var __defProp = Object.defineProperty;
var __defProps = Object.defineProperties;
var __getOwnPropDescs = Object.getOwnPropertyDescriptors;
var __getOwnPropSymbols = Object.getOwnPropertySymbols;
var __getProtoOf = Object.getPrototypeOf;
var __hasOwnProp = Object.prototype.hasOwnProperty;
var __propIsEnum = Object.prototype.propertyIsEnumerable;
var __reflectGet = Reflect.get;
var __knownSymbol = (name2, symbol) => (symbol = Symbol[name2]) ? symbol : Symbol.for("Symbol." + name2);
var __typeError = (msg) => {
  throw TypeError(msg);
};
var __pow = Math.pow;
var __defNormalProp = (obj, key, value) => key in obj ? __defProp(obj, key, { enumerable: true, configurable: true, writable: true, value }) : obj[key] = value;
var __spreadValues = (a, b) => {
  for (var prop in b || (b = {}))
    if (__hasOwnProp.call(b, prop))
      __defNormalProp(a, prop, b[prop]);
  if (__getOwnPropSymbols)
    for (var prop of __getOwnPropSymbols(b)) {
      if (__propIsEnum.call(b, prop))
        __defNormalProp(a, prop, b[prop]);
    }
  return a;
};
var __spreadProps = (a, b) => __defProps(a, __getOwnPropDescs(b));
var __objRest = (source, exclude) => {
  var target = {};
  for (var prop in source)
    if (__hasOwnProp.call(source, prop) && exclude.indexOf(prop) < 0)
      target[prop] = source[prop];
  if (source != null && __getOwnPropSymbols)
    for (var prop of __getOwnPropSymbols(source)) {
      if (exclude.indexOf(prop) < 0 && __propIsEnum.call(source, prop))
        target[prop] = source[prop];
    }
  return target;
};
var __publicField = (obj, key, value) => __defNormalProp(obj, typeof key !== "symbol" ? key + "" : key, value);
var __superGet = (cls, obj, key) => __reflectGet(__getProtoOf(cls), key, obj);
var __async = (__this, __arguments, generator) => {
  return new Promise((resolve, reject) => {
    var fulfilled = (value) => {
      try {
        step(generator.next(value));
      } catch (e) {
        reject(e);
      }
    };
    var rejected = (value) => {
      try {
        step(generator.throw(value));
      } catch (e) {
        reject(e);
      }
    };
    var step = (x) => x.done ? resolve(x.value) : Promise.resolve(x.value).then(fulfilled, rejected);
    step((generator = generator.apply(__this, __arguments)).next());
  });
};
var __await = function(promise, isYieldStar) {
  this[0] = promise;
  this[1] = isYieldStar;
};
var __yieldStar = (value) => {
  var obj = value[__knownSymbol("asyncIterator")], isAwait = false, method, it = {};
  if (obj == null) {
    obj = value[__knownSymbol("iterator")]();
    method = (k) => it[k] = (x) => obj[k](x);
  } else {
    obj = obj.call(value);
    method = (k) => it[k] = (v) => {
      if (isAwait) {
        isAwait = false;
        if (k === "throw") throw v;
        return v;
      }
      isAwait = true;
      return {
        done: false,
        value: new __await(new Promise((resolve) => {
          var x = obj[k](v);
          if (!(x instanceof Object)) __typeError("Object expected");
          resolve(x);
        }), 1)
      };
    };
  }
  return it[__knownSymbol("iterator")] = () => it, method("next"), "throw" in obj ? method("throw") : it.throw = (x) => {
    throw x;
  }, "return" in obj && method("return"), it;
};
var _a;
function _mergeNamespaces(n, m) {
  for (var i = 0; i < m.length; i++) {
    const e = m[i];
    if (typeof e !== "string" && !Array.isArray(e)) {
      for (const k in e) {
        if (k !== "default" && !(k in n)) {
          const d = Object.getOwnPropertyDescriptor(e, k);
          if (d) {
            Object.defineProperty(n, k, d.get ? d : {
              enumerable: true,
              get: () => e[k]
            });
          }
        }
      }
    }
  }
  return Object.freeze(Object.defineProperty(n, Symbol.toStringTag, { value: "Module" }));
}
(function polyfill() {
  const relList = document.createElement("link").relList;
  if (relList && relList.supports && relList.supports("modulepreload")) {
    return;
  }
  for (const link of document.querySelectorAll('link[rel="modulepreload"]')) {
    processPreload(link);
  }
  new MutationObserver((mutations) => {
    for (const mutation of mutations) {
      if (mutation.type !== "childList") {
        continue;
      }
      for (const node of mutation.addedNodes) {
        if (node.tagName === "LINK" && node.rel === "modulepreload")
          processPreload(node);
      }
    }
  }).observe(document, { childList: true, subtree: true });
  function getFetchOpts(link) {
    const fetchOpts = {};
    if (link.integrity) fetchOpts.integrity = link.integrity;
    if (link.referrerPolicy) fetchOpts.referrerPolicy = link.referrerPolicy;
    if (link.crossOrigin === "use-credentials")
      fetchOpts.credentials = "include";
    else if (link.crossOrigin === "anonymous") fetchOpts.credentials = "omit";
    else fetchOpts.credentials = "same-origin";
    return fetchOpts;
  }
  function processPreload(link) {
    if (link.ep)
      return;
    link.ep = true;
    const fetchOpts = getFetchOpts(link);
    fetch(link.href, fetchOpts);
  }
})();
function initPlugins() {
}
try {
  initPlugins();
} catch (err) {
  console.error("[wxt] Failed to initialize plugins", err);
}
const _OffscreenManager = class _OffscreenManager {
  constructor() {
    __publicField(this, "isCreated", false);
    __publicField(this, "isCreating", false);
    __publicField(this, "createPromise", null);
  }
  /**
   * Get singleton instance
   */
  static getInstance() {
    if (!_OffscreenManager.instance) {
      _OffscreenManager.instance = new _OffscreenManager();
    }
    return _OffscreenManager.instance;
  }
  /**
   * Ensure offscreen document exists
   */
  ensureOffscreenDocument() {
    return __async(this, null, function* () {
      if (this.isCreated) {
        return;
      }
      if (this.isCreating && this.createPromise) {
        return this.createPromise;
      }
      this.isCreating = true;
      this.createPromise = this._doCreateOffscreenDocument().finally(() => {
        this.isCreating = false;
      });
      return this.createPromise;
    });
  }
  _doCreateOffscreenDocument() {
    return __async(this, null, function* () {
      try {
        if (!chrome.offscreen) {
          throw new Error("Offscreen API not available. Chrome 109+ required.");
        }
        const existingContexts = yield chrome.runtime.getContexts({
          contextTypes: ["OFFSCREEN_DOCUMENT"]
        });
        if (existingContexts && existingContexts.length > 0) {
          console.log("OffscreenManager: Offscreen document already exists");
          this.isCreated = true;
          return;
        }
        yield chrome.offscreen.createDocument({
          url: "offscreen.html",
          reasons: ["WORKERS"],
          justification: "Need to run semantic similarity engine with workers"
        });
        this.isCreated = true;
        console.log("OffscreenManager: Offscreen document created successfully");
      } catch (error) {
        console.error("OffscreenManager: Failed to create offscreen document:", error);
        this.isCreated = false;
        throw error;
      }
    });
  }
  /**
   * Check if offscreen document is created
   */
  isOffscreenDocumentCreated() {
    return this.isCreated;
  }
  /**
   * Close offscreen document
   */
  closeOffscreenDocument() {
    return __async(this, null, function* () {
      try {
        if (chrome.offscreen && this.isCreated) {
          yield chrome.offscreen.closeDocument();
          this.isCreated = false;
          console.log("OffscreenManager: Offscreen document closed");
        }
      } catch (error) {
        console.error("OffscreenManager: Failed to close offscreen document:", error);
      }
    });
  }
  /**
   * Reset state (for testing)
   */
  reset() {
    this.isCreated = false;
    this.isCreating = false;
    this.createPromise = null;
  }
};
__publicField(_OffscreenManager, "instance", null);
let OffscreenManager = _OffscreenManager;
OffscreenManager.getInstance();
function dispatchCallback(progress_callback, data) {
  if (progress_callback) progress_callback(data);
}
function reverseDictionary(data) {
  return Object.fromEntries(Object.entries(data).map(([key, value]) => [value, key]));
}
function escapeRegExp(string) {
  return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&");
}
const Callable = (
  /** @type {any} */
  class {
    /**
    * Creates a new instance of the Callable class.
    */
    constructor() {
      let closure = function(...args) {
        return closure._call(...args);
      };
      return Object.setPrototypeOf(closure, new.target.prototype);
    }
    /**
     * This method should be implemented in subclasses to provide the
     * functionality of the callable object.
     *
     * @param {any[]} args
     * @throws {Error} If the subclass does not implement the `_call` method.
     */
    _call(...args) {
      throw Error("Must implement _call method in subclass");
    }
  }
);
function isTypedArray(val) {
  var _a2, _b, _c;
  return ((_c = (_b = (_a2 = val == null ? void 0 : val.prototype) == null ? void 0 : _a2.__proto__) == null ? void 0 : _b.constructor) == null ? void 0 : _c.name) === "TypedArray";
}
function isIntegralNumber(x) {
  return Number.isInteger(x) || typeof x === "bigint";
}
function exists(x) {
  return x !== void 0 && x !== null;
}
function mergeArrays(...arrs) {
  return Array.prototype.concat.apply([], arrs);
}
const sharp = {};
const ONNX_NODE = /* @__PURE__ */ Object.freeze(/* @__PURE__ */ Object.defineProperty({
  __proto__: null,
  default: sharp
}, Symbol.toStringTag, { value: "Module" }));
function getDefaultExportFromCjs(x) {
  return x && x.__esModule && Object.prototype.hasOwnProperty.call(x, "default") ? x["default"] : x;
}
function getAugmentedNamespace(n) {
  if (Object.prototype.hasOwnProperty.call(n, "__esModule")) return n;
  var f = n.default;
  if (typeof f == "function") {
    var a = function a2() {
      if (this instanceof a2) {
        return Reflect.construct(f, arguments, this.constructor);
      }
      return f.apply(this, arguments);
    };
    a.prototype = f.prototype;
  } else a = {};
  Object.defineProperty(a, "__esModule", { value: true });
  Object.keys(n).forEach(function(k) {
    var d = Object.getOwnPropertyDescriptor(n, k);
    Object.defineProperty(a, k, d.get ? d : {
      enumerable: true,
      get: function() {
        return n[k];
      }
    });
  });
  return a;
}
var ortWeb_min$1 = { exports: {} };
const backends = {};
const backendsSortedByPriority = [];
const registerBackend = (name2, backend, priority) => {
  if (backend && typeof backend.init === "function" && typeof backend.createSessionHandler === "function") {
    const currentBackend = backends[name2];
    if (currentBackend === void 0) {
      backends[name2] = { backend, priority };
    } else if (currentBackend.priority > priority) {
      return;
    } else if (currentBackend.priority === priority) {
      if (currentBackend.backend !== backend) {
        throw new Error(`cannot register backend "${name2}" using priority ${priority}`);
      }
    }
    if (priority >= 0) {
      const i = backendsSortedByPriority.indexOf(name2);
      if (i !== -1) {
        backendsSortedByPriority.splice(i, 1);
      }
      for (let i2 = 0; i2 < backendsSortedByPriority.length; i2++) {
        if (backends[backendsSortedByPriority[i2]].priority <= priority) {
          backendsSortedByPriority.splice(i2, 0, name2);
          return;
        }
      }
      backendsSortedByPriority.push(name2);
    }
    return;
  }
  throw new TypeError("not a valid backend");
};
const resolveBackend = (backendHints) => __async(null, null, function* () {
  const backendNames = backendHints.length === 0 ? backendsSortedByPriority : backendHints;
  const errors = [];
  for (const backendName of backendNames) {
    const backendInfo = backends[backendName];
    if (backendInfo) {
      if (backendInfo.initialized) {
        return backendInfo.backend;
      } else if (backendInfo.aborted) {
        continue;
      }
      const isInitializing = !!backendInfo.initPromise;
      try {
        if (!isInitializing) {
          backendInfo.initPromise = backendInfo.backend.init();
        }
        yield backendInfo.initPromise;
        backendInfo.initialized = true;
        return backendInfo.backend;
      } catch (e) {
        if (!isInitializing) {
          errors.push({ name: backendName, err: e });
        }
        backendInfo.aborted = true;
      } finally {
        delete backendInfo.initPromise;
      }
    }
  }
  throw new Error(`no available backend found. ERR: ${errors.map((e) => `[${e.name}] ${e.err}`).join(", ")}`);
});
class EnvImpl {
  constructor() {
    this.wasm = {};
    this.webgl = {};
    this.logLevelInternal = "warning";
  }
  // TODO standadize the getter and setter convention in env for other fields.
