }), a = AC({ inputs: { x: r }, backend: n }), i = eo({ inputs: { input: s }, backend: n }), o = Od({ inputs: { x: i }, backend: n }), u = An({ inputs: { real: a, imag: o }, backend: n });
    return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
  } else
    return uv({ backend: n, attrs: { shape: s.shape, value: 1, dtype: s.dtype } });
}
var Eq = { kernelName: No, backendName: "cpu", kernelFunc: AC };
function EC(e) {
  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
  if (t.length === 1)
    return Fd({ 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 = Fd({ inputs: { input: c }, backend: n, attrs: { dim: r } });
    return o.push(p), p;
  }), l = to({ inputs: u, backend: n, attrs: { axis: r } });
  return o.forEach((c) => n.disposeIntermediateTensorInfo(c)), l;
}
var Rq = { kernelName: $o, backendName: "cpu", kernelFunc: EC };
function Dq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { paddings: a, constantValue: i } = s;
  ve(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((C, T) => C + u[T]), k = w.locToIndex(x, f, m);
    g[k] = l[y];
  }
  return { dataId: n.write(g, o, r.dtype), shape: o, dtype: r.dtype };
}
var RC = { kernelName: Xa, backendName: "cpu", kernelFunc: Dq };
var Fq = At((e, t) => Math.pow(e, t));
var Oq = Gt(Ya, Fq);
var Pq = { kernelName: Ya, backendName: "cpu", kernelFunc: Oq };
function zq(e) {
  let { backend: t, attrs: n } = e, { start: s, stop: r, dtype: a, step: i } = n, o = nv(s, r, i, a);
  return t.makeTensorInfo([o.length], a, o);
}
var Mq = { kernelName: wl, backendName: "cpu", kernelFunc: zq };
var Lq = st(kl, (e) => 1 / e);
var Bq = { kernelName: kl, backendName: "cpu", kernelFunc: Lq };
function Vq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s;
  ve(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 C = 0; C < p; C++)
    for (let T = 0; T < l; T++) {
      let E;
      i ? E = x * (T + 0.5) - 0.5 : E = x * T;
      let A = Math.max(0, Math.floor(E)), P = E - A, R = Math.min(d - 1, Math.ceil(E)), F = C * u[0] + A * u[1], $ = C * u[0] + R * u[1];
      for (let z = 0; z < c; z++) {
        let W;
        i ? W = k * (z + 0.5) - 0.5 : W = k * z;
        let q = Math.max(0, Math.floor(W)), K = W - q, Y = Math.min(h - 1, Math.ceil(W)), Z = F + q * u[2], te = $ + q * u[2], ee = F + Y * u[2], se = $ + Y * u[2];
        for (let ne = 0; ne < f; ne++) {
          let oe = m[Z + ne], re = m[te + ne], le = m[ee + ne], me = m[se + ne], we = oe + (le - oe) * K, Se = re + (me - re) * K, Ee = we + (Se - we) * P;
          g[v++] = Ee;
        }
      }
    }
  return n.makeTensorInfo([p, l, c, f], "float32", g);
}
var Wq = { kernelName: Ja, backendName: "cpu", kernelFunc: Vq };
function Uq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s;
  ve([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 C = k * o[0];
    for (let T = 0; T < d; T++) {
      let E = T * b, A = Math.floor(E), P = Math.min(Math.ceil(E), l - 1), R = C + A * o[1], F = C + P * o[1], $ = E - A, z = 1 - $;
      for (let W = 0; W < h; W++) {
        let q = W * y, K = Math.floor(q), Y = Math.min(Math.ceil(q), c - 1), Z = q - K, te = 1 - Z, ee = R + K * o[2], se = R + Y * o[2], ne = F + K * o[2], oe = F + Y * o[2], re = z * te, le = z * Z, me = $ * te, we = $ * Z;
        for (let Se = 0; Se < p; Se++) {
          let Ee = v[x++];
          f[ee + Se] += Ee * re, f[se + Se] += Ee * le, f[ne + Se] += Ee * me, f[oe + Se] += Ee * we;
        }
      }
    }
  }
  return n.makeTensorInfo([u, c, l, p], "float32", f);
}
var Gq = { kernelName: Cg, backendName: "cpu", kernelFunc: Uq };
function Hq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s;
  ve(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 C = 0; C < p; C++) {
    let T = C * u[0];
    for (let E = 0; E < l; E++) {
      let A = i ? v * (E + 0.5) : v * E, P = Math.min(d - 1, a ? Math.round(A) : Math.floor(A));
      i && (P = Math.max(0, P));
      let R = T + P * u[1];
      for (let F = 0; F < c; F++) {
        let $ = i ? x * (F + 0.5) : x * F, z = Math.min(h - 1, a ? Math.round($) : Math.floor($));
        i && (z = Math.max(0, z));
        let W = R + z * u[2];
        for (let q = 0; q < f; q++) {
          let K = m[W + q];
          g[k++] = K;
        }
      }
    }
  }
  return n.makeTensorInfo([p, l, c, f], r.dtype, g);
}
var qq = { kernelName: Il, backendName: "cpu", kernelFunc: Hq };
function jq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s;
  ve([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, C = 1 / x, T = Math.ceil(k) * 2 + 2, E = Math.ceil(C) * 2 + 2;
  for (let A = 0; A < l; A++) {
    let P = A * o[0];
    for (let R = 0; R < c; R++) {
      let F = P + R * o[1], $ = Math.floor(R * k), z = Math.floor($ - T / 2);
      for (let W = 0; W < p; W++) {
        let q = F + W * o[2], K = Math.floor(W * C), Y = Math.floor(K - E / 2);
        for (let Z = 0; Z < d; Z++) {
          let te = 0;
          for (let ee = 0; ee < T; ee++) {
            let se = ee + z;
            if (se < 0 || se >= h)
              continue;
            let ne = P + se * u[1], oe = se * v, re = Math.min(c - 1, i ? Math.round(oe) : Math.floor(oe));
            if (R === re)
              for (let le = 0; le < E; le++) {
                let me = le + Y;
                if (me < 0 || me >= f)
                  continue;
                let we = ne + me * u[2], Se = me * x, Ee = Math.min(p - 1, i ? Math.round(Se) : Math.floor(Se));
                W === Ee && (te += g[we + Z]);
              }
          }
          m[q + Z] = te;
        }
      }
    }
  }
  return n.makeTensorInfo(r.shape, r.dtype, m);
}
var Kq = { kernelName: Sg, backendName: "cpu", kernelFunc: jq };
function Xq(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dims: a } = s;
  ve(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 Vt(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 Yq = { kernelName: Eo, backendName: "cpu", kernelFunc: Xq };
var Qq = { kernelName: Ho, 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] = N.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 C = 0; C < c; C++) {
      let T = C * (p * d);
      for (let E = 0; E < p; E++) {
        let A = E * d;
        for (let P = 0; P < d; P++) {
          let R = [l, C, E, P], F = R[2], $ = R[1], z = (F - h) * b - ($ - f) * g, W = (F - h) * g + ($ - f) * b;
          z = Math.round(z + h), W = Math.round(W + f);
          let q = a;
          if (typeof a != "number" && (P === 3 ? q = m : q = a[P]), z >= 0 && z < p && W >= 0 && W < c) {
            let Y = W * (p * d), Z = z * d, te = k + Y + Z + P;
            q = y[te];
          }
          let K = k + T + A + P;
          u[K] = q;
        }
      }
    }
  }
  return { dataId: o.write(u, s.shape, s.dtype), shape: s.shape, dtype: s.dtype };
} };
var Zq = st(Ro, (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 Jq = { kernelName: Ro, backendName: "cpu", kernelFunc: Zq };
function DC(e, t, n, s, r, a, i, o, u, l) {
  let c = [s / r, r], p = e.values, d = t.values;
  if (s === 0)
    return De(n, t.dtype);
  let h = De(c, t.dtype);
  h.values.fill(u);
  for (let f = 0; f < a; f++) {
    let m = [], g = 0;
    for (let b = 0; b < i; b++) {
      let y = p[f * i + b];
      m.push(y), g += y * o[b];
    }
    if (g < 0 || g >= s / r)
      throw new Error(`Invalid indices: ${m} does not index into ${n}`);
    for (let b = 0; b < r; b++)
      l ? h.values[g * r + b] += d[f * r + b] : h.values[g * r + b] = t.rank === 0 ? d[0] : d[f * r + b];
  }
  return h;
}
function ej(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 } = N.calculateShapes(a, r, i), d = true, h = n.bufferSync(r), f = n.bufferSync(a), m = DC(h, f, i, p, l, u, o, c, 0, d);
  return n.makeTensorInfo(i, m.dtype, m.values);
}
var tj = { kernelName: Do, backendName: "cpu", kernelFunc: ej };
function nj(e) {
  let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t;
  ve([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 = yn(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 sj = { kernelName: Fo, backendName: "cpu", kernelFunc: nj };
var rj = N.SELU_SCALEALPHA;
var aj = N.SELU_SCALE;
var ij = st(Sl, (e) => e >= 0 ? aj * e : rj * (Math.exp(e) - 1));
var oj = { kernelName: Sl, backendName: "cpu", kernelFunc: ij };
var uj = st(Cl, (e) => e < 0 ? -1 : e > 0 ? 1 : 0);
var lj = { kernelName: Cl, backendName: "cpu", kernelFunc: uj };
var cj = st(ni, (e) => Math.sin(e));
var dj = { kernelName: ni, backendName: "cpu", kernelFunc: cj };
var pj = st(Po, (e) => Math.sinh(e));
var hj = { kernelName: Po, backendName: "cpu", kernelFunc: pj };
var fj = 11920928955078125e-23;
var Jx = Math.log(fj) + 2;
var mj = st(Nl, (e) => {
  let t = e > -Jx, n = e < Jx, s = Math.exp(e), r;
  return n ? r = s : t ? r = e : r = Math.log(1 + s), r;
});
var gj = { kernelName: Nl, backendName: "cpu", kernelFunc: mj };
function bj(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockShape: a, paddings: i } = s;
  ve([r], "spaceToBatchND");
  let o = w.sizeFromShape(a), u = [[0, 0]];
  u.push(...i);
  for (let C = 1 + a.length; C < r.shape.length; ++C)
    u.push([0, 0]);
  let l = RC.kernelFunc({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), c = N.getReshaped(l.shape, a, o, false), p = N.getPermuted(c.length, a.length, false), d = N.getReshapedPermuted(l.shape, a, o, false), m = mt({ inputs: { x: l }, backend: n, attrs: { shape: c } }), y = Vn({ inputs: { x: m }, backend: n, attrs: { perm: p } }), k = mt({ inputs: { x: y }, backend: n, attrs: { shape: d } });
  return n.disposeIntermediateTensorInfo(l), n.disposeIntermediateTensorInfo(m), n.disposeIntermediateTensorInfo(y), k;
}
var yj = { kernelName: zo, backendName: "cpu", kernelFunc: bj };
function vj(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] = aC(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 xj = { kernelName: np, backendName: "cpu", kernelFunc: vj };
function wj(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] = iC(o, s.shape, s.dtype, i, u);
  return [n.makeTensorInfo(c, s.dtype, l), n.makeTensorInfo([p.length], a.dtype, new Int32Array(p))];
}
var kj = { kernelName: Tl, backendName: "cpu", kernelFunc: wj };
function Ij(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] = sv(i, s.shape, s.dtype, o, u, true);
  return n.makeTensorInfo(c, s.dtype, l);
}
var Sj = { kernelName: sp, backendName: "cpu", kernelFunc: Ij };
function Cj(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] = sv(i, s.shape, s.dtype, o, u);
  return n.makeTensorInfo(c, s.dtype, l);
}
var Nj = { kernelName: rp, backendName: "cpu", kernelFunc: Cj };
function Tj(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 } = N.calculateShapes(a, r, o), h = false, f = n.bufferSync(r), m = n.bufferSync(a), g = n.data.get(i.dataId).values[0], b = DC(f, m, o, d, c, l, u, p, g, h);
  return n.makeTensorInfo(o, b.dtype, b.values);
}
var $j = { kernelName: ap, backendName: "cpu", kernelFunc: Tj };
function _j(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 = N.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 = fa({ inputs: { x: r }, backend: n, attrs: { begin: l, size: d } });
    return l[o] += p, h;
  });
}
var Aj = { kernelName: Mo, backendName: "cpu", kernelFunc: _j };
var Ej = { kernelName: $l, backendName: "cpu", kernelFunc: ({ inputs: e, backend: t }) => {
  let { x: n } = e, s = t;
  ve(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 Rj = st(di, (e, t) => {
  let n = t;
  return isNaN(e) ? NaN : e > 0 ? 1 : n.alpha;
});
var Dj = { kernelName: di, backendName: "cpu", kernelFunc: Rj };
function Fj(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;
  ve(r, "stridedSlice");
  let { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = wt.sliceInfo(r.shape, a, i, o, u, l, c, p, d), k;
  if (m)
    k = mt({ inputs: { x: r }, backend: n, attrs: { shape: f } });
  else if (g || b) {
    w.assert(r.shape.length >= 1, () => `Input must have rank at least 1, got: ${r.shape.length}`);
    let C = wt.computeOutShape(y, v, x), T = fa({ inputs: { x: r }, backend: n, attrs: { begin: y, size: C } });
    k = mt({ inputs: { x: T }, backend: n, attrs: { shape: f } }), n.disposeIntermediateTensorInfo(T);
  } else {
    let C = n.bufferSync(r), T = uC(h, C, x, y);
    k = n.makeTensorInfo(f, T.dtype, T.values);
  }
  return k;
}
var Oj = { kernelName: Lo, backendName: "cpu", kernelFunc: Fj };
function Pj(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] = lC(d, h, r, a, i, o, u, l);
  return [n.makeTensorInfo([f.length], "string", f), n.makeTensorInfo(p.shape, "int32", m)];
}
var zj = { kernelName: ip, backendName: "cpu", kernelFunc: Pj };
function Mj(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] = cC(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 Lj = { kernelName: Ng, backendName: "cpu", kernelFunc: Mj };
function Bj(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 = dC(i, r);
  return n.makeTensorInfo(a.shape, "int32", o);
}
var Vj = { kernelName: Tg, backendName: "cpu", kernelFunc: Bj };
var Wj = st(Bo, (e) => Math.tan(e));
var Uj = { kernelName: Bo, backendName: "cpu", kernelFunc: Wj };
var Gj = st(li, (e) => Math.tanh(e));
var Hj = { kernelName: li, backendName: "cpu", kernelFunc: Gj };
function qj(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reps: a } = s;
  ve(r, "tile");
  let i = hC(n.bufferSync(r), a);
  return n.makeTensorInfo(i.shape, i.dtype, i.values);
}
var jj = { kernelName: Sr, backendName: "cpu", kernelFunc: qj };
function Kj(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { k: a, sorted: i } = s;
  ve(r, "topk");
  let o = n.data.get(r.dataId).values, [u, l] = mC(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 Xj = { kernelName: Vo, backendName: "cpu", kernelFunc: Kj };
function Yj(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 C = s.data.get(r.dataId).values, T = s.data.get(a.dataId).values;
  for (let A = 0; A < c; ++A) {
    let P = a.shape[0] === 1 ? T : T.subarray(A * 8, A * 8 + 8);
    for (let R = 0; R < f; ++R)
      for (let F = 0; F < m; ++F)
        for (let $ = 0; $ < h; ++$) {
          let z, W = P[6] * F + P[7] * R + 1;
          if (W === 0)
            continue;
          let q = (P[0] * F + P[1] * R + P[2]) / W, K = (P[3] * F + P[4] * R + P[5]) / W, Y = ew(q, d, o), Z = ew(K, p, o);
          switch (i) {
            case "nearest":
              z = n5(C, p, d, y, v, x, A, Z, Y, $, u);
              break;
            case "bilinear":
              z = s5(C, p, d, y, v, x, A, Z, Y, $, u);
              break;
            default:
              throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${i}`);
          }
          let te = A * y + R * v + F * x + $;
          k[te] = z;
        }
    return s.makeTensorInfo(g, r.dtype, k);
  }
  return { dataId: s.write(k, g, r.dtype), shape: r.shape, dtype: r.dtype };
}
var Qj = { kernelName: Wo, backendName: "cpu", kernelFunc: Yj };
function ew(e, t, n) {
  switch (n) {
    case "reflect":
      return Zj(e, t);
    case "wrap":
      return Jj(e, t);
    case "nearest":
      return t5(e, t);
    case "constant":
    default:
      return e5(e, t);
  }
}
function Zj(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 Jj(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 e5(e, t) {
  return e;
}
function t5(e, t) {
  return w.clamp(0, e, t - 1);
}
function Ru(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 n5(e, t, n, s, r, a, i, o, u, l, c) {
  let p = Math.round(o), d = Math.round(u);
  return Ru(e, t, n, s, r, a, i, p, d, l, c);
}
function s5(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) * Ru(e, t, n, s, r, a, i, p, d, l, c) + (u - d) * Ru(e, t, n, s, r, a, i, p, f, l, c), g = (f - u) * Ru(e, t, n, s, r, a, i, h, d, l, c) + (u - d) * Ru(e, t, n, s, r, a, i, h, f, l, c);
  return (h - o) * m + (o - p) * g;
}
function r5(e) {
  let { inputs: t, attrs: n, backend: s } = e, { axis: r } = n, { x: a } = t;
  ve(a, "unique");
  let i = s.data.get(a.dataId).values, { outputValues: o, outputShape: u, indices: l } = gC(i, r, a.shape, a.dtype);
  return [s.makeTensorInfo(u, a.dtype, o), s.makeTensorInfo([l.length], "int32", l)];
}
var a5 = { kernelName: $g, backendName: "cpu", kernelFunc: r5 };
function i5(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 = fa({ inputs: { x: r }, backend: n, attrs: { begin: c, size: p } });
    d[h] = mt({ inputs: { x: f }, backend: n, attrs: { shape: u } }), n.disposeIntermediateTensorInfo(f);
  }
  return d;
}
var o5 = { kernelName: Uo, backendName: "cpu", kernelFunc: i5 };
function u5(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, segmentIds: a } = t, { numSegments: i } = s;
  ve(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 = Fd({ 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 = MS({ inputs: { a: g, b: d }, backend: n }), y = xr({ inputs: { x: b }, backend: n, attrs: { dtype: "float32" } }), v = Gp({ inputs: { a: y, b: r }, backend: n }), x = Kl({ 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 = EC({ inputs: l, backend: n, attrs: { axis: 0 } });
  return c.forEach((f) => n.disposeIntermediateTensorInfo(f)), h;
}
var l5 = { kernelName: op, backendName: "cpu", kernelFunc: u5 };
var c5 = [bG, cU, vG, wG, gU, IG, CG, TG, _G, EG, DG, OG, zG, BG, WG, HG, jG, XG, QG, mG, JG, tH, sH, aH, fU, yU, oH, dU, lH, dH, pH, fH, gH, yH, xH, kH, SH, NH, $H, AH, RH, FH, OH, zH, LH, VH, WH, UH, GH, jH, uG, XH, vU, s6, xU, r6, kU, c6, d6, h6, SU, g6, y6, x6, k6, S6, NU, $U, pU, N6, cH, $6, A6, R6, lG, AU, RU, F6, FU, P6, L6, V6, G6, q6, K6, X6, PU, Q6, J6, tq, sq, aq, oq, lq, MU, dq, fq, yq, BU, WU, wq, Sq, Tq, GU, _q, Eq, Rq, RC, Pq, dG, jU, Mq, hU, Pm, Bq, pG, hG, fG, Wq, Gq, qq, Kq, Yq, Qq, Jq, XU, tj, sj, oj, QU, lj, dj, hj, ZU, gq, gj, yj, xj, kj, Sj, Nj, $j, Aj, tG, Ej, sG, Dj, Oj, zj, Lj, Vj, oG, HH, Uj, Hj, jj, Xj, Qj, HU, a5, o5, l5, Aq];
for (let e of c5)
  _l(e);
var d5 = {};
Ae(d5, { assertNotComplex: () => tu, bindCanvasToFramebuffer: () => S5, bindColorTextureToFramebuffer: () => rd, bindTextureToProgramUniformSampler: () => KC, bindTextureUnit: () => HC, bindVertexBufferToProgramAttribute: () => Mm, callAndCheck: () => fe, canBeRepresented: () => FC, createFragmentShader: () => zC, createFramebuffer: () => GC, createProgram: () => MC, createStaticIndexBuffer: () => VC, createStaticVertexBuffer: () => BC, createTexture: () => WC, createVertexShader: () => PC, getBatchDim: () => ma, getExtensionOrThrow: () => Du, getFramebufferErrorMessage: () => XC, getMaxTexturesInShader: () => JC, getNumChannels: () => k5, getProgramUniformLocation: () => jC, getProgramUniformLocationOrThrow: () => qC, getRowsCols: () => ga, getShapeAs3D: () => ad, getTextureShapeFromLogicalShape: () => QC, getWebGLDisjointQueryTimerVersion: () => e1, getWebGLErrorMessage: () => OC, getWebGLMaxTextureSize: () => ZC, hasExtension: () => Mn, isCapableOfRenderingToFloatTexture: () => t1, isDownloadFloatTextureEnabled: () => n1, isReshapeFree: () => Ju, isWebGLFenceEnabled: () => s1, isWebGLVersionEnabled: () => Bm, linkProgram: () => LC, resetMaxTextureSize: () => C5, resetMaxTexturesInShader: () => N5, unbindColorTextureFromFramebuffer: () => Lm, unbindTextureUnit: () => I5, validateFramebuffer: () => Fu, validateProgram: () => sd, validateTextureSize: () => UC });
var jr = {};
var Hf = { alpha: false, antialias: false, premultipliedAlpha: false, preserveDrawingBuffer: false, depth: false, stencil: false, failIfMajorPerformanceCaveat: true };
function p5(e, t) {
  jr[e] = t;
}
function vs(e, t) {
  if (!(e in jr) || t != null) {
    let s = f5(e, t);
    if (s !== null)
      jr[e] = s;
    else
      return console.log("Could not get context for WebGL version", e), null;
  }
  let n = jr[e];
  return n == null || n.isContextLost() ? (delete jr[e], vs(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), jr[e]);
}
function h5(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 f5(e, t) {
  if (e !== 1 && e !== 2)
    throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");
  let n = t == null ? h5(e) : t;
  return n.addEventListener("webglcontextlost", (s) => {
    s.preventDefault(), delete jr[e];
  }, false), e === 1 ? n.getContext("webgl", Hf) || n.getContext("experimental-webgl", Hf) : n.getContext("webgl2", Hf);
}
function Xl(e, t) {
  return [t, e];
}
function m5(e, t) {
  return e * t;
}
function Xc(e) {
  let t = w.sizeFromShape(e), n = Math.ceil(t / 4);
  return w.sizeToSquarishShape(n);
}
function eu(e, t) {
  return [Math.max(1, Math.ceil(t / 2)), Math.max(1, Math.ceil(e / 2))];
}
function g5(e, t) {
  let [n, s] = eu(e, t);
  return n * s * 4;
}
function lv(e, t) {
  let n = e, s, r, a, i, o, u, l, c, p, d;
  return X().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 X().getBool("DEBUG") && b5(e), n;
}
function b5(e) {
  let t = e.getError();
  if (t !== e.NO_ERROR)
    throw new Error("WebGL Error: " + OC(e, t));
}
var y5 = 596e-10;
var v5 = 65504;
function FC(e) {
  return !!(X().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || e === 0 || y5 < Math.abs(e) && Math.abs(e) < v5);
}
function OC(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 Du(e, t) {
  return Qs(e, () => e.getExtension(t), 'Extension "' + t + '" not supported on this browser.');
}
function PC(e, t) {
  let n = Qs(e, () => e.createShader(e.VERTEX_SHADER), "Unable to create vertex WebGLShader.");
  if (fe(e, () => e.shaderSource(n, t)), fe(e, () => e.compileShader(n)), e.getShaderParameter(n, e.COMPILE_STATUS) === false)
    throw console.log(e.getShaderInfoLog(n)), new Error("Failed to compile vertex shader.");
  return n;
}
function zC(e, t) {
  let n = Qs(e, () => e.createShader(e.FRAGMENT_SHADER), "Unable to create fragment WebGLShader.");
  if (fe(e, () => e.shaderSource(n, t)), fe(e, () => e.compileShader(n)), e.getShaderParameter(n, e.COMPILE_STATUS) === false)
    throw w5(t, e.getShaderInfoLog(n)), new Error("Failed to compile fragment shader.");
  return n;
}
var x5 = /ERROR: [0-9]+:([0-9]+):/g;
function w5(e, t) {
  let n = x5.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 ${w.rightPad(l[0], o)}`, "border:1px solid red; background-color:#e3d2d2; color:#a61717"), console.log(c.join(`
`));
}
function MC(e) {
  return Qs(e, () => e.createProgram(), "Unable to create WebGLProgram.");
}
function LC(e, t) {
  if (fe(e, () => e.linkProgram(t)), e.getProgramParameter(t, e.LINK_STATUS) === false)
    throw console.log(e.getProgramInfoLog(t)), new Error("Failed to link vertex and fragment shaders.");
}
function sd(e, t) {
  if (fe(e, () => e.validateProgram(t)), e.getProgramParameter(t, e.VALIDATE_STATUS) === false)
    throw console.log(e.getProgramInfoLog(t)), new Error("Shader program validation failed.");
}
function BC(e, t) {
  let n = Qs(e, () => e.createBuffer(), "Unable to create WebGLBuffer");
  return fe(e, () => e.bindBuffer(e.ARRAY_BUFFER, n)), fe(e, () => e.bufferData(e.ARRAY_BUFFER, t, e.STATIC_DRAW)), n;
}
function VC(e, t) {
  let n = Qs(e, () => e.createBuffer(), "Unable to create WebGLBuffer");
  return fe(e, () => e.bindBuffer(e.ELEMENT_ARRAY_BUFFER, n)), fe(e, () => e.bufferData(e.ELEMENT_ARRAY_BUFFER, t, e.STATIC_DRAW)), n;
}
function k5() {
  return X().getNumber("WEBGL_VERSION") === 2 ? 1 : 4;
}
function WC(e) {
  return Qs(e, () => e.createTexture(), "Unable to create WebGLTexture.");
}
function UC(e, t) {
  let n = X().getNumber("WEBGL_MAX_TEXTURE_SIZE");
  if (e <= 0 || t <= 0) {
    let s = `[${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 GC(e) {
  return Qs(e, () => e.createFramebuffer(), "Unable to create WebGLFramebuffer.");
}
function Mm(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 HC(e, t, n) {
  YC(e, n), fe(e, () => e.activeTexture(e.TEXTURE0 + n)), fe(e, () => e.bindTexture(e.TEXTURE_2D, t));
}
function I5(e, t) {
  YC(e, t), fe(e, () => e.activeTexture(e.TEXTURE0 + t)), fe(e, () => e.bindTexture(e.TEXTURE_2D, null));
}
function qC(e, t, n) {
  return Qs(e, () => e.getUniformLocation(t, n), 'uniform "' + n + '" not present in program.');
}
function jC(e, t, n) {
  return e.getUniformLocation(t, n);
}
function KC(e, t, n, s) {
  fe(e, () => HC(e, t, s)), fe(e, () => e.uniform1i(n, s));
}
function S5(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 rd(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 Lm(e, t) {
  fe(e, () => e.bindFramebuffer(e.FRAMEBUFFER, t)), fe(e, () => e.framebufferTexture2D(e.FRAMEBUFFER, e.COLOR_ATTACHMENT0, e.TEXTURE_2D, null, 0));
}
function Fu(e) {
  let t = e.checkFramebufferStatus(e.FRAMEBUFFER);
  if (t !== e.FRAMEBUFFER_COMPLETE)
    throw new Error("Error binding framebuffer: " + XC(e, t));
}
function XC(e, t) {
  switch (t) {
    case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:
      return "FRAMEBUFFER_INCOMPLETE_ATTACHMENT";
    case e.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:
      return "FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";
    case e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:
      return "FRAMEBUFFER_INCOMPLETE_DIMENSIONS";
    case e.FRAMEBUFFER_UNSUPPORTED:
      return "FRAMEBUFFER_UNSUPPORTED";
    default:
      return `unknown error ${t}`;
  }
}
function Qs(e, t, n) {
  let s = fe(e, () => t());
  if (s == null)
    throw new Error(n);
  return s;
}
function YC(e, t) {
  let n = e.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1, s = t + e.TEXTURE0;
  if (s < e.TEXTURE0 || s > n) {
    let r = `[gl.TEXTURE0, gl.TEXTURE${n}]`;
    throw new Error(`textureUnit must be in ${r}.`);
  }
}
function ma(e, t = 2) {
  return w.sizeFromShape(e.slice(0, e.length - t));
}
function ga(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 ad(e) {
  let t = [1, 1, 1];
  return e.length === 0 || e.length === 1 && e[0] === 1 || (t = [ma(e), ...ga(e)]), t;
}
function QC(e, t = false) {
  let n = X().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 = ma(e), a = 2, i = 2;
    return e.length && ([a, i] = ga(e)), s = r * (a / 2) * (i / 2), w.sizeToSquarishShape(s).map((o) => o * 2);
  }
  return w.sizeToSquarishShape(s);
}
function Yc(e) {
  return e % 2 === 0;
}
function Ju(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 || Yc(n) && Yc(s) && (e[0] === 1 || t[0] === 1))
      return true;
  }
  return e[1] === t[1] && Yc(e[0]) && Yc(t[0]);
}
var id;
var od;
function ZC(e) {
  if (id == null) {
    let t = vs(e);
    id = t.getParameter(t.MAX_TEXTURE_SIZE);
  }
  return id;
}
function C5() {
  id = null;
}
function N5() {
  od = null;
}
function JC(e) {
  if (od == null) {
    let t = vs(e);
    od = t.getParameter(t.MAX_TEXTURE_IMAGE_UNITS);
  }
  return Math.min(16, od);
}
function e1(e) {
  if (e === 0)
    return 0;
  let t, n = vs(e);
  return Mn(n, "EXT_disjoint_timer_query_webgl2") && e === 2 ? t = 2 : Mn(n, "EXT_disjoint_timer_query") ? t = 1 : t = 0, t;
}
function Mn(e, t) {
  return e.getExtension(t) != null;
}
function Bm(e) {
  try {
    if (vs(e) != null)
      return true;
  } catch (t) {
    return console.log("Error when getting WebGL context: ", t), false;
  }
  return false;
}
function t1(e) {
  if (e === 0)
    return false;
  let t = vs(e);
  if (e === 1) {
    if (!Mn(t, "OES_texture_float"))
      return false;
  } else if (!Mn(t, "EXT_color_buffer_float"))
    return false;
  return Vm(t);
}
function n1(e) {
  if (e === 0)
    return false;
  let t = vs(e);
  if (e === 1) {
    if (!Mn(t, "OES_texture_float") || !Mn(t, "WEBGL_color_buffer_float"))
      return false;
  } else {
    if (Mn(t, "EXT_color_buffer_float"))
      return Vm(t);
    let s = "EXT_color_buffer_half_float";
    if (Mn(t, s)) {
      let r = t.getExtension(s);
      return T5(t, r);
    }
    return false;
  }
  return Vm(t);
}
function Vm(e) {
  let t = lv(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 T5(e, t) {
  let n = lv(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 : vs(e).fenceSync != null;
}
function tu(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 = X();
Ne.registerFlag("HAS_WEBGL", () => Ne.getNumber("WEBGL_VERSION") > 0);
Ne.registerFlag("WEBGL_VERSION", () => Bm(2) ? 2 : Bm(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", () => ZC(Ne.getNumber("WEBGL_VERSION")));
Ne.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => JC(Ne.getNumber("WEBGL_VERSION")));
Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", () => {
  let e = Ne.getNumber("WEBGL_VERSION");
  return e === 0 ? 0 : e1(e);
});
Ne.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => Ne.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !cp.isMobile());
Ne.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => t1(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", () => n1(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", () => cp.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 hn() {
  let e, t, n, s, r, a, i, o, u, l;
  return X().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 gi(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 Hp(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 $5(e, t) {
  let n = e.length, s = e.map((a) => `${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 _5(e, t, n = "index") {
  let s = e.map((a, i) => i), r = $5(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 cv(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 dv() {
  return `
  int getFlatIndex(ivec3 coords) {
    return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
  }
`;
}
var r1 = `
  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: a1 } = N;
function A5(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 } = pv(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 ? `[${h.arrayIndex}]` : ""};`);
  });
  let r = s.join(`
`), a = e.map((h) => E5(h, t, n.packedInputs, n.enableShapeUniforms)).join(`
`), i = t.texShape, o = hn(), u = F5(o), l, c, p = z5(o);
  return t.isPacked ? (l = R5(t.logicalShape, i, n.enableShapeUniforms), c = P5(o)) : (l = D5(t.logicalShape, i, n.enableShapeUniforms), c = O5(o)), n.packedInputs && (p += V5), [p, u, c, r, l, a, n.userCode].join(`
`);
}
function nu(e, t = false) {
  let n = e.shapeInfo.logicalShape;
  switch (n.length) {
    case 0:
      return J5(e, t);
    case 1:
      return tK(e, t);
    case 2:
      return sK(e, t);
    case 3:
      return aK(e, t);
    case 4:
      return oK(e, t);
    case 5:
      return uK(e);
    case 6:
      return lK(e);
    default:
      throw new Error(`${n.length}-D input sampling is not yet supported`);
  }
}
function i1(e, t) {
  switch (e.shapeInfo.logicalShape.length) {
    case 0:
      return Z5(e);
    case 1:
      return eK(e, t);
    case 2:
      return nK(e, t);
    case 3:
      return rK(e, t);
    default:
      return iK(e, t);
  }
}
function E5(e, t, n = false, s) {
  let r = "";
  n ? r += i1(e, s) : r += nu(e, s);
  let a = e.shapeInfo.logicalShape, i = t.logicalShape;
  return a.length <= i.length && (n ? r += cK(e, t) : r += dK(e, t)), r;
}
function R5(e, t, n) {
  switch (e.length) {
    case 0:
      return o1();
    case 1:
      return W5(e, t, n);
    case 2:
      return Y5(e, t, n);
    case 3:
      return G5(e, t, n);
    default:
      return q5(e, t, n);
  }
}
function D5(e, t, n) {
  switch (e.length) {
    case 0:
      return o1();
    case 1:
      return U5(e, t, n);
    case 2:
      return Q5(e, t, n);
    case 3:
      return H5(e, t, n);
    case 4:
      return j5(e, t, n);
    case 5:
      return K5(e, t);
    case 6:
      return X5(e, t);
    default:
      throw new Error(`${e.length}-D output sampling is not yet supported`);
  }
}
function F5(e) {
  return `
    float sampleTexture(sampler2D textureSampler, vec2 uv) {
      return ${e.texture2D}(textureSampler, uv).r;
    }
  `;
}
function O5(e) {
  return `
    void setOutput(float val) {
      ${e.output} = vec4(val, 0, 0, 0);
    }
  `;
}
function P5(e) {
  return `
    void setOutput(vec4 val) {
      ${e.output} = val;
    }
  `;
}
function z5(e) {
  return `${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);
    }

    ${M5}
    ${L5}
    ${B5}
  `;
}
var M5 = `
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 L5 = `
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 B5 = `
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 V5 = `
  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 o1() {
  return `
    int getOutputCoords() {
      return 0;
    }
  `;
}
function W5(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 U5(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 G5(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 H5(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;
    ${Hp(["r", "c", "d"], e)}
    return ivec3(r, c, d);
  }
`;
  let s = gi(["r", "c", "d"], e);
  return `
    ivec3 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
                             vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${s}
      return ivec3(r, c, d);
    }
  `;
}
function q5(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 j5(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;
      ${Hp(["r", "c", "d", "d2"], e)}
      return ivec4(r, c, d, d2);
    }
  `;
  let s = gi(["r", "c", "d", "d2"], e);
  return `
    ivec4 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx *
        vec2(${t[0]}, ${t[1]}));
      int index = resTexRC.x * ${t[1]} + resTexRC.y;
      ${s}
      return ivec4(r, c, d, d2);
    }
  `;
}
function K5(e, t) {
  let n = gi(["r", "c", "d", "d2", "d3"], e);
  return `
    ivec5 getOutputCoords() {
      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
                             ${t[1]}));

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

      ${n}

      ivec5 outShape = ivec5(r, c, d, d2, d3);
      return outShape;
    }
  `;
}
function X5(e, t) {
  let n = gi(["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 Y5(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 Q5(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 * ${e[1]};
      return ivec2(r, c);
    }
  `;
}
function bi(e) {
  return `offset${e}`;
}
function Z5(e) {
  let t = e.name, n = "get" + t.charAt(0).toUpperCase() + t.slice(1), s = hn();
  return `
    vec4 ${n}() {
      return ${s.texture2D}(${t}, halfCR);
    }
  `;
}
function J5(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 = bi(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 eK(e, t) {
  let n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1), r = e.shapeInfo.texShape, a = hn();
  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 tK(e, t) {
  let n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1);
  if (e.shapeInfo.isUniform)
    return `
      float ${s}(int index) {
        ${su(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 = bi(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 nK(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 = hn();
  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 sK(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 = ru(e, u), h = ["row", "col"];
    return `
      ${nu(d, t)}
      float ${r}(int row, int col) {
        return ${r}(${au(h, o)});
      }
    `;
  }
  if (e.shapeInfo.isUniform)
    return `
      float ${r}(int row, int col) {
        int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
        ${su(e)}
      }
    `;
  let l = a[0], c = a[1], p = bi(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 rK(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 = ru(e, d), m = ["b", "row", "col"];
    return `
        ${i1(f, t)}
        vec4 ${r}(int b, int row, int col) {
          return ${r}(${au(m, h)});
        }
      `;
  }
  let o = hn();
  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 aK(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 = ru(e, l), g = ["row", "col", "depth"];
    return `
        ${nu(m, t)}
        float ${r}(int row, int col, int depth) {
          return ${r}(${au(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)));
        ${su(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 = bi(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 iK(e, t) {
  let n = e.name, s = "get" + n.charAt(0).toUpperCase() + n.slice(1), r = hn();
  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 oK(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 = ru(e, u), v = ["row", "col", "depth", "depth2"];
    return `
      ${nu(y, t)}
      float ${r}(int row, int col, int depth, int depth2) {
        return ${r}(${au(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)));
        ${su(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 = bi(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 uK(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 = ru(e, u), g = ["row", "col", "depth", "depth2", "depth3"];
    return `
      ${nu(m)}
      float ${s}(int row, int col, int depth, int depth2, int depth3) {
        return ${s}(${au(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;
        ${su(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 = bi(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 lK(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 = ru(e, r), b = ["row", "col", "depth", "depth2", "depth3", "depth4"];
    return `
      ${nu(g)}
      float ${s}(int row, int col, int depth,
                    int depth2, int depth3, int depth4) {
        return ${s}(${au(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)));
        ${su(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 = bi(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 su(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 ${t}[i];
      }
    }
  `;
}
function cK(e, t) {
  let n = e.name, s = n.charAt(0).toUpperCase() + n.slice(1), r = "get" + s + "AtOutCoords", a = e.shapeInfo.logicalShape.length, i = t.logicalShape.length, o = a1(e.shapeInfo.logicalShape, t.logicalShape), u = ot(i), l = i - a, c, p = ["x", "y", "z", "w", "u", "v"];
  a === 0 ? c = "" : i < 2 && o.length >= 1 ? c = "coords = 0;" : c = o.map((y) => `coords.${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 dK(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 = a1(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 ${e} is not yet supported`);
}
function pv(e, t, n) {
  let { newShape: s, keptDims: r } = w.squeezeShape(t), a = t.length, i = e && a === 3 && t[0] === 1, o = i ? t.slice(1) : s, u = !e && a > 1 && !w.arraysEqual(t, n) && s.length < a || i;
  return { useSqueezeShape: u, uniformShape: u ? o : t, keptDims: r };
}
function ru(e, t) {
  let n = JSON.parse(JSON.stringify(e));
  return n.shapeInfo.logicalShape = t, n;
}
function au(e, t) {
  return t.map((n) => e[n]).join(", ");
}
function pK(e, t, n, s) {
  let r = n.map((x, k) => {
    let C = { logicalShape: x.shape, texShape: x.isUniform ? null : x.texData.texShape, isUniform: x.isUniform, isPacked: x.isUniform ? false : x.texData.isPacked, flatOffset: null };
    return x.texData != null && x.texData.slice != null && x.texData.slice.flatOffset > 0 && (C.flatOffset = x.texData.slice.flatOffset), { name: t.variableNames[k], shapeInfo: C };
  }), a = r.map((x) => x.shapeInfo), i = { logicalShape: s.shape, texShape: s.texData.texShape, isUniform: false, isPacked: s.texData.isPacked, flatOffset: null }, o = A5(r, i, t), u = zC(e.gl, o), l = e.createProgram(u), c = null, p = e.getUniformLocation(l, "NAN", false);
  X().getNumber("WEBGL_VERSION") === 1 && (c = e.getUniformLocation(l, "INFINITY", false));
  let d = false, h = {}, f = {}, m = {};
  for (let x = 0; x < t.variableNames.length; x++) {
    let k = t.variableNames[x];
    h[k] = e.getUniformLocation(l, k, d), h[`offset${k}`] = e.getUniformLocation(l, `offset${k}`, d), t.enableShapeUniforms && (f[`${k}Shape`] = e.getUniformLocation(l, `${k}Shape`, d), m[`${k}TexShape`] = e.getUniformLocation(l, `${k}TexShape`, d));
  }
  let g, b, y;
  t.enableShapeUniforms && (g = e.getUniformLocation(l, "outShape", d), y = e.getUniformLocation(l, "outShapeStrides", d), b = e.getUniformLocation(l, "outTexShape", d));
  let v = [];
  return t.customUniforms && t.customUniforms.forEach((x, k) => {
    v[k] = e.getUniformLocation(l, x.name, d);
  }), { program: t, fragmentShader: u, source: o, webGLProgram: l, uniformLocations: h, customUniformLocations: v, inShapeInfos: a, outShapeInfo: i, infLoc: c, nanLoc: p, inShapesLocations: f, inTexShapesLocations: m, outShapeLocation: g, outShapeStridesLocation: y, outTexShapeLocation: b };
}
function tw(e, t) {
  if (e.length !== t.length)
    throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);
  e.forEach((n, s) => {
    let r = n.logicalShape, a = t[s], i = a.shape;
    if (!w.arraysEqual(r, i))
      throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);
    if (n.isUniform && a.isUniform)
      return;
    let o = n.texShape, u = a.isUniform ? null : a.texData.texShape;
    if (!w.arraysEqual(o, u))
      throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${u} must match`);
  });
}
function hK(e, t, n, s, r) {
  t.program.enableShapeUniforms || (tw(t.inShapeInfos, n), tw([t.outShapeInfo], [s]));
  let a = s.texData.texture, i = s.texData.texShape;
  s.texData.isPacked ? e.setOutputPackedMatrixTexture(a.texture, i[0], i[1]) : e.setOutputMatrixTexture(a.texture, i[0], i[1]), e.setProgram(t.webGLProgram), X().getNumber("WEBGL_VERSION") === 1 && t.infLoc !== null && e.gl.uniform1f(t.infLoc, 1 / 0), t.nanLoc !== null && e.gl.uniform1f(t.nanLoc, NaN), n.forEach((u, l) => {
    let c = t.program.variableNames[l], p = t.uniformLocations[c], d = t.uniformLocations[`offset${c}`], h = t.inShapesLocations[`${c}Shape`], f = t.inTexShapesLocations[`${c}TexShape`];
    if (h) {
      let { uniformShape: m } = pv(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 ${u.type} is not supported yet.`);
  }), e.executeProgram();
}
function fK(e, t, n) {
  let s = "";
  t.concat(n).forEach((i) => {
    let o = i.texData != null && i.texData.slice != null && i.texData.slice.flatOffset > 0;
    if (e.enableShapeUniforms && !i.isUniform) {
      let u = i.texData.texShape, { useSqueezeShape: l, uniformShape: c, keptDims: p } = pv(e.packedInputs, i.shape, u), d = "", h = "", f = "";
      if (c.length === 1 && e.packedInputs) {
        let k = [Math.ceil(u[0] / 2), Math.ceil(u[1] / 2)];
        d = `${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]}_${k[k.length - 1] === u[1]}`;
      }
      let m = i.shape.length, g = c.length === 2 && w.arraysEqual(i.shape, u), b = w.sizeFromShape(i.shape) === 1, y = N.getBroadcastDims(i.shape, n.shape), v = !e.packedInputs && m === n.shape.length && w.arraysEqual(u, n.texData.texShape), x = e.packedInputs || c.length > 2 ? "" : `${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 + `${X().getNumber("WEBGL_VERSION")}`, a;
}
function kn(e) {
  return X().getBool("WEBGL_USE_SHAPES_UNIFORMS") && e <= 4;
}
var mK = class {
  constructor(e) {
    this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outPackingScheme = 0, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
    let t = hn();
    this.outputShape = e, this.enableShapeUniforms = kn(this.outputShape.length), this.userCode = `
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms ? Hp(["r", "c", "d"], e) : gi(["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 gK = class {
  constructor(e) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outPackingScheme = 0, this.customUniforms = [{ name: "texShape", type: "ivec2" }];
    let t = hn();
    this.outputShape = e, this.enableShapeUniforms = kn(this.outputShape.length), this.userCode = `
      ivec3 outCoordsFromFlatIndex(int index) {
        ${this.enableShapeUniforms ? Hp(["r", "c", "d"], e) : gi(["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 bK = class {
  constructor(e) {
    this.variableNames = ["A"], this.outTexUsage = 3;
    let t = hn();
    this.outputShape = e, this.userCode = `
      ${r1}

      void main() {
        float x = getAAtOutCoords();
        ${t.output} = encode_float(x);
      }
    `;
  }
};
var yK = class {
  constructor(e) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outTexUsage = 3;
    let t = hn();
    this.outputShape = e, this.userCode = `
      ${r1}

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

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

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

          ${s}

          ${n.output} = ${r};
        }
    `;
  }
};
var wK = {};
Ae(wK, { bindVertexProgramAttributeStreams: () => g1, createBufferFromOutputTexture: () => v1, createFloat16MatrixTexture: () => p1, createFloat16PackedMatrixTexture: () => m1, createFloat32MatrixTexture: () => d1, createIndexBuffer: () => c1, createPackedMatrixTexture: () => f1, createUnsignedBytesMatrixTexture: () => h1, createVertexBuffer: () => l1, createVertexShader: () => u1, downloadByteEncodedFloatMatrixFromOutputTexture: () => w1, downloadFloat32MatrixFromBuffer: () => x1, downloadMatrixFromPackedOutputTexture: () => I1, downloadPackedMatrixFromBuffer: () => k1, getInternalFormatForFloat16MatrixTexture: () => fv, getInternalFormatForFloat16PackedMatrixTexture: () => bv, getInternalFormatForFloat32MatrixTexture: () => hv, getInternalFormatForPackedMatrixTexture: () => gv, getInternalFormatForUnsignedBytesMatrixTexture: () => mv, uploadDenseMatrixToTexture: () => b1, uploadPixelDataToTexture: () => y1 });
function u1(e) {
  let t = hn(), n = `${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 PC(e, n);
}
function l1(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 BC(e, t);
}
function c1(e) {
  let t = new Uint16Array([0, 1, 2, 2, 1, 3]);
  return VC(e, t);
}
function Yl(e, t, n, s, r, a) {
  UC(t, n);
  let i = WC(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)), X().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 hv(e) {
  return e.internalFormatFloat;
}
function d1(e, t, n, s) {
  let [r, a] = Xl(t, n);
  return Yl(e, r, a, hv(s), s.textureFormatFloat, e.FLOAT);
}
function fv(e) {
  return e.internalFormatHalfFloat;
}
function p1(e, t, n, s) {
  let [r, a] = Xl(t, n);
  return Yl(e, r, a, fv(s), s.textureFormatFloat, s.textureTypeHalfFloat);
}
function mv(e) {
  return e.downloadTextureFormat;
}
function h1(e, t, n, s) {
  let [r, a] = Xl(t, n);
  return Yl(e, r, a, mv(s), e.RGBA, e.UNSIGNED_BYTE);
}
function gv(e) {
  return e.internalFormatPackedFloat;
}
function f1(e, t, n, s) {
  let [r, a] = eu(t, n);
  return Yl(e, r, a, gv(s), e.RGBA, e.FLOAT);
}
function bv(e) {
  return e.internalFormatPackedHalfFloat;
}
function m1(e, t, n, s) {
  let [r, a] = eu(t, n);
  return Yl(e, r, a, bv(s), e.RGBA, s.textureTypeHalfFloat);
}
function g1(e, t, n) {
  return fe(e, () => e.bindBuffer(e.ARRAY_BUFFER, n)), Mm(e, t, "clipSpacePos", n, 3, 20, 0) && Mm(e, t, "uv", n, 2, 20, 12);
}
function b1(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), X().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 y1(e, t, n) {
  fe(e, () => e.bindTexture(e.TEXTURE_2D, t)), n.data instanceof Uint8Array ? X().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)) : X().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 v1(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 x1(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] = Xl(t, n), i = 4, o = new Uint8Array(m5(t * n, i));
  return fe(e, () => e.readPixels(0, 0, r, a, s.downloadTextureFormat, e.UNSIGNED_BYTE, o)), new Float32Array(o.buffer);
}
function k1(e, t, n, s, r, a, i, o) {
  let u = e, l = new Float32Array(g5(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 I1(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 qf = class {
  constructor(e) {
    this.outputTexture = null, this.program = null, this.disposed = false, this.vertexAttrsAreBound = false, this.itemsToPoll = [];
    let t = X().getNumber("WEBGL_VERSION");
    e != null ? (this.gl = e, p5(t, e)) : this.gl = vs(t);
    let n = "WEBGL_color_buffer_float", s = "EXT_color_buffer_half_float";
    if (X().getNumber("WEBGL_VERSION") === 1) {
      let r = "OES_texture_float", a = "OES_texture_half_float";
      if (this.textureFloatExtension = Du(this.gl, r), Mn(this.gl, a))
        this.textureHalfFloatExtension = Du(this.gl, a);
      else if (X().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), Mn(this.gl, s))
        this.colorBufferHalfFloatExtension = Du(this.gl, s);
      else if (X().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", Mn(this.gl, n))
      this.colorBufferFloatExtension = this.gl.getExtension(n);
    else if (Mn(this.gl, s))
      this.colorBufferHalfFloatExtension = this.gl.getExtension(s);
    else
      throw new Error("GL context does not support color renderable floats");
    this.vertexBuffer = l1(this.gl), this.indexBuffer = c1(this.gl), this.framebuffer = GC(this.gl), this.textureConfig = lv(this.gl, this.textureHalfFloatExtension);
  }
  get debug() {
    return X().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(), d1(this.gl, e, t, this.textureConfig);
  }
  createFloat16MatrixTexture(e, t) {
    return this.throwIfDisposed(), p1(this.gl, e, t, this.textureConfig);
  }
  createUnsignedBytesMatrixTexture(e, t) {
    return this.throwIfDisposed(), h1(this.gl, e, t, this.textureConfig);
  }
  uploadPixelDataToTexture(e, t) {
    this.throwIfDisposed(), y1(this.gl, e, t);
  }
  uploadDenseMatrixToTexture(e, t, n, s) {
    this.throwIfDisposed(), b1(this.gl, e, t, n, s, this.textureConfig);
  }
  createFloat16PackedMatrixTexture(e, t) {
    return this.throwIfDisposed(), m1(this.gl, e, t, this.textureConfig);
  }
  createPackedMatrixTexture(e, t) {
    return this.throwIfDisposed(), f1(this.gl, e, t, this.textureConfig);
  }
  deleteMatrixTexture(e) {
    this.throwIfDisposed(), this.outputTexture === e && (Lm(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 k1(this.gl, e, t, n, s, r, a, this.textureConfig);
  }
  downloadFloat32MatrixFromBuffer(e, t) {
    return x1(this.gl, e, t);
  }
  createBufferFromTexture(e, t, n) {
    this.bindTextureToFrameBuffer(e);
    let s = v1(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 (X().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
      X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 ? (t = this.beginQuery(), this.endQuery(), n = () => this.isQueryAvailable(t, X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))) : n = () => true;
    return { query: t, isFencePassed: n };
  }
  downloadMatrixFromPackedTexture(e, t, n) {
    return this.downloadMatrixDriver(e, () => I1(this.gl, t, n));
  }
  createProgram(e) {
    this.throwIfDisposed();
    let t = this.gl;
    this.vertexShader == null && (this.vertexShader = u1(t));
    let n = MC(t);
    return fe(t, () => t.attachShader(n, this.vertexShader)), fe(t, () => t.attachShader(n, e)), LC(t, n), this.debug && sd(t, n), this.vertexAttrsAreBound || (this.setProgram(n), this.vertexAttrsAreBound = g1(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 && sd(this.gl, this.program), fe(this.gl, () => this.gl.useProgram(e));
  }
  getUniformLocation(e, t, n = true) {
    return this.throwIfDisposed(), n ? qC(this.gl, e, t) : jC(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(), KC(this.gl, e, t, n);
  }
  setOutputMatrixTexture(e, t, n) {
    this.setOutputMatrixTextureDriver(e, n, t);
  }
  setOutputPackedMatrixTexture(e, t, n) {
    this.throwIfDisposed();
    let [s, r] = eu(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 && sd(this.gl, this.program), Fu(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 = Du(this.gl, X().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 (X().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 (X().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, X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))), this.getQueryTime(e, X().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 = kK(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(), rd(this.gl, e, this.framebuffer), this.debug && Fu(this.gl);
  }
  unbindTextureToFrameBuffer() {
    this.outputTexture != null ? (rd(this.gl, this.outputTexture, this.framebuffer), this.debug && Fu(this.gl)) : Lm(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;
    rd(s, e, this.framebuffer), this.debug && Fu(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 kK(e) {
  let t = 0;
  for (; t < e.length && e[t](); ++t)
    ;
  return t - 1;
}
var { addImpl: IK, bincountImpl: S1, bincountReduceImpl: SK, ceilImpl: CK, concatImpl: NK, equalImpl: TK, expImpl: $K, expm1Impl: _K, floorImpl: AK, gatherNdImpl: EK, gatherV2Impl: RK, greaterImpl: DK, greaterEqualImpl: FK, lessImpl: OK, lessEqualImpl: PK, linSpaceImpl: zK, logImpl: MK, maxImpl: LK, maximumImpl: BK, minimumImpl: VK, multiplyImpl: WK, negImpl: UK, notEqualImpl: GK, prodImpl: HK, rangeImpl: qK, rsqrtImpl: jK, sigmoidImpl: KK, simpleAbsImpl: C1, sliceImpl: XK, sparseFillEmptyRowsImpl: YK, sparseReshapeImpl: QK, sparseSegmentReductionImpl: N1, sqrtImpl: ZK, stridedSliceImpl: JK, stringNGramsImpl: eX, stringSplitImpl: tX, stringToHashBucketFastImpl: nX, subImpl: sX, tileImpl: rX, topKImpl: aX, transposeImpl: yv, uniqueImpl: iX } = Yy;
function T1(e, t) {
  return ["x", "y", "z", "w", "u", "v"].slice(0, t).map((n) => `${e}.${n}`);
}
function un(e, t) {
  return t === 1 ? [e] : T1(e, t);
}
function oX(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 uX = class {
  constructor(e) {
    if (this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true, this.outputShape = e, this.rank = e.length, this.enableShapeUniforms = kn(this.outputShape.length), this.rank === 0)
      this.userCode = `
        void main() {
          setOutput(vec4(getA(), 0., 0., 0.));
        }
      `;
    else {
      let t = un("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 $1 = class {
  constructor(e, t) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "inputShape", type: "ivec3" }], this.outputShape = e, this.enableShapeUniforms = kn(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 = `
      ${lX(t, this.enableShapeUniforms)}
      ${this.enableShapeUniforms ? dv() : cv(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 lX(e, t) {
  return `
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${t ? _5(["r", "c", "d"], "inputShape") : gi(["r", "c", "d"], e)}
      return ivec3(r, c, d);
    }
  `;
}
var cX = 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 = sw(t, n), r = rw(e, s, n);
    r in this.freeTextures || (this.freeTextures[r] = []), r in this.usedTextures || (this.usedTextures[r] = []);
    let a = nw(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 = sw(n, s), a = rw(t, r, s);
    a in this.freeTextures || (this.freeTextures[a] = []);
    let i = nw(t, r, this.gpgpu.gl, this.gpgpu.textureConfig, s), o = X().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 dX(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 nw(e, t, n, s, r) {
  let a = pX(t, s), i;
  if (r) {
    let [u, l] = eu(e[0], e[1]);
    i = u * l;
  } else {
    let [u, l] = Xl(e[0], e[1]);
    i = u * l;
  }
  let o = dX(n, a);
  return i * o;
}
function pX(e, t) {
  switch (e) {
    case 3:
      return gv(t);
    case 4:
      return bv(t);
    case 1:
      return hv(t);
    case 0:
      return fv(t);
    case 2:
      return mv(t);
    default:
      throw new Error(`Unknown physical texture type ${e}`);
  }
}
function hX(e) {
  return X().getBool("WEBGL_RENDER_FLOAT32_ENABLED") ? e ? 3 : 1 : e ? 4 : 0;
}
function sw(e, t) {
  if (e === 1)
    return 3;
  if (e === 0 || e == null)
    return hX(t);
  if (e === 3 || e === 2)
    return 2;
  throw new Error(`Unknown logical texture type ${e}`);
}
function rw(e, t, n) {
  return `${e[0]}_${e[1]}_${t}_${n}`;
}
var Hs = class {
  constructor(e, t) {
    this.variableNames = ["A"], this.outputShape = e, this.enableShapeUniforms = kn(this.outputShape.length), this.userCode = `
      float unaryOperation(float x) {
        ${t}
      }

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

        setOutput(y);
      }
    `;
  }
};
var ts = "if (isnan(x)) return x;";
var fX = "return x;";
var aw = "return abs(x);";
var mX = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
var gX = ts + `
  return (x < 0.0) ? 0.0 : x;
`;
var bX = ts + `
  return (x < 0.0) ? 0.0 : min(6.0, x);
`;
var Pi = "return x;";
var yX = "return 1.0 / (1.0 + exp(-1.0 * x));";
var vX = "return x;";
var xX = `
  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 wX = `
  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 kX = `
  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 IX = "return 1.0 / (1.0 + exp(-1.0 * x));";
var Yr = class {
  constructor(e, t) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.enableShapeUniforms = kn(this.outputShape.length), this.userCode = `
      vec4 unaryOperation(vec4 x) {
        ${t}
      }

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

        setOutput(y);
      }
    `;
  }
};
var SX = class {
  constructor(e) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = false, this.outputShape = e, this.enableShapeUniforms = kn(this.outputShape.length);
    let t = e.length, n = un("rc", t), s = ot(t), r = oX(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 CX = xs.whereImpl;
var NX = 1e-7;
var TX = 1e-4;
var Qc = {};
function $X(e) {
  return e in Qc || (Qc[e] = {}), Qc[e];
}
var _X = X().getNumber("CPU_HANDOFF_SIZE_THRESHOLD");
var AX = 600;
function EX() {
  return X().global.screen == null ? 1024 : X().global.screen.height * X().global.screen.width * window.devicePixelRatio * AX / 1024 / 1024;
}
var _1 = class extends tl {
  constructor(e) {
    super();
    if (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, !X().getBool("HAS_WEBGL"))
      throw new Error("WebGL is not supported on this device");
    let t;
    if (e != null) {
      if (e instanceof qf)
        t = e;
      else {
        let n = vs(X().getNumber("WEBGL_VERSION"), e);
        t = new qf(n);
      }
      this.binaryCache = {}, this.gpgpuCreatedLocally = false;
    } else {
      let n = vs(X().getNumber("WEBGL_VERSION"));
      t = new qf(n), this.binaryCache = $X(X().getNumber("WEBGL_VERSION")), this.gpgpuCreatedLocally = true;
    }
    this.gpgpu = t, this.canvas = this.gpgpu.gl.canvas, this.textureManager = new cX(this.gpgpu), this.numMBBeforeWarning = EX(), this.texData = new Wd(this, Ss());
  }
  nextDataId() {
    return _1.nextDataId++;
  }
  numDataIds() {
    return this.texData.numDataIds() - this.pendingDeletes;
  }
  write(e, t, n) {
    if ((X().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS") || X().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 (X().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 Yr(i, Pi) : p = new Hs(i, Pi);
      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 = N.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 Yr(s, Pi) : h = new Hs(s, Pi);
      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 (X().getBool("DEBUG") && !X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED") && X().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" && X().get("WEBGL_BUFFER_SUPPORTED")) {
      l = this.decode(e);
      let h = this.texData.get(l.dataId);
      u = this.gpgpu.createBufferFromTexture(h.texture.texture, ...Xc(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 = N.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) && Ss().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 Yr(r, Pi) : d = new Hs(r, Pi);
      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 = Ss().makeTensorFromDataId(l.dataId, l.shape, l.dtype), p = this.texData.get(l.dataId);
    return { tensorRef: c, ...p.texture };
  }
  bufferSync(e) {
    let t = this.readSync(e.dataId), n = t;
    if (e.dtype === "string")
      try {
        n = t.map((s) => w.decodeString(s));
      } catch (s) {
        throw new Error("Failed to decode encoded string bytes into utf-8");
      }
    return De(e.shape, e.dtype, n);
  }
  checkNumericalProblems(e) {
    if (e != null)
      for (let t = 0; t < e.length; t++) {
        let n = e[t];
        if (!FC(n))
          throw X().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 ${n} cannot be represented on this device.`);
      }
  }
  getValuesFromTexture(e) {
    let { shape: t, dtype: n, isPacked: s } = this.texData.get(e), r = w.sizeFromShape(t);
    if (X().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")) {
      let p = this.decode(e), d = this.texData.get(p.dataId), h = this.gpgpu.downloadMatrixFromPackedTexture(d.texture.texture, ...Xc(t)).subarray(0, r);
      return this.disposeIntermediateTensorInfo(p), h;
    }
    let a = X().getBool("WEBGL_PACK") && s === true, i = a ? ad(t) : t, o = a ? new yK(i) : new bK(i), u = this.runWebGLProgram(o, [{ shape: i, dtype: n, dataId: e }], "float32"), l = this.texData.get(u.dataId), c = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture.texture, l.texShape[0], l.texShape[1]).subarray(0, r);
    return this.disposeIntermediateTensorInfo(u), c;
  }
  timerAvailable() {
    return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0;
  }
  time(e) {
    let t = this.activeTimers, n = [], s = false;
    this.programTimersStack == null ? (this.programTimersStack = n, s = true) : this.activeTimers.push(n), this.activeTimers = n, e();
    let r = w.flatten(this.activeTimers.map((o) => o.query)).filter((o) => o != null), a = w.flatten(this.activeTimers.map((o) => o.name)).filter((o) => o != null);
    this.activeTimers = t, s && (this.programTimersStack = null);
    let i = { uploadWaitMs: this.uploadWaitMs, downloadWaitMs: this.downloadWaitMs, kernelMs: null, wallMs: null };
    return (async () => {
      if (X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
        let o = await Promise.all(r);
        i.kernelMs = w.sum(o), i.getExtraProfileInfo = () => o.map((u, l) => ({ name: a[l], ms: u })).map((u) => `${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 X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0 ? this.gpgpu.beginQuery() : { startMs: w.now(), endMs: null };
  }
  endTimer(e) {
    return X().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0 ? (this.gpgpu.endQuery(), e) : (e.endMs = w.now(), e);
  }
  async getQueryTime(e) {
    if (X().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 = _X) {
    return X().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) {
    N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");
    let t = e.dataSync();
    return CX(e.shape, t);
  }
  packedUnaryOp(e, t, n) {
    let s = new Yr(e.shape, t), r = this.compileAndRun(s, [e], n);
    return Ss().makeTensorFromDataId(r.dataId, r.shape, r.dtype);
  }
  abs(e) {
    if (this.shouldExecuteOnCPU([e]) && e.dtype !== "complex64") {
      let s = C1(this.texData.get(e.dataId).values);
      return this.makeOutput(e.shape, e.dtype, s);
    }
    if (X().getBool("WEBGL_PACK_UNARY_OPERATIONS"))
      return this.packedUnaryOp(e, aw, e.dtype);
    let t = new Hs(e.shape, aw), n = this.compileAndRun(t, [e]);
    return Ss().makeTensorFromDataId(n.dataId, n.shape, n.dtype);
  }
  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) {
    let { dataId: s } = this.makeTensorInfo(e, t, n);
    return Ss().makeTensorFromDataId(s, e, t, this);
  }
  unpackTensor(e) {
    let t = new SX(e.shape);
    return this.runWebGLProgram(t, [e], e.dtype);
  }
  packTensor(e) {
    let t = new uX(e.shape), n = true;
    return this.runWebGLProgram(t, [e], e.dtype, null, n);
  }
  packedReshape(e, t) {
    let n = [ma(e.shape), ...ga(e.shape)], s = { dtype: e.dtype, shape: n, dataId: e.dataId }, r = [ma(t), ...ga(t)], a = new $1(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 = ad(r), o;
    s ? o = new gK(i) : o = new mK(i);
    let u = true, l = [t != null ? t : Xc(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 : Xc(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) <= X().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 && !Ju(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 = fK(e, l, c), d = this.getAndSaveBinary(p, () => pK(this.gpgpu, e, l, c)), h = this.activeTimers != null, f;
    h && (f = this.startTimer()), hK(this.gpgpu, d, l, c, s), u.forEach((g) => this.disposeIntermediateTensorInfo(g)), h && (f = this.endTimer(f), this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(f) }));
    let m = X().get("WEBGL_FLUSH_THRESHOLD");
    if (m > 0) {
      let g = w.now();
      g - this.lastGlFlushTime > m && (this.gpgpu.gl.flush(), this.lastGlFlushTime = g);
    }
    if (!X().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 || (X().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 = j(() => {
      if (!X().get("WEBGL_RENDER_FLOAT32_ENABLED")) {
        let e = X().getBool("DEBUG");
        X().set("DEBUG", false);
        let t = this.abs(Ie(1e-8)).dataSync()[0];
        if (X().set("DEBUG", e), t > 0)
          return 32;
      }
      return 16;
    })), this.floatPrecisionValue;
  }
  epsilon() {
    return this.floatPrecision() === 32 ? NX : TX;
  }
  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 = QC(n, o), t.texShape = c), r != null) {
      let p = ad(n), d, h = c[1], f = c[0], m = r instanceof Uint8Array || r instanceof Uint8ClampedArray;
      (o || !m) && ([h, f] = eu(c[0], c[1])), o ? d = new xK(p, m) : d = new vK(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), C = this.texData.get(k.dataId);
      t.texture = C.texture, t.texShape = C.texShape, t.isPacked = C.isPacked, t.usage = C.usage, this.disposeIntermediateTensorInfo(b), this.texData.delete(k.dataId), t.values = null, 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 = RX(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);
  }
};
var A1 = _1;
A1.nextDataId = 0;
function RX(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 xpe = "0.0.0";
function DX() {
  X().set("WEBGL_FORCE_F16_TEXTURES", true);
}
cp.isBrowser() && dp("webgl", () => new A1(), 2);
var wpe = { forceHalfFloat: DX };
var E1 = `
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`;
var no = class {
  constructor(e, t, n) {
    this.variableNames = ["A", "B"], this.outputShape = N.assertAndGetBroadcastShape(t, n), this.enableShapeUniforms = kn(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 qp = `
  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 Ql = class {
  constructor(e, t, n, s = false) {
    this.variableNames = ["A", "B"], this.supportsBroadcasting = true, this.packedInputs = true, this.packedOutput = true, this.outputShape = N.assertAndGetBroadcastShape(t, n);
    let r = this.outputShape.length;
    this.enableShapeUniforms = kn(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) >= ${this.outputShape[0]} ? 0. : result.y;
            result.z = 0.;
            result.w = 0.;
          `;
      else {
        let o = un("coords", r);
        this.enableShapeUniforms ? a += `
            bool nextRowOutOfBounds =
              (${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 En(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 FX = { kernelName: Ma, backendName: "webgl", kernelFunc: En };
function Er(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 = En({ inputs: { x: s }, backend: n }), u = En({ inputs: { x: r }, backend: n });
  return i.complexTensorInfos = { real: o, imag: u }, a;
}
var OX = { kernelName: qd, backendName: "webgl", kernelFunc: Er };
var R1 = "return (a < 0.) ? b * a : a;";
var D1 = `
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;
function PX(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s, i = n.makeTensorInfo([], "float32", w.createScalarValue(a, "float32")), o = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Ql(D1, r.shape, i.shape) : new no(R1, r.shape, i.shape), u = n.runWebGLProgram(o, [r, i], "float32");
  return n.disposeIntermediateTensorInfo(i), u;
}
var zX = { kernelName: La, backendName: "webgl", kernelFunc: PX };
var F1 = "return (a < 0.) ? b * a : a;";
var O1 = `
  vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
  return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;
function MX(e) {
  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Ql(O1, s.shape, r.shape) : new no(F1, s.shape, r.shape);
  return n.runWebGLProgram(a, [s, r], "float32");
}
var LX = { kernelName: Qa, backendName: "webgl", kernelFunc: MX };
var iu = "if (isnan(x)) return x;";
var BX = `
  if (isnan(a)) return a;
  if (isnan(b)) return b;
`;
var VX = `
  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 = X().getBool("WEBGL_PACK_UNARY_OPERATIONS") && t != null, c;
    return l ? c = new Yr(i.shape, t) : c = new Hs(i.shape, e), o.runWebGLProgram(c, [i], u);
  };
}
function qt({ 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, C = { dataId: x.dataId, dtype: x.dtype, shape: u.shape }, T = { dataId: k.dataId, dtype: k.dtype, shape: l.shape }, E = new no(e, u.shape, l.shape);
        return c.runWebGLProgram(E, [C, T], yn(x.dtype, k.dtype));
      }), y = Er({ inputs: { real: g, imag: b }, backend: c });
      return c.disposeIntermediateTensorInfo(g), c.disposeIntermediateTensorInfo(b), y;
    }
    let p = a || yn(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" ? N.fromUint8ToStringArray(f) : f, b = u.dtype === "string" ? N.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 = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") && t != null, h;
    return d ? h = new Ql(t, u.shape, l.shape, n) : h = new no(e, u.shape, l.shape), c.runWebGLProgram(h, [u, l], p);
  };
}
function jp(e, t = false) {
  if (e === "linear")
    return t ? vX : fX;
  if (e === "relu")
    return t ? wX : gX;
  if (e === "elu")
    return t ? xX : mX;
  if (e === "relu6")
    return t ? kX : bX;
  if (e === "prelu")
    return t ? O1 : F1;
  if (e === "leakyrelu")
    return t ? D1 : R1;
  if (e === "sigmoid")
    return t ? IX : yX;
  throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`);
}
var P1 = 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 = kn(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 iw = { REAL: "return areal * breal - aimag * bimag;", IMAG: "return areal * bimag + aimag * breal;" };
var ow = class {
  constructor(e, t, n) {
    this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.outputShape = N.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 uw = "return a * b;";
function vv(e) {
  let { inputs: t, backend: n } = e, { a: s, b: r } = t, a = N.upcastType(s.dtype, r.dtype);
  if (s.dtype === "complex64") {
    let o = n.texData.get(s.dataId), u = n.texData.get(r.dataId), l = new ow(iw.REAL, s.shape, r.shape), c = new ow(iw.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 = Er({ 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] = WK(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 X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? i = new Ql(uw, s.shape, r.shape) : i = new no(uw, s.shape, r.shape), n.runWebGLProgram(i, [s, r], a);
}
var WX = { kernelName: Ka, backendName: "webgl", kernelFunc: vv };
function UX(e, t, n) {
  let s = [ma(e.shape), ...ga(e.shape)], r = { dtype: e.dtype, shape: s, dataId: e.dataId }, a = [ma(t), ...ga(t)], i = new $1(a, s), o = true, u = [s], l = n.runWebGLProgram(i, [r], e.dtype, u, o);
  return { dataId: l.dataId, shape: t, dtype: l.dtype };
}
function he(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { shape: a } = s, i = n, o = w.sizeFromShape(r.shape), u = w.inferFromImplicitShape(a, o), l = w.sizeFromShape(u);
  w.assert(o === l, () => `The new shape (${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 && !Ju(r.shape, u) && !(c.texture !== null && Ju(c.shape, u)) ? UX(r, u, i) : (i.incRef(r.dataId), { dataId: r.dataId, shape: u, dtype: r.dtype });
}
var GX = { kernelName: Ao, backendName: "webgl", kernelFunc: he };
var lw = 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);

          ${u}
        }
        setOutput(sumValue);
      }
    `;
  }
};
var HX = class {
  constructor(e, t) {
    this.variableNames = ["x"];
    let { windowSize: n, batchSize: s, inSize: r, outSize: a } = e;
    this.outputShape = [s, a];
    let i = "0.0", o = "";
    t === "prod" ? i = "1.0" : t === "min" ? (i = "1.0 / 1e-20", o = "min") : t === "max" && (i = "-1.0 / 1e-20", o = "max");
    let u = `${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 qX(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 = N.computeOptimalWindowSize(n);
    t.push({ inSize: n, windowSize: s, outSize: Math.ceil(n / s) });
  }
  return t;
}
function yi(e, t, n, s) {
  let r = qX(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 lw({ windowSize: u, inSize: o, batchSize: e.shape[0], outSize: l }, o) : new lw({ windowSize: u, inSize: o, batchSize: e.shape[0], outSize: l }) : c = new HX({ 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 jX = 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 = KX(t);
    this.userCode = `
    void main() {
      ${s} resRC = getOutputCoords();
      setOutput(getA(${r}));
    }
    `;
  }
};
function KX(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 XX = 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 ${this.rank} is not yet supported.`);
    let s = ot(this.rank), r = T1("rc", this.rank), a = new Array(this.rank);
    for (let l = 0; l < t.length; l++)
      a[t[l]] = r[l];
    let i = `vec2(${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 Kp(e, t, n) {
  let s = X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new XX(e.shape, t) : new jX(e.shape, t);
  return n.runWebGLProgram(s, [e], e.dtype);
}
function YX(e, t, n, s) {
  let r = t, a = e.shape.length, i = w.parseAxisParam(r, e.shape), o = i, u = N.getAxesPermutation(o, a), l = u != null, c = e;
  l && (c = Kp(e, u, s), o = N.getInnerMostAxes(o.length, a)), N.assertAxesAreInnerMostDims("sum", o, a);
  let [p, d] = N.computeOutAndReduceShapes(c.shape, o), h = p;
  n && (h = N.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 = lp(e.dtype), v = yi(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 Xp(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
  return YX(r, a, i, n);
}
var QX = { kernelName: ai, backendName: "webgl", kernelFunc: Xp };
function cn(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 = yv(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 = Kp(r, a, i);
  return l;
}
var ZX = { kernelName: ci, backendName: "webgl", kernelFunc: cn };
var z1 = 1e3;
function Pd({ 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], C = s ? [y, f, d] : [y, d, f], T = he({ inputs: { x: e }, backend: r, attrs: { shape: k } }), E = he({ inputs: { x: t }, backend: r, attrs: { shape: C } }), A = [T, E], P = Math.max(b, y), R = n ? T.shape[1] : T.shape[2], F = a != null, $ = i != null, z = u === "leakyrelu", W = u != null ? jp(u, true) : null, q = F || $ || z || W != null, K;
  if ((h === 1 || f === 1) && R > z1 && q === false) {
    let Z = T, te = E;
    n && (Z = cn({ inputs: { x: T }, backend: r, attrs: { perm: [0, 2, 1] } }), A.push(Z)), s && (te = cn({ inputs: { x: E }, backend: r, attrs: { perm: [0, 2, 1] } }), A.push(te));
    let ee = f !== 1, se = f === 1, ne = Z;
    ee && (ne = he({ inputs: { x: Z }, backend: r, attrs: { shape: [P, R, 1] } }), A.push(ne));
    let oe = f === 1 ? 2 : 1, re = te;
    se && (re = he({ inputs: { x: te }, backend: r, attrs: { shape: [P, 1, R] } }), A.push(re));
    let le = vv({ inputs: { a: ne, b: re }, backend: r });
    K = Xp({ inputs: { x: le }, backend: r, attrs: { axis: oe, keepDims: true } }), A.push(le);
  } else {
    let Z = yn(e.dtype, t.dtype), te = new P1(k, C, [P, h, f], n, s, F, W, $, z), ee = [T, E];
    if (a != null && ee.push(a), $ && ee.push(i), z) {
      let se = r.makeTensorInfo([], "float32", w.createScalarValue(o, "float32"));
      ee.push(se), A.push(se);
    }
    K = r.runWebGLProgram(te, ee, Z);
  }
  let Y = he({ inputs: { x: K }, backend: r, attrs: { shape: x } });
  A.push(K);
  for (let Z of A)
    r.disposeIntermediateTensorInfo(Z);
  return Y;
}
function JX(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 Pd({ a: r, b: a, transposeA: u, transposeB: l, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: p, activation: c });
}
var e8 = { kernelName: na, backendName: "webgl", kernelFunc: JX };
var cw = "return abs(x);";
function t8(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 = C1(a.values);
    return n.makeTensorInfo(s.shape, s.dtype, i);
  }
  let r;
  return X().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? r = new Yr(s.shape, cw) : r = new Hs(s.shape, cw), n.runWebGLProgram(r, [s], s.dtype);
}
var n8 = { kernelName: ao, backendName: "webgl", kernelFunc: t8 };
var s8 = ts + `
  if (abs(x) > 1.) {
    return NAN;
  }
  return acos(x);
`;
var r8 = Ke({ opSnippet: s8 });
var a8 = { kernelName: nl, backendName: "webgl", kernelFunc: r8 };
var i8 = ts + `
  if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`;
var o8 = Ke({ opSnippet: i8 });
var u8 = { kernelName: sl, backendName: "webgl", kernelFunc: o8 };
var dw = "return a + b;";
var l8 = qt({ opSnippet: dw, packedOpSnippet: dw, supportsComplex: true, cpuKernelImpl: IK });
var c8 = { kernelName: kr, backendName: "webgl", kernelFunc: l8 };
var d8 = 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 = ${s};
        setOutput(result);
      }
    `;
  }
};
var p8 = class {
  constructor(e, t) {
    this.outputShape = [], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.variableNames = t.map((r, a) => `T${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 ud(e) {
  let { inputs: t, backend: n } = e, s = t;
  if (s.length === 1)
    return En({ inputs: { x: s[0] }, backend: n });
  if (s.length > X().get("WEBGL_MAX_TEXTURES_IN_SHADER")) {
    let u = Math.floor(s.length / 2), l = ud({ inputs: s.slice(0, u), backend: n }), c = ud({ inputs: s.slice(u), backend: n });
    return ud({ inputs: [l, c], backend: n });
  }
  let r = s.map((u) => u.dtype).reduce((u, l) => yn(u, l)), a = s.map((u) => u.shape), o = X().getBool("WEBGL_PACK") ? new p8(s[0].shape, a) : new d8(s[0].shape, a);
  return n.runWebGLProgram(o, s, r);
}
var h8 = { kernelName: xa, backendName: "webgl", kernelFunc: ud };
function f8(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 = N.getAxesPermutation(l, o), p = r;
  c != null && (p = cn({ inputs: { x: r }, backend: n, attrs: { perm: c } }), l = N.getInnerMostAxes(l.length, o)), N.assertAxesAreInnerMostDims("all", l, o);
  let [d, h] = N.computeOutAndReduceShapes(p.shape, l), f = w.sizeFromShape(h), m = he({ inputs: { x: p }, backend: n, attrs: { shape: [-1, f] } }), g = yi(m, m.dtype, "all", n), b;
  if (i) {
    let y = N.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 m8 = { kernelName: rl, backendName: "webgl", kernelFunc: f8 };
function g8(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 = N.getAxesPermutation(l, o), p = r;
  c != null && (p = cn({ inputs: { x: r }, backend: n, attrs: { perm: c } }), l = N.getInnerMostAxes(l.length, o)), N.assertAxesAreInnerMostDims("any", l, o);
  let [d, h] = N.computeOutAndReduceShapes(p.shape, l), f = w.sizeFromShape(h), m = he({ inputs: { x: p }, backend: n, attrs: { shape: [-1, f] } }), g = yi(m, m.dtype, "any", n), b;
  if (i) {
    let y = N.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 b8 = { kernelName: al, backendName: "webgl", kernelFunc: g8 };
var y8 = class {
  constructor(e, t, n) {
    this.variableNames = ["A"];
    let { windowSize: s, batchSize: r, outSize: a } = e;
    n || this.variableNames.push("bestIndicesA"), this.outputShape = [r, a];
    let i = t === "max" ? ">" : "<", o = n ? "inOffset + i;" : "round(getBestIndicesA(batch, inOffset + i));";
    this.userCode = `
      void main() {
        ivec2 coords = getOutputCoords();
        int batch = coords[0];
        int outIdx = coords[1];
        int inOffset = outIdx * ${s};

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

        for (int i = 0; i < ${s}; i++) {
          int inIdx = ${o};
          float candidate = getA(batch, inIdx);
          if (candidate ${i} bestValue) {
            bestValue = candidate;
            bestIndex = inIdx;
          }
        }
        setOutput(float(bestIndex));
      }
    `;
  }
};
var v8 = class {
  constructor(e, t, n, s) {
    this.variableNames = ["A"], this.packedInputs = true, this.packedOutput = true, w.assert(e.length > 2, () => `Packed arg${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 = un("coords", o), c, p;
    if (a === 1) {
      p = o + 1;
      let T = ot(p);
      c = `
        ${T} sourceLocR = ${T}(${l.join()}, 0);
        ++${l[o - 1]};
        ${T} sourceLocG = ${T}(${l.join()}, 0);
        ++${l[o - 2]};
        ${T} sourceLocA = ${T}(${l.join()}, 0);
        --${l[o - 1]};
        ${T} sourceLocB = ${T}(${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((T) => "int " + T), m = un("sourceLocR", p - 1).concat("inIdx.r"), g = un("sourceLocG", p - 1).concat("inIdx.g"), b = un("sourceLocB", p - 1).concat("inIdx.b"), y = un("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.)`, C = 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()}));
      }
      ${C}
      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 M1(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 = N.computeOptimalWindowSize(a), o = { windowSize: i, inSize: a, batchSize: r, outSize: Math.ceil(a / i) }, u = new y8(o, n, s == null), l = [t];
  s != null && l.push(s);
  let c = e.runWebGLProgram(u, l, "int32");
  if (c.shape[1] === 1)
    return c;
  let p = M1(e, t, n, c);
  return e.disposeIntermediateTensorInfo(c), p;
}
function L1(e, t, n, s = null) {
  let r = s != null ? s.shape : t.shape, a = r[r.length - 1], i = N.computeOptimalWindowSize(a), o = new v8(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 = L1(e, t, n, l);
    return e.disposeIntermediateTensorInfo(l), c;
  }
  return l;
}
function B1(e, t, n, s) {
  let r = [n];
  if (N.assertAxesAreInnerMostDims("arg" + s.charAt(0).toUpperCase() + s.slice(1), r, t.shape.length), !X().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] = N.computeOutAndReduceShapes(u.shape, r), p = w.sizeFromShape(c), d = he({ inputs: { x: u }, backend: e, attrs: { shape: [-1, p] } });
    a.push(d);
    let h = M1(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 L1(e, t, s);
}
function x8(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s, i = w.parseAxisParam(a, r.shape), o = N.getAxesPermutation(i, r.shape.length), u = r, l = [];
  o != null && (u = cn({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = N.getInnerMostAxes(i.length, u.shape.length)), N.assertAxesAreInnerMostDims("argMax", [i[0]], u.shape.length);
  let c = B1(n, u, i[0], "max");
  return l.forEach((p) => n.disposeIntermediateTensorInfo(p)), c;
}
var w8 = { kernelName: wa, backendName: "webgl", kernelFunc: x8 };
function k8(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s, i = w.parseAxisParam(a, r.shape), o = N.getAxesPermutation(i, r.shape.length), u = r, l = [];
  o != null && (u = cn({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = N.getInnerMostAxes(i.length, u.shape.length)), N.assertAxesAreInnerMostDims("argMin", [i[0]], u.shape.length);
  let c = B1(n, u, i[0], "min");
  return l.forEach((p) => n.disposeIntermediateTensorInfo(p)), c;
}
var I8 = { kernelName: il, backendName: "webgl", kernelFunc: k8 };
var S8 = ts + `
  if (abs(x) > 1.) {
    return NAN;
  }
  return asin(x);
`;
var C8 = Ke({ opSnippet: S8 });
var N8 = { kernelName: ol, backendName: "webgl", kernelFunc: C8 };
var T8 = ts + "return log(x + sqrt(x * x + 1.0));";
var $8 = Ke({ opSnippet: T8 });
var _8 = { kernelName: ul, backendName: "webgl", kernelFunc: $8 };
var A8 = ts + `
  return atan(x);
`;
var E8 = Ke({ opSnippet: A8 });
var R8 = { kernelName: ll, backendName: "webgl", kernelFunc: E8 };
var D8 = BX + `
  return atan(a, b);
`;
var F8 = `
  vec4 result = atan(a, b);
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  ` + VX + `
  return result;
`;
var O8 = qt({ opSnippet: D8, packedOpSnippet: F8 });
var P8 = { kernelName: dl, backendName: "webgl", kernelFunc: O8 };
var z8 = ts + `
  if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;
var M8 = Ke({ opSnippet: z8 });
var L8 = { kernelName: cl, backendName: "webgl", kernelFunc: M8 };
var el = 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) * ${e.inChannels} + d`, b = "0.0";
    if (f || (b = "-1.0 / 1e-20"), n) {
      let T = ">=";
      this.userCode = `
        const ivec2 strides = ivec2(${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 ${T} 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, C = `
      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)
            );

            ${C}
          }

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

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

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

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

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

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

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

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

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

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

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

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

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

                float value = getX(batch, xD, xR, xC, ch);

                // If a min / max value has already been found, use it. If not,
                // use the current value.
                float currMinMaxValue = mix(
                    value, minMaxValue, minMaxValueFound);
                if (value ${A} currMinMaxValue) {
                  minMaxValue = value;
                  minMaxValueFound = 1.0;
                  minMaxPosition = ${s ? r ? `(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch` : `((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch` : `wD * ${h} * ${f} +
                      wR * ${f} + wC`};
                }
              }
            }
          }
          setOutput(float(minMaxPosition));
        }
      `;
      return;
    }
    let x = "max", k = `${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
    t === "avg" && (k = "avgValue / count");
    let C = Math.floor(a / 4) * 4, T = a % 4, E = `
      if (${y}) {
        avgValue += dot(values, ones);
      } else {
        minMaxValue = ${x}(values, minMaxValue);
      }
    `;
    this.userCode = `
      const ivec3 strides =
        ivec3(${i}, ${o}, ${u});
      const ivec3 pads = ivec3(${m}, ${g}, ${b});
      const float initializationValue = ${v};
      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);

      float count = 0.0;

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

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

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

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

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

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

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

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

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

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

              ${E}
            }

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

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

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

              ${E}
            }
          }
          setOutput(${k});
        }
      }
    `;
  }
};
function B8(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t;
  tu(r, "avgPool");
  let { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1;
  w.assert(N.eitherStridesOrDilationsAreOne(i, l), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);
  let c = N.computePool2DInfo(r.shape, a, i, l, o, u);
  if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
    return En({ inputs: { x: r }, backend: n });
  let p = new el(c, "avg", false);
  return n.runWebGLProgram(p, [r], "float32");
}
var V8 = { kernelName: ka, backendName: "webgl", kernelFunc: B8 };
function W8(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 = N.computePool3DInfo(r.shape, a, i, c, o, u, l), d = new xv(p, "avg", false);
  return n.runWebGLProgram(d, [r], "float32");
}
var U8 = { kernelName: Hd, backendName: "webgl", kernelFunc: W8 };
var G8 = 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 H8 = 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 q8(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 = N.computePool3DInfo(i.shape, o, u, p, l, c), h = new H8(d);
  return n.runWebGLProgram(h, [r], i.dtype);
}
var j8 = { kernelName: ag, backendName: "webgl", kernelFunc: q8 };
function K8(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a } = t, i = a;
  tu([r, a], "avgPoolGrad");
  let { filterSize: o, strides: u, pad: l } = s, c = N.computePool2DInfo(i.shape, o, u, 1, l), p = new G8(c);
  return n.runWebGLProgram(p, [r], i.dtype);
}
var X8 = { kernelName: rg, backendName: "webgl", kernelFunc: K8 };
function Y8(e) {
  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
  return Pd({ a: r, b: a, transposeA: i, transposeB: o, backend: n });
}
var Q8 = { kernelName: Ia, backendName: "webgl", kernelFunc: Y8 };
var Z8 = class {
  constructor(e, t, n, s, r, a) {
    this.outputShape = [], this.variableNames = ["x", "mean", "variance"], N.assertAndGetBroadcastShape(e, t), N.assertAndGetBroadcastShape(e, n);
    let i = "0.0";
    s != null && (N.assertAndGetBroadcastShape(e, s), this.variableNames.push("offset"), i = "getOffsetAtOutCoords()");
    let o = "1.0";
    r != null && (N.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 J8 = class {
  constructor(e, t, n, s, r, a) {
    this.packedInputs = true, this.packedOutput = true, this.variableNames = ["x", "mean", "variance"], N.assertAndGetBroadcastShape(e, t), N.assertAndGetBroadcastShape(e, n);
    let i = "vec4(0.0)";
    s != null && (N.assertAndGetBroadcastShape(e, s), this.variableNames.push("offset"), i = "getOffsetAtOutCoords()");
    let o = "vec4(1.0)";
    r != null && (N.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 eY = ({ 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 = X().getBool("WEBGL_PACK_NORMALIZATION") ? new J8(s.shape, r.shape, a.shape, c, p, u) : new Z8(s.shape, r.shape, a.shape, c, p, u);
  return t.runWebGLProgram(d, l, l[0].dtype);
};
var tY = { kernelName: Pa, backendName: "webgl", kernelFunc: eY };
var nY = 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 = sY(this.rank), s, r = e.map((a, i) => `sourceLoc.${Wm[i]} = start[${i}] + coords.${Wm[i]};`);
    s = `
        ${t} sourceLoc;
        ${t} coords = getOutputCoords();
        ${r.join(`
`)}
      `, this.userCode = `
      void main() {
        ${s}
        setOutput(getSource(${n}));
      }
    `;
  }
};
var Wm = ["x", "y", "z", "w", "u", "v"];
function sY(e) {
  if (e === 1)
    return "sourceLoc";
  if (e <= 6)
    return Wm.slice(0, e).map((t) => "sourceLoc." + t).join(",");
  throw Error(`Slicing for rank ${e} is not yet supported`);
}
var rY = class {
  constructor(e) {
    this.variableNames = ["source"], this.packedInputs = true, this.packedOutput = true, this.outputShape = e, this.rank = e.length, this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
    let t = ot(this.rank), n = un("coords", this.rank), s = un("sourceLoc", this.rank), r = this.rank === 1 ? "sourceLoc" : `vec2(${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 aY(e, t, n, s) {
  let r = s.texData.get(e.dataId), a = s.makeTensorInfo(n, e.dtype), i = s.texData.get(a.dataId);
  Object.assign(i, r), i.refCount = 1, i.shape = n, i.dtype = e.dtype;
  let o = wt.computeFlatOffset(t, w.computeStrides(e.shape));
  r.slice && (o += r.slice.flatOffset), i.slice = { flatOffset: o, origDataId: r.slice && r.slice.origDataId || e.dataId };
  let u = s.dataRefCount.get(i.slice.origDataId) || 1;
  return s.dataRefCount.set(i.slice.origDataId, u + 1), a;
}
function ou(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, size: i } = s, [o, u] = wt.parseSliceParams(r, a, i);
  if (wt.assertParamsValid(r, o, u), w.sizeFromShape(u) === 0)
    return n.makeTensorInfo(u, r.dtype, []);
  if (n.shouldExecuteOnCPU([r]) || r.dtype === "string") {
    let p = n.texData.get(r.dataId), d = XK(p.values, o, u, r.shape, r.dtype);
    return n.makeTensorInfo(u, r.dtype, d);
  }
  let { isPacked: l } = n.texData.get(r.dataId), c = wt.isSliceContinous(r.shape, o, u);
  if (l || !c) {
    let p = X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new rY(u) : new nY(u), d = [o];
    return n.runWebGLProgram(p, [r], r.dtype, d);
  }
  return n.uploadToGPU(r.dataId), aY(r, o, u, n);
}
var iY = { kernelName: Oo, backendName: "webgl", kernelFunc: ou };
var oY = (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 = N.getReshaped(r.shape, a, o), l = N.getPermuted(u.length, a.length), c = N.getReshapedPermuted(r.shape, a, o), p = N.getSliceBeginCoords(i, a.length), d = N.getSliceSize(c, i, a.length), h = [], f = he({ inputs: { x: r }, backend: n, attrs: { shape: u } }), m = cn({ inputs: { x: f }, backend: n, attrs: { perm: l } }), g = he({ inputs: { x: m }, backend: n, attrs: { shape: c } }), b = ou({ 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 uY = { kernelName: io, backendName: "webgl", kernelFunc: oY };
function lY(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 = S1(o, u, a.dtype, a.shape, i);
  return n.makeTensorInfo([i], a.dtype, l);
}
var cY = { kernelName: ig, backendName: "webgl", kernelFunc: lY };
function dY(e) {
  let { inputs: t, backend: n } = e, { s0: s, s1: r } = t, a = n.readSync(s.dataId), i = n.readSync(r.dataId), o = N.assertAndGetBroadcastShape(Array.from(a), Array.from(i));
  return n.makeTensorInfo([o.length], "int32", Int32Array.from(o));
}
var pY = { kernelName: og, backendName: "webgl", kernelFunc: dY };
var hY = "return float(a != b);";
var V1 = qt({ opSnippet: hY, cpuKernelImpl: GK, dtype: "bool" });
var fY = { kernelName: Io, backendName: "webgl", kernelFunc: V1 };
function Zl(e) {
  let { inputs: t, backend: n } = e, { input: s } = t, r = n.texData.get(s.dataId);
  return En({ inputs: { x: r.complexTensorInfos.real }, backend: n });
}
var mY = { kernelName: tp, backendName: "webgl", kernelFunc: Zl };
var gY = "return float(int(x));";
function bY(e, t) {
  let n = new Hs(e.shape, gY), s = t.runWebGLProgram(n, [e], "int32");
  return { dataId: s.dataId, shape: s.shape, dtype: s.dtype };
}
function Um(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dtype: a } = s;
  if (a === "complex64") {
    if (r.dtype === "complex64")
      return En({ inputs: { x: r }, backend: n });
    let i = $t(r.shape), o = Um({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = Er({ inputs: { real: o, imag: i }, backend: n });
    return i.dispose(), n.disposeIntermediateTensorInfo(o), u;
  }
  if (r.dtype === "complex64") {
    let i = Zl({ inputs: { input: r }, backend: n }), o = Um({ inputs: { x: i }, backend: n, attrs: { dtype: a } });
    return n.disposeIntermediateTensorInfo(i), o;
  }
  if (!w.hasEncodingLoss(r.dtype, a)) {
    let i = En({ inputs: { x: r }, backend: n });
    return { dataId: i.dataId, shape: i.shape, dtype: a };
  }
  if (a === "int32")
    return bY(r, n);
  if (a === "bool") {
    let i = n.makeTensorInfo([], "bool", w.getTypedArrayFromDType("bool", 1)), u = V1({ inputs: { a: r, b: i }, backend: n });
    return n.disposeIntermediateTensorInfo(i), u;
  }
  throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`);
}
var yY = { kernelName: Sa, backendName: "webgl", kernelFunc: Um };
var pw = "return ceil(x);";
var vY = Ke({ opSnippet: pw, packedOpSnippet: pw, cpuKernelImpl: CK });
var xY = { kernelName: Ca, backendName: "webgl", kernelFunc: vY };
var wY = 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 kY = 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 IY(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { clipValueMin: a, clipValueMax: i } = s, o;
  X().getBool("WEBGL_PACK_CLIP") ? o = new kY(r.shape) : o = new wY(r.shape);
  let u = [[a], [i]];
  return n.runWebGLProgram(o, [r], r.dtype, u);
}
var SY = { kernelName: Ir, backendName: "webgl", kernelFunc: IY };
var CY = 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 hw(e, t) {
  return { dataId: t.dataId, dtype: t.dtype, shape: e.shape };
}
function NY(e) {
  let { inputs: t, backend: n } = e, { x: s } = t, r = n.texData.get(s.dataId), a = new CY(s.shape), i = [hw(s, r.complexTensorInfos.real), hw(s, r.complexTensorInfos.imag)];
  return n.runWebGLProgram(a, i, i[0].dtype);
}
var TY = { kernelName: jd, backendName: "webgl", kernelFunc: NY };
var $Y = class {
  constructor(e) {
    this.outputShape = [], this.outputShape = N.computeOutShape(e, 1), this.variableNames = e.map((a, i) => `T${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;