  set logLevel(value) {
    if (value === void 0) {
      return;
    }
    if (typeof value !== "string" || ["verbose", "info", "warning", "error", "fatal"].indexOf(value) === -1) {
      throw new Error(`Unsupported logging level: ${value}`);
    }
    this.logLevelInternal = value;
  }
  get logLevel() {
    return this.logLevelInternal;
  }
}
const env$2 = new EnvImpl();
const isBigInt64ArrayAvailable = typeof BigInt64Array !== "undefined" && typeof BigInt64Array.from === "function";
const isBigUint64ArrayAvailable = typeof BigUint64Array !== "undefined" && typeof BigUint64Array.from === "function";
const NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP = /* @__PURE__ */ new Map([
  ["float32", Float32Array],
  ["uint8", Uint8Array],
  ["int8", Int8Array],
  ["uint16", Uint16Array],
  ["int16", Int16Array],
  ["int32", Int32Array],
  ["bool", Uint8Array],
  ["float64", Float64Array],
  ["uint32", Uint32Array]
]);
const NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP = /* @__PURE__ */ new Map([
  [Float32Array, "float32"],
  [Uint8Array, "uint8"],
  [Int8Array, "int8"],
  [Uint16Array, "uint16"],
  [Int16Array, "int16"],
  [Int32Array, "int32"],
  [Float64Array, "float64"],
  [Uint32Array, "uint32"]
]);
if (isBigInt64ArrayAvailable) {
  NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set("int64", BigInt64Array);
  NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(BigInt64Array, "int64");
}
if (isBigUint64ArrayAvailable) {
  NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set("uint64", BigUint64Array);
  NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(BigUint64Array, "uint64");
}
const calculateSize = (dims) => {
  let size = 1;
  for (let i = 0; i < dims.length; i++) {
    const dim = dims[i];
    if (typeof dim !== "number" || !Number.isSafeInteger(dim)) {
      throw new TypeError(`dims[${i}] must be an integer, got: ${dim}`);
    }
    if (dim < 0) {
      throw new RangeError(`dims[${i}] must be a non-negative integer, got: ${dim}`);
    }
    size *= dim;
  }
  return size;
};
let Tensor$2 = class Tensor2 {
  constructor(arg0, arg1, arg2) {
    let type;
    let data;
    let dims;
    if (typeof arg0 === "string") {
      type = arg0;
      dims = arg2;
      if (arg0 === "string") {
        if (!Array.isArray(arg1)) {
          throw new TypeError("A string tensor's data must be a string array.");
        }
        data = arg1;
      } else {
        const typedArrayConstructor = NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.get(arg0);
        if (typedArrayConstructor === void 0) {
          throw new TypeError(`Unsupported tensor type: ${arg0}.`);
        }
        if (Array.isArray(arg1)) {
          data = typedArrayConstructor.from(arg1);
        } else if (arg1 instanceof typedArrayConstructor) {
          data = arg1;
        } else {
          throw new TypeError(`A ${type} tensor's data must be type of ${typedArrayConstructor}`);
        }
      }
    } else {
      dims = arg1;
      if (Array.isArray(arg0)) {
        if (arg0.length === 0) {
          throw new TypeError("Tensor type cannot be inferred from an empty array.");
        }
        const firstElementType = typeof arg0[0];
        if (firstElementType === "string") {
          type = "string";
          data = arg0;
        } else if (firstElementType === "boolean") {
          type = "bool";
          data = Uint8Array.from(arg0);
        } else {
          throw new TypeError(`Invalid element type of data array: ${firstElementType}.`);
        }
      } else {
        const mappedType = NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.get(arg0.constructor);
        if (mappedType === void 0) {
          throw new TypeError(`Unsupported type for tensor data: ${arg0.constructor}.`);
        }
        type = mappedType;
        data = arg0;
      }
    }
    if (dims === void 0) {
      dims = [data.length];
    } else if (!Array.isArray(dims)) {
      throw new TypeError("A tensor's dims must be a number array");
    }
    const size = calculateSize(dims);
    if (size !== data.length) {
      throw new Error(`Tensor's size(${size}) does not match data length(${data.length}).`);
    }
    this.dims = dims;
    this.type = type;
    this.data = data;
    this.size = size;
  }
  // #endregion
  /**
   * Create a new tensor object from image object
   *
   * @param buffer - Extracted image buffer data - assuming RGBA format
   * @param imageFormat - input image configuration - required configurations height, width, format
   * @param tensorFormat - output tensor configuration - Default is RGB format
   */
  static bufferToTensor(buffer, options) {
    if (buffer === void 0) {
      throw new Error("Image buffer must be defined");
    }
    if (options.height === void 0 || options.width === void 0) {
      throw new Error("Image height and width must be defined");
    }
    const { height, width } = options;
    const norm = options.norm;
    let normMean;
    let normBias;
    if (norm === void 0 || norm.mean === void 0) {
      normMean = 255;
    } else {
      normMean = norm.mean;
    }
    if (norm === void 0 || norm.bias === void 0) {
      normBias = 0;
    } else {
      normBias = norm.bias;
    }
    const inputformat = options.bitmapFormat !== void 0 ? options.bitmapFormat : "RGBA";
    const outputformat = options.tensorFormat !== void 0 ? options.tensorFormat !== void 0 ? options.tensorFormat : "RGB" : "RGB";
    const offset = height * width;
    const float32Data = outputformat === "RGBA" ? new Float32Array(offset * 4) : new Float32Array(offset * 3);
    let step = 4, rImagePointer = 0, gImagePointer = 1, bImagePointer = 2, aImagePointer = 3;
    let rTensorPointer = 0, gTensorPointer = offset, bTensorPointer = offset * 2, aTensorPointer = -1;
    if (inputformat === "RGB") {
      step = 3;
      rImagePointer = 0;
      gImagePointer = 1;
      bImagePointer = 2;
      aImagePointer = -1;
    }
    if (outputformat === "RGBA") {
      aTensorPointer = offset * 3;
    } else if (outputformat === "RBG") {
      rTensorPointer = 0;
      bTensorPointer = offset;
      gTensorPointer = offset * 2;
    } else if (outputformat === "BGR") {
      bTensorPointer = 0;
      gTensorPointer = offset;
      rTensorPointer = offset * 2;
    }
    for (let i = 0; i < offset; i++, rImagePointer += step, bImagePointer += step, gImagePointer += step, aImagePointer += step) {
      float32Data[rTensorPointer++] = (buffer[rImagePointer] + normBias) / normMean;
      float32Data[gTensorPointer++] = (buffer[gImagePointer] + normBias) / normMean;
      float32Data[bTensorPointer++] = (buffer[bImagePointer] + normBias) / normMean;
      if (aTensorPointer !== -1 && aImagePointer !== -1) {
        float32Data[aTensorPointer++] = (buffer[aImagePointer] + normBias) / normMean;
      }
    }
    const outputTensor = outputformat === "RGBA" ? new Tensor2("float32", float32Data, [1, 4, height, width]) : new Tensor2("float32", float32Data, [1, 3, height, width]);
    return outputTensor;
  }
  static fromImage(image, options) {
    return __async(this, null, function* () {
      const isHTMLImageEle = typeof HTMLImageElement !== "undefined" && image instanceof HTMLImageElement;
      const isImageDataEle = typeof ImageData !== "undefined" && image instanceof ImageData;
      const isImageBitmap = typeof ImageBitmap !== "undefined" && image instanceof ImageBitmap;
      const isURL = typeof String !== "undefined" && (image instanceof String || typeof image === "string");
      let data;
      let tensorConfig = {};
      if (isHTMLImageEle) {
        const canvas = document.createElement("canvas");
        const pixels2DContext = canvas.getContext("2d");
        if (pixels2DContext != null) {
          let height = image.naturalHeight;
          let width = image.naturalWidth;
          if (options !== void 0 && options.resizedHeight !== void 0 && options.resizedWidth !== void 0) {
            height = options.resizedHeight;
            width = options.resizedWidth;
          }
          if (options !== void 0) {
            tensorConfig = options;
            if (options.tensorFormat !== void 0) {
              throw new Error("Image input config format must be RGBA for HTMLImageElement");
            } else {
              tensorConfig.tensorFormat = "RGBA";
            }
            if (options.height !== void 0 && options.height !== height) {
              throw new Error("Image input config height doesn't match HTMLImageElement height");
            } else {
              tensorConfig.height = height;
            }
            if (options.width !== void 0 && options.width !== width) {
              throw new Error("Image input config width doesn't match HTMLImageElement width");
            } else {
              tensorConfig.width = width;
            }
          } else {
            tensorConfig.tensorFormat = "RGBA";
            tensorConfig.height = height;
            tensorConfig.width = width;
          }
          canvas.width = width;
          canvas.height = height;
          pixels2DContext.drawImage(image, 0, 0, width, height);
          data = pixels2DContext.getImageData(0, 0, width, height).data;
        } else {
          throw new Error("Can not access image data");
        }
      } else if (isImageDataEle) {
        const format = "RGBA";
        let height;
        let width;
        if (options !== void 0 && options.resizedWidth !== void 0 && options.resizedHeight !== void 0) {
          height = options.resizedHeight;
          width = options.resizedWidth;
        } else {
          height = image.height;
          width = image.width;
        }
        if (options !== void 0) {
          tensorConfig = options;
          if (options.bitmapFormat !== void 0 && options.bitmapFormat !== format) {
            throw new Error("Image input config format must be RGBA for ImageData");
          } else {
            tensorConfig.bitmapFormat = "RGBA";
          }
        } else {
          tensorConfig.bitmapFormat = "RGBA";
        }
        tensorConfig.height = height;
        tensorConfig.width = width;
        if (options !== void 0) {
          const tempCanvas = document.createElement("canvas");
          tempCanvas.width = width;
          tempCanvas.height = height;
          const pixels2DContext = tempCanvas.getContext("2d");
          if (pixels2DContext != null) {
            pixels2DContext.putImageData(image, 0, 0);
            data = pixels2DContext.getImageData(0, 0, width, height).data;
          } else {
            throw new Error("Can not access image data");
          }
        } else {
          data = image.data;
        }
      } else if (isImageBitmap) {
        if (options === void 0) {
          throw new Error("Please provide image config with format for Imagebitmap");
        }
        if (options.bitmapFormat !== void 0) {
          throw new Error("Image input config format must be defined for ImageBitmap");
        }
        const pixels2DContext = document.createElement("canvas").getContext("2d");
        if (pixels2DContext != null) {
          const height = image.height;
          const width = image.width;
          pixels2DContext.drawImage(image, 0, 0, width, height);
          data = pixels2DContext.getImageData(0, 0, width, height).data;
          if (options !== void 0) {
            if (options.height !== void 0 && options.height !== height) {
              throw new Error("Image input config height doesn't match ImageBitmap height");
            } else {
              tensorConfig.height = height;
            }
            if (options.width !== void 0 && options.width !== width) {
              throw new Error("Image input config width doesn't match ImageBitmap width");
            } else {
              tensorConfig.width = width;
            }
          } else {
            tensorConfig.height = height;
            tensorConfig.width = width;
          }
          return Tensor2.bufferToTensor(data, tensorConfig);
        } else {
          throw new Error("Can not access image data");
        }
      } else if (isURL) {
        return new Promise((resolve, reject) => {
          const canvas = document.createElement("canvas");
          const context = canvas.getContext("2d");
          if (!image || !context) {
            return reject();
          }
          const newImage = new Image();
          newImage.crossOrigin = "Anonymous";
          newImage.src = image;
          newImage.onload = () => {
            canvas.width = newImage.width;
            canvas.height = newImage.height;
            context.drawImage(newImage, 0, 0, canvas.width, canvas.height);
            const img = context.getImageData(0, 0, canvas.width, canvas.height);
            if (options !== void 0) {
              if (options.height !== void 0 && options.height !== canvas.height) {
                throw new Error("Image input config height doesn't match ImageBitmap height");
              } else {
                tensorConfig.height = canvas.height;
              }
              if (options.width !== void 0 && options.width !== canvas.width) {
                throw new Error("Image input config width doesn't match ImageBitmap width");
              } else {
                tensorConfig.width = canvas.width;
              }
            } else {
              tensorConfig.height = canvas.height;
              tensorConfig.width = canvas.width;
            }
            resolve(Tensor2.bufferToTensor(img.data, tensorConfig));
          };
        });
      } else {
        throw new Error("Input data provided is not supported - aborted tensor creation");
      }
      if (data !== void 0) {
        return Tensor2.bufferToTensor(data, tensorConfig);
      } else {
        throw new Error("Input data provided is not supported - aborted tensor creation");
      }
    });
  }
  toImageData(options) {
    var _a2, _b;
    const pixels2DContext = document.createElement("canvas").getContext("2d");
    let image;
    if (pixels2DContext != null) {
      const width = this.dims[3];
      const height = this.dims[2];
      const channels = this.dims[1];
      const inputformat = options !== void 0 ? options.format !== void 0 ? options.format : "RGB" : "RGB";
      const normMean = options !== void 0 ? ((_a2 = options.norm) === null || _a2 === void 0 ? void 0 : _a2.mean) !== void 0 ? options.norm.mean : 255 : 255;
      const normBias = options !== void 0 ? ((_b = options.norm) === null || _b === void 0 ? void 0 : _b.bias) !== void 0 ? options.norm.bias : 0 : 0;
      const offset = height * width;
      if (options !== void 0) {
        if (options.height !== void 0 && options.height !== height) {
          throw new Error("Image output config height doesn't match tensor height");
        }
        if (options.width !== void 0 && options.width !== width) {
          throw new Error("Image output config width doesn't match tensor width");
        }
        if (options.format !== void 0 && (channels === 4 && options.format !== "RGBA") || channels === 3 && (options.format !== "RGB" && options.format !== "BGR")) {
          throw new Error("Tensor format doesn't match input tensor dims");
        }
      }
      const step = 4;
      let rImagePointer = 0, gImagePointer = 1, bImagePointer = 2, aImagePointer = 3;
      let rTensorPointer = 0, gTensorPointer = offset, bTensorPointer = offset * 2, aTensorPointer = -1;
      if (inputformat === "RGBA") {
        rTensorPointer = 0;
        gTensorPointer = offset;
        bTensorPointer = offset * 2;
        aTensorPointer = offset * 3;
      } else if (inputformat === "RGB") {
        rTensorPointer = 0;
        gTensorPointer = offset;
        bTensorPointer = offset * 2;
      } else if (inputformat === "RBG") {
        rTensorPointer = 0;
        bTensorPointer = offset;
        gTensorPointer = offset * 2;
      }
      image = pixels2DContext.createImageData(width, height);
      for (let i = 0; i < height * width; rImagePointer += step, gImagePointer += step, bImagePointer += step, aImagePointer += step, i++) {
        image.data[rImagePointer] = (this.data[rTensorPointer++] - normBias) * normMean;
        image.data[gImagePointer] = (this.data[gTensorPointer++] - normBias) * normMean;
        image.data[bImagePointer] = (this.data[bTensorPointer++] - normBias) * normMean;
        image.data[aImagePointer] = aTensorPointer === -1 ? 255 : (this.data[aTensorPointer++] - normBias) * normMean;
      }
    } else {
      throw new Error("Can not access image data");
    }
    return image;
  }
  // #endregion
  // #region tensor utilities
  reshape(dims) {
    return new Tensor2(this.type, this.data, dims);
  }
};
const Tensor$1 = Tensor$2;
let InferenceSession$2 = class InferenceSession2 {
  constructor(handler) {
    this.handler = handler;
  }
  run(feeds, arg1, arg2) {
    return __async(this, null, function* () {
      const fetches = {};
      let options = {};
      if (typeof feeds !== "object" || feeds === null || feeds instanceof Tensor$1 || Array.isArray(feeds)) {
        throw new TypeError("'feeds' must be an object that use input names as keys and OnnxValue as corresponding values.");
      }
      let isFetchesEmpty = true;
      if (typeof arg1 === "object") {
        if (arg1 === null) {
          throw new TypeError("Unexpected argument[1]: cannot be null.");
        }
        if (arg1 instanceof Tensor$1) {
          throw new TypeError("'fetches' cannot be a Tensor");
        }
        if (Array.isArray(arg1)) {
          if (arg1.length === 0) {
            throw new TypeError("'fetches' cannot be an empty array.");
          }
          isFetchesEmpty = false;
          for (const name2 of arg1) {
            if (typeof name2 !== "string") {
              throw new TypeError("'fetches' must be a string array or an object.");
            }
            if (this.outputNames.indexOf(name2) === -1) {
              throw new RangeError(`'fetches' contains invalid output name: ${name2}.`);
            }
            fetches[name2] = null;
          }
          if (typeof arg2 === "object" && arg2 !== null) {
            options = arg2;
          } else if (typeof arg2 !== "undefined") {
            throw new TypeError("'options' must be an object.");
          }
        } else {
          let isFetches = false;
          const arg1Keys = Object.getOwnPropertyNames(arg1);
          for (const name2 of this.outputNames) {
            if (arg1Keys.indexOf(name2) !== -1) {
              const v = arg1[name2];
              if (v === null || v instanceof Tensor$1) {
                isFetches = true;
                isFetchesEmpty = false;
                fetches[name2] = v;
              }
            }
          }
          if (isFetches) {
            if (typeof arg2 === "object" && arg2 !== null) {
              options = arg2;
            } else if (typeof arg2 !== "undefined") {
              throw new TypeError("'options' must be an object.");
            }
          } else {
            options = arg1;
          }
        }
      } else if (typeof arg1 !== "undefined") {
        throw new TypeError("Unexpected argument[1]: must be 'fetches' or 'options'.");
      }
      for (const name2 of this.inputNames) {
        if (typeof feeds[name2] === "undefined") {
          throw new Error(`input '${name2}' is missing in 'feeds'.`);
        }
      }
      if (isFetchesEmpty) {
        for (const name2 of this.outputNames) {
          fetches[name2] = null;
        }
      }
      const results = yield this.handler.run(feeds, fetches, options);
      const returnValue = {};
      for (const key in results) {
        if (Object.hasOwnProperty.call(results, key)) {
          returnValue[key] = new Tensor$1(results[key].type, results[key].data, results[key].dims);
        }
      }
      return returnValue;
    });
  }
  static create(arg0, arg1, arg2, arg3) {
    return __async(this, null, function* () {
      let filePathOrUint8Array;
      let options = {};
      if (typeof arg0 === "string") {
        filePathOrUint8Array = arg0;
        if (typeof arg1 === "object" && arg1 !== null) {
          options = arg1;
        } else if (typeof arg1 !== "undefined") {
          throw new TypeError("'options' must be an object.");
        }
      } else if (arg0 instanceof Uint8Array) {
        filePathOrUint8Array = arg0;
        if (typeof arg1 === "object" && arg1 !== null) {
          options = arg1;
        } else if (typeof arg1 !== "undefined") {
          throw new TypeError("'options' must be an object.");
        }
      } else if (arg0 instanceof ArrayBuffer || typeof SharedArrayBuffer !== "undefined" && arg0 instanceof SharedArrayBuffer) {
        const buffer = arg0;
        let byteOffset = 0;
        let byteLength = arg0.byteLength;
        if (typeof arg1 === "object" && arg1 !== null) {
          options = arg1;
        } else if (typeof arg1 === "number") {
          byteOffset = arg1;
          if (!Number.isSafeInteger(byteOffset)) {
            throw new RangeError("'byteOffset' must be an integer.");
          }
          if (byteOffset < 0 || byteOffset >= buffer.byteLength) {
            throw new RangeError(`'byteOffset' is out of range [0, ${buffer.byteLength}).`);
          }
          byteLength = arg0.byteLength - byteOffset;
          if (typeof arg2 === "number") {
            byteLength = arg2;
            if (!Number.isSafeInteger(byteLength)) {
              throw new RangeError("'byteLength' must be an integer.");
            }
            if (byteLength <= 0 || byteOffset + byteLength > buffer.byteLength) {
              throw new RangeError(`'byteLength' is out of range (0, ${buffer.byteLength - byteOffset}].`);
            }
            if (typeof arg3 === "object" && arg3 !== null) {
              options = arg3;
            } else if (typeof arg3 !== "undefined") {
              throw new TypeError("'options' must be an object.");
            }
          } else if (typeof arg2 !== "undefined") {
            throw new TypeError("'byteLength' must be a number.");
          }
        } else if (typeof arg1 !== "undefined") {
          throw new TypeError("'options' must be an object.");
        }
        filePathOrUint8Array = new Uint8Array(buffer, byteOffset, byteLength);
      } else {
        throw new TypeError("Unexpected argument[0]: must be 'path' or 'buffer'.");
      }
      const eps = options.executionProviders || [];
      const backendHints = eps.map((i) => typeof i === "string" ? i : i.name);
      const backend = yield resolveBackend(backendHints);
      const handler = yield backend.createSessionHandler(filePathOrUint8Array, options);
      return new InferenceSession2(handler);
    });
  }
  startProfiling() {
    this.handler.startProfiling();
  }
  endProfiling() {
    this.handler.endProfiling();
  }
  get inputNames() {
    return this.handler.inputNames;
  }
  get outputNames() {
    return this.handler.outputNames;
  }
};
const InferenceSession$1 = InferenceSession$2;
const lib = /* @__PURE__ */ Object.freeze(/* @__PURE__ */ Object.defineProperty({
  __proto__: null,
  InferenceSession: InferenceSession$1,
  Tensor: Tensor$1,
  env: env$2,
  registerBackend
}, Symbol.toStringTag, { value: "Module" }));
const require$$0 = /* @__PURE__ */ getAugmentedNamespace(lib);
/*!
* ONNX Runtime Web v1.14.0
* Copyright (c) Microsoft Corporation. All rights reserved.
* Licensed under the MIT License.
*/
var hasRequiredOrtWeb_min;
function requireOrtWeb_min() {
  if (hasRequiredOrtWeb_min) return ortWeb_min$1.exports;
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            return -1;
          }, Pa: function(t3, e3) {
            t3 = new Date(1e3 * Bt(t3)), i()[e3 >> 2 >>> 0] = t3.getUTCSeconds(), i()[e3 + 4 >> 2 >>> 0] = t3.getUTCMinutes(), i()[e3 + 8 >> 2 >>> 0] = t3.getUTCHours(), i()[e3 + 12 >> 2 >>> 0] = t3.getUTCDate(), i()[e3 + 16 >> 2 >>> 0] = t3.getUTCMonth(), i()[e3 + 20 >> 2 >>> 0] = t3.getUTCFullYear() - 1900, i()[e3 + 24 >> 2 >>> 0] = t3.getUTCDay(), t3 = (t3.getTime() - Date.UTC(t3.getUTCFullYear(), 0, 1, 0, 0, 0, 0)) / 864e5 | 0, i()[e3 + 28 >> 2 >>> 0] = t3;
          }, Qa: function(t3, e3) {
            t3 = new Date(1e3 * Bt(t3)), i()[e3 >> 2 >>> 0] = t3.getSeconds(), i()[e3 + 4 >> 2 >>> 0] = t3.getMinutes(), i()[e3 + 8 >> 2 >>> 0] = t3.getHours(), i()[e3 + 12 >> 2 >>> 0] = t3.getDate(), i()[e3 + 16 >> 2 >>> 0] = t3.getMonth(), i()[e3 + 20 >> 2 >>> 0] = t3.getFullYear() - 1900, i()[e3 + 24 >> 2 >>> 0] = t3.getDay();
            var n2 = new Date(t3.getFullYear(), 0, 1), r3 = (t3.getTime() - n2.getTime()) / 864e5 | 0;
            i()[e3 + 28 >> 2 >>> 0] = r3, i()[e3 + 36 >> 2 >>> 0] = -60 * t3.getTimezoneOffset(), r3 = new Date(t3.getFullYear(), 6, 1).getTimezoneOffset(), t3 = 0 | (r3 != (n2 = n2.getTimezoneOffset()) && t3.getTimezoneOffset() == Math.min(n2, r3)), i()[e3 + 32 >> 2 >>> 0] = t3;
          }, Ra: function(t3) {
            var e3 = new Date(i()[t3 + 20 >> 2 >>> 0] + 1900, i()[t3 + 16 >> 2 >>> 0], i()[t3 + 12 >> 2 >>> 0], i()[t3 + 8 >> 2 >>> 0], i()[t3 + 4 >> 2 >>> 0], i()[t3 >> 2 >>> 0], 0), n2 = i()[t3 + 32 >> 2 >>> 0], r3 = e3.getTimezoneOffset(), o2 = new Date(e3.getFullYear(), 0, 1), a2 = new Date(e3.getFullYear(), 6, 1).getTimezoneOffset(), s2 = o2.getTimezoneOffset(), u2 = Math.min(s2, a2);
            return 0 > n2 ? i()[t3 + 32 >> 2 >>> 0] = Number(a2 != s2 && u2 == r3) : 0 < n2 != (u2 == r3) && (a2 = Math.max(s2, a2), e3.setTime(e3.getTime() + 6e4 * ((0 < n2 ? u2 : a2) - r3))), i()[t3 + 24 >> 2 >>> 0] = e3.getDay(), n2 = (e3.getTime() - o2.getTime()) / 864e5 | 0, i()[t3 + 28 >> 2 >>> 0] = n2, i()[t3 >> 2 >>> 0] = e3.getSeconds(), i()[t3 + 4 >> 2 >>> 0] = e3.getMinutes(), i()[t3 + 8 >> 2 >>> 0] = e3.getHours(), i()[t3 + 12 >> 2 >>> 0] = e3.getDate(), i()[t3 + 16 >> 2 >>> 0] = e3.getMonth(), e3.getTime() / 1e3 | 0;
          }, Aa: zt, Ba: Gt, Sa: function t3(e3, n2, r3) {
            t3.Ac || (t3.Ac = true, Wt(e3, n2, r3));
          }, y: function() {
            it("");
          }, U: function() {
            if (!w && !v) {
              var t3 = "Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread";
              _t || (_t = {}), _t[t3] || (_t[t3] = 1, w && (t3 = "warning: " + t3), P(t3));
            }
          }, ra: function() {
            return 4294901760;
          }, B: vt, Ia: function(t3, e3, n2) {
            r2().copyWithin(t3 >>> 0, e3 >>> 0, e3 + n2 >>> 0);
          }, F: function() {
            return w ? n(3993).cpus().length : navigator.hardwareConcurrency;
          }, Da: function(t3, e3, n2) {
            Yt.length = e3, n2 >>= 3;
            for (var r3 = 0; r3 < e3; r3++) Yt[r3] = a()[n2 + r3 >>> 0];
            return (0 > t3 ? st[-t3 - 1] : pe[t3]).apply(null, Yt);
          }, qa: function(t3) {
            var e3 = r2().length;
            if ((t3 >>>= 0) <= e3 || 4294901760 < t3) return false;
            for (var n2 = 1; 4 >= n2; n2 *= 2) {
              var i2 = e3 * (1 + 0.2 / n2);
              i2 = Math.min(i2, t3 + 100663296);
              var o2 = Math;
              i2 = Math.max(t3, i2), o2 = o2.min.call(o2, 4294901760, i2 + (65536 - i2 % 65536) % 65536);
              t: {
                try {
                  $.grow(o2 - C.byteLength + 65535 >>> 16), H($.buffer);
                  var a2 = 1;
                  break t;
                } catch (t4) {
                }
                a2 = void 0;
              }
              if (a2) return true;
            }
            return false;
          }, Na: function() {
            throw "unwind";
          }, Ga: Jt, Ha: Qt, J: ft, I: te, S: ee, ga: ne, R: oe, d: function() {
            return ae;
          }, na: function t3(r3, i2) {
            t3.lc || (t3.lc = function() {
              if ("object" == typeof crypto && "function" == typeof crypto.getRandomValues) {
                var t4 = new Uint8Array(1);
                return () => (crypto.getRandomValues(t4), t4[0]);
              }
              if (w) try {
                var e3 = n(Object(function() {
                  var t5 = new Error("Cannot find module 'crypto'");
                  throw t5.code = "MODULE_NOT_FOUND", t5;
                }()));
                return () => e3.randomBytes(1)[0];
              } catch (t5) {
              }
              return () => it("randomDevice");
            }());
            for (var o2 = 0; o2 < i2; o2++) e2()[r3 + o2 >> 0 >>> 0] = t3.lc();
            return 0;
          }, ia: function(t3, e3, n2) {
            var r3 = Ae();
            try {
              return yt(t3)(e3, n2);
            } catch (t4) {
              if (Ee(r3), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, ja: function(t3, e3, n2) {
            var r3 = Ae();
            try {
              return yt(t3)(e3, n2);
            } catch (t4) {
              if (Ee(r3), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, K: function(t3) {
            var e3 = Ae();
            try {
              return yt(t3)();
            } catch (t4) {
              if (Ee(e3), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, f: function(t3, e3) {
            var n2 = Ae();
            try {
              return yt(t3)(e3);
            } catch (t4) {
              if (Ee(n2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, P: function(t3, e3, n2) {
            var r3 = Ae();
            try {
              return yt(t3)(e3, n2);
            } catch (t4) {
              if (Ee(r3), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, Q: function(t3, e3, n2) {
            var r3 = Ae();
            try {
              return yt(t3)(e3, n2);
            } catch (t4) {
              if (Ee(r3), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, k: function(t3, e3, n2) {
            var r3 = Ae();
            try {
              return yt(t3)(e3, n2);
            } catch (t4) {
              if (Ee(r3), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, p: function(t3, e3, n2, r3) {
            var i2 = Ae();
            try {
              return yt(t3)(e3, n2, r3);
            } catch (t4) {
              if (Ee(i2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, q: function(t3, e3, n2, r3, i2) {
            var o2 = Ae();
            try {
              return yt(t3)(e3, n2, r3, i2);
            } catch (t4) {
              if (Ee(o2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, N: function(t3, e3, n2, r3, i2, o2) {
            var a2 = Ae();
            try {
              return yt(t3)(e3, n2, r3, i2, o2);
            } catch (t4) {
              if (Ee(a2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, s: function(t3, e3, n2, r3, i2, o2) {
            var a2 = Ae();
            try {
              return yt(t3)(e3, n2, r3, i2, o2);
            } catch (t4) {
              if (Ee(a2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, w: function(t3, e3, n2, r3, i2, o2, a2) {
            var s2 = Ae();
            try {
              return yt(t3)(e3, n2, r3, i2, o2, a2);
            } catch (t4) {
              if (Ee(s2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, L: function(t3, e3, n2, r3, i2, o2, a2, s2) {
            var u2 = Ae();
            try {
              return yt(t3)(e3, n2, r3, i2, o2, a2, s2);
            } catch (t4) {
              if (Ee(u2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, E: function(t3, e3, n2, r3, i2, o2, a2, s2, u2, c2, l2, p2) {
            var f2 = Ae();
            try {
              return yt(t3)(e3, n2, r3, i2, o2, a2, s2, u2, c2, l2, p2);
            } catch (t4) {
              if (Ee(f2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, aa: function(t3, e3, n2, r3, i2, o2, a2, s2) {
            var u2 = Ae();
            try {
              return Me(t3, e3, n2, r3, i2, o2, a2, s2);
            } catch (t4) {
              if (Ee(u2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, _: function(t3, e3, n2, r3, i2, o2, a2) {
            var s2 = Ae();
            try {
              return ke(t3, e3, n2, r3, i2, o2, a2);
            } catch (t4) {
              if (Ee(s2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, Z: function(t3, e3, n2, r3, i2) {
            var o2 = Ae();
            try {
              return Ue(t3, e3, n2, r3, i2);
            } catch (t4) {
              if (Ee(o2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, ca: function(t3, e3, n2, r3) {
            var i2 = Ae();
            try {
              return Re(t3, e3, n2, r3);
            } catch (t4) {
              if (Ee(i2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, $: function(t3) {
            var e3 = Ae();
            try {
              return $e(t3);
            } catch (t4) {
              if (Ee(e3), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, ba: function(t3, e3) {
            var n2 = Ae();
            try {
              return je(t3, e3);
            } catch (t4) {
              if (Ee(n2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, Y: function(t3, e3, n2) {
            var r3 = Ae();
            try {
              return Ce(t3, e3, n2);
            } catch (t4) {
              if (Ee(r3), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, g: function(t3) {
            var e3 = Ae();
            try {
              yt(t3)();
            } catch (t4) {
              if (Ee(e3), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, r: function(t3, e3) {
            var n2 = Ae();
            try {
              yt(t3)(e3);
            } catch (t4) {
              if (Ee(n2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, i: function(t3, e3, n2) {
            var r3 = Ae();
            try {
              yt(t3)(e3, n2);
            } catch (t4) {
              if (Ee(r3), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, ha: function(t3, e3, n2, r3) {
            var i2 = Ae();
            try {
              yt(t3)(e3, n2, r3);
            } catch (t4) {
              if (Ee(i2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, m: function(t3, e3, n2, r3) {
            var i2 = Ae();
            try {
              yt(t3)(e3, n2, r3);
            } catch (t4) {
              if (Ee(i2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, v: function(t3, e3, n2, r3, i2) {
            var o2 = Ae();
            try {
              yt(t3)(e3, n2, r3, i2);
            } catch (t4) {
              if (Ee(o2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, u: function(t3, e3, n2, r3, i2, o2) {
            var a2 = Ae();
            try {
              yt(t3)(e3, n2, r3, i2, o2);
            } catch (t4) {
              if (Ee(a2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, O: function(t3, e3, n2, r3, i2, o2, a2) {
            var s2 = Ae();
            try {
              yt(t3)(e3, n2, r3, i2, o2, a2);
            } catch (t4) {
              if (Ee(s2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, A: function(t3, e3, n2, r3, i2, o2, a2, s2) {
            var u2 = Ae();
            try {
              yt(t3)(e3, n2, r3, i2, o2, a2, s2);
            } catch (t4) {
              if (Ee(u2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, ka: function(t3, e3, n2, r3, i2, o2, a2, s2, u2) {
            var c2 = Ae();
            try {
              yt(t3)(e3, n2, r3, i2, o2, a2, s2, u2);
            } catch (t4) {
              if (Ee(c2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, C: function(t3, e3, n2, r3, i2, o2, a2, s2, u2, c2, l2) {
            var p2 = Ae();
            try {
              yt(t3)(e3, n2, r3, i2, o2, a2, s2, u2, c2, l2);
            } catch (t4) {
              if (Ee(p2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, D: function(t3, e3, n2, r3, i2, o2, a2, s2, u2, c2, l2, p2, f2, d2, h2, g2) {
            var b2 = Ae();
            try {
              yt(t3)(e3, n2, r3, i2, o2, a2, s2, u2, c2, l2, p2, f2, d2, h2, g2);
            } catch (t4) {
              if (Ee(b2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, fa: function(t3, e3, n2, r3, i2, o2, a2, s2) {
            var u2 = Ae();
            try {
              Fe(t3, e3, n2, r3, i2, o2, a2, s2);
            } catch (t4) {
              if (Ee(u2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, da: function(t3, e3, n2, r3, i2, o2, a2, s2, u2, c2, l2, p2) {
            var f2 = Ae();
            try {
              Le(t3, e3, n2, r3, i2, o2, a2, s2, u2, c2, l2, p2);
            } catch (t4) {
              if (Ee(f2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, ea: function(t3, e3, n2, r3, i2, o2) {
            var a2 = Ae();
            try {
              Ne(t3, e3, n2, r3, i2, o2);
            } catch (t4) {
              if (Ee(a2), t4 !== t4 + 0) throw t4;
              Se(1, 0);
            }
          }, o: function(t3) {
            return t3;
          }, a: $ || s.wasmMemory, G: function(t3) {
            ae = t3;
          }, la: le, z: function(t3, e3, n2, r3) {
            return le(t3, e3, n2, r3);
          } };
          !function() {
            function t3(t4, e4) {
              s.asm = t4.exports, dt.qc.push(s.asm.sb), q = s.asm.ub, Y.unshift(s.asm.Va), k = e4, x || (et--, s.monitorRunDependencies && s.monitorRunDependencies(et), 0 == et && (rt && (t4 = rt, rt = null, t4())));
            }
            function e3(e4) {
              t3(e4.instance, e4.module);
            }
            function n2(t4) {
              return function() {
                if (!E && (_ || v)) {
                  if ("function" == typeof fetch && !tt.startsWith("file://")) return fetch(tt, { credentials: "same-origin" }).then(function(t5) {
                    if (!t5.ok) throw "failed to load wasm binary file at '" + tt + "'";
                    return t5.arrayBuffer();
                  }).catch(function() {
                    return at();
                  });
                  if (p) return new Promise(function(t5, e4) {
                    p(tt, function(e5) {
                      t5(new Uint8Array(e5));
                    }, e4);
                  });