        ${n.join(`
        `)}
      }
    `;
  }
};
var _Y = class {
  constructor(e, t) {
    this.packedInputs = true, this.packedOutput = true, this.outputShape = [], this.outputShape = N.computeOutShape(e, t);
    let n = this.outputShape, s = n.length, r = ot(s), a = un("coords", s), i = ["x", "y", "z", "w", "u", "v"].slice(0, s);
    this.variableNames = e.map((f, m) => `T${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}(${Zc(i, u, m)}),
            vec2(${Zc(l, u, m)}));
        }`;
    }
    let d = o.length, h = o[o.length - 1];
    p += `
        return getChannel(
          getT${d}(${Zc(i, u, h)}),
          vec2(${Zc(l, u, h)}));`, this.userCode = `
      float getValue(${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 Zc(e, t, n) {
  let s = e.indexOf(t);
  return e.map((a, i) => i === s ? `${a} - ${n}` : a).join();
}
function Yp(e) {
  let { inputs: t, backend: n } = e, { input: s } = t, r = n.texData.get(s.dataId);
  return En({ inputs: { x: r.complexTensorInfos.imag }, backend: n });
}
var AY = { kernelName: Qd, backendName: "webgl", kernelFunc: Yp };
function Wi(e, t, n) {
  let s = e[0].dtype;
  if (s === "complex64") {
    let c = e.map((m) => Zl({ inputs: { input: m }, backend: n })), p = e.map((m) => Yp({ inputs: { input: m }, backend: n })), d = Wi(c, t, n), h = Wi(p, t, n), f = Er({ 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 = N.computeOutShape(c.map((b) => b.shape), 1), h = c[0].shape[0] === 1, f = NK(p, d, s, h), m = N.computeOutShape(e.map((b) => b.shape), t), g = n.makeTensorInfo(m, s, f);
    return c.forEach((b) => n.disposeIntermediateTensorInfo(b)), g;
  }
  if (e.length > X().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")) {
    let c = Math.floor(e.length / 2), p = Wi(e.slice(0, c), t, n), d = Wi(e.slice(c), t, n), h = Wi([p, d], t, n);
    return n.disposeIntermediateTensorInfo(p), n.disposeIntermediateTensorInfo(d), h;
  }
  if (X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") && e[0].shape.length > 1) {
    let c = new _Y(e.map((p) => p.shape), t);
    return n.runWebGLProgram(c, e, s);
  }
  let { tensors2D: a, outShape: i } = EY(e, t, n), o = new $Y(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 EY(e, t, n) {
  let s = N.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 W1(e) {
  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s, a = w.parseAxisParam(r, t[0].shape)[0], i = N.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 En({ inputs: { x: o[0] }, backend: n });
  let u = o.map((l) => l.shape);
  return N.assertParamsConsistent(u, a), Wi(o, a, n);
}
var RY = { kernelName: oo, backendName: "webgl", kernelFunc: W1 };
var U1 = 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}
        ${x}
        setOutput(result);
      }
    `;
  }
};
var DY = class {
  constructor(e) {
    this.variableNames = ["x", "W"], this.outputShape = e.outShape;
    let t = e.padInfo.front, n = e.padInfo.top, s = e.padInfo.left, r = e.strideDepth, a = e.strideHeight, i = e.strideWidth, o = e.dilationDepth, u = e.dilationHeight, l = e.dilationWidth, c = e.filterDepth, p = e.filterHeight, d = e.filterWidth, h = Math.floor(e.inChannels / 4) * 4, f = e.inChannels % 4;
    this.userCode = `
      const ivec3 strides = ivec3(${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 FY = 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 = kn(this.outputShape.length);
    let { dataFormat: n } = t, s = hn(), 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 G1({ 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 (!((p === 1 || d === 1) && c > z1) && 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 }, C = l.shape;
    l.shape = l.shape.slice(), l.shape[l.shape.length - 2]++, w.assert(Ju(l.shape, k.shape), () => `packed reshape ${l.shape} to ${k.shape} isn't free`);
    let T = he({ inputs: { x: t }, backend: s, attrs: { shape: [1, n.inChannels, n.outChannels] } });
    b.push(T);
    let E = Pd({ a: k, b: T, backend: s, transposeA: f, transposeB: m, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i }), A = s.texData.get(E.dataId);
    w.assert(A.isPacked, () => "batchMatMul result is expected to be packed"), l.shape = C, A.shape = n.outShape, g = En({ inputs: { x: E }, backend: s }), g.shape = n.outShape, b.push(E);
  } else {
    let x = h ? u[0] * u[1] * u[2] : u[0] * u[2] * u[3], k = he({ inputs: { x: e }, backend: s, attrs: { shape: [1, x, n.inChannels] } }), C = he({ inputs: { x: t }, backend: s, attrs: { shape: [1, n.inChannels, n.outChannels] } }), T = Pd({ a: k, b: C, transposeA: f, transposeB: m, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i });
    g = he({ inputs: { x: T }, backend: s, attrs: { shape: n.outShape } }), b.push(k), b.push(C), b.push(T);
  }
  for (let x of b)
    s.disposeIntermediateTensorInfo(x);
  return g;
}
function H1({ 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 = [], k = he({ inputs: { x: e }, backend: s, attrs: { shape: e.shape.slice(1) } }), C = he({ inputs: { x: t }, backend: s, attrs: { shape: [1, m, w.sizeFromShape(t.shape) / m] } });
  x.push(k), x.push(C);
  let T = new FY(b, n), E = [k.shape, [n.padInfo.top, n.padInfo.left], [n.strideHeight, n.strideWidth], [n.dilationHeight, n.dilationWidth], [n.inChannels], [n.filterWidth * n.inChannels], [n.outWidth]], A = s.runWebGLProgram(T, [k], "float32", E), P = he({ inputs: { x: A }, backend: s, attrs: { shape: [1, b[0], b[1]] } });
  x.push(A), x.push(P);
  let R = r != null, F = a != null, $ = o === "leakyrelu", z = o ? jp(o, true) : null, W = new P1(P.shape, C.shape, [1, g, n.outChannels], y, v, R, z, F, $), q = [P, C];
  if (r && q.push(r), F && q.push(a), $) {
    let te = s.makeTensorInfo([], "float32", w.createScalarValue(i, "float32"));
    q.push(te), x.push(te);
  }
  let K = s.runWebGLProgram(W, q, "float32"), Y = f ? [1, d, p, n.outChannels] : [1, n.outChannels, d, p], Z = he({ inputs: { x: K }, backend: s, attrs: { shape: Y } });
  x.push(K);
  for (let te of x)
    s.disposeIntermediateTensorInfo(te);
  return Z;
}
function OY(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 = N.convertConv2DDataFormat(u), d = N.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 = G1({ x: r, filter: a, convInfo: d, backend: n });
  else if (X().getBool("WEBGL_CONV_IM2COL") && r.shape[0] === 1)
    h = H1({ x: r, filter: a, convInfo: d, backend: n });
  else {
    let m = new U1(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 PY = { kernelName: Na, backendName: "webgl", kernelFunc: OY };
var zY = 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 MY = 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 LY = 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 BY = 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 VY(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 = N.convertConv2DDataFormat(u), d = N.computeConv2DInfo(r.shape, c, i, 1, o, l, false, p), h = new zY(d);
  return n.runWebGLProgram(h, [r, a], "float32");
}
var WY = { kernelName: ug, backendName: "webgl", kernelFunc: VY };
function UY(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 = N.convertConv2DDataFormat(l), d = N.computeConv2DInfo(i, a.shape, o, 1, u, c, false, p), h = new MY(d);
  return n.runWebGLProgram(h, [r, a], "float32");
}
var GY = { kernelName: Ta, backendName: "webgl", kernelFunc: UY };
function HY(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u } = s, l = N.computeConv3DInfo(r.shape, a.shape, i, u, o), c = new DY(l);
  return n.runWebGLProgram(c, [r, a], "float32");
}
var qY = { kernelName: Kd, backendName: "webgl", kernelFunc: HY };
function jY(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, dy: a } = t, { strides: i, pad: o, filterShape: u } = s, l = N.computeConv3DInfo(r.shape, u, i, 1, o), c = new LY(l);
  return n.runWebGLProgram(c, [r, a], "float32");
}
var KY = { kernelName: lg, backendName: "webgl", kernelFunc: jY };
function XY(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, filter: a } = t, { pad: i, strides: o, inputShape: u } = s, l = N.computeConv3DInfo(u, a.shape, o, 1, i), c = new BY(l);
  return n.runWebGLProgram(c, [r, a], "float32");
}
var YY = { kernelName: cg, backendName: "webgl", kernelFunc: XY };
var QY = iu + `
  return cos(x);
`;
var ZY = Ke({ opSnippet: QY });
var JY = { kernelName: $a, backendName: "webgl", kernelFunc: ZY };
var e9 = `
  float e2x = exp(-x);
  return (e2x + 1.0 / e2x) / 2.0;
`;
var t9 = Ke({ opSnippet: e9 });
var n9 = { kernelName: _a, backendName: "webgl", kernelFunc: t9 };
var s9 = class {
  constructor(e, t, n, s, r) {
    this.variableNames = ["Image", "Boxes", "BoxInd"], this.outputShape = [];
    let [a, i, o, u] = e, [l] = t, [c, p] = n;
    this.outputShape = [l, c, p, u];
    let d = s === "bilinear" ? 1 : 0, [h, f] = [`${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 r9 = (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 s9(r.shape, a.shape, o, u, l);
  return n.runWebGLProgram(c, [r, a, i], "float32");
};
var a9 = { kernelName: lo, backendName: "webgl", kernelFunc: r9 };
var fw = class {
  constructor(e, t, n) {
    this.variableNames = ["x"], this.customUniforms = [{ name: "index", type: "float" }], this.outputShape = e;
    let s = e.length, r = t ? "0.0" : `getX(${mw(s, "coords")})`, a = e[e.length - 1], i = "", o = "";
    t ? (i = n ? `end != ${a - 1}` : "end != 0", o = n ? "end + 1" : "end - 1") : (i = n ? `end + pow2 < ${a}` : "end >= pow2", o = n ? "end + pow2" : "end - pow2"), this.userCode = `
      void main() {
        ${ot(s)} coords = getOutputCoords();
        int end = ${gw(s, "coords")};
        float val = ${r};
        int pow2 = int(pow(2.0, index));
        if (${i}) {
          int idx = ${o};
          ${gw(s, "coords")} = idx;
          val += getX(${mw(s, "coords")});
        }
        setOutput(val);
      }
    `;
  }
};
function mw(e, t) {
  if (e === 1)
    return `${t}`;
  if (e === 2)
    return `${t}.x, ${t}.y`;
  if (e === 3)
    return `${t}.x, ${t}.y, ${t}.z`;
  if (e === 4)
    return `${t}.x, ${t}.y, ${t}.z, ${t}.w`;
  throw Error(`Cumulative sum for rank ${e} is not yet supported`);
}
function gw(e, t) {
  if (e === 1)
    return `${t}`;
  if (e === 2)
    return `${t}.y`;
  if (e === 3)
    return `${t}.z`;
  if (e === 4)
    return `${t}.w`;
  throw Error(`Cumulative sum for rank ${e} is not yet supported`);
}
function i9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, exclusive: i, reverse: o } = s, u = r.shape.length, l = N.getAxesPermutation([a], u), c = r;
  l != null && (c = cn({ inputs: { x: r }, backend: n, attrs: { perm: l } }));
  let p = N.getInnerMostAxes(1, u)[0];
  if (p !== u - 1)
    throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length - 1} but got axis=${a}`);
  let d = c.shape[p], h = En({ inputs: { x: c }, backend: n });
  for (let f = 0; f <= Math.ceil(Math.log2(d)) - 1; f++) {
    let m = new fw(c.shape, false, o), g = [[f]], b = h;
    h = n.runWebGLProgram(m, [h], h.dtype, g), n.disposeIntermediateTensorInfo(b);
  }
  if (i) {
    let f = new fw(c.shape, i, o), m = h;
    h = n.runWebGLProgram(f, [h], h.dtype), n.disposeIntermediateTensorInfo(m);
  }
  if (l != null) {
    let f = N.getUndoAxesPermutation(l), m = cn({ inputs: { x: h }, backend: n, attrs: { perm: f } });
    return n.disposeIntermediateTensorInfo(h), n.disposeIntermediateTensorInfo(c), m;
  }
  return h;
}
var o9 = { kernelName: uo, backendName: "webgl", kernelFunc: i9 };
function u9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, weights: a } = t, { size: i, binaryOutput: o } = s;
  if (r.shape.length === 1) {
    let u = n.readSync(r.dataId), l = n.readSync(a.dataId), c = S1(u, l, a.dtype, a.shape, i);
    return n.makeTensorInfo([i], a.dtype, c);
  } else if (r.shape.length === 2) {
    let u = n.bufferSync(r), l = n.bufferSync(a), c = SK(u, l, i, o);
    return n.makeTensorInfo(c.shape, a.dtype, c.values);
  }
  throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`);
}
var l9 = { kernelName: dg, backendName: "webgl", kernelFunc: u9 };
var c9 = 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 d9(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 c9(f, a, i);
  return n.runWebGLProgram(m, [r], r.dtype);
}
var p9 = { kernelName: co, backendName: "webgl", kernelFunc: d9 };
var q1 = 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 = kn(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}
        ${l}
        setOutput(result);
      }
    `;
  }
};
var j1 = class {
  constructor(e, t = false, n = null, s = false, r = false) {
    this.variableNames = ["x", "W"], this.packedInputs = true, this.packedOutput = true, this.customUniforms = [{ name: "pads", type: "ivec2" }, { name: "strides", type: "ivec2" }, { name: "dilations", type: "ivec2" }, { name: "inDims", type: "ivec2" }], this.outputShape = e.outShape, this.enableShapeUniforms = kn(this.outputShape.length);
    let a = e.outChannels / e.inChannels, i = e.padInfo.left, o = e.strideWidth, u = e.dilationWidth, l = e.filterHeight, c = e.filterWidth, p = c, d = `
      int xR; int xC; int xCOffset;
      vec4 wTexel; vec4 previous; vec4 final;`;
    for (let g = 0; g < c; g++)
      d += `
          vec4 xTexelC${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 h9(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(N.eitherStridesOrDilationsAreOne(i, c), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${c}'`);
  let p = N.computeConv2DInfo(r.shape, a.shape, i, c, o, l, true), d;
  X().getBool("WEBGL_PACK_DEPTHWISECONV") && p.strideWidth <= 2 && p.outChannels / p.inChannels === 1 ? d = new j1(p) : d = new q1(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 f9 = { kernelName: Aa, backendName: "webgl", kernelFunc: h9 };
var m9 = class {
  constructor(e) {
    this.variableNames = ["x", "dy"], this.outputShape = e.filterShape;
    let t = e.strideHeight, n = e.strideWidth, s = e.padInfo.top, r = e.padInfo.left, a = e.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 g9 = class {
  constructor(e) {
    this.variableNames = ["dy", "W"], this.outputShape = e.inShape;
    let t = e.filterHeight, n = e.filterWidth, s = e.strideHeight, r = e.strideWidth, a = 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 b9(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 = N.computeConv2DInfo(r.shape, c, i, o, u, l, true), d = new m9(p);
  return n.runWebGLProgram(d, [r, a], "float32");
}
var y9 = { kernelName: pg, backendName: "webgl", kernelFunc: b9 };
function v9(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 = N.computeConv2DInfo(c, a.shape, i, o, u, l, true), d = new g9(p);
  return n.runWebGLProgram(d, [r, a], "float32");
}
var x9 = { kernelName: hg, backendName: "webgl", kernelFunc: v9 };
var w9 = 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 k9(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 w9(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 I9 = { kernelName: fg, backendName: "webgl", kernelFunc: k9 };
var S9 = 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 C9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u } = s, l = N.computeDilation2DInfo(r.shape, a.shape, i, o, "NHWC", u), c, p = new S9(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 N9 = { kernelName: Xd, backendName: "webgl", kernelFunc: C9 };
function T9(e) {
  let { inputs: t, backend: n, attrs: s } = e, { equation: r } = s, a = t, { allDims: i, summedDims: o, idDims: u } = N.decodeEinsumEquation(r, a.length);
  N.checkEinsumDimSizes(i.length, u, a);
  let { path: l, steps: c } = N.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 } = N.getEinsumPermutation(h, u[g]), v;
      N.isIdentityPermutation(b) ? v = a[g] : (v = cn({ 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 = vv({ inputs: { a: v, b: d }, backend: n }), f.push(d));
    }
    m < p - 1 && (l[m] >= 0 && (d = Xp({ 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 $9 = { kernelName: Yd, backendName: "webgl", kernelFunc: T9 };
var _9 = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
var A9 = `
  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 E9 = Ke({ opSnippet: _9, packedOpSnippet: A9 });
var R9 = { kernelName: Ra, backendName: "webgl", kernelFunc: E9 };
var D9 = "return (b >= 1.0) ? a : a * (b + 1.0);";
var F9 = `
  vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
  return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`;
var O9 = (e) => {
  let { inputs: t, backend: n } = e, { dy: s, y: r } = t, a = X().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new Ql(F9, s.shape, r.shape) : new no(D9, s.shape, r.shape);
  return n.runWebGLProgram(a, [s, r], s.dtype);
};
var P9 = { kernelName: mg, backendName: "webgl", kernelFunc: O9 };
var z9 = `
  return vec4(equal(a, b));
`;
var M9 = "return float(a == b);";
var L9 = qt({ opSnippet: M9, packedOpSnippet: z9, dtype: "bool", cpuKernelImpl: TK });
var B9 = { kernelName: po, backendName: "webgl", kernelFunc: L9 };
var V9 = `
  // Error function is calculated approximately with elementary function.
  // See "Handbook of Mathematical Functions with Formulas,
  // Graphs, and Mathematical Tables", Abramowitz and Stegun.
  float p = ${N.ERF_P};
  float a1 = ${N.ERF_A1};
  float a2 = ${N.ERF_A2};
  float a3 = ${N.ERF_A3};
  float a4 = ${N.ERF_A4};
  float a5 = ${N.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 W9 = Ke({ opSnippet: V9 });
var U9 = { kernelName: pl, backendName: "webgl", kernelFunc: W9 };
var G9 = iu + `
  return exp(x);
`;
var H9 = `
  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 K1 = Ke({ opSnippet: G9, packedOpSnippet: H9, cpuKernelImpl: $K, dtype: "float32" });
var q9 = { kernelName: Da, backendName: "webgl", kernelFunc: K1 };
function Gm(e) {
  let { inputs: t, attrs: n, backend: s } = e, { dim: r } = n, { input: a } = t, i = a.shape.length, o = a.shape.slice(), u = r;
  return r < 0 && (w.assert(-(i + 1) <= r, () => `Axis must be in the interval [${-(i + 1)}, ${i}]`), u = i + r + 1), o.splice(u, 0, 1), he({ inputs: { x: a }, backend: s, attrs: { shape: o } });
}
var j9 = { kernelName: ho, backendName: "webgl", kernelFunc: Gm };
var bw = "return exp(x) - 1.0;";
var K9 = Ke({ opSnippet: bw, packedOpSnippet: bw, cpuKernelImpl: _K });
var X9 = { kernelName: fo, backendName: "webgl", kernelFunc: K9 };
var yw = 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 X1(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 yw("real", u, t), c = new yw("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 = Er({ 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 Y9(e) {
  let { inputs: t, backend: n } = e, { input: s } = t;
  return X1(s, false, n);
}
var Q9 = { kernelName: gg, backendName: "webgl", kernelFunc: Y9 };
var Z9 = 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 Jl(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 Z9(s, r), o = [[r]];
    return t.runWebGLProgram(i, [], a, o);
  }
}
var J9 = { kernelName: hl, backendName: "webgl", kernelFunc: Jl };
var eQ = 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 tQ = { kernelName: mo, backendName: "webgl", kernelFunc: ({ inputs: e, backend: t }) => {
  let { image: n } = e, s = t, r = new eQ(n.shape);
  return s.runWebGLProgram(r, [n], n.dtype);
} };
var vw = "return floor(x);";
var nQ = Ke({ opSnippet: vw, packedOpSnippet: vw, cpuKernelImpl: AK });
var sQ = { kernelName: Fa, backendName: "webgl", kernelFunc: nQ };
var rQ = `
  float s = sign(a) * sign(b);
  int ia = round(a);
  int ib = round(b);
  if (ib != 0) {
    // Windows (D3D) wants guaranteed non-zero int division at compile-time.
    return float(idiv(ia, ib, s));
  } else {
    return NAN;
  }
`;
var aQ = `
  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 iQ = qt({ opSnippet: rQ, packedOpSnippet: aQ, dtype: "int32" });
var oQ = { kernelName: Oa, backendName: "webgl", kernelFunc: iQ };
var uQ = class {
  constructor(e) {
    this.variableNames = ["A"];
    let t = hn(), [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 lQ = class {
  constructor(e) {
    this.variableNames = ["A"], this.packedInputs = false, this.packedOutput = true;
    let t = hn(), [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 cQ = { kernelName: hd, backendName: "webgl", kernelFunc: dQ };
var zi;
function dQ(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) && (zi == null && (zi = document.createElement("canvas").getContext("2d")), zi.canvas.width = u, zi.canvas.height = l, zi.drawImage(r, 0, 0, u, l), r = zi.canvas);
  let d = n.makeTensorInfo(c, "int32");
  n.texData.get(d.dataId).usage = 2, n.gpgpu.uploadPixelDataToTexture(n.getTexture(d.dataId), r);
  let h = X().getBool("WEBGL_PACK") ? new lQ(p) : new uQ(p), f = n.runWebGLProgram(h, [d], "int32");
  return n.disposeData(d.dataId), f;
}
function pQ(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 = N.convertConv2DDataFormat(c), g = N.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 = G1({ x: r, filter: a, convInfo: g, backend: n, bias: i, activation: h, preluActivationWeights: o, leakyreluAlpha: f });
  else if (X().getBool("WEBGL_CONV_IM2COL") && r.shape[0] === 1)
    b = H1({ x: r, filter: a, convInfo: g, backend: n, bias: i, activation: h, preluActivationWeights: o, leakyreluAlpha: f });
  else {
    let x = i != null, k = o != null, C = h === "leakyrelu", T = h ? jp(h, false) : null, E = new U1(g, x, T, k, C), A = [r, a];
    if (i && A.push(i), o && A.push(o), C) {
      let P = n.makeTensorInfo([], "float32", w.createScalarValue(f, "float32"));
      A.push(P), y.push(P);
    }
    b = n.runWebGLProgram(E, A, "float32");
  }
  let v = he({ inputs: { x: b }, backend: n, attrs: { shape: g.outShape } });
  return y.push(b), y.forEach((x) => n.disposeIntermediateTensorInfo(x)), v;
}
var hQ = { kernelName: sa, backendName: "webgl", kernelFunc: pQ };
function fQ(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(N.eitherStridesOrDilationsAreOne(u, m), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${m}'`);
  let g = N.computeConv2DInfo(r.shape, a.shape, u, m, l, p, true), b = X().getBool("WEBGL_PACK_DEPTHWISECONV") && g.strideWidth <= 2 && g.outChannels / g.inChannels === 1, y = d ? jp(d, b) : null, v = [r, a], x = i != null, k = o != null, C = d === "leakyrelu";
  if (x && v.push(i), k && v.push(o), C) {
    let P = n.makeTensorInfo([], "float32", w.createScalarValue(h, "float32"));
    v.push(P), f.push(P);
  }
  let T;
  b ? T = new j1(g, x, y, k, C) : T = new q1(g, x, y, k, C);
  let E = [[g.padInfo.top, g.padInfo.left], [g.strideHeight, g.strideWidth], [g.dilationHeight, g.dilationWidth], [g.inHeight, g.inWidth]], A = n.runWebGLProgram(T, v, "float32", E);
  return f.forEach((P) => n.disposeIntermediateTensorInfo(P)), A;
}
var mQ = { kernelName: ra, backendName: "webgl", kernelFunc: fQ };
var gQ = 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 bQ(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] = N.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 = EK(b, y, s.dtype, l, i, c, p, s.shape, o);
    return n.makeTensorInfo(u, s.dtype, v.values);
  }
  let f = new gQ(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 yQ = { kernelName: bo, backendName: "webgl", kernelFunc: bQ };
var vQ = class {
  constructor(e, t) {
    this.variableNames = ["A", "indices"], this.outputShape = t, this.rank = t.length;
    let n = ot(this.rank), s = xQ(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 xQ(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 Y1(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 (X().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 = N.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 = RK(v, y, f);
    return p.forEach((k) => n.disposeIntermediateTensorInfo(k)), n.makeTensorInfo(l.outputShape, x.dtype, x.values);
  }
  let m = new vQ(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 wQ = { kernelName: go, backendName: "webgl", kernelFunc: Y1 };
var kQ = "return float(a > b);";
var IQ = `
  return vec4(greaterThan(a, b));
`;
var SQ = qt({ opSnippet: kQ, packedOpSnippet: IQ, cpuKernelImpl: DK, dtype: "bool" });
var CQ = { kernelName: yo, backendName: "webgl", kernelFunc: SQ };
var NQ = "return float(a >= b);";
var TQ = `
  return vec4(greaterThanEqual(a, b));
`;
var $Q = qt({ opSnippet: NQ, packedOpSnippet: TQ, dtype: "bool", cpuKernelImpl: FK });
var _Q = { kernelName: za, backendName: "webgl", kernelFunc: $Q };
function AQ(e) {
  let { inputs: t, backend: n } = e, { input: s } = t;
  return X1(s, true, n);
}
var EQ = { kernelName: bg, backendName: "webgl", kernelFunc: AQ };
var RQ = "return float(!isnan(x) && !isinf(x));";
var DQ = Ke({ opSnippet: RQ, dtype: "bool" });
var FQ = { kernelName: fl, backendName: "webgl", kernelFunc: DQ };
var OQ = "return float(isinf(x));";
var PQ = Ke({ opSnippet: OQ, dtype: "bool" });
var zQ = { kernelName: ml, backendName: "webgl", kernelFunc: PQ };
var MQ = "return float(isnan(x));";
var LQ = Ke({ opSnippet: MQ, dtype: "bool" });
var BQ = { kernelName: gl, backendName: "webgl", kernelFunc: LQ };
var VQ = "return float(a < b);";
var WQ = `
  return vec4(lessThan(a, b));
`;
var UQ = qt({ opSnippet: VQ, packedOpSnippet: WQ, cpuKernelImpl: OK, dtype: "bool" });
var GQ = { kernelName: vo, backendName: "webgl", kernelFunc: UQ };
var HQ = "return float(a <= b);";
var qQ = `
  return vec4(lessThanEqual(a, b));
`;
var jQ = qt({ opSnippet: HQ, packedOpSnippet: qQ, cpuKernelImpl: PK, dtype: "bool" });
var KQ = { kernelName: xo, backendName: "webgl", kernelFunc: jQ };
function XQ(e) {
  let { backend: t, attrs: n } = e, { start: s, stop: r, num: a } = n, i = zK(s, r, a);
  return t.makeTensorInfo([i.length], "float32", i);
}
var YQ = { kernelName: yg, backendName: "webgl", kernelFunc: XQ };
var QQ = iu + `
  return x < 0.0 ? 0./0. : log(x);
`;
var ZQ = `
  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 JQ = Ke({ opSnippet: QQ, packedOpSnippet: ZQ, cpuKernelImpl: MK });
var eZ = { kernelName: Ba, backendName: "webgl", kernelFunc: JQ };
var tZ = iu + `
  return log(1.0 + x);
`;
var nZ = Ke({ opSnippet: tZ });
var sZ = { kernelName: bl, backendName: "webgl", kernelFunc: nZ };
var rZ = "return float(a >= 1.0 && b >= 1.0);";
var aZ = `
  return vec4(
    vec4(greaterThanEqual(a, vec4(1.0))) *
    vec4(greaterThanEqual(b, vec4(1.0))));
`;
var iZ = qt({ opSnippet: rZ, packedOpSnippet: aZ, dtype: "bool" });
var oZ = { kernelName: wo, backendName: "webgl", kernelFunc: iZ };
var uZ = "return float(!(x >= 1.0));";
var lZ = Ke({ opSnippet: uZ });
var cZ = { kernelName: yl, backendName: "webgl", kernelFunc: lZ };
var dZ = "return float(a >= 1.0 || b >= 1.0);";
var pZ = `
  return min(
    vec4(greaterThanEqual(a, vec4(1.0))) +
    vec4(greaterThanEqual(b, vec4(1.0))),
    vec4(1.0));
`;
var hZ = qt({ opSnippet: dZ, packedOpSnippet: pZ, dtype: "bool" });
var fZ = { kernelName: Zd, backendName: "webgl", kernelFunc: hZ };
var mZ = 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 * ${o};
        setOutput(val);
      }
    `;
  }
};
var gZ = class {
  constructor(e, t, n, s, r) {
    this.variableNames = ["x"], this.outputShape = [], this.packedInputs = true, this.packedOutput = true;
    let a = t, i = e[3] - 1;
    this.outputShape = e;
    let o, u = `float(${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 bZ = (e) => {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { depthRadius: a, bias: i, alpha: o, beta: u } = s, l = X().getBool("WEBGL_PACK_NORMALIZATION") ? new gZ(r.shape, a, i, o, u) : new mZ(r.shape, a, i, o, u);
  return n.runWebGLProgram(l, [r], r.dtype);
};
var yZ = { kernelName: Jd, backendName: "webgl", kernelFunc: bZ };
var vZ = 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 xZ = (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 vZ(r.shape, o, u, l, c);
  return n.runWebGLProgram(p, [r, a, i], r.dtype);
};
var wZ = { kernelName: vg, backendName: "webgl", kernelFunc: xZ };
function kZ(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 = yi(o, e.dtype, "max", s), l = he({ inputs: { x: u }, attrs: { shape: n }, backend: s });
  return s.disposeIntermediateTensorInfo(o), s.disposeIntermediateTensorInfo(u), l;
}
function Q1(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 = N.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 T = 0; T < x.length; T++)
        x[T] = r.shape[c[T]];
      let k = yv(v, r.shape, r.dtype, c, x);
      h = n.makeTensorInfo(x, r.dtype);
      let C = n.texData.get(h.dataId);
      C.values = k;
    } else
      h = Kp(r, c, n);
    l = N.getInnerMostAxes(l.length, o);
  }
  N.assertAxesAreInnerMostDims("max", l, o);
  let [f, m] = N.computeOutAndReduceShapes(h.shape, l), g = f;
  i && (g = N.expandShapeToKeepDim(f, u));
  let b;
  if (d) {
    let v = n.texData.get(h.dataId).values, x = LK(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 = kZ(h, m, g, n);
  return p && n.disposeIntermediateTensorInfo(h), b;
}
var IZ = { kernelName: Va, backendName: "webgl", kernelFunc: Q1 };
var SZ = E1 + `
  return max(a, b);
`;
var CZ = `
  vec4 result = vec4(max(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  ` + qp + `
  return result;
`;
var NZ = qt({ opSnippet: SZ, packedOpSnippet: CZ, cpuKernelImpl: BK });
var TZ = { kernelName: Wa, backendName: "webgl", kernelFunc: NZ };
function $Z(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t;
  tu(r, "maxPool");
  let { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1;
  w.assert(N.eitherStridesOrDilationsAreOne(i, l), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);
  let c = N.computePool2DInfo(r.shape, a, i, l, o, u);
  if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
    return En({ inputs: { x: r }, backend: n });
  let p = new el(c, "max", false);
  return n.runWebGLProgram(p, [r], r.dtype);
}
var _Z = { kernelName: Ua, backendName: "webgl", kernelFunc: $Z };
function AZ(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dataFormat: u, dimRoundingMode: l } = s, c = [1, 1, 1], p = N.computePool3DInfo(r.shape, a, i, c, o, l, u), d = new xv(p, "max", false);
  return n.runWebGLProgram(d, [r], r.dtype);
}
var EZ = { kernelName: ep, backendName: "webgl", kernelFunc: AZ };
var RZ = class {
  constructor(e) {
    this.variableNames = ["dy", "maxPos"], this.outputShape = e.inShape;
    let t = e.strideHeight, n = e.strideWidth, s = e.dilationHeight, r = e.effectiveFilterHeight, a = e.effectiveFilterWidth, i = r - 1 - e.padInfo.top, o = a - 1 - e.padInfo.left, u = r * a - 1;
    this.userCode = `
      const ivec2 pads = ivec2(${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 DZ = 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 FZ(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 = N.computePool3DInfo(i.shape, o, u, p, l, c), h = new xv(d, "max", true), f = n.runWebGLProgram(h, [i], i.dtype), m = new DZ(d), g = n.runWebGLProgram(m, [r, f], i.dtype);
  return n.disposeIntermediateTensorInfo(f), g;
}
var OZ = { kernelName: wg, backendName: "webgl", kernelFunc: FZ };
function PZ(e) {
  let { inputs: t, backend: n, attrs: s } = e, { dy: r, input: a, output: i } = t, o = a;
  tu([a, i], "maxPoolGrad");
  let { filterSize: u, strides: l, pad: c, dimRoundingMode: p } = s, d = N.computePool2DInfo(o.shape, u, l, 1, c, p), h = true, f = new el(d, "max", h), m = n.runWebGLProgram(f, [o], o.dtype), g = new RZ(d), b = n.runWebGLProgram(g, [r, m], o.dtype);
  return n.disposeIntermediateTensorInfo(m), b;
}
var zZ = { kernelName: xg, backendName: "webgl", kernelFunc: PZ };
function MZ(e, t, n, s) {
  let r = new el(n, "max", false), a = s.runWebGLProgram(r, [e], "float32");
  r = new el(n, "max", true, true, t);
  let i = s.runWebGLProgram(r, [e], "float32");
  return [a, i];
}
var LZ = { kernelName: kg, 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(N.eitherStridesOrDilationsAreOne(a, l), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${l}'`);
  let c = N.computePool2DInfo(s.shape, r, a, l, i), [p, d] = MZ(s, o, c, u);
  return [p, d];
} };
function BZ(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 = yi(o, "float32", "mean", s), l = he({ inputs: { x: u }, attrs: { shape: n }, backend: s });
  return s.disposeIntermediateTensorInfo(o), s.disposeIntermediateTensorInfo(u), l;
}
var VZ = { kernelName: Ga, 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 = N.getAxesPermutation(l, o), p = c != null, d = i.shouldExecuteOnCPU([s]), h = [], f = s;
  if (p) {
    if (d) {
      let x = i.texData.get(f.dataId).values, k = new Array(o);
      for (let E = 0; E < k.length; E++)
        k[E] = s.shape[c[E]];
      let C = yv(x, s.shape, s.dtype, c, k);
      f = i.makeTensorInfo(k, s.dtype);
      let T = i.texData.get(f.dataId);
      T.values = C;
    } else
      f = Kp(s, c, i);
    h.push(f), l = N.getInnerMostAxes(l.length, o);
  }
  N.assertAxesAreInnerMostDims("sum", l, o);
  let [m, g] = N.computeOutAndReduceShapes(f.shape, l), b = m;
  r && (b = N.expandShapeToKeepDim(m, u));
  let y = BZ(f, g, b, i);
  for (let v of h)
    i.disposeIntermediateTensorInfo(v);
  return y;
} };
function WZ(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 = N.getAxesPermutation(l, o), p = r;
  c != null && (p = cn({ inputs: { x: r }, backend: n, attrs: { perm: c } }), l = N.getInnerMostAxes(l.length, r.shape.length)), N.assertAxesAreInnerMostDims("min", l, o);
  let [d, h] = N.computeOutAndReduceShapes(p.shape, l), f = w.sizeFromShape(h), m = he({ inputs: { x: p }, backend: n, attrs: { shape: [-1, f] } }), g = yi(m, m.dtype, "min", n), b;
  if (i) {
    let y = N.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 UZ = { kernelName: Ha, backendName: "webgl", kernelFunc: WZ };
var GZ = E1 + `
  return min(a, b);
`;
var HZ = `
  vec4 result = vec4(min(a, b));
  vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
  ` + qp + `
  return result;
`;
var qZ = qt({ opSnippet: GZ, packedOpSnippet: HZ, cpuKernelImpl: VK });
var jZ = { kernelName: qa, backendName: "webgl", kernelFunc: qZ };
var KZ = 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 XZ = 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 = un("rc", s), u = un("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 YZ = ({ inputs: e, backend: t, attrs: n }) => {
  let { x: s } = e, { paddings: r, mode: a } = n, i = X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new XZ(s.shape, r, a) : new KZ(s.shape, r, a);
  return t.runWebGLProgram(i, [s], s.dtype);
};
var QZ = { kernelName: ja, backendName: "webgl", kernelFunc: YZ };
var ZZ = `if (b == 0.0) return NAN;
  return mod(a, b);`;
var JZ = `
  vec4 result = mod(a, b);
  vec4 isNaN = vec4(equal(b, vec4(0.0)));
  ` + qp + `
  return result;
`;
var e7 = qt({ opSnippet: ZZ, packedOpSnippet: JZ });
var t7 = { kernelName: vl, backendName: "webgl", kernelFunc: e7 };
var n7 = 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 s7 = `
if (a == b) {
  return 1.0;
};
return a / b;`;
var r7 = `
  // 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 Z1 = qt({ opSnippet: s7, packedOpSnippet: r7, checkOutOfBounds: true });
var a7 = { kernelName: Ea, backendName: "webgl", kernelFunc: Z1 };
var xw = "return a - b;";
var J1 = qt({ opSnippet: xw, packedOpSnippet: xw, supportsComplex: true, cpuKernelImpl: sX });
var i7 = { kernelName: ui, backendName: "webgl", kernelFunc: J1 };
function e2(e) {
  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, i = w.parseAxisParam([a], r.shape), o = Q1({ inputs: { x: r }, backend: n, attrs: { reductionIndices: i, keepDims: false } }), u = N.expandShapeToKeepDim(o.shape, i), l = he({ inputs: { x: o }, backend: n, attrs: { shape: u } }), c = J1({ inputs: { a: r, b: l }, backend: n }), p = K1({ inputs: { x: c }, backend: n }), d = Xp({ inputs: { x: p }, backend: n, attrs: { axis: i, keepDims: false } }), h = he({ inputs: { x: d }, backend: n, attrs: { shape: u } }), f = Z1({ 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 o7 = { kernelName: ii, backendName: "webgl", kernelFunc: e2 };
function u7(e) {
  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { numSamples: a, seed: i, normalized: o } = s, u = o ? r : e2({ inputs: { logits: r }, backend: n, attrs: { dim: r.shape.length - 1 } }), l = u.shape[0], c = u.shape[1], p = new n7(l, c, a), d = [[i]], h = n.runWebGLProgram(p, [u], "int32", d);
  return o || n.disposeIntermediateTensorInfo(u), h;
}
var l7 = { kernelName: Ig, backendName: "webgl", kernelFunc: u7 };
var c7 = ts + `
  return -x;
`;
var d7 = `
  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 p7(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  if (n.shouldExecuteOnCPU([s])) {
    let a = n.texData.get(s.dataId), [i, o] = UK(a.values, s.shape, s.dtype);
    return n.makeTensorInfo(o, s.dtype, i);
  }
  let r;
  return X().getBool("WEBGL_PACK_UNARY_OPERATIONS") ? r = new Yr(s.shape, d7) : r = new Hs(s.shape, c7), n.runWebGLProgram(r, [s], s.dtype);
}
var h7 = { kernelName: ko, backendName: "webgl", kernelFunc: p7 };
var f7 = xs.nonMaxSuppressionV3Impl;
function m7(e) {
  N.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 } = f7(l, c, i, o, u);
  return n.makeTensorInfo([p.length], "int32", new Int32Array(p));
}
var g7 = { kernelName: So, backendName: "webgl", kernelFunc: m7 };
var b7 = xs.nonMaxSuppressionV4Impl;
function y7(e) {
  N.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 } = b7(c, p, i, o, u, l);
  return [n.makeTensorInfo([d.length], "int32", new Int32Array(d)), n.makeTensorInfo([], "int32", new Int32Array([h]))];
}
var v7 = { kernelName: xl, backendName: "webgl", kernelFunc: y7 };
var x7 = xs.nonMaxSuppressionV5Impl;
function w7(e) {
  N.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 } = x7(c, p, d, h, f, m);
  return [n.makeTensorInfo([g.length], "int32", new Int32Array(g)), n.makeTensorInfo([b.length], "float32", new Float32Array(b))];
}
var k7 = { kernelName: Co, backendName: "webgl", kernelFunc: w7 };
var I7 = 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 S7 = (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 I7(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 C7 = { kernelName: To, backendName: "webgl", kernelFunc: S7 };
function zd(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  if (s.dtype === "complex64") {
    let r = Zl({ inputs: { input: s }, backend: n }), a = zd({ inputs: { x: r }, backend: n }), i = Yp({ inputs: { input: s }, backend: n }), o = zd({ inputs: { x: i }, backend: n }), u = Er({ inputs: { real: a, imag: o }, backend: n });
    return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
  } else
    return Jl({ attrs: { shape: s.shape, dtype: s.dtype, value: s.dtype === "string" ? "" : 0 }, backend: n });
}
var N7 = { kernelName: Go, backendName: "webgl", kernelFunc: zd };
function t2(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 = Zl({ inputs: { input: s }, backend: n }), a = t2({ inputs: { x: r }, backend: n }), i = Yp({ inputs: { input: s }, backend: n }), o = zd({ inputs: { x: i }, backend: n }), u = Er({ inputs: { real: a, imag: o }, backend: n });
    return n.disposeIntermediateTensorInfo(r), n.disposeIntermediateTensorInfo(a), n.disposeIntermediateTensorInfo(i), n.disposeIntermediateTensorInfo(o), u;
  } else
    return Jl({ attrs: { shape: s.shape, dtype: s.dtype, value: 1 }, backend: n });
}
var T7 = { kernelName: No, backendName: "webgl", kernelFunc: t2 };
function $7(e) {
  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
  if (t.length === 1)
    return Gm({ 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 = Gm({ inputs: { input: c }, backend: n, attrs: { dim: r } });
    return o.push(p), p;
  }), l = W1({ inputs: u, backend: n, attrs: { axis: r } });
  return o.forEach((c) => n.disposeIntermediateTensorInfo(c)), l;
}
var _7 = { kernelName: $o, backendName: "webgl", kernelFunc: $7 };
var A7 = 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 E7 = 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 = un("rc", s), u = un("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 n2 = (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 Jl({ backend: n, attrs: { shape: l, value: i, dtype: r.dtype } });
  }
  let o = X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new E7(r.shape, a, i) : new A7(r.shape, a, i), u = [[i]];
  return n.runWebGLProgram(o, [r], r.dtype, u);
};
var R7 = { kernelName: Xa, backendName: "webgl", kernelFunc: n2 };
var D7 = `
  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 F7 = `
  // 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));
  ` + qp + `
  return result;
`;
var O7 = qt({ opSnippet: D7, packedOpSnippet: F7 });
var P7 = { kernelName: Ya, backendName: "webgl", kernelFunc: O7 };
function z7(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 = N.getAxesPermutation(c, o), d = r;
  p != null && (d = cn({ inputs: { x: r }, backend: n, attrs: { perm: p } }), c = N.getInnerMostAxes(c.length, o), u.push(d)), N.assertAxesAreInnerMostDims("prod", c, o);
  let h;
  if (n.shouldExecuteOnCPU([d])) {
    let f = n.texData.get(d.dataId).values, { outVals: m, outShape: g, outDtype: b } = HK(d.shape, d.dtype, f, c);
    h = n.makeTensorInfo(g, b, m);
  } else {
    let [f, m] = N.computeOutAndReduceShapes(d.shape, c), g = w.sizeFromShape(m), b = he({ inputs: { x: d }, backend: n, attrs: { shape: [-1, g] } }), y = lp(r.dtype), v = yi(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 = N.expandShapeToKeepDim(h.shape, l);
    h = he({ inputs: { x: h }, backend: n, attrs: { shape: f } });
  }
  return u.forEach((f) => n.disposeIntermediateTensorInfo(f)), h;
}
var M7 = { kernelName: _o, backendName: "webgl", kernelFunc: z7 };
var s2 = (e) => {
  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = qK(s, r, a, i);
  return t.makeTensorInfo([o.length], i, o);
};
var L7 = { kernelName: wl, backendName: "webgl", kernelFunc: s2 };
var B7 = "return 1.0 / x;";
var V7 = Ke({ opSnippet: B7 });
var W7 = { kernelName: kl, backendName: "webgl", kernelFunc: V7 };
var U7 = ts + `
  return (x < 0.0) ? 0.0 : x;
`;
var G7 = `
  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 H7 = Ke({ opSnippet: U7, packedOpSnippet: G7 });
var q7 = { kernelName: Za, backendName: "webgl", kernelFunc: H7 };
var j7 = ts + `
  return (x < 0.0) ? 0.0 : min(6.0, x);
`;
var K7 = `
  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 X7 = Ke({ opSnippet: j7, packedOpSnippet: K7 });
var Y7 = { kernelName: ei, backendName: "webgl", kernelFunc: X7 };
var Q7 = 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 Z7 = 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 J7(e) {
  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, c = X().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new Z7(r.shape, u, l, a, i) : new Q7(r.shape, u, l, a, i);
  return n.runWebGLProgram(c, [r], "float32");
}
var eJ = { kernelName: Ja, backendName: "webgl", kernelFunc: J7 };
var tJ = 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 nJ(e) {
  let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s, o = new tJ(a.shape, r.shape, i);
  return n.runWebGLProgram(o, [a], a.dtype);
}
var sJ = { kernelName: Cg, backendName: "webgl", kernelFunc: nJ };
var rJ = 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 aJ = 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 iJ(e) {
  let { inputs: t, backend: n, attrs: s } = e, { images: r } = t, { alignCorners: a, halfPixelCenters: i, size: o } = s, [u, l] = o, c = X().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new aJ(r.shape, u, l, a, i) : new rJ(r.shape, u, l, a, i);
  return n.runWebGLProgram(c, [r], r.dtype);
}
var oJ = { kernelName: Il, backendName: "webgl", kernelFunc: iJ };
var uJ = 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 lJ(e) {
  let { inputs: t, backend: n, attrs: s } = e, { images: r, dy: a } = t, { alignCorners: i } = s, o = new uJ(a.shape, r.shape, i);
  return n.runWebGLProgram(o, [a], a.dtype);
}
var cJ = { kernelName: Sg, backendName: "webgl", kernelFunc: lJ };
var dJ = 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 pJ = 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-${n} tensor is not yet supported`);
    this.outputShape = e;
    let s = un("rc", n), r = `${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 hJ(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 En({ inputs: { x: r }, backend: n });
  let u = X().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new pJ(r.shape, o) : new dJ(r.shape, o);
  return n.runWebGLProgram(u, [r], r.dtype);
}
var fJ = { kernelName: Eo, backendName: "webgl", kernelFunc: hJ };
var mJ = 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 < ${n}) {
            outputValue = getImage(coords[0], coordY, coordX, coords[3]);
          }
          setOutput(outputValue);
        }
    `;
  }
};
var gJ = { kernelName: Ho, backendName: "webgl", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
  let { image: s } = e, { radians: r, fillValue: a, center: i } = t, o = n, u = new mJ(s.shape, a), [l, c] = N.getImageCenter(i, s.shape[1], s.shape[2]), p = [[l, c, Math.sin(r), Math.cos(r)]];
  return o.runWebGLProgram(u, [s], s.dtype, p);
} };
var bJ = `
  // OpenGL ES does not support round function.
  // The algorithm is based on banker's rounding.
  float base = floor(x);
  if ((x - base) < 0.5) {
    return floor(x);
  } else if ((x - base) > 0.5) {
    return ceil(x);
  } else {
    if (mod(base, 2.0) == 0.0) {
      return base;
    } else {
      return base + 1.0;
    }
  }
`;
var yJ = Ke({ opSnippet: bJ });
var vJ = { kernelName: Ro, backendName: "webgl", kernelFunc: yJ };
var xJ = "return inversesqrt(x);";
var wJ = Ke({ opSnippet: xJ, cpuKernelImpl: jK });
var kJ = { kernelName: ti, backendName: "webgl", kernelFunc: wJ };
var r2 = class {
  constructor(e, t, n, s, r, a, i = true) {
    this.variableNames = ["updates", "indices", "defaultValue"], this.outputShape = a;
    let o = ot(r.length), u = ot(a.length), l = "";
    n === 1 ? l = "i" : n === 2 && (l = "i, j");
    let c = `getIndices(${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 IJ(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 } = N.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 r2(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 SJ = { kernelName: Do, backendName: "webgl", kernelFunc: IJ };
var CJ = 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 NJ(e) {
  let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, i = new CJ(s.shape.length, r.shape, r.shape.length);
  return n.runWebGLProgram(i, [s, r, a], yn(r.dtype, a.dtype));
}
var TJ = { kernelName: Fo, backendName: "webgl", kernelFunc: NJ };
var $J = `
  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
  // see: https://arxiv.org/abs/1706.02515
  float scaleAlpha = ${N.SELU_SCALEALPHA};
  float scale = ${N.SELU_SCALE};
  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`;
var _J = Ke({ opSnippet: $J });
var AJ = { kernelName: Sl, backendName: "webgl", kernelFunc: _J };
var EJ = iu + `
  return 1.0 / (1.0 + exp(-1.0 * x));
`;
var RJ = `
  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 DJ = Ke({ opSnippet: EJ, packedOpSnippet: RJ, cpuKernelImpl: KK });
var FJ = { kernelName: si, backendName: "webgl", kernelFunc: DJ };
var OJ = `
  if (isnan(x)) { return 0.0; }
  return sign(x);
`;
var PJ = Ke({ opSnippet: OJ });
var zJ = { kernelName: Cl, backendName: "webgl", kernelFunc: PJ };
var MJ = iu + `
  return sin(x);
`;
var LJ = Ke({ opSnippet: MJ });
var BJ = { kernelName: ni, backendName: "webgl", kernelFunc: LJ };
var VJ = `
  float e2x = exp(x);
  return (e2x - 1.0 / e2x) / 2.0;
`;
var WJ = Ke({ opSnippet: VJ });
var UJ = { kernelName: Po, backendName: "webgl", kernelFunc: WJ };
var GJ = `
  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 HJ = Ke({ opSnippet: GJ });
var qJ = { kernelName: Nl, backendName: "webgl", kernelFunc: HJ };
var jJ = (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 = n2({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), p = N.getReshaped(c.shape, a, o, false), d = N.getPermuted(p.length, a.length, false), h = N.getReshapedPermuted(c.shape, a, o, false), f = he({ inputs: { x: c }, backend: n, attrs: { shape: p } }), m = cn({ 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 KJ = { kernelName: zo, backendName: "webgl", kernelFunc: jJ };
function XJ(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] = YK(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 YJ = { kernelName: np, backendName: "webgl", kernelFunc: XJ };
function QJ(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] = QK(o, s.shape, s.dtype, i, u);
  return [n.makeTensorInfo(c, s.dtype, l), n.makeTensorInfo([p.length], a.dtype, new Int32Array(p))];
}
var ZJ = { kernelName: Tl, backendName: "webgl", kernelFunc: QJ };
function JJ(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] = N1(i, s.shape, s.dtype, o, u, true);
  return n.makeTensorInfo(c, s.dtype, l);
}
var eee = { kernelName: sp, backendName: "webgl", kernelFunc: JJ };
function tee(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] = N1(i, s.shape, s.dtype, o, u);
  return n.makeTensorInfo(c, s.dtype, l);
}
var nee = { kernelName: rp, backendName: "webgl", kernelFunc: tee };
function see(e) {
  let { inputs: t, backend: n, attrs: s } = e, { sparseIndices: r, sparseValues: a, defaultValue: i } = t, { outputShape: o } = s, { sliceRank: u, numUpdates: l, strides: c, outputSize: p } = N.calculateShapes(a, r, o), d = false, h = new r2(l, u, r.shape.length, a.shape.length, c, [p, 1], d), f = n.runWebGLProgram(h, [a, r, i], a.dtype), m = he({ inputs: { x: f }, backend: n, attrs: { shape: o } });
  return n.disposeIntermediateTensorInfo(f), m;
}
var ree = { kernelName: ap, backendName: "webgl", kernelFunc: see };
function aee(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 = N.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 = ou({ inputs: { x: r }, backend: n, attrs: { begin: c, size: h } });
    return c[o] += d, f;
  });
}
var iee = { kernelName: Mo, backendName: "webgl", kernelFunc: aee };
var ww = "return sqrt(x);";
var oee = Ke({ opSnippet: ww, packedOpSnippet: ww, cpuKernelImpl: ZK });
var uee = { kernelName: ri, backendName: "webgl", kernelFunc: oee };
var lee = "return x * x;";
var cee = Ke({ opSnippet: lee });
var dee = { kernelName: $l, backendName: "webgl", kernelFunc: cee };
var kw = "return (a - b) * (a - b);";
var pee = qt({ opSnippet: kw, packedOpSnippet: kw });
var hee = { kernelName: oi, backendName: "webgl", kernelFunc: pee };
function fee({ inputs: e, attrs: t, backend: n }) {
  let { x: s } = e, r = ts + `
    return x > 0.0 ? 1.0 : float(${t.alpha});
  `, a = new Hs(s.shape, r);
  return n.runWebGLProgram(a, [s], s.dtype);
}
var mee = { kernelName: di, backendName: "webgl", kernelFunc: fee };
var gee = 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 bee(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, end: i, strides: o, beginMask: u, endMask: l, ellipsisMask: c, newAxisMask: p, shrinkAxisMask: d } = s, { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = wt.sliceInfo(r.shape, a, i, o, u, l, c, p, d), k;
  if (m)
    k = he({ inputs: { x: r }, backend: n, attrs: { shape: f } });
  else if (g || b) {
    w.assert(r.shape.length >= 1, () => `Input must have rank at least 1, got: ${r.shape.length}`);
    let T = wt.computeOutShape(y, v, x), E = ou({ inputs: { x: r }, backend: n, attrs: { begin: y, size: T } });
    k = he({ inputs: { x: E }, backend: n, attrs: { shape: f } }), n.disposeIntermediateTensorInfo(E);
  } else if (n.shouldExecuteOnCPU([r])) {
    let E = n.readSync(r.dataId), A = De(r.shape, r.dtype, E), P = JK(h, A, x, y);
    k = n.makeTensorInfo(f, r.dtype, P.values);
  } else {
    let E = new gee(y, x, h);
    k = n.runWebGLProgram(E, [r], r.dtype);
  }
  let C = he({ inputs: { x: k }, backend: n, attrs: { shape: f } });
  return n.disposeIntermediateTensorInfo(k), C;
}
var yee = { kernelName: Lo, backendName: "webgl", kernelFunc: bee };
function vee(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] = eX(d, h, r, a, i, o, u, l);
  return [n.makeTensorInfo([f.length], "string", f), n.makeTensorInfo(p.shape, "int32", m)];
}
var xee = { kernelName: ip, backendName: "webgl", kernelFunc: vee };
function wee(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] = tX(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 kee = { kernelName: Ng, backendName: "webgl", kernelFunc: wee };
function Iee(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 = nX(i, r);
  return n.makeTensorInfo(a.shape, "int32", o);
}
var See = { kernelName: Tg, backendName: "webgl", kernelFunc: Iee };
var Cee = "return tan(x);";
var Nee = Ke({ opSnippet: Cee });
var Tee = { kernelName: Bo, backendName: "webgl", kernelFunc: Nee };
var $ee = `
  float e2x = exp(-2.0 * abs(x));
  return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`;
var _ee = Ke({ opSnippet: $ee });
var Aee = { kernelName: li, backendName: "webgl", kernelFunc: _ee };
var Eee = 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 = Ree(e);
    this.userCode = `
      void main() {
        ${s} resRC = getOutputCoords();
        setOutput(getA(${r}));
      }
    `;
  }
};
function Ree(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 a2(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reps: a } = s;
  if (r.dtype === "string" || r.shape.length > 5) {
    let u = n.readSync(r.dataId), l = r.dtype === "string" ? u.map((d) => w.decodeString(d)) : u, c = De(r.shape, r.dtype, l), p = rX(c, a);
    return n.makeTensorInfo(p.shape, p.dtype, p.values);
  }
  let i = new Eee(r.shape, a);
  return n.runWebGLProgram(i, [r], r.dtype);
}
var Dee = { kernelName: Sr, backendName: "webgl", kernelFunc: a2 };
var Fee = 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 Oee = 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 Wr(e, t) {
  t !== null && e.disposeIntermediateTensorInfo(t);
}
function Iw(e) {
  let t = 1;
  for (; t < e; )
    t *= 2;
  return t;
}
function Pee(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { k: a, sorted: i } = s, o = X().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"), u = X().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"), l = r.shape, c = l[l.length - 1];
  if (n.shouldExecuteOnCPU([r]) || c < o || a > u) {
    let P = n.readSync(r.dataId), [R, F] = aX(P, l, r.dtype, a, i);
    return [n.makeTensorInfo(R.shape, R.dtype, R.values), n.makeTensorInfo(F.shape, F.dtype, F.values)];
  }
  if (a === 0)
    return l[l.length - 1] = 0, [n.makeTensorInfo(l, r.dtype, []), n.makeTensorInfo(l, "int32", [])];
  if (c === 1)
    return [r, Jl({ 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 && Wr(n, h);
  let b = Iw(a), y = Iw(c), v = null, x = () => v === null ? [g, g] : [g, v], k = (P, R, F) => {
    let $ = x(), z = new Fee(F), q = [[c], [v === null ? 1 : 0], [Number.NEGATIVE_INFINITY], [P], [R]], K = v;
    v = n.runWebGLProgram(z, $, "int32", q), Wr(n, K);
  };
  for (let P = 1; P < b; P *= 2) {
    let R = P * 2;
    for (let F = P; F >= 1; F /= 2)
      k(R, F, [m, y]);
  }
  for (let P = y; P > b; P /= 2) {
    let R = x(), F = new Oee([m, P / 2]), z = [[c], [v === null ? 1 : 0], [b]], W = v;
    v = n.runWebGLProgram(F, R, "int32", z), Wr(n, W);
    let q = b / 2, K = q * 2;
    for (let Y = q; Y >= 1; Y /= 2)
      k(K, Y, v.shape);
  }
  let C = v;
  v = ou({ inputs: { x: v }, backend: n, attrs: { begin: 0, size: [m, a] } }), Wr(n, C);
  let T = Y1({ inputs: { x: g, indices: v }, backend: n, attrs: { axis: 1, batchDims: 1 } });
  Wr(n, g);
  let E = l.slice(0, -1);
  E.push(a), C = v, v = he({ inputs: { x: v }, attrs: { shape: E }, backend: n }), Wr(n, C);
  let A = T;
  return T = he({ inputs: { x: T }, attrs: { shape: E }, backend: n }), Wr(n, A), [T, v];
}
var zee = { kernelName: Vo, backendName: "webgl", kernelFunc: Pee };
var Mee = class {
  constructor(e, t, n, s, r, a) {
    this.variableNames = ["Image", "Transforms"], this.outputShape = a;
    let i = n === "nearest" ? 1 : 2, o;
    switch (s) {
      case "constant":
        o = 1;
        break;
      case "reflect":
        o = 2;
        break;
      case "wrap":
        o = 3;
        break;
      case "nearest":
        o = 4;
        break;
      default:
        o = 1;
        break;
    }
    this.userCode = `
            float mapCoord(float outCoord, float len) {
              float inCoord = outCoord;
              if(${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 Lee(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 Mee(p, d, i, o, u, g);
  return n.runWebGLProgram(b, [r, a], "float32");
}
var Bee = { kernelName: Wo, backendName: "webgl", kernelFunc: Lee };
function Vee(e) {
  let { inputs: t, attrs: n, backend: s } = e, { axis: r } = n, { x: a } = t;
  tu(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 } = iX(i, r, a.shape, a.dtype);
  return [s.makeTensorInfo(u, a.dtype, o), s.makeTensorInfo([l.length], "int32", l)];
}
var Wee = { kernelName: $g, backendName: "webgl", kernelFunc: Vee };
function Uee(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 = ou({ 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 Gee = { kernelName: Uo, backendName: "webgl", kernelFunc: Uee };
var Hee = 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 qee(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 = N.getAxesPermutation([l], o), p = r;
  c != null && (p = cn({ inputs: { x: r }, backend: n, attrs: { perm: c } }), u.push(p), l = N.getInnerMostAxes(1, o)[0]);
  let d = N.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 = lp(r.dtype), g = (x, k, C, T, E) => {
    let A = x.shape[0], P = x.shape[1], R = N.segment_util.segOpComputeOptimalWindowSize(P, E), F = { windowSize: R, inSize: P, batchSize: A, numSegments: E }, $ = new Hee(F, k), z = n.compileAndRun($, [x, C], T);
    if (u.push(z), z.shape[1] === E)
      return z;
    let W = s2({ backend: n, attrs: { start: 0, stop: E, step: 1, dtype: "float32" } }), q = a2({ inputs: { x: W }, backend: n, attrs: { reps: [P / R] } });
    return u.push(W), u.push(q), g(z, k, q, T, E);
  }, b = g(f, "unsortedSegmentSum", a, m, i), y = he({ inputs: { x: b }, backend: n, attrs: { shape: d } }), v = y;
  if (c != null) {
    u.push(y);
    let x = N.getUndoAxesPermutation(c);
    v = cn({ inputs: { x: v }, backend: n, attrs: { perm: x } });
  }
  return u.forEach((x) => n.disposeIntermediateTensorInfo(x)), v;
}
var jee = { kernelName: op, backendName: "webgl", kernelFunc: qee };
var Kee = [e8, n8, a8, u8, c8, h8, m8, b8, w8, I8, N8, _8, R8, P8, L8, V8, U8, j8, X8, Q8, tY, uY, cY, pY, yY, xY, SY, OX, TY, RY, PY, WY, GY, qY, KY, YY, JY, n9, a9, o9, l9, p9, f9, y9, x9, I9, N9, $9, R9, P9, B9, U9, q9, j9, X9, Q9, J9, tQ, sQ, oQ, cQ, hQ, mQ, yQ, wQ, CQ, _Q, FX, EQ, AY, FQ, zQ, BQ, zX, GQ, KQ, YQ, eZ, sZ, oZ, cZ, fZ, yZ, wZ, IZ, TZ, _Z, EZ, OZ, zZ, LZ, VZ, UZ, jZ, QZ, t7, l7, WX, h7, g7, v7, k7, fY, C7, T7, _7, R7, P7, LX, M7, L7, mY, a7, W7, q7, Y7, GX, eJ, sJ, oJ, cJ, fJ, gJ, vJ, kJ, SJ, TJ, AJ, FJ, zJ, BJ, UJ, iY, o7, qJ, KJ, YJ, ZJ, eee, nee, ree, iee, uee, dee, hee, mee, yee, xee, kee, See, i7, QX, Tee, Aee, Dee, zee, Bee, ZX, Wee, Gee, jee, N7];
for (let e of Kee)
  _l(e);
var zs = X();
zs.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE", () => 15);
zs.registerFlag("WEBGPU_CPU_FORWARD", () => true);
zs.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD", () => 4);
zs.registerFlag("WEBGPU_USE_NAIVE_CONV2D", () => false);
zs.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE", () => false);
zs.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER", () => false);
zs.registerFlag("WEBGPU_USE_LOW_POWER_GPU", () => false);
zs.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e3);
zs.registerFlag("WEBGPU_USE_PROFILE_TOOL", () => false);
zs.registerFlag("WEBGPU_USE_IMPORT", () => false);
function Xee(e, t) {
  if (Math.max(...e) > 3)
    throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");
  let n = e.length, s = e.map((a) => `${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 Wt(e) {
  if (e <= 1)
    return "i32";
  if (e === 2)
    return "vec2<i32>";
  if (e === 3)
    return "vec3<i32>";
  if (e === 4)
    return "vec4<i32>";
  throw Error(`GPU for rank ${e} is not yet supported`);
}
function ld(e, t) {
  return e === "float32" ? t ? "vec4<f32>" : "f32" : e === "int32" || e === "bool" ? t ? "vec4<i32>" : "i32" : e;
}
function wv() {
  return `
  @stage(compute) @workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)
`;
}
function Rr() {
  return `
  ${wv()}
  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 `
    ${Rr()}
      let index = getGlobalIndex();
`;
}
function Yee(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 Matrix0 {
        numbers: array<${ld(t.dtype, n.isVec4)}>;
      };
      struct Uniform {
        size            : i32;
        numChannels     : i32;
        outShapeStrides : vec2<i32>;
        dispatchSize    : vec3<u32>;
      };

      @group(0) @binding(0) var<storage, write> result : Matrix0;
      @group(0) @binding(2) var<uniform> uniforms: Uniform;
    `), [Sw, r.join(`
`), Cw(t.shape), n.getUserCode()].join(`
`);
  let a = "struct Uniforms { NAN : f32; ";
  n.variableNames.forEach((p, d) => {
    a += `${p.charAt(0).toLowerCase() + p.slice(1)}Shape : ${Wt(e[d].shape.length)}; `;
  }), a += `outShape : ${Wt(t.shape.length)} ; `;
  let i = t.shape.length - 1;
  a += `
       outShapeStrides: ${Wt(i)}; `, n.size && (a += "size : i32; "), n.uniforms && (a += n.uniforms), a += "};", r.push(a), n.atomic ? r.push(`
    struct Matrix0 {
        numbers: array<atomic<i32>>;
    };

    @group(0) @binding(0) var<storage, read_write> result : Matrix0;
  `) : r.push(`
    struct Matrix0 {
        numbers: array<${ld(t.dtype, n.isVec4)}>;
    };

    @group(0) @binding(0) var<storage, write> result : Matrix0;
  `), n.variableNames.forEach((p, d) => {
    r.push(`
    struct Matrix${1 + d} {
      numbers: array<${ld(e[d].dtype, n.isVec4)}>;
    };
    @group(0) @binding(${1 + d}) var<storage, read> ${p} : Matrix${1 + d};
    `);
  }), a !== "" && r.push(`
    @group(0) @binding(${1 + n.variableNames.length}) var<uniform> uniforms : Uniforms;
    `);
  let [o, u] = nte(t.shape, n.dispatchLayout), l = [Sw, r.join(`
`), Cw(t.shape), o, Qee(t.shape.length)];
  if (n.atomic || l.push(Zee(t.shape, t.dtype, n.isVec4)), u === t.shape.length) {
    let p = e.map((d) => Jee(d, t.shape, n.isVec4, n.dispatchLayout.x.length === t.shape.length)).join(`
`);
    l.push(p);
  }
  return l.push(n.getUserCode()), l.join(`
`);
}
var Sw = `
  // 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 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 Qee(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;
    default:
      w.assert(false, () => `Unsupported ${e}D shape`);
      break;
  }
  return t;
}
function Zee(e, t, n) {
  let s = e.length, r = ld(t, n), a;
  if (n ? a = `fn setOutputAtIndex(flatIndex : i32, value : vec4<f32>) {
      result.numbers[flatIndex] = ${r}(value);
    }
    fn setOutputAtIndexI32(flatIndex : i32, value : vec4<i32>) {
      result.numbers[flatIndex] = ${r}(value);
    }` : a = `fn setOutputAtIndex(flatIndex : i32, value : f32) {
      result.numbers[flatIndex] = ${r}(value);
    }
    fn setOutputAtIndexI32(flatIndex : i32, value : i32) {
      result.numbers[flatIndex] = ${r}(value);
    }`, s >= 2) {
    let i = ["d0", "d1", "d2", "d3"].slice(0, s), o = Wt(s);
    n ? a += `
      fn setOutputAtCoords(${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}(${i.join(", ")}));
        setOutputAtIndexI32(flatIndex, value);
      }
    `;
  }
  return a;
}
function Jee(e, t, n, s) {
  let r = ete(e, n);
  return e.shape.length <= t.length && (r += tte(e, t, n, s)), r;
}
function ete(e, t) {
  let n = e.name, s = e.shape.length, r = Wt(s), a = "get" + n.charAt(0).toUpperCase() + n.slice(1), i = ["d0", "d1", "d2", "d3"].slice(0, s), o = i.map((c) => `${c} : i32`).join(", ");
  if (s < 1)
    return t ? `
        fn ${a}() -> vec4<f32> {
          return vec4<f32>(${n}.numbers[0]);
        }
      ` : `
      fn ${a}() ->f32 {
        return f32(${n}.numbers[0]);
      }
    `;
  let 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>(${n}.numbers[getIndexFromCoords${l}(${r}(${i.join(",")}),
          ${u}) / 4]);
      }
      ` : `
    fn ${a}(${o}) -> f32 {
      return f32(${n}.numbers[getIndexFromCoords${l}(${r}(${i.join(",")}),
        ${u})]);
    }
   `;
}
function tte(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 = Wt(u);
  if (w.arraysEqual(e.shape, t) && s)
    return n ? `
        fn ${i}Index(globalIndex : i32) -> vec4<f32> {
          return vec4<f32>(${r}.numbers[globalIndex]);
        }

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

      fn ${i}Coords(coords : ${l}) -> f32 {
        return f32(${r}.numbers[${u > 1 ? "getOutputIndexFromCoords(coords)" : "coords"}]);
      }
      `;
  let c = N.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[${g + p}] = 0;`).join(`
`);
  let h = "";
  if (u < 2 && o > 0)
    h = "coords";
  else if (u > 1) {
    let g = Wt(o), b = e.shape.map((y, v) => `coords[${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 ${r}.numbers[getIndexFromCoords${m}(${h}, ${f}) / 4];
      }

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

    fn ${i}Coords(coordsIn : ${l}) -> f32 {
      var coords = coordsIn;
      ${d}
      return f32(${r}.numbers[getIndexFromCoords${m}(${h}, ${f})]);
    }
  `;
}
function nte(e, t) {
  let { x: n, y: s = [], z: r = [] } = t, a = e.length;
  if (n.length === a)
    return [`fn getOutputCoords() -> ${Wt(a)}{
      let globalIndex = getGlobalIndex();
      return getCoordsFromIndex(globalIndex);
    }
    `, a];
  let i = "", o = [n, s, r], u = 0;
  for (let d = 0; d < o.length; d++) {
    let h = o[d];
    if (h.length !== 0)
      if (u += h.length, h.length === 1)
        i += `let d${h[0]} = i32(globalId[${d}]);`;
      else {
        let f = Xee(h, "uniforms.outShape");
        i += `var index${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 = Wt(u), p = `fn getOutputCoords() -> ${c} {
    ${i}
  `;
  return l.length === 0 ? p += `return ${c}(0); }` : p += `return ${c}(${l.join(",")}); }`, [p, u];
}
function Cw(e) {
  let t = e.length;
  if (t <= 1)
    return "fn getCoordsFromIndex(index : i32) -> i32 { return index; }";
  let n = w.computeStrides(e), s = Wt(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 = "var index2 = index;" + n.map((i, o) => {
    let u = `let ${r[o]} = index2 / uniforms.outShapeStrides[${o}]`, l = o === n.length - 1 ? `let ${r[o + 1]} = index2 - ${r[o]} * uniforms.outShapeStrides[${o}]` : `index2 = index2 - ${r[o]} * uniforms.outShapeStrides[${o}]`;
    return `${u}; ${l};`;
  }).join("");
  return `
    fn getCoordsFromIndex(index : i32) -> ${s} {
      ${a}
      return ${s}(${r.join(",")});
    }
  `;
}
var i2 = {};
Ae(i2, { ArrayBufferToTypedArray: () => u2, GPUBytesPerElement: () => Hm, computeDispatch: () => _e, computeWorkGroupSizeForConv2d: () => kv, computeWorkGroupSizeForMatMul: () => o2, computeWorkPerThreadForConv2d: () => Iv, flatDispatchLayout: () => Be, isWebGPUSupported: () => Sv, tilesFitEvenlyIntoShape: () => Ks });
var ea = (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(ea(e.x.map((o) => t[o])) / (n[0] * s[0])), e.y ? Math.ceil(ea(e.y.map((o) => t[o])) / (n[1] * s[1])) : 1, e.z ? Math.ceil(ea(e.z.map((o) => t[o])) / (n[2] * s[2])) : 1];
  return [r, a, i];
}
function kv(e, t) {
  let n = ea(e.x.map((r) => t[r])), s = ea(e.y.map((r) => t[r]));
  return n <= 4 ? [4, 16, 1] : s <= 4 ? [16, 4, 1] : [16, 16, 1];
}
function o2(e, t, n) {
  return e === 1 ? [32, 1, 1] : n === 1 ? [1, 32, 1] : [8, 8, 1];
}
function Iv(e, t) {
  let n = ea(e.x.map((r) => t[r])), s = ea(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 Hm(e) {
  if (e === "float32" || e === "int32" || e === "bool" || e === "string")
    return 4;
  if (e === "complex64")
    return 8;
  throw new Error(`Unknown dtype ${e}`);
}
function u2(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 Sv() {
  return (typeof window != "undefined" || typeof WorkerGlobalScope != "undefined") && !!navigator.gpu;
}
var ste = "return a + b;";
var rte = "return areal * breal - aimag * bimag;";
var ate = "return areal * bimag + aimag * breal;";
var ite = "return a / b;";
var ote = "return a * b;";
var ute = "return (a - b) * (a - b);";
var lte = "return a - b;";
var cte = "return f32(a == b);";
var dte = "return vec4<f32>(a == b);";
var pte = "return f32(a > b);";
var hte = "return vec4<f32>(a > b);";
var fte = "return f32(a >= b);";
var mte = "return vec4<f32>(a >= b);";
var gte = "return f32(a < b);";
var bte = "return vec4<f32>(a < b);";
var yte = "return f32(a <= b);";
var vte = "return vec4<f32>(a <= b);";
var xte = "return f32(f32(a) >= 1.0 && f32(b) >= 1.0);";
var wte = `return (vec4<f32>(a >= vec4<f32>(1.0)) *
  vec4<f32>(b >= vec4<f32>(1.0)));`;
var kte = `
  if (isnan(a)) { return a; }
  if (isnan(b)) { return b; }
  `;
var l2 = `
  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 Ite = `
  let s = sign(a) * sign(b);
  let ia = i32(round(a));
  let ib = i32(round(b));
  return f32(idiv(ia, ib, s));
  `;
var Ste = `
  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 Cte = "return f32(a != b);";
var Nte = "return vec4<f32>(a != b);";
var Tte = `
  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 $te = `
  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;
  ${l2}
  return resultTemp;
  `;
var _te = "if (a < 0.0) { return b * a; }  return a;";
var Ate = `
  let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
  return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
  `;
function Nw(e, t) {
  let n = t ? l2 : kte;
  return t ? `
    var resultTemp = vec4<f32>(${e}(a, b));
    let isNaN = isnanVec4(a) | isnanVec4(b);
    ` + n + `
    return resultTemp;
  ` : n + `
    return ${e}(a, b);
  `;
}
function ec(e, t) {
  switch (e) {
    case 0:
      return ote;
    case 1:
      return ste;
    case 2:
      return lte;
    case 3:
      return ite;
    case 4:
      return t ? dte : cte;
    case 5:
      return t ? hte : pte;
    case 6:
      return t ? mte : fte;
    case 7:
      return t ? bte : gte;
    case 8:
      return t ? vte : yte;
    case 9:
      return t ? wte : xte;
    case 10:
      return t ? Nte : Cte;
    case 11:
      return ute;
    case 12:
      return t ? Ste : Ite;
    case 14:
      return t ? Ate : _te;
    case 15:
      return Nw("max", t);
    case 16:
      return Nw("min", t);
    case 13:
      return t ? $te : Tte;
    case 17:
      return rte;
    case 18:
      return ate;
    default:
      throw new Error(`BinaryType ${e} is not implemented!`);
  }
}
var Ete = "return abs(a);";
var Rte = "return ceil(a);";
var Dte = "return cos(a);";
var Fte = `
  let e2x = exp(-a);
  return (e2x + 1.0 / e2x) / 2.0;
`;
var Ote = "return exp(a) - 1.0;";
var Pte = "if (a >= 0.0) { return a; }  return (exp(a) - 1.0);";
var zte = `
  var resFloat = exp(a) - vec4<f32>(1.0);
  if (a.r >= 0.0) {
    resFloat.r = a.r;
  }
  if (a.g >= 0.0) {
    resFloat.g = a.g;
  }
  if (a.b >= 0.0) {
    resFloat.b = a.b;
  }
  if (a.a >= 0.0) {
    resFloat.a = a.a;
  }
  return resFloat;
`;
var Mte = "return exp(a);";
var Lte = "return floor(a);";
var Bte = "return a;";
var Vte = `if (a < 0.0) { return 1.0/0.0; }
  return log(a);`;
var Wte = "return f32(!(a >= 1.0));";
var Ute = "return -a;";
var Gte = "return (a < 0.0) ? b * a : a;";
var Hte = "if (a < 0.0) { return uniforms.alpha * a; } return a;";
var qte = "if(a < 0.0) { return 0.0; } return a;";
var jte = "return clamp(a, 0.0, 6.0);";
var Kte = "return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));";
var Xte = `
  var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
  let isNaN = isnanVec4(a);

  if (isNaN.r) {
    resFloat.r = a.r;
  }
  if (isNaN.g) {
    resFloat.g = a.g;
  }
  if (isNaN.b) {
    resFloat.b = a.b;
  }
  if (isNaN.a) {
    resFloat.a = a.a;
  }
  return resFloat;
`;
var Yte = "return 1.0/sqrt(a);";
var Qte = "return 1.0 / (1.0 + exp(-1.0 * a));";
var Zte = "return sin(a);";
var Jte = `
  let e2x = exp(a);
  return (e2x - 1.0 / e2x) / 2.0;
`;
var ene = "return sqrt(a);";
var tne = "return a * a;";
var nne = `
  let e2x = exp(-2.0 * abs(a));
  return sign(a) * (1.0 - e2x) / (1.0 + e2x);
`;
var sne = "return f32(i32((a)));";
function Ui(e, t) {
  switch (e) {
    case 0:
      return Ete;
    case 2:
      return Dte;
    case 3:
      return Fte;
    case 1:
      return Rte;
    case 4:
      return t ? zte : Pte;
    case 5:
      return Mte;
    case 6:
      return Ote;
    case 7:
      return Lte;
    case 8:
      return Bte;
    case 9:
      return Vte;
    case 10:
      return Wte;
    case 11:
      return Ute;
    case 12:
      return Gte;
    case 15:
      return Hte;
    case 13:
      return t ? Xte : qte;
    case 14:
      return t ? Kte : jte;
    case 16:
      return Yte;
    case 19:
      return Qte;
    case 17:
      return Zte;
    case 18:
      return Jte;
    case 20:
      return ene;
    case 21:
      return tne;
    case 22:
      return nne;
    case 23:
      return sne;
    default:
      throw new Error(`BinaryType ${e} is not implemented!`);
  }
}
function Zs(e, t = false) {
  if (e === null)
    return null;
  if (e === "linear")
    return Ui(8);
  if (e === "relu")
    return Ui(13, t);
  if (e === "elu")
    return Ui(4, t);
  if (e === "relu6")
    return Ui(14, t);
  if (e === "prelu")
    return ec(14, t);
  if (e === "sigmoid")
    return Ui(19);
  throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`);
}
function c2(e, t, n, s) {
  return w.assert(s % 4 === 0 && e[0] === 4, () => "tileInner must be divisible by 4. And ColPerThread must be 4"), `
  var<workgroup> mm_Asub : array<array<vec4<f32>, ${s / e[0]}>, ${t}>;
  var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n / e[0]}>, ${s}>;

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

  ${Rr()}

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

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

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

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

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

        workgroupBarrier();

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

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

        workgroupBarrier();
    }

    for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
        mm_write(globalRow + innerRow,
                 globalCol,
                 acc[innerRow], globalId);
    }
  }`;
}
var rne = 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, 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 i = s != null, o = a != null;
    i && this.variableNames.push("bias"), o && 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 = i, this.activation = r, this.hasPreluActivationWeights = o, [this.fitA, this.fitB] = this.getShapeFit(), this.shaderKey = `matMulPackedVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`;
  }
  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.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col]" : `if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
            return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col];
        }
        return vec4<f32>(0.0)`, t = this.fitB ? "return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]" : `if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
             return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
        }
        return vec4<f32>(0.0)`, n = "", s = "";
    if (this.activation) {
      let i = Zs(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> {
        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> {
        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);
        }
      }
      ${c2(this.elementsPerThread, this.tileAOuter, this.tileBOuter, this.tileInner)}
    `;
  }
};
function Cv(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}>;
    ${Rr()}
      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 ane(e) {
  return `
    let TileSize = ${e[0] * 4};
    var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;

    ${Rr()}
      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 d2 = 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 = [16, 16, 1], this.outputShape = t, this.dispatchLayout = { x: [2], y: [1], z: [0] };
    let u = s ? e[1] : e[2];
    this.workGroupSize = o2(t[1], u, 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 l = a != null, c = o != null;
    l && this.variableNames.push("bias"), c && this.variableNames.push("preluActivationWeights"), this.workPerThread = n, this.aShape = e, this.transposeA = s, this.transposeB = r, this.addBias = l, this.activation = i, this.hasPreluActivationWeights = c;
    let p = this.outputShape[2], d = this.transposeB ? [this.outputShape[0], p, u] : [this.outputShape[0], u, p];
    [this.fitA, this.fitB] = this.getShapeFit(d), this.shaderKey = `matMulPacked_${this.workPerThread}_${s}_${r}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1] > 1}`;
  }
  getShapeFit(e) {
    let t = this.workGroupSize[1] * this.workPerThread, n = this.workGroupSize[0] * this.workPerThread, s = t > n ? t : n;
    this.outputShape[1] === 1 && (s *= 4), 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.numbers[batch * batchASize + row * uniforms.dimInner + col];" : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
             return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
           }
           return 0.0;` : e = this.fitA ? "return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];" : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
             return A.numbers[batch* batchASize + col * uniforms.dimAOuter + row];
           }
           return 0.0;`;
    let t;
    this.transposeB === false ? t = this.fitB ? "return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];" : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
             return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
           }
           return 0.0;` : t = this.fitB ? "return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];" : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
             return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];
           }
           return 0.0;`;
    let n = "", s = "";
    if (this.activation) {
      let i = Zs(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 {
        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
        let batch = i32(globalId.z);
        ${e}
      }

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

      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
        var value = valueIn;
        let batch = i32(globalId.z);
        let outCoord = vec3<i32>(batch, row, col);
        ${r}
        ${s}
        setOutputAtCoords(batch, row, col, value);
      }
      ${this.outputShape[1] > 1 ? Cv([this.workPerThread, this.workPerThread, 1], this.workGroupSize) : ane(this.workGroupSize)}
    `;
  }
};
function ine() {
  return `
    var<workgroup> sumValues : array<f32, workGroupSizeX>;
    ${Rr()}
      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 one = class {
  constructor(e, t = false, n = false, s = null, r = null, a = null) {
    this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32; dimBOuter : i32; dimInner : i32;", this.workGroupSize = [256, 1, 1], this.outputShape = e, this.dispatchLayout = { x: [], y: [1, 2], z: [0] }, this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize);
    let i = s != null, o = a != null;
    i && this.variableNames.push("bias"), o && this.variableNames.push("preluActivationWeights"), this.transposeA = t, this.transposeB = n, this.addBias = i, this.activation = r, this.hasPreluActivationWeights = o, this.shaderKey = `matMulReduce_${this.activation}_${t}_${n}`;
  }
  getUserCode() {
    let e;
    this.transposeA === false ? e = "return A.numbers[batch * batchASize + row * uniforms.dimInner + col];" : e = "return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];";
    let t;
    this.transposeB === false ? t = "return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];" : t = "return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];";
    let n = "", s = "";
    if (this.activation) {
      let i = Zs(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(batch: i32, row : i32, col : i32) -> f32 {
        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
        ${e}
      }

      fn mm_readB(batch: i32, row : i32, col : i32) -> f32 {
        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);
      }
      ${ine()}
    `;
  }
};
function une(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.
  ${Rr()}
    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 >= ${t} && writeCol >= 0) {
      mm_write(writeCol, globalCol, acc, globalId);
    }
  }
  `;
}
var lne = class {
  constructor(e, t, n, s = null, r = null, a = null) {
    this.variableNames = ["A", "B"], this.uniforms = "dimAOuter : i32; dimBOuter : i32; dimInner : i32;", this.workGroupSize = [8, 16, 1], w.assert(e[1] <= 16 || t[2] <= 16, () => "This program can be only used when A width or B Height are small"), this.outputShape = n, this.dispatchLayout = { x: [2], y: [1], z: [0] }, this.dispatch = [Math.ceil(n[2] / this.workGroupSize[0]), Math.ceil(n[1] * 2 / this.workGroupSize[1]), n[0]];
    let i = s != null;
    i && this.variableNames.push("bias");
    let o = a != null;
    o && this.variableNames.push("preluActivationWeights"), this.addBias = i, this.activation = r, this.hasPreluActivationWeights = o, this.shaderKey = `matMulSmallOutputSize_${this.activation}`;
  }
  getUserCode() {
    let e = `if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
          return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
        }
        return 0.0;`, t = `if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
           return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
         }
         return 0.0;`, n = "", s = "";
    if (this.activation) {
      let i = Zs(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 {
        let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
        let batch = i32(globalId.z);
        ${e}
      }
      fn mm_readB(row : i32, col : i32,  globalId : vec3<u32>) -> f32 {
        let batch = i32(globalId.z);
        let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
        ${t}
      }
      fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
        if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {
          let batch = i32(globalId.z);
          let outCoord = vec3<i32>(batch, row, col);
          var value = valueIn;
          ${r}
          ${s}
          setOutputAtCoords(batch, row, col, value);
        }
      }
      ${une(this.workGroupSize)}
    `;
  }
};
function Me(e) {
  let { inputs: t, attrs: n } = e, { x: s } = t, { shape: r } = n, a = w.sizeFromShape(s.shape), i = w.inferFromImplicitShape(r, a), o = w.sizeFromShape(i);
  return w.assert(a === o, () => `The new shape (${i}) has ${o} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`), e.backend.incRef(s.dataId), { dataId: s.dataId, shape: i, dtype: s.dtype };
}
var cne = { kernelName: Ao, backendName: "webgpu", kernelFunc: Me };
function Nv({ a: e, b: t, transposeA: n, transposeB: s, backend: r, bias: a = null, preluActivationWeights: i = null, leakyreluAlpha: o = 0, activation: u = null }) {
  let l = e.shape.length, c = t.shape.length, p = n ? e.shape[l - 2] : e.shape[l - 1], d = s ? t.shape[c - 1] : t.shape[c - 2], h = n ? e.shape[l - 1] : e.shape[l - 2], f = s ? t.shape[c - 2] : t.shape[c - 1], m = e.shape.slice(0, -2), g = t.shape.slice(0, -2), b = w.sizeFromShape(m), y = w.sizeFromShape(g), x = qo.assertAndGetBroadcastShape(e.shape.slice(0, -2), t.shape.slice(0, -2)).concat([h, f]);
  w.assert(p === d, () => `Error in matMul: inner shapes (${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], C = s ? [y, f, d] : [y, d, f], T = Me({ inputs: { x: e }, backend: r, attrs: { shape: k } }), E = Me({ inputs: { x: t }, backend: r, attrs: { shape: C } }), A = [T, E], P = Math.max(b, y), R = p % 4 === 0 && f % 4 === 0 && !n && !s && f >= 32, F;
  h * f <= 32 ? F = new one([P, h, f], n, s, a, u, i) : !n && !s && (h <= 16 && (f <= 512 || d >= 2 * f) || f <= 16 && (h <= 512 || p >= 2 * h)) ? F = new lne(k, C, [P, h, f], a, u, i) : R ? F = new rne(k, [P, h, f], X().get("WEBGPU_MATMUL_WORK_PER_THREAD"), a, u, i) : F = new d2(k, [P, h, f], X().get("WEBGPU_MATMUL_WORK_PER_THREAD"), n, s, a, u, i);
  let $ = [T, E];
  a && $.push(a), i && $.push(i);
  let z = [{ type: "int32", data: [h] }, { type: "int32", data: [f] }, { type: "int32", data: [p] }], W = r.runWebGPUProgram(F, $, e.dtype, z), q = Me({ inputs: { x: W }, backend: r, attrs: { shape: x } });
  A.push(W);
  for (let K of A)
    r.disposeData(K.dataId);
  return q;
}
function dne(e) {
  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: i, preluActivationWeights: o } = t, { transposeA: u, transposeB: l, activation: c, leakyreluAlpha: p } = s;
  return Nv({ a: r, b: a, transposeA: u, transposeB: l, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: p, activation: c });
}
var pne = { kernelName: na, backendName: "webgpu", kernelFunc: dne };
var Tw = class {
  constructor(e, t, n) {
    this.variableNames = ["AReal", "AImag", "BReal", "BImag"], this.workGroupSize = [128, 1, 1], this.size = true, this.outputShape = N.assertAndGetBroadcastShape(t, n), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `binaryOpComplex_${e}`, this.op = e;
  }
  getUserCode() {
    return `
      fn binaryOpComplex(
          areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
        ${ec(this.op, false)}
      }