                }
                return Promise.resolve().then(function() {
                  return at();
                });
              }().then(function(t5) {
                return WebAssembly.instantiate(t5, r3);
              }).then(function(t5) {
                return t5;
              }).then(t4, function(t5) {
                P("failed to asynchronously prepare wasm: " + t5), it(t5);
              });
            }
            var r3 = { a: fe };
            if (x || (et++, s.monitorRunDependencies && s.monitorRunDependencies(et)), s.instantiateWasm) try {
              return s.instantiateWasm(r3, t3);
            } catch (t4) {
              return P("Module.instantiateWasm callback failed with error: " + t4), false;
            }
            (E || "function" != typeof WebAssembly.instantiateStreaming || ot() || tt.startsWith("file://") || w || "function" != typeof fetch ? n2(e3) : fetch(tt, { credentials: "same-origin" }).then(function(t4) {
              return WebAssembly.instantiateStreaming(t4, r3).then(e3, function(t5) {
                return P("wasm streaming compile failed: " + t5), P("falling back to ArrayBuffer instantiation"), n2(e3);
              });
            })).catch(c);
          }(), s.___wasm_call_ctors = function() {
            return (s.___wasm_call_ctors = s.asm.Va).apply(null, arguments);
          }, s._OrtInit = function() {
            return (s._OrtInit = s.asm.Wa).apply(null, arguments);
          }, s._OrtCreateSessionOptions = function() {
            return (s._OrtCreateSessionOptions = s.asm.Xa).apply(null, arguments);
          }, s._OrtAppendExecutionProvider = function() {
            return (s._OrtAppendExecutionProvider = s.asm.Ya).apply(null, arguments);
          }, s._OrtAddSessionConfigEntry = function() {
            return (s._OrtAddSessionConfigEntry = s.asm.Za).apply(null, arguments);
          }, s._OrtReleaseSessionOptions = function() {
            return (s._OrtReleaseSessionOptions = s.asm._a).apply(null, arguments);
          }, s._OrtCreateSession = function() {
            return (s._OrtCreateSession = s.asm.$a).apply(null, arguments);
          }, s._OrtReleaseSession = function() {
            return (s._OrtReleaseSession = s.asm.ab).apply(null, arguments);
          }, s._OrtGetInputCount = function() {
            return (s._OrtGetInputCount = s.asm.bb).apply(null, arguments);
          }, s._OrtGetOutputCount = function() {
            return (s._OrtGetOutputCount = s.asm.cb).apply(null, arguments);
          }, s._OrtGetInputName = function() {
            return (s._OrtGetInputName = s.asm.db).apply(null, arguments);
          }, s._OrtGetOutputName = function() {
            return (s._OrtGetOutputName = s.asm.eb).apply(null, arguments);
          }, s._OrtFree = function() {
            return (s._OrtFree = s.asm.fb).apply(null, arguments);
          }, s._OrtCreateTensor = function() {
            return (s._OrtCreateTensor = s.asm.gb).apply(null, arguments);
          }, s._OrtGetTensorData = function() {
            return (s._OrtGetTensorData = s.asm.hb).apply(null, arguments);
          }, s._OrtReleaseTensor = function() {
            return (s._OrtReleaseTensor = s.asm.ib).apply(null, arguments);
          }, s._OrtCreateRunOptions = function() {
            return (s._OrtCreateRunOptions = s.asm.jb).apply(null, arguments);
          }, s._OrtAddRunConfigEntry = function() {
            return (s._OrtAddRunConfigEntry = s.asm.kb).apply(null, arguments);
          }, s._OrtReleaseRunOptions = function() {
            return (s._OrtReleaseRunOptions = s.asm.lb).apply(null, arguments);
          }, s._OrtRun = function() {
            return (s._OrtRun = s.asm.mb).apply(null, arguments);
          }, s._OrtEndProfiling = function() {
            return (s._OrtEndProfiling = s.asm.nb).apply(null, arguments);
          };
          var de = s._pthread_self = function() {
            return (de = s._pthread_self = s.asm.ob).apply(null, arguments);
          }, he = s._malloc = function() {
            return (he = s._malloc = s.asm.pb).apply(null, arguments);
          }, ge = s._free = function() {
            return (ge = s._free = s.asm.qb).apply(null, arguments);
          }, be = s._fflush = function() {
            return (be = s._fflush = s.asm.rb).apply(null, arguments);
          };
          s.__emscripten_tls_init = function() {
            return (s.__emscripten_tls_init = s.asm.sb).apply(null, arguments);
          };
          var me = s.___funcs_on_exit = function() {
            return (me = s.___funcs_on_exit = s.asm.tb).apply(null, arguments);
          }, ye = s.__emscripten_thread_init = function() {
            return (ye = s.__emscripten_thread_init = s.asm.vb).apply(null, arguments);
          };
          s.__emscripten_thread_crashed = function() {
            return (s.__emscripten_thread_crashed = s.asm.wb).apply(null, arguments);
          };
          var _e, ve = s._emscripten_run_in_main_runtime_thread_js = function() {
            return (ve = s._emscripten_run_in_main_runtime_thread_js = s.asm.xb).apply(null, arguments);
          }, we = s.__emscripten_proxy_execute_task_queue = function() {
            return (we = s.__emscripten_proxy_execute_task_queue = s.asm.yb).apply(null, arguments);
          }, xe = s.__emscripten_thread_free_data = function() {
            return (xe = s.__emscripten_thread_free_data = s.asm.zb).apply(null, arguments);
          }, Te = s.__emscripten_thread_exit = function() {
            return (Te = s.__emscripten_thread_exit = s.asm.Ab).apply(null, arguments);
          }, Se = s._setThrew = function() {
            return (Se = s._setThrew = s.asm.Bb).apply(null, arguments);
          }, Oe = s._emscripten_stack_set_limits = function() {
            return (Oe = s._emscripten_stack_set_limits = s.asm.Cb).apply(null, arguments);
          }, Ae = s.stackSave = function() {
            return (Ae = s.stackSave = s.asm.Db).apply(null, arguments);
          }, Ee = s.stackRestore = function() {
            return (Ee = s.stackRestore = s.asm.Eb).apply(null, arguments);
          }, Ie = s.stackAlloc = function() {
            return (Ie = s.stackAlloc = s.asm.Fb).apply(null, arguments);
          }, Pe = s.___cxa_can_catch = function() {
            return (Pe = s.___cxa_can_catch = s.asm.Gb).apply(null, arguments);
          }, De = s.___cxa_is_pointer_type = function() {
            return (De = s.___cxa_is_pointer_type = s.asm.Hb).apply(null, arguments);
          }, $e = s.dynCall_j = function() {
            return ($e = s.dynCall_j = s.asm.Ib).apply(null, arguments);
          }, ke = s.dynCall_iiiiij = function() {
            return (ke = s.dynCall_iiiiij = s.asm.Jb).apply(null, arguments);
          }, Ce = s.dynCall_jii = function() {
            return (Ce = s.dynCall_jii = s.asm.Kb).apply(null, arguments);
          }, Fe = s.dynCall_viiiiij = function() {
            return (Fe = s.dynCall_viiiiij = s.asm.Lb).apply(null, arguments);
          }, Ne = s.dynCall_vjji = function() {
            return (Ne = s.dynCall_vjji = s.asm.Mb).apply(null, arguments);
          }, Le = s.dynCall_viiijjjii = function() {
            return (Le = s.dynCall_viiijjjii = s.asm.Nb).apply(null, arguments);
          }, Re = s.dynCall_iij = function() {
            return (Re = s.dynCall_iij = s.asm.Ob).apply(null, arguments);
          }, je = s.dynCall_ji = function() {
            return (je = s.dynCall_ji = s.asm.Pb).apply(null, arguments);
          }, Me = s.dynCall_iiiiiij = function() {
            return (Me = s.dynCall_iiiiiij = s.asm.Qb).apply(null, arguments);
          }, Ue = s.dynCall_iiij = function() {
            return (Ue = s.dynCall_iiij = s.asm.Rb).apply(null, arguments);
          };
          function Ve() {
            function t3() {
              if (!_e && (_e = true, s.calledRun = true, !M) && (x || ht(Y), u(s), s.onRuntimeInitialized && s.onRuntimeInitialized(), !x)) {
                if (s.postRun) for ("function" == typeof s.postRun && (s.postRun = [s.postRun]); s.postRun.length; ) {
                  var t4 = s.postRun.shift();
                  Z.unshift(t4);
                }
                ht(Z);
              }
            }
            if (!(0 < et)) if (x) u(s), x || ht(Y), postMessage({ cmd: "loaded" });
            else {
              if (s.preRun) for ("function" == typeof s.preRun && (s.preRun = [s.preRun]); s.preRun.length; ) Q();
              ht(X), 0 < et || (s.setStatus ? (s.setStatus("Running..."), setTimeout(function() {
                setTimeout(function() {
                  s.setStatus("");
                }, 1), t3();
              }, 1)) : t3());
            }
          }
          if (s.UTF8ToString = B, s.stringToUTF8 = function(t3, e3, n2) {
            return z(t3, r2(), e3, n2);
          }, s.lengthBytesUTF8 = G, s.keepRuntimeAlive = J, s.wasmMemory = $, s.stackSave = Ae, s.stackRestore = Ee, s.stackAlloc = Ie, s.ExitStatus = ut, s.PThread = dt, rt = function t3() {
            _e || Ve(), _e || (rt = t3);
          }, s.preInit) for ("function" == typeof s.preInit && (s.preInit = [s.preInit]); 0 < s.preInit.length; ) s.preInit.pop()();
          return Ve(), t2.ready;
        });
        t.exports = r;
      }, 932: (t, e, n) => {
        var _scriptDir, r = (_scriptDir = (_scriptDir = "undefined" != typeof document && document.currentScript ? document.currentScript.src : void 0) || "/index.js", function(t2) {
          var e2, r2, i;
          t2 = t2 || {}, e2 || (e2 = void 0 !== t2 ? t2 : {}), e2.ready = new Promise(function(t3, e3) {
            r2 = t3, i = e3;
          });
          var o, a, s, u, c, l, p = Object.assign({}, e2), f = "./this.program", d = (t3, e3) => {
            throw e3;
          }, h = "object" == typeof window, g = "function" == typeof importScripts, b = "object" == typeof process && "object" == typeof process.versions && "string" == typeof process.versions.node, m = "";
          b ? (m = g ? n(908).dirname(m) + "/" : "//", l = () => {
            c || (u = n(1384), c = n(908));
          }, o = function(t3, e3) {
            return l(), t3 = c.normalize(t3), u.readFileSync(t3, e3 ? void 0 : "utf8");
          }, s = (t3) => ((t3 = o(t3, true)).buffer || (t3 = new Uint8Array(t3)), t3), a = (t3, e3, n2) => {
            l(), t3 = c.normalize(t3), u.readFile(t3, function(t4, r3) {
              t4 ? n2(t4) : e3(r3.buffer);
            });
          }, 1 < process.argv.length && (f = process.argv[1].replace(/\\/g, "/")), process.argv.slice(2), process.on("uncaughtException", function(t3) {
            if (!(t3 instanceof K)) throw t3;
          }), process.on("unhandledRejection", function(t3) {
            throw t3;
          }), d = (t3, e3) => {
            if (w || 0 < U) throw process.exitCode = t3, e3;
            e3 instanceof K || v("exiting due to exception: " + e3), process.exit(t3);
          }, e2.inspect = function() {
            return "[Emscripten Module object]";
          }) : (h || g) && (g ? m = self.location.href : "undefined" != typeof document && document.currentScript && (m = document.currentScript.src), _scriptDir && (m = _scriptDir), m = 0 !== m.indexOf("blob:") ? m.substr(0, m.replace(/[?#].*/, "").lastIndexOf("/") + 1) : "", o = (t3) => {
            var e3 = new XMLHttpRequest();
            return e3.open("GET", t3, false), e3.send(null), e3.responseText;
          }, g && (s = (t3) => {
            var e3 = new XMLHttpRequest();
            return e3.open("GET", t3, false), e3.responseType = "arraybuffer", e3.send(null), new Uint8Array(e3.response);
          }), a = (t3, e3, n2) => {
            var r3 = new XMLHttpRequest();
            r3.open("GET", t3, true), r3.responseType = "arraybuffer", r3.onload = () => {
              200 == r3.status || 0 == r3.status && r3.response ? e3(r3.response) : n2();
            }, r3.onerror = n2, r3.send(null);
          });
          var y, _ = e2.print || console.log.bind(console), v = e2.printErr || console.warn.bind(console);
          Object.assign(e2, p), p = null, e2.thisProgram && (f = e2.thisProgram), e2.quit && (d = e2.quit), e2.wasmBinary && (y = e2.wasmBinary);
          var w = e2.noExitRuntime || false;
          "object" != typeof WebAssembly && W("no native wasm support detected");
          var x, T, S, O, A, E, I = false, P = "undefined" != typeof TextDecoder ? new TextDecoder("utf8") : void 0;
          function D(t3, e3, n2) {
            var r3 = (e3 >>>= 0) + n2;
            for (n2 = e3; t3[n2] && !(n2 >= r3); ) ++n2;
            if (16 < n2 - e3 && t3.buffer && P) return P.decode(t3.subarray(e3, n2));
            for (r3 = ""; e3 < n2; ) {
              var i2 = t3[e3++];
              if (128 & i2) {
                var o2 = 63 & t3[e3++];
                if (192 == (224 & i2)) r3 += String.fromCharCode((31 & i2) << 6 | o2);
                else {
                  var a2 = 63 & t3[e3++];
                  65536 > (i2 = 224 == (240 & i2) ? (15 & i2) << 12 | o2 << 6 | a2 : (7 & i2) << 18 | o2 << 12 | a2 << 6 | 63 & t3[e3++]) ? r3 += String.fromCharCode(i2) : (i2 -= 65536, r3 += String.fromCharCode(55296 | i2 >> 10, 56320 | 1023 & i2));
                }
              } else r3 += String.fromCharCode(i2);
            }
            return r3;
          }
          function $(t3, e3) {
            return (t3 >>>= 0) ? D(O, t3, e3) : "";
          }
          function k(t3, e3, n2, r3) {
            if (!(0 < r3)) return 0;
            var i2 = n2 >>>= 0;
            r3 = n2 + r3 - 1;
            for (var o2 = 0; o2 < t3.length; ++o2) {
              var a2 = t3.charCodeAt(o2);
              if (55296 <= a2 && 57343 >= a2 && (a2 = 65536 + ((1023 & a2) << 10) | 1023 & t3.charCodeAt(++o2)), 127 >= a2) {
                if (n2 >= r3) break;
                e3[n2++ >>> 0] = a2;
              } else {
                if (2047 >= a2) {
                  if (n2 + 1 >= r3) break;
                  e3[n2++ >>> 0] = 192 | a2 >> 6;
                } else {
                  if (65535 >= a2) {
                    if (n2 + 2 >= r3) break;
                    e3[n2++ >>> 0] = 224 | a2 >> 12;
                  } else {
                    if (n2 + 3 >= r3) break;
                    e3[n2++ >>> 0] = 240 | a2 >> 18, e3[n2++ >>> 0] = 128 | a2 >> 12 & 63;
                  }
                  e3[n2++ >>> 0] = 128 | a2 >> 6 & 63;
                }
                e3[n2++ >>> 0] = 128 | 63 & a2;
              }
            }
            return e3[n2 >>> 0] = 0, n2 - i2;
          }
          function C(t3) {
            for (var e3 = 0, n2 = 0; n2 < t3.length; ++n2) {
              var r3 = t3.charCodeAt(n2);
              127 >= r3 ? e3++ : 2047 >= r3 ? e3 += 2 : 55296 <= r3 && 57343 >= r3 ? (e3 += 4, ++n2) : e3 += 3;