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

              ${t}
              setOutputAtIndex(flatIndex, binaryOperation(a, b));
            }
          }
        }
        `;
  }
};
var fne = class {
  constructor(e, t, n) {
    this.variableNames = ["A", "B"], this.workPerThread = 4, this.isVec4 = true, this.size = true;
    let s = 128;
    this.workGroupSize = [s, 1, 1], this.outputShape = N.assertAndGetBroadcastShape(t, n), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.op = e, this.shaderKey = `binaryVec4_${e}`;
  }
  getUserCode() {
    return `
      fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
        ${ec(this.op, this.isVec4)}
      }
      ${Ue()}
        if (index < uniforms.size) {
          let a = getAByOutputIndex(index);
          let b = getBByOutputIndex(index);
          setOutputAtIndex(index, binaryOperation(a, b));
        }
      }
    `;
  }
};
var p2 = class {
  constructor(e, t, n) {
    this.variableNames = ["A", "B"], this.size = true;
    let s = 128;
    this.workGroupSize = [s, 1, 1], this.outputShape = N.assertAndGetBroadcastShape(t, n), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.shaderKey = `binary_${e}`, this.op = e;
  }
  getUserCode() {
    return `
      fn binaryOperation(a : f32, b : f32) -> f32 {
        ${ec(this.op, false)}
      }
      ${Ue()}
        if (index < uniforms.size) {
          let a = getAByOutputIndex(index);
          let b = getBByOutputIndex(index);
          setOutputAtIndex(index, binaryOperation(a, b));
        }
      }
      `;
  }
};
function $w(e, t, n) {
  if (w.arraysEqual(t, n) && w.sizeFromShape(t) % 4 === 0)
    return new fne(e, t, n);
  let r = t.length === 1 && n.length > 1 && t[0] < 1024, a = n.length === 1 && t.length > 1 && n[0] < 1024;
  return r || a ? new hne(e, t, n, a) : new p2(e, t, n);
}
function Zn(e) {
  let { inputs: t } = e, { x: n } = t;
  return e.backend.incRef(n.dataId), { dataId: n.dataId, shape: n.shape, dtype: n.dtype };
}
var mne = { kernelName: Ma, backendName: "webgpu", kernelFunc: Zn };
function uu(e) {
  let { inputs: t, backend: n } = e, { real: s, imag: r } = t, a = n.makeTensorInfo(s.shape, "complex64"), i = n.tensorMap.get(a.dataId), o = Zn({ inputs: { x: s }, backend: n }), u = Zn({ inputs: { x: r }, backend: n });
  return i.complexTensorInfos = { real: o, imag: u }, a;
}
var gne = { kernelName: qd, backendName: "webgpu", kernelFunc: uu };
var tc = class {
  constructor(e, t) {
    this.variableNames = ["A"], this.size = true;
    let n = 128;
    this.workGroupSize = [n, 1, 1], this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.op = t, this.shaderKey = `unary_${t}`;
  }
  getUserCode() {
    return `
      fn unaryOperation(a : f32) -> f32 {
        ${Ui(this.op, false)}
      }
      ${Ue()}
        if (index < uniforms.size) {
          let a = getAByOutputIndex(index);
          setOutputAtIndex(index, unaryOperation(a));
        }
      }
      `;
  }
};
function jt({ 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 tc(a.shape, e);
    return i.runWebGPUProgram(u, [a], o);
  };
}
function fn({ 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 = $w(e, i.shape, o.shape);
          return u.runWebGPUProgram(k, [v, x], yn(b.dtype, y.dtype));
        });
      else {
        let g = new Tw(17, i.shape, o.shape), b = new Tw(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 = uu({ inputs: { real: h, imag: f }, backend: u });
      return u.disposeData(h.dataId), u.disposeData(f.dataId), m;
    }
    let l = s || yn(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" ? N.fromUint8ToStringArray(p) : p, f = i.dtype === "string" ? N.fromUint8ToStringArray(d) : d, [m, g] = t(i.shape, o.shape, h, f, l);
      return u.makeTensorInfo(g, l, m);
    }
    let c = $w(e, i.shape, o.shape);
    return u.runWebGPUProgram(c, [i, o], l);
  };
}
var { addImpl: bne, ceilImpl: yne, concatImpl: vne, equalImpl: xne, expImpl: wne, expm1Impl: kne, floorImpl: Ine, gatherNdImpl: Sne, gatherV2Impl: Cne, greaterEqualImpl: Nne, greaterImpl: Tne, lessEqualImpl: $ne, lessImpl: _ne, logImpl: Ane, maxImpl: Ene, maximumImpl: Rne, minimumImpl: Dne, multiplyImpl: Fne, negImpl: One, notEqualImpl: Pne, prodImpl: zne, rangeImpl: Mne, rsqrtImpl: Lne, simpleAbsImpl: Bne, sliceImpl: Vne, stridedSliceImpl: Wne, stringNGramsImpl: Une, subImpl: Gne, tileImpl: Hne, topKImpl: qne, transposeImpl: jne, uniqueImpl: kpe } = Yy;
var Kne = jt({ opType: 0, cpuKernelImpl: Bne });
var Xne = { kernelName: ao, backendName: "webgpu", kernelFunc: Kne };
var Yne = fn({ opSnippet: 1, cpuKernelImpl: bne, supportsComplex: true });
var Qne = { kernelName: kr, backendName: "webgpu", kernelFunc: Yne };
var Zne = class {
  constructor(e) {
    this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e[0], this.variableNames = e.map((t, n) => `T${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, ${t});
          }
        }
      }
    `;
  }
};
function Jne(e) {
  let { inputs: t, backend: n } = e, s = t;
  if (s.length === 1)
    return Zn({ inputs: { x: s[0] }, backend: n });
  let r = s.map((o) => o.dtype).reduce((o, u) => yn(o, u)), a = s.map((o) => o.shape), i = new Zne(a);
  return n.runWebGPUProgram(i, s, r);
}
var ese = { kernelName: xa, backendName: "webgpu", kernelFunc: Jne };
var h2 = class {
  constructor(e, t, n) {
    this.workGroupSize = [64, 1, 1], this.variableNames = ["x"], this.uniforms = "axis : i32; infinityValue : f32;", this.size = true;
    let s = [t];
    N.assertAxesAreInnerMostDims("arg" + n.charAt(0).toUpperCase() + n.slice(1), s, e.length), this.op = n === "min" ? "<" : ">";
    let [r] = N.computeOutAndReduceShapes(e, s);
    this.outputShape = r.length === 0 ? [1] : r, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, [1, 1, 1]), this.inputShape = e, this.shaderKey = `argMinMax${this.op}`;
  }
  getUserCode() {
    let e = `
      var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
      var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
    `, t = (r, a) => this.outputShape.length === 1 ? r : `${r}[${a}]`, n = (r) => this.inputShape.length === 1 ? "uniforms.xShape" : `uniforms.xShape[${r}]`;
    return `
      fn DIV_CEIL(a : u32, b : u32) -> u32 {
        return ((a - 1u) / b + 1u);
      }

      ${e}

      // In order to get a flattened index into the input tensor, we need to
      // add back the index along the reduced dimension to |outputCoords|.
      // This function outputs the offset to the first value along
      // |axis| and the stride to get the next value of the input along |axis|.
      fn getInputCoordInfo(outputIndex : i32) -> vec2<i32>{
        let outputCoords = getCoordsFromIndex(outputIndex);
        var i = ${this.outputShape.length - 1};

        var stride = 1;
        var inputStride = 1;
        var offset = 0;

        for (var r = 1; r <= ${this.inputShape.length}; r = r + 1) {
          let length = ${n(`${this.inputShape.length} - r`)};
          if (${this.inputShape.length} - r == uniforms.axis) {
            inputStride = stride;
          } else {
            offset = offset + ${t("outputCoords", "i")} * stride;
            i = i - 1;
          }
          stride = stride * length;
        }

        return vec2<i32>(offset, inputStride);
      }

      fn getInputIndex(coordInfo : vec2<i32>, index : i32) -> i32{
        return coordInfo[0] + coordInfo[1] * index;
      }

      ${Ue()}
        let outputIndex = index / i32(workGroupSizeX);
        let coordInfo = getInputCoordInfo(outputIndex);
        let Length = ${n("uniforms.axis")};

        var bestIndex = i32(localId.x);
        var bestValue = uniforms.infinityValue;

        for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
            k = k + i32(workGroupSizeX)) {
          let candidate = f32(x.numbers[getInputIndex(coordInfo, 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(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];
            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 tse = 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]}>;
      ${wv()}
      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.numbers[y * width + x];
        }
        workgroupBarrier();

        x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);
        y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);
        if (x < height && y < width) {
          setOutputAtIndex((y * height + x), tile[localId.x]
            [localId.y]);
        }
      }
    `;
  }
};
var nse = 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 = Wt(this.outputShape.length), t = sse(this.newDim);
    return `
      ${Ue()}

        for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
          let flatIndex = index * ${this.workPerThread} + i;
          if(flatIndex < uniforms.size) {
            let resRC = getCoordsFromIndex(flatIndex);
            setOutputAtIndex(flatIndex, A.numbers[getIndexFromCoords${this.outputShape.length}D(
              ${e}(${t}), uniforms.aShape)]);
          }
        }
      }
    `;
  }
};
function sse(e) {
  let t = e.length;
  if (t > 4)
    throw Error(`Transpose for rank ${t} is not yet supported`);
  let n = new Array(t);
  for (let s = 0; s < e.length; s++)
    n[e[s]] = `resRC[${s}]`;
  return n.join();
}
function vi(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 = jne(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 tse(r.shape, a);
    return i.runWebGPUProgram(c, [r], r.dtype);
  }
  let l = new nse(r.shape, a);
  return i.runWebGPUProgram(l, [r], r.dtype);
}
var rse = { kernelName: ci, backendName: "webgpu", kernelFunc: vi };
function ase(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s, i = w.parseAxisParam(a, r.shape), o = N.getAxesPermutation(i, r.shape.length), u = r, l = [];
  o != null && (u = vi({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = N.getInnerMostAxes(i.length, u.shape.length)), N.assertAxesAreInnerMostDims("argMax", [i[0]], u.shape.length);
  let c = new h2(u.shape, i[0], "max"), p = [{ type: "int32", data: [i[0]] }, { type: "float32", data: [Number.NEGATIVE_INFINITY] }], d = n.runWebGPUProgram(c, [u], "int32", p);
  return l.forEach((h) => n.disposeData(h.dataId)), d;
}
var ise = { kernelName: wa, backendName: "webgpu", kernelFunc: ase };
function ose(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a } = s, i = w.parseAxisParam(a, r.shape), o = N.getAxesPermutation(i, r.shape.length), u = r, l = [];
  o != null && (u = vi({ inputs: { x: r }, backend: n, attrs: { perm: o } }), l.push(u), i = N.getInnerMostAxes(i.length, u.shape.length)), N.assertAxesAreInnerMostDims("argMin", [i[0]], u.shape.length);
  let c = new h2(u.shape, i[0], "min"), p = [{ type: "int32", data: [i[0]] }, { type: "float32", data: [Number.POSITIVE_INFINITY] }], d = n.runWebGPUProgram(c, [u], "int32", p);
  return l.forEach((h) => n.disposeData(h.dataId)), d;
}
var use = { kernelName: il, backendName: "webgpu", kernelFunc: ose };
var f2 = 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 m2 = 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 lse(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1, c = N.computePool2DInfo(r.shape, a, i, l, o, u);
  if (c.filterWidth === 1 && c.filterHeight === 1 && w.arraysEqual(c.inShape, c.outShape))
    return Zn({ inputs: { x: r }, backend: n });
  let p, d = [{ type: "int32", data: [c.strideHeight, c.strideWidth] }];
  return c.filterHeight === 1 && c.filterWidth === 1 ? p = new m2(c) : (p = new f2(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 cse = { kernelName: ka, backendName: "webgpu", kernelFunc: lse };
function dse(e) {
  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
  return Nv({ a: r, b: a, transposeA: i, transposeB: o, backend: n });
}
var pse = { kernelName: Ia, backendName: "webgpu", kernelFunc: dse };
var hse = 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 : ${Wt(e.length)}; `, this.shaderKey = "slice";
  }
  getUserCode() {
    let e = Wt(this.rank), t = fse(this.rank), n;
    return this.start.length === 1 ? n = this.outputShape.map((r, a) => "sourceLoc = uniforms.start + coords;") : n = this.outputShape.map((r, a) => `sourceLoc.${qm[a]} = uniforms.start[${a}] + coords.${qm[a]};`), `
      ${Ue()}
        if (index < uniforms.size) {
          var sourceLoc : ${e};
          let coords = getCoordsFromIndex(index);
          ${n.join(`
`)}
          setOutputAtIndex(index, getSource(${t}));
        }
      }
    `;
  }
};
var qm = ["x", "y", "z", "w", "u", "v"];
function fse(e) {
  if (e === 1)
    return "sourceLoc";
  if (e <= 6)
    return qm.slice(0, e).map((t) => `sourceLoc.${t}`).join(",");
  throw Error(`Slicing for rank ${e} is not yet supported`);
}
function lu(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, size: i } = s, [o, u] = wt.parseSliceParams(r, a, i);
  if (wt.assertParamsValid(r, o, u), n.shouldExecuteOnCPU([r]) || r.dtype === "string") {
    let p = n.tensorMap.get(r.dataId), d = Vne(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 hse(o, u), c = [{ type: "int32", data: o }];
  return n.runWebGPUProgram(l, [r], r.dtype, c);
}
var mse = { kernelName: Oo, backendName: "webgpu", kernelFunc: lu };
var gse = (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 = N.getReshaped(r.shape, a, o), l = N.getPermuted(u.length, a.length), c = N.getReshapedPermuted(r.shape, a, o), p = N.getSliceBeginCoords(i, a.length), d = N.getSliceSize(c, i, a.length), h = [], f = Me({ inputs: { x: r }, backend: n, attrs: { shape: u } }), m = vi({ inputs: { x: f }, backend: n, attrs: { perm: l } }), g = Me({ inputs: { x: m }, backend: n, attrs: { shape: c } }), b = lu({ 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 bse = { kernelName: io, backendName: "webgpu", kernelFunc: gse };
var g2 = fn({ opSnippet: 10, dtype: "bool", cpuKernelImpl: Pne });
var yse = { kernelName: Io, backendName: "webgpu", kernelFunc: g2 };
function nc(e) {
  let { inputs: t, backend: n } = e, { input: s } = t, r = n.tensorMap.get(s.dataId);
  return Zn({ inputs: { x: r.complexTensorInfos.real }, backend: n });
}
var vse = { kernelName: tp, backendName: "webgpu", kernelFunc: nc };
function xse(e, t) {
  let n = new tc(e.shape, 23), s = t.runWebGPUProgram(n, [e], "int32");
  return { dataId: s.dataId, shape: s.shape, dtype: s.dtype };
}
function jm(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { dtype: a } = s;
  if (a === "complex64") {
    if (r.dtype === "complex64")
      return Zn({ inputs: { x: r }, backend: n });
    let i = $t(r.shape), o = jm({ inputs: { x: r }, backend: n, attrs: { dtype: "float32" } }), u = uu({ inputs: { real: o, imag: i }, backend: n });
    return i.dispose(), n.disposeData(o.dataId), 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.disposeData(i.dataId), o;
  }
  if (!w.hasEncodingLoss(r.dtype, a)) {
    let i = Zn({ inputs: { x: r }, backend: n });
    return { dataId: i.dataId, shape: i.shape, dtype: a };
  }
  if (a === "int32")
    return xse(r, n);
  if (a === "bool") {
    let i = n.makeTensorInfo([], "bool", w.getTypedArrayFromDType("bool", 1)), u = g2({ 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 wse = { kernelName: Sa, backendName: "webgpu", kernelFunc: jm };
var kse = jt({ opType: 1, cpuKernelImpl: yne });
var Ise = { kernelName: Ca, backendName: "webgpu", kernelFunc: kse };
var Sse = 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 Cse = 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 Nse(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 Sse(r.shape) : o = new Cse(r.shape), n.runWebGPUProgram(o, [r], r.dtype, u);
}
var Tse = { kernelName: Ir, backendName: "webgpu", kernelFunc: Nse };
var $se = class {
  constructor(e) {
    this.uniforms = "", this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = N.computeOutShape(e, 1), this.variableNames = e.map((t, n) => `T${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 Qp(e) {
  let { inputs: t, backend: n } = e, { input: s } = t, r = n.tensorMap.get(s.dataId);
  return Zn({ inputs: { x: r.complexTensorInfos.imag }, backend: n });
}
var _se = { kernelName: Qd, backendName: "webgpu", kernelFunc: Qp };
function Km(e, t, n) {
  let s = e[0].dtype;
  if (s === "complex64") {
    let h = e.map((y) => nc({ inputs: { input: y }, backend: n })), f = e.map((y) => Qp({ inputs: { input: y }, backend: n })), m = Km(h, t, n), g = Km(f, t, n), b = uu({ 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 Me({ inputs: { x }, backend: n, attrs: { shape: [-1, k] } });
    }), f = h.map((x) => ({ vals: n.readSync(x.dataId), shape: x.shape })), m = N.computeOutShape(h.map((x) => x.shape), 1), g = h[0].shape[0] === 1, b = vne(f, m, s, g), y = N.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 } = Ase(e, t, n), o = a.map((h) => h.shape), u = new $se(o), l = [], c = new Array(o.length - 1);
  if (c.length > 0) {
    c[0] = o[0][1], l.push({ type: "int32", data: [c[0]] });
    for (let h = 1; h < c.length; h++)
      c[h] = c[h - 1] + o[h][1], l.push({ type: "int32", data: [c[h]] });
  }
  let p = n.runWebGPUProgram(u, a, a[0].dtype, l);
  a.forEach((h) => n.disposeData(h.dataId));
  let d = Me({ inputs: { x: p }, backend: n, attrs: { shape: i } });
  return n.disposeData(p.dataId), d;
}
function Ase(e, t, n) {
  let s = N.computeOutShape(e.map((a) => a.shape), t);
  return { tensors2D: e.map((a) => Me({ inputs: { x: a }, backend: n, attrs: { shape: [w.sizeFromShape(a.shape.slice(0, t)), w.sizeFromShape(a.shape.slice(t))] } })), outShape: s };
}
function b2(e) {
  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s, a = w.parseAxisParam(r, t[0].shape)[0], i = N.computeOutShape(t.map((l) => l.shape), a);
  if (w.sizeFromShape(i) === 0)
    return n.makeTensorInfo(i, t[0].dtype, []);
  let o = t.filter((l) => w.sizeFromShape(l.shape) > 0);
  if (o.length === 1)
    return Zn({ inputs: { x: o[0] }, backend: n });
  let u = o.map((l) => l.shape);
  return N.assertParamsConsistent(u, a), Km(o, a, n);
}
var Ese = { kernelName: oo, backendName: "webgpu", kernelFunc: b2 };
var Rse = class {
  constructor(e, t) {
    this.variableNames = ["A"], this.uniforms = `pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; outWidth : i32; itemsPerBlockRow : i32;
      inChannels : i32;`, this.workPerThread = 4, this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]), this.isChannelsLast = t, this.shaderKey = `im2col_${this.isChannelsLast}`;
  }
  getUserCode() {
    let e = this.isChannelsLast ? 0 : 1, t = this.isChannelsLast ? 1 : 2;
    return `
    ${Ue()}

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

        let rc = getCoordsFromIndex(flatIndex);

        if(flatIndex < uniforms.size) {
          let blockIndex = rc[0];
          let pos = rc[1];

          let offsetY = blockIndex / uniforms.outWidth * uniforms.stride[1] - uniforms.pad[1];
          let d0 = offsetY + uniforms.dilation[1] * pos / uniforms.itemsPerBlockRow;
          var value = 0.0;
          if(d0 < uniforms.aShape[${e}] && d0 >= 0) {
            let offsetX = (blockIndex % uniforms.outWidth) * uniforms.stride[0] -
              uniforms.pad[0];
            let d1 = offsetX + uniforms.dilation[0] * ((pos %
              uniforms.itemsPerBlockRow) / uniforms.inChannels);
            let ch = pos % uniforms.inChannels;
            if(d1 < uniforms.aShape[${t}] && d1 >= 0) {
              value = getA(d0, d1, ch);
            }
          }
          setOutputAtIndex(flatIndex, value);
        }
      }
    }
  `;
  }
};
var Dse = class {
  constructor(e, t = false, n = null, s = false, r = 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.hasLeakyreluAlpha = r, this.addBias && this.variableNames.push("bias"), this.hasPreluActivationWeights && this.variableNames.push("preluActivationWeights"), this.hasLeakyreluAlpha && this.variableNames.push("leakyreluAlpha"), this.tileAOuter = this.outputShape[1] === 1 ? 1 : this.workGroupSize[1] * this.elementsPerThread[1], this.tileBOuter = this.workGroupSize[0] * this.elementsPerThread[0], this.tileInner = this.tileBOuter, [this.fitA, this.fitB] = this.getShapeFit(), this.shaderKey = `conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}_${this.elementsPerThread}`;
  }
  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])];
  }
  getSampleAWithRemainder(e) {
    return `let flatIndex${e} = getIndexFromCoords4D(coord, uniforms.xShape);
    let divBy4Remainder${e} = flatIndex${e} % 4;
    let divBy4Index${e} = flatIndex${e} / 4;
    let curData${e} = x.numbers[divBy4Index${e}];
    if (divBy4Remainder${e} == 0) {
      temp = curData${e};
    } else {
      // TODO: This could end up being a redundant load with another one in
      // the same shader invocation. Perhaps there's an opportunity for
      // optimization
      let nextData${e} = x.numbers[divBy4Index${e} + 1];
      if (divBy4Remainder${e} == 1) {
        temp = vec4<f32>(curData${e}.yzw, nextData${e}.x);
      } else if (divBy4Remainder${e} == 2) {
        temp = vec4<f32>(curData${e}.zw, nextData${e}.xy);
      } else if (divBy4Remainder${e} == 3) {
        temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);
      }
    }
    `;
  }
  getUserCode() {
    let e = c2(this.elementsPerThread, this.tileAOuter, this.tileBOuter, this.tileInner), s = `let outRow = r / uniforms.outShape[2];
        let outCol = r % uniforms.outShape[2];
        let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
        let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
        let inChCoord = c % uniforms.xShape[3];
        var coord = vec4<i32>(
            batch,
            outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
            outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
            inChCoord);
        var resData = vec4<f32>(0.0);
        ${this.convInfo.inChannels % 4 === 0 ? `// The bounds checking is always needed since we use it to pad zero for
          // the 'same' padding type.
          if (coordsInBounds4D(coord, uniforms.xShape)) {
            resData = x.numbers[getIndexFromCoords4D(coord, uniforms.xShape) / 4];
          } else {
            resData = vec4<f32>(0.0); }` : `var temp = vec4<f32>(0.0);
          ${this.getSampleAWithRemainder(1)}
          resData = temp;
          if (WCol == (uniforms.filterDims[1] - 1)) {
            coord = vec4<i32>(
              coord.x, coord.y + 1, coord.z + 1 - uniforms.filterDims[1], 0);
              ${this.getSampleAWithRemainder(2)}
            if (inChCoord == 0) {
              resData = vec4<f32>(resData.xyz, temp.x);
            } else if (inChCoord == 1) {
              resData = vec4<f32>(resData.xy, temp.xy);
            } else {
              resData = vec4<f32>(resData.x, temp.xyz);
            }
          }
          `}
        return resData;`, r = this.fitA ? `${s}` : `if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
          ${s}
         }
         return vec4<f32>(0.0);
        `, a = this.fitB ? "return W.numbers[row * uniforms.dimBOuter / 4 + col];" : `if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
           return W.numbers[row * uniforms.dimBOuter / 4 + col];
         }
         return vec4<f32>(0.0);
        `, i = "", o = "";
    if (this.activation) {
      let c = Zs(this.activation, this.isVec4);
      if (this.hasPreluActivationWeights)
        i = `fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
          let b = getPreluActivationWeightsByOutputCoords(outCoord);
          ${c}
        }`;
      else {
        if (this.hasLeakyreluAlpha)
          throw i = `fn activation(outCoord: vec4<f32>) -> vec4<f32> {
          let b = getLeakyreluAlphaByOutputCoords(outCoord);
          ${c}
        }`, new Error("Leakyrelu is not supported.");
        i = `
        fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<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>) -> vec4<f32> {
          let r = row;
          let c = col * 4;
          var batch = i32(globalId.z);
          ${r}
        }

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

        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);
            ${u}
            ${o}
            setOutputAtCoords(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
              value);
          }
        }
        ${e}
      `;
  }
};
var Fse = class {
  constructor(e, t = false, n = null, s = false) {
    this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;", this.outputShape = e.outShape, w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), this.dispatchLayout = { x: [3], y: [1, 2], z: [0] }, this.workGroupSize = kv(this.dispatchLayout, this.outputShape), this.elementsPerThread = Iv(this.dispatchLayout, this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, this.elementsPerThread), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivationWeights = s, [this.fitA, this.fitB] = this.getShapeFit(), this.shaderKey = `conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`;
  }
  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.outputShape[1] * this.outputShape[2], i = this.outputShape[3], o = this.convInfo.filterHeight * this.convInfo.filterWidth * this.convInfo.inChannels;
    return [Ks(s, [a, o]), Ks(r, [o, i])];
  }
  getUserCode() {
    let e = Cv(this.elementsPerThread, this.workGroupSize), t = `
    let outRow = row / uniforms.outShape[2];
    let outCol = row % uniforms.outShape[2];

    let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);
    let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];
    let coord = vec4<i32>(
        batch,
        outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
        outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
        col % uniforms.xShape[3]);
    // The bounds checking is always needed since we use it to pad zero for the
    // 'same' padding type.
    if(coordsInBounds4D(coord, uniforms.xShape)) {
      return x.numbers[getIndexFromCoords4D(coord, uniforms.xShape)];
    }
    return 0.0;`, n = this.fitA ? `${t}` : `if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
      ${t}
    }
    return 0.0;
    `, s = this.fitB ? "return W.numbers[row * uniforms.dimBOuter + col];" : `if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
           return W.numbers[row * uniforms.dimBOuter + col];
	 }
	 return 0.0;
	 `, r = "", a = "";
    if (this.activation) {
      let u = Zs(this.activation, false);
      this.hasPreluActivationWeights ? r = `fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
                  let b = getPreluActivationWeightsByOutputCoords(outCoord);
                  ${u}
                }` : r = `
                  fn activation(a : f32, outCoord : vec4<i32>) -> 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>) -> f32 {
      var batch = i32(globalId.z);
      ${n}
    }

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

    fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
      var batch = i32(globalId.z);
      var value = valueInput;
      let outCoord = vec4<i32>(
          batch,
          row / uniforms.outShape[2],
          row % uniforms.outShape[2],
          col);
      ${i}
      ${a}
      result.numbers[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
    }
    ${e}
  `;
  }
};
var Ose = class {
  constructor(e, t = false, n = null, s = false) {
    this.variableNames = ["x", "W"], this.uniforms = "filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;", this.workGroupSize = [128, 1, 1], this.outputShape = e.outShape, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivationWeights = s, this.shaderKey = `conv2DNaive_${this.activation}`;
  }
  getUserCode() {
    let e = "", t = "";
    if (this.activation) {
      let r = Zs(this.activation);
      this.hasPreluActivationWeights ? 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 = "value = activation(value, outCoord);";
    }
    let n = this.addBias ? "value = value + getBiasByOutputCoords(outCoord);" : "";
    return `
      ${e}
      fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32 {
        let coord = vec4<i32>(batch, row, col, chan);
        if(coordsInBounds4D(coord, uniforms.xShape)) {
          return getX(batch, row, col, chan);
        }
        return 0.0;
      }

      fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{
        let coord = vec4<i32>(row, col, xChannel, outChannel);
        if(coordsInBounds4D(coord, uniforms.wShape)) {
          return getW(row, col, xChannel, outChannel);
        }
        return 0.0;
      }

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

      ${Rr()}
        let coords = getOutputCoords();
        let batch = coords[0];
        let outChannel = coords[3];

        var acc = 0.0;

        for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {
          for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {
            for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) {
              let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];
              let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];
              let v = readInp(batch, coordRow, coordCol, xChannel);
              let f = readFilt(row, col, xChannel, outChannel);
              acc = acc + v * f;
            }
          }
        }

        writeResult(batch, coords[1], coords[2], outChannel, acc);
      }
    `;
  }
};
function Pse({ 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 = n.dataFormat === "channelsLast", c = false, p = false, d = n.filterHeight === n.inHeight && n.filterWidth === n.inWidth && n.padInfo.type === "VALID", h, f;
  if (d) {
    let b = n.inHeight * n.inWidth * n.inChannels;
    h = Me({ inputs: { x: e }, backend: s, attrs: { shape: [1, n.batchSize, b] } }), f = Me({ inputs: { x: t }, backend: s, attrs: { shape: [1, b, n.outChannels] } });
  } else {
    let b = l ? u[0] * u[1] * u[2] : u[0] * u[2] * u[3];
    h = Me({ inputs: { x: e }, backend: s, attrs: { shape: [1, b, n.inChannels] } }), f = Me({ inputs: { x: t }, backend: s, attrs: { shape: [1, n.inChannels, n.outChannels] } });
  }
  let m = Nv({ a: h, b: f, transposeA: c, transposeB: p, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i }), g = Me({ inputs: { x: m }, backend: s, attrs: { shape: n.outShape } });
  return s.disposeData(h.dataId), s.disposeData(f.dataId), s.disposeData(m.dataId), g;
}
function zse({ 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, strideWidth: p, strideHeight: d, padInfo: h, outWidth: f, outHeight: m, dilationWidth: g, dilationHeight: b, dataFormat: y } = n, v = y === "channelsLast", x = u * l * c, k = m * f, C = [k, x], T = false, E = false, A = [], P = Me({ inputs: { x: e }, backend: s, attrs: { shape: e.shape.slice(1) } }), R = Me({ inputs: { x: t }, backend: s, attrs: { shape: [1, x, -1] } });
  A.push(P), A.push(R);
  let F = new Rse(C, v), $ = [{ type: "int32", data: [h.left, h.top] }, { type: "int32", data: [p, d] }, { type: "int32", data: [g, b] }, { type: "int32", data: [f] }, { type: "int32", data: [c * u] }, { type: "int32", data: [c] }], z = s.runWebGPUProgram(F, [P], P.dtype, $), W = Me({ inputs: { x: z }, backend: s, attrs: { shape: [1, C[0], C[1]] } });
  A.push(z), A.push(W);
  let q = [1, C[0], C[1]], K = new d2(q, [1, k, n.outChannels], X().get("WEBGPU_MATMUL_WORK_PER_THREAD"), T, E, r, o, a), Y = q[1], Z = q[2], te = n.outChannels, ee = [{ type: "int32", data: [Y] }, { type: "int32", data: [te] }, { type: "int32", data: [Z] }], se = [W, R];
  r && se.push(r), a && se.push(a);
  let ne = s.runWebGPUProgram(K, se, W.dtype, ee), oe = v ? [1, m, f, n.outChannels] : [1, n.outChannels, m, f], re = Me({ inputs: { x: ne }, backend: s, attrs: { shape: oe } });
  A.push(ne);
  for (let le of A)
    s.disposeData(le.dataId);
  return re;
}
function y2({ 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;
  if (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 Pse({ x: e, filter: t, convInfo: n, backend: s, bias: r, activation: o, preluActivationWeights: a, leakyreluAlpha: i });
  if (X().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER") && e.shape[0] === 1)
    return zse({ x: e, filter: t, convInfo: n, backend: s, bias: r, preluActivationWeights: a, leakyreluAlpha: i, activation: o });
  let d = X().getBool("WEBGPU_USE_NAIVE_CONV2D"), h = (n.inChannels % 4 === 0 || n.inChannels === 3 && n.padInfo.type === "VALID") && n.outChannels % 4 === 0 && n.outChannels >= 32, 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] }];
  if (d)
    c = new Ose(n, u, o, l);
  else {
    h ? c = new Dse(n, u, o, l) : c = new Fse(n, u, o, l);
    let b = n.outShape[1] * n.outShape[2], y = n.outShape[3], v = n.filterHeight * n.filterWidth * n.inShape[3];
    m.push({ type: "int32", data: [b] }, { type: "int32", data: [y] }, { type: "int32", data: [v] });
  }
  let g = [e, t];
  return u && g.push(r), l && g.push(a), s.runWebGPUProgram(c, g, e.dtype, m);
}
function Mse(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 = N.convertConv2DDataFormat(u), d = N.computeConv2DInfo(r.shape, a.shape, i, l, o, c, false, p);
  return y2({ x: r, filter: a, convInfo: d, backend: s });
}
var Lse = { kernelName: Na, backendName: "webgpu", kernelFunc: Mse };
var Bse = 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 = kv(this.dispatchLayout, this.outputShape), this.elementsPerThread = Iv(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.numbers[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.numbers[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.numbers[getIndexFromCoords4D(outCoord, uniforms.outShape)] = value;
    }

    ${Cv(this.elementsPerThread, this.workGroupSize)}
  `;
  }
};
var Vse = class {
  constructor(e) {
    this.variableNames = ["dy", "W"], this.uniforms = "filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>;", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = e.inShape, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.isChannelsLast = e.dataFormat === "channelsLast", this.shaderKey = `conv2DDerInput_${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 Wse(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 = N.convertConv2DDataFormat(l), d = N.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 (X().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))
    f = new Vse(d);
  else {
    f = new Bse(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 Use = { kernelName: Ta, backendName: "webgpu", kernelFunc: Wse };
var Gse = jt({ opType: 2 });
var Hse = { kernelName: $a, backendName: "webgpu", kernelFunc: Gse };
var qse = jt({ opType: 3 });
var jse = { kernelName: _a, backendName: "webgpu", kernelFunc: qse };
var Kse = class {
  constructor(e, t, n, s) {
    this.variableNames = ["Image", "Boxes", "BoxInd"], this.uniforms = "extrapolationValue : f32;", this.workGroupSize = [64, 1, 1], this.size = true;
    let [r] = t;
    this.outputShape = [r, n[0], n[1], e], this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), this.methodId = s === "bilinear" ? 1 : 0, this.cropHeightBiggerThan1 = this.outputShape[1] > 1, this.cropWidthBiggerThan1 = this.outputShape[2] > 1, this.shaderKey = `cropAndResize_${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 Xse = (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 Kse(r.shape[3], a.shape, o, u), p = [{ type: "float32", data: [l] }];
  return n.runWebGPUProgram(c, [r, a, i], "float32", p);
};
var Yse = { kernelName: lo, backendName: "webgpu", kernelFunc: Xse };
var Qse = 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 = ${this.getInputSamplingString()};
          setOutputAtIndex(index, rlt);
        }
      }`;
  }
  getHeightCoordString() {
    return this.dataFormat === "NHWC" ? "coords[1]" : "coords[2]";
  }
  getWidthCoordString() {
    return this.dataFormat === "NHWC" ? "coords[2]" : "coords[3]";
  }
  getDepthCoordString() {
    return this.dataFormat === "NHWC" ? "coords[3]" : "coords[1]";
  }
  getOutputDepthSize() {
    return this.dataFormat === "NHWC" ? "uniforms.outShape[3]" : "uniforms.outShape[1]";
  }
  getInputSamplingString() {
    return this.dataFormat === "NHWC" ? "getX(b, in_h, in_w, in_d)" : "getX(b, in_d, in_h, in_w)";
  }
};
function Zse(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { blockSize: a, dataFormat: i } = s, o = r.shape[0], u = i === "NHWC" ? r.shape[1] : r.shape[2], l = i === "NHWC" ? r.shape[2] : r.shape[3], c = i === "NHWC" ? r.shape[3] : r.shape[1], p = u * a, d = l * a, h = c / (a * a), f = i === "NHWC" ? [o, p, d, h] : [o, h, p, d], m = [{ type: "int32", data: [a] }], g = new Qse(f, i);
  return n.runWebGPUProgram(g, [r], r.dtype, m);
}
var Jse = { kernelName: co, backendName: "webgpu", kernelFunc: Zse };
var v2 = class {
  constructor(e, t = false, n = null, s = false) {
    this.variableNames = ["x", "W"], this.uniforms = "pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;", this.workGroupSize = [4, 4, 4], this.isVec4 = true, this.outputShape = e.outShape, this.dispatchLayout = { x: [0, 1], y: [2], z: [3] }, this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [1, 4, 4]), w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivation = s, this.shaderKey = `depthwise3x3_${n}`;
  }
  getUserCode() {
    let e = "", t = "";
    if (this.activation) {
      let r = Zs(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}

      ${wv()}
      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}
            ${t}
            setOutputAtCoords(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
          }
        }
      }
    `;
  }
};
var x2 = class {
  constructor(e, t = false, n = null, s = false) {
    this.variableNames = ["x", "W"], this.uniforms = `pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
      inDims : vec2<i32>; filterHeight : i32; filterWidth : i32;
      channelMul : i32;`, this.workGroupSize = [256, 1, 1], this.outputShape = e.outShape, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), w.assert(e.dataFormat === "channelsLast", () => "TODO: NCHW is unimplemented"), t && this.variableNames.push("bias"), s && this.variableNames.push("preluActivationWeights"), this.convInfo = e, this.addBias = t, this.activation = n, this.hasPreluActivation = s, this.shaderKey = `depthwise_${this.activation}`;
  }
  getUserCode() {
    let e = "", t = "";
    if (this.activation) {
      let r = Zs(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);
        }
      }

      ${Rr()}
        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}
        ${t}
        writeResult(batch, coords[1], coords[2], d2, dotProd);
      }
    `;
  }
};
function ere(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r, filter: a } = t, { strides: i, pad: o, dilations: u, dimRoundingMode: l } = s, c = u;
  c == null && (c = [1, 1]);
  let p = N.computeConv2DInfo(r.shape, a.shape, i, c, o, l, true), d = [{ type: "int32", data: [p.padInfo.top, p.padInfo.left] }, { type: "int32", data: [p.strideHeight, p.strideWidth] }, { type: "int32", data: [p.dilationHeight, p.dilationWidth] }, { type: "int32", data: [p.inHeight, p.inWidth] }], h;
  return p.batchSize === 1 && p.inHeight === p.outHeight && p.inWidth === p.outWidth && p.strideHeight === 1 && p.strideWidth === 1 && p.filterHeight === p.filterWidth && p.inChannels === p.outChannels && p.filterHeight === 3 && p.inChannels % 4 === 0 ? h = new v2(p) : (h = new x2(p), d.push({ type: "int32", data: [p.filterHeight] }, { type: "int32", data: [p.filterWidth] }, { type: "int32", data: [p.outChannels / p.inChannels] })), n.runWebGPUProgram(h, [r, a], r.dtype, d);
}
var tre = { kernelName: Aa, backendName: "webgpu", kernelFunc: ere };
var w2 = fn({ opSnippet: 0, cpuKernelImpl: Fne, supportsComplex: true });
var nre = { kernelName: Ka, backendName: "webgpu", kernelFunc: w2 };
var sre = class {
  constructor(e, t) {
    this.workGroupSize = [64, 1, 1], this.variableNames = ["x"], this.uniforms = "reduceSize : i32;", this.size = true, this.inputShape = [e.batchSize, e.inSize];
    let [n] = N.computeOutAndReduceShapes(this.inputShape, [1]);
    this.outputShape = n.length === 0 ? [1] : n, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, [1, 1, 1]), this.reduceType = t, this.shaderKey = `reduce_${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.numbers[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.numbers[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) {
          ${n}
        }
       }
     `;
  }
};
function sc(e, t, n, s, r) {
  let a = e.shape.length, i = [], o = w.parseAxisParam(t, e.shape), u = o, l = N.getAxesPermutation(u, a), c = e;
  l != null && (c = vi({ inputs: { x: e }, attrs: { perm: l }, backend: r }), u = N.getInnerMostAxes(u.length, a), i.push(c)), N.assertAxesAreInnerMostDims(s, u, a);
  let [p, d] = N.computeOutAndReduceShapes(c.shape, u), h = p;
  n && (h = N.expandShapeToKeepDim(p, o));
  let f;
  if ((s === "max" || s === "prod") && r.shouldExecuteOnCPU([c])) {
    let m = r.tensorMap.get(c.dataId).values;
    switch (s) {
      case "max":
        let g = Ene(m, w.sizeFromShape(d), h, e.dtype);
        f = r.makeTensorInfo(h, e.dtype, g);
        break;
      case "prod":
        let { outVals: b, outShape: y, outDtype: v } = zne(c.shape, c.dtype, m, u);
        f = r.makeTensorInfo(y, v, b);
        break;
      default:
        throw new Error(`${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" : lp(e.dtype), x = [{ type: "int32", data: [m] }], k = new sre(y, s), C = r.runWebGPUProgram(k, [c], v, x);
    i.push(C), f = Me({ inputs: { x: C }, attrs: { shape: h }, backend: r });
  }
  return i.forEach((m) => r.disposeData(m.dataId)), f;
}
function Tv(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
  return sc(r, a, i, "sum", n);
}
var rre = { kernelName: ai, backendName: "webgpu", kernelFunc: Tv };
function are(e) {
  let { inputs: t, backend: n, attrs: s } = e, { equation: r } = s, a = t, { allDims: i, summedDims: o, idDims: u } = N.decodeEinsumEquation(r, a.length);
  N.checkEinsumDimSizes(i.length, u, a);
  let { path: l, steps: c } = N.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 } = N.getEinsumPermutation(h, u[g]), v;
      N.isIdentityPermutation(b) ? v = a[g] : (v = vi({ 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 = Me({ inputs: { x: v }, backend: n, attrs: { shape: x } }), f.push(v)), d === null ? d = v : (d = w2({ inputs: { a: v, b: d }, backend: n }), f.push(d));
    }
    m < p - 1 && (l[m] >= 0 && (d = Tv({ 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 ire = { kernelName: Yd, backendName: "webgpu", kernelFunc: are };
var ore = jt({ opType: 4 });
var ure = { kernelName: Ra, backendName: "webgpu", kernelFunc: ore };
var lre = fn({ opSnippet: 4, dtype: "bool", cpuKernelImpl: xne });
var cre = { kernelName: po, backendName: "webgpu", kernelFunc: lre };
var k2 = jt({ opType: 5, cpuKernelImpl: wne, dtype: "float32" });
var dre = { kernelName: Da, backendName: "webgpu", kernelFunc: k2 };
function Xm(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), Me({ inputs: { x: a }, backend: s, attrs: { shape: o } });
}
var pre = { kernelName: ho, backendName: "webgpu", kernelFunc: Xm };
var hre = jt({ opType: 6, cpuKernelImpl: kne });
var fre = { kernelName: fo, backendName: "webgpu", kernelFunc: hre };
var mre = 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 cu(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 mre(s), o = [{ type: "float32", data: [r] }];
    return t.runWebGPUProgram(i, [], a, o);
  }
}
var gre = { kernelName: hl, backendName: "webgpu", kernelFunc: cu };
var bre = 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 yre = { kernelName: mo, backendName: "webgpu", kernelFunc: ({ inputs: e, backend: t }) => {
  let { image: n } = e, s = t, r = new bre(n.shape);
  return s.runWebGPUProgram(r, [n], n.dtype);
} };
var vre = jt({ opType: 7, cpuKernelImpl: Ine });
var xre = { kernelName: Fa, backendName: "webgpu", kernelFunc: vre };
var wre = fn({ opSnippet: 12, dtype: "int32" });
var kre = { kernelName: Oa, backendName: "webgpu", kernelFunc: wre };
var Ire = (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 I2 = (e, t, n, s, r, a = false) => {
  let i = { dtype: r.dtype, shape: r.shape }, o = Yee(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 S2(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;
}
function _w(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 = n.makeTensorInfo(r, "int32"), c = n.getFromPixelsProgram(a ? "import" : "copyExternal");
  c.updateOutputShape(r);
  let p = [l.shape], d = [l.dtype, a ? "import" : "copyExternal"], h = S2(c, p, d), f = c.getLayout(n.device), m = n.getAndSavePipeline(h, () => I2(n.device, c, f.pipelineLayout, [], l, true));
  c.setPipeline(m), a || n.queue.copyExternalImageToTexture({ source: t, origin: { x: 0, y: 0 } }, { texture: c.makeInputTexture(n.device, r[1], r[0]) }, [r[1], r[0]]);
  let g = n.tensorMap.get(l.dataId);
  g.bufferInfo.buffer = n.acquireBuffer(g.bufferInfo.byteSize);
  let b = [o, i, ...u, ...c.dispatch];
  c.setUniform(n.device, b);
  let y;
  if (a) {
    let v = { source: t };
    y = n.device.importExternalTexture(v);
  } else
    y = c.inputTexture.createView();
  return n.runFromPixelsProgram(c, g.bufferInfo.buffer, f, y, l.dataId), l;
}
var Sre = { kernelName: hd, backendName: "webgpu", kernelFunc: Cre };
var Mi;
function Cre(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 (X().getBool("WEBGPU_USE_IMPORT") && i)
    return _w({ externalImage: r, backend: n, attrs: s, outShape: d, useImport: true });
  if ((i || o) && (Mi == null && (Mi = document.createElement("canvas").getContext("2d")), Mi.canvas.width = c, Mi.canvas.height = p, Mi.drawImage(r, 0, 0, c, p), r = Mi.canvas), l || u || i || o)
    return _w({ 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;
}
var Nre = class {
  constructor(e, t, n, s, r) {
    this.uniforms = "varianceEpsilon : f32;", this.workGroupSize = [128, 1, 1], this.size = true, this.variableNames = ["x", "mean", "variance"], N.assertAndGetBroadcastShape(e, t), N.assertAndGetBroadcastShape(e, n), this.outputShape = e, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), s != null && (N.assertAndGetBroadcastShape(e, s), this.variableNames.push("offset")), r != null && (N.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 Tre = { kernelName: Pa, 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 Nre(s.shape, i.shape, o.shape, p, d), f = [{ type: "float32", data: [u] }];
  return l.runWebGPUProgram(h, c, s.dtype, f);
} };
function $re(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 = N.convertConv2DDataFormat(c), g = N.computeConv2DInfo(r.shape, a.shape, u, p, l, d, false, m);
  return y2({ x: r, filter: a, convInfo: g, backend: n, bias: i, preluActivationWeights: o, leakyreluAlpha: f, activation: h });
}
var _re = { kernelName: sa, backendName: "webgpu", kernelFunc: $re };
function Are(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 } = s, h = c;
  h == null && (h = [1, 1]), w.assert(N.eitherStridesOrDilationsAreOne(u, h), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${h}'`);
  let f = N.computeConv2DInfo(r.shape, a.shape, u, h, l, p, true), m = [r, a], g = i != null, b = o != null;
  g && m.push(i), b && m.push(o);
  let y = [{ type: "int32", data: [f.padInfo.top, f.padInfo.left] }, { type: "int32", data: [f.strideHeight, f.strideWidth] }, { type: "int32", data: [f.dilationHeight, f.dilationWidth] }, { type: "int32", data: [f.inHeight, f.inWidth] }], v;
  return f.batchSize === 1 && f.inHeight === f.outHeight && f.inWidth === f.outWidth && f.strideHeight === 1 && f.strideWidth === 1 && f.filterHeight === f.filterWidth && f.inChannels === f.outChannels && f.filterHeight === 3 && f.inChannels % 4 === 0 ? v = new v2(f, g, d, b) : (v = new x2(f, g, d, b), y.push({ type: "int32", data: [f.filterHeight] }, { type: "int32", data: [f.filterWidth] }, { type: "int32", data: [f.outChannels / f.inChannels] })), n.runWebGPUProgram(v, m, "float32", y);
}
var Ere = { kernelName: ra, backendName: "webgpu", kernelFunc: Are };
var Rre = 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 : ${Wt(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 Dre(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] = N.prepareAndValidate(s, r), d = Me({ inputs: { x: r }, backend: n, attrs: { shape: [l, i] } }), h = Me({ 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 = Sne(y, v, s.dtype, l, i, c, p, s.shape, o);
    return n.makeTensorInfo(u, s.dtype, x.values);
  }
  let f = new Rre(i, [l, c]), m = [{ type: "int32", data: [i] }, { type: "int32", data: p }], g = n.runWebGPUProgram(f, [h, d], h.dtype, m), b = Me({ inputs: { x: g }, backend: n, attrs: { shape: u } });
  return n.disposeData(d.dataId), n.disposeData(h.dataId), n.disposeData(g.dataId), b;
}
var Fre = { kernelName: bo, backendName: "webgpu", kernelFunc: Dre };
var Ore = 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 = Pre(this.aShape, "i32");
    return `
      ${Ue()}
        if (index < uniforms.size) {
          let resRC = getCoordsFromIndex(index);
          setOutputAtIndex(index, getA(${e}));
        }
      }
    `;
  }
};
function Pre(e, t = "int") {
  let n = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"], s = [];
  for (let r = 0; r < e.length; r++)
    r === 2 ? s.push(`${t}(getIndices(resRC.x, resRC.z))`) : s.push(`${n[r]}`);
  return s.join();
}
function C2(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 = N.segment_util.collectGatherOpShapeInfo(r, a, u, o), c = w.sizeFromShape(a.shape), p = [], d = Me({ inputs: { x: r }, backend: n, attrs: { shape: [l.batchSize, l.outerSize, l.dimSize, l.sliceSize] } }), h = Me({ inputs: { x: a }, backend: n, attrs: { shape: [l.batchSize, c / l.batchSize] } });
  p.push(d), p.push(h);
  let f = [l.batchSize, l.outerSize, c / l.batchSize, l.sliceSize];
  if (n.shouldExecuteOnCPU([r, a])) {
    let v = n.tensorMap.get(h.dataId).values, x = De(h.shape, h.dtype, v), C = n.tensorMap.get(d.dataId).values, T = De(d.shape, d.dtype, C), E = Cne(T, x, f);
    return p.forEach((A) => n.disposeData(A.dataId)), n.makeTensorInfo(l.outputShape, E.dtype, E.values);
  }
  let m = new Ore(d.shape, f), g = n.runWebGPUProgram(m, [d, h], d.dtype);
  p.push(g);
  let b = Me({ inputs: { x: g }, backend: n, attrs: { shape: l.outputShape } });
  return p.forEach((y) => n.disposeData(y.dataId)), b;
}
var zre = { kernelName: go, backendName: "webgpu", kernelFunc: C2 };
var Mre = fn({ opSnippet: 5, cpuKernelImpl: Tne, dtype: "bool" });
var Lre = { kernelName: yo, backendName: "webgpu", kernelFunc: Mre };
var Bre = fn({ opSnippet: 6, dtype: "bool", cpuKernelImpl: Nne });
var Vre = { kernelName: za, backendName: "webgpu", kernelFunc: Bre };
function Wre(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { alpha: a } = s, i = [{ type: "float32", data: [a] }], o = new tc(r.shape, 15);
  return o.uniforms = "alpha : f32;", n.runWebGPUProgram(o, [r], "float32", i);
}
var Ure = { kernelName: La, backendName: "webgpu", kernelFunc: Wre };
var Gre = fn({ opSnippet: 7, dtype: "bool", cpuKernelImpl: _ne });
var Hre = { kernelName: vo, backendName: "webgpu", kernelFunc: Gre };
var qre = fn({ opSnippet: 8, dtype: "bool", cpuKernelImpl: $ne });
var jre = { kernelName: xo, backendName: "webgpu", kernelFunc: qre };
var Kre = jt({ opType: 9, cpuKernelImpl: Ane });
var Xre = { kernelName: Ba, backendName: "webgpu", kernelFunc: Kre };
var Yre = fn({ opSnippet: 9, dtype: "bool" });
var Qre = { kernelName: wo, backendName: "webgpu", kernelFunc: Yre };
var Zre = jt({ opType: 10 });
var Jre = { kernelName: yl, backendName: "webgpu", kernelFunc: Zre };
function N2(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reductionIndices: a, keepDims: i } = s;
  return sc(r, a, i, "max", n);
}
var eae = { kernelName: Va, backendName: "webgpu", kernelFunc: N2 };
var tae = fn({ opSnippet: 15, cpuKernelImpl: Rne });
var nae = { kernelName: Wa, backendName: "webgpu", kernelFunc: tae };
function sae(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { filterSize: a, strides: i, pad: o, dimRoundingMode: u } = s, l = 1, c = N.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 Zn({ inputs: { x: r }, backend: n });
    p = new m2(c), d.push({ type: "int32", data: [c.strideHeight, c.strideWidth] });
  } else
    p = new f2(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 rae = { kernelName: Ua, backendName: "webgpu", kernelFunc: sae };
function aae(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { keepDims: a, axis: i } = s;
  return sc(r, i, a, "mean", n);
}
var iae = { kernelName: Ga, backendName: "webgpu", kernelFunc: aae };
function oae(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
  return sc(r, a, i, "min", n);
}
var uae = { kernelName: Ha, backendName: "webgpu", kernelFunc: oae };
var lae = fn({ opSnippet: 16, cpuKernelImpl: Dne });
var cae = { kernelName: qa, backendName: "webgpu", kernelFunc: lae };
var dae = 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 = Wt(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 pae = { kernelName: ja, 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 dae(s.shape, r, a);
  return i.runWebGPUProgram(u, [s], s.dtype, o);
} };
function hae(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  if (n.shouldExecuteOnCPU([s])) {
    let a = n.tensorMap.get(s.dataId), [i, o] = One(a.values, s.shape, s.dtype);
    return n.makeTensorInfo(o, s.dtype, i);
  }
  let r = new tc(s.shape, 11);
  return n.runWebGPUProgram(r, [s], s.dtype);
}
var fae = { kernelName: ko, backendName: "webgpu", kernelFunc: hae };
function mae(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 } = xs.nonMaxSuppressionV3Impl(l, c, i, o, u);
  return n.makeTensorInfo([p.length], "int32", new Int32Array(p));
}
var gae = { kernelName: So, backendName: "webgpu", kernelFunc: mae };
function bae(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 } = xs.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 yae = { kernelName: Co, backendName: "webgpu", kernelFunc: bae };
function Md(e) {
  let { inputs: t, backend: n } = e, { x: s } = t;
  if (s.dtype === "complex64") {
    let r = nc({ inputs: { input: s }, backend: n }), a = Md({ inputs: { x: r }, backend: n }), i = Qp({ inputs: { input: s }, backend: n }), o = Md({ inputs: { x: i }, backend: n }), u = uu({ 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 cu({ attrs: { shape: s.shape, dtype: s.dtype, value: s.dtype === "string" ? "" : 0 }, backend: n });
}
var vae = { kernelName: Go, backendName: "webgpu", kernelFunc: Md };
function T2(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 = T2({ inputs: { x: r }, backend: n }), i = Qp({ inputs: { input: s }, backend: n }), o = Md({ inputs: { x: i }, backend: n }), u = uu({ 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 cu({ attrs: { shape: s.shape, dtype: s.dtype, value: 1 }, backend: n });
}
var xae = { kernelName: No, backendName: "webgpu", kernelFunc: T2 };
function wae(e) {
  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
  if (t.length === 1)
    return Xm({ 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 = Xm({ inputs: { input: c }, backend: n, attrs: { dim: r } });
    return o.push(p), p;
  }), l = b2({ inputs: u, backend: n, attrs: { axis: r } });
  return o.forEach((c) => n.disposeData(c.dataId)), l;
}
var kae = { kernelName: $o, backendName: "webgpu", kernelFunc: wae };
var Iae = class {
  constructor(e, t) {
    this.variableNames = ["x"], this.uniforms = "constantValue : f32;", this.workGroupSize = [64, 1, 1], this.size = true, this.outputShape = t.map((n, s) => n[0] + e[s] + n[1]), this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize), t.map((n, s) => {
      this.uniforms += ` pad${s} : vec2<i32>;`;
    }), this.xShape = e, this.shaderKey = "pad";
  }
  getUserCode() {
    let e = this.xShape.length, t = Wt(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 $2 = (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 Zn({ 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 cu({ 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 Iae(r.shape, a);
  return n.runWebGPUProgram(u, [r], r.dtype, o);
};
var Sae = { kernelName: Xa, backendName: "webgpu", kernelFunc: $2 };
var Cae = fn({ opSnippet: 13 });
var Nae = { kernelName: Ya, backendName: "webgpu", kernelFunc: Cae };
function Tae(e) {
  let { inputs: t, backend: n } = e, { x: s, alpha: r } = t, a = new p2(14, s.shape, r.shape);
  return n.runWebGPUProgram(a, [s, r], "float32");
}
var $ae = { kernelName: Qa, backendName: "webgpu", kernelFunc: Tae };
function _ae(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { axis: a, keepDims: i } = s;
  return sc(r, a, i, "prod", n);
}
var Aae = { kernelName: _o, backendName: "webgpu", kernelFunc: _ae };
var Eae = (e) => {
  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = Mne(s, r, a, i);
  return t.makeTensorInfo([o.length], i, o);
};
var Rae = { kernelName: wl, backendName: "webgpu", kernelFunc: Eae };
var _2 = fn({ opSnippet: 3 });
var Dae = { kernelName: Ea, backendName: "webgpu", kernelFunc: _2 };
var Fae = jt({ opType: 13 });
var Oae = { kernelName: Za, backendName: "webgpu", kernelFunc: Fae };
var Pae = jt({ opType: 14 });
var zae = { kernelName: ei, backendName: "webgpu", kernelFunc: Pae };
var Mae = 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 Lae(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 Mae(r.shape, u, l);
  return n.runWebGPUProgram(f, [r], "float32", h);
}
var Bae = { kernelName: Ja, backendName: "webgpu", kernelFunc: Lae };
var Vae = 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 Wae(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 Vae(r.shape, u, l, i);
  return n.runWebGPUProgram(f, [r], r.dtype, h);
}
var Uae = { kernelName: Il, backendName: "webgpu", kernelFunc: Wae };
var Gae = 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));
            ${this.fillSnippet}
            if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
                coordY < uniforms.xShape[1]) {
              outputValue = getX(coords[0], coordY, coordX, coords[3]);
            }
            setOutputAtIndex(index, outputValue);
          }
        }
      `;
  }
};
var Hae = { kernelName: Ho, backendName: "webgpu", kernelFunc: ({ inputs: e, attrs: t, backend: n }) => {
  let { image: s } = e, { radians: r, fillValue: a, center: i } = t, o = n, u = new Gae(s.shape, a), [l, c] = N.getImageCenter(i, s.shape[1], s.shape[2]), p = [{ type: "float32", data: [l] }, { type: "float32", data: [c] }, { type: "float32", data: [Math.sin(r)] }, { type: "float32", data: [Math.cos(r)] }];
  return typeof a == "number" ? p.push({ type: "float32", data: [Number.parseFloat(a.toFixed(2))] }) : p.push({ type: "float32", data: a }), o.runWebGPUProgram(u, [s], s.dtype, p);
} };
var qae = jt({ opType: 16, cpuKernelImpl: Lne });
var jae = { kernelName: ti, backendName: "webgpu", kernelFunc: qae };
var Kae = class {
  constructor(e, t, n, s, r, a, i) {
    this.variableNames = ["updates", "indices"], this.workGroupSize = [64, 1, 1], this.atomic = true, this.outputShape = a, this.type = i, this.dispatchLayout = Be(e), this.dispatch = _e(this.dispatchLayout, e, this.workGroupSize), this.sliceDimGreaterThanOne = t > 1, this.shaderKey = `scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${i}`;
    let o = Wt(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.numbers[flatIndex]), i32(updateValue));" : `
     var assumed = atomicLoad(&(result.numbers[flatIndex]));
     var success = 0;
     for (; success == 0;) {
       let new = bitcast<f32>(assumed) + updateValue;
       let newI32 = bitcast<i32>(new);
       let resValue = atomicCompareExchangeWeak(&(result.numbers[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 Xae(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 } = N.calculateShapes(a, r, i), d = [p / l, l];
  if (p === 0)
    return n.makeTensorInfo(i, r.dtype);
  let h = Me({ inputs: { x: r }, backend: n, attrs: { shape: [u, o] } }), f = Me({ inputs: { x: a }, backend: n, attrs: { shape: [u, l] } }), m = f.dtype, g = cu({ 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 Kae(f.shape, o, h.shape.length, f.shape.length, c, d, m), x = n.runWebGPUProgram(v, [f, h], m, y, g), k = Me({ inputs: { x }, backend: n, attrs: { shape: i } });
  return n.disposeData(h.dataId), n.disposeData(f.dataId), n.disposeData(x.dataId), k;
}
var Yae = { kernelName: Do, backendName: "webgpu", kernelFunc: Xae };
var Qae = 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 Zae(e) {
  let { inputs: t, backend: n } = e, { condition: s, t: r, e: a } = t, i = new Qae(s.shape.length, r.shape, r.shape.length);
  return n.runWebGPUProgram(i, [s, r, a], yn(r.dtype, a.dtype));
}
var Jae = { kernelName: Fo, backendName: "webgpu", kernelFunc: Zae };
var eie = jt({ opType: 19 });
var tie = { kernelName: si, backendName: "webgpu", kernelFunc: eie };
var nie = jt({ opType: 17 });
var sie = { kernelName: ni, backendName: "webgpu", kernelFunc: nie };
var rie = jt({ opType: 18 });
var aie = { kernelName: Po, backendName: "webgpu", kernelFunc: rie };
var A2 = fn({ opSnippet: 2, cpuKernelImpl: Gne, supportsComplex: true });
var iie = { kernelName: ui, backendName: "webgpu", kernelFunc: A2 };
function oie(e) {
  let { inputs: t, backend: n, attrs: s } = e, { logits: r } = t, { dim: a } = s, i = w.parseAxisParam([a], r.shape), o = N2({ inputs: { x: r }, backend: n, attrs: { reductionIndices: i, keepDims: false } }), u = N.expandShapeToKeepDim(o.shape, i), l = Me({ inputs: { x: o }, backend: n, attrs: { shape: u } }), c = A2({ inputs: { a: r, b: l }, backend: n }), p = k2({ inputs: { x: c }, backend: n }), d = Tv({ inputs: { x: p }, backend: n, attrs: { axis: i, keepDims: false } }), h = Me({ inputs: { x: d }, backend: n, attrs: { shape: u } }), f = _2({ 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 uie = { kernelName: ii, backendName: "webgpu", kernelFunc: oie };
var lie = (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 = $2({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), p = N.getReshaped(c.shape, a, o, false), d = N.getPermuted(p.length, a.length, false), h = N.getReshapedPermuted(c.shape, a, o, false), f = Me({ inputs: { x: c }, backend: n, attrs: { shape: p } }), m = vi({ inputs: { x: f }, backend: n, attrs: { perm: d } }), g = Me({ inputs: { x: m }, backend: n, attrs: { shape: h } });
  return l.push(c), l.push(f), l.push(m), l.forEach((b) => n.disposeData(b.dataId)), g;
};
var cie = { kernelName: zo, backendName: "webgpu", kernelFunc: lie };
var die = class {
  constructor(e, t, n, s, r, a, i = true) {
    this.variableNames = ["updates", "indices", "defaultValue"], this.workGroupSize = [64, 1, 1], this.workPerThread = 4, this.size = true, this.outputShape = a, this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]);
    let o = t > 1;
    this.shaderKey = `scatter_${n}_${s}_${o}`;
    let u = Wt(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 pie(e) {
  let { inputs: t, backend: n, attrs: s } = e, { sparseIndices: r, sparseValues: a, defaultValue: i } = t, { outputShape: o } = s, { sliceRank: u, numUpdates: l, strides: c, outputSize: p } = N.calculateShapes(a, r, o), d = false, h = [{ type: "int32", data: [l] }, { type: "int32", data: [u] }, { type: "int32", data: c }], f = new die(l, u, r.shape.length, a.shape.length, c, [p, 1], d), m = n.runWebGPUProgram(f, [a, r, i], a.dtype, h), g = Me({ inputs: { x: m }, backend: n, attrs: { shape: o } });
  return n.disposeData(m.dataId), g;
}
var hie = { kernelName: ap, backendName: "webgpu", kernelFunc: pie };
function fie(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 = N.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 = lu({ inputs: { x: r }, backend: n, attrs: { begin: c, size: h } });
    return c[o] += d, f;
  });
}
var mie = { kernelName: Mo, backendName: "webgpu", kernelFunc: fie };
var gie = jt({ opType: 20 });
var bie = { kernelName: ri, backendName: "webgpu", kernelFunc: gie };
var yie = { kernelName: $l, backendName: "webgpu", kernelFunc: ({ inputs: e, backend: t }) => {
  let { x: n } = e, s = t, r = new tc(n.shape, 21);
  return s.runWebGPUProgram(r, [n], n.dtype);
} };
var vie = fn({ opSnippet: 11 });
var xie = { kernelName: oi, backendName: "webgpu", kernelFunc: vie };
var wie = 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 = Wt(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 kie(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { begin: a, end: i, strides: o, beginMask: u, endMask: l, ellipsisMask: c, newAxisMask: p, shrinkAxisMask: d } = s, { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = wt.sliceInfo(r.shape, a, i, o, u, l, c, p, d), k;
  if (m)
    k = Me({ 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 C = wt.computeOutShape(y, v, x), T = lu({ inputs: { x: r }, backend: n, attrs: { begin: y, size: C } });
    k = Me({ inputs: { x: T }, backend: n, attrs: { shape: f } }), n.disposeData(T.dataId);
  } else if (n.shouldExecuteOnCPU([r])) {
    let T = n.readSync(r.dataId), E = De(r.shape, r.dtype, T), A = Wne(h, E, x, y);
    k = n.makeTensorInfo(f, r.dtype, A.values);
  } else {
    let T = new wie(h), E = [{ type: "int32", data: y }, { type: "int32", data: x }], A = n.runWebGPUProgram(T, [r], r.dtype, E);
    k = Me({ inputs: { x: A }, backend: n, attrs: { shape: f } }), n.disposeData(A.dataId);
  }
  return k;
}
var Iie = { kernelName: Lo, backendName: "webgpu", kernelFunc: kie };
function Sie(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] = Une(d, h, r, a, i, o, u, l);
  return [n.makeTensorInfo([f.length], "string", f), n.makeTensorInfo(p.shape, "int32", m)];
}
var Cie = { kernelName: ip, backendName: "webgpu", kernelFunc: Sie };
var Nie = jt({ opType: 22 });
var Tie = { kernelName: li, backendName: "webgpu", kernelFunc: Nie };
var $ie = 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 = _ie(this.rank, "uniforms.");
    return `
      ${Ue()}
        if (index < uniforms.size) {
          let resRC = getCoordsFromIndex(index);
          setOutputAtIndex(index, getA(${e}));
        }
      }
    `;
  }
};
function _ie(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 Aie(e) {
  let { inputs: t, backend: n, attrs: s } = e, { x: r } = t, { reps: a } = s;
  if (n.shouldExecuteOnCPU([r]) || r.dtype === "string" || r.shape.length >= 5) {
    let u = n.readSync(r.dataId), l = r.dtype === "string" ? u.map((d) => w.decodeString(d)) : u, c = De(r.shape, r.dtype, l), p = Hne(c, a);
    return n.makeTensorInfo(p.shape, p.dtype, p.values);
  }
  let i = new $ie(r.shape, a);
  return n.runWebGPUProgram(i, [r], r.dtype);
}
var Eie = { kernelName: Sr, backendName: "webgpu", kernelFunc: Aie };
var Rie = 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 Die = 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 Li(e, t) {
  t !== null && e.disposeData(t.dataId);
}
function Aw(e) {
  let t = 1;
  for (; t < e; )
    t *= 2;
  return t;
}
function Fie(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), [C, T] = qne(k, o, r.dtype, a, i);
    return [n.makeTensorInfo(C.shape, C.dtype, C.values), n.makeTensorInfo(T.shape, T.dtype, T.values)];
  }
  if (a === 0)
    return o[o.length - 1] = 0, [n.makeTensorInfo(o, r.dtype, []), n.makeTensorInfo(o, "int32", [])];
  if (u === 1)
    return [r, cu({ attrs: { shape: o, dtype: "int32", value: 0 }, backend: n })];
  let c = w.sizeFromShape(o) / u, p = Me({ inputs: { x: r }, attrs: { shape: [c, u] }, backend: n }), d = Aw(a), h = Aw(u), f = null, m = () => f === null ? [p, p] : [p, f], g = (k, C, T) => {
    let E = m(), A = new Rie(T), R = [{ type: "int32", data: [u] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "float32", data: [Number.NEGATIVE_INFINITY] }, { type: "int32", data: [k] }, { type: "int32", data: [C] }], F = f;
    f = n.runWebGPUProgram(A, E, "int32", R), Li(n, F);
  };
  for (let k = 1; k < d; k *= 2) {
    let C = k * 2;
    for (let T = k; T >= 1; T /= 2)
      g(C, T, [c, h]);
  }
  for (let k = h; k > d; k /= 2) {
    let C = m(), T = new Die([c, k / 2]), A = [{ type: "int32", data: [u] }, { type: "int32", data: [f === null ? 1 : 0] }, { type: "int32", data: [d] }], P = f;
    f = n.runWebGPUProgram(T, C, "int32", A), Li(n, P);
    let R = d / 2, F = R * 2;
    for (let $ = R; $ >= 1; $ /= 2)
      g(F, $, f.shape);
  }
  let b = f;
  f = lu({ inputs: { x: f }, backend: n, attrs: { begin: 0, size: [c, a] } }), Li(n, b);
  let y = C2({ inputs: { x: p, indices: f }, backend: n, attrs: { axis: 1, batchDims: 1 } });
  Li(n, p);
  let v = o.slice(0, -1);
  v.push(a), b = f, f = Me({ inputs: { x: f }, attrs: { shape: v }, backend: n }), Li(n, b);
  let x = y;
  return y = Me({ inputs: { x: y }, attrs: { shape: v }, backend: n }), Li(n, x), [y, f];
}
var Oie = { kernelName: Vo, backendName: "webgpu", kernelFunc: Fie };
var Pie = 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 zie(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 Pie(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 Mie = { kernelName: Wo, backendName: "webgpu", kernelFunc: zie };
function Lie(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 = lu({ inputs: { x: i }, backend: n, attrs: { begin: d, size: h } }), b = Me({ inputs: { x: g }, backend: n, attrs: { shape: l } });
    f[m] = b, p.push(g);
  }
  return p.forEach((m) => n.disposeData(m.dataId)), f;
}
var Bie = { kernelName: Uo, backendName: "webgpu", kernelFunc: Lie };
var Vie = [pne, Xne, Qne, ese, ise, use, cse, pse, bse, wse, Ise, Tse, gne, Ese, Lse, Use, Hse, jse, Yse, Jse, tre, ire, ure, cre, dre, pre, fre, gre, yre, Sre, xre, kre, Tre, _re, Ere, Fre, zre, Lre, Vre, mne, _se, Ure, Hre, jre, Xre, Qre, Jre, eae, nae, rae, iae, uae, cae, pae, nre, fae, gae, yae, yse, xae, kae, Sae, Nae, $ae, Aae, Rae, vse, Dae, Oae, zae, cne, Bae, Uae, Hae, jae, Yae, Jae, tie, sie, aie, mse, Iie, Cie, uie, cie, hie, mie, bie, yie, xie, iie, rre, Tie, Eie, Oie, Mie, rse, Bie, vae];
for (let e of Vie)
  _l(e);
var Wie = 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 = Ew(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 = Ew(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 Ew(e, t) {
  return `${e}_${t}`;
}
var E2 = class {
  constructor() {
    this.outputShape = [0], this.variableNames = [], this.workGroupSize = [256, 1, 1], this.lastUniformData = [], this.inputTexture = null, this.layout = null, this.lastPixelSize = { width: 0, height: 0 }, this.disposed = false, this.shaderKey = "fromPixels", this.useImport = false;
  }
  updateOutputShape(e) {
    w.arraysEqual(this.outputShape, e) || (this.outputShape = e, this.workPerThread = e[2], this.dispatchLayout = Be(this.outputShape), this.dispatch = _e(this.dispatchLayout, this.outputShape, this.workGroupSize, [this.workPerThread, 1, 1]));
  }
  makeFromPixelsSource() {
    let e = this.useImport ? "textureLoad(src, vec2<i32>(coords.yx));" : "textureLoad(src, vec2<i32>(coords.yx), 0)";
    return `
      @binding(1) @group(0) var src: ${this.useImport ? "texture_external" : "texture_2d<f32>"};

      ${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.numbers[flatIndex] = i32(floor(255.0 * values[i]));
          }
        }
      }
  `;
  }
  getUserCode() {
    return this.makeFromPixelsSource();
  }
  setPipeline(e) {
    this.pipeline = e;
  }
  setUniform(e, t) {
    if (!this.uniform) {
      let n = e.createBuffer({ size: t.length * 4, usage: GPUBufferUsage.UNIFORM | GPUBufferUsage.COPY_DST });
      this.uniform = n;
    }
    !t || t.length === this.lastUniformData.length && t.every((n, s) => n === this.lastUniformData[s]) || (e.queue.writeBuffer(this.uniform, 0, new Uint32Array(t)), this.lastUniformData = t);
  }
  makeInputTexture(e, t, n) {
    return (!this.inputTexture || this.lastPixelSize.width !== t || this.lastPixelSize.height !== n) && (this.inputTexture && this.inputTexture.destroy(), this.inputTexture = e.createTexture({ size: [t, n], format: "rgba8unorm", usage: GPUTextureUsage.COPY_DST | GPUTextureUsage.RENDER_ATTACHMENT | GPUTextureUsage.TEXTURE_BINDING }), this.lastPixelSize.width = t, this.lastPixelSize.height = n), this.inputTexture;
  }
  dispose() {
    this.disposed || (this.uniform && this.uniform.destroy(), this.inputTexture && this.inputTexture.destroy(), this.disposed = true);
  }
  getLayout(e) {
    return this.layout === null && (this.layout = this.createTextureLayout(e)), this.layout;
  }
  createTextureLayout(e) {
    let t = [];
    t.push({ binding: 0, visibility: GPUShaderStage.COMPUTE, buffer: { type: "storage" } }), t.push({ binding: 1, visibility: GPUShaderStage.COMPUTE, texture: {} }), t.push({ binding: 2, visibility: GPUShaderStage.COMPUTE, buffer: {} });
    let n = e.createBindGroupLayout({ entries: t }), s = e.createPipelineLayout({ bindGroupLayouts: [n] });
    return { bindGroupLayout: n, pipelineLayout: s };
  }
};
var Uie = class extends E2 {
  constructor() {
    super(...arguments);
    this.layout = null, this.useImport = true;
  }
  getUserCode() {
    return this.makeFromPixelsSource();
  }
  getLayout(e) {
    return this.layout === null && (this.layout = this.createTextureImportLayout(e)), this.layout;
  }
  createTextureImportLayout(e) {
    let t = [];
    t.push({ binding: 0, visibility: GPUShaderStage.COMPUTE, buffer: { type: "storage" } }), t.push({ binding: 1, visibility: GPUShaderStage.COMPUTE, externalTexture: {} }), t.push({ binding: 2, visibility: GPUShaderStage.COMPUTE, buffer: {} });
    let n = e.createBindGroupLayout({ entries: t }), s = e.createPipelineLayout({ bindGroupLayouts: [n] });
    return { bindGroupLayout: n, pipelineLayout: s };
  }
};
var Gie = X().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD");
var Rw = (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 R2 = class extends tl {
  constructor(e, t = false) {
    super();
    if (this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.tensorDisposalQueue = [], this.uniformDisposalQueue = [], this.stagingDisposalQueue = [], this.disposed = false, this.uploadWaitMs = 0, this.downloadWaitMs = 0, this.dispatchNumberInEncoder = 0, !Sv())
      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 Wie(this.device), this.tensorMap = new Wd(this, Ss()), this.supportTimeQuery && (this.querySet = this.device.createQuerySet({ type: "timestamp", count: 2 })), X().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 R2.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.tensorDisposalQueue = [], this.uniformDisposalQueue = [], this.stagingDisposalQueue = [];
  }
  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;
  }
  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) * Hm(n);
    return this.tensorMap.set(s, { dtype: n, values: e, bufferInfo: { byteSize: r, usage: this.defaultGpuBufferUsage() }, refCount: 1 }), s;
  }
  move(e, t, n, s, r) {
    if (s === "complex64")
      throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");
    let a = w.sizeFromShape(n) * Hm(s);
    this.tensorMap.set(e, { dtype: s, values: t, bufferInfo: { byteSize: a, usage: this.defaultGpuBufferUsage() }, refCount: r });
  }
  submitQueue() {
    this.ensureComputePassEnded(), this.queue.submit([this.currentCommandEncoder.finish()]), this.currentCommandEncoder = null, this.dispatchNumberInEncoder = 0, this.commandQueueOwnedIds = /* @__PURE__ */ new WeakSet(), this.flushDisposalQueue();
  }
  getBuffer(e) {
    return this.uploadToGPU(e), this.tensorMap.get(e).bufferInfo.buffer;
  }
  getFromPixelsProgram(e) {
    switch (e) {
      case "copyExternal":
        return this.fromPixelProgram || (this.fromPixelProgram = new E2()), this.fromPixelProgram;
      case "import":
        return this.fromPixelImportProgram || (this.fromPixelImportProgram = new Uie()), this.fromPixelImportProgram;
      default:
        w.assert(false, () => "Unsupported fromPixels shape");
        return;
    }
  }
  ensureCommandEncoderReady() {
    this.currentCommandEncoder || (this.currentCommandEncoder = this.device.createCommandEncoder());
  }
  ensureComputePassEnded() {
    this.currentComputePass && (this.currentComputePass.endPass(), this.currentComputePass = null);
  }
  getComputePass() {
    return this.currentComputePass || (this.currentComputePass = this.currentCommandEncoder.beginComputePass()), this.currentComputePass;
  }
  async getBufferData(e) {
    if (e.values != null)
      return e.values;
    let t = this.acquireBuffer(e.bufferInfo.byteSize, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ);
    this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer, 0, t, 0, e.bufferInfo.byteSize), this.submitQueue(), await t.mapAsync(GPUMapMode.READ);
    let n = t.getMappedRange().slice(0);
    return t.unmap(), t != null && this.bufferManager.releaseBuffer(t, e.bufferInfo.byteSize, GPUBufferUsage.COPY_DST | GPUBufferUsage.MAP_READ), X().getBool("WEBGPU_USE_PROFILE_TOOL") && (w.assert(this.dummyContext !== void 0, () => "Fail to get context for profiling tool"), this.dummyContext.getCurrentTexture()), n;
  }
  convertAndCacheOnCPU(e, t) {
    let n = this.tensorMap.get(e);
    return this.maybeReleaseBuffer(e), n.values = t, n.values;
  }
  readSync(e) {
    let t = this.tensorMap.get(e), { values: n } = t;
    if (n == null)
      throw new Error("WebGPU readSync is only available for CPU-resident tensors.");
    return n;
  }
  async read(e) {
    if (!this.tensorMap.has(e))
      throw new Error(`Tensor ${e} was not registered!`);
    let t = this.tensorMap.get(e), { values: n } = t;
    if (n != null)
      return this.convertAndCacheOnCPU(e, n);
    let s;
    if (t.dtype === "complex64") {
      let r = await Promise.all([this.read(t.complexTensorInfos.real.dataId), this.read(t.complexTensorInfos.imag.dataId)]), a = r[0], i = r[1];
      s = N.mergeRealAndImagArrays(a, i);
    } else {
      let r = await this.getBufferData(t);
      s = u2(r, t.dtype);
    }
    return this.convertAndCacheOnCPU(e, s), s;
  }
  bufferSync(e) {
    let t = this.readSync(e.dataId), n = t;
    if (e.dtype === "string")
      try {
        n = t.map((s) => w.decodeString(s));
      } catch (s) {
        throw new Error("Failed to decode encoded string bytes into utf-8");
      }
    return De(e.shape, e.dtype, n);
  }
  async time(e) {
    let t = this.activeTimers, n = [], s = false;
    this.programTimersStack == null ? (this.programTimersStack = n, s = true) : this.activeTimers.push(n), this.activeTimers = n, e();
    let r = w.flatten(this.activeTimers.map((u) => u.query)).filter((u) => u != null), a = w.flatten(this.activeTimers.map((u) => u.name)).filter((u) => u != null);
    this.activeTimers = t, s && (this.programTimersStack = null);
    let i = { uploadWaitMs: this.uploadWaitMs, downloadWaitMs: this.downloadWaitMs, kernelMs: null, wallMs: null }, o = await Promise.all(r);
    return i.kernelMs = w.sum(o), i.getExtraProfileInfo = () => o.map((u, l) => ({ name: a[l], ms: u })).map((u) => `${u.name}: ${u.ms}`).join(", "), this.uploadWaitMs = 0, this.downloadWaitMs = 0, i;
  }
  getAndSavePipeline(e, t) {
    return e in this.pipelineCache || (this.pipelineCache[e] = t()), this.pipelineCache[e];
  }
  makeTensorInfo(e, t, n) {
    let s;
    if (t === "string" && n != null && n.length > 0 && w.isString(n[0])) {
      let r = n.map((a) => w.encodeString(a));
      s = this.write(r, e, t);
    } else
      s = this.write(n, e, t);
    return { dataId: s, shape: e, dtype: t };
  }
  tensorToBinding(e) {
    if (!e)
      return null;
    let t = this.tensorMap.get(e.dataId);
    return { offset: 0, size: t.bufferInfo.byteSize, buffer: t.bufferInfo.buffer };
  }
  async getQueryTime(e) {
    return this.supportTimeQuery ? this.getTimeFromQuerySet(e) : 0;
  }
  uploadToGPU(e) {
    let t = this.tensorMap.get(e);
    if (t.bufferInfo.buffer == null && (t.bufferInfo.buffer = this.acquireBuffer(t.bufferInfo.byteSize), t.values)) {
      let n = this.bufferManager.acquireUploadBuffer(t.bufferInfo.byteSize, GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC), s = n.getMappedRange();
      t.dtype === "int32" || t.dtype === "bool" ? new Int32Array(s).set(t.values) : new Float32Array(s).set(t.values), n.unmap(), this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.copyBufferToBuffer(n, 0, t.bufferInfo.buffer, 0, t.bufferInfo.byteSize);
      let r = { byteSize: t.bufferInfo.byteSize, usage: GPUBufferUsage.MAP_WRITE | GPUBufferUsage.COPY_SRC, buffer: n };
      this.stagingDisposalQueue.push(r);
    }
  }
  makeUniforms(e) {
    let t = 0, n = [];
    e.forEach((a) => {
      a.data.length === 0 && (a.data = [1]);
      let i;
      switch (a.data.length) {
        case 1:
          i = 4;
          break;
        case 2:
          i = 8;
          break;
        case 3:
          i = 16;
          break;
        case 4:
          i = 16;
          break;
        default:
          w.assert(false, () => `Unsupported ${a.data.length}D shape`);
      }
      t = Math.ceil(t / i) * i, n.push(t), t += a.data.length * 4;
    });
    let s = new ArrayBuffer(t);
    e.forEach((a, i) => {
      let o = n[i];
      a.type === "int32" ? new Int32Array(s, o, a.data.length).set(a.data) : a.type === "uint32" ? new Uint32Array(s, o, a.data.length).set(a.data) : new Float32Array(s, o, a.data.length).set(a.data);
    });
    let r = this.acquireBuffer(t, GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM);
    return this.queue.writeBuffer(r, 0, s, 0, t), { offset: 0, size: t, buffer: r };
  }
  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 T = this.tensorMap.get(r.dataId);
        return T.values = w.getTypedArrayFromDType(r.dtype, 0), r;
      }
      this.uploadToGPU(r.dataId);
    }
    e.dispatch = Rw(this.device, e);
    let a = [{ type: "float32", data: [NaN] }], i = t.concat(r).map((T) => T.shape), o = "int32";
    i.map((T) => {
      a.push({ type: o, data: T });
    });
    let u = w.computeStrides(r.shape);
    if (a.push({ type: o, data: u }), e.size) {
      let T = w.sizeFromShape(e.outputShape);
      a.push({ type: o, data: [e.isVec4 ? T / 4 : T] });
    }
    s && (a = [...a, ...s]);
    let l = this.makeUniforms(a), c = t.map((T, E) => {
      if (T.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(T.dataId), { dtype: this.tensorMap.get(T.dataId).dtype, shape: T.shape, name: e.variableNames[E] };
    }), p = c.map((T) => T.dtype).concat(r.dtype), d = c.map((T) => N.getBroadcastDims(T.shape, r.shape)), h = c.map((T) => w.arraysEqual(T.shape, r.shape)).join("_"), f = d.map((T) => T.join("_")).join(";"), m = S2(e, i, p, f, h), { bindGroupLayout: g, pipelineLayout: b } = this.getCachedOrCreateLayout(e.variableNames.length), y = this.getAndSavePipeline(m, () => I2(this.device, e, b, c, r)), v = this.activeTimers != null, x = Ire(this.device, g, t.map((T) => this.tensorToBinding(T)), this.tensorToBinding(r), l);
    this.ensureCommandEncoderReady();
    let k = this.getComputePass();
    v && this.supportTimeQuery && k.writeTimestamp(this.querySet, 0), k.setPipeline(y), k.setBindGroup(0, x), k.dispatch(e.dispatch[0], e.dispatch[1], e.dispatch[2]), v && this.supportTimeQuery && k.writeTimestamp(this.querySet, 1), this.dispatchNumberInEncoder++, t.forEach((T) => {
      this.commandQueueOwnedIds.add(T.dataId);
    }), this.commandQueueOwnedIds.add(r.dataId);
    let C = { byteSize: l.size, usage: GPUBufferUsage.COPY_DST | GPUBufferUsage.UNIFORM, buffer: l.buffer };
    return this.uniformDisposalQueue.push(C), X().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE") <= this.dispatchNumberInEncoder && this.submitQueue(), v && this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(this.querySet) }), r;
  }
  runFromPixelsProgram(e, t, n, s, r) {
    e.dispatch = Rw(this.device, e);
    let a = this.device.createBindGroup({ layout: n.bindGroupLayout, entries: [{ binding: 0, resource: { buffer: t } }, { binding: 1, resource: s }, { binding: 2, resource: { buffer: e.uniform } }] });
    this.ensureCommandEncoderReady();
    let i = this.getComputePass(), o = this.activeTimers != null;
    o && this.supportTimeQuery && i.writeTimestamp(this.querySet, 0), i.setPipeline(e.pipeline), i.setBindGroup(0, a), i.dispatch(e.dispatch[0], e.dispatch[1], e.dispatch[2]), o && this.supportTimeQuery && i.writeTimestamp(this.querySet, 1), this.commandQueueOwnedIds.add(r), this.submitQueue(), o && this.activeTimers.push({ name: e.constructor.name, query: this.getQueryTime(this.querySet) });
  }
  async getTimeFromQuerySet(e) {
    let t = this.acquireBuffer(16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE), n = this.acquireBuffer(16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST);
    this.ensureCommandEncoderReady(), this.ensureComputePassEnded(), this.currentCommandEncoder.resolveQuerySet(e, 0, 2, t, 0), this.currentCommandEncoder.copyBufferToBuffer(t, 0, n, 0, 16), this.submitQueue(), await n.mapAsync(GPUMapMode.READ);
    let s = new BigUint64Array(n.getMappedRange()), r = Number(s[1] - s[0]);
    return n.unmap(), this.bufferManager.releaseBuffer(n, 16, GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST), this.bufferManager.releaseBuffer(t, 16, GPUBufferUsage.COPY_SRC | GPUBufferUsage.QUERY_RESOLVE), r / 1e6;
  }
  shouldExecuteOnCPU(e, t = Gie) {
    return X().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.fromPixelProgram && this.fromPixelProgram.dispose(), this.fromPixelImportProgram && this.fromPixelImportProgram.dispose(), this.disposed = true);
  }
};
var $v = R2;
$v.nextDataId = 0;
var Hie = {};
Ae(Hie, { WebGPUBackend: () => $v, webgpu_util: () => i2 });
Sv() && dp("webgpu", async () => {
  X().set("CHECK_COMPUTATION_FOR_ERRORS", false);
  let e = { powerPreference: X().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 $v(a, r);
}, 3);
var Ct = ((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))(Ct || {});
var Zp = ((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))(Zp || {});
var D2;
function qie(e) {
  D2 = e.wasm.cwrap(na, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function jie(e) {
  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a, bias: i, preluActivationWeights: o } = t;
  if (r.dtype !== "float32" || a.dtype !== "float32")
    throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");
  let { transposeA: u, transposeB: l, activation: c, leakyreluAlpha: p } = s, d = n.dataIdMap.get(r.dataId).id, h = n.dataIdMap.get(a.dataId).id, f = 0;
  if (i != null) {
    let E = n.dataIdMap.get(i.dataId);
    if (E.shape.length !== 1)
      throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);
    f = E.id;
  }
  let m = o == null ? 0 : n.dataIdMap.get(o.dataId).id, g = Zp[c];
  if (g == null)
    throw new Error(`${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, C = new Uint8Array(new Int32Array(r.shape).buffer), T = new Uint8Array(new Int32Array(a.shape).buffer);
  return D2(d, C, r.shape.length, h, T, a.shape.length, u, l, g, f, m, p || 0, k), x;
}
var Kie = { kernelName: na, backendName: "wasm", setupFunc: qie, kernelFunc: jie };
function Kt(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, Ct[o.dtype], c), l;
  }
  return { kernelName: e, backendName: "wasm", setupFunc: s, kernelFunc: r };
}
var Xie = Kt(ao);
function mn(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 = N.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, Ct[l.dtype], y))(), m;
  }
  return { kernelName: e, backendName: "wasm", setupFunc: r, kernelFunc: a };
}
var Yie = true;
var Qie = mn(kr, Yie);
var F2;
function Zie(e) {
  F2 = e.wasm.cwrap(xa, null, ["array", "number", "number", "number"]);
}
function Jie(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 F2(a, r.length, Ct[s.dtype], i), s;
}
var eoe = { kernelName: xa, backendName: "wasm", setupFunc: Zie, kernelFunc: Jie };
function Jp(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 toe = { kernelName: Ma, backendName: "wasm", kernelFunc: Jp };
var O2;
function noe(e) {
  O2 = e.wasm.cwrap(ci, null, ["number", "array", "number", "number", "number", "array", "number"]);
}
function so(e) {
  let { inputs: t, backend: n, attrs: s } = e, [r, a] = roe(t.x.shape, s.perm), i = true;
  for (let f = 0; f < a.length; f++)
    a[f] !== f && (i = false);
  let o = soe(t.x.shape, s.perm), u = { dataId: t.x.dataId, shape: r, dtype: t.x.dtype };
  if (i) {
    let f = Jp({ 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 O2(c, h, u.shape.length, Ct[u.dtype], p, d, a.length), l;
}
function soe(e, t) {
  let n = new Array(e.length);
  for (let s = 0; s < n.length; s++)
    n[s] = e[t[s]];
  return n;
}
function roe(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 aoe = { kernelName: ci, backendName: "wasm", kernelFunc: so, setupFunc: noe };
function Dr(e, t, n) {
  let s = e.shape, r = e.shape.length, a = w.parseAxisParam(t, s), i = a, o = N.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 = N.getInnerMostAxes(i.length, r), u = so({ 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 P2;
function ioe(e) {
  P2 = e.wasm.cwrap(rl, null, ["number, number, number"]);
}
function ooe(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 } = Dr(i, r, t);
  if (h) {
    let v = t.dataIdMap.get(c.dataId).id;
    l = c, u = v;
  }
  let f = l.shape.length;
  N.assertAxesAreInnerMostDims("all", p, f);
  let [m, g] = N.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;
    P2(u, b, v);
  }
  if (h && t.disposeData(c.dataId), a) {
    let v = N.expandShapeToKeepDim(y.shape, d);
    y.shape = v;
  }
  return y;
}
var uoe = { kernelName: rl, backendName: "wasm", setupFunc: ioe, kernelFunc: ooe };
var z2;
function loe(e) {
  z2 = e.wasm.cwrap(al, null, ["number, number, number"]);
}
function coe(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 } = Dr(i, r, t);
  if (h) {
    let v = t.dataIdMap.get(c.dataId).id;
    l = c, u = v;
  }
  let f = l.shape.length;
  N.assertAxesAreInnerMostDims("any", p, f);
  let [m, g] = N.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;
    z2(u, b, v);
  }
  if (h && t.disposeData(c.dataId), a) {
    let v = N.expandShapeToKeepDim(y.shape, d);
    y.shape = v;
  }
  return y;
}
var doe = { kernelName: al, backendName: "wasm", setupFunc: loe, kernelFunc: coe };
var M2;
function poe(e) {
  M2 = e.wasm.cwrap(wa, null, ["number", "number", "number", "number", "number"]);
}
function hoe(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 } = Dr(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 M2(o, Ct[u.dtype], m, g, f), p && t.disposeData(l.dataId), h;
}
var foe = { kernelName: wa, backendName: "wasm", kernelFunc: hoe, setupFunc: poe };
var L2;
function moe(e) {
  L2 = e.wasm.cwrap(ka, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function goe(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 = N.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 L2(a, r.shape[0], r.shape[1], r.shape[2], p, d, h, f, m, g, b, y, v, k), x;
}
var boe = { kernelName: ka, backendName: "wasm", setupFunc: moe, kernelFunc: goe };
function bn(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: ${s.shape}. New shape and old shape must have the same number of elements.`), e.backend.incRef(s.dataId), { dataId: s.dataId, shape: i, dtype: s.dtype };
}
var yoe = { kernelName: Ao, backendName: "wasm", kernelFunc: bn };
var B2;
function voe(e) {
  B2 = e.wasm.cwrap(Ia, null, ["number", "array", "number", "number", "array", "number", "number", "number", "number"]);
}
function xoe(e) {
  let { inputs: t, backend: n, attrs: s } = e, { a: r, b: a } = t, { transposeA: i, transposeB: o } = s;
  if (r.dtype !== "float32" || a.dtype !== "float32")
    throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");
  let u = r.shape.length, l = a.shape.length, c = i ? r.shape[u - 2] : r.shape[u - 1], p = o ? a.shape[l - 1] : a.shape[l - 2], d = i ? r.shape[u - 1] : r.shape[u - 2], h = o ? a.shape[l - 2] : a.shape[l - 1], f = r.shape.slice(0, -2), m = a.shape.slice(0, -2), g = w.sizeFromShape(f), b = w.sizeFromShape(m), v = qo.assertAndGetBroadcastShape(r.shape.slice(0, -2), a.shape.slice(0, -2)).concat([d, h]);
  w.assert(c === p, () => `Error in matMul: inner shapes (${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], C = bn({ inputs: { x: r }, backend: n, attrs: { shape: x } }), T = bn({ inputs: { x: a }, backend: n, attrs: { shape: k } }), E = n.dataIdMap.get(C.dataId).id, A = n.dataIdMap.get(T.dataId).id, P = i ? C.shape[2] : C.shape[1], R = o ? T.shape[1] : T.shape[2], F = Math.max(g, b), $ = n.makeOutput([F, P, R], C.dtype), z = n.dataIdMap.get($.dataId).id, W = new Uint8Array(new Int32Array(C.shape).buffer), q = new Uint8Array(new Int32Array(T.shape).buffer);
  return B2(E, W, C.shape.length, A, q, T.shape.length, i, o, z), n.disposeData(C.dataId), n.disposeData(T.dataId), $.shape = v, $;
}
var woe = { kernelName: Ia, backendName: "wasm", setupFunc: voe, kernelFunc: xoe };
function ba(e) {
  let { inputs: { x: t }, attrs: { begin: n, size: s }, backend: r } = e, [a, i] = wt.parseSliceParams(t, n, s), o = wt.isSliceContinous(t.shape, a, i), u = r.readSync(t.dataId), l = r.makeOutput(i, t.dtype), c = w.computeStrides(t.shape), p = r.dataIdMap.get(l.dataId);
  if (o) {
    let f = wt.computeFlatOffset(a, c);
    return t.dtype === "string" ? p.stringBytes = u.slice(f, f + w.sizeFromShape(i)) : r.typedArrayFromHeap(l).set(u.subarray(f, f + w.sizeFromShape(i))), l;
  }
  if (t.dtype === "string") {
    let f = Dd(u, a, i, t.shape, t.dtype);
    return p.stringBytes = f, l;
  }
  let d = r.typedArrayFromHeap(l), h = t.shape.length;
  if (h === 2)
    koe(u, c[0], d, a, i);
  else if (h === 3)
    Ioe(u, c[0], c[1], d, a, i);
  else if (h === 4)
    Soe(u, c[0], c[1], c[2], d, a, i);
  else {
    let f = Dd(u, a, i, t.shape, t.dtype);
    d.set(f);
  }
  return l;
}
function koe(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 Ioe(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 Soe(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 Coe = { kernelName: Oo, backendName: "wasm", kernelFunc: ba };
function Noe(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 = N.getReshaped(r.shape, a, o), l = N.getPermuted(u.length, a.length), c = N.getReshapedPermuted(r.shape, a, o), p = N.getSliceBeginCoords(i, a.length), d = N.getSliceSize(c, i, a.length), h = bn({ inputs: { x: r }, backend: n, attrs: { shape: u } }), f = so({ inputs: { x: h }, backend: n, attrs: { perm: l } }), m = bn({ inputs: { x: f }, backend: n, attrs: { shape: c } }), g = ba({ 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 Toe = { kernelName: io, backendName: "wasm", kernelFunc: Noe };
function rc(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 $oe = { kernelName: Sa, backendName: "wasm", kernelFunc: rc };
var _oe = Kt(Ca);
var V2;
function Aoe(e) {
  V2 = e.wasm.cwrap(Ir, null, ["number", "number", "number", "number"]);
}
function Eoe(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 V2(o, a, i, l), u;
}
var Roe = { kernelName: Ir, backendName: "wasm", setupFunc: Aoe, kernelFunc: Eoe };
function W2(e) {
  let { inputs: t, backend: n } = e, s = w.parseAxisParam(e.attrs.axis, t[0].shape)[0], r = N.computeOutShape(t.map((h) => h.shape), s), a = t.filter((h) => w.sizeFromShape(h.shape) > 0);
  if (a.length === 1)
    return Jp({ 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 (N.assertParamsConsistent(o, s), a[0].dtype === "string") {
    let h = a.map((v) => {
      let x = w.sizeFromShape(v.shape.slice(s));
      return bn({ inputs: { x: v }, backend: n, attrs: { shape: [-1, x] } });
    }), f = h.map((v) => ({ vals: n.readSync(v.dataId), shape: v.shape }));
    r = N.computeOutShape(h.map((v) => v.shape), 1);
    let m = h[0].shape[0] === 1, g = Jy(f, r, t[0].dtype, m), b = N.computeOutShape(a.map((v) => v.shape), s);
    i.shape = b;
    let y = n.dataIdMap.get(i.dataId);
    return y.stringBytes = N.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 Doe = { kernelName: oo, backendName: "wasm", kernelFunc: W2 };
var U2;
function Foe(e) {
  U2 = e.wasm.cwrap(Na, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Ooe(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 = N.convertConv2DDataFormat(d), f = N.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, C = f.dilationWidth, T = f.strideHeight, E = f.strideWidth, A = f.inChannels, P = f.outChannels, R = f.padInfo.type === "SAME" ? 1 : 0;
  if (f.dataFormat !== "channelsLast")
    throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);
  let F = s.makeOutput(f.outShape, "float32"), $ = s.dataIdMap.get(F.dataId).id;
  return U2(i, r.shape[0], r.shape[1], r.shape[2], o, m, g, b, y, v, x, R, k, C, T, E, A, P, $), F;
}
var Poe = { kernelName: Na, backendName: "wasm", setupFunc: Foe, kernelFunc: Ooe };
var G2;
function zoe(e) {
  G2 = e.wasm.cwrap(Ta, 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 Moe(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 = N.convertConv2DDataFormat(u), h = N.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: C, strideHeight: T, strideWidth: E } = h, A = m - 1 - h.padInfo.top, P = g - 1 - h.padInfo.left, R = h.dataFormat === "channelsLast", F = w.computeStrides(h.inShape), $ = w.computeStrides(r.shape), [z, W, q] = w.computeStrides(a.shape), K = F[0], Y = R ? F[1] : F[2], Z = R ? F[2] : 1, te = R ? 1 : F[1], ee = $[0], se = R ? $[1] : $[2], ne = R ? $[2] : 1, oe = R ? 1 : $[1], re = t.makeOutput(h.inShape, "float32"), le = t.dataIdMap.get(re.dataId).id, me = t.dataIdMap.get(r.dataId).id, we = t.dataIdMap.get(a.dataId).id;
  return G2(me, we, f, m, g, y, v, b, k, C, x, T, E, A, P, z, W, q, K, Y, Z, te, ee, se, ne, oe, le), re;
}
var Loe = { kernelName: Ta, backendName: "wasm", setupFunc: zoe, kernelFunc: Moe };
var Boe = Kt($a);
var Voe = Kt(_a);
var H2 = ((e) => (e[e.bilinear = 0] = "bilinear", e[e.nearest = 1] = "nearest", e))(H2 || {});
var q2;
function Woe(e) {
  q2 = e.wasm.cwrap(lo, null, ["number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
}
function Uoe(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 = rc({ 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 q2(g, b, y, c, k, p, d, H2[r], a, x), m != null && t.disposeData(m.dataId), v;
}
var Goe = { kernelName: lo, backendName: "wasm", setupFunc: Woe, kernelFunc: Uoe };
var j2;
function Hoe(e) {
  j2 = e.wasm.cwrap(uo, null, ["number", "number", "number", "number", "number", "number"]);
}
function qoe(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 = N.getAxesPermutation([a], u), c = r;
  l !== null && (c = so({ inputs: { x: r }, attrs: { perm: l }, backend: n }));
  let p = N.getInnerMostAxes(1, u)[0];
  N.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;
  j2(f, i ? 1 : 0, o ? 1 : 0, h, m, Ct[r.dtype]);
  let g = d;
  if (l !== null) {
    let b = N.getUndoAxesPermutation(l);
    g = so({ inputs: { x: d }, attrs: { perm: b }, backend: n }), n.disposeData(c.dataId), n.disposeData(d.dataId);
  }
  return g;
}
var joe = { kernelName: uo, backendName: "wasm", setupFunc: Hoe, kernelFunc: qoe };
var K2;
function Koe(e) {
  K2 = e.wasm.cwrap(co, null, ["number", "number", "number", "array", "number", "array", "array", "number", "number"]);
}
function Xoe(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 K2(b, a, i === "NHWC" ? 1 : 0, y, r.shape.length - 1, v, x, f.length, k), m;
}
var Yoe = { kernelName: co, backendName: "wasm", setupFunc: Koe, kernelFunc: Xoe };
var X2;
function Qoe(e) {
  X2 = 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 Zoe(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 = N.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, C = h.strideHeight, T = h.strideWidth, E = h.inChannels, A = h.outChannels, P = h.padInfo.type === "SAME" ? 1 : 0;
  if (h.dataFormat !== "channelsLast")
    throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);
  let R = s.makeOutput(h.outShape, "float32"), F = s.dataIdMap.get(R.dataId).id;
  return X2(i, r.shape[0], r.shape[1], r.shape[2], o, f, m, g, b, y, v, P, x, k, C, T, E, A, F), R;
}
var Joe = { kernelName: Aa, backendName: "wasm", setupFunc: Qoe, kernelFunc: Zoe };
var eue = Kt(Ra);
var tue = false;
var nue = mn(po, tue, "bool");
var sue = Kt(Da, "float32");
function Ym(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), bn({ inputs: { x: r }, backend: s, attrs: { shape: o } });
}
var rue = { kernelName: ho, backendName: "wasm", kernelFunc: Ym };
function Y2(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 aue = { kernelName: hl, backendName: "wasm", kernelFunc: Y2 };
var Q2;
function iue(e) {
  Q2 = e.wasm.cwrap(mo, null, ["number", "number", "number", "number", "number", "number"]);
}
function oue(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 Q2(a, o, u, l, c, i), r;
}
var uue = { kernelName: mo, backendName: "wasm", kernelFunc: oue, setupFunc: iue };
var lue = Kt(Fa);
var cue = false;
var due = mn(Oa, cue);
var Z2;
function pue(e) {
  Z2 = e.wasm.cwrap(Pa, null, ["number", "number", "number", "number", "number", "number", "number"]);
}
function hue(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 Z2(c, p, d, h, f, r, g), m;
}
var fue = { kernelName: Pa, backendName: "wasm", setupFunc: pue, kernelFunc: hue };
var J2;
function mue(e) {
  J2 = e.wasm.cwrap(sa, 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 gue(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 = N.computeConv2DInfo(r.shape, a.shape, u, c, l, d), g = Zp[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, C = m.filterWidth, T = m.padInfo.top, E = m.padInfo.right, A = m.padInfo.bottom, P = m.padInfo.left, R = m.dilationHeight, F = m.dilationWidth, $ = m.strideHeight, z = m.strideWidth, W = m.inChannels, q = m.padInfo.type === "SAME" ? 1 : 0, K = 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"), ee = s.dataIdMap.get(te.dataId).id, se = o == null ? 0 : s.dataIdMap.get(o.dataId).id;
  return J2(b, K, Y, Z, y, k, C, x, T, E, A, P, q, R, F, $, z, W, v, g, se, f || 0, ee), te;
}
var bue = { kernelName: sa, backendName: "wasm", setupFunc: mue, kernelFunc: gue };
var eN;
function yue(e) {
  eN = e.wasm.cwrap(ra, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function vue(e) {
  let { inputs: t, attrs: n, backend: s } = e, { x: r, filter: a, bias: i, preluActivationWeights: o } = t, { strides: u, pad: l, dilations: c, dataFormat: p, dimRoundingMode: d, activation: h, leakyreluAlpha: f } = n, m = N.computeConv2DInfo(r.shape, a.shape, u, c, l, d, true), g = Zp[h];
  if (g == null)
    throw new Error(`${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, C = m.filterWidth, T = m.padInfo.top, E = m.padInfo.right, A = m.padInfo.bottom, P = m.padInfo.left, R = m.dilationHeight, F = m.dilationWidth, $ = m.strideHeight, z = m.strideWidth, W = m.inChannels, q = m.padInfo.type === "SAME" ? 1 : 0, K = 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"), ee = s.dataIdMap.get(te.dataId).id, se = o == null ? 0 : s.dataIdMap.get(o.dataId).id;
  return eN(b, K, Y, Z, y, k, C, x, T, E, A, P, q, R, F, $, z, W, v, g, se, f || 0, ee), te;
}
var xue = { kernelName: ra, backendName: "wasm", setupFunc: yue, kernelFunc: vue };
var tN;
function wue(e) {
  tN = e.wasm.cwrap(bo, null, ["number", "number", "number", "number", "number", "number", "array", "number"]);
}
function kue(e) {
  let { backend: t, inputs: n } = e, { params: s, indices: r } = n, [a, i, o, u] = kk.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 tN(h, Ct[s.dtype], m, i, p, o, g, b), l;
}
var Iue = { kernelName: bo, backendName: "wasm", setupFunc: wue, kernelFunc: kue };
var nN;
function Sue(e) {
  nN = e.wasm.cwrap("Gather", null, ["number", "number", "array", "number", "number", "number", "array", "number"]);
}
function Cue(e) {
  let { backend: t, inputs: n, attrs: s } = e, { x: r, indices: a } = n, { axis: i, batchDims: o } = s, u = w.parseAxisParam(i, r.shape)[0], l = t.readSync(a.dataId), c = r.shape[u];
  for (let A = 0; A < l.length; ++A) {
    let P = l[A];
    w.assert(P <= c - 1 && P >= 0, () => `GatherV2: the index value ${P} is not in [0, ${c - 1}]`);
  }
  let p = N.segment_util.collectGatherOpShapeInfo(r, a, u, o), d = bn({ inputs: { x: r }, attrs: { shape: [p.batchSize, p.outerSize, p.dimSize, p.sliceSize] }, backend: t }), h = w.sizeFromShape(a.shape), f = bn({ 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, C = t.dataIdMap.get(g.dataId).id, T = new Uint8Array(new Int32Array(w.computeStrides(d.shape)).buffer), E = new Uint8Array(new Int32Array(w.computeStrides(m)).buffer);
  return nN(v, Ct[r.dtype], T, b, k, p.batchSize, E, C), t.disposeData(d.dataId), t.disposeData(f.dataId), g.shape = p.outputShape, g;
}
var Nue = { kernelName: go, backendName: "wasm", setupFunc: Sue, kernelFunc: Cue };
var Tue = false;
var $ue = mn(yo, Tue, "bool");
var _ue = false;
var Aue = mn(za, _ue, "bool");
var sN;
function Eue(e) {
  sN = e.wasm.cwrap(La, null, ["number", "number", "number", "number"]);
}
function Rue(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, Ct[t.dtype], n, i);
  }
  return a;
}
var Due = { kernelName: La, backendName: "wasm", setupFunc: Eue, kernelFunc: Rue };
var Fue = false;
var Oue = mn(vo, Fue, "bool");
var Pue = false;
var zue = mn(xo, Pue, "bool");
var Mue = Kt(Ba);
var Lue = false;
var Bue = mn(wo, Lue, "bool");
var rN;
function Vue(e) {
  rN = e.wasm.cwrap(Va, null, ["number", "number", "number", "number"]);
}
function Wue(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 } = Dr(i, r, t);
  if (h) {
    let v = t.dataIdMap.get(c.dataId).id;
    l = c, u = v;
  }
  let f = l.shape.length;
  N.assertAxesAreInnerMostDims("max", p, f);
  let [m, g] = N.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;
    rN(u, Ct[i.dtype], b, v);
  }
  if (h && t.disposeData(c.dataId), a) {
    let v = N.expandShapeToKeepDim(y.shape, d);
    y.shape = v;
  }
  return y;
}
var Uue = { kernelName: Va, backendName: "wasm", setupFunc: Vue, kernelFunc: Wue };
var Gue = false;
var Hue = mn(Wa, Gue);
var aN;
function que(e) {
  aN = e.wasm.cwrap(Ua, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function jue(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 ${r.dtype}.`);
  let { filterSize: i, strides: o, pad: u, dimRoundingMode: l } = n, c = N.computePool2DInfo(r.shape, i, o, 1, u, l), p = c.filterHeight, d = c.filterWidth, h = c.padInfo.top, f = c.padInfo.right, m = c.padInfo.bottom, g = c.padInfo.left, b = c.dilationHeight, y = c.dilationWidth, v = c.strideHeight, x = c.strideWidth, k = c.inChannels, C = c.outChannels;
  if (c.dataFormat !== "channelsLast")
    throw new Error(`wasm backend does not support dataFormat:'${c.dataFormat}'. Please use 'channelsLast'.`);
  let T = s.makeOutput(c.outShape, "float32"), E = s.dataIdMap.get(T.dataId).id;
  return aN(a, r.shape[0], r.shape[1], r.shape[2], p, d, h, f, m, g, b, y, v, x, k, C, E), T;
}
var Kue = { kernelName: Ua, backendName: "wasm", setupFunc: que, kernelFunc: jue };
var iN;
function Xue(e) {
  iN = e.wasm.cwrap(Ga, null, ["number, number, number"]);
}
function Yue(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 } = Dr(i, r, t), f = p;
  if (h) {
    let x = t.dataIdMap.get(c.dataId).id;
    x !== o && (l = c, u = x, f = N.getInnerMostAxes(f.length, l.shape.length));
  }
  N.assertAxesAreInnerMostDims("mean", f, l.shape.length);
  let [m, g] = N.computeOutAndReduceShapes(l.shape, f), b = w.sizeFromShape(g), y = l;
  l.dtype !== "float32" && (y = rc({ backend: t, inputs: { x: l }, attrs: { dtype: "float32" } }), u = t.dataIdMap.get(y.dataId).id);
  let v = t.makeOutput(m, "float32");
  if (w.sizeFromShape(l.shape) !== 0) {
    let x = t.dataIdMap.get(v.dataId).id;
    iN(u, b, x);
  }
  if (h && t.disposeData(c.dataId), a) {
    let x = N.expandShapeToKeepDim(v.shape, d);
    v.shape = x;
  }
  return l.dtype !== "float32" && t.disposeData(y.dataId), v;
}
var Que = { kernelName: Ga, backendName: "wasm", setupFunc: Xue, kernelFunc: Yue };
var oN;
function Zue(e) {
  oN = e.wasm.cwrap(Ha, null, ["number", "number", "number", "number"]);
}
function Jue(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 } = Dr(i, r, t);
  if (h) {
    let v = t.dataIdMap.get(c.dataId).id;
    v !== o && (l = c, u = v);
  }
  let f = l.shape.length;
  N.assertAxesAreInnerMostDims("min", p, f);
  let [m, g] = N.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;
    oN(u, Ct[i.dtype], b, v);
  }
  if (h && t.disposeData(c.dataId), a) {
    let v = N.expandShapeToKeepDim(y.shape, d);
    y.shape = v;
  }
  return y;
}
var ele = { kernelName: Ha, backendName: "wasm", setupFunc: Zue, kernelFunc: Jue };
var tle = false;
var nle = mn(qa, tle);
var uN = ((e) => (e[e.reflect = 0] = "reflect", e[e.symmetric = 1] = "symmetric", e))(uN || {});
var lN;
function sle(e) {
  lN = e.wasm.cwrap(ja, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
}
function rle(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 lN(i, l, t.shape.length, Ct[t.dtype], d, h, uN[r], u), o;
}
var ale = { kernelName: ja, backendName: "wasm", kernelFunc: rle, setupFunc: sle };
var ile = true;
var ole = mn(Ka, ile);
var ule = Kt(ko);
function _v(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 cN;
function lle(e) {
  cN = e.wasm.cwrap(So, "number", ["number", "number", "number", "number", "number"]);
}
function cle(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 = cN(l, c, a, r, i), { pSelectedIndices: d, selectedSize: h, pSelectedScores: f, pValidOutputs: m } = _v(t, p);
  return t.wasm._free(f), t.wasm._free(m), t.makeOutput([h], "int32", d);
}
var dle = { kernelName: So, backendName: "wasm", setupFunc: lle, kernelFunc: cle };
var dN;
function ple(e) {
  dN = e.wasm.cwrap(xl, "number", ["number", "number", "number", "number", "number", "bool"]);
}
function hle(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 = dN(c, p, a, r, i, o), { pSelectedIndices: h, selectedSize: f, pSelectedScores: m, pValidOutputs: g } = _v(t, d);
  t.wasm._free(m);
  let b = t.makeOutput([f], "int32", h), y = t.makeOutput([], "int32", g);
  return [b, y];
}
var fle = { kernelName: xl, backendName: "wasm", setupFunc: ple, kernelFunc: hle };
var pN;
function mle(e) {
  pN = e.wasm.cwrap(Co, "number", ["number", "number", "number", "number", "number", "number"]);
}
function gle(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 = pN(c, p, a, r, i, o), { pSelectedIndices: h, selectedSize: f, pSelectedScores: m, pValidOutputs: g } = _v(t, d);
  t.wasm._free(g);
  let b = t.makeOutput([f], "int32", h), y = t.makeOutput([f], "float32", m);
  return [b, y];
}
var ble = { kernelName: Co, backendName: "wasm", setupFunc: mle, kernelFunc: gle };
var yle = false;
var vle = mn(Io, yle, "bool");
var hN;
function xle(e) {
  hN = e.wasm.cwrap(To, null, ["number", "number", "number", "number", "number"]);
}
function wle(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 hN(p, a, i, o, l), u;
}
var kle = { kernelName: To, backendName: "wasm", setupFunc: xle, kernelFunc: wle };
function Ile(e) {
  let { inputs: { x: t }, backend: n } = e, s = n.makeOutput(t.shape, t.dtype);
  return n.typedArrayFromHeap(s).fill(1), s;
}
var Sle = { kernelName: No, backendName: "wasm", kernelFunc: Ile };
function Cle(e) {
  let { inputs: t, backend: n, attrs: s } = e, { axis: r } = s;
  if (t.length === 1)
    return Ym({ 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 = Ym({ inputs: { input: c }, backend: n, attrs: { dim: r } });
    return o.push(p), p;
  }), l = W2({ inputs: u, backend: n, attrs: { axis: r } });
  return o.forEach((c) => n.disposeData(c.dataId)), l;
}
var Nle = { kernelName: $o, backendName: "wasm", kernelFunc: Cle };
var fN;
function Tle(e) {
  fN = e.wasm.cwrap(Xa, null, ["number", "array", "number", "number", "array", "array", "number", "number"]);
}
function $le(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 Y2({ 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, Ct[t.dtype], h, f, r, l), o;
}
var mN = { kernelName: Xa, backendName: "wasm", kernelFunc: $le, setupFunc: Tle };
var _le = false;
var Ale = mn(Ya, _le);
var gN;
function Ele(e) {
  gN = e.wasm.cwrap(Qa, null, ["number", "number", "number"]);
}
function Rle(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 = rc({ 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 gN(o, i, p), u.dtype !== "float32" && n.disposeData(l.dataId), c;
}
var Dle = { kernelName: Qa, backendName: "wasm", setupFunc: Ele, kernelFunc: Rle };
var bN;
function Fle(e) {
  bN = e.wasm.cwrap(_o, null, ["number", "number", "number", "number"]);
}
function Ole(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 } = Dr(i, r, t), f = p;
  if (h) {
    let v = t.dataIdMap.get(c.dataId).id;
    v !== o && (l = c, u = v, f = N.getInnerMostAxes(f.length, l.shape.length));
  }
  N.assertAxesAreInnerMostDims("prod", f, l.shape.length);
  let [m, g] = N.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;
    bN(u, b, Ct[y.dtype], v);
  }
  if (h && t.disposeData(c.dataId), a) {
    let v = N.expandShapeToKeepDim(y.shape, d);
    y.shape = v;
  }
  return y;
}
var Ple = { kernelName: _o, backendName: "wasm", setupFunc: Fle, kernelFunc: Ole };
var zle = (e) => {
  let { backend: t, attrs: n } = e, { start: s, stop: r, step: a, dtype: i } = n, o = nv(s, r, a, i), u = t.makeOutput([o.length], i);
  return t.typedArrayFromHeap(u).set(o), u;
};
var Mle = { kernelName: wl, backendName: "wasm", kernelFunc: zle };
var Lle = true;
var Ble = mn(Ea, Lle);
var Vle = Kt(Za);
var Wle = Kt(ei);
var yN;
function Ule(e) {
  yN = e.wasm.cwrap(Ja, null, ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function Gle(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 = rc({ 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 yN(b, c, p, d, h, u, l, a ? 1 : 0, i ? 1 : 0, v), g != null && t.disposeData(g.dataId), y;
}
var Hle = { kernelName: Ja, backendName: "wasm", setupFunc: Ule, kernelFunc: Gle };
var vN;
function qle(e) {
  vN = e.wasm.cwrap(Eo, null, ["number", "array", "number", "array", "number", "number"]);
}
function jle(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 Jp({ 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);
  vN(u, c, i.length, p, r.shape.length, l);
  let d = bn({ inputs: { x: o }, attrs: { shape: r.shape }, backend: n });
  return n.disposeData(o.dataId), d;
}
var Kle = { kernelName: Eo, backendName: "wasm", kernelFunc: jle, setupFunc: qle };
var xN;
function Xle(e) {
  xN = e.wasm.cwrap(Ho, null, ["number", "number", "number", "number", "number", "number", "number", "number", "array", "number", "number"]);
}
function Yle(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] = N.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 xN(l, p, d, h, f, a, m, g, x, v.length, c), u;
}
var Qle = { kernelName: Ho, backendName: "wasm", kernelFunc: Yle, setupFunc: Xle };
var Zle = Kt(Ro);
var Jle = Kt(ti);
var wN;
function ece(e) {
  wN = e.wasm.cwrap(Do, null, ["number", "number", "number", "number", "number", "number", "array", "number", "number"]);
}
function tce(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 } = Sk.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 wN(f, g, Ct[a.dtype], u, l, c, b, d, y), o;
}
var nce = { kernelName: Do, backendName: "wasm", setupFunc: ece, kernelFunc: tce };
var kN;
function sce(e) {
  kN = e.wasm.cwrap("SelectV2", null, ["number", "number", "number", "number", "number"]);
}
function rce(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 kN(i, o, u, h, c), l;
}
var ace = { kernelName: Fo, backendName: "wasm", kernelFunc: rce, setupFunc: sce };
var IN;
function ice(e) {
  IN = e.wasm.cwrap(si, null, ["number", "number"]);
}
function oce(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 || IN(s, a), r;
}
var uce = { kernelName: "Sigmoid", backendName: "wasm", setupFunc: ice, kernelFunc: oce };
var lce = Kt(ni);
var SN;
function cce(e) {
  SN = e.wasm.cwrap(ii, null, ["number", "number", "number", "number"]);
}
function dce(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 || SN(r, i, o, u), a;
}
var pce = { kernelName: ii, backendName: "wasm", setupFunc: cce, kernelFunc: dce };
function hce(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 C = 1 + a.length; C < r.shape.length; ++C)
    u.push([0, 0]);
  let l = mN.kernelFunc({ inputs: { x: r }, backend: n, attrs: { paddings: u, constantValue: 0 } }), c = N.getReshaped(l.shape, a, o, false), p = N.getPermuted(c.length, a.length, false), d = N.getReshapedPermuted(l.shape, a, o, false), m = bn({ inputs: { x: l }, backend: n, attrs: { shape: c } }), y = so({ inputs: { x: m }, backend: n, attrs: { perm: p } }), k = bn({ inputs: { x: y }, backend: n, attrs: { shape: d } });
  return n.disposeData(l.dataId), n.disposeData(m.dataId), n.disposeData(y.dataId), k;
}
var fce = { kernelName: zo, backendName: "wasm", kernelFunc: hce };
var CN;
function mce(e) {
  CN = e.wasm.cwrap("SparseFillEmptyRows", "number", ["number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function gce(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, C = t.makeOutput([4], "int32"), T = t.dataIdMap.get(C.dataId).id, E = CN(p, d, Ct[r.dtype], o, l, u, h, m, b, v, k, T), A = t.readSync(C.dataId), P;
  switch (A[0]) {
    case 1: {
      P = N.getSparseFillEmptyRowsIndicesDenseShapeMismatch(A[1]);
      break;
    }
    case 2: {
      P = N.getSparseFillEmptyRowsNegativeIndexErrorMessage(A[1], A[2]);
      break;
    }
    case 3:
      P = N.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(A[1], A[2], A[3]);
      break;
    default:
      P = "";
  }
  if (t.disposeData(C.dataId), P)
    throw t.disposeData(f.dataId), t.disposeData(g.dataId), t.disposeData(y.dataId), t.disposeData(x.dataId), new Error(P);
  let R = f, F = g;
  return E !== c[0] && (R = ba({ inputs: { x: f }, attrs: { begin: 0, size: [E, u] }, backend: t }), F = ba({ inputs: { x: g }, attrs: { begin: 0, size: E }, backend: t }), t.disposeData(f.dataId), t.disposeData(g.dataId)), [R, F, y, x];
}
var bce = { kernelName: np, backendName: "wasm", setupFunc: mce, kernelFunc: gce };
var NN;
function yce(e) {
  NN = e.wasm.cwrap(Tl, null, ["number", "number", "number", "number", "number", "number", "number"]);
}
function vce(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;
  NN(i, o, u, l, d, f, g);
  let b = t.readSync(m.dataId), y;
  switch (b[0]) {
    case 0: {
      y = N.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(b[1], b[2]);
      break;
    }
    case 1: {
      y = N.getSparseReshapeNegativeOutputDimErrorMessage(b[1], b[2]);
      break;
    }
    case 2:
      y = N.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();
      break;
    case 3: {
      let v = Array.from(t.readSync(r.dataId)), x = Array.from(t.readSync(h.dataId));
      y = N.getSparseReshapeInputOutputMultipleErrorMessage(v, x);
      break;
    }
    case 4: {
      let v = Array.from(t.readSync(r.dataId)), x = Array.from(t.readSync(h.dataId));
      y = N.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 xce = { kernelName: Tl, backendName: "wasm", setupFunc: yce, kernelFunc: vce };
var TN;
function $N(e) {
  TN = e.wasm.cwrap("SparseSegmentReduction", null, ["number", "number", "number", "number", "number", "number", "number", "number", "number"]);
}
function _N(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(N.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;
  TN(d, Ct[r.dtype], r.shape[0], h, f, g, y, t, 0);
  let v = n.readSync(b.dataId), x;
  switch (v[0]) {
    case 0: {
      x = N.getSparseSegmentReductionNegativeSegmentIdsErrorMessage();
      break;
    }
    case 1: {
      x = N.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage();
      break;
    }
    case 2:
      x = N.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(v[1], v[2]);
      break;
    case 3:
      x = N.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 wce(e) {
  return _N(e, true);
}
var kce = { kernelName: sp, backendName: "wasm", setupFunc: $N, kernelFunc: wce };
function Ice(e) {
  return _N(e, false);
}
var Sce = { kernelName: rp, backendName: "wasm", setupFunc: $N, kernelFunc: Ice };
function Cce(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 = N.prepareSplitSize(r, a, o), l = new Array(r.shape.length).fill(0), c = r.shape.slice();
  return u.map((p) => {
    let d = [...c];
    d[o] = p;
    let h = ba({ inputs: { x: r }, attrs: { begin: l, size: d }, backend: s });
    return l[o] += p, h;
  });
}
var Nce = { kernelName: Mo, backendName: "wasm", kernelFunc: Cce };
var Tce = Kt(ri);
var $ce = Kt($l);
var _ce = true;
var Ace = mn(oi, _ce);
var AN;
function Ece(e) {
  AN = e.wasm.cwrap(di, null, ["number", "number", "number", "number"]);
}
function Rce(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 AN(i, r, Ct[a.dtype], u), o;
}
var Dce = { kernelName: di, backendName: "wasm", setupFunc: Ece, kernelFunc: Rce };
var EN;
function Fce(e) {
  EN = e.wasm.cwrap(Lo, null, ["number", "array", "number", "array", "array", "array", "array", "array", "number", "number"]);
}
function Oce(e) {
  let { backend: t, inputs: n, attrs: s } = e, { x: r } = n, { begin: a, end: i, strides: o, beginMask: u, endMask: l, ellipsisMask: c, newAxisMask: p, shrinkAxisMask: d } = s, { finalShapeSparse: h, finalShape: f, isIdentity: m, sliceDim0: g, isSimpleSlice: b, begin: y, end: v, strides: x } = wt.sliceInfo(r.shape, a, i, o, u, l, c, p, d), k;
  if (m)
    k = bn({ 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 C = wt.computeOutShape(y, v, x), T = ba({ inputs: { x: r }, backend: t, attrs: { begin: y, size: C } });
    k = bn({ inputs: { x: T }, backend: t, attrs: { shape: f } }), t.disposeData(T.dataId);
  } else {
    let C = t.makeOutput(h, "float32"), T = t.dataIdMap.get(r.dataId).id, E = new Uint8Array(new Int32Array(w.computeStrides(r.shape)).buffer), A = new Uint8Array(new Int32Array(y).buffer), P = new Uint8Array(new Int32Array(v).buffer), R = new Uint8Array(new Int32Array(x).buffer), F = new Uint8Array(new Int32Array(h).buffer), $ = new Uint8Array(new Int32Array(w.computeStrides(h)).buffer), z = t.dataIdMap.get(C.dataId).id;
    EN(T, E, r.shape.length, A, P, R, F, $, h.length, z), k = bn({ inputs: { x: C }, backend: t, attrs: { shape: f } }), t.disposeData(C.dataId);
  }
  return k;
}
var Pce = { kernelName: Lo, backendName: "wasm", setupFunc: Fce, kernelFunc: Oce };
var zce = true;
var Mce = mn(ui, zce);
var RN;
function Lce(e) {
  RN = e.wasm.cwrap(ai, null, ["number", "number", "number", "number"]);
}
function Bce(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 } = Dr(i, r, t), f = p;
  if (h) {
    let v = t.dataIdMap.get(c.dataId).id;
    v !== o && (l = c, u = v, f = N.getInnerMostAxes(f.length, l.shape.length));
  }
  N.assertAxesAreInnerMostDims("sum", f, l.shape.length);
  let [m, g] = N.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;
    RN(u, b, Ct[y.dtype], v);
  }
  if (h && t.disposeData(c.dataId), a) {
    let v = N.expandShapeToKeepDim(y.shape, d);
    y.shape = v;
  }
  return y;
}
var Vce = { kernelName: ai, backendName: "wasm", setupFunc: Lce, kernelFunc: Bce };
var Wce = Kt(Bo);
var Uce = Kt(li);
var DN;
function Gce(e) {
  DN = e.wasm.cwrap(Sr, null, ["number", "array", "number", "array", "number", "number"]);
}
function Hce(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 DN(a, u, r.shape.length, l, o.length, Ct[c.dtype], p), c;
}
var qce = { kernelName: Sr, backendName: "wasm", setupFunc: Gce, kernelFunc: Hce };
var FN;
function jce(e) {
  FN = e.wasm.cwrap(Vo, null, ["number", "array", "number", "number", "number", "bool", "number", "number"]);
}
var Kce = ({ 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 FN(i, o, s.shape.length, Ct[s.dtype], r, a, c, d), [l, p];
};
var Xce = { kernelName: Vo, backendName: "wasm", setupFunc: jce, kernelFunc: Kce };
var ON;
function Yce(e) {
  ON = e.wasm.cwrap(Wo, null, ["number", "number", "bool", "number", "number", "number", "number", "number", "number", "array", "number", "number", "number", "number", "number"]);
}
function Qce(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 = t.dataIdMap.get(a.dataId).id, E = i === "nearest" ? 1 : 2, A;
  switch (o) {
    case "constant":
      A = 1;
      break;
    case "reflect":
      A = 2;
      break;
    case "wrap":
      A = 3;
      break;
    case "nearest":
      A = 4;
      break;
    default:
      A = 1;
      break;
  }
  return ON(k, T, a.shape[0] > 1, c, f, m, h, d, p, b, r.shape.length - 1, E, A, u, v), y;
}
var Zce = { kernelName: Wo, backendName: "wasm", setupFunc: Yce, kernelFunc: Qce };
function Jce(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] = ba({ inputs: { x: r }, attrs: { begin: p, size: d }, backend: n });
  return c.map(({ dataId: h, dtype: f }) => ({ dataId: h, dtype: f, shape: u }));
}
var ede = { kernelName: Uo, backendName: "wasm", kernelFunc: Jce };
function tde(e) {
  let { inputs: { x: t }, backend: n } = e, s = n.makeOutput(t.shape, t.dtype);
  return n.typedArrayFromHeap(s).fill(0), s;
}
var nde = { kernelName: Go, backendName: "wasm", kernelFunc: tde };
var sde = [Kie, Xie, Qie, eoe, uoe, doe, foe, boe, woe, Toe, $oe, _oe, Roe, Doe, Poe, Loe, Boe, Voe, Goe, joe, Yoe, Joe, eue, nue, sue, rue, aue, uue, lue, due, fue, bue, xue, Iue, Nue, $ue, Aue, toe, Due, Oue, zue, Mue, Bue, Uue, Hue, Kue, Que, ele, nle, ale, ole, ule, dle, fle, ble, vle, kle, Sle, Nle, mN, Ale, Dle, Ple, Mle, Ble, Vle, Wle, yoe, Hle, Kle, Qle, Zle, Jle, nce, ace, uce, lce, Coe, pce, fce, bce, xce, kce, Sce, Nce, Tce, $ce, Ace, Dce, Pce, Mce, Vce, Wce, Uce, qce, Xce, Zce, aoe, ede, nde];
for (let e of sde)
  _l(e);
var Qm = X();
Qm.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])));
Qm.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT", async () => {
  if (Qm.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 Dw = ya(YT());
var rde = `"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 ade = ya(QT());
var ide = class extends tl {
  constructor(e) {
    super();
    this.wasm = e, this.dataIdNextNumber = 1, this.wasm.tfjs.initWithThreadsCount(PN), Zm = this.wasm.tfjs.getThreadsCount(), this.dataIdMap = new Wd(this, Ss());
  }
  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 lde(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 ${t}`);
    }
  }
};
function ode(e) {
  return (t, n) => (w.fetch(e, { credentials: "same-origin" }).then((s) => {
    s.ok || t.env.a(`failed to load wasm binary file at '${e}'`), s.arrayBuffer().then((r) => {
      WebAssembly.instantiate(r, t).then((a) => {
        n(a.instance, a.module);
      });
    });
  }), {});
}
function Fw(e, t, n) {
  if (Ld != null)
    return Ld;
  let s = "tfjs-backend-wasm.wasm";
  return e && t ? s = "tfjs-backend-wasm-threaded-simd.wasm" : e && (s = "tfjs-backend-wasm-simd.wasm"), Mu != null && Mu[s] != null ? Mu[s] : n + s;
}
async function ude() {
  let [e, t] = await Promise.all([X().getAsync("WASM_HAS_SIMD_SUPPORT"), X().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);
  return new Promise((n, s) => {
    let r = {};
    r.locateFile = (o, u) => {
      if (o.endsWith(".worker.js")) {
        let l = rde, c = new Blob([l], { type: "application/javascript" });
        return URL.createObjectURL(c);
      }
      return o.endsWith(".wasm") ? Fw(e, t, Ou != null ? Ou : u) : u + o;
    }, Av && (r.instantiateWasm = ode(Fw(e, t, Ou != null ? Ou : "")));
    let a = false;
    r.onAbort = () => {
      if (a || Lu)
        return;
      Lu = 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 && Ld == null ? (r.mainScriptUrlOrBlob = new Blob(["var WasmBackendModuleThreadedSimd = " + Dw.default.toString()], { type: "text/javascript" }), i = (0, Dw.default)(r)) : i = (0, ade.default)(r), i.then((o) => {
      a = true, Lu = 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 lde(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 cde = ["tfjs-backend-wasm.wasm", "tfjs-backend-wasm-simd.wasm", "tfjs-backend-wasm-threaded-simd.wasm"];
var Ld = null;
var Ou = null;
var Mu = {};
var Lu = false;
var Av = false;
function Ipe(e, t = false) {
  if (Mk("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."), Lu)
    throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");
  Ld = e, Av = t;
}
function Spe(e, t = false) {
  if (Lu)
    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")
    Ou = e;
  else {
    Mu = e;
    let n = cde.filter((s) => Mu[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.`);
  }
  Av = t;
}
var PN = -1;
var Zm = -1;
function Cpe(e) {
  PN = e;
}
function Npe() {
  if (Zm === -1)
    throw new Error("WASM backend not initialized.");
  return Zm;
}
var Tpe = "0.0.0";
var dde = 2;
dp("wasm", async () => {
  let { wasm: e } = await ude();
  return new ide(e);
}, dde);
var sr = "3.14.0-20220307";
var $pe = { 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");
  this.gl = gl2;
  if (!gl2) {
    log("filter: cannot get webgl context");
    return;
  }
  function resize(width, height) {
    if (width === fxcanvas.width && height === fxcanvas.height)
      return;
    fxcanvas.width = width;
    fxcanvas.height = height;
    if (!vertexBuffer) {
      const vertices = new Float32Array([-1, -1, 0, 1, 1, -1, 1, 1, -1, 1, 0, 0, -1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 0]);
      vertexBuffer = gl2.createBuffer();
      gl2.bindBuffer(gl2.ARRAY_BUFFER, vertexBuffer);
      gl2.bufferData(gl2.ARRAY_BUFFER, vertices, gl2.STATIC_DRAW);
      gl2.pixelStorei(gl2.UNPACK_PREMULTIPLY_ALPHA_WEBGL, true);
    }
    gl2.viewport(0, 0, fxcanvas.width, fxcanvas.height);
    tempFramebuffers = [null, null];
  }
  function createFramebufferTexture(width, height) {
    const fbo = gl2.createFramebuffer();
    gl2.bindFramebuffer(gl2.FRAMEBUFFER, fbo);
    const renderbuffer = gl2.createRenderbuffer();
    gl2.bindRenderbuffer(gl2.RENDERBUFFER, renderbuffer);
    const texture = gl2.createTexture();
    gl2.bindTexture(gl2.TEXTURE_2D, texture);
    gl2.texImage2D(gl2.TEXTURE_2D, 0, gl2.RGBA, width, height, 0, gl2.RGBA, gl2.UNSIGNED_BYTE, null);
    gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_MAG_FILTER, gl2.LINEAR);
    gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_MIN_FILTER, gl2.LINEAR);
    gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_WRAP_S, gl2.CLAMP_TO_EDGE);
    gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_WRAP_T, gl2.CLAMP_TO_EDGE);
    gl2.framebufferTexture2D(gl2.FRAMEBUFFER, gl2.COLOR_ATTACHMENT0, gl2.TEXTURE_2D, texture, 0);
    gl2.bindTexture(gl2.TEXTURE_2D, null);
    gl2.bindFramebuffer(gl2.FRAMEBUFFER, null);
    return { fbo, texture };
  }
  function getTempFramebuffer(index2) {
    tempFramebuffers[index2] = tempFramebuffers[index2] || createFramebufferTexture(fxcanvas.width, fxcanvas.height);
    return tempFramebuffers[index2];
  }
  function draw(flags = 0) {
    if (!currentProgram)
      return;
    let source = null;
    let target = null;
    let flipY = false;
    if (drawCount === 0)
      source = sourceTexture;
    else
      source = getTempFramebuffer(currentFramebufferIndex).texture || null;
    drawCount++;
    if (lastInChain && !(flags & DRAW.INTERMEDIATE)) {
      target = null;
      flipY = drawCount % 2 === 0;
    } else {
      currentFramebufferIndex = (currentFramebufferIndex + 1) % 2;
      target = getTempFramebuffer(currentFramebufferIndex).fbo || null;
    }
    gl2.bindTexture(gl2.TEXTURE_2D, source);
    gl2.bindFramebuffer(gl2.FRAMEBUFFER, target);
    gl2.uniform1f(currentProgram.uniform["flipY"], flipY ? -1 : 1);
    gl2.drawArrays(gl2.TRIANGLES, 0, 6);
  }
  function compileShader(fragmentSource) {
    if (shaderProgramCache[fragmentSource]) {
      currentProgram = shaderProgramCache[fragmentSource];
      gl2.useProgram((currentProgram ? currentProgram.id : null) || null);
      return currentProgram;
    }
    currentProgram = new GLProgram(gl2, vertexIdentity, fragmentSource);
    if (!currentProgram) {
      log("filter: could not get webgl program");
      return null;
    }
    const floatSize = Float32Array.BYTES_PER_ELEMENT;
    const vertSize = 4 * floatSize;
    gl2.enableVertexAttribArray(currentProgram.attribute["pos"]);
    gl2.vertexAttribPointer(currentProgram.attribute["pos"], 2, gl2.FLOAT, false, vertSize, 0 * floatSize);
    gl2.enableVertexAttribArray(currentProgram.attribute["uv"]);
    gl2.vertexAttribPointer(currentProgram.attribute["uv"], 2, gl2.FLOAT, false, vertSize, 2 * floatSize);
    shaderProgramCache[fragmentSource] = currentProgram;
    return currentProgram;
  }
  const filter = {
    colorMatrix: (matrix) => {
      const m = new Float32Array(matrix);
      m[4] /= 255;
      m[9] /= 255;
      m[14] /= 255;
      m[19] /= 255;
      const shader = m[18] === 1 && m[3] === 0 && m[8] === 0 && m[13] === 0 && m[15] === 0 && m[16] === 0 && m[17] === 0 && m[19] === 0 ? colorMatrixWithoutAlpha : colorMatrixWithAlpha;
      const program = compileShader(shader);
      if (!program)
        return;
      gl2.uniform1fv(program.uniform["m"], m);
      draw();
    },
    brightness: (brightness) => {
      const b = (brightness || 0) + 1;
      filter.colorMatrix([
        b,
        0,
        0,
        0,
        0,
        0,
        b,
        0,
        0,
        0,
        0,
        0,
        b,
        0,
        0,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    saturation: (amount) => {
      const x = (amount || 0) * 2 / 3 + 1;
      const y = (x - 1) * -0.5;
      filter.colorMatrix([
        x,
        y,
        y,
        0,
        0,
        y,
        x,
        y,
        0,
        0,
        y,
        y,
        x,
        0,
        0,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    desaturate: () => {
      filter.saturation(-1);
    },
    contrast: (amount) => {
      const v = (amount || 0) + 1;
      const o = -128 * (v - 1);
      filter.colorMatrix([
        v,
        0,
        0,
        0,
        o,
        0,
        v,
        0,
        0,
        o,
        0,
        0,
        v,
        0,
        o,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    negative: () => {
      filter.contrast(-2);
    },
    hue: (rotation) => {
      rotation = (rotation || 0) / 180 * Math.PI;
      const cos = Math.cos(rotation);
      const sin = Math.sin(rotation);
      const lumR = 0.213;
      const lumG = 0.715;
      const lumB = 0.072;
      filter.colorMatrix([
        lumR + cos * (1 - lumR) + sin * -lumR,
        lumG + cos * -lumG + sin * -lumG,
        lumB + cos * -lumB + sin * (1 - lumB),
        0,
        0,
        lumR + cos * -lumR + sin * 0.143,
        lumG + cos * (1 - lumG) + sin * 0.14,
        lumB + cos * -lumB + sin * -0.283,
        0,
        0,
        lumR + cos * -lumR + sin * -(1 - lumR),
        lumG + cos * -lumG + sin * lumG,
        lumB + cos * (1 - lumB) + sin * lumB,
        0,
        0,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    desaturateLuminance: () => {
      filter.colorMatrix([
        0.2764723,
        0.929708,
        0.0938197,
        0,
        -37.1,
        0.2764723,
        0.929708,
        0.0938197,
        0,
        -37.1,
        0.2764723,
        0.929708,
        0.0938197,
        0,
        -37.1,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    sepia: () => {
      filter.colorMatrix([
        0.393,
        0.7689999,
        0.18899999,
        0,
        0,
        0.349,
        0.6859999,
        0.16799999,
        0,
        0,
        0.272,
        0.5339999,
        0.13099999,
        0,
        0,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    brownie: () => {
      filter.colorMatrix([
        0.5997023498159715,
        0.34553243048391263,
        -0.2708298674538042,
        0,
        47.43192855600873,
        -0.037703249837783157,
        0.8609577587992641,
        0.15059552388459913,
        0,
        -36.96841498319127,
        0.24113635128153335,
        -0.07441037908422492,
        0.44972182064877153,
        0,
        -7.562075277591283,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    vintagePinhole: () => {
      filter.colorMatrix([
        0.6279345635605994,
        0.3202183420819367,
        -0.03965408211312453,
        0,
        9.651285835294123,
        0.02578397704808868,
        0.6441188644374771,
        0.03259127616149294,
        0,
        7.462829176470591,
        0.0466055556782719,
        -0.0851232987247891,
        0.5241648018700465,
        0,
        5.159190588235296,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    kodachrome: () => {
      filter.colorMatrix([
        1.1285582396593525,
        -0.3967382283601348,
        -0.03992559172921793,
        0,
        63.72958762196502,
        -0.16404339962244616,
        1.0835251566291304,
        -0.05498805115633132,
        0,
        24.732407896706203,
        -0.16786010706155763,
        -0.5603416277695248,
        1.6014850761964943,
        0,
        35.62982807460946,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    technicolor: () => {
      filter.colorMatrix([
        1.9125277891456083,
        -0.8545344976951645,
        -0.09155508482755585,
        0,
        11.793603434377337,
        -0.3087833385928097,
        1.7658908555458428,
        -0.10601743074722245,
        0,
        -70.35205161461398,
        -0.231103377548616,
        -0.7501899197440212,
        1.847597816108189,
        0,
        30.950940869491138,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    polaroid: () => {
      filter.colorMatrix([
        1.438,
        -0.062,
        -0.062,
        0,
        0,
        -0.122,
        1.378,
        -0.122,
        0,
        0,
        -0.016,
        -0.016,
        1.483,
        0,
        0,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    shiftToBGR: () => {
      filter.colorMatrix([
        0,
        0,
        1,
        0,
        0,
        0,
        1,
        0,
        0,
        0,
        1,
        0,
        0,
        0,
        0,
        0,
        0,
        0,
        1,
        0
      ]);
    },
    convolution: (matrix) => {
      const m = new Float32Array(matrix);
      const pixelSizeX = 1 / fxcanvas.width;
      const pixelSizeY = 1 / fxcanvas.height;
      const program = compileShader(convolution);
      if (!program)
        return;
      gl2.uniform1fv(program.uniform["m"], m);
      gl2.uniform2f(program.uniform["px"], pixelSizeX, pixelSizeY);
      draw();
    },
    detectEdges: () => {
      filter.convolution.call(this, [
        0,
        1,
        0,
        1,
        -4,
        1,
        0,
        1,
        0
      ]);
    },
    sobelX: () => {
      filter.convolution.call(this, [
        -1,
        0,
        1,
        -2,
        0,
        2,
        -1,
        0,
        1
      ]);
    },
    sobelY: () => {
      filter.convolution.call(this, [
        -1,
        -2,
        -1,
        0,
        0,
        0,
        1,
        2,
        1
      ]);
    },
    sharpen: (amount) => {
      const a = amount || 1;
      filter.convolution.call(this, [
        0,
        -1 * a,
        0,
        -1 * a,
        1 + 4 * a,
        -1 * a,
        0,
        -1 * a,
        0
      ]);
    },
    emboss: (size2) => {
      const s = size2 || 1;
      filter.convolution.call(this, [
        -2 * s,
        -1 * s,
        0,
        -1 * s,
        1,
        1 * s,
        0,
        1 * s,
        2 * s
      ]);
    },
    blur: (size2) => {
      const blurSizeX = size2 / 7 / fxcanvas.width;
      const blurSizeY = size2 / 7 / fxcanvas.height;
      const program = compileShader(blur);
      if (!program)
        return;
      gl2.uniform2f(program.uniform["px"], 0, blurSizeY);
      draw(DRAW.INTERMEDIATE);
      gl2.uniform2f(program.uniform["px"], blurSizeX, 0);
      draw();
    },
    pixelate: (size2) => {
      const blurSizeX = size2 / fxcanvas.width;
      const blurSizeY = size2 / fxcanvas.height;
      const program = compileShader(pixelate);
      if (!program)
        return;
      gl2.uniform2f(program.uniform["size"], blurSizeX, blurSizeY);
      draw();
    }
  };
  this.add = function(name) {
    const args = Array.prototype.slice.call(arguments, 1);
    const func = filter[name];
    filterChain.push({ func, args });
  };
  this.reset = function() {
    filterChain = [];
  };
  this.get = function() {
    return filterChain;
  };
  this.apply = function(image) {
    resize(image.width, image.height);
    drawCount = 0;
    if (!sourceTexture)
      sourceTexture = gl2.createTexture();
    gl2.bindTexture(gl2.TEXTURE_2D, sourceTexture);
    gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_WRAP_S, gl2.CLAMP_TO_EDGE);
    gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_WRAP_T, gl2.CLAMP_TO_EDGE);
    gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_MIN_FILTER, gl2.NEAREST);
    gl2.texParameteri(gl2.TEXTURE_2D, gl2.TEXTURE_MAG_FILTER, gl2.NEAREST);
    gl2.texImage2D(gl2.TEXTURE_2D, 0, gl2.RGBA, gl2.RGBA, gl2.UNSIGNED_BYTE, image);
    for (let i = 0; i < filterChain.length; i++) {
      lastInChain = i === filterChain.length - 1;
      const f = filterChain[i];
      f.func.apply(this, f.args || []);
    }
    return fxcanvas;
  };
  this.draw = function(image) {
    this.add("brightness", 0);
    return this.apply(image);
  };
}