            }
            return e3;
          }
          function F() {
            var t3 = x.buffer;
            T = t3, e2.HEAP8 = S = new Int8Array(t3), e2.HEAP16 = new Int16Array(t3), e2.HEAP32 = A = new Int32Array(t3), e2.HEAPU8 = O = new Uint8Array(t3), e2.HEAPU16 = new Uint16Array(t3), e2.HEAPU32 = E = new Uint32Array(t3), e2.HEAPF32 = new Float32Array(t3), e2.HEAPF64 = new Float64Array(t3);
          }
          var N, L = [], R = [], j = [], M = [], U = 0;
          function V() {
            var t3 = e2.preRun.shift();
            L.unshift(t3);
          }
          var B, z = 0, H = null;
          function W(t3) {
            throw e2.onAbort && e2.onAbort(t3), v(t3 = "Aborted(" + t3 + ")"), I = true, t3 = new WebAssembly.RuntimeError(t3 + ". Build with -sASSERTIONS for more info."), i(t3), t3;
          }
          function q() {
            return B.startsWith("data:application/octet-stream;base64,");
          }
          if (B = "ort-wasm.wasm", !q()) {
            var X = B;
            B = e2.locateFile ? e2.locateFile(X, m) : m + X;
          }
          function Y() {
            var t3 = B;
            try {
              if (t3 == B && y) return new Uint8Array(y);
              if (s) return s(t3);
              throw "both async and sync fetching of the wasm failed";
            } catch (t4) {
              W(t4);
            }
          }
          function K(t3) {
            this.name = "ExitStatus", this.message = "Program terminated with exit(" + t3 + ")", this.status = t3;
          }
          function Z(t3) {
            for (; 0 < t3.length; ) t3.shift()(e2);
          }
          var J = [], Q = 0, tt = 0;
          function et(t3) {
            this.Db = t3, this.zb = t3 - 24, this.Ub = function(t4) {
              E[this.zb + 4 >> 2 >>> 0] = t4;
            }, this.Eb = function() {
              return E[this.zb + 4 >> 2 >>> 0];
            }, this.Sb = function(t4) {
              E[this.zb + 8 >> 2 >>> 0] = t4;
            }, this.Wb = function() {
              return E[this.zb + 8 >> 2 >>> 0];
            }, this.Tb = function() {
              A[this.zb >> 2 >>> 0] = 0;
            }, this.Ib = function(t4) {
              S[this.zb + 12 >> 0 >>> 0] = t4 ? 1 : 0;
            }, this.Pb = function() {
              return 0 != S[this.zb + 12 >> 0 >>> 0];
            }, this.Jb = function(t4) {
              S[this.zb + 13 >> 0 >>> 0] = t4 ? 1 : 0;
            }, this.Lb = function() {
              return 0 != S[this.zb + 13 >> 0 >>> 0];
            }, this.Rb = function(t4, e3) {
              this.Fb(0), this.Ub(t4), this.Sb(e3), this.Tb(), this.Ib(false), this.Jb(false);
            }, this.Nb = function() {
              A[this.zb >> 2 >>> 0] += 1;
            }, this.Xb = function() {
              var t4 = A[this.zb >> 2 >>> 0];
              return A[this.zb >> 2 >>> 0] = t4 - 1, 1 === t4;
            }, this.Fb = function(t4) {
              E[this.zb + 16 >> 2 >>> 0] = t4;
            }, this.Ob = function() {
              return E[this.zb + 16 >> 2 >>> 0];
            }, this.Qb = function() {
              if (Et(this.Eb())) return E[this.Db >> 2 >>> 0];
              var t4 = this.Ob();
              return 0 !== t4 ? t4 : this.Db;
            };
          }
          function nt(t3) {
            return _t(new et(t3).zb);
          }
          var rt = [];
          function it(t3) {
            var e3 = rt[t3];
            return e3 || (t3 >= rt.length && (rt.length = t3 + 1), rt[t3] = e3 = N.get(t3)), e3;
          }
          function ot(t3) {
            var e3 = C(t3) + 1, n2 = yt(e3);
            return n2 && k(t3, S, n2, e3), n2;
          }
          var at = {};
          function st() {
            if (!ut) {
              var t3, e3 = { USER: "web_user", LOGNAME: "web_user", PATH: "/", PWD: "/", HOME: "/home/web_user", LANG: ("object" == typeof navigator && navigator.languages && navigator.languages[0] || "C").replace("-", "_") + ".UTF-8", _: f || "./this.program" };
              for (t3 in at) void 0 === at[t3] ? delete e3[t3] : e3[t3] = at[t3];
              var n2 = [];
              for (t3 in e3) n2.push(t3 + "=" + e3[t3]);
              ut = n2;
            }
            return ut;
          }
          var ut, ct = [null, [], []];
          function lt(t3, e3) {
            var n2 = ct[t3];
            0 === e3 || 10 === e3 ? ((1 === t3 ? _ : v)(D(n2, 0)), n2.length = 0) : n2.push(e3);
          }
          var pt = 0;
          function ft(t3) {
            return 0 == t3 % 4 && (0 != t3 % 100 || 0 == t3 % 400);
          }
          var dt = [31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31], ht = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31];
          function gt(t3, e3, n2, r3) {
            function i2(t4, e4, n3) {
              for (t4 = "number" == typeof t4 ? t4.toString() : t4 || ""; t4.length < e4; ) t4 = n3[0] + t4;
              return t4;
            }
            function o2(t4, e4) {
              return i2(t4, e4, "0");
            }
            function a2(t4, e4) {
              function n3(t5) {
                return 0 > t5 ? -1 : 0 < t5 ? 1 : 0;
              }
              var r4;
              return 0 === (r4 = n3(t4.getFullYear() - e4.getFullYear())) && 0 === (r4 = n3(t4.getMonth() - e4.getMonth())) && (r4 = n3(t4.getDate() - e4.getDate())), r4;
            }
            function s2(t4) {
              switch (t4.getDay()) {
                case 0:
                  return new Date(t4.getFullYear() - 1, 11, 29);
                case 1:
                  return t4;
                case 2:
                  return new Date(t4.getFullYear(), 0, 3);
                case 3:
                  return new Date(t4.getFullYear(), 0, 2);
                case 4:
                  return new Date(t4.getFullYear(), 0, 1);
                case 5:
                  return new Date(t4.getFullYear() - 1, 11, 31);
                case 6:
                  return new Date(t4.getFullYear() - 1, 11, 30);
              }
            }
            function u2(t4) {
              var e4 = t4.Bb;
              for (t4 = new Date(new Date(t4.Cb + 1900, 0, 1).getTime()); 0 < e4; ) {
                var n3 = t4.getMonth(), r4 = (ft(t4.getFullYear()) ? dt : ht)[n3];
                if (!(e4 > r4 - t4.getDate())) {
                  t4.setDate(t4.getDate() + e4);
                  break;
                }
                e4 -= r4 - t4.getDate() + 1, t4.setDate(1), 11 > n3 ? t4.setMonth(n3 + 1) : (t4.setMonth(0), t4.setFullYear(t4.getFullYear() + 1));
              }
              return n3 = new Date(t4.getFullYear() + 1, 0, 4), e4 = s2(new Date(t4.getFullYear(), 0, 4)), n3 = s2(n3), 0 >= a2(e4, t4) ? 0 >= a2(n3, t4) ? t4.getFullYear() + 1 : t4.getFullYear() : t4.getFullYear() - 1;
            }
            var c2 = A[r3 + 40 >> 2 >>> 0];
            for (var l2 in r3 = { $b: A[r3 >> 2 >>> 0], Zb: A[r3 + 4 >> 2 >>> 0], Gb: A[r3 + 8 >> 2 >>> 0], Kb: A[r3 + 12 >> 2 >>> 0], Hb: A[r3 + 16 >> 2 >>> 0], Cb: A[r3 + 20 >> 2 >>> 0], Ab: A[r3 + 24 >> 2 >>> 0], Bb: A[r3 + 28 >> 2 >>> 0], bc: A[r3 + 32 >> 2 >>> 0], Yb: A[r3 + 36 >> 2 >>> 0], ac: c2 ? $(c2) : "" }, n2 = $(n2), c2 = { "%c": "%a %b %d %H:%M:%S %Y", "%D": "%m/%d/%y", "%F": "%Y-%m-%d", "%h": "%b", "%r": "%I:%M:%S %p", "%R": "%H:%M", "%T": "%H:%M:%S", "%x": "%m/%d/%y", "%X": "%H:%M:%S", "%Ec": "%c", "%EC": "%C", "%Ex": "%m/%d/%y", "%EX": "%H:%M:%S", "%Ey": "%y", "%EY": "%Y", "%Od": "%d", "%Oe": "%e", "%OH": "%H", "%OI": "%I", "%Om": "%m", "%OM": "%M", "%OS": "%S", "%Ou": "%u", "%OU": "%U", "%OV": "%V", "%Ow": "%w", "%OW": "%W", "%Oy": "%y" }) n2 = n2.replace(new RegExp(l2, "g"), c2[l2]);
            var p2 = "Sunday Monday Tuesday Wednesday Thursday Friday Saturday".split(" "), f2 = "January February March April May June July August September October November December".split(" ");
            for (l2 in c2 = { "%a": function(t4) {
              return p2[t4.Ab].substring(0, 3);
            }, "%A": function(t4) {
              return p2[t4.Ab];
            }, "%b": function(t4) {
              return f2[t4.Hb].substring(0, 3);
            }, "%B": function(t4) {
              return f2[t4.Hb];
            }, "%C": function(t4) {
              return o2((t4.Cb + 1900) / 100 | 0, 2);
            }, "%d": function(t4) {
              return o2(t4.Kb, 2);
            }, "%e": function(t4) {
              return i2(t4.Kb, 2, " ");
            }, "%g": function(t4) {
              return u2(t4).toString().substring(2);
            }, "%G": function(t4) {
              return u2(t4);
            }, "%H": function(t4) {
              return o2(t4.Gb, 2);
            }, "%I": function(t4) {
              return 0 == (t4 = t4.Gb) ? t4 = 12 : 12 < t4 && (t4 -= 12), o2(t4, 2);
            }, "%j": function(t4) {
              for (var e4 = 0, n3 = 0; n3 <= t4.Hb - 1; e4 += (ft(t4.Cb + 1900) ? dt : ht)[n3++]) ;
              return o2(t4.Kb + e4, 3);
            }, "%m": function(t4) {
              return o2(t4.Hb + 1, 2);
            }, "%M": function(t4) {
              return o2(t4.Zb, 2);
            }, "%n": function() {
              return "\n";
            }, "%p": function(t4) {
              return 0 <= t4.Gb && 12 > t4.Gb ? "AM" : "PM";
            }, "%S": function(t4) {
              return o2(t4.$b, 2);
            }, "%t": function() {
              return "	";
            }, "%u": function(t4) {
              return t4.Ab || 7;
            }, "%U": function(t4) {
              return o2(Math.floor((t4.Bb + 7 - t4.Ab) / 7), 2);
            }, "%V": function(t4) {
              var e4 = Math.floor((t4.Bb + 7 - (t4.Ab + 6) % 7) / 7);
              if (2 >= (t4.Ab + 371 - t4.Bb - 2) % 7 && e4++, e4) 53 == e4 && (4 == (n3 = (t4.Ab + 371 - t4.Bb) % 7) || 3 == n3 && ft(t4.Cb) || (e4 = 1));
              else {
                e4 = 52;
                var n3 = (t4.Ab + 7 - t4.Bb - 1) % 7;
                (4 == n3 || 5 == n3 && ft(t4.Cb % 400 - 1)) && e4++;
              }
              return o2(e4, 2);
            }, "%w": function(t4) {
              return t4.Ab;
            }, "%W": function(t4) {
              return o2(Math.floor((t4.Bb + 7 - (t4.Ab + 6) % 7) / 7), 2);
            }, "%y": function(t4) {
              return (t4.Cb + 1900).toString().substring(2);
            }, "%Y": function(t4) {
              return t4.Cb + 1900;
            }, "%z": function(t4) {
              var e4 = 0 <= (t4 = t4.Yb);
              return t4 = Math.abs(t4) / 60, (e4 ? "+" : "-") + String("0000" + (t4 / 60 * 100 + t4 % 60)).slice(-4);
            }, "%Z": function(t4) {
              return t4.ac;
            }, "%%": function() {
              return "%";
            } }, n2 = n2.replace(/%%/g, "\0\0"), c2) n2.includes(l2) && (n2 = n2.replace(new RegExp(l2, "g"), c2[l2](r3)));
            return l2 = function(t4) {
              var e4 = Array(C(t4) + 1);
              return k(t4, e4, 0, e4.length), e4;
            }(n2 = n2.replace(/\0\0/g, "%")), l2.length > e3 ? 0 : (S.set(l2, t3 >>> 0), l2.length - 1);
          }
          var bt = { a: function(t3) {
            return yt(t3 + 24) + 24;
          }, m: function(t3) {
            return (t3 = new et(t3)).Pb() || (t3.Ib(true), Q--), t3.Jb(false), J.push(t3), t3.Nb(), t3.Qb();
          }, ia: function(t3) {
            throw v("Unexpected exception thrown, this is not properly supported - aborting"), I = true, t3;
          }, w: function() {
            xt(0);
            var t3 = J.pop();
            if (t3.Xb() && !t3.Lb()) {
              var e3 = t3.Wb();
              e3 && it(e3)(t3.Db), nt(t3.Db);
            }
            tt = 0;
          }, d: function() {
            var t3 = tt;
            if (!t3) return pt = 0;
            var e3 = new et(t3);
            e3.Fb(t3);
            var n2 = e3.Eb();
            if (!n2) return pt = 0, t3;
            for (var r3 = Array.prototype.slice.call(arguments), i2 = 0; i2 < r3.length; i2++) {
              var o2 = r3[i2];
              if (0 === o2 || o2 === n2) break;
              if (At(o2, n2, e3.zb + 16)) return pt = o2, t3;
            }
            return pt = n2, t3;
          }, k: function() {
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          }, e2._OrtInit = function() {
            return (e2._OrtInit = e2.asm.Ma).apply(null, arguments);
          }, e2._OrtCreateSessionOptions = function() {
            return (e2._OrtCreateSessionOptions = e2.asm.Na).apply(null, arguments);
          }, e2._OrtAppendExecutionProvider = function() {
            return (e2._OrtAppendExecutionProvider = e2.asm.Oa).apply(null, arguments);
          }, e2._OrtAddSessionConfigEntry = function() {
            return (e2._OrtAddSessionConfigEntry = e2.asm.Pa).apply(null, arguments);
          }, e2._OrtReleaseSessionOptions = function() {
            return (e2._OrtReleaseSessionOptions = e2.asm.Qa).apply(null, arguments);
          }, e2._OrtCreateSession = function() {
            return (e2._OrtCreateSession = e2.asm.Ra).apply(null, arguments);
          }, e2._OrtReleaseSession = function() {
            return (e2._OrtReleaseSession = e2.asm.Sa).apply(null, arguments);
          }, e2._OrtGetInputCount = function() {
            return (e2._OrtGetInputCount = e2.asm.Ta).apply(null, arguments);
          }, e2._OrtGetOutputCount = function() {
            return (e2._OrtGetOutputCount = e2.asm.Ua).apply(null, arguments);
          }, e2._OrtGetInputName = function() {
            return (e2._OrtGetInputName = e2.asm.Va).apply(null, arguments);
          }, e2._OrtGetOutputName = function() {
            return (e2._OrtGetOutputName = e2.asm.Wa).apply(null, arguments);
          }, e2._OrtFree = function() {
            return (e2._OrtFree = e2.asm.Xa).apply(null, arguments);
          }, e2._OrtCreateTensor = function() {
            return (e2._OrtCreateTensor = e2.asm.Ya).apply(null, arguments);
          }, e2._OrtGetTensorData = function() {
            return (e2._OrtGetTensorData = e2.asm.Za).apply(null, arguments);
          }, e2._OrtReleaseTensor = function() {
            return (e2._OrtReleaseTensor = e2.asm._a).apply(null, arguments);
          }, e2._OrtCreateRunOptions = function() {
            return (e2._OrtCreateRunOptions = e2.asm.$a).apply(null, arguments);
          }, e2._OrtAddRunConfigEntry = function() {
            return (e2._OrtAddRunConfigEntry = e2.asm.ab).apply(null, arguments);
          }, e2._OrtReleaseRunOptions = function() {
            return (e2._OrtReleaseRunOptions = e2.asm.bb).apply(null, arguments);
          }, e2._OrtRun = function() {
            return (e2._OrtRun = e2.asm.cb).apply(null, arguments);
          }, e2._OrtEndProfiling = function() {