// src/image/enhance.ts
async function histogramEqualization(inputImage) {
  const squeeze = inputImage.shape.length === 4 ? mr(inputImage) : inputImage;
  const channels = Ln(squeeze, 3, 2);
  const min = [pm(channels[0]), pm(channels[1]), pm(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 = Qn([enh[0], enh[1], enh[2]], 2);
  const reshape = G(rgb2, [1, squeeze.shape[0], squeeze.shape[1], 3]);
  Re([...channels, ...min, ...max, ...sub, ...range, ...fact, ...enh, rgb2, squeeze]);
  return reshape;
}

// src/image/image.ts
var maxSize = 2048;
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 = On(input, 0);
      } else if (input.shape[2] === 4) {
        const rgb2 = ab(input, [0, 0, 0], [-1, -1, 3]);
        tensor = On(rgb2, 0);
        Re(rgb2);
      }
    } else if (input.shape.length === 4) {
      if (input.shape[3] === 3) {
        tensor = lr(input);
      } else if (input.shape[3] === 4) {
        tensor = wd(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 = ce(tensor, "float32");
      Re(tensor);
      tensor = cast;
    }
    return { tensor, canvas: config3.filter.return ? outCanvas : null };
  } else {
    if (typeof input["readyState"] !== "undefined" && input["readyState"] <= 2) {
      if (config3.debug)
        log("input stream is not ready");
      return { tensor: null, canvas: inCanvas };
    }
    const originalWidth = input["naturalWidth"] || input["videoWidth"] || input["width"] || input["shape"] && input["shape"][1] > 0;
    const originalHeight = input["naturalHeight"] || input["videoHeight"] || input["height"] || input["shape"] && input["shape"][2] > 0;
    if (!originalWidth || !originalHeight) {
      if (config3.debug)
        log("cannot determine input dimensions");
      return { tensor: null, canvas: inCanvas };
    }
    let targetWidth = originalWidth;
    let targetHeight = originalHeight;
    if (targetWidth > maxSize) {
      targetWidth = maxSize;
      targetHeight = Math.trunc(targetWidth * originalHeight / originalWidth);
    }
    if (targetHeight > maxSize) {
      targetHeight = maxSize;
      targetWidth = Math.trunc(targetHeight * originalWidth / originalHeight);
    }
    if ((config3.filter.width || 0) > 0)
      targetWidth = config3.filter.width;
    else if ((config3.filter.height || 0) > 0)
      targetWidth = originalWidth * ((config3.filter.height || 0) / originalHeight);
    if ((config3.filter.height || 0) > 0)
      targetHeight = config3.filter.height;
    else if ((config3.filter.width || 0) > 0)
      targetHeight = originalHeight * ((config3.filter.width || 0) / originalWidth);
    if (!targetWidth || !targetHeight)
      throw new Error("input error: cannot determine dimension");
    if (!inCanvas || (inCanvas == null ? void 0 : inCanvas.width) !== targetWidth || (inCanvas == null ? void 0 : inCanvas.height) !== targetHeight)
      inCanvas = canvas(targetWidth, targetHeight);
    const inCtx = inCanvas.getContext("2d");
    if (typeof ImageData !== "undefined" && input instanceof ImageData) {
      inCtx.putImageData(input, 0, 0);
    } else {
      if (config3.filter.flip && typeof inCtx.translate !== "undefined") {
        inCtx.translate(originalWidth, 0);
        inCtx.scale(-1, 1);
        inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height);
        inCtx.setTransform(1, 0, 0, 1, 0, 0);
      } else {
        inCtx.drawImage(input, 0, 0, originalWidth, originalHeight, 0, 0, inCanvas == null ? void 0 : inCanvas.width, inCanvas == null ? void 0 : inCanvas.height);
      }
    }
    if (!outCanvas || inCanvas.width !== outCanvas.width || (inCanvas == null ? void 0 : inCanvas.height) !== (outCanvas == null ? void 0 : outCanvas.height))
      outCanvas = canvas(inCanvas.width, inCanvas.height);
    if (config3.filter.enabled && env.webgl.supported) {
      if (!fx2)
        fx2 = env.browser ? new GLImageFilter() : null;
      env.filter = !!fx2;
      if (!fx2 || !fx2.add) {
        if (config3.debug)
          log("input process error: cannot initialize filters");
        return { tensor: null, canvas: inCanvas };
      }
      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 && xk) {
        pixels = xk ? xk.fromPixels(input) : null;
      } else {
        depth = input["data"].length / input["height"] / input["width"];
        const arr = new Uint8Array(input["data"]["buffer"]);
        pixels = hs(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 (xk && env.browser) {
        if (config3.backend === "webgl" || config3.backend === "humangl" || config3.backend === "webgpu") {
          pixels = xk.fromPixels(outCanvas);
        } else {
          tmpCanvas = copy(outCanvas);
          pixels = xk.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 = hs(arr, [targetWidth, targetHeight, depth]);
      }
    }
    if (depth === 4) {
      const rgb2 = ab(pixels, [0, 0, 0], [-1, -1, 3]);
      Re(pixels);
      pixels = rgb2;
    }
    if (!pixels)
      throw new Error("input error: cannot create tensor");
    const casted = ce(pixels, "float32");
    const tensor = config3.filter.equalization ? await histogramEqualization(casted) : On(casted, 0);
    Re([pixels, casted]);
    return { tensor, canvas: config3.filter.return ? outCanvas : null };
  }
}
async function skip(config3, input) {
  let skipFrame = false;
  if (config3.cacheSensitivity === 0 || !input.shape || input.shape.length !== 4 || input.shape[1] > 2048 || input.shape[2] > 2048)
    return skipFrame;
  if (!last.inputTensor) {
    last.inputTensor = lr(input);
  } else if (last.inputTensor.shape[1] !== input.shape[1] || last.inputTensor.shape[2] !== input.shape[2]) {
    Re(last.inputTensor);
    last.inputTensor = lr(input);
  } else {
    const t = {};
    t.diff = ge(input, last.inputTensor);
    t.squared = V(t.diff, t.diff);
    t.sum = ye(t.squared);
    const diffSum = await t.sum.data();
    const diffRelative = diffSum[0] / (input.shape[1] || 1) / (input.shape[2] || 1) / 255 / 3;
    Re([last.inputTensor, t.diff, t.squared, t.sum]);
    last.inputTensor = 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] ? ds.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 = ye(t.squared);
  const diffSum = await t.sum.data();
  const diffRelative = diffSum[0] / (input1.shape[1] || 1) / (input1.shape[2] || 1) / 255 / 3;
  Re([t.input1, t.input2, t.diff, t.squared, t.sum]);
  return diffRelative;
}

// src/util/env.ts
var Env = class {
  constructor() {
    __publicField(this, "browser");
    __publicField(this, "node");
    __publicField(this, "worker");
    __publicField(this, "platform", "");
    __publicField(this, "agent", "");
    __publicField(this, "backends", []);
    __publicField(this, "initial");
    __publicField(this, "filter");
    __publicField(this, "tfjs");
    __publicField(this, "offscreen");
    __publicField(this, "perfadd", false);
    __publicField(this, "wasm", {
      supported: void 0,
      backend: void 0,
      simd: void 0,
      multithread: void 0
    });
    __publicField(this, "webgl", {
      supported: void 0,
      backend: void 0,
      version: void 0,
      renderer: void 0
    });
    __publicField(this, "webgpu", {
      supported: void 0,
      backend: void 0,
      adapter: void 0
    });
    __publicField(this, "cpu", {
      model: void 0,
      flags: []
    });
    __publicField(this, "kernels", []);
    __publicField(this, "Canvas");
    __publicField(this, "Image");
    __publicField(this, "ImageData");
    this.browser = typeof navigator !== "undefined";
    this.node = typeof process !== "undefined" && typeof process.versions !== "undefined" && typeof process.versions.node !== "undefined";
    this.tfjs = { version: $pe["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(Ss().registryFactory);
    this.wasm.supported = typeof WebAssembly !== "undefined";
    this.wasm.backend = this.backends.includes("wasm");
    if (this.wasm.supported && this.wasm.backend && Cde() === "wasm") {
      this.wasm.simd = await X().getAsync("WASM_HAS_SIMD_SUPPORT");
      this.wasm.multithread = await X().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 && (Cde() === "webgl" || Cde() === "humangl")) {
      const gl2 = $A().gpgpu !== "undefined" ? await $A().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 = Qf(Cde()).map((kernel) => kernel.kernelName.toLowerCase());
    } catch (e) {
    }
  }
  async updateCPU() {
    const cpu = { model: "", flags: [] };
    if (this.node && this.platform.startsWith("linux")) {
    }
    if (!this["cpu"])
      Object.defineProperty(this, "cpu", { value: cpu });
    else
      this["cpu"] = cpu;
  }
};
var env = new Env();

// src/tfjs/load.ts
var options = {
  cacheModels: false,
  verbose: true,
  debug: false,
  modelBasePath: ""
};
async function httpHandler(url, init2) {
  if (options.debug)
    log("load model fetch:", url, init2);
  return fetch(url, init2);
}
function setModelLoadOptions(config3) {
  options.cacheModels = config3.cacheModels;
  options.verbose = config3.debug;
  options.modelBasePath = config3.modelBasePath;
}
async function loadModel(modelPath) {
  const modelUrl = join(options.modelBasePath, modelPath || "");
  const modelPathSegments = modelUrl.split("/");
  const cachedModelName = "indexeddb://" + modelPathSegments[modelPathSegments.length - 1].replace(".json", "");
  const cachedModels = await _n.listModels();
  const modelCached = options.cacheModels && Object.keys(cachedModels).includes(cachedModelName);
  const tfLoadOptions = typeof fetch === "undefined" ? {} : { fetchFunc: (url, init2) => httpHandler(url, init2) };
  const model18 = new w4(modelCached ? cachedModelName : modelUrl, tfLoadOptions);
  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"]);
  } catch (err) {
    log("error loading model:", modelUrl, err);
  }
  if (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 = "2.6.4";

// 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 = ds.cropAndResize(image, box, [0], [model.inputs[0].shape[2], model.inputs[0].shape[1]]);
    const obj = { age: 0, gender: "unknown", genderScore: 0, race: [] };
    if ((_a3 = config3.face["gear"]) == null ? void 0 : _a3.enabled)
      [t.age, t.gender, t.race] = model.execute(t.resize, ["age_output", "gender_output", "race_output"]);
    const gender = await t.gender.data();
    obj.gender = gender[0] > gender[1] ? "male" : "female";
    obj.genderScore = Math.round(100 * (gender[0] > gender[1] ? gender[0] : gender[1])) / 100;
    const race = await t.race.data();
    for (let i = 0; i < race.length; i++) {
      if (race[i] > (((_b3 = config3.face["gear"]) == null ? void 0 : _b3.minConfidence) || 0.2))
        obj.race.push({ score: Math.round(100 * race[i]) / 100, race: raceNames[i] });
    }
    obj.race.sort((a, b) => b.score - a.score);
    const ageDistribution = Array.from(await t.age.data());
    const ageSorted = ageDistribution.map((a, i) => [ageWeights[i], a]).sort((a, b) => b[1] - a[1]);
    let age = ageSorted[0][0];
    for (let i = 1; i < ageSorted.length; i++)
      age += ageSorted[i][1] * (ageSorted[i][0] - age);
    obj.age = Math.round(10 * age) / 10;
    Object.keys(t).forEach((tensor) => Re(t[tensor]));
    last2[idx] = obj;
    lastCount = count2;
    lastTime = now();
    resolve(obj);
  });
}

// src/tfjs/constants.ts
var constants = {
  tf255: 255,
  tf1: 1,
  tf2: 2,
  tf05: 0.5,
  tf127: 127.5,
  rgb: [0.2989, 0.587, 0.114]
};
function init() {
  constants.tf255 = Ie(255, "float32");
  constants.tf1 = Ie(1, "float32");
  constants.tf2 = Ie(2, "float32");
  constants.tf05 = Ie(0.5, "float32");
  constants.tf127 = Ie(127.5, "float32");
  constants.rgb = Qt([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 = ds.resizeBilinear(image, [model2.inputs[0].shape[2], model2.inputs[0].shape[1]], false);
    t.enhance = V(t.resize, constants.tf255);
    const obj = { age: 0 };
    if (config3.face["ssrnet"].enabled)
      t.age = model2.execute(t.enhance);
    if (t.age) {
      const data = await t.age.data();
      obj.age = Math.trunc(10 * data[0]) / 10;
    }
    Object.keys(t).forEach((tensor) => Re(t[tensor]));
    last3[idx] = obj;
    lastCount2 = count2;
    lastTime2 = now();
    resolve(obj);
  });
}

// src/gear/ssrnet-gender.ts
var model3;
var last4 = [];
var lastCount3 = 0;
var lastTime3 = 0;
var skipped3 = Number.MAX_SAFE_INTEGER;
var rgb = [0.2989, 0.587, 0.114];
async function load3(config3) {
  if (env.initial)
    model3 = null;
  if (!model3)
    model3 = await loadModel(config3.face["ssrnet"].modelPathGender);
  else if (config3.debug)
    log("cached model:", model3["modelUrl"]);
  return model3;
}
async function predict3(image, config3, idx, count2) {
  var _a2, _b2, _c, _d2;
  if (!model3)
    return { gender: "unknown", genderScore: 0 };
  const skipFrame = skipped3 < (((_a2 = config3.face["ssrnet"]) == null ? void 0 : _a2.skipFrames) || 0);
  const skipTime = (((_b2 = config3.face["ssrnet"]) == null ? void 0 : _b2.skipTime) || 0) > now() - lastTime3;
  if (config3.skipAllowed && skipFrame && skipTime && lastCount3 === count2 && ((_c = last4[idx]) == null ? void 0 : _c.gender) && ((_d2 = last4[idx]) == null ? void 0 : _d2.genderScore) > 0) {
    skipped3++;
    return last4[idx];
  }
  skipped3 = 0;
  return new Promise(async (resolve) => {
    if (!(model3 == null ? void 0 : model3.inputs[0].shape))
      return;
    const t = {};
    t.resize = ds.resizeBilinear(image, [model3.inputs[0].shape[2], model3.inputs[0].shape[1]], false);
    t.enhance = j(() => {
      const [red, green, blue] = Ln(t.resize, 3, 3);
      const redNorm = V(red, rgb[0]);
      const greenNorm = V(green, rgb[1]);
      const blueNorm = V(blue, rgb[2]);
      const grayscale = LA([redNorm, greenNorm, blueNorm]);
      const normalize = V(ge(grayscale, constants.tf05), 2);
      return normalize;
    });
    const obj = { gender: "unknown", genderScore: 0 };
    if (config3.face["ssrnet"].enabled)
      t.gender = model3.execute(t.enhance);
    const data = await t.gender.data();
    obj.gender = data[0] > data[1] ? "female" : "male";
    obj.genderScore = data[0] > data[1] ? Math.trunc(100 * data[0]) / 100 : Math.trunc(100 * data[1]) / 100;
    Object.keys(t).forEach((tensor) => Re(t[tensor]));
    last4[idx] = obj;
    lastCount3 = count2;
    lastTime3 = now();
    resolve(obj);
  });
}

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

// src/face/facemeshcoords.ts
var meshAnnotations = {
  silhouette: [
    10,
    338,
    297,
    332,
    284,
    251,
    389,
    356,
    454,
    323,
    361,
    288,
    397,
    365,
    379,
    378,
    400,
    377,
    152,
    148,
    176,
    149,
    150,
    136,
    172,
    58,
    132,
    93,
    234,
    127,
    162,
    21,
    54,
    103,
    67,
    109
  ],
  lipsUpperOuter: [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291],
  lipsLowerOuter: [146, 91, 181, 84, 17, 314, 405, 321, 375, 291],
  lipsUpperInner: [78, 191, 80, 81, 82, 13, 312, 311, 310, 415, 308],
  lipsLowerInner: [78, 95, 88, 178, 87, 14, 317, 402, 318, 324, 308],
  rightEyeUpper0: [246, 161, 160, 159, 158, 157, 173],
  rightEyeLower0: [33, 7, 163, 144, 145, 153, 154, 155, 133],
  rightEyeUpper1: [247, 30, 29, 27, 28, 56, 190],
  rightEyeLower1: [130, 25, 110, 24, 23, 22, 26, 112, 243],
  rightEyeUpper2: [113, 225, 224, 223, 222, 221, 189],
  rightEyeLower2: [226, 31, 228, 229, 230, 231, 232, 233, 244],
  rightEyeLower3: [143, 111, 117, 118, 119, 120, 121, 128, 245],
  rightEyebrowUpper: [156, 70, 63, 105, 66, 107, 55, 193],
  rightEyebrowLower: [35, 124, 46, 53, 52, 65],
  rightEyeIris: [473, 474, 475, 476, 477],
  leftEyeUpper0: [466, 388, 387, 386, 385, 384, 398],
  leftEyeLower0: [263, 249, 390, 373, 374, 380, 381, 382, 362],
  leftEyeUpper1: [467, 260, 259, 257, 258, 286, 414],
  leftEyeLower1: [359, 255, 339, 254, 253, 252, 256, 341, 463],
  leftEyeUpper2: [342, 445, 444, 443, 442, 441, 413],
  leftEyeLower2: [446, 261, 448, 449, 450, 451, 452, 453, 464],
  leftEyeLower3: [372, 340, 346, 347, 348, 349, 350, 357, 465],
  leftEyebrowUpper: [383, 300, 293, 334, 296, 336, 285, 417],
  leftEyebrowLower: [265, 353, 276, 283, 282, 295],
  leftEyeIris: [468, 469, 470, 471, 472],
  midwayBetweenEyes: [168],
  noseTip: [1],
  noseBottom: [2],
  noseRightCorner: [98],
  noseLeftCorner: [327],
  rightCheek: [205],
  leftCheek: [425]
};
var meshLandmarks = {
  count: 468,
  mouth: 13,
  symmetryLine: [13, meshAnnotations["midwayBetweenEyes"][0]]
};
var blazeFaceLandmarks = {
  leftEye: 0,
  rightEye: 1,
  nose: 2,
  mouth: 3,
  leftEar: 4,
  rightEar: 5,
  symmetryLine: [3, 2]
};
var MESH_TO_IRIS_INDICES_MAP = [
  { 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] }
];
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],
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var VTX68 = [
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  78,
  81,
  13,
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  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]);

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

// src/face/blazeface.ts
var keypointsCount = 6;
var faceBoxScaleFactor = 1.2;
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 = Ie(inputSize, "int32");
  anchors = ji(generateAnchors(inputSize));
  return model5;
}
function decodeBounds(boxOutputs) {
  const t = {};
  t.boxStarts = He(boxOutputs, [0, 1], [-1, 2]);
  t.centers = ie(t.boxStarts, anchors);
  t.boxSizes = He(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 = FE([t.startNormalized, t.endNormalized], 1);
  Object.keys(t).forEach((tensor) => Re(t[tensor]));
  return boxes;
}
async function getBoxes(inputImage, config3) {
  var _a2, _b2, _c, _d2;
  if (!inputImage || inputImage["isDisposedInternal"] || inputImage.shape.length !== 4 || inputImage.shape[1] < 1 || inputImage.shape[2] < 1)
    return [];
  const t = {};
  t.resized = ds.resizeBilinear(inputImage, [inputSize, inputSize]);
  t.div = xe(t.resized, constants.tf127);
  t.normalized = ge(t.div, constants.tf05);
  const res = model5 == null ? void 0 : model5.execute(t.normalized);
  if (Array.isArray(res)) {
    const sorted = res.sort((a, b) => a.size - b.size);
    t.concat384 = Ft([sorted[0], sorted[2]], 2);
    t.concat512 = Ft([sorted[1], sorted[3]], 2);
    t.concat = Ft([t.concat512, t.concat384], 1);
    t.batch = mr(t.concat, 0);
  } else {
    t.batch = mr(res);
  }
  Re(res);
  t.boxes = decodeBounds(t.batch);
  t.logits = He(t.batch, [0, 0], [-1, 1]);
  t.sigmoid = qs(t.logits);
  t.scores = mr(t.sigmoid);
  t.nms = await ds.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 = He(t.boxes, [nms[i], 0], [1, -1]);
      b.slice = He(t.batch, [nms[i], keypointsCount - 1], [1, -1]);
      b.squeeze = mr(b.slice);
      b.landmarks = G(b.squeeze, [keypointsCount, -1]);
      const points = await b.bbox.data();
      const rawBox = {
        startPoint: [points[0], points[1]],
        endPoint: [points[2], points[3]],
        landmarks: await b.landmarks.array(),
        confidence
      };
      const scaledBox = scaleBoxCoordinates(rawBox, [(inputImage.shape[2] || 0) / inputSize, (inputImage.shape[1] || 0) / inputSize]);
      const enlargedBox = enlargeBox(scaledBox, config3.face["scale"] || faceBoxScaleFactor);
      const squaredBox = squarifyBox(enlargedBox);
      boxes.push(squaredBox);
      Object.keys(b).forEach((tensor) => Re(b[tensor]));
    }
  }
  Object.keys(t).forEach((tensor) => Re(t[tensor]));
  return boxes;
}

// src/body/blazeposecoords.ts
var blazeposecoords_exports = {};
__export(blazeposecoords_exports, {
  connected: () => connected,
  kpt: () => kpt
});
var kpt = [
  "nose",
  "leftEyeInside",
  "leftEye",
  "leftEyeOutside",
  "rightEyeInside",
  "rightEye",
  "rightEyeOutside",
  "leftEar",
  "rightEar",
  "leftMouth",
  "rightMouth",
  "leftShoulder",
  "rightShoulder",
  "leftElbow",
  "rightElbow",
  "leftWrist",
  "rightWrist",
  "leftPinky",
  "rightPinky",
  "leftIndex",
  "rightIndex",
  "leftThumb",
  "rightThumb",
  "leftHip",
  "rightHip",
  "leftKnee",
  "rightKnee",
  "leftAnkle",
  "rightAnkle",
  "leftHeel",
  "rightHeel",
  "leftFoot",
  "rightFoot",
  "bodyCenter",
  "bodyTop",
  "leftPalm",
  "leftHand",
  "rightPalm",
  "rightHand"
];
var connected = {
  shoulders: ["leftShoulder", "rightShoulder"],
  hips: ["rightHip", "leftHip"],
  mouth: ["leftMouth", "rightMouth"],
  leftLegUpper: ["leftHip", "leftKnee"],
  leftLegLower: ["leftKnee", "leftAnkle"],
  leftFoot: ["leftAnkle", "leftHeel", "leftFoot"],
  leftTorso: ["leftShoulder", "leftHip"],
  leftArmUpper: ["leftShoulder", "leftElbow"],
  leftArmLower: ["leftElbow", "leftWrist"],
  leftHand: ["leftWrist", "leftPalm"],
  leftHandPinky: ["leftPalm", "leftPinky"],
  leftHandIndex: ["leftPalm", "leftIndex"],
  leftHandThumb: ["leftPalm", "leftThumb"],
  leftEyeOutline: ["leftEyeInside", "leftEyeOutside"],
  rightLegUpper: ["rightHip", "rightKnee"],
  rightLegLower: ["rightKnee", "rightAnkle"],
  rightFoot: ["rightAnkle", "rightHeel", "rightFoot"],
  rightTorso: ["rightShoulder", "rightHip"],
  rightArmUpper: ["rightShoulder", "rightElbow"],
  rightArmLower: ["rightElbow", "rightWrist"],
  rightHand: ["rightWrist", "rightPalm"],
  rightHandPinky: ["rightPalm", "rightPinky"],
  rightHandIndex: ["rightPalm", "rightIndex"],
  rightHandThumb: ["rightPalm", "rightThumb"],
  rightEyeOutline: ["rightEyeInside", "rightEyeOutside"]
};

// src/body/blazeposedetector.ts
var inputSize2 = 224;
var anchorTensor;
var numLayers = 5;
var strides = [8, 16, 32, 32, 32];
async function createAnchors() {
  const anchors3 = [];
  let layerId = 0;
  while (layerId < numLayers) {
    let anchorCount = 0;
    let lastSameStrideLayer = layerId;
    while (lastSameStrideLayer < strides.length && strides[lastSameStrideLayer] === strides[layerId]) {
      anchorCount += 2;
      lastSameStrideLayer++;
    }
    const stride = strides[layerId];
    const featureMapHeight = Math.ceil(inputSize2 / stride);
    const featureMapWidth = Math.ceil(inputSize2 / stride);
    for (let y = 0; y < featureMapHeight; ++y) {
      for (let x = 0; x < featureMapWidth; ++x) {
        for (let anchorId = 0; anchorId < anchorCount; ++anchorId) {
          anchors3.push({ x: (x + 0.5) / featureMapWidth, y: (y + 0.5) / featureMapHeight });
        }
      }
    }
    layerId = lastSameStrideLayer;
  }
  anchorTensor = { x: Qt(anchors3.map((a) => a.x)), y: Qt(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 = ds.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 = pi(t.cropped || input, padding);
    t.resize = ds.resizeBilinear(t.pad, [size2, size2]);
    final = xe(t.resize, constants.tf255);
  } else if (input.shape[1] !== size2) {
    t.resize = ds.resizeBilinear(t.cropped || input, [size2, size2]);
    final = xe(t.resize, constants.tf255);
  } else {
    final = xe(t.cropped || input, constants.tf255);
  }
  Object.keys(t).forEach((tensor) => Re(t[tensor]));
  return final;
}
function rescaleKeypoints(keypoints, outputSize2) {
  for (const kpt4 of keypoints) {
    kpt4.position = [
      Math.trunc(kpt4.position[0] * (outputSize2[0] + padding[2][0] + padding[2][1]) / outputSize2[0] - padding[2][0]),
      Math.trunc(kpt4.position[1] * (outputSize2[1] + padding[1][0] + padding[1][1]) / outputSize2[1] - padding[1][0]),
      kpt4.position[2]
    ];
    kpt4.positionRaw = [kpt4.position[0] / outputSize2[0], kpt4.position[1] / outputSize2[1], 2 * kpt4.position[2] / (outputSize2[0] + outputSize2[1])];
  }
  if (cropBox) {
    for (const kpt4 of keypoints) {
      kpt4.positionRaw = [
        kpt4.positionRaw[0] + cropBox[1],
        kpt4.positionRaw[1] + cropBox[0],
        kpt4.positionRaw[2]
      ];
      kpt4.position = [
        Math.trunc(kpt4.positionRaw[0] * outputSize2[0]),
        Math.trunc(kpt4.positionRaw[1] * outputSize2[1]),
        kpt4.positionRaw[2]
      ];
    }
  }
  return keypoints;
}
async function fixKeypoints(keypoints) {
  const leftPalm = keypoints.find((k) => k.part === "leftPalm");
  const leftWrist = keypoints.find((k) => k.part === "leftWrist");
  const leftIndex = keypoints.find((k) => k.part === "leftIndex");
  leftPalm.position[2] = ((leftWrist.position[2] || 0) + (leftIndex.position[2] || 0)) / 2;
  const rightPalm = keypoints.find((k) => k.part === "rightPalm");
  const rightWrist = keypoints.find((k) => k.part === "rightWrist");
  const rightIndex = keypoints.find((k) => k.part === "rightIndex");
  rightPalm.position[2] = ((rightWrist.position[2] || 0) + (rightIndex.position[2] || 0)) / 2;
}
async function detectLandmarks(input, config3, outputSize2) {
  var _a2;
  const t = {};
  [t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = (_a2 = models.landmarks) == null ? void 0 : _a2.execute(input, outputNodes.landmarks);
  const poseScore = (await t.poseflag.data())[0];
  const points = await t.ld.data();
  const distances = await t.world.data();
  Object.keys(t).forEach((tensor) => Re(t[tensor]));
  const keypointsRelative = [];
  const depth = 5;
  for (let i = 0; i < points.length / depth; i++) {
    const score = sigmoid(points[depth * i + 3]);
    const presence = sigmoid(points[depth * i + 4]);
    const adjScore = Math.trunc(100 * score * presence * poseScore) / 100;
    const positionRaw = [points[depth * i + 0] / inputSize3.landmarks[0], points[depth * i + 1] / inputSize3.landmarks[1], points[depth * i + 2] + 0];
    const position = [Math.trunc(outputSize2[0] * positionRaw[0]), Math.trunc(outputSize2[1] * positionRaw[1]), positionRaw[2]];
    const distance2 = [distances[depth * i + 0], distances[depth * i + 1], distances[depth * i + 2] + 0];
    keypointsRelative.push({ part: kpt[i], positionRaw, position, distance: distance2, score: adjScore });
  }
  if (poseScore < (config3.body.minConfidence || 0))
    return null;
  fixKeypoints(keypointsRelative);
  const keypoints = rescaleKeypoints(keypointsRelative, outputSize2);
  const kpts = keypoints.map((k) => k.position);
  const boxes = calc(kpts, [outputSize2[0], outputSize2[1]]);
  const annotations2 = {};
  for (const [name, indexes] of Object.entries(connected)) {
    const pt2 = [];
    for (let i = 0; i < indexes.length - 1; i++) {
      const pt0 = keypoints.find((kpt4) => kpt4.part === indexes[i]);
      const pt1 = keypoints.find((kpt4) => kpt4.part === indexes[i + 1]);
      if (pt0 && pt1)
        pt2.push([pt0.position, pt1.position]);
    }
    annotations2[name] = pt2;
  }
  const body4 = { id: 0, score: Math.trunc(100 * poseScore) / 100, box: boxes.box, boxRaw: boxes.boxRaw, keypoints, annotations: annotations2 };
  return body4;
}
async function predict5(input, config3) {
  const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
  const skipTime = (config3.body.skipTime || 0) > now() - lastTime5;
  const skipFrame = skipped5 < (config3.body.skipFrames || 0);
  if (config3.skipAllowed && skipTime && skipFrame && cache !== null) {
    skipped5++;
  } else {
    const t = {};
    t.landmarks = await prepareImage(input, 256);
    cache = await detectLandmarks(t.landmarks, config3, outputSize2);
    Object.keys(t).forEach((tensor) => Re(t[tensor]));
    lastTime5 = now();
    skipped5 = 0;
  }
  return cache ? [cache] : [];
}

// src/object/labels.ts
var labels = [
  { class: 1, label: "person" },
  { class: 2, label: "bicycle" },
  { class: 3, label: "car" },
  { class: 4, label: "motorcycle" },
  { class: 5, label: "airplane" },
  { class: 6, label: "bus" },
  { class: 7, label: "train" },
  { class: 8, label: "truck" },
  { class: 9, label: "boat" },
  { class: 10, label: "traffic light" },
  { class: 11, label: "fire hydrant" },
  { class: 12, label: "stop sign" },
  { class: 13, label: "parking meter" },
  { class: 14, label: "bench" },
  { class: 15, label: "bird" },
  { class: 16, label: "cat" },
  { class: 17, label: "dog" },
  { class: 18, label: "horse" },
  { class: 19, label: "sheep" },
  { class: 20, label: "cow" },
  { class: 21, label: "elephant" },
  { class: 22, label: "bear" },
  { class: 23, label: "zebra" },
  { class: 24, label: "giraffe" },
  { class: 25, label: "backpack" },
  { class: 26, label: "umbrella" },
  { class: 27, label: "handbag" },
  { class: 28, label: "tie" },
  { class: 29, label: "suitcase" },
  { class: 30, label: "frisbee" },
  { class: 31, label: "skis" },
  { class: 32, label: "snowboard" },
  { class: 33, label: "sports ball" },
  { class: 34, label: "kite" },
  { class: 35, label: "baseball bat" },
  { class: 36, label: "baseball glove" },
  { class: 37, label: "skateboard" },
  { class: 38, label: "surfboard" },
  { class: 39, label: "tennis racket" },
  { class: 40, label: "bottle" },
  { class: 41, label: "wine glass" },
  { class: 42, label: "cup" },
  { class: 43, label: "fork" },
  { class: 44, label: "knife" },
  { class: 45, label: "spoon" },
  { class: 46, label: "bowl" },
  { class: 47, label: "banana" },
  { class: 48, label: "apple" },
  { class: 49, label: "sandwich" },
  { class: 50, label: "orange" },
  { class: 51, label: "broccoli" },
  { class: 52, label: "carrot" },
  { class: 53, label: "hot dog" },
  { class: 54, label: "pizza" },
  { class: 55, label: "donut" },
  { class: 56, label: "cake" },
  { class: 57, label: "chair" },
  { class: 58, label: "couch" },
  { class: 59, label: "potted plant" },
  { class: 60, label: "bed" },
  { class: 61, label: "dining table" },
  { class: 62, label: "toilet" },
  { class: 63, label: "tv" },
  { class: 64, label: "laptop" },
  { class: 65, label: "mouse" },
  { class: 66, label: "remote" },
  { class: 67, label: "keyboard" },
  { class: 68, label: "cell phone" },
  { class: 69, label: "microwave" },
  { class: 70, label: "oven" },
  { class: 71, label: "toaster" },
  { class: 72, label: "sink" },
  { class: 73, label: "refrigerator" },
  { class: 74, label: "book" },
  { class: 75, label: "clock" },
  { class: 76, label: "vase" },
  { class: 77, label: "scissors" },
  { class: 78, label: "teddy bear" },
  { class: 79, label: "hair drier" },
  { class: 80, label: "toothbrush" }
];

// src/object/centernet.ts
var model6;
var inputSize4 = 0;
var last5 = [];
var lastTime6 = 0;
var skipped6 = Number.MAX_SAFE_INTEGER;
async function load6(config3) {
  if (env.initial)
    model6 = null;
  if (!model6) {
    model6 = await loadModel(config3.object.modelPath);
    const inputs = Object.values(model6.modelSignature["inputs"]);
    inputSize4 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
  } else if (config3.debug)
    log("cached model:", model6["modelUrl"]);
  return model6;
}
async function process3(res, outputShape, config3) {
  if (!res)
    return [];
  const t = {};
  const results = [];
  const detections = await res.array();
  t.squeeze = mr(res);
  const arr = Ln(t.squeeze, 6, 1);
  t.stack = Qn([arr[1], arr[0], arr[3], arr[2]], 1);
  t.boxes = mr(t.stack);
  t.scores = mr(arr[4]);
  t.classes = mr(arr[5]);
  Re([res, ...arr]);
  t.nms = await ds.nonMaxSuppressionAsync(t.boxes, t.scores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence || 0);
  const nms = await t.nms.data();
  let i = 0;
  for (const id2 of Array.from(nms)) {
    const score = Math.trunc(100 * detections[0][id2][4]) / 100;
    const classVal = detections[0][id2][5];
    const label = labels[classVal].label;
    const [x, y] = [
      detections[0][id2][0] / inputSize4,
      detections[0][id2][1] / inputSize4
    ];
    const boxRaw = [
      x,
      y,
      detections[0][id2][2] / inputSize4 - x,
      detections[0][id2][3] / inputSize4 - y
    ];
    const box = [
      Math.trunc(boxRaw[0] * outputShape[0]),
      Math.trunc(boxRaw[1] * outputShape[1]),
      Math.trunc(boxRaw[2] * outputShape[0]),
      Math.trunc(boxRaw[3] * outputShape[1])
    ];
    results.push({ id: i++, score, class: classVal, label, box, boxRaw });
  }
  Object.keys(t).forEach((tensor) => Re(t[tensor]));
  return results;
}
async function predict6(input, config3) {
  const skipTime = (config3.object.skipTime || 0) > now() - lastTime6;
  const skipFrame = skipped6 < (config3.object.skipFrames || 0);
  if (config3.skipAllowed && skipTime && skipFrame && last5.length > 0) {
    skipped6++;
    return last5;
  }
  skipped6 = 0;
  return new Promise(async (resolve) => {
    const outputSize2 = [input.shape[2] || 0, input.shape[1] || 0];
    const resize = ds.resizeBilinear(input, [inputSize4, inputSize4]);
    const objectT = config3.object.enabled ? model6 == null ? void 0 : model6.execute(resize, ["tower_0/detections"]) : null;
    lastTime6 = now();
    Re(resize);
    const obj = await process3(objectT, outputSize2, config3);
    last5 = obj;
    resolve(obj);
  });
}

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

// src/body/efficientpose.ts
var model7;
var lastTime7 = 0;
var cache2 = { id: 0, keypoints: [], box: [0, 0, 0, 0], boxRaw: [0, 0, 0, 0], score: 0, annotations: {} };
var skipped7 = Number.MAX_SAFE_INTEGER;
async function load7(config3) {
  if (env.initial)
    model7 = null;
  if (!model7)
    model7 = await loadModel(config3.body.modelPath);
  else if (config3.debug)
    log("cached model:", model7["modelUrl"]);
  return model7;
}
async function max2d(inputs, minScore) {
  const [width, height] = inputs.shape;
  const reshaped = G(inputs, [height * width]);
  const max = As(reshaped, 0);
  const newScore = (await max.data())[0];
  Re([reshaped, max]);
  if (newScore > minScore) {
    const coordinates = Gu(reshaped, 0);
    const mod = dD(coordinates, width);
    const x = (await mod.data())[0];
    const div = xe(coordinates, Ie(width, "int32"));
    const y = (await div.data())[0];
    Re([mod, div]);
    return [x, y, newScore];
  }
  return [0, 0, newScore];
}
async function predict7(image, config3) {
  const skipTime = (config3.body.skipTime || 0) > now() - lastTime7;
  const skipFrame = skipped7 < (config3.body.skipFrames || 0);
  if (config3.skipAllowed && skipTime && skipFrame && Object.keys(cache2.keypoints).length > 0) {
    skipped7++;
    return [cache2];
  }
  skipped7 = 0;
  return new Promise(async (resolve) => {
    var _a2;
    const tensor = j(() => {
      if (!(model7 == null ? void 0 : model7.inputs[0].shape))
        return null;
      const resize = ds.resizeBilinear(image, [model7.inputs[0].shape[2], model7.inputs[0].shape[1]], false);
      const enhance2 = V(resize, constants.tf2);
      const norm = ge(enhance2, constants.tf1);
      return norm;
    });
    let resT;
    if (config3.body.enabled)
      resT = model7 == null ? void 0 : model7.execute(tensor);
    lastTime7 = now();
    Re(tensor);
    if (resT) {
      cache2.keypoints.length = 0;
      const squeeze = resT.squeeze();
      Re(resT);
      const stack = squeeze.unstack(2);
      Re(squeeze);
      for (let id2 = 0; id2 < stack.length; id2++) {
        const [x10, y10, partScore] = await max2d(stack[id2], config3.body.minConfidence);
        if (partScore > (((_a2 = config3.body) == null ? void 0 : _a2.minConfidence) || 0)) {
          cache2.keypoints.push({
            score: Math.round(100 * partScore) / 100,
            part: kpt2[id2],
            positionRaw: [
              x10 / model7.inputs[0].shape[2],
              y10 / model7.inputs[0].shape[1]
            ],
            position: [
              Math.round(image.shape[2] * x10 / model7.inputs[0].shape[2]),
              Math.round(image.shape[1] * y10 / model7.inputs[0].shape[1])
            ]
          });
        }
      }
      stack.forEach((s) => Re(s));
    }
    cache2.score = cache2.keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
    const x = cache2.keypoints.map((a) => a.position[0]);
    const y = cache2.keypoints.map((a) => a.position[1]);
    cache2.box = [
      Math.min(...x),
      Math.min(...y),
      Math.max(...x) - Math.min(...x),
      Math.max(...y) - Math.min(...y)
    ];
    const xRaw = cache2.keypoints.map((a) => a.positionRaw[0]);
    const yRaw = cache2.keypoints.map((a) => a.positionRaw[1]);
    cache2.boxRaw = [
      Math.min(...xRaw),
      Math.min(...yRaw),
      Math.max(...xRaw) - Math.min(...xRaw),
      Math.max(...yRaw) - Math.min(...yRaw)
    ];
    for (const [name, indexes] of Object.entries(connected2)) {
      const pt2 = [];
      for (let i = 0; i < indexes.length - 1; i++) {
        const pt0 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i]);
        const pt1 = cache2.keypoints.find((kpt4) => kpt4.part === indexes[i + 1]);
        if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
          pt2.push([pt0.position, pt1.position]);
      }
      cache2.annotations[name] = pt2;
    }
    resolve([cache2]);
  });
}

// src/gear/emotion.ts
var annotations = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"];
var model8;
var last6 = [];
var lastCount5 = 0;
var lastTime8 = 0;
var skipped8 = Number.MAX_SAFE_INTEGER;
async function load8(config3) {
  var _a2;
  if (env.initial)
    model8 = null;
  if (!model8)
    model8 = await loadModel((_a2 = config3.face.emotion) == null ? void 0 : _a2.modelPath);
  else if (config3.debug)
    log("cached model:", model8["modelUrl"]);
  return model8;
}
async function predict8(image, config3, idx, count2) {
  var _a2, _b2;
  if (!model8)
    return [];
  const skipFrame = skipped8 < (((_a2 = config3.face.emotion) == null ? void 0 : _a2.skipFrames) || 0);
  const skipTime = (((_b2 = config3.face.emotion) == null ? void 0 : _b2.skipTime) || 0) > now() - lastTime8;
  if (config3.skipAllowed && skipTime && skipFrame && lastCount5 === count2 && last6[idx] && last6[idx].length > 0) {
    skipped8++;
    return last6[idx];
  }
  skipped8 = 0;
  return new Promise(async (resolve) => {
    var _a3, _b3;
    const obj = [];
    if ((_a3 = config3.face.emotion) == null ? void 0 : _a3.enabled) {
      const t = {};
      const inputSize10 = (model8 == null ? void 0 : model8.inputs[0].shape) ? model8.inputs[0].shape[2] : 0;
      t.resize = ds.resizeBilinear(image, [inputSize10, inputSize10], false);
      t.channels = V(t.resize, constants.rgb);
      t.grayscale = ye(t.channels, 3, true);
      t.grayscaleSub = ge(t.grayscale, constants.tf05);
      t.grayscaleMul = V(t.grayscaleSub, constants.tf2);
      t.emotion = model8 == null ? void 0 : model8.execute(t.grayscaleMul);
      lastTime8 = now();
      const data = await t.emotion.data();
      for (let i = 0; i < data.length; i++) {
        if (data[i] > (((_b3 = config3.face.emotion) == null ? void 0 : _b3.minConfidence) || 0))
          obj.push({ score: Math.min(0.99, Math.trunc(100 * data[i]) / 100), emotion: annotations[i] });
      }
      obj.sort((a, b) => b.score - a.score);
      Object.keys(t).forEach((tensor) => Re(t[tensor]));
    }
    last6[idx] = obj;
    lastCount5 = count2;
    resolve(obj);
  });
}

// src/face/mobilefacenet.ts
var model9;
var last7 = [];
var lastCount6 = 0;
var lastTime9 = 0;
var skipped9 = Number.MAX_SAFE_INTEGER;
async function load9(config3) {
  if (env.initial)
    model9 = null;
  if (!model9)
    model9 = await loadModel(config3.face["mobilefacenet"].modelPath);
  else if (config3.debug)
    log("cached model:", model9["modelUrl"]);
  return model9;
}
async function predict9(input, config3, idx, count2) {
  var _a2, _b2;
  if (!model9)
    return [];
  const skipFrame = skipped9 < (((_a2 = config3.face["embedding"]) == null ? void 0 : _a2.skipFrames) || 0);
  const skipTime = (((_b2 = config3.face["embedding"]) == null ? void 0 : _b2.skipTime) || 0) > now() - lastTime9;
  if (config3.skipAllowed && skipTime && skipFrame && lastCount6 === count2 && last7[idx]) {
    skipped9++;
    return last7[idx];
  }
  return new Promise(async (resolve) => {
    var _a3;
    let data = [];
    if (((_a3 = config3.face["embedding"]) == null ? void 0 : _a3.enabled) && (model9 == null ? void 0 : model9.inputs[0].shape)) {
      const t = {};
      t.crop = ds.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 replaceRawCoordinates(rawCoords, newCoords, prefix, keys) {
  for (let i = 0; i < MESH_TO_IRIS_INDICES_MAP.length; i++) {
    const { key, indices } = MESH_TO_IRIS_INDICES_MAP[i];
    const originalIndices = meshAnnotations[`${prefix}${key}`];
    if (!keys || keys.includes(key)) {
      for (let j10 = 0; j10 < indices.length; j10++) {
        const index2 = indices[j10];
        rawCoords[originalIndices[j10]] = [
          newCoords[index2][0],
          newCoords[index2][1],
          (newCoords[index2][2] + rawCoords[originalIndices[j10]][2]) / 2
        ];
      }
    }
  }
}
var getLeftToRightEyeDepthDifference = (rawCoords) => {
  const leftEyeZ = rawCoords[eyeLandmarks.leftBounds[0]][2];
  const rightEyeZ = rawCoords[eyeLandmarks.rightBounds[0]][2];
  return leftEyeZ - rightEyeZ;
};
var getEyeBox = (rawCoords, face4, eyeInnerCornerIndex, eyeOuterCornerIndex, meshSize, flip = false) => {
  const box = squarifyBox(enlargeBox(calculateLandmarksBoundingBox([rawCoords[eyeInnerCornerIndex], rawCoords[eyeOuterCornerIndex]]), irisEnlarge));
  const boxSize = getBoxSize(box);
  let crop = ds.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 = ds.flipLeftRight(crop);
    Re(crop);
    crop = flipped;
  }
  return { box, boxSize, crop };
};
var getEyeCoords = (eyeData, eyeBox, eyeBoxSize, flip = false) => {
  const eyeRawCoords = [];
  for (let i = 0; i < irisLandmarks.numCoordinates; i++) {
    const x = eyeData[i * 3];
    const y = eyeData[i * 3 + 1];
    const z = eyeData[i * 3 + 2];
    eyeRawCoords.push([
      (flip ? 1 - x / inputSize5 : x / inputSize5) * eyeBoxSize[0] + eyeBox.startPoint[0],
      y / inputSize5 * eyeBoxSize[1] + eyeBox.startPoint[1],
      z
    ]);
  }
  return { rawCoords: eyeRawCoords, iris: eyeRawCoords.slice(irisLandmarks.index) };
};
var getAdjustedIrisCoords = (rawCoords, irisCoords, direction) => {
  const upperCenterZ = rawCoords[meshAnnotations[`${direction}EyeUpper0`][irisLandmarks.upperCenter]][2];
  const lowerCenterZ = rawCoords[meshAnnotations[`${direction}EyeLower0`][irisLandmarks.lowerCenter]][2];
  const averageZ = (upperCenterZ + lowerCenterZ) / 2;
  return irisCoords.map((coord, i) => {
    let z = averageZ;
    if (i === 2) {
      z = upperCenterZ;
    } else if (i === 4) {
      z = lowerCenterZ;
    }
    return [coord[0], coord[1], z];
  });
};
async function augmentIris(rawCoords, face4, config3, meshSize) {
  if (!model10) {
    if (config3.debug)
      log("face mesh iris detection requested, but model is not loaded");
    return rawCoords;
  }
  const { box: leftEyeBox, boxSize: leftEyeBoxSize, crop: leftEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.leftBounds[0], eyeLandmarks.leftBounds[1], meshSize, true);
  const { box: rightEyeBox, boxSize: rightEyeBoxSize, crop: rightEyeCrop } = getEyeBox(rawCoords, face4, eyeLandmarks.rightBounds[0], eyeLandmarks.rightBounds[1], meshSize, true);
  const combined = Ft([leftEyeCrop, rightEyeCrop]);
  Re(leftEyeCrop);
  Re(rightEyeCrop);
  const eyePredictions = model10.execute(combined);
  Re(combined);
  const eyePredictionsData = await eyePredictions.data();
  Re(eyePredictions);
  const leftEyeData = eyePredictionsData.slice(0, irisLandmarks.numCoordinates * 3);
  const { rawCoords: leftEyeRawCoords, iris: leftIrisRawCoords } = getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true);
  const rightEyeData = eyePredictionsData.slice(irisLandmarks.numCoordinates * 3);
  const { rawCoords: rightEyeRawCoords, iris: rightIrisRawCoords } = getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize);
  const leftToRightEyeDepthDifference = getLeftToRightEyeDepthDifference(rawCoords);
  if (Math.abs(leftToRightEyeDepthDifference) < 30) {
    replaceRawCoordinates(rawCoords, leftEyeRawCoords, "left", null);
    replaceRawCoordinates(rawCoords, rightEyeRawCoords, "right", null);
  } else if (leftToRightEyeDepthDifference < 1) {
    replaceRawCoordinates(rawCoords, leftEyeRawCoords, "left", ["EyeUpper0", "EyeLower0"]);
  } else {
    replaceRawCoordinates(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/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;
  const skipTime = (((_a2 = config3.face.detector) == null ? void 0 : _a2.skipTime) || 0) > now() - cache3.timestamp;
  const skipFrame = cache3.skipped < (((_b2 = config3.face.detector) == null ? void 0 : _b2.skipFrames) || 0);
  if (!config3.skipAllowed || !skipTime || !skipFrame || cache3.boxes.length === 0) {
    cache3.boxes = await getBoxes(input, config3);
    cache3.timestamp = now();
    cache3.skipped = 0;
  } else {
    cache3.skipped++;
  }
  const faces = [];
  const newCache = [];
  let id2 = 0;
  for (let i = 0; i < cache3.boxes.length; i++) {
    const box = cache3.boxes[i];
    let angle = 0;
    let rotationMatrix;
    const face4 = {
      id: id2++,
      mesh: [],
      meshRaw: [],
      box: [0, 0, 0, 0],
      boxRaw: [0, 0, 0, 0],
      score: 0,
      boxScore: 0,
      faceScore: 0,
      annotations: {}
    };
    [angle, rotationMatrix, face4.tensor] = correctFaceRotation((_c = config3.face.detector) == null ? void 0 : _c.rotation, box, input, ((_d2 = config3.face.mesh) == null ? void 0 : _d2.enabled) ? inputSize6 : size());
    if ((_e2 = config3 == null ? void 0 : config3.filter) == null ? void 0 : _e2.equalization) {
      const equilized = await histogramEqualization(face4.tensor);
      Re(face4.tensor);
      face4.tensor = equilized;
    }
    face4.boxScore = Math.round(100 * box.confidence) / 100;
    if (!((_f = config3.face.mesh) == null ? void 0 : _f.enabled)) {
      face4.box = clampBox(box, input);
      face4.boxRaw = getRawBox(box, input);
      face4.score = face4.boxScore;
      face4.mesh = box.landmarks.map((pt2) => [
        (box.startPoint[0] + box.endPoint[0]) / 2 + (box.endPoint[0] + box.startPoint[0]) * pt2[0] / size(),
        (box.startPoint[1] + box.endPoint[1]) / 2 + (box.endPoint[1] + box.startPoint[1]) * pt2[1] / size()
      ]);
      face4.meshRaw = face4.mesh.map((pt2) => [pt2[0] / (input.shape[2] || 0), pt2[1] / (input.shape[1] || 0), (pt2[2] || 0) / inputSize6]);
      for (const key of Object.keys(blazeFaceLandmarks))
        face4.annotations[key] = [face4.mesh[blazeFaceLandmarks[key]]];
    } else if (!model11) {
      if (config3.debug)
        log("face mesh detection requested, but model is not loaded");
    } else {
      const [contours, confidence, contourCoords] = model11.execute(face4.tensor);
      const faceConfidence = await confidence.data();
      face4.faceScore = Math.round(100 * faceConfidence[0]) / 100;
      const coordsReshaped = G(contourCoords, [-1, 3]);
      let rawCoords = await coordsReshaped.array();
      Re([contourCoords, coordsReshaped, confidence, contours]);
      if (face4.faceScore < (((_g2 = config3.face.detector) == null ? void 0 : _g2.minConfidence) || 1)) {
        box.confidence = face4.faceScore;
      } else {
        if ((_h = config3.face.iris) == null ? void 0 : _h.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);
      }
    }
    if (face4.score > (((_i = config3.face.detector) == null ? void 0 : _i.minConfidence) || 1))
      faces.push(face4);
    else
      Re(face4.tensor);
  }
  cache3.boxes = newCache;
  return faces;
}
async function load11(config3) {
  var _a2;
  if (env.initial)
    model11 = null;
  if (!model11)
    model11 = await loadModel((_a2 = config3.face.mesh) == null ? void 0 : _a2.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 = ds.resizeBilinear(tensor, [model12.inputs[0].shape[2], model12.inputs[0].shape[1]], false);
  const norm = V(crop, constants.tf255);
  Re(crop);
  return norm;
}
async function predict11(image, config3, idx, count2) {
  var _a2, _b2, _c, _d2;
  if (!model12)
    return { age: 0, gender: "unknown", genderScore: 0, descriptor: [] };
  const skipFrame = skipped10 < (((_a2 = config3.face.description) == null ? void 0 : _a2.skipFrames) || 0);
  const skipTime = (((_b2 = config3.face.description) == null ? void 0 : _b2.skipTime) || 0) > now() - lastTime10;
  if (config3.skipAllowed && skipFrame && skipTime && lastCount7 === count2 && ((_c = last8[idx]) == null ? void 0 : _c.age) && ((_d2 = last8[idx]) == null ? void 0 : _d2.age) > 0) {
    skipped10++;
    return last8[idx];
  }
  skipped10 = 0;
  return new Promise(async (resolve) => {
    var _a3, _b3;
    const obj = {
      age: 0,
      gender: "unknown",
      genderScore: 0,
      descriptor: []
    };
    if ((_a3 = config3.face.description) == null ? void 0 : _a3.enabled) {
      const enhanced = enhance(image);
      const resT = model12 == null ? void 0 : model12.execute(enhanced);
      lastTime10 = now();
      Re(enhanced);
      const genderT = await resT.find((t) => t.shape[1] === 1);
      const gender = await genderT.data();
      const confidence = Math.trunc(200 * Math.abs(gender[0] - 0.5)) / 100;
      if (confidence > (((_b3 = config3.face.description) == null ? void 0 : _b3.minConfidence) || 0)) {
        obj.gender = gender[0] <= 0.5 ? "female" : "male";
        obj.genderScore = Math.min(0.99, confidence);
      }
      const argmax = Gu(resT.find((t) => t.shape[1] === 100), 1);
      const age = (await argmax.data())[0];
      Re(argmax);
      const ageT = resT.find((t) => t.shape[1] === 100);
      const all2 = await ageT.data();
      obj.age = Math.round(all2[age - 1] > all2[age + 1] ? 10 * age - 100 * all2[age - 1] : 10 * age + 100 * all2[age + 1]) / 10;
      const desc = resT.find((t) => t.shape[1] === 1024);
      const descriptor = desc ? await desc.data() : [];
      obj.descriptor = Array.from(descriptor);
      resT.forEach((t) => Re(t));
    }
    last8[idx] = obj;
    lastCount7 = count2;
    resolve(obj);
  });
}

// src/hand/handposeutil.ts
function getBoxSize2(box) {
  return [
    Math.abs(box.endPoint[0] - box.startPoint[0]),
    Math.abs(box.endPoint[1] - box.startPoint[1])
  ];
}
function getBoxCenter2(box) {
  return [
    box.startPoint[0] + (box.endPoint[0] - box.startPoint[0]) / 2,
    box.startPoint[1] + (box.endPoint[1] - box.startPoint[1]) / 2
  ];
}
function cutBoxFromImageAndResize(box, image, cropSize) {
  const h = image.shape[1];
  const w10 = image.shape[2];
  const boxes = [[
    box.startPoint[1] / h,
    box.startPoint[0] / w10,
    box.endPoint[1] / h,
    box.endPoint[0] / w10
  ]];
  return ds.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 },
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  { 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 = ji(this.anchors);
    this.inputSize = this.model && this.model.inputs && this.model.inputs[0].shape ? this.model.inputs[0].shape[2] : 0;
    this.inputSizeTensor = Qt([this.inputSize, this.inputSize]);
    this.doubleInputSizeTensor = Qt([this.inputSize * 2, this.inputSize * 2]);
  }
  normalizeBoxes(boxes) {
    const t = {};
    t.boxOffsets = He(boxes, [0, 0], [-1, 2]);
    t.boxSizes = He(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 = FE([t.startPoints, t.endPoints], 1);
    Object.keys(t).forEach((tensor) => Re(t[tensor]));
    return res;
  }
  normalizeLandmarks(rawPalmLandmarks, index2) {
    const t = {};
    t.reshape = G(rawPalmLandmarks, [-1, 7, 2]);
    t.div = xe(t.reshape, this.inputSizeTensor);
    t.landmarks = ie(t.div, this.anchors[index2]);
    const res = V(t.landmarks, this.inputSizeTensor);
    Object.keys(t).forEach((tensor) => Re(t[tensor]));
    return res;
  }
  async predict(input, config3) {
    const t = {};
    t.resize = ds.resizeBilinear(input, [this.inputSize, this.inputSize]);
    t.div = xe(t.resize, constants.tf127);
    t.image = ge(t.div, constants.tf1);
    t.batched = this.model.execute(t.image);
    t.predictions = mr(t.batched);
    t.slice = He(t.predictions, [0, 0], [-1, 1]);
    t.sigmoid = qs(t.slice);
    t.scores = mr(t.sigmoid);
    const scores = await t.scores.data();
    t.boxes = He(t.predictions, [0, 1], [-1, 4]);
    t.norm = this.normalizeBoxes(t.boxes);
    t.nms = await ds.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 = He(t.norm, [index2, 0], [1, -1]);
      p.slice = He(t.predictions, [index2, 5], [1, 14]);
      p.norm = this.normalizeLandmarks(p.slice, index2);
      p.palmLandmarks = G(p.norm, [-1, 2]);
      const box = await p.box.data();
      const startPoint = box.slice(0, 2);
      const endPoint = box.slice(2, 4);
      const palmLandmarks = await p.palmLandmarks.array();
      const hand3 = { startPoint, endPoint, palmLandmarks, confidence: scores[index2] };
      const scaled = scaleBoxCoordinates2(hand3, [input.shape[2] / this.inputSize, input.shape[1] / this.inputSize]);
      hands.push(scaled);
      Object.keys(p).forEach((tensor) => Re(p[tensor]));
    }
    Object.keys(t).forEach((tensor) => Re(t[tensor]));
    return hands;
  }
};

// src/hand/handposepipeline.ts
var palmBoxEnlargeFactor = 5;
var handBoxEnlargeFactor = 1.65;
var palmLandmarkIds = [0, 5, 9, 13, 17, 1, 2];
var palmLandmarksPalmBase = 0;
var palmLandmarksMiddleFingerBase = 2;
var lastTime11 = 0;
var HandPipeline = class {
  constructor(handDetector, handPoseModel2) {
    __publicField(this, "handDetector");
    __publicField(this, "handPoseModel");
    __publicField(this, "inputSize");
    __publicField(this, "storedBoxes");
    __publicField(this, "skipped");
    __publicField(this, "detectedHands");
    this.handDetector = handDetector;
    this.handPoseModel = handPoseModel2;
    this.inputSize = this.handPoseModel && this.handPoseModel.inputs[0].shape ? this.handPoseModel.inputs[0].shape[2] : 0;
    this.storedBoxes = [];
    this.skipped = Number.MAX_SAFE_INTEGER;
    this.detectedHands = 0;
  }
  calculateLandmarksBoundingBox(landmarks) {
    const xs2 = landmarks.map((d) => d[0]);
    const ys2 = landmarks.map((d) => d[1]);
    const startPoint = [Math.min(...xs2), Math.min(...ys2)];
    const endPoint = [Math.max(...xs2), Math.max(...ys2)];
    return { startPoint, endPoint };
  }
  getBoxForPalmLandmarks(palmLandmarks, rotationMatrix) {
    const rotatedPalmLandmarks = palmLandmarks.map((coord) => rotatePoint2([...coord, 1], rotationMatrix));
    const boxAroundPalm = this.calculateLandmarksBoundingBox(rotatedPalmLandmarks);
    return enlargeBox2(squarifyBox2(boxAroundPalm), palmBoxEnlargeFactor);
  }
  getBoxForHandLandmarks(landmarks) {
    const boundingBox = this.calculateLandmarksBoundingBox(landmarks);
    const boxAroundHand = enlargeBox2(squarifyBox2(boundingBox), handBoxEnlargeFactor);
    boxAroundHand.palmLandmarks = [];
    for (let i = 0; i < palmLandmarkIds.length; i++) {
      boxAroundHand.palmLandmarks.push(landmarks[palmLandmarkIds[i]].slice(0, 2));
    }
    return boxAroundHand;
  }
  transformRawCoords(rawCoords, box2, angle, rotationMatrix) {
    const boxSize = getBoxSize2(box2);
    const scaleFactor = [boxSize[0] / this.inputSize, boxSize[1] / this.inputSize, (boxSize[0] + boxSize[1]) / this.inputSize / 2];
    const coordsScaled = rawCoords.map((coord) => [
      scaleFactor[0] * (coord[0] - this.inputSize / 2),
      scaleFactor[1] * (coord[1] - this.inputSize / 2),
      scaleFactor[2] * coord[2]
    ]);
    const coordsRotationMatrix = buildRotationMatrix2(angle, [0, 0]);
    const coordsRotated = coordsScaled.map((coord) => {
      const rotated = rotatePoint2(coord, coordsRotationMatrix);
      return [...rotated, coord[2]];
    });
    const inverseRotationMatrix = invertTransformMatrix2(rotationMatrix);
    const boxCenter = [...getBoxCenter2(box2), 1];
    const originalBoxCenter = [
      dot2(boxCenter, inverseRotationMatrix[0]),
      dot2(boxCenter, inverseRotationMatrix[1])
    ];
    return coordsRotated.map((coord) => [
      Math.trunc(coord[0] + originalBoxCenter[0]),
      Math.trunc(coord[1] + originalBoxCenter[1]),
      Math.trunc(coord[2])
    ]);
  }
  async estimateHands(image, config3) {
    let useFreshBox = false;
    let boxes;
    const skipTime = (config3.hand.skipTime || 0) > now() - lastTime11;
    const skipFrame = this.skipped < (config3.hand.skipFrames || 0);
    if (config3.skipAllowed && skipTime && skipFrame) {
      boxes = await this.handDetector.predict(image, config3);
      this.skipped = 0;
    }
    if (config3.skipAllowed)
      this.skipped++;
    if (boxes && boxes.length > 0 && (boxes.length !== this.detectedHands && this.detectedHands !== config3.hand.maxDetected || !config3.hand.landmarks)) {
      this.detectedHands = 0;
      this.storedBoxes = [...boxes];
      if (this.storedBoxes.length > 0)
        useFreshBox = true;
    }
    const hands = [];
    for (let i = 0; i < this.storedBoxes.length; i++) {
      const currentBox = this.storedBoxes[i];
      if (!currentBox)
        continue;
      if (config3.hand.landmarks) {
        const angle = config3.hand.rotation ? computeRotation2(currentBox.palmLandmarks[palmLandmarksPalmBase], currentBox.palmLandmarks[palmLandmarksMiddleFingerBase]) : 0;
        const palmCenter = getBoxCenter2(currentBox);
        const palmCenterNormalized = [palmCenter[0] / image.shape[2], palmCenter[1] / image.shape[1]];
        const rotatedImage = config3.hand.rotation && env.kernels.includes("rotatewithoffset") ? ds.rotateWithOffset(image, angle, 0, palmCenterNormalized) : image.clone();
        const rotationMatrix = buildRotationMatrix2(-angle, palmCenter);
        const newBox = useFreshBox ? this.getBoxForPalmLandmarks(currentBox.palmLandmarks, rotationMatrix) : currentBox;
        const croppedInput = cutBoxFromImageAndResize(newBox, rotatedImage, [this.inputSize, this.inputSize]);
        const handImage = xe(croppedInput, constants.tf255);
        Re(croppedInput);
        Re(rotatedImage);
        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage);
        lastTime11 = now();
        Re(handImage);
        const confidence = (await confidenceT.data())[0];
        Re(confidenceT);
        if (confidence >= config3.hand.minConfidence / 4) {
          const keypointsReshaped = G(keypoints, [-1, 3]);
          const rawCoords = await keypointsReshaped.array();
          Re(keypoints);
          Re(keypointsReshaped);
          const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);
          const nextBoundingBox = this.getBoxForHandLandmarks(coords);
          this.storedBoxes[i] = { ...nextBoundingBox, confidence };
          const result = {
            landmarks: coords,
            confidence,
            boxConfidence: currentBox.confidence,
            fingerConfidence: confidence,
            box: { topLeft: nextBoundingBox.startPoint, bottomRight: nextBoundingBox.endPoint }
          };
          hands.push(result);
        } else {
          this.storedBoxes[i] = null;
        }
        Re(keypoints);
      } else {
        const enlarged = enlargeBox2(squarifyBox2(currentBox), handBoxEnlargeFactor);
        const result = {
          confidence: currentBox.confidence,
          boxConfidence: currentBox.confidence,
          fingerConfidence: 0,
          box: { topLeft: enlarged.startPoint, bottomRight: enlarged.endPoint },
          landmarks: []
        };
        hands.push(result);
      }
    }
    this.storedBoxes = this.storedBoxes.filter((a) => a !== null);
    this.detectedHands = hands.length;
    if (hands.length > config3.hand.maxDetected)
      hands.length = config3.hand.maxDetected;
    return hands;
  }
};

// src/hand/fingerdef.ts
var Finger = {
  thumb: 0,
  index: 1,
  middle: 2,
  ring: 3,
  pinky: 4,
  all: [0, 1, 2, 3, 4],
  nameMapping: { 0: "thumb", 1: "index", 2: "middle", 3: "ring", 4: "pinky" },
  pointsMapping: {
    0: [[0, 1], [1, 2], [2, 3], [3, 4]],
    1: [[0, 5], [5, 6], [6, 7], [7, 8]],
    2: [[0, 9], [9, 10], [10, 11], [11, 12]],
    3: [[0, 13], [13, 14], [14, 15], [15, 16]],
    4: [[0, 17], [17, 18], [18, 19], [19, 20]]
  },
  getName: (value) => Finger.nameMapping[value],
  getPoints: (value) => Finger.pointsMapping[value]
};
var FingerCurl = {
  none: 0,
  half: 1,
  full: 2,
  nameMapping: { 0: "none", 1: "half", 2: "full" },
  getName: (value) => FingerCurl.nameMapping[value]
};
var FingerDirection = {
  verticalUp: 0,
  verticalDown: 1,
  horizontalLeft: 2,
  horizontalRight: 3,
  diagonalUpRight: 4,
  diagonalUpLeft: 5,
  diagonalDownRight: 6,
  diagonalDownLeft: 7,
  nameMapping: { 0: "verticalUp", 1: "verticalDown", 2: "horizontalLeft", 3: "horizontalRight", 4: "diagonalUpRight", 5: "diagonalUpLeft", 6: "diagonalDownRight", 7: "diagonalDownLeft" },
  getName: (value) => FingerDirection.nameMapping[value]
};
var FingerGesture = class {
  constructor(name) {
    __publicField(this, "name");
    __publicField(this, "curls");
    __publicField(this, "directions");
    __publicField(this, "weights");
    __publicField(this, "weightsRelative");
    this.name = name;
    this.curls = {};
    this.directions = {};
    this.weights = [1, 1, 1, 1, 1];
    this.weightsRelative = [1, 1, 1, 1, 1];
  }
  curl(finger, curl, confidence) {
    if (typeof this.curls[finger] === "undefined")
      this.curls[finger] = [];
    this.curls[finger].push([curl, confidence]);
  }
  direction(finger, position, confidence) {
    if (!this.directions[finger])
      this.directions[finger] = [];
    this.directions[finger].push([position, confidence]);
  }
  weight(finger, weight) {
    this.weights[finger] = weight;
    const total = this.weights.reduce((a, b) => a + b, 0);
    this.weightsRelative = this.weights.map((el2) => el2 * 5 / total);
  }
  matchAgainst(detectedCurls, detectedDirections) {
    let confidence = 0;
    for (const fingerIdx in detectedCurls) {
      const detectedCurl = detectedCurls[fingerIdx];
      const expectedCurls = this.curls[fingerIdx];
      if (typeof expectedCurls === "undefined") {
        confidence += this.weightsRelative[fingerIdx];
        continue;
      }
      for (const [expectedCurl, score] of expectedCurls) {
        if (detectedCurl === expectedCurl) {
          confidence += score * this.weightsRelative[fingerIdx];
          break;
        }
      }
    }
    for (const fingerIdx in detectedDirections) {
      const detectedDirection = detectedDirections[fingerIdx];
      const expectedDirections = this.directions[fingerIdx];
      if (typeof expectedDirections === "undefined") {
        confidence += this.weightsRelative[fingerIdx];
        continue;
      }
      for (const [expectedDirection, score] of expectedDirections) {
        if (detectedDirection === expectedDirection) {
          confidence += score * this.weightsRelative[fingerIdx];
          break;
        }
      }
    }
    return confidence / 10;
  }
};

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

// src/hand/fingerpose.ts
var minConfidence = 0.7;
var options2 = {
  HALF_CURL_START_LIMIT: 60,
  NO_CURL_START_LIMIT: 130,
  DISTANCE_VOTE_POWER: 1.1,
  SINGLE_ANGLE_VOTE_POWER: 0.9,
  TOTAL_ANGLE_VOTE_POWER: 1.6
};
function calculateSlope(point1x, point1y, point2x, point2y) {
  const value = (point1y - point2y) / (point1x - point2x);
  let slope = Math.atan(value) * 180 / Math.PI;
  if (slope <= 0)
    slope = -slope;
  else if (slope > 0)
    slope = 180 - slope;
  return slope;
}
function getSlopes(point1, point2) {
  if (!point1 || !point2)
    return [0, 0];
  const slopeXY = calculateSlope(point1[0], point1[1], point2[0], point2[1]);
  if (point1.length === 2)
    return slopeXY;
  const slopeYZ = calculateSlope(point1[1], point1[2], point2[1], point2[2]);
  return [slopeXY, slopeYZ];
}
function angleOrientationAt(angle, weightageAt = 1) {
  let isVertical = 0;
  let isDiagonal = 0;
  let isHorizontal = 0;
  if (angle >= 75 && angle <= 105)
    isVertical = 1 * weightageAt;
  else if (angle >= 25 && angle <= 155)
    isDiagonal = 1 * weightageAt;
  else
    isHorizontal = 1 * weightageAt;
  return [isVertical, isDiagonal, isHorizontal];
}
function estimateFingerCurl(startPoint, midPoint, endPoint) {
  const start_mid_x_dist = startPoint[0] - midPoint[0];
  const start_end_x_dist = startPoint[0] - endPoint[0];
  const mid_end_x_dist = midPoint[0] - endPoint[0];
  const start_mid_y_dist = startPoint[1] - midPoint[1];
  const start_end_y_dist = startPoint[1] - endPoint[1];
  const mid_end_y_dist = midPoint[1] - endPoint[1];
  const start_mid_z_dist = startPoint[2] - midPoint[2];
  const start_end_z_dist = startPoint[2] - endPoint[2];
  const mid_end_z_dist = midPoint[2] - endPoint[2];
  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist + start_mid_z_dist * start_mid_z_dist);
  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist + start_end_z_dist * start_end_z_dist);
  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist + mid_end_z_dist * mid_end_z_dist);
  let cos_in = (mid_end_dist * mid_end_dist + start_mid_dist * start_mid_dist - start_end_dist * start_end_dist) / (2 * mid_end_dist * start_mid_dist);
  if (cos_in > 1)
    cos_in = 1;
  else if (cos_in < -1)
    cos_in = -1;
  let angleOfCurve = Math.acos(cos_in);
  angleOfCurve = 57.2958 * angleOfCurve % 180;
  let fingerCurl;
  if (angleOfCurve > options2.NO_CURL_START_LIMIT)
    fingerCurl = FingerCurl.none;
  else if (angleOfCurve > options2.HALF_CURL_START_LIMIT)
    fingerCurl = FingerCurl.half;
  else
    fingerCurl = FingerCurl.full;
  return fingerCurl;
}
function estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {
  let estimatedDirection;
  if (max_dist_x === Math.abs(start_end_x_dist)) {
    if (start_end_x_dist > 0)
      estimatedDirection = FingerDirection.horizontalLeft;
    else
      estimatedDirection = FingerDirection.horizontalRight;
  } else if (max_dist_x === Math.abs(start_mid_x_dist)) {
    if (start_mid_x_dist > 0)
      estimatedDirection = FingerDirection.horizontalLeft;
    else
      estimatedDirection = FingerDirection.horizontalRight;
  } else {
    if (mid_end_x_dist > 0)
      estimatedDirection = FingerDirection.horizontalLeft;
    else
      estimatedDirection = FingerDirection.horizontalRight;
  }
  return estimatedDirection;
}
function estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) {
  let estimatedDirection;
  if (max_dist_y === Math.abs(start_end_y_dist)) {
    if (start_end_y_dist < 0)
      estimatedDirection = FingerDirection.verticalDown;
    else
      estimatedDirection = FingerDirection.verticalUp;
  } else if (max_dist_y === Math.abs(start_mid_y_dist)) {
    if (start_mid_y_dist < 0)
      estimatedDirection = FingerDirection.verticalDown;
    else
      estimatedDirection = FingerDirection.verticalUp;
  } else {
    if (mid_end_y_dist < 0)
      estimatedDirection = FingerDirection.verticalDown;
    else
      estimatedDirection = FingerDirection.verticalUp;
  }
  return estimatedDirection;
}
function estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) {
  let estimatedDirection;
  const reqd_vertical_direction = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);
  const reqd_horizontal_direction = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
  if (reqd_vertical_direction === FingerDirection.verticalUp) {
    if (reqd_horizontal_direction === FingerDirection.horizontalLeft)
      estimatedDirection = FingerDirection.diagonalUpLeft;
    else
      estimatedDirection = FingerDirection.diagonalUpRight;
  } else {
    if (reqd_horizontal_direction === FingerDirection.horizontalLeft)
      estimatedDirection = FingerDirection.diagonalDownLeft;
    else
      estimatedDirection = FingerDirection.diagonalDownRight;
  }
  return estimatedDirection;
}
function calculateFingerDirection(startPoint, midPoint, endPoint, fingerSlopes) {
  const start_mid_x_dist = startPoint[0] - midPoint[0];
  const start_end_x_dist = startPoint[0] - endPoint[0];
  const mid_end_x_dist = midPoint[0] - endPoint[0];
  const start_mid_y_dist = startPoint[1] - midPoint[1];
  const start_end_y_dist = startPoint[1] - endPoint[1];
  const mid_end_y_dist = midPoint[1] - endPoint[1];
  const max_dist_x = Math.max(Math.abs(start_mid_x_dist), Math.abs(start_end_x_dist), Math.abs(mid_end_x_dist));
  const max_dist_y = Math.max(Math.abs(start_mid_y_dist), Math.abs(start_end_y_dist), Math.abs(mid_end_y_dist));
  let voteVertical = 0;
  let voteDiagonal = 0;
  let voteHorizontal = 0;
  const start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 1e-5);
  if (start_end_x_y_dist_ratio > 1.5)
    voteVertical += options2.DISTANCE_VOTE_POWER;
  else if (start_end_x_y_dist_ratio > 0.66)
    voteDiagonal += options2.DISTANCE_VOTE_POWER;
  else
    voteHorizontal += options2.DISTANCE_VOTE_POWER;
  const start_mid_dist = Math.sqrt(start_mid_x_dist * start_mid_x_dist + start_mid_y_dist * start_mid_y_dist);
  const start_end_dist = Math.sqrt(start_end_x_dist * start_end_x_dist + start_end_y_dist * start_end_y_dist);
  const mid_end_dist = Math.sqrt(mid_end_x_dist * mid_end_x_dist + mid_end_y_dist * mid_end_y_dist);
  const max_dist = Math.max(start_mid_dist, start_end_dist, mid_end_dist);
  let calc_start_point_x = startPoint[0];
  let calc_start_point_y = startPoint[1];
  let calc_end_point_x = endPoint[0];
  let calc_end_point_y = endPoint[1];
  if (max_dist === start_mid_dist) {
    calc_end_point_x = endPoint[0];
    calc_end_point_y = endPoint[1];
  } else if (max_dist === mid_end_dist) {
    calc_start_point_x = midPoint[0];
    calc_start_point_y = midPoint[1];
  }
  const calcStartPoint = [calc_start_point_x, calc_start_point_y];
  const calcEndPoint = [calc_end_point_x, calc_end_point_y];
  const totalAngle = getSlopes(calcStartPoint, calcEndPoint);
  const votes = angleOrientationAt(totalAngle, options2.TOTAL_ANGLE_VOTE_POWER);
  voteVertical += votes[0];
  voteDiagonal += votes[1];
  voteHorizontal += votes[2];
  for (const fingerSlope of fingerSlopes) {
    const fingerVotes = angleOrientationAt(fingerSlope, options2.SINGLE_ANGLE_VOTE_POWER);
    voteVertical += fingerVotes[0];
    voteDiagonal += fingerVotes[1];
    voteHorizontal += fingerVotes[2];
  }
  let estimatedDirection;
  if (voteVertical === Math.max(voteVertical, voteDiagonal, voteHorizontal)) {
    estimatedDirection = estimateVerticalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y);
  } else if (voteHorizontal === Math.max(voteDiagonal, voteHorizontal)) {
    estimatedDirection = estimateHorizontalDirection(start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
  } else {
    estimatedDirection = estimateDiagonalDirection(start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y, start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x);
  }
  return estimatedDirection;
}
function estimate(landmarks) {
  const slopesXY = [];
  const slopesYZ = [];
  const fingerCurls = [];
  const fingerDirections = [];
  if (!landmarks)
    return { curls: fingerCurls, directions: fingerDirections };
  for (const finger of Finger.all) {
    const points = Finger.getPoints(finger);
    const slopeAtXY = [];
    const slopeAtYZ = [];
    for (const point2 of points) {
      const point1 = landmarks[point2[0]];
      const point22 = landmarks[point2[1]];
      const slopes = getSlopes(point1, point22);
      const slopeXY = slopes[0];
      const slopeYZ = slopes[1];
      slopeAtXY.push(slopeXY);
      slopeAtYZ.push(slopeYZ);
    }
    slopesXY.push(slopeAtXY);
    slopesYZ.push(slopeAtYZ);
  }
  for (const finger of Finger.all) {
    const pointIndexAt = finger === Finger.thumb ? 1 : 0;
    const fingerPointsAt = Finger.getPoints(finger);
    const startPoint = landmarks[fingerPointsAt[pointIndexAt][0]];
    const midPoint = landmarks[fingerPointsAt[pointIndexAt + 1][1]];
    const endPoint = landmarks[fingerPointsAt[3][1]];
    const fingerCurled = estimateFingerCurl(startPoint, midPoint, endPoint);
    const fingerPosition = calculateFingerDirection(startPoint, midPoint, endPoint, slopesXY[finger].slice(pointIndexAt));
    fingerCurls[finger] = fingerCurled;
    fingerDirections[finger] = fingerPosition;
  }
  return { curls: fingerCurls, directions: fingerDirections };
}
function analyze(keypoints) {
  if (!keypoints || keypoints.length === 0)
    return null;
  const estimatorRes = estimate(keypoints);
  const landmarks = {};
  for (const fingerIdx of Finger.all) {
    landmarks[Finger.getName(fingerIdx)] = {
      curl: FingerCurl.getName(estimatorRes.curls[fingerIdx]),
      direction: FingerDirection.getName(estimatorRes.directions[fingerIdx])
    };
  }
  return landmarks;
}
function match(keypoints) {
  const poses = [];
  if (!keypoints || keypoints.length === 0)
    return poses;
  const estimatorRes = estimate(keypoints);
  for (const gesture2 of fingergesture_default) {
    const confidence = gesture2.matchAgainst(estimatorRes.curls, estimatorRes.directions);
    if (confidence >= minConfidence)
      poses.push({ name: gesture2.name, confidence });
  }
  return poses;
}

// src/hand/handpose.ts
var meshAnnotations2 = {
  thumb: [1, 2, 3, 4],
  index: [5, 6, 7, 8],
  middle: [9, 10, 11, 12],
  ring: [13, 14, 15, 16],
  pinky: [17, 18, 19, 20],
  palm: [0]
};
var handDetectorModel;
var handPoseModel;
var handPipeline;
async function predict12(input, config3) {
  const predictions = await handPipeline.estimateHands(input, config3);
  if (!predictions)
    return [];
  const hands = [];
  for (let i = 0; i < predictions.length; i++) {
    const annotations2 = {};
    if (predictions[i].landmarks) {
      for (const key of Object.keys(meshAnnotations2)) {
        annotations2[key] = meshAnnotations2[key].map((index2) => predictions[i].landmarks[index2]);
      }
    }
    const keypoints = predictions[i].landmarks;
    let box = [Number.MAX_SAFE_INTEGER, Number.MAX_SAFE_INTEGER, 0, 0];
    let boxRaw = [0, 0, 0, 0];
    if (keypoints && keypoints.length > 0) {
      for (const pt2 of keypoints) {
        if (pt2[0] < box[0])
          box[0] = pt2[0];
        if (pt2[1] < box[1])
          box[1] = pt2[1];
        if (pt2[0] > box[2])
          box[2] = pt2[0];
        if (pt2[1] > box[3])
          box[3] = pt2[1];
      }
      box[2] -= box[0];
      box[3] -= box[1];
      boxRaw = [box[0] / (input.shape[2] || 0), box[1] / (input.shape[1] || 0), box[2] / (input.shape[2] || 0), box[3] / (input.shape[1] || 0)];
    } else {
      box = predictions[i].box ? [
        Math.trunc(Math.max(0, predictions[i].box.topLeft[0])),
        Math.trunc(Math.max(0, predictions[i].box.topLeft[1])),
        Math.trunc(Math.min(input.shape[2] || 0, predictions[i].box.bottomRight[0]) - Math.max(0, predictions[i].box.topLeft[0])),
        Math.trunc(Math.min(input.shape[1] || 0, predictions[i].box.bottomRight[1]) - Math.max(0, predictions[i].box.topLeft[1]))
      ] : [0, 0, 0, 0];
      boxRaw = [
        predictions[i].box.topLeft[0] / (input.shape[2] || 0),
        predictions[i].box.topLeft[1] / (input.shape[1] || 0),
        (predictions[i].box.bottomRight[0] - predictions[i].box.topLeft[0]) / (input.shape[2] || 0),
        (predictions[i].box.bottomRight[1] - predictions[i].box.topLeft[1]) / (input.shape[1] || 0)
      ];
    }
    const landmarks = analyze(keypoints);
    hands.push({
      id: i,
      score: Math.round(100 * predictions[i].confidence) / 100,
      boxScore: Math.round(100 * predictions[i].boxConfidence) / 100,
      fingerScore: Math.round(100 * predictions[i].fingerConfidence) / 100,
      label: "hand",
      box,
      boxRaw,
      keypoints,
      annotations: annotations2,
      landmarks
    });
  }
  return hands;
}
async function load13(config3) {
  var _a2, _b2;
  if (env.initial) {
    handDetectorModel = null;
    handPoseModel = null;
  }
  if (!handDetectorModel || !handPoseModel) {
    [handDetectorModel, handPoseModel] = await Promise.all([
      config3.hand.enabled ? loadModel((_a2 = config3.hand.detector) == null ? void 0 : _a2.modelPath) : null,
      config3.hand.landmarks ? loadModel((_b2 = config3.hand.skeleton) == null ? void 0 : _b2.modelPath) : null
    ]);
  } else {
    if (config3.debug)
      log("cached model:", handDetectorModel["modelUrl"]);
    if (config3.debug)
      log("cached model:", handPoseModel["modelUrl"]);
  }
  const handDetector = new HandDetector(handDetectorModel);
  handPipeline = new HandPipeline(handDetector, handPoseModel);
  return [handDetectorModel, handPoseModel];
}

// src/hand/handtrack.ts
var models2 = [null, null];
var modelOutputNodes = ["StatefulPartitionedCall/Postprocessor/Slice", "StatefulPartitionedCall/Postprocessor/ExpandDims_1"];
var inputSize7 = [[0, 0], [0, 0]];
var classes = ["hand", "fist", "pinch", "point", "face", "tip", "pinchtip"];
var faceIndex = 4;
var boxExpandFact = 1.6;
var maxDetectorResolution = 512;
var detectorExpandFact = 1.4;
var skipped11 = Number.MAX_SAFE_INTEGER;
var lastTime12 = 0;
var outputSize = [0, 0];
var cache4 = {
  boxes: [],
  hands: []
};
var fingerMap = {
  thumb: [1, 2, 3, 4],
  index: [5, 6, 7, 8],
  middle: [9, 10, 11, 12],
  ring: [13, 14, 15, 16],
  pinky: [17, 18, 19, 20],
  base: [0],
  palm: [0, 17, 13, 9, 5, 1, 0]
};
async function loadDetect2(config3) {
  var _a2;
  if (env.initial)
    models2[0] = null;
  if (!models2[0]) {
    fakeOps(["tensorlistreserve", "enter", "tensorlistfromtensor", "merge", "loopcond", "switch", "exit", "tensorliststack", "nextiteration", "tensorlistsetitem", "tensorlistgetitem", "reciprocal", "shape", "split", "where"], config3);
    models2[0] = await loadModel((_a2 = config3.hand.detector) == null ? void 0 : _a2.modelPath);
    const inputs = Object.values(models2[0].modelSignature["inputs"]);
    inputSize7[0][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
    inputSize7[0][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
  } else if (config3.debug)
    log("cached model:", models2[0]["modelUrl"]);
  return models2[0];
}
async function loadSkeleton(config3) {
  var _a2;
  if (env.initial)
    models2[1] = null;
  if (!models2[1]) {
    models2[1] = await loadModel((_a2 = config3.hand.skeleton) == null ? void 0 : _a2.modelPath);
    const inputs = Object.values(models2[1].modelSignature["inputs"]);
    inputSize7[1][0] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[1].size) : 0;
    inputSize7[1][1] = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
  } else if (config3.debug)
    log("cached model:", models2[1]["modelUrl"]);
  return models2[1];
}
async function detectHands(input, config3) {
  const hands = [];
  if (!input || !models2[0])
    return hands;
  const t = {};
  const ratio = (input.shape[2] || 1) / (input.shape[1] || 1);
  const height = Math.min(Math.round((input.shape[1] || 0) / 8) * 8, maxDetectorResolution);
  const width = Math.round(height * ratio / 8) * 8;
  t.resize = ds.resizeBilinear(input, [height, width]);
  t.cast = ce(t.resize, "int32");
  [t.rawScores, t.rawBoxes] = await models2[0].executeAsync(t.cast, modelOutputNodes);
  t.boxes = mr(t.rawBoxes, [0, 2]);
  t.scores = mr(t.rawScores, [0]);
  const classScores = Fs(t.scores, 1);
  Re(classScores[faceIndex]);
  classScores.splice(faceIndex, 1);
  t.filtered = Qn(classScores, 1);
  Re(classScores);
  t.max = As(t.filtered, 1);
  t.argmax = Gu(t.filtered, 1);
  let id2 = 0;
  t.nms = await ds.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 = He(t.boxes, nmsIndex, 1);
    const boxYX = await boxSlice.data();
    Re(boxSlice);
    const boxData = [boxYX[1], boxYX[0], boxYX[3] - boxYX[1], boxYX[2] - boxYX[0]];
    const boxRaw = scale(boxData, detectorExpandFact);
    const boxFull = [Math.trunc(boxData[0] * outputSize[0]), Math.trunc(boxData[1] * outputSize[1]), Math.trunc(boxData[2] * outputSize[0]), Math.trunc(boxData[3] * outputSize[1])];
    const score = scores[nmsIndex];
    const label = classes[classNum[nmsIndex]];
    const hand3 = { id: id2++, score, box: boxFull, boxRaw, label };
    hands.push(hand3);
  }
  Object.keys(t).forEach((tensor) => Re(t[tensor]));
  hands.sort((a, b) => b.score - a.score);
  if (hands.length > (config3.hand.maxDetected || 1))
    hands.length = config3.hand.maxDetected || 1;
  return hands;
}
async function detectFingers(input, h, config3) {
  const hand3 = {
    id: h.id,
    score: Math.round(100 * h.score) / 100,
    boxScore: Math.round(100 * h.score) / 100,
    fingerScore: 0,
    box: h.box,
    boxRaw: h.boxRaw,
    label: h.label,
    keypoints: [],
    landmarks: {},
    annotations: {}
  };
  if (input && models2[1] && config3.hand.landmarks && h.score > (config3.hand.minConfidence || 0)) {
    const t = {};
    const boxCrop = [h.boxRaw[1], h.boxRaw[0], h.boxRaw[3] + h.boxRaw[1], h.boxRaw[2] + h.boxRaw[0]];
    t.crop = ds.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 = G(t.keypoints, [-1, 3]);
      const coordsData = await t.reshaped.array();
      const coordsRaw = coordsData.map((kpt4) => [kpt4[0] / inputSize7[1][1], kpt4[1] / inputSize7[1][0], kpt4[2] || 0]);
      const coordsNorm = coordsRaw.map((kpt4) => [kpt4[0] * h.boxRaw[2], kpt4[1] * h.boxRaw[3], kpt4[2] || 0]);
      hand3.keypoints = coordsNorm.map((kpt4) => [outputSize[0] * (kpt4[0] + h.boxRaw[0]), outputSize[1] * (kpt4[1] + h.boxRaw[1]), kpt4[2] || 0]);
      hand3.landmarks = analyze(hand3.keypoints);
      for (const key of Object.keys(fingerMap)) {
        hand3.annotations[key] = fingerMap[key].map((index2) => hand3.landmarks && hand3.keypoints[index2] ? hand3.keypoints[index2] : null);
      }
    }
    Object.keys(t).forEach((tensor) => Re(t[tensor]));
  }
  return hand3;
}
async function predict13(input, config3) {
  var _a2, _b2;
  if (!models2[0] || !models2[1] || !((_a2 = models2[0]) == null ? void 0 : _a2.inputs[0].shape) || !((_b2 = models2[1]) == null ? void 0 : _b2.inputs[0].shape))
    return [];
  outputSize = [input.shape[2] || 0, input.shape[1] || 0];
  skipped11++;
  const skipTime = (config3.hand.skipTime || 0) > now() - lastTime12;
  const skipFrame = skipped11 < (config3.hand.skipFrames || 0);
  if (config3.skipAllowed && skipTime && skipFrame) {
    return cache4.hands;
  }
  return new Promise(async (resolve) => {
    const skipTimeExtended = 3 * (config3.hand.skipTime || 0) > now() - lastTime12;
    const skipFrameExtended = skipped11 < 3 * (config3.hand.skipFrames || 0);
    if (config3.skipAllowed && cache4.hands.length === config3.hand.maxDetected) {
      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
    } else if (config3.skipAllowed && skipTimeExtended && skipFrameExtended && cache4.hands.length > 0) {
      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
    } else {
      cache4.boxes = await detectHands(input, config3);
      lastTime12 = now();
      cache4.hands = await Promise.all(cache4.boxes.map((handBox) => detectFingers(input, handBox, config3)));
      skipped11 = 0;
    }
    const oldCache = [...cache4.boxes];
    cache4.boxes.length = 0;
    if (config3.cacheSensitivity > 0) {
      for (let i = 0; i < cache4.hands.length; i++) {
        const boxKpt = square(cache4.hands[i].keypoints, outputSize);
        if (boxKpt.box[2] / (input.shape[2] || 1) > 0.05 && boxKpt.box[3] / (input.shape[1] || 1) > 0.05 && cache4.hands[i].fingerScore && cache4.hands[i].fingerScore > (config3.hand.minConfidence || 0)) {
          const boxScale = scale(boxKpt.box, boxExpandFact);
          const boxScaleRaw = scale(boxKpt.boxRaw, boxExpandFact);
          cache4.boxes.push({ ...oldCache[i], box: boxScale, boxRaw: boxScaleRaw });
        }
      }
    }
    for (let i = 0; i < cache4.hands.length; i++) {
      const bbox = calc(cache4.hands[i].keypoints, outputSize);
      cache4.hands[i].box = bbox.box;
      cache4.hands[i].boxRaw = bbox.boxRaw;
    }
    resolve(cache4.hands);
  });
}

// src/face/liveness.ts
var model13;
var cached2 = [];
var skipped12 = Number.MAX_SAFE_INTEGER;
var lastCount8 = 0;
var lastTime13 = 0;
async function load14(config3) {
  var _a2;
  if (env.initial)
    model13 = null;
  if (!model13)
    model13 = await loadModel((_a2 = config3.face.liveness) == null ? void 0 : _a2.modelPath);
  else if (config3.debug)
    log("cached model:", model13["modelUrl"]);
  return model13;
}
async function predict14(image, config3, idx, count2) {
  var _a2, _b2;
  if (!model13)
    return 0;
  const skipTime = (((_a2 = config3.face.liveness) == null ? void 0 : _a2.skipTime) || 0) > now() - lastTime13;
  const skipFrame = skipped12 < (((_b2 = config3.face.liveness) == null ? void 0 : _b2.skipFrames) || 0);
  if (config3.skipAllowed && skipTime && skipFrame && lastCount8 === count2 && cached2[idx]) {
    skipped12++;
    return cached2[idx];
  }
  skipped12 = 0;
  return new Promise(async (resolve) => {
    const resize = ds.resizeBilinear(image, [(model13 == null ? void 0 : model13.inputs[0].shape) ? model13.inputs[0].shape[2] : 0, (model13 == null ? void 0 : model13.inputs[0].shape) ? model13.inputs[0].shape[1] : 0], false);
    const res = model13 == null ? void 0 : model13.execute(resize);
    const num = (await res.data())[0];
    cached2[idx] = Math.round(100 * num) / 100;
    lastCount8 = count2;
    lastTime13 = now();
    Re([resize, res]);
    resolve(cached2[idx]);
  });
}

// src/body/movenetcoords.ts
var movenetcoords_exports = {};
__export(movenetcoords_exports, {
  connected: () => connected3,
  horizontal: () => horizontal,
  kpt: () => kpt3,
  relative: () => relative,
  vertical: () => vertical
});
var kpt3 = [
  "nose",
  "leftEye",
  "rightEye",
  "leftEar",
  "rightEar",
  "leftShoulder",
  "rightShoulder",
  "leftElbow",
  "rightElbow",
  "leftWrist",
  "rightWrist",
  "leftHip",
  "rightHip",
  "leftKnee",
  "rightKnee",
  "leftAnkle",
  "rightAnkle"
];
var horizontal = [
  ["leftEye", "rightEye"],
  ["leftEar", "rightEar"],
  ["leftShoulder", "rightShoulder"],
  ["leftElbow", "rightElbow"],
  ["leftWrist", "rightWrist"],
  ["leftHip", "rightHip"],
  ["leftKnee", "rightKnee"],
  ["leftAnkle", "rightAnkle"]
];
var vertical = [
  ["leftKnee", "leftShoulder"],
  ["rightKnee", "rightShoulder"],
  ["leftAnkle", "leftKnee"],
  ["rightAnkle", "rightKnee"]
];
var relative = [
  [["leftHip", "rightHip"], ["leftShoulder", "rightShoulder"]],
  [["leftElbow", "rightElbow"], ["leftShoulder", "rightShoulder"]]
];
var connected3 = {
  leftLeg: ["leftHip", "leftKnee", "leftAnkle"],
  rightLeg: ["rightHip", "rightKnee", "rightAnkle"],
  torso: ["leftShoulder", "rightShoulder", "rightHip", "leftHip", "leftShoulder"],
  leftArm: ["leftShoulder", "leftElbow", "leftWrist"],
  rightArm: ["rightShoulder", "rightElbow", "rightWrist"],
  head: []
};

// src/body/movenetfix.ts
var maxJitter = 5e-3;
var cache5 = {
  keypoints: [],
  padding: [[0, 0], [0, 0], [0, 0], [0, 0]]
};
function bodyParts(body4) {
  for (const pair of horizontal) {
    const left = body4.keypoints.findIndex((kp2) => kp2.part === pair[0]);
    const right = body4.keypoints.findIndex((kp2) => kp2.part === pair[1]);
    if (body4.keypoints[left] && body4.keypoints[right]) {
      if (body4.keypoints[left].position[0] < body4.keypoints[right].position[0]) {
        const tmp = body4.keypoints[left];
        body4.keypoints[left] = body4.keypoints[right];
        body4.keypoints[right] = tmp;
      }
    }
  }
  for (const pair of vertical) {
    const lower = body4.keypoints.findIndex((kp2) => kp2 && kp2.part === pair[0]);
    const higher = body4.keypoints.findIndex((kp2) => kp2 && kp2.part === pair[1]);
    if (body4.keypoints[lower] && body4.keypoints[higher]) {
      if (body4.keypoints[lower].position[1] < body4.keypoints[higher].position[1]) {
        body4.keypoints.splice(lower, 1);
      }
    }
  }
  for (const [pair, compare2] of relative) {
    const left = body4.keypoints.findIndex((kp2) => kp2 && kp2.part === pair[0]);
    const right = body4.keypoints.findIndex((kp2) => kp2 && kp2.part === pair[1]);
    const leftTo = body4.keypoints.findIndex((kp2) => kp2 && kp2.part === compare2[0]);
    const rightTo = body4.keypoints.findIndex((kp2) => kp2 && kp2.part === compare2[1]);
    if (!body4.keypoints[leftTo] || !body4.keypoints[rightTo])
      continue;
    const distanceLeft = body4.keypoints[left] ? [
      Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[left].position[0]),
      Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[left].position[0])
    ] : [0, 0];
    const distanceRight = body4.keypoints[right] ? [
      Math.abs(body4.keypoints[rightTo].position[0] - body4.keypoints[right].position[0]),
      Math.abs(body4.keypoints[leftTo].position[0] - body4.keypoints[right].position[0])
    ] : [0, 0];
    if (distanceLeft[0] > distanceLeft[1] || distanceRight[0] > distanceRight[1]) {
      const tmp = body4.keypoints[left];
      body4.keypoints[left] = body4.keypoints[right];
      body4.keypoints[right] = tmp;
    }
  }
}
function jitter(keypoints) {
  for (let i = 0; i < keypoints.length; i++) {
    if (keypoints[i] && cache5.keypoints[i]) {
      const diff = [Math.abs(keypoints[i].positionRaw[0] - cache5.keypoints[i].positionRaw[0]), Math.abs(keypoints[i].positionRaw[1] - cache5.keypoints[i].positionRaw[1])];
      if (diff[0] < maxJitter && diff[1] < maxJitter) {
        keypoints[i] = cache5.keypoints[i];
      } else {
        cache5.keypoints[i] = keypoints[i];
      }
    } else {
      cache5.keypoints[i] = keypoints[i];
    }
  }
  return keypoints;
}
function padInput(input, inputSize10) {
  const t = {};
  if (!input.shape || !input.shape[1] || !input.shape[2])
    return input;
  cache5.padding = [
    [0, 0],
    [input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0, input.shape[2] > input.shape[1] ? Math.trunc((input.shape[2] - input.shape[1]) / 2) : 0],
    [input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0, input.shape[1] > input.shape[2] ? Math.trunc((input.shape[1] - input.shape[2]) / 2) : 0],
    [0, 0]
  ];
  t.pad = pi(input, cache5.padding);
  t.resize = ds.resizeBilinear(t.pad, [inputSize10, inputSize10]);
  const final = ce(t.resize, "int32");
  Object.keys(t).forEach((tensor) => Re(t[tensor]));
  return final;
}
function rescaleBody(body4, outputSize2) {
  body4.keypoints = body4.keypoints.filter((kpt4) => kpt4 && kpt4.position);
  for (const kpt4 of body4.keypoints) {
    kpt4.position = [
      kpt4.position[0] * (outputSize2[0] + cache5.padding[2][0] + cache5.padding[2][1]) / outputSize2[0] - cache5.padding[2][0],
      kpt4.position[1] * (outputSize2[1] + cache5.padding[1][0] + cache5.padding[1][1]) / outputSize2[1] - cache5.padding[1][0]
    ];
    kpt4.positionRaw = [
      kpt4.position[0] / outputSize2[0],
      kpt4.position[1] / outputSize2[1]
    ];
  }
  const rescaledBoxes = calc(body4.keypoints.map((pt2) => pt2.position), outputSize2);
  body4.box = rescaledBoxes.box;
  body4.boxRaw = rescaledBoxes.boxRaw;
  return body4;
}

// src/body/movenet.ts
var model14;
var inputSize8 = 0;
var skipped13 = Number.MAX_SAFE_INTEGER;
var cache6 = {
  boxes: [],
  bodies: [],
  last: 0
};
async function load15(config3) {
  if (env.initial)
    model14 = null;
  if (!model14) {
    fakeOps(["size"], config3);
    model14 = await loadModel(config3.body.modelPath);
  } else if (config3.debug)
    log("cached model:", model14["modelUrl"]);
  inputSize8 = model14.inputs[0].shape ? model14.inputs[0].shape[2] : 0;
  if (inputSize8 < 64)
    inputSize8 = 256;
  return model14;
}
async function parseSinglePose(res, config3, image) {
  const kpt4 = res[0][0];
  const keypoints = [];
  let score = 0;
  for (let id2 = 0; id2 < kpt4.length; id2++) {
    score = kpt4[id2][2];
    if (score > config3.body.minConfidence) {
      const positionRaw = [kpt4[id2][1], kpt4[id2][0]];
      keypoints.push({
        score: Math.round(100 * score) / 100,
        part: kpt3[id2],
        positionRaw,
        position: [
          Math.round((image.shape[2] || 0) * positionRaw[0]),
          Math.round((image.shape[1] || 0) * positionRaw[1])
        ]
      });
    }
  }
  score = keypoints.reduce((prev, curr) => curr.score > prev ? curr.score : prev, 0);
  const bodies = [];
  const newBox = calc(keypoints.map((pt2) => pt2.position), [image.shape[2], image.shape[1]]);
  const annotations2 = {};
  for (const [name, indexes] of Object.entries(connected3)) {
    const pt2 = [];
    for (let i = 0; i < indexes.length - 1; i++) {
      const pt0 = keypoints.find((kp2) => kp2.part === indexes[i]);
      const pt1 = keypoints.find((kp2) => kp2.part === indexes[i + 1]);
      if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
        pt2.push([pt0.position, pt1.position]);
    }
    annotations2[name] = pt2;
  }
  const body4 = { id: 0, score, box: newBox.box, boxRaw: newBox.boxRaw, keypoints, annotations: annotations2 };
  bodyParts(body4);
  bodies.push(body4);
  return bodies;
}
async function parseMultiPose(res, config3, image) {
  const bodies = [];
  for (let id2 = 0; id2 < res[0].length; id2++) {
    const kpt4 = res[0][id2];
    const totalScore = Math.round(100 * kpt4[51 + 4]) / 100;
    if (totalScore > config3.body.minConfidence) {
      const keypoints = [];
      for (let i = 0; i < 17; i++) {
        const score = kpt4[3 * i + 2];
        if (score > config3.body.minConfidence) {
          const positionRaw = [kpt4[3 * i + 1], kpt4[3 * i + 0]];
          keypoints.push({
            part: kpt3[i],
            score: Math.round(100 * score) / 100,
            positionRaw,
            position: [Math.round((image.shape[2] || 0) * positionRaw[0]), Math.round((image.shape[1] || 0) * positionRaw[1])]
          });
        }
      }
      const newBox = calc(keypoints.map((pt2) => pt2.position), [image.shape[2], image.shape[1]]);
      const annotations2 = {};
      for (const [name, indexes] of Object.entries(connected3)) {
        const pt2 = [];
        for (let i = 0; i < indexes.length - 1; i++) {
          const pt0 = keypoints.find((kp2) => kp2.part === indexes[i]);
          const pt1 = keypoints.find((kp2) => kp2.part === indexes[i + 1]);
          if (pt0 && pt1 && pt0.score > (config3.body.minConfidence || 0) && pt1.score > (config3.body.minConfidence || 0))
            pt2.push([pt0.position, pt1.position]);
        }
        annotations2[name] = pt2;
      }
      const body4 = { id: id2, score: totalScore, box: newBox.box, boxRaw: newBox.boxRaw, keypoints: [...keypoints], annotations: annotations2 };
      bodyParts(body4);
      bodies.push(body4);
    }
  }
  bodies.sort((a, b) => b.score - a.score);
  if (bodies.length > config3.body.maxDetected)
    bodies.length = config3.body.maxDetected;
  return bodies;
}
async function predict15(input, config3) {
  if (!model14 || !(model14 == null ? void 0 : model14.inputs[0].shape))
    return [];
  if (!config3.skipAllowed)
    cache6.boxes.length = 0;
  skipped13++;
  const skipTime = (config3.body.skipTime || 0) > now() - cache6.last;
  const skipFrame = skipped13 < (config3.body.skipFrames || 0);
  if (config3.skipAllowed && skipTime && skipFrame) {
    return cache6.bodies;
  }
  return new Promise(async (resolve) => {
    const t = {};
    skipped13 = 0;
    t.input = padInput(input, inputSize8);
    t.res = model14 == null ? void 0 : model14.execute(t.input);
    cache6.last = now();
    const res = await t.res.array();
    cache6.bodies = t.res.shape[2] === 17 ? await parseSinglePose(res, config3, input) : await parseMultiPose(res, config3, input);
    for (const body4 of cache6.bodies) {
      rescaleBody(body4, [input.shape[2] || 1, input.shape[1] || 1]);
      jitter(body4.keypoints);
    }
    Object.keys(t).forEach((tensor) => Re(t[tensor]));
    resolve(cache6.bodies);
  });
}