            return (e2._OrtEndProfiling = e2.asm.db).apply(null, arguments);
          };
          var mt, yt = e2._malloc = function() {
            return (yt = e2._malloc = e2.asm.eb).apply(null, arguments);
          }, _t = e2._free = function() {
            return (_t = e2._free = e2.asm.fb).apply(null, arguments);
          }, vt = e2._fflush = function() {
            return (vt = e2._fflush = e2.asm.gb).apply(null, arguments);
          }, wt = e2.___funcs_on_exit = function() {
            return (wt = e2.___funcs_on_exit = e2.asm.hb).apply(null, arguments);
          }, xt = e2._setThrew = function() {
            return (xt = e2._setThrew = e2.asm.jb).apply(null, arguments);
          }, Tt = e2.stackSave = function() {
            return (Tt = e2.stackSave = e2.asm.kb).apply(null, arguments);
          }, St = e2.stackRestore = function() {
            return (St = e2.stackRestore = e2.asm.lb).apply(null, arguments);
          }, Ot = e2.stackAlloc = function() {
            return (Ot = e2.stackAlloc = e2.asm.mb).apply(null, arguments);
          }, At = e2.___cxa_can_catch = function() {
            return (At = e2.___cxa_can_catch = e2.asm.nb).apply(null, arguments);
          }, Et = e2.___cxa_is_pointer_type = function() {
            return (Et = e2.___cxa_is_pointer_type = e2.asm.ob).apply(null, arguments);
          }, It = e2.dynCall_j = function() {
            return (It = e2.dynCall_j = e2.asm.pb).apply(null, arguments);
          }, Pt = e2.dynCall_iiiiij = function() {
            return (Pt = e2.dynCall_iiiiij = e2.asm.qb).apply(null, arguments);
          }, Dt = e2.dynCall_jii = function() {
            return (Dt = e2.dynCall_jii = e2.asm.rb).apply(null, arguments);
          }, $t = e2.dynCall_viiiiij = function() {
            return ($t = e2.dynCall_viiiiij = e2.asm.sb).apply(null, arguments);
          }, kt = e2.dynCall_vjji = function() {
            return (kt = e2.dynCall_vjji = e2.asm.tb).apply(null, arguments);
          }, Ct = e2.dynCall_viiijjjii = function() {
            return (Ct = e2.dynCall_viiijjjii = e2.asm.ub).apply(null, arguments);
          }, Ft = e2.dynCall_iij = function() {
            return (Ft = e2.dynCall_iij = e2.asm.vb).apply(null, arguments);
          }, Nt = e2.dynCall_ji = function() {
            return (Nt = e2.dynCall_ji = e2.asm.wb).apply(null, arguments);
          }, Lt = e2.dynCall_iiiiiij = function() {
            return (Lt = e2.dynCall_iiiiiij = e2.asm.xb).apply(null, arguments);
          }, Rt = e2.dynCall_iiij = function() {
            return (Rt = e2.dynCall_iiij = e2.asm.yb).apply(null, arguments);
          };
          function jt() {
            function t3() {
              if (!mt && (mt = true, e2.calledRun = true, !I)) {
                if (Z(R), r2(e2), e2.onRuntimeInitialized && e2.onRuntimeInitialized(), e2.postRun) for ("function" == typeof e2.postRun && (e2.postRun = [e2.postRun]); e2.postRun.length; ) {
                  var t4 = e2.postRun.shift();
                  M.unshift(t4);
                }
                Z(M);
              }
            }
            if (!(0 < z)) {
              if (e2.preRun) for ("function" == typeof e2.preRun && (e2.preRun = [e2.preRun]); e2.preRun.length; ) V();
              Z(L), 0 < z || (e2.setStatus ? (e2.setStatus("Running..."), setTimeout(function() {
                setTimeout(function() {
                  e2.setStatus("");
                }, 1), t3();
              }, 1)) : t3());
            }
          }
          if (e2.UTF8ToString = $, e2.stringToUTF8 = function(t3, e3, n2) {
            return k(t3, O, e3, n2);
          }, e2.lengthBytesUTF8 = C, e2.stackSave = Tt, e2.stackRestore = St, e2.stackAlloc = Ot, H = function t3() {
            mt || jt(), mt || (H = t3);
          }, e2.preInit) for ("function" == typeof e2.preInit && (e2.preInit = [e2.preInit]); 0 < e2.preInit.length; ) e2.preInit.pop()();
          return jt(), t2.ready;
        });
        t.exports = r;
      }, 4537: (t) => {
        t.exports = function(t2, e) {
          for (var n = new Array(arguments.length - 1), r = 0, i = 2, o = true; i < arguments.length; ) n[r++] = arguments[i++];
          return new Promise(function(i2, a) {
            n[r] = function(t3) {
              if (o) if (o = false, t3) a(t3);
              else {
                for (var e2 = new Array(arguments.length - 1), n2 = 0; n2 < e2.length; ) e2[n2++] = arguments[n2];
                i2.apply(null, e2);
              }
            };
            try {
              t2.apply(e || null, n);
            } catch (t3) {
              o && (o = false, a(t3));
            }
          });
        };
      }, 7419: (t, e) => {
        var n = e;
        n.length = function(t2) {
          var e2 = t2.length;
          if (!e2) return 0;
          for (var n2 = 0; --e2 % 4 > 1 && "=" === t2.charAt(e2); ) ++n2;
          return Math.ceil(3 * t2.length) / 4 - n2;
        };
        for (var r = new Array(64), i = new Array(123), o = 0; o < 64; ) i[r[o] = o < 26 ? o + 65 : o < 52 ? o + 71 : o < 62 ? o - 4 : o - 59 | 43] = o++;
        n.encode = function(t2, e2, n2) {
          for (var i2, o2 = null, a2 = [], s = 0, u = 0; e2 < n2; ) {
            var c = t2[e2++];
            switch (u) {
              case 0:
                a2[s++] = r[c >> 2], i2 = (3 & c) << 4, u = 1;
                break;
              case 1:
                a2[s++] = r[i2 | c >> 4], i2 = (15 & c) << 2, u = 2;
                break;
              case 2:
                a2[s++] = r[i2 | c >> 6], a2[s++] = r[63 & c], u = 0;
            }
            s > 8191 && ((o2 || (o2 = [])).push(String.fromCharCode.apply(String, a2)), s = 0);
          }
          return u && (a2[s++] = r[i2], a2[s++] = 61, 1 === u && (a2[s++] = 61)), o2 ? (s && o2.push(String.fromCharCode.apply(String, a2.slice(0, s))), o2.join("")) : String.fromCharCode.apply(String, a2.slice(0, s));
        };
        var a = "invalid encoding";
        n.decode = function(t2, e2, n2) {
          for (var r2, o2 = n2, s = 0, u = 0; u < t2.length; ) {
            var c = t2.charCodeAt(u++);
            if (61 === c && s > 1) break;
            if (void 0 === (c = i[c])) throw Error(a);
            switch (s) {
              case 0:
                r2 = c, s = 1;
                break;
              case 1:
                e2[n2++] = r2 << 2 | (48 & c) >> 4, r2 = c, s = 2;
                break;
              case 2:
                e2[n2++] = (15 & r2) << 4 | (60 & c) >> 2, r2 = c, s = 3;
                break;
              case 3:
                e2[n2++] = (3 & r2) << 6 | c, s = 0;
            }
          }
          if (1 === s) throw Error(a);
          return n2 - o2;
        }, n.test = function(t2) {
          return /^(?:[A-Za-z0-9+/]{4})*(?:[A-Za-z0-9+/]{2}==|[A-Za-z0-9+/]{3}=)?$/.test(t2);
        };
      }, 9211: (t) => {
        function e() {
          this._listeners = {};
        }
        t.exports = e, e.prototype.on = function(t2, e2, n) {
          return (this._listeners[t2] || (this._listeners[t2] = [])).push({ fn: e2, ctx: n || this }), this;
        }, e.prototype.off = function(t2, e2) {
          if (void 0 === t2) this._listeners = {};
          else if (void 0 === e2) this._listeners[t2] = [];
          else for (var n = this._listeners[t2], r = 0; r < n.length; ) n[r].fn === e2 ? n.splice(r, 1) : ++r;
          return this;
        }, e.prototype.emit = function(t2) {
          var e2 = this._listeners[t2];
          if (e2) {
            for (var n = [], r = 1; r < arguments.length; ) n.push(arguments[r++]);
            for (r = 0; r < e2.length; ) e2[r].fn.apply(e2[r++].ctx, n);
          }
          return this;
        };
      }, 945: (t) => {
        function e(t2) {
          return "undefined" != typeof Float32Array ? function() {
            var e2 = new Float32Array([-0]), n2 = new Uint8Array(e2.buffer), r2 = 128 === n2[3];
            function i2(t3, r3, i3) {
              e2[0] = t3, r3[i3] = n2[0], r3[i3 + 1] = n2[1], r3[i3 + 2] = n2[2], r3[i3 + 3] = n2[3];
            }
            function o2(t3, r3, i3) {
              e2[0] = t3, r3[i3] = n2[3], r3[i3 + 1] = n2[2], r3[i3 + 2] = n2[1], r3[i3 + 3] = n2[0];
            }
            function a(t3, r3) {
              return n2[0] = t3[r3], n2[1] = t3[r3 + 1], n2[2] = t3[r3 + 2], n2[3] = t3[r3 + 3], e2[0];
            }
            function s(t3, r3) {
              return n2[3] = t3[r3], n2[2] = t3[r3 + 1], n2[1] = t3[r3 + 2], n2[0] = t3[r3 + 3], e2[0];
            }
            t2.writeFloatLE = r2 ? i2 : o2, t2.writeFloatBE = r2 ? o2 : i2, t2.readFloatLE = r2 ? a : s, t2.readFloatBE = r2 ? s : a;
          }() : function() {
            function e2(t3, e3, n2, r2) {
              var i2 = e3 < 0 ? 1 : 0;
              if (i2 && (e3 = -e3), 0 === e3) t3(1 / e3 > 0 ? 0 : 2147483648, n2, r2);
              else if (isNaN(e3)) t3(2143289344, n2, r2);
              else if (e3 > 34028234663852886e22) t3((i2 << 31 | 2139095040) >>> 0, n2, r2);
              else if (e3 < 11754943508222875e-54) t3((i2 << 31 | Math.round(e3 / 1401298464324817e-60)) >>> 0, n2, r2);
              else {
                var o2 = Math.floor(Math.log(e3) / Math.LN2);
                t3((i2 << 31 | o2 + 127 << 23 | 8388607 & Math.round(e3 * Math.pow(2, -o2) * 8388608)) >>> 0, n2, r2);
              }
            }
            function a(t3, e3, n2) {
              var r2 = t3(e3, n2), i2 = 2 * (r2 >> 31) + 1, o2 = r2 >>> 23 & 255, a2 = 8388607 & r2;
              return 255 === o2 ? a2 ? NaN : i2 * (1 / 0) : 0 === o2 ? 1401298464324817e-60 * i2 * a2 : i2 * Math.pow(2, o2 - 150) * (a2 + 8388608);
            }
            t2.writeFloatLE = e2.bind(null, n), t2.writeFloatBE = e2.bind(null, r), t2.readFloatLE = a.bind(null, i), t2.readFloatBE = a.bind(null, o);
          }(), "undefined" != typeof Float64Array ? function() {
            var e2 = new Float64Array([-0]), n2 = new Uint8Array(e2.buffer), r2 = 128 === n2[7];
            function i2(t3, r3, i3) {
              e2[0] = t3, r3[i3] = n2[0], r3[i3 + 1] = n2[1], r3[i3 + 2] = n2[2], r3[i3 + 3] = n2[3], r3[i3 + 4] = n2[4], r3[i3 + 5] = n2[5], r3[i3 + 6] = n2[6], r3[i3 + 7] = n2[7];
            }
            function o2(t3, r3, i3) {
              e2[0] = t3, r3[i3] = n2[7], r3[i3 + 1] = n2[6], r3[i3 + 2] = n2[5], r3[i3 + 3] = n2[4], r3[i3 + 4] = n2[3], r3[i3 + 5] = n2[2], r3[i3 + 6] = n2[1], r3[i3 + 7] = n2[0];
            }
            function a(t3, r3) {
              return n2[0] = t3[r3], n2[1] = t3[r3 + 1], n2[2] = t3[r3 + 2], n2[3] = t3[r3 + 3], n2[4] = t3[r3 + 4], n2[5] = t3[r3 + 5], n2[6] = t3[r3 + 6], n2[7] = t3[r3 + 7], e2[0];
            }
            function s(t3, r3) {
              return n2[7] = t3[r3], n2[6] = t3[r3 + 1], n2[5] = t3[r3 + 2], n2[4] = t3[r3 + 3], n2[3] = t3[r3 + 4], n2[2] = t3[r3 + 5], n2[1] = t3[r3 + 6], n2[0] = t3[r3 + 7], e2[0];
            }
            t2.writeDoubleLE = r2 ? i2 : o2, t2.writeDoubleBE = r2 ? o2 : i2, t2.readDoubleLE = r2 ? a : s, t2.readDoubleBE = r2 ? s : a;
          }() : function() {
            function e2(t3, e3, n2, r2, i2, o2) {
              var a2 = r2 < 0 ? 1 : 0;
              if (a2 && (r2 = -r2), 0 === r2) t3(0, i2, o2 + e3), t3(1 / r2 > 0 ? 0 : 2147483648, i2, o2 + n2);
              else if (isNaN(r2)) t3(0, i2, o2 + e3), t3(2146959360, i2, o2 + n2);
              else if (r2 > 17976931348623157e292) t3(0, i2, o2 + e3), t3((a2 << 31 | 2146435072) >>> 0, i2, o2 + n2);
              else {
                var s;
                if (r2 < 22250738585072014e-324) t3((s = r2 / 5e-324) >>> 0, i2, o2 + e3), t3((a2 << 31 | s / 4294967296) >>> 0, i2, o2 + n2);
                else {
                  var u = Math.floor(Math.log(r2) / Math.LN2);
                  1024 === u && (u = 1023), t3(4503599627370496 * (s = r2 * Math.pow(2, -u)) >>> 0, i2, o2 + e3), t3((a2 << 31 | u + 1023 << 20 | 1048576 * s & 1048575) >>> 0, i2, o2 + n2);
                }
              }
            }
            function a(t3, e3, n2, r2, i2) {
              var o2 = t3(r2, i2 + e3), a2 = t3(r2, i2 + n2), s = 2 * (a2 >> 31) + 1, u = a2 >>> 20 & 2047, c = 4294967296 * (1048575 & a2) + o2;
              return 2047 === u ? c ? NaN : s * (1 / 0) : 0 === u ? 5e-324 * s * c : s * Math.pow(2, u - 1075) * (c + 4503599627370496);
            }
            t2.writeDoubleLE = e2.bind(null, n, 0, 4), t2.writeDoubleBE = e2.bind(null, r, 4, 0), t2.readDoubleLE = a.bind(null, i, 0, 4), t2.readDoubleBE = a.bind(null, o, 4, 0);
          }(), t2;
        }
        function n(t2, e2, n2) {
          e2[n2] = 255 & t2, e2[n2 + 1] = t2 >>> 8 & 255, e2[n2 + 2] = t2 >>> 16 & 255, e2[n2 + 3] = t2 >>> 24;
        }
        function r(t2, e2, n2) {
          e2[n2] = t2 >>> 24, e2[n2 + 1] = t2 >>> 16 & 255, e2[n2 + 2] = t2 >>> 8 & 255, e2[n2 + 3] = 255 & t2;
        }
        function i(t2, e2) {
          return (t2[e2] | t2[e2 + 1] << 8 | t2[e2 + 2] << 16 | t2[e2 + 3] << 24) >>> 0;
        }
        function o(t2, e2) {
          return (t2[e2] << 24 | t2[e2 + 1] << 16 | t2[e2 + 2] << 8 | t2[e2 + 3]) >>> 0;
        }
        t.exports = e(e);
      }, 7199: (module) => {
        function inquire(moduleName) {
          try {
            var mod = eval("quire".replace(/^/, "re"))(moduleName);
            if (mod && (mod.length || Object.keys(mod).length)) return mod;
          } catch (t) {
          }
          return null;
        }
        module.exports = inquire;
      }, 6662: (t) => {
        t.exports = function(t2, e, n) {
          var r = n || 8192, i = r >>> 1, o = null, a = r;
          return function(n2) {
            if (n2 < 1 || n2 > i) return t2(n2);
            a + n2 > r && (o = t2(r), a = 0);
            var s = e.call(o, a, a += n2);
            return 7 & a && (a = 1 + (7 | a)), s;
          };
        };
      }, 4997: (t, e) => {
        var n = e;
        n.length = function(t2) {
          for (var e2 = 0, n2 = 0, r = 0; r < t2.length; ++r) (n2 = t2.charCodeAt(r)) < 128 ? e2 += 1 : n2 < 2048 ? e2 += 2 : 55296 == (64512 & n2) && 56320 == (64512 & t2.charCodeAt(r + 1)) ? (++r, e2 += 4) : e2 += 3;
          return e2;
        }, n.read = function(t2, e2, n2) {
          if (n2 - e2 < 1) return "";
          for (var r, i = null, o = [], a = 0; e2 < n2; ) (r = t2[e2++]) < 128 ? o[a++] = r : r > 191 && r < 224 ? o[a++] = (31 & r) << 6 | 63 & t2[e2++] : r > 239 && r < 365 ? (r = ((7 & r) << 18 | (63 & t2[e2++]) << 12 | (63 & t2[e2++]) << 6 | 63 & t2[e2++]) - 65536, o[a++] = 55296 + (r >> 10), o[a++] = 56320 + (1023 & r)) : o[a++] = (15 & r) << 12 | (63 & t2[e2++]) << 6 | 63 & t2[e2++], a > 8191 && ((i || (i = [])).push(String.fromCharCode.apply(String, o)), a = 0);
          return i ? (a && i.push(String.fromCharCode.apply(String, o.slice(0, a))), i.join("")) : String.fromCharCode.apply(String, o.slice(0, a));
        }, n.write = function(t2, e2, n2) {
          for (var r, i, o = n2, a = 0; a < t2.length; ++a) (r = t2.charCodeAt(a)) < 128 ? e2[n2++] = r : r < 2048 ? (e2[n2++] = r >> 6 | 192, e2[n2++] = 63 & r | 128) : 55296 == (64512 & r) && 56320 == (64512 & (i = t2.charCodeAt(a + 1))) ? (r = 65536 + ((1023 & r) << 10) + (1023 & i), ++a, e2[n2++] = r >> 18 | 240, e2[n2++] = r >> 12 & 63 | 128, e2[n2++] = r >> 6 & 63 | 128, e2[n2++] = 63 & r | 128) : (e2[n2++] = r >> 12 | 224, e2[n2++] = r >> 6 & 63 | 128, e2[n2++] = 63 & r | 128);
          return n2 - o;
        };
      }, 3442: (t, e) => {
        e.__esModule = true;
        var n = function() {
          function t2(e2) {
            if (!e2) throw new TypeError("Invalid argument; `value` has no value.");
            this.value = t2.EMPTY, e2 && t2.isGuid(e2) && (this.value = e2);
          }
          return t2.isGuid = function(e2) {
            var n2 = e2.toString();
            return e2 && (e2 instanceof t2 || t2.validator.test(n2));
          }, t2.create = function() {
            return new t2([t2.gen(2), t2.gen(1), t2.gen(1), t2.gen(1), t2.gen(3)].join("-"));
          }, t2.createEmpty = function() {
            return new t2("emptyguid");
          }, t2.parse = function(e2) {
            return new t2(e2);
          }, t2.raw = function() {
            return [t2.gen(2), t2.gen(1), t2.gen(1), t2.gen(1), t2.gen(3)].join("-");
          }, t2.gen = function(t3) {
            for (var e2 = "", n2 = 0; n2 < t3; n2++) e2 += (65536 * (1 + Math.random()) | 0).toString(16).substring(1);
            return e2;
          }, t2.prototype.equals = function(e2) {
            return t2.isGuid(e2) && this.value === e2.toString();
          }, t2.prototype.isEmpty = function() {
            return this.value === t2.EMPTY;
          }, t2.prototype.toString = function() {
            return this.value;
          }, t2.prototype.toJSON = function() {
            return { value: this.value };
          }, t2.validator = new RegExp("^[a-z0-9]{8}-[a-z0-9]{4}-[a-z0-9]{4}-[a-z0-9]{4}-[a-z0-9]{12}$", "i"), t2.EMPTY = "00000000-0000-0000-0000-000000000000", t2;
        }();
        e.Guid = n;
      }, 3720: (t) => {
        t.exports = n;
        var e = null;
        try {
          e = 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 (t2) {
        }
        function n(t2, e2, n2) {
          this.low = 0 | t2, this.high = 0 | e2, this.unsigned = !!n2;
        }
        function r(t2) {
          return true === (t2 && t2.__isLong__);
        }
        n.prototype.__isLong__, Object.defineProperty(n.prototype, "__isLong__", { value: true }), n.isLong = r;
        var i = {}, o = {};
        function a(t2, e2) {
          var n2, r2, a2;
          return e2 ? (a2 = 0 <= (t2 >>>= 0) && t2 < 256) && (r2 = o[t2]) ? r2 : (n2 = u(t2, (0 | t2) < 0 ? -1 : 0, true), a2 && (o[t2] = n2), n2) : (a2 = -128 <= (t2 |= 0) && t2 < 128) && (r2 = i[t2]) ? r2 : (n2 = u(t2, t2 < 0 ? -1 : 0, false), a2 && (i[t2] = n2), n2);
        }
        function s(t2, e2) {
          if (isNaN(t2)) return e2 ? m : b;
          if (e2) {
            if (t2 < 0) return m;
            if (t2 >= d) return x;
          } else {
            if (t2 <= -h) return T;
            if (t2 + 1 >= h) return w;
          }
          return t2 < 0 ? s(-t2, e2).neg() : u(t2 % f | 0, t2 / f | 0, e2);
        }
        function u(t2, e2, r2) {
          return new n(t2, e2, r2);
        }
        n.fromInt = a, n.fromNumber = s, n.fromBits = u;
        var c = Math.pow;
        function l(t2, e2, n2) {
          if (0 === t2.length) throw Error("empty string");
          if ("NaN" === t2 || "Infinity" === t2 || "+Infinity" === t2 || "-Infinity" === t2) return b;
          if ("number" == typeof e2 ? (n2 = e2, e2 = false) : e2 = !!e2, (n2 = n2 || 10) < 2 || 36 < n2) throw RangeError("radix");
          var r2;
          if ((r2 = t2.indexOf("-")) > 0) throw Error("interior hyphen");
          if (0 === r2) return l(t2.substring(1), e2, n2).neg();
          for (var i2 = s(c(n2, 8)), o2 = b, a2 = 0; a2 < t2.length; a2 += 8) {
            var u2 = Math.min(8, t2.length - a2), p2 = parseInt(t2.substring(a2, a2 + u2), n2);
            if (u2 < 8) {
              var f2 = s(c(n2, u2));
              o2 = o2.mul(f2).add(s(p2));
            } else o2 = (o2 = o2.mul(i2)).add(s(p2));
          }
          return o2.unsigned = e2, o2;
        }
        function p(t2, e2) {
          return "number" == typeof t2 ? s(t2, e2) : "string" == typeof t2 ? l(t2, e2) : u(t2.low, t2.high, "boolean" == typeof e2 ? e2 : t2.unsigned);
        }
        n.fromString = l, n.fromValue = p;
        var f = 4294967296, d = f * f, h = d / 2, g = a(1 << 24), b = a(0);
        n.ZERO = b;
        var m = a(0, true);
        n.UZERO = m;
        var y = a(1);
        n.ONE = y;
        var _ = a(1, true);
        n.UONE = _;
        var v = a(-1);
        n.NEG_ONE = v;
        var w = u(-1, 2147483647, false);
        n.MAX_VALUE = w;
        var x = u(-1, -1, true);
        n.MAX_UNSIGNED_VALUE = x;
        var T = u(0, -2147483648, false);
        n.MIN_VALUE = T;
        var S = n.prototype;
        S.toInt = function() {
          return this.unsigned ? this.low >>> 0 : this.low;
        }, S.toNumber = function() {
          return this.unsigned ? (this.high >>> 0) * f + (this.low >>> 0) : this.high * f + (this.low >>> 0);
        }, S.toString = function(t2) {
          if ((t2 = t2 || 10) < 2 || 36 < t2) throw RangeError("radix");
          if (this.isZero()) return "0";
          if (this.isNegative()) {
            if (this.eq(T)) {
              var e2 = s(t2), n2 = this.div(e2), r2 = n2.mul(e2).sub(this);
              return n2.toString(t2) + r2.toInt().toString(t2);
            }
            return "-" + this.neg().toString(t2);
          }
          for (var i2 = s(c(t2, 6), this.unsigned), o2 = this, a2 = ""; ; ) {
            var u2 = o2.div(i2), l2 = (o2.sub(u2.mul(i2)).toInt() >>> 0).toString(t2);
            if ((o2 = u2).isZero()) return l2 + a2;
            for (; l2.length < 6; ) l2 = "0" + l2;
            a2 = "" + l2 + a2;
          }
        }, S.getHighBits = function() {
          return this.high;
        }, S.getHighBitsUnsigned = function() {
          return this.high >>> 0;
        }, S.getLowBits = function() {
          return this.low;
        }, S.getLowBitsUnsigned = function() {
          return this.low >>> 0;
        }, S.getNumBitsAbs = function() {
          if (this.isNegative()) return this.eq(T) ? 64 : this.neg().getNumBitsAbs();
          for (var t2 = 0 != this.high ? this.high : this.low, e2 = 31; e2 > 0 && 0 == (t2 & 1 << e2); e2--) ;
          return 0 != this.high ? e2 + 33 : e2 + 1;
        }, S.isZero = function() {
          return 0 === this.high && 0 === this.low;
        }, S.eqz = S.isZero, S.isNegative = function() {
          return !this.unsigned && this.high < 0;
        }, S.isPositive = function() {
          return this.unsigned || this.high >= 0;
        }, S.isOdd = function() {
          return 1 == (1 & this.low);
        }, S.isEven = function() {
          return 0 == (1 & this.low);
        }, S.equals = function(t2) {
          return r(t2) || (t2 = p(t2)), (this.unsigned === t2.unsigned || this.high >>> 31 != 1 || t2.high >>> 31 != 1) && this.high === t2.high && this.low === t2.low;
        }, S.eq = S.equals, S.notEquals = function(t2) {
          return !this.eq(t2);
        }, S.neq = S.notEquals, S.ne = S.notEquals, S.lessThan = function(t2) {
          return this.comp(t2) < 0;
        }, S.lt = S.lessThan, S.lessThanOrEqual = function(t2) {
          return this.comp(t2) <= 0;
        }, S.lte = S.lessThanOrEqual, S.le = S.lessThanOrEqual, S.greaterThan = function(t2) {
          return this.comp(t2) > 0;
        }, S.gt = S.greaterThan, S.greaterThanOrEqual = function(t2) {
          return this.comp(t2) >= 0;
        }, S.gte = S.greaterThanOrEqual, S.ge = S.greaterThanOrEqual, S.compare = function(t2) {
          if (r(t2) || (t2 = p(t2)), this.eq(t2)) return 0;
          var e2 = this.isNegative(), n2 = t2.isNegative();
          return e2 && !n2 ? -1 : !e2 && n2 ? 1 : this.unsigned ? t2.high >>> 0 > this.high >>> 0 || t2.high === this.high && t2.low >>> 0 > this.low >>> 0 ? -1 : 1 : this.sub(t2).isNegative() ? -1 : 1;
        }, S.comp = S.compare, S.negate = function() {
          return !this.unsigned && this.eq(T) ? T : this.not().add(y);
        }, S.neg = S.negate, S.add = function(t2) {
          r(t2) || (t2 = p(t2));
          var e2 = this.high >>> 16, n2 = 65535 & this.high, i2 = this.low >>> 16, o2 = 65535 & this.low, a2 = t2.high >>> 16, s2 = 65535 & t2.high, c2 = t2.low >>> 16, l2 = 0, f2 = 0, d2 = 0, h2 = 0;
          return d2 += (h2 += o2 + (65535 & t2.low)) >>> 16, f2 += (d2 += i2 + c2) >>> 16, l2 += (f2 += n2 + s2) >>> 16, l2 += e2 + a2, u((d2 &= 65535) << 16 | (h2 &= 65535), (l2 &= 65535) << 16 | (f2 &= 65535), this.unsigned);
        }, S.subtract = function(t2) {
          return r(t2) || (t2 = p(t2)), this.add(t2.neg());
        }, S.sub = S.subtract, S.multiply = function(t2) {
          if (this.isZero()) return b;
          if (r(t2) || (t2 = p(t2)), e) return u(e.mul(this.low, this.high, t2.low, t2.high), e.get_high(), this.unsigned);
          if (t2.isZero()) return b;
          if (this.eq(T)) return t2.isOdd() ? T : b;
          if (t2.eq(T)) return this.isOdd() ? T : b;
          if (this.isNegative()) return t2.isNegative() ? this.neg().mul(t2.neg()) : this.neg().mul(t2).neg();
          if (t2.isNegative()) return this.mul(t2.neg()).neg();
          if (this.lt(g) && t2.lt(g)) return s(this.toNumber() * t2.toNumber(), this.unsigned);
          var n2 = this.high >>> 16, i2 = 65535 & this.high, o2 = this.low >>> 16, a2 = 65535 & this.low, c2 = t2.high >>> 16, l2 = 65535 & t2.high, f2 = t2.low >>> 16, d2 = 65535 & t2.low, h2 = 0, m2 = 0, y2 = 0, _2 = 0;
          return y2 += (_2 += a2 * d2) >>> 16, m2 += (y2 += o2 * d2) >>> 16, y2 &= 65535, m2 += (y2 += a2 * f2) >>> 16, h2 += (m2 += i2 * d2) >>> 16, m2 &= 65535, h2 += (m2 += o2 * f2) >>> 16, m2 &= 65535, h2 += (m2 += a2 * l2) >>> 16, h2 += n2 * d2 + i2 * f2 + o2 * l2 + a2 * c2, u((y2 &= 65535) << 16 | (_2 &= 65535), (h2 &= 65535) << 16 | (m2 &= 65535), this.unsigned);
        }, S.mul = S.multiply, S.divide = function(t2) {
          if (r(t2) || (t2 = p(t2)), t2.isZero()) throw Error("division by zero");
          var n2, i2, o2;
          if (e) return this.unsigned || -2147483648 !== this.high || -1 !== t2.low || -1 !== t2.high ? u((this.unsigned ? e.div_u : e.div_s)(this.low, this.high, t2.low, t2.high), e.get_high(), this.unsigned) : this;
          if (this.isZero()) return this.unsigned ? m : b;
          if (this.unsigned) {
            if (t2.unsigned || (t2 = t2.toUnsigned()), t2.gt(this)) return m;
            if (t2.gt(this.shru(1))) return _;
            o2 = m;
          } else {
            if (this.eq(T)) return t2.eq(y) || t2.eq(v) ? T : t2.eq(T) ? y : (n2 = this.shr(1).div(t2).shl(1)).eq(b) ? t2.isNegative() ? y : v : (i2 = this.sub(t2.mul(n2)), o2 = n2.add(i2.div(t2)));
            if (t2.eq(T)) return this.unsigned ? m : b;
            if (this.isNegative()) return t2.isNegative() ? this.neg().div(t2.neg()) : this.neg().div(t2).neg();
            if (t2.isNegative()) return this.div(t2.neg()).neg();
            o2 = b;
          }
          for (i2 = this; i2.gte(t2); ) {
            n2 = Math.max(1, Math.floor(i2.toNumber() / t2.toNumber()));
            for (var a2 = Math.ceil(Math.log(n2) / Math.LN2), l2 = a2 <= 48 ? 1 : c(2, a2 - 48), f2 = s(n2), d2 = f2.mul(t2); d2.isNegative() || d2.gt(i2); ) d2 = (f2 = s(n2 -= l2, this.unsigned)).mul(t2);
            f2.isZero() && (f2 = y), o2 = o2.add(f2), i2 = i2.sub(d2);
          }
          return o2;
        }, S.div = S.divide, S.modulo = function(t2) {
          return r(t2) || (t2 = p(t2)), e ? u((this.unsigned ? e.rem_u : e.rem_s)(this.low, this.high, t2.low, t2.high), e.get_high(), this.unsigned) : this.sub(this.div(t2).mul(t2));
        }, S.mod = S.modulo, S.rem = S.modulo, S.not = function() {
          return u(~this.low, ~this.high, this.unsigned);
        }, S.and = function(t2) {
          return r(t2) || (t2 = p(t2)), u(this.low & t2.low, this.high & t2.high, this.unsigned);
        }, S.or = function(t2) {
          return r(t2) || (t2 = p(t2)), u(this.low | t2.low, this.high | t2.high, this.unsigned);
        }, S.xor = function(t2) {
          return r(t2) || (t2 = p(t2)), u(this.low ^ t2.low, this.high ^ t2.high, this.unsigned);
        }, S.shiftLeft = function(t2) {
          return r(t2) && (t2 = t2.toInt()), 0 == (t2 &= 63) ? this : t2 < 32 ? u(this.low << t2, this.high << t2 | this.low >>> 32 - t2, this.unsigned) : u(0, this.low << t2 - 32, this.unsigned);
        }, S.shl = S.shiftLeft, S.shiftRight = function(t2) {
          return r(t2) && (t2 = t2.toInt()), 0 == (t2 &= 63) ? this : t2 < 32 ? u(this.low >>> t2 | this.high << 32 - t2, this.high >> t2, this.unsigned) : u(this.high >> t2 - 32, this.high >= 0 ? 0 : -1, this.unsigned);
        }, S.shr = S.shiftRight, S.shiftRightUnsigned = function(t2) {
          if (r(t2) && (t2 = t2.toInt()), 0 == (t2 &= 63)) return this;
          var e2 = this.high;
          return t2 < 32 ? u(this.low >>> t2 | e2 << 32 - t2, e2 >>> t2, this.unsigned) : u(32 === t2 ? e2 : e2 >>> t2 - 32, 0, this.unsigned);
        }, S.shru = S.shiftRightUnsigned, S.shr_u = S.shiftRightUnsigned, S.toSigned = function() {
          return this.unsigned ? u(this.low, this.high, false) : this;
        }, S.toUnsigned = function() {
          return this.unsigned ? this : u(this.low, this.high, true);
        }, S.toBytes = function(t2) {
          return t2 ? this.toBytesLE() : this.toBytesBE();
        }, S.toBytesLE = function() {
          var t2 = this.high, e2 = this.low;
          return [255 & e2, e2 >>> 8 & 255, e2 >>> 16 & 255, e2 >>> 24, 255 & t2, t2 >>> 8 & 255, t2 >>> 16 & 255, t2 >>> 24];
        }, S.toBytesBE = function() {
          var t2 = this.high, e2 = this.low;
          return [t2 >>> 24, t2 >>> 16 & 255, t2 >>> 8 & 255, 255 & t2, e2 >>> 24, e2 >>> 16 & 255, e2 >>> 8 & 255, 255 & e2];
        }, n.fromBytes = function(t2, e2, r2) {
          return r2 ? n.fromBytesLE(t2, e2) : n.fromBytesBE(t2, e2);
        }, n.fromBytesLE = function(t2, e2) {
          return new n(t2[0] | t2[1] << 8 | t2[2] << 16 | t2[3] << 24, t2[4] | t2[5] << 8 | t2[6] << 16 | t2[7] << 24, e2);
        }, n.fromBytesBE = function(t2, e2) {
          return new n(t2[4] << 24 | t2[5] << 16 | t2[6] << 8 | t2[7], t2[0] << 24 | t2[1] << 16 | t2[2] << 8 | t2[3], e2);