// src/object/nanodet.ts
var model15;
var last9 = [];
var lastTime14 = 0;
var skipped14 = Number.MAX_SAFE_INTEGER;
var inputSize9 = 0;
var scaleBox = 2.5;
async function load16(config3) {
  if (!model15 || env.initial) {
    model15 = await loadModel(config3.object.modelPath);
    const inputs = Object.values(model15.modelSignature["inputs"]);
    inputSize9 = Array.isArray(inputs) ? parseInt(inputs[0].tensorShape.dim[2].size) : 0;
  } else if (config3.debug)
    log("cached model:", model15["modelUrl"]);
  return model15;
}
async function process4(res, outputShape, config3) {
  let id2 = 0;
  let results = [];
  for (const strideSize of [1, 2, 4]) {
    j(async () => {
      const baseSize = strideSize * 13;
      const scoresT = mr(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) === labels.length));
      const featuresT = mr(res.find((a) => a.shape[1] === baseSize ** 2 && (a.shape[2] || 0) < labels.length));
      const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]);
      const boxIdx = await boxesMax.argMax(2).array();
      const scores = await scoresT.array();
      for (let i = 0; i < scoresT.shape[0]; i++) {
        for (let j10 = 0; j10 < scoresT.shape[1]; j10++) {
          const score = scores[i][j10];
          if (score > (config3.object.minConfidence || 0) && j10 !== 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: j10 + 1,
              label: labels[j10].label,
              box: box.map((a) => Math.trunc(a)),
              boxRaw
            };
            results.push(result);
          }
        }
      }
    });
  }
  res.forEach((t) => Re(t));
  const nmsBoxes = results.map((a) => [a.boxRaw[1], a.boxRaw[0], a.boxRaw[3], a.boxRaw[2]]);
  const nmsScores = results.map((a) => a.score);
  let nmsIdx = [];
  if (nmsBoxes && nmsBoxes.length > 0) {
    const nms = await ds.nonMaxSuppressionAsync(nmsBoxes, nmsScores, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
    nmsIdx = await nms.data();
    Re(nms);
  }
  results = results.filter((_val, idx) => nmsIdx.includes(idx)).sort((a, b) => b.score - a.score);
  return results;
}
async function predict16(image, config3) {
  const skipTime = (config3.object.skipTime || 0) > now() - lastTime14;
  const skipFrame = skipped14 < (config3.object.skipFrames || 0);
  if (config3.skipAllowed && skipTime && skipFrame && last9.length > 0) {
    skipped14++;
    return last9;
  }
  skipped14 = 0;
  if (!env.kernels.includes("mod") || !env.kernels.includes("sparsetodense"))
    return last9;
  return new Promise(async (resolve) => {
    const outputSize2 = [image.shape[2] || 0, image.shape[1] || 0];
    const resize = ds.resizeBilinear(image, [inputSize9, inputSize9], false);
    const norm = xe(resize, constants.tf255);
    const transpose = norm.transpose([0, 3, 1, 2]);
    Re(norm);
    Re(resize);
    let objectT;
    if (config3.object.enabled)
      objectT = model15.execute(transpose);
    lastTime14 = now();
    Re(transpose);
    const obj = await process4(objectT, outputSize2, config3);
    last9 = obj;
    resolve(obj);
  });
}

// src/body/posenetutils.ts
var partNames = [
  "nose",
  "leftEye",
  "rightEye",
  "leftEar",
  "rightEar",
  "leftShoulder",
  "rightShoulder",
  "leftElbow",
  "rightElbow",
  "leftWrist",
  "rightWrist",
  "leftHip",
  "rightHip",
  "leftKnee",
  "rightKnee",
  "leftAnkle",
  "rightAnkle"
];
var count = partNames.length;
var partIds = partNames.reduce((result, jointName, i) => {
  result[jointName] = i;
  return result;
}, {});
var connectedPartNames = [
  ["leftHip", "leftShoulder"],
  ["leftElbow", "leftShoulder"],
  ["leftElbow", "leftWrist"],
  ["leftHip", "leftKnee"],
  ["leftKnee", "leftAnkle"],
  ["rightHip", "rightShoulder"],
  ["rightElbow", "rightShoulder"],
  ["rightElbow", "rightWrist"],
  ["rightHip", "rightKnee"],
  ["rightKnee", "rightAnkle"],
  ["leftShoulder", "rightShoulder"],
  ["leftHip", "rightHip"]
];
var connectedPartIndices = connectedPartNames.map(([jointNameA, jointNameB]) => [partIds[jointNameA], partIds[jointNameB]]);
var poseChain = [
  ["nose", "leftEye"],
  ["leftEye", "leftEar"],
  ["nose", "rightEye"],
  ["rightEye", "rightEar"],
  ["nose", "leftShoulder"],
  ["leftShoulder", "leftElbow"],
  ["leftElbow", "leftWrist"],
  ["leftShoulder", "leftHip"],
  ["leftHip", "leftKnee"],
  ["leftKnee", "leftAnkle"],
  ["nose", "rightShoulder"],
  ["rightShoulder", "rightElbow"],
  ["rightElbow", "rightWrist"],
  ["rightShoulder", "rightHip"],
  ["rightHip", "rightKnee"],
  ["rightKnee", "rightAnkle"]
];
function getBoundingBox(keypoints) {
  const coord = keypoints.reduce(({ maxX, maxY, minX, minY }, { position: { x, y } }) => ({
    maxX: Math.max(maxX, x),
    maxY: Math.max(maxY, y),
    minX: Math.min(minX, x),
    minY: Math.min(minY, y)
  }), {
    maxX: Number.NEGATIVE_INFINITY,
    maxY: Number.NEGATIVE_INFINITY,
    minX: Number.POSITIVE_INFINITY,
    minY: Number.POSITIVE_INFINITY
  });
  return [coord.minX, coord.minY, coord.maxX - coord.minX, coord.maxY - coord.minY];
}
function scalePoses(poses, [height, width], [inputResolutionHeight, inputResolutionWidth]) {
  const scaleY = height / inputResolutionHeight;
  const scaleX = width / inputResolutionWidth;
  const scalePose = (pose, i) => ({
    id: i,
    score: pose.score,
    boxRaw: [pose.box[0] / inputResolutionWidth, pose.box[1] / inputResolutionHeight, pose.box[2] / inputResolutionWidth, pose.box[3] / inputResolutionHeight],
    box: [Math.trunc(pose.box[0] * scaleX), Math.trunc(pose.box[1] * scaleY), Math.trunc(pose.box[2] * scaleX), Math.trunc(pose.box[3] * scaleY)],
    keypoints: pose.keypoints.map(({ score, part, position }) => ({
      score,
      part,
      position: [Math.trunc(position.x * scaleX), Math.trunc(position.y * scaleY)],
      positionRaw: [position.x / inputResolutionHeight, position.y / inputResolutionHeight]
    })),
    annotations: {}
  });
  const scaledPoses = poses.map((pose, i) => scalePose(pose, i));
  return scaledPoses;
}
var MaxHeap = class {
  constructor(maxSize2, getElementValue) {
    __publicField(this, "priorityQueue");
    __publicField(this, "numberOfElements");
    __publicField(this, "getElementValue");
    this.priorityQueue = new Array(maxSize2);
    this.numberOfElements = -1;
    this.getElementValue = getElementValue;
  }
  enqueue(x) {
    this.priorityQueue[++this.numberOfElements] = x;
    this.swim(this.numberOfElements);
  }
  dequeue() {
    const max = this.priorityQueue[0];
    this.exchange(0, this.numberOfElements--);
    this.sink(0);
    this.priorityQueue[this.numberOfElements + 1] = null;
    return max;
  }
  empty() {
    return this.numberOfElements === -1;
  }
  size() {
    return this.numberOfElements + 1;
  }
  all() {
    return this.priorityQueue.slice(0, this.numberOfElements + 1);
  }
  max() {
    return this.priorityQueue[0];
  }
  swim(k) {
    while (k > 0 && this.less(Math.floor(k / 2), k)) {
      this.exchange(k, Math.floor(k / 2));
      k = Math.floor(k / 2);
    }
  }
  sink(k) {
    while (2 * k <= this.numberOfElements) {
      let j10 = 2 * k;
      if (j10 < this.numberOfElements && this.less(j10, j10 + 1))
        j10++;
      if (!this.less(k, j10))
        break;
      this.exchange(k, j10);
      k = j10;
    }
  }
  getValueAt(i) {
    return this.getElementValue(this.priorityQueue[i]);
  }
  less(i, j10) {
    return this.getValueAt(i) < this.getValueAt(j10);
  }
  exchange(i, j10) {
    const t = this.priorityQueue[i];
    this.priorityQueue[i] = this.priorityQueue[j10];
    this.priorityQueue[j10] = 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 = j(() => {
    if (!model16.inputs[0].shape)
      return [];
    const resized = ds.resizeBilinear(input, [model16.inputs[0].shape[2], model16.inputs[0].shape[1]]);
    const normalized = ge(xe(ce(resized, "float32"), 127.5), 1);
    const results = model16.execute(normalized, poseNetOutputs);
    const results3d = results.map((y) => mr(y, [0]));
    results3d[1] = qs(results3d[1]);
    return results3d;
  });
  const buffers = await Promise.all(res.map((tensor) => tensor.buffer()));
  for (const t of res)
    Re(t);
  const decoded = await decode(buffers[0], buffers[1], buffers[2], buffers[3], config3.body.maxDetected, config3.body.minConfidence);
  if (!model16.inputs[0].shape)
    return [];
  const scaled = scalePoses(decoded, [input.shape[1], input.shape[2]], [model16.inputs[0].shape[2], model16.inputs[0].shape[1]]);
  return scaled;
}
async function load17(config3) {
  if (!model16 || env.initial)
    model16 = await loadModel(config3.body.modelPath);
  else if (config3.debug)
    log("cached model:", model16["modelUrl"]);
  return model16;
}

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

// src/models.ts
var Models = class {
  constructor() {
    __publicField(this, "ssrnetage", null);
    __publicField(this, "gear", null);
    __publicField(this, "blazeposedetect", null);
    __publicField(this, "blazepose", null);
    __publicField(this, "centernet", null);
    __publicField(this, "efficientpose", null);
    __publicField(this, "mobilefacenet", null);
    __publicField(this, "emotion", null);
    __publicField(this, "facedetect", null);
    __publicField(this, "faceiris", null);
    __publicField(this, "facemesh", null);
    __publicField(this, "faceres", null);
    __publicField(this, "ssrnetgender", null);
    __publicField(this, "handpose", null);
    __publicField(this, "handskeleton", null);
    __publicField(this, "handtrack", null);
    __publicField(this, "liveness", null);
    __publicField(this, "movenet", null);
    __publicField(this, "nanodet", null);
    __publicField(this, "posenet", null);
    __publicField(this, "segmentation", null);
    __publicField(this, "antispoof", null);
  }
};
function reset(instance) {
  for (const model18 of Object.keys(instance.models))
    instance.models[model18] = null;
}
async function load19(instance) {
  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t2, _u2, _v2, _w2, _x2, _y2, _z2, _A2, _B2, _C2, _D2;
  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) && !instance.models.faceiris)
    instance.models.faceiris = load10(instance.config);
  if (instance.config.face.enabled && ((_r2 = instance.config.face.mesh) == null ? void 0 : _r2.enabled) && !instance.models.facemesh)
    instance.models.facemesh = load11(instance.config);
  if (instance.config.face.enabled && ((_s2 = instance.config.face["gear"]) == null ? void 0 : _s2.enabled) && !instance.models.gear)
    instance.models.gear = load(instance.config);
  if (instance.config.face.enabled && ((_t2 = instance.config.face["ssrnet"]) == null ? void 0 : _t2.enabled) && !instance.models.ssrnetage)
    instance.models.ssrnetage = load2(instance.config);
  if (instance.config.face.enabled && ((_u2 = instance.config.face["ssrnet"]) == null ? void 0 : _u2.enabled) && !instance.models.ssrnetgender)
    instance.models.ssrnetgender = load3(instance.config);
  if (instance.config.face.enabled && ((_v2 = instance.config.face["mobilefacenet"]) == null ? void 0 : _v2.enabled) && !instance.models.mobilefacenet)
    instance.models.mobilefacenet = load9(instance.config);
  if (instance.config.hand.enabled && !instance.models.handtrack && ((_x2 = (_w2 = instance.config.hand.detector) == null ? void 0 : _w2.modelPath) == null ? void 0 : _x2.includes("handtrack")))
    instance.models.handtrack = loadDetect2(instance.config);
  if (instance.config.hand.enabled && instance.config.hand.landmarks && !instance.models.handskeleton && ((_z2 = (_y2 = instance.config.hand.detector) == null ? void 0 : _y2.modelPath) == null ? void 0 : _z2.includes("handtrack")))
    instance.models.handskeleton = loadSkeleton(instance.config);
  if (instance.config.object.enabled && !instance.models.centernet && ((_B2 = (_A2 = instance.config.object) == null ? void 0 : _A2.modelPath) == null ? void 0 : _B2.includes("centernet")))
    instance.models.centernet = load6(instance.config);
  if (instance.config.object.enabled && !instance.models.nanodet && ((_D2 = (_C2 = instance.config.object) == null ? void 0 : _C2.modelPath) == null ? void 0 : _D2.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 Ss().registry && (!config2.gl || !config2.gl.getParameter(config2.gl.VERSION))) {
    log("error: humangl backend invalid context");
    reset(instance);
  }
  if (!Tde(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 {
      p5(2, config2.gl);
    } catch (err) {
      log("error: cannot set WebGL context:", err);
      return;
    }
    try {
      const ctx = new qf(config2.gl);
      dp(config2.name, () => new A1(ctx), config2.priority);
    } catch (err) {
      log("error: cannot register WebGL backend:", err);
      return;
    }
    try {
      const kernels = Qf("webgl");
      kernels.forEach((kernelConfig) => {
        const newKernelConfig = { ...kernelConfig, backendName: config2.name };
        _l(newKernelConfig);
      });
    } catch (err) {
      log("error: cannot update WebGL backend registration:", err);
      return;
    }
    const current = $A().getGPGPUContext ? $A().getGPGPUContext().gl : null;
    if (current) {
      log(`humangl webgl version:${current.getParameter(current.VERSION)} renderer:${current.getParameter(current.RENDERER)}`);
    } else {
      log("error: no current gl context:", current, config2.gl);
      return;
    }
    try {
      jw.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: Cde(),
      kernelFunc: (op2) => j(() => ge(op2.inputs.a, V(xe(op2.inputs.a, op2.inputs.b), op2.inputs.b)))
    };
    _l(kernelMod);
    env.kernels.push("mod");
  }
  if (!env.kernels.includes("floormod")) {
    const kernelMod = {
      kernelName: "FloorMod",
      backendName: Cde(),
      kernelFunc: (op2) => j(() => Lk(op2.inputs.a / op2.inputs.b) * op2.inputs.b + dD(op2.inputs.a, op2.inputs.b))
    };
    _l(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 && Cde() !== 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(Ss().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 Spe(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 X().getAsync("WASM_HAS_SIMD_SUPPORT");
        const mt2 = await X().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");
        if (instance.config.debug)
          log(`wasm execution: ${simd ? "SIMD" : "no SIMD"} ${mt2 ? "multithreaded" : "singlethreaded"}`);
        if (instance.config.debug && !simd)
          log("warning: wasm simd support is not enabled");
      }
      try {
        await Ide(instance.config.backend);
        await Sde();
        init();
      } catch (err) {
        log("error: cannot set backend:", instance.config.backend, err);
        return false;
      }
    }
    if (Cde() === "humangl") {
      jw.set("CHECK_COMPUTATION_FOR_ERRORS", false);
      jw.set("WEBGL_CPU_FORWARD", true);
      jw.set("WEBGL_USE_SHAPES_UNIFORMS", true);
      jw.set("CPU_HANDOFF_SIZE_THRESHOLD", 256);
      if (typeof instance.config["deallocate"] !== "undefined" && instance.config["deallocate"]) {
        log("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:", true);
        jw.set("WEBGL_DELETE_TEXTURE_THRESHOLD", 0);
      }
      if ($A().getGPGPUContext) {
        const gl2 = await $A().getGPGPUContext().gl;
        if (instance.config.debug)
          log(`gl version:${gl2.getParameter(gl2.VERSION)} renderer:${gl2.getParameter(gl2.RENDERER)}`);
      }
    }
    if (Cde() === "webgpu") {
    }
    bde();
    await Sde();
    instance.performance.initBackend = Math.trunc(now() - timeStamp);
    instance.config.backend = Cde();
    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);
      }
    };
    _l(kernelConfig);
  }
  env.kernels = Qf(Cde()).map((kernel) => kernel.kernelName.toLowerCase());
}

// src/util/draw.ts
var options3 = {
  color: "rgba(173, 216, 230, 0.6)",
  labelColor: "rgba(173, 216, 230, 1)",
  shadowColor: "black",
  font: 'small-caps 16px "Segoe UI"',
  lineHeight: 18,
  lineWidth: 4,
  pointSize: 2,
  roundRect: 8,
  drawPoints: false,
  drawLabels: true,
  drawBoxes: true,
  drawGestures: true,
  drawPolygons: true,
  drawGaze: true,
  fillPolygons: false,
  useDepth: true,
  useCurves: false
};
var drawTime = 0;
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);
function point(ctx, x, y, z, localOptions) {
  z = z || 0;
  ctx.fillStyle = localOptions.useDepth && z ? `rgba(${127.5 + 2 * z}, ${127.5 - 2 * z}, 255, 0.3)` : localOptions.color;
  ctx.beginPath();
  ctx.arc(x, y, localOptions.pointSize, 0, 2 * Math.PI);
  ctx.fill();
}
function rect(ctx, x, y, width, height, localOptions) {
  ctx.beginPath();
  ctx.lineWidth = localOptions.lineWidth;
  if (localOptions.useCurves) {
    const cx2 = (x + x + width) / 2;
    const cy2 = (y + y + height) / 2;
    ctx.ellipse(cx2, cy2, width / 2, height / 2, 0, 0, 2 * Math.PI);
  } else {
    ctx.moveTo(x + localOptions.roundRect, y);
    ctx.lineTo(x + width - localOptions.roundRect, y);
    ctx.quadraticCurveTo(x + width, y, x + width, y + localOptions.roundRect);
    ctx.lineTo(x + width, y + height - localOptions.roundRect);
    ctx.quadraticCurveTo(x + width, y + height, x + width - localOptions.roundRect, y + height);
    ctx.lineTo(x + localOptions.roundRect, y + height);
    ctx.quadraticCurveTo(x, y + height, x, y + height - localOptions.roundRect);
    ctx.lineTo(x, y + localOptions.roundRect);
    ctx.quadraticCurveTo(x, y, x + localOptions.roundRect, y);
    ctx.closePath();
  }
  ctx.stroke();
}
function lines(ctx, points, localOptions) {
  if (points.length < 2)
    return;
  ctx.beginPath();
  ctx.moveTo(points[0][0], points[0][1]);
  for (const pt2 of points) {
    const z = pt2[2] || 0;
    ctx.strokeStyle = localOptions.useDepth && z !== 0 ? `rgba(${127.5 + 2 * z}, ${127.5 - 2 * z}, 255, 0.3)` : localOptions.color;
    ctx.fillStyle = localOptions.useDepth && z !== 0 ? `rgba(${127.5 + 2 * z}, ${127.5 - 2 * z}, 255, 0.3)` : localOptions.color;
    ctx.lineTo(pt2[0], Math.round(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();
}
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 j10 = 0; j10 < result.length; j10++) {
      let where = [];
      let what = [];
      [where, what] = Object.entries(result[j10]);
      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;
      }
    }
  }
}
async function face(inCanvas2, result, drawOptions) {
  var _a2, _b2, _c, _d2, _e2;
  const localOptions = mergeDeep(options3, drawOptions);
  if (!result || !inCanvas2)
    return;
  const ctx = getCanvasContext(inCanvas2);
  if (!ctx)
    return;
  for (const f of result) {
    ctx.font = localOptions.font;
    ctx.strokeStyle = localOptions.color;
    ctx.fillStyle = localOptions.color;
    if (localOptions.drawBoxes)
      rect(ctx, f.box[0], f.box[1], f.box[2], f.box[3], localOptions);
    if (localOptions.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 = localOptions.color;
      for (let i = labels2.length - 1; i >= 0; i--) {
        const x = Math.max(f.box[0], 0);
        const y = i * localOptions.lineHeight + f.box[1];
        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
          ctx.fillStyle = localOptions.shadowColor;
          ctx.fillText(labels2[i], x + 5, y + 16);
        }
        ctx.fillStyle = localOptions.labelColor;
        ctx.fillText(labels2[i], x + 4, y + 15);
      }
    }
    ctx.lineWidth = 2;
    if (f.mesh && f.mesh.length > 0) {
      if (localOptions.drawPoints) {
        for (const pt2 of f.mesh)
          point(ctx, pt2[0], pt2[1], pt2[2], localOptions);
      }
      if (localOptions.drawPolygons) {
        if (f.mesh.length > 450) {
          for (let i = 0; i < TRI468.length / 3; i++) {
            const points = [
              TRI468[i * 3 + 0],
              TRI468[i * 3 + 1],
              TRI468[i * 3 + 2]
            ].map((index2) => f.mesh[index2]);
            lines(ctx, points, localOptions);
          }
        }
        if (f.annotations && f.annotations["leftEyeIris"] && f.annotations["leftEyeIris"][0]) {
          ctx.strokeStyle = localOptions.useDepth ? "rgba(255, 200, 255, 0.3)" : localOptions.color;
          ctx.beginPath();
          const sizeX = Math.abs(f.annotations["leftEyeIris"][3][0] - f.annotations["leftEyeIris"][1][0]) / 2;
          const sizeY = Math.abs(f.annotations["leftEyeIris"][4][1] - f.annotations["leftEyeIris"][2][1]) / 2;
          ctx.ellipse(f.annotations["leftEyeIris"][0][0], f.annotations["leftEyeIris"][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);
          ctx.stroke();
          if (localOptions.fillPolygons) {
            ctx.fillStyle = localOptions.useDepth ? "rgba(255, 255, 200, 0.3)" : localOptions.color;
            ctx.fill();
          }
        }
        if (f.annotations && f.annotations["rightEyeIris"] && f.annotations["rightEyeIris"][0]) {
          ctx.strokeStyle = localOptions.useDepth ? "rgba(255, 200, 255, 0.3)" : localOptions.color;
          ctx.beginPath();
          const sizeX = Math.abs(f.annotations["rightEyeIris"][3][0] - f.annotations["rightEyeIris"][1][0]) / 2;
          const sizeY = Math.abs(f.annotations["rightEyeIris"][4][1] - f.annotations["rightEyeIris"][2][1]) / 2;
          ctx.ellipse(f.annotations["rightEyeIris"][0][0], f.annotations["rightEyeIris"][0][1], sizeX, sizeY, 0, 0, 2 * Math.PI);
          ctx.stroke();
          if (localOptions.fillPolygons) {
            ctx.fillStyle = localOptions.useDepth ? "rgba(255, 255, 200, 0.3)" : localOptions.color;
            ctx.fill();
          }
        }
        if (localOptions.drawGaze && ((_a2 = f.rotation) == null ? void 0 : _a2.angle) && typeof Path2D !== "undefined") {
          ctx.strokeStyle = "pink";
          const valX = f.box[0] + f.box[2] / 2 - f.box[3] * rad2deg(f.rotation.angle.yaw) / 90;
          const valY = f.box[1] + f.box[3] / 2 + f.box[2] * rad2deg(f.rotation.angle.pitch) / 90;
          const pathV = new Path2D(`
            M ${f.box[0] + f.box[2] / 2} ${f.box[1]}
            C
              ${valX} ${f.box[1]},
              ${valX} ${f.box[1] + f.box[3]},
              ${f.box[0] + f.box[2] / 2} ${f.box[1] + f.box[3]}
          `);
          const pathH = new Path2D(`
            M ${f.box[0]} ${f.box[1] + f.box[3] / 2}
            C 
              ${f.box[0]} ${valY},
              ${f.box[0] + f.box[2]} ${valY},
              ${f.box[0] + f.box[2]} ${f.box[1] + f.box[3] / 2}
          `);
          ctx.stroke(pathH);
          ctx.stroke(pathV);
        }
        if (localOptions.drawGaze && ((_c = (_b2 = f.rotation) == null ? void 0 : _b2.gaze) == null ? void 0 : _c.strength) && ((_e2 = (_d2 = f.rotation) == null ? void 0 : _d2.gaze) == null ? void 0 : _e2.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);
        }
      }
    }
  }
}
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 ${100 * result[i].score}%`, result[i].box[0] + 2, 0 + result[i].box[1] + localOptions.lineHeight, result[i].box[2]);
      }
    }
    if (localOptions.drawPoints && result[i].keypoints) {
      for (let pt2 = 0; pt2 < result[i].keypoints.length; pt2++) {
        if (!result[i].keypoints[pt2].score || result[i].keypoints[pt2].score === 0)
          continue;
        ctx.fillStyle = localOptions.useDepth && result[i].keypoints[pt2].position[2] ? `rgba(${127.5 + 2 * (result[i].keypoints[pt2].position[2] || 0)}, ${127.5 - 2 * (result[i].keypoints[pt2].position[2] || 0)}, 255, 0.5)` : localOptions.color;
        point(ctx, result[i].keypoints[pt2].position[0], result[i].keypoints[pt2].position[1], 0, localOptions);
      }
    }
    if (localOptions.drawLabels && result[i].keypoints) {
      ctx.font = localOptions.font;
      for (const pt2 of result[i].keypoints) {
        if (!pt2.score || pt2.score === 0)
          continue;
        ctx.fillStyle = localOptions.useDepth && pt2.position[2] ? `rgba(${127.5 + 2 * pt2.position[2]}, ${127.5 - 2 * pt2.position[2]}, 255, 0.5)` : localOptions.color;
        ctx.fillText(`${pt2.part} ${Math.trunc(100 * pt2.score)}%`, pt2.position[0] + 4, pt2.position[1] + 4);
      }
    }
    if (localOptions.drawPolygons && result[i].keypoints && result[i].annotations) {
      for (const part of Object.values(result[i].annotations)) {
        for (const connected4 of part)
          curves(ctx, connected4, localOptions);
      }
    }
  }
}
async function hand(inCanvas2, result, drawOptions) {
  const localOptions = mergeDeep(options3, drawOptions);
  if (!result || !inCanvas2)
    return;
  const ctx = getCanvasContext(inCanvas2);
  if (!ctx)
    return;
  ctx.lineJoin = "round";
  ctx.font = localOptions.font;
  for (const h of result) {
    if (localOptions.drawBoxes) {
      ctx.strokeStyle = localOptions.color;
      ctx.fillStyle = localOptions.color;
      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);
      if (localOptions.drawLabels) {
        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
          ctx.fillStyle = localOptions.shadowColor;
          ctx.fillText(`hand:${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:${Math.trunc(100 * h.score)}%`, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);
      }
      ctx.stroke();
    }
    if (localOptions.drawPoints) {
      if (h.keypoints && h.keypoints.length > 0) {
        for (const pt2 of h.keypoints) {
          ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + 2 * (pt2[2] || 0)}, ${127.5 - 2 * (pt2[2] || 0)}, 255, 0.5)` : localOptions.color;
          point(ctx, pt2[0], pt2[1], 0, localOptions);
        }
      }
    }
    if (localOptions.drawLabels && h.annotations) {
      const addHandLabel = (part, title) => {
        if (!part || part.length === 0 || !part[0])
          return;
        const z = part[part.length - 1][2] || 0;
        ctx.fillStyle = localOptions.useDepth ? `rgba(${127.5 + 2 * z}, ${127.5 - 2 * z}, 255, 0.5)` : localOptions.color;
        ctx.fillText(title, part[part.length - 1][0] + 4, part[part.length - 1][1] + 4);
      };
      ctx.font = localOptions.font;
      addHandLabel(h.annotations["index"], "index");
      addHandLabel(h.annotations["middle"], "middle");
      addHandLabel(h.annotations["ring"], "ring");
      addHandLabel(h.annotations["pinky"], "pinky");
      addHandLabel(h.annotations["thumb"], "thumb");
      addHandLabel(h.annotations["palm"], "palm");
    }
    if (localOptions.drawPolygons && h.annotations) {
      const addHandLine = (part) => {
        if (!part || part.length === 0 || !part[0])
          return;
        for (let i = 0; i < part.length; i++) {
          ctx.beginPath();
          const z = part[i][2] || 0;
          ctx.strokeStyle = localOptions.useDepth ? `rgba(${127.5 + i * z}, ${127.5 - i * z}, 255, 0.5)` : localOptions.color;
          ctx.moveTo(part[i > 0 ? i - 1 : 0][0], part[i > 0 ? i - 1 : 0][1]);
          ctx.lineTo(part[i][0], part[i][1]);
          ctx.stroke();
        }
      };
      ctx.lineWidth = localOptions.lineWidth;
      addHandLine(h.annotations["index"]);
      addHandLine(h.annotations["middle"]);
      addHandLine(h.annotations["ring"]);
      addHandLine(h.annotations["pinky"]);
      addHandLine(h.annotations["thumb"]);
    }
  }
}
async function object(inCanvas2, result, drawOptions) {
  const localOptions = mergeDeep(options3, drawOptions);
  if (!result || !inCanvas2)
    return;
  const ctx = getCanvasContext(inCanvas2);
  if (!ctx)
    return;
  ctx.lineJoin = "round";
  ctx.font = localOptions.font;
  for (const h of result) {
    if (localOptions.drawBoxes) {
      ctx.strokeStyle = localOptions.color;
      ctx.fillStyle = localOptions.color;
      rect(ctx, h.box[0], h.box[1], h.box[2], h.box[3], localOptions);
      if (localOptions.drawLabels) {
        const label = `${h.label} ${Math.round(100 * h.score)}%`;
        if (localOptions.shadowColor && localOptions.shadowColor !== "") {
          ctx.fillStyle = localOptions.shadowColor;
          ctx.fillText(label, h.box[0] + 3, 1 + h.box[1] + localOptions.lineHeight, h.box[2]);
        }
        ctx.fillStyle = localOptions.labelColor;
        ctx.fillText(label, h.box[0] + 2, 0 + h.box[1] + localOptions.lineHeight, h.box[2]);
      }
      ctx.stroke();
    }
  }
}
async function person(inCanvas2, result, drawOptions) {
  const localOptions = mergeDeep(options3, drawOptions);
  if (!result || !inCanvas2)
    return;
  const ctx = getCanvasContext(inCanvas2);
  if (!ctx)
    return;
  ctx.lineJoin = "round";
  ctx.font = localOptions.font;
  for (let i = 0; i < result.length; i++) {
    if (localOptions.drawBoxes) {
      ctx.strokeStyle = localOptions.color;
      ctx.fillStyle = localOptions.color;
      rect(ctx, result[i].box[0], result[i].box[1], result[i].box[2], result[i].box[3], localOptions);
      if (localOptions.drawLabels) {
        const label = `person #${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 j10 = polygon.length - 1;
  for (let i = 0; i < polygon.length; j10 = i++) {
    if (polygon[i].y > y !== polygon[j10].y > y && x < (polygon[j10].x - polygon[i].x) * (y - polygon[i].y) / (polygon[j10].y - polygon[i].y) + polygon[i].x)
      inside = !inside;
  }
  return inside;
}
async function mask(face4) {
  if (!face4.tensor)
    return face4.tensor;
  if (!face4.mesh || face4.mesh.length < 100)
    return face4.tensor;
  const width = face4.tensor.shape[2] || 0;
  const height = face4.tensor.shape[1] || 0;
  const buffer = await face4.tensor.buffer();
  let silhouette = [];
  for (const pt2 of meshAnnotations.silhouette)
    silhouette.push({ x: (face4.mesh[pt2][0] - face4.box[0]) / face4.box[2], y: (face4.mesh[pt2][1] - face4.box[1]) / face4.box[3] });
  if (expandFact && expandFact > 0)
    silhouette = silhouette.map((pt2) => ({ x: pt2.x > 0.5 ? pt2.x + expandFact : pt2.x - expandFact, y: pt2.y > 0.5 ? pt2.y + expandFact : pt2.y - expandFact }));
  for (let x = 0; x < width; x++) {
    for (let y = 0; y < height; y++) {
      const inside = insidePoly(x / width, y / width, silhouette);
      if (!inside) {
        buffer.set(alpha * buffer.get(0, y, x, 0), 0, y, x, 0);
        buffer.set(alpha * buffer.get(0, y, x, 1), 0, y, x, 1);
        buffer.set(alpha * buffer.get(0, y, x, 2), 0, y, x, 2);
      }
    }
  }
  const output = buffer.toTensor();
  Re(buffer);
  return output;
}

// src/face/angles.ts
var calculateGaze = (face4) => {
  const radians = (pt1, pt2) => Math.atan2(pt1[1] - pt2[1], pt1[0] - pt2[0]);
  if (!face4.annotations["rightEyeIris"] || !face4.annotations["leftEyeIris"])
    return { bearing: 0, strength: 0 };
  const offsetIris = [0, -0.1];
  const eyeRatio = 1;
  const left = (face4.mesh[33][2] || 0) > (face4.mesh[263][2] || 0);
  const irisCenter = left ? face4.mesh[473] : face4.mesh[468];
  const eyeCenter = left ? [(face4.mesh[133][0] + face4.mesh[33][0]) / 2, (face4.mesh[133][1] + face4.mesh[33][1]) / 2] : [(face4.mesh[263][0] + face4.mesh[362][0]) / 2, (face4.mesh[263][1] + face4.mesh[362][1]) / 2];
  const eyeSize = left ? [face4.mesh[133][0] - face4.mesh[33][0], face4.mesh[23][1] - face4.mesh[27][1]] : [face4.mesh[263][0] - face4.mesh[362][0], face4.mesh[253][1] - face4.mesh[257][1]];
  const eyeDiff = [
    (eyeCenter[0] - irisCenter[0]) / eyeSize[0] - offsetIris[0],
    eyeRatio * (irisCenter[1] - eyeCenter[1]) / eyeSize[1] - offsetIris[1]
  ];
  let strength = Math.sqrt(eyeDiff[0] * eyeDiff[0] + eyeDiff[1] * eyeDiff[1]);
  strength = Math.min(strength, face4.boxRaw[2] / 2, face4.boxRaw[3] / 2);
  const bearing = (radians([0, 0], eyeDiff) + Math.PI / 2) % Math.PI;
  return { bearing, strength };
};
var calculateFaceAngle = (face4, imageSize) => {
  const normalize = (v) => {
    const length = Math.sqrt(v[0] * v[0] + v[1] * v[1] + v[2] * v[2]);
    v[0] /= length;
    v[1] /= length;
    v[2] /= length;
    return v;
  };
  const subVectors = (a, b) => {
    const x = a[0] - b[0];
    const y = a[1] - b[1];
    const z = a[2] - b[2];
    return [x, y, z];
  };
  const crossVectors = (a, b) => {
    const x = a[1] * b[2] - a[2] * b[1];
    const y = a[2] * b[0] - a[0] * b[2];
    const z = a[0] * b[1] - a[1] * b[0];
    return [x, y, z];
  };
  const rotationMatrixToEulerAngle = (r) => {
    const [r00, _r01, _r02, r10, r11, r12, r20, r21, r22] = r;
    let thetaX;
    let thetaY;
    let thetaZ;
    if (r10 < 1) {
      if (r10 > -1) {
        thetaZ = Math.asin(r10);
        thetaY = Math.atan2(-r20, r00);
        thetaX = Math.atan2(-r12, r11);
      } else {
        thetaZ = -Math.PI / 2;
        thetaY = -Math.atan2(r21, r22);
        thetaX = 0;
      }
    } else {
      thetaZ = Math.PI / 2;
      thetaY = Math.atan2(r21, r22);
      thetaX = 0;
    }
    if (isNaN(thetaX))
      thetaX = 0;
    if (isNaN(thetaY))
      thetaY = 0;
    if (isNaN(thetaZ))
      thetaZ = 0;
    return { pitch: 2 * -thetaX, yaw: 2 * -thetaY, roll: 2 * -thetaZ };
  };
  const mesh = face4.meshRaw;
  if (!mesh || mesh.length < 300)
    return { angle: { pitch: 0, yaw: 0, roll: 0 }, matrix: [1, 0, 0, 0, 1, 0, 0, 0, 1], gaze: { bearing: 0, strength: 0 } };
  const size2 = Math.max(face4.boxRaw[2] * imageSize[0], face4.boxRaw[3] * imageSize[1]) / 1.5;
  const pts = [mesh[10], mesh[152], mesh[234], mesh[454]].map((pt2) => [pt2[0] * imageSize[0] / size2, pt2[1] * imageSize[1] / size2, pt2[2]]);
  const y_axis = normalize(subVectors(pts[1], pts[0]));
  let x_axis = normalize(subVectors(pts[3], pts[2]));
  const z_axis = normalize(crossVectors(x_axis, y_axis));
  x_axis = crossVectors(y_axis, z_axis);
  const matrix = [
    x_axis[0],
    x_axis[1],
    x_axis[2],
    y_axis[0],
    y_axis[1],
    y_axis[2],
    z_axis[0],
    z_axis[1],
    z_axis[2]
  ];
  const angle = rotationMatrixToEulerAngle(matrix);
  const gaze = mesh.length === 478 ? calculateGaze(face4) : { bearing: 0, strength: 0 };
  return { angle, matrix, gaze };
};

// src/face/face.ts
var detectFace = async (instance, input) => {
  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t2, _u2, _v2;
  let timeStamp = now();
  let ageRes;
  let gearRes;
  let genderRes;
  let emotionRes;
  let mobilefacenetRes;
  let antispoofRes;
  let livenessRes;
  let descRes;
  const faceRes = [];
  instance.state = "run:face";
  const faces = await predict10(input, instance.config);
  instance.performance.face = env.perfadd ? (instance.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
  if (!input.shape || input.shape.length !== 4)
    return [];
  if (!faces)
    return [];
  for (let i = 0; i < faces.length; i++) {
    instance.analyze("Get Face");
    if (!faces[i].tensor || faces[i].tensor["isDisposedInternal"]) {
      log("Face object is disposed:", faces[i].tensor);
      continue;
    }
    if ((_a2 = instance.config.face.detector) == null ? void 0 : _a2.mask) {
      const masked = await mask(faces[i]);
      Re(faces[i].tensor);
      faces[i].tensor = masked;
    }
    const rotation = faces[i].mesh && faces[i].mesh.length > 200 ? calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]) : null;
    instance.analyze("Start Emotion:");
    if (instance.config.async) {
      emotionRes = ((_b2 = instance.config.face.emotion) == null ? void 0 : _b2.enabled) ? predict8(faces[i].tensor || hs([]), 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 || hs([]), 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 || hs([]), 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 || hs([]), 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 || hs([]), 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 || hs([]), 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 || hs([]), 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 || hs([]), 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 || hs([]), instance.config, i, faces.length) : null;
      genderRes = ((_k2 = instance.config.face["ssrnet"]) == null ? void 0 : _k2.enabled) ? predict3(faces[i].tensor || hs([]), 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 || hs([]), instance.config, i, faces.length) : null;
      genderRes = ((_m2 = instance.config.face["ssrnet"]) == null ? void 0 : _m2.enabled) ? await predict3(faces[i].tensor || hs([]), 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 || hs([]), 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 || hs([]), 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 || hs([]), 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 || hs([]), instance.config, i, faces.length) : null;
      instance.performance.description = env.perfadd ? (instance.performance.description || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
    }
    instance.analyze("End Description:");
    if (instance.config.async) {
      [ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes] = await Promise.all([ageRes, genderRes, emotionRes, mobilefacenetRes, descRes, gearRes, antispoofRes, livenessRes]);
    }
    instance.analyze("Finish Face:");
    if (((_r2 = instance.config.face["ssrnet"]) == null ? void 0 : _r2.enabled) && ageRes && genderRes) {
      descRes = {
        ...descRes,
        age: ageRes.age,
        gender: genderRes.gender,
        genderScore: genderRes.genderScore
      };
    }
    if (((_s2 = instance.config.face["gear"]) == null ? void 0 : _s2.enabled) && gearRes) {
      descRes = {
        ...descRes,
        age: gearRes.age,
        gender: gearRes.gender,
        genderScore: gearRes.genderScore,
        race: gearRes.race
      };
    }
    if (((_t2 = instance.config.face["mobilefacenet"]) == null ? void 0 : _t2.enabled) && mobilefacenetRes) {
      descRes.descriptor = mobilefacenetRes;
    }
    if (!((_u2 = instance.config.face.iris) == null ? void 0 : _u2.enabled)) {
    }
    const irisSize = faces[i].annotations && faces[i].annotations.leftEyeIris && faces[i].annotations.leftEyeIris[0] && faces[i].annotations.rightEyeIris && faces[i].annotations.rightEyeIris[0] && faces[i].annotations.leftEyeIris.length > 0 && faces[i].annotations.rightEyeIris.length > 0 && faces[i].annotations.leftEyeIris[0] !== null && faces[i].annotations.rightEyeIris[0] !== null ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2] : 0;
    const tensor = ((_v2 = instance.config.face.detector) == null ? void 0 : _v2.return) ? mr(faces[i].tensor) : null;
    Re(faces[i].tensor);
    if (faces[i].tensor)
      delete faces[i].tensor;
    const res = {
      ...faces[i],
      id: i
    };
    if (descRes == null ? void 0 : descRes.age)
      res.age = descRes.age;
    if (descRes == null ? void 0 : descRes.gender)
      res.gender = descRes.gender;
    if (descRes == null ? void 0 : descRes.genderScore)
      res.genderScore = descRes == null ? void 0 : descRes.genderScore;
    if (descRes == null ? void 0 : descRes.descriptor)
      res.embedding = descRes == null ? void 0 : descRes.descriptor;
    if (descRes == null ? void 0 : descRes.race)
      res.race = descRes == null ? void 0 : descRes.race;
    if (emotionRes)
      res.emotion = emotionRes;
    if (antispoofRes)
      res.real = antispoofRes;
    if (livenessRes)
      res.live = livenessRes;
    if (irisSize && irisSize !== 0)
      res.iris = Math.trunc(500 / irisSize / 11.7) / 100;
    if (rotation)
      res.rotation = rotation;
    if (tensor)
      res.tensor = tensor;
    faceRes.push(res);
    instance.analyze("End Face");
  }
  instance.analyze("End FaceMesh:");
  if (instance.config.async) {
    if (instance.performance.face)
      delete instance.performance.face;
    if (instance.performance.age)
      delete instance.performance.age;
    if (instance.performance.gender)
      delete instance.performance.gender;
    if (instance.performance.emotion)
      delete instance.performance.emotion;
  }
  return faceRes;
};

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

// src/util/interpolate.ts
var bufferedResult = { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };
var interpolateTime = 0;
function calc2(newResult, config3) {
  var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t2, _u2, _v2, _w2, _x2, _y2, _z2, _A2;
  const t02 = now();
  if (!newResult)
    return { face: [], body: [], hand: [], gesture: [], object: [], persons: [], performance: {}, timestamp: 0, error: null };
  const elapsed = Date.now() - newResult.timestamp;
  const bufferedFactor = elapsed < 1e3 ? 8 - Math.log(elapsed + 1) : 1;
  if (newResult.canvas)
    bufferedResult.canvas = newResult.canvas;
  if (newResult.error)
    bufferedResult.error = newResult.error;
  if (!bufferedResult.body || newResult.body.length !== bufferedResult.body.length) {
    bufferedResult.body = JSON.parse(JSON.stringify(newResult.body));
  } else {
    for (let i = 0; i < newResult.body.length; i++) {
      const box = newResult.body[i].box.map((newBoxCoord, j10) => ((bufferedFactor - 1) * bufferedResult.body[i].box[j10] + newBoxCoord) / bufferedFactor);
      const boxRaw = newResult.body[i].boxRaw.map((newBoxCoord, j10) => ((bufferedFactor - 1) * bufferedResult.body[i].boxRaw[j10] + newBoxCoord) / bufferedFactor);
      const keypoints = newResult.body[i].keypoints.map((newKpt, j10) => {
        var _a3, _b3, _c2, _d3, _e3, _f2, _g3, _h2, _i2;
        return {
          score: newKpt.score,
          part: newKpt.part,
          position: [
            bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j10].position[0] || 0) + (newKpt.position[0] || 0)) / bufferedFactor : newKpt.position[0],
            bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j10].position[1] || 0) + (newKpt.position[1] || 0)) / bufferedFactor : newKpt.position[1],
            bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j10].position[2] || 0) + (newKpt.position[2] || 0)) / bufferedFactor : newKpt.position[2]
          ],
          positionRaw: [
            bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j10].positionRaw[0] || 0) + (newKpt.positionRaw[0] || 0)) / bufferedFactor : newKpt.positionRaw[0],
            bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j10].positionRaw[1] || 0) + (newKpt.positionRaw[1] || 0)) / bufferedFactor : newKpt.positionRaw[1],
            bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (bufferedResult.body[i].keypoints[j10].positionRaw[2] || 0) + (newKpt.positionRaw[2] || 0)) / bufferedFactor : newKpt.positionRaw[2]
          ],
          distance: [
            bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (((_a3 = bufferedResult.body[i].keypoints[j10].distance) == null ? void 0 : _a3[0]) || 0) + (((_b3 = newKpt.distance) == null ? void 0 : _b3[0]) || 0)) / bufferedFactor : (_c2 = newKpt.distance) == null ? void 0 : _c2[0],
            bufferedResult.body[i].keypoints[j10] ? ((bufferedFactor - 1) * (((_d3 = bufferedResult.body[i].keypoints[j10].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[j10] ? ((bufferedFactor - 1) * (((_g3 = bufferedResult.body[i].keypoints[j10].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 j10 = 0; j10 < indexes.length - 1; j10++) {
          const pt0 = keypoints.find((kp2) => kp2.part === indexes[j10]);
          const pt1 = keypoints.find((kp2) => kp2.part === indexes[j10 + 1]);
          if (pt0 && pt1)
            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, j10) => ((bufferedFactor - 1) * bufferedResult.hand[i].box[j10] + b) / bufferedFactor);
      const boxRaw = newResult.hand[i].boxRaw.map((b, j10) => ((bufferedFactor - 1) * bufferedResult.hand[i].boxRaw[j10] + b) / bufferedFactor);
      if (bufferedResult.hand[i].keypoints.length !== newResult.hand[i].keypoints.length)
        bufferedResult.hand[i].keypoints = newResult.hand[i].keypoints;
      const keypoints = newResult.hand[i].keypoints && newResult.hand[i].keypoints.length > 0 ? newResult.hand[i].keypoints.map((landmark, j10) => landmark.map((coord, k) => ((bufferedFactor - 1) * (bufferedResult.hand[i].keypoints[j10][k] || 1) + (coord || 0)) / bufferedFactor)) : [];
      let annotations2 = {};
      if (Object.keys(bufferedResult.hand[i].annotations).length !== Object.keys(newResult.hand[i].annotations).length) {
        bufferedResult.hand[i].annotations = newResult.hand[i].annotations;
        annotations2 = bufferedResult.hand[i].annotations;
      } else if (newResult.hand[i].annotations) {
        for (const key of Object.keys(newResult.hand[i].annotations)) {
          annotations2[key] = newResult.hand[i].annotations[key] && newResult.hand[i].annotations[key][0] ? newResult.hand[i].annotations[key].map((val, j10) => val.map((coord, k) => ((bufferedFactor - 1) * bufferedResult.hand[i].annotations[key][j10][k] + coord) / bufferedFactor)) : null;
        }
      }
      bufferedResult.hand[i] = { ...newResult.hand[i], box, boxRaw, keypoints, annotations: annotations2 };
    }
  }
  if (!bufferedResult.face || newResult.face.length !== bufferedResult.face.length) {
    bufferedResult.face = JSON.parse(JSON.stringify(newResult.face));
  } else {
    for (let i = 0; i < newResult.face.length; i++) {
      const box = newResult.face[i].box.map((b, j10) => ((bufferedFactor - 1) * bufferedResult.face[i].box[j10] + b) / bufferedFactor);
      const boxRaw = newResult.face[i].boxRaw.map((b, j10) => ((bufferedFactor - 1) * bufferedResult.face[i].boxRaw[j10] + b) / bufferedFactor);
      if (newResult.face[i].rotation) {
        const rotation = { matrix: [0, 0, 0, 0, 0, 0, 0, 0, 0], angle: { roll: 0, yaw: 0, pitch: 0 }, gaze: { bearing: 0, strength: 0 } };
        rotation.matrix = (_g2 = newResult.face[i].rotation) == null ? void 0 : _g2.matrix;
        rotation.angle = {
          roll: ((bufferedFactor - 1) * (((_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, j10) => ((bufferedFactor - 1) * bufferedResult.object[i].box[j10] + b) / bufferedFactor);
      const boxRaw = newResult.object[i].boxRaw.map((b, j10) => ((bufferedFactor - 1) * bufferedResult.object[i].boxRaw[j10] + b) / bufferedFactor);
      bufferedResult.object[i] = { ...newResult.object[i], box, boxRaw };
    }
  }
  if (newResult.persons) {
    const newPersons = newResult.persons;
    if (!bufferedResult.persons || newPersons.length !== bufferedResult.persons.length) {
      bufferedResult.persons = JSON.parse(JSON.stringify(newPersons));
    } else {
      for (let i = 0; i < newPersons.length; i++) {
        bufferedResult.persons[i].box = newPersons[i].box.map((box, j10) => ((bufferedFactor - 1) * bufferedResult.persons[i].box[j10] + box) / bufferedFactor);
      }
    }
  }
  if (newResult.gesture)
    bufferedResult.gesture = newResult.gesture;
  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
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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AAABAAAARgEoAAMAAAABAAIAAAExAAIAAAARAAAATgAAAAAAAABgAAAAAQAAAGAAAAABcGFpbnQu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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 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 };
  }
  let res;
  return new Promise(async (resolve) => {
    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);
    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 = $pe["tfjs-core"].includes("-") ? "https://vladmandic.github.io/tfjs/dist/" : `https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${gde}/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);
    if (userConfig)
      this.config = mergeDeep(this.config, userConfig);
    this.config.cacheModels = typeof indexedDB !== "undefined";
    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 Sde();
      if (this.env.browser) {
        if (this.config.debug)
          log("configuration:", this.config);
        if (this.config.debug)
          log("environment:", this.env);
        if (this.config.debug)
          log("tf flags:", this.tf.ENV["flags"]);
      }
    }
    await load19(this);
    if (this.env.initial && this.config.debug)
      log("tf engine state:", this.tf.engine().state.numBytes, "bytes", this.tf.engine().state.numTensors, "tensors");
    this.env.initial = false;
    const loaded = Object.values(this.models).filter((model18) => model18).length;
    if (loaded !== count2) {
      await validate2(this);
      this.emit("load");
    }
    const current = Math.trunc(now() - timeStamp);
    if (current > (this.performance.loadModels || 0))
      this.performance.loadModels = this.env.perfadd ? (this.performance.loadModels || 0) + current : current;
  }
  next(result = this.result) {
    return calc2(result, this.config);
  }
  async warmup(userConfig) {
    const t02 = now();
    const res = await warmup(this, userConfig);
    const t12 = now();
    this.performance.warmup = Math.trunc(t12 - t02);
    return res;
  }
  async profile(input, userConfig) {
    const profile = await this.tf.profile(() => this.detect(input, userConfig));
    const kernels = {};
    for (const kernel of profile.kernels) {
      if (kernels[kernel.name])
        kernels[kernel.name] += kernel.kernelTimeMs;
      else
        kernels[kernel.name] = kernel.kernelTimeMs;
    }
    const kernelArr = [];
    Object.entries(kernels).forEach((key) => kernelArr.push({ name: key[0], ms: key[1] }));
    kernelArr.sort((a, b) => b.ms - a.ms);
    kernelArr.length = 20;
    const res = {};
    for (const kernel of kernelArr)
      res[kernel.name] = kernel.ms;
    return res;
  }
  async detect(input, userConfig) {
    this.state = "detect";
    return new Promise(async (resolve) => {
      var _a2, _b2, _c, _d2, _e2, _f, _g2, _h, _i, _j2, _k2, _l2, _m2, _n2, _o2, _p2, _q2, _r2, _s2, _t2, _u2, _v2;
      this.state = "config";
      let timeStamp;
      this.config = mergeDeep(this.config, userConfig);
      this.state = "check";
      const error = __privateGet(this, _sanity).call(this, input);
      if (error) {
        log(error, input);
        this.emit("error");
        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error });
      }
      const timeStart = now();
      await check(this);
      await this.load();
      timeStamp = now();
      this.state = "image";
      const img = await process2(input, this.config);
      this.process = img;
      this.performance.inputProcess = this.env.perfadd ? (this.performance.inputProcess || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
      this.analyze("Get Image:");
      if (!img.tensor) {
        if (this.config.debug)
          log("could not convert input to tensor");
        this.emit("error");
        resolve({ face: [], body: [], hand: [], gesture: [], object: [], performance: this.performance, timestamp: now(), persons: [], error: "could not convert input to tensor" });
        return;
      }
      this.emit("image");
      timeStamp = now();
      this.config.skipAllowed = await skip(this.config, img.tensor);
      if (!this.performance.totalFrames)
        this.performance.totalFrames = 0;
      if (!this.performance.cachedFrames)
        this.performance.cachedFrames = 0;
      this.performance.totalFrames++;
      if (this.config.skipAllowed)
        this.performance.cachedFrames++;
      this.performance.cacheCheck = this.env.perfadd ? (this.performance.cacheCheck || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
      this.analyze("Check Changed:");
      let faceRes = [];
      let bodyRes = [];
      let handRes = [];
      let objectRes = [];
      this.state = "detect:face";
      if (this.config.async) {
        faceRes = this.config.face.enabled ? detectFace(this, img.tensor) : [];
        if (this.performance.face)
          delete this.performance.face;
      } else {
        timeStamp = now();
        faceRes = this.config.face.enabled ? await detectFace(this, img.tensor) : [];
        this.performance.face = this.env.perfadd ? (this.performance.face || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
      }
      if (this.config.async && (this.config.body.maxDetected === -1 || this.config.hand.maxDetected === -1))
        faceRes = await faceRes;
      this.analyze("Start Body:");
      this.state = "detect:body";
      const bodyConfig = this.config.body.maxDetected === -1 ? mergeDeep(this.config, { body: { maxDetected: this.config.face.enabled ? 1 * faceRes.length : 1 } }) : this.config;
      if (this.config.async) {
        if ((_a2 = this.config.body.modelPath) == null ? void 0 : _a2.includes("posenet"))
          bodyRes = this.config.body.enabled ? predict17(img.tensor, bodyConfig) : [];
        else if ((_b2 = this.config.body.modelPath) == null ? void 0 : _b2.includes("blazepose"))
          bodyRes = this.config.body.enabled ? predict5(img.tensor, bodyConfig) : [];
        else if ((_c = this.config.body.modelPath) == null ? void 0 : _c.includes("efficientpose"))
          bodyRes = this.config.body.enabled ? predict7(img.tensor, bodyConfig) : [];
        else if ((_d2 = this.config.body.modelPath) == null ? void 0 : _d2.includes("movenet"))
          bodyRes = this.config.body.enabled ? predict15(img.tensor, bodyConfig) : [];
        if (this.performance.body)
          delete this.performance.body;
      } else {
        timeStamp = now();
        if ((_e2 = this.config.body.modelPath) == null ? void 0 : _e2.includes("posenet"))
          bodyRes = this.config.body.enabled ? await predict17(img.tensor, bodyConfig) : [];
        else if ((_f = this.config.body.modelPath) == null ? void 0 : _f.includes("blazepose"))
          bodyRes = this.config.body.enabled ? await predict5(img.tensor, bodyConfig) : [];
        else if ((_g2 = this.config.body.modelPath) == null ? void 0 : _g2.includes("efficientpose"))
          bodyRes = this.config.body.enabled ? await predict7(img.tensor, bodyConfig) : [];
        else if ((_h = this.config.body.modelPath) == null ? void 0 : _h.includes("movenet"))
          bodyRes = this.config.body.enabled ? await predict15(img.tensor, bodyConfig) : [];
        this.performance.body = this.env.perfadd ? (this.performance.body || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
      }
      this.analyze("End Body:");
      this.analyze("Start Hand:");
      this.state = "detect:hand";
      const handConfig = this.config.hand.maxDetected === -1 ? mergeDeep(this.config, { hand: { maxDetected: this.config.face.enabled ? 2 * faceRes.length : 1 } }) : this.config;
      if (this.config.async) {
        if ((_j2 = (_i = this.config.hand.detector) == null ? void 0 : _i.modelPath) == null ? void 0 : _j2.includes("handdetect"))
          handRes = this.config.hand.enabled ? predict12(img.tensor, handConfig) : [];
        else if ((_l2 = (_k2 = this.config.hand.detector) == null ? void 0 : _k2.modelPath) == null ? void 0 : _l2.includes("handtrack"))
          handRes = this.config.hand.enabled ? predict13(img.tensor, handConfig) : [];
        if (this.performance.hand)
          delete this.performance.hand;
      } else {
        timeStamp = now();
        if ((_n2 = (_m2 = this.config.hand.detector) == null ? void 0 : _m2.modelPath) == null ? void 0 : _n2.includes("handdetect"))
          handRes = this.config.hand.enabled ? await predict12(img.tensor, handConfig) : [];
        else if ((_p2 = (_o2 = this.config.hand.detector) == null ? void 0 : _o2.modelPath) == null ? void 0 : _p2.includes("handtrack"))
          handRes = this.config.hand.enabled ? await predict13(img.tensor, handConfig) : [];
        this.performance.hand = this.env.perfadd ? (this.performance.hand || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
      }
      this.analyze("End Hand:");
      this.analyze("Start Object:");
      this.state = "detect:object";
      if (this.config.async) {
        if ((_q2 = this.config.object.modelPath) == null ? void 0 : _q2.includes("nanodet"))
          objectRes = this.config.object.enabled ? predict16(img.tensor, this.config) : [];
        else if ((_r2 = this.config.object.modelPath) == null ? void 0 : _r2.includes("centernet"))
          objectRes = this.config.object.enabled ? predict6(img.tensor, this.config) : [];
        if (this.performance.object)
          delete this.performance.object;
      } else {
        timeStamp = now();
        if ((_s2 = this.config.object.modelPath) == null ? void 0 : _s2.includes("nanodet"))
          objectRes = this.config.object.enabled ? await predict16(img.tensor, this.config) : [];
        else if ((_t2 = this.config.object.modelPath) == null ? void 0 : _t2.includes("centernet"))
          objectRes = this.config.object.enabled ? await predict6(img.tensor, this.config) : [];
        this.performance.object = this.env.perfadd ? (this.performance.object || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
      }
      this.analyze("End Object:");
      this.state = "detect:await";
      if (this.config.async)
        [faceRes, bodyRes, handRes, objectRes] = await Promise.all([faceRes, bodyRes, handRes, objectRes]);
      this.state = "detect:gesture";
      let gestureRes = [];
      if (this.config.gesture.enabled) {
        timeStamp = now();
        gestureRes = [...face2(faceRes), ...body2(bodyRes), ...hand2(handRes), ...iris(faceRes)];
        if (!this.config.async)
          this.performance.gesture = this.env.perfadd ? (this.performance.gesture || 0) + Math.trunc(now() - timeStamp) : Math.trunc(now() - timeStamp);
        else if (this.performance.gesture)
          delete this.performance.gesture;
      }
      this.performance.total = this.env.perfadd ? (this.performance.total || 0) + Math.trunc(now() - timeStart) : Math.trunc(now() - timeStart);
      const shape = ((_v2 = (_u2 = this.process) == null ? void 0 : _u2.tensor) == null ? void 0 : _v2.shape) || [];
      this.result = {
        face: faceRes,
        body: bodyRes,
        hand: handRes,
        gesture: gestureRes,
        object: objectRes,
        performance: this.performance,
        canvas: this.process.canvas,
        timestamp: Date.now(),
        error: null,
        get persons() {
          return join2(faceRes, bodyRes, handRes, gestureRes, shape);
        }
      };
      Re(img.tensor);
      this.emit("detect");
      this.state = "idle";
      resolve(this.result);
    });
  }
};
_numTensors = new WeakMap();
_analyzeMemoryLeaks = new WeakMap();
_checkSanity = new WeakMap();
_sanity = new WeakMap();
export {
  Human,
  Human as default,
  config as defaults,
  env
};
/**
 * @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.
 * =============================================================================
 */
/**
 * 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