        };
      }, 1446: (t, e, n) => {
        var r, i, o, a = n(2100), s = a.Reader, u = a.Writer, c = a.util, l = a.roots.default || (a.roots.default = {});
        l.onnx = ((o = {}).Version = (r = {}, (i = Object.create(r))[r[0] = "_START_VERSION"] = 0, i[r[1] = "IR_VERSION_2017_10_10"] = 1, i[r[2] = "IR_VERSION_2017_10_30"] = 2, i[r[3] = "IR_VERSION_2017_11_3"] = 3, i[r[4] = "IR_VERSION_2019_1_22"] = 4, i[r[5] = "IR_VERSION"] = 5, i), o.AttributeProto = function() {
          function t2(t3) {
            if (this.floats = [], this.ints = [], this.strings = [], this.tensors = [], this.graphs = [], t3) for (var e2 = Object.keys(t3), n2 = 0; n2 < e2.length; ++n2) null != t3[e2[n2]] && (this[e2[n2]] = t3[e2[n2]]);
          }
          return t2.prototype.name = "", t2.prototype.refAttrName = "", t2.prototype.docString = "", t2.prototype.type = 0, t2.prototype.f = 0, t2.prototype.i = c.Long ? c.Long.fromBits(0, 0, false) : 0, t2.prototype.s = c.newBuffer([]), t2.prototype.t = null, t2.prototype.g = null, t2.prototype.floats = c.emptyArray, t2.prototype.ints = c.emptyArray, t2.prototype.strings = c.emptyArray, t2.prototype.tensors = c.emptyArray, t2.prototype.graphs = c.emptyArray, t2.create = function(e2) {
            return new t2(e2);
          }, t2.encode = function(t3, e2) {
            if (e2 || (e2 = u.create()), null != t3.name && t3.hasOwnProperty("name") && e2.uint32(10).string(t3.name), null != t3.f && t3.hasOwnProperty("f") && e2.uint32(21).float(t3.f), null != t3.i && t3.hasOwnProperty("i") && e2.uint32(24).int64(t3.i), null != t3.s && t3.hasOwnProperty("s") && e2.uint32(34).bytes(t3.s), null != t3.t && t3.hasOwnProperty("t") && l.onnx.TensorProto.encode(t3.t, e2.uint32(42).fork()).ldelim(), null != t3.g && t3.hasOwnProperty("g") && l.onnx.GraphProto.encode(t3.g, e2.uint32(50).fork()).ldelim(), null != t3.floats && t3.floats.length) {
              e2.uint32(58).fork();
              for (var n2 = 0; n2 < t3.floats.length; ++n2) e2.float(t3.floats[n2]);
              e2.ldelim();
            }
            if (null != t3.ints && t3.ints.length) {
              for (e2.uint32(66).fork(), n2 = 0; n2 < t3.ints.length; ++n2) e2.int64(t3.ints[n2]);
              e2.ldelim();
            }
            if (null != t3.strings && t3.strings.length) for (n2 = 0; n2 < t3.strings.length; ++n2) e2.uint32(74).bytes(t3.strings[n2]);
            if (null != t3.tensors && t3.tensors.length) for (n2 = 0; n2 < t3.tensors.length; ++n2) l.onnx.TensorProto.encode(t3.tensors[n2], e2.uint32(82).fork()).ldelim();
            if (null != t3.graphs && t3.graphs.length) for (n2 = 0; n2 < t3.graphs.length; ++n2) l.onnx.GraphProto.encode(t3.graphs[n2], e2.uint32(90).fork()).ldelim();
            return null != t3.docString && t3.hasOwnProperty("docString") && e2.uint32(106).string(t3.docString), null != t3.type && t3.hasOwnProperty("type") && e2.uint32(160).int32(t3.type), null != t3.refAttrName && t3.hasOwnProperty("refAttrName") && e2.uint32(170).string(t3.refAttrName), e2;
          }, t2.encodeDelimited = function(t3, e2) {
            return this.encode(t3, e2).ldelim();
          }, t2.decode = function(t3, e2) {
            t3 instanceof s || (t3 = s.create(t3));
            for (var n2 = void 0 === e2 ? t3.len : t3.pos + e2, r2 = new l.onnx.AttributeProto(); t3.pos < n2; ) {
              var i2 = t3.uint32();
              switch (i2 >>> 3) {
                case 1:
                  r2.name = t3.string();
                  break;
                case 21:
                  r2.refAttrName = t3.string();
                  break;
                case 13:
                  r2.docString = t3.string();
                  break;
                case 20:
                  r2.type = t3.int32();
                  break;
                case 2:
                  r2.f = t3.float();
                  break;
                case 3:
                  r2.i = t3.int64();
                  break;
                case 4:
                  r2.s = t3.bytes();
                  break;
                case 5:
                  r2.t = l.onnx.TensorProto.decode(t3, t3.uint32());
                  break;
                case 6:
                  r2.g = l.onnx.GraphProto.decode(t3, t3.uint32());
                  break;
                case 7:
                  if (r2.floats && r2.floats.length || (r2.floats = []), 2 == (7 & i2)) for (var o2 = t3.uint32() + t3.pos; t3.pos < o2; ) r2.floats.push(t3.float());
                  else r2.floats.push(t3.float());
                  break;
                case 8:
                  if (r2.ints && r2.ints.length || (r2.ints = []), 2 == (7 & i2)) for (o2 = t3.uint32() + t3.pos; t3.pos < o2; ) r2.ints.push(t3.int64());
                  else r2.ints.push(t3.int64());
                  break;
                case 9:
                  r2.strings && r2.strings.length || (r2.strings = []), r2.strings.push(t3.bytes());
                  break;
                case 10:
                  r2.tensors && r2.tensors.length || (r2.tensors = []), r2.tensors.push(l.onnx.TensorProto.decode(t3, t3.uint32()));
                  break;
                case 11:
                  r2.graphs && r2.graphs.length || (r2.graphs = []), r2.graphs.push(l.onnx.GraphProto.decode(t3, t3.uint32()));
                  break;
                default:
                  t3.skipType(7 & i2);
              }
            }
            return r2;
          }, t2.decodeDelimited = function(t3) {
            return t3 instanceof s || (t3 = new s(t3)), this.decode(t3, t3.uint32());
          }, t2.verify = function(t3) {
            if ("object" != typeof t3 || null === t3) return "object expected";
            if (null != t3.name && t3.hasOwnProperty("name") && !c.isString(t3.name)) return "name: string expected";
            if (null != t3.refAttrName && t3.hasOwnProperty("refAttrName") && !c.isString(t3.refAttrName)) return "refAttrName: string expected";
            if (null != t3.docString && t3.hasOwnProperty("docString") && !c.isString(t3.docString)) return "docString: string expected";
            if (null != t3.type && t3.hasOwnProperty("type")) switch (t3.type) {
              default:
                return "type: enum value expected";
              case 0:
              case 1:
              case 2:
              case 3:
              case 4:
              case 5:
              case 6:
              case 7:
              case 8:
              case 9:
              case 10:
            }
            if (null != t3.f && t3.hasOwnProperty("f") && "number" != typeof t3.f) return "f: number expected";
            if (null != t3.i && t3.hasOwnProperty("i") && !(c.isInteger(t3.i) || t3.i && c.isInteger(t3.i.low) && c.isInteger(t3.i.high))) return "i: integer|Long expected";
            if (null != t3.s && t3.hasOwnProperty("s") && !(t3.s && "number" == typeof t3.s.length || c.isString(t3.s))) return "s: buffer expected";
            if (null != t3.t && t3.hasOwnProperty("t") && (n2 = l.onnx.TensorProto.verify(t3.t))) return "t." + n2;
            if (null != t3.g && t3.hasOwnProperty("g") && (n2 = l.onnx.GraphProto.verify(t3.g))) return "g." + n2;
            if (null != t3.floats && t3.hasOwnProperty("floats")) {
              if (!Array.isArray(t3.floats)) return "floats: array expected";
              for (var e2 = 0; e2 < t3.floats.length; ++e2) if ("number" != typeof t3.floats[e2]) return "floats: number[] expected";
            }
            if (null != t3.ints && t3.hasOwnProperty("ints")) {
              if (!Array.isArray(t3.ints)) return "ints: array expected";
              for (e2 = 0; e2 < t3.ints.length; ++e2) if (!(c.isInteger(t3.ints[e2]) || t3.ints[e2] && c.isInteger(t3.ints[e2].low) && c.isInteger(t3.ints[e2].high))) return "ints: integer|Long[] expected";
            }
            if (null != t3.strings && t3.hasOwnProperty("strings")) {
              if (!Array.isArray(t3.strings)) return "strings: array expected";
              for (e2 = 0; e2 < t3.strings.length; ++e2) if (!(t3.strings[e2] && "number" == typeof t3.strings[e2].length || c.isString(t3.strings[e2]))) return "strings: buffer[] expected";
            }
            if (null != t3.tensors && t3.hasOwnProperty("tensors")) {
              if (!Array.isArray(t3.tensors)) return "tensors: array expected";
              for (e2 = 0; e2 < t3.tensors.length; ++e2) if (n2 = l.onnx.TensorProto.verify(t3.tensors[e2])) return "tensors." + n2;
            }
            if (null != t3.graphs && t3.hasOwnProperty("graphs")) {
              if (!Array.isArray(t3.graphs)) return "graphs: array expected";
              for (e2 = 0; e2 < t3.graphs.length; ++e2) {
                var n2;
                if (n2 = l.onnx.GraphProto.verify(t3.graphs[e2])) return "graphs." + n2;
              }
            }
            return null;
          }, t2.fromObject = function(t3) {
            if (t3 instanceof l.onnx.AttributeProto) return t3;
            var e2 = new l.onnx.AttributeProto();
            switch (null != t3.name && (e2.name = String(t3.name)), null != t3.refAttrName && (e2.refAttrName = String(t3.refAttrName)), null != t3.docString && (e2.docString = String(t3.docString)), t3.type) {
              case "UNDEFINED":
              case 0:
                e2.type = 0;
                break;
              case "FLOAT":
              case 1:
                e2.type = 1;
                break;
              case "INT":
              case 2:
                e2.type = 2;
                break;
              case "STRING":
              case 3:
                e2.type = 3;
                break;
              case "TENSOR":
              case 4:
                e2.type = 4;
                break;
              case "GRAPH":
              case 5:
                e2.type = 5;
                break;
              case "FLOATS":
              case 6:
                e2.type = 6;
                break;
              case "INTS":
              case 7:
                e2.type = 7;
                break;
              case "STRINGS":
              case 8:
                e2.type = 8;
                break;
              case "TENSORS":
              case 9:
                e2.type = 9;
                break;
              case "GRAPHS":
              case 10:
                e2.type = 10;
            }
            if (null != t3.f && (e2.f = Number(t3.f)), null != t3.i && (c.Long ? (e2.i = c.Long.fromValue(t3.i)).unsigned = false : "string" == typeof t3.i ? e2.i = parseInt(t3.i, 10) : "number" == typeof t3.i ? e2.i = t3.i : "object" == typeof t3.i && (e2.i = new c.LongBits(t3.i.low >>> 0, t3.i.high >>> 0).toNumber())), null != t3.s && ("string" == typeof t3.s ? c.base64.decode(t3.s, e2.s = c.newBuffer(c.base64.length(t3.s)), 0) : t3.s.length && (e2.s = t3.s)), null != t3.t) {
              if ("object" != typeof t3.t) throw TypeError(".onnx.AttributeProto.t: object expected");
              e2.t = l.onnx.TensorProto.fromObject(t3.t);
            }
            if (null != t3.g) {
              if ("object" != typeof t3.g) throw TypeError(".onnx.AttributeProto.g: object expected");
              e2.g = l.onnx.GraphProto.fromObject(t3.g);
            }
            if (t3.floats) {
              if (!Array.isArray(t3.floats)) throw TypeError(".onnx.AttributeProto.floats: array expected");
              e2.floats = [];
              for (var n2 = 0; n2 < t3.floats.length; ++n2) e2.floats[n2] = Number(t3.floats[n2]);
            }
            if (t3.ints) {
              if (!Array.isArray(t3.ints)) throw TypeError(".onnx.AttributeProto.ints: array expected");
              for (e2.ints = [], n2 = 0; n2 < t3.ints.length; ++n2) c.Long ? (e2.ints[n2] = c.Long.fromValue(t3.ints[n2])).unsigned = false : "string" == typeof t3.ints[n2] ? e2.ints[n2] = parseInt(t3.ints[n2], 10) : "number" == typeof t3.ints[n2] ? e2.ints[n2] = t3.ints[n2] : "object" == typeof t3.ints[n2] && (e2.ints[n2] = new c.LongBits(t3.ints[n2].low >>> 0, t3.ints[n2].high >>> 0).toNumber());
            }
            if (t3.strings) {
              if (!Array.isArray(t3.strings)) throw TypeError(".onnx.AttributeProto.strings: array expected");
              for (e2.strings = [], n2 = 0; n2 < t3.strings.length; ++n2) "string" == typeof t3.strings[n2] ? c.base64.decode(t3.strings[n2], e2.strings[n2] = c.newBuffer(c.base64.length(t3.strings[n2])), 0) : t3.strings[n2].length && (e2.strings[n2] = t3.strings[n2]);
            }
            if (t3.tensors) {
              if (!Array.isArray(t3.tensors)) throw TypeError(".onnx.AttributeProto.tensors: array expected");
              for (e2.tensors = [], n2 = 0; n2 < t3.tensors.length; ++n2) {
                if ("object" != typeof t3.tensors[n2]) throw TypeError(".onnx.AttributeProto.tensors: object expected");
                e2.tensors[n2] = l.onnx.TensorProto.fromObject(t3.tensors[n2]);
              }
            }
            if (t3.graphs) {
              if (!Array.isArray(t3.graphs)) throw TypeError(".onnx.AttributeProto.graphs: array expected");
              for (e2.graphs = [], n2 = 0; n2 < t3.graphs.length; ++n2) {
                if ("object" != typeof t3.graphs[n2]) throw TypeError(".onnx.AttributeProto.graphs: object expected");
                e2.graphs[n2] = l.onnx.GraphProto.fromObject(t3.graphs[n2]);
              }
            }
            return e2;
          }, t2.toObject = function(t3, e2) {
            e2 || (e2 = {});
            var n2 = {};
            if ((e2.arrays || e2.defaults) && (n2.floats = [], n2.ints = [], n2.strings = [], n2.tensors = [], n2.graphs = []), e2.defaults) {
              if (n2.name = "", n2.f = 0, c.Long) {
                var r2 = new c.Long(0, 0, false);
                n2.i = e2.longs === String ? r2.toString() : e2.longs === Number ? r2.toNumber() : r2;
              } else n2.i = e2.longs === String ? "0" : 0;
              e2.bytes === String ? n2.s = "" : (n2.s = [], e2.bytes !== Array && (n2.s = c.newBuffer(n2.s))), n2.t = null, n2.g = null, n2.docString = "", n2.type = e2.enums === String ? "UNDEFINED" : 0, n2.refAttrName = "";
            }
            if (null != t3.name && t3.hasOwnProperty("name") && (n2.name = t3.name), null != t3.f && t3.hasOwnProperty("f") && (n2.f = e2.json && !isFinite(t3.f) ? String(t3.f) : t3.f), null != t3.i && t3.hasOwnProperty("i") && ("number" == typeof t3.i ? n2.i = e2.longs === String ? String(t3.i) : t3.i : n2.i = e2.longs === String ? c.Long.prototype.toString.call(t3.i) : e2.longs === Number ? new c.LongBits(t3.i.low >>> 0, t3.i.high >>> 0).toNumber() : t3.i), null != t3.s && t3.hasOwnProperty("s") && (n2.s = e2.bytes === String ? c.base64.encode(t3.s, 0, t3.s.length) : e2.bytes === Array ? Array.prototype.slice.call(t3.s) : t3.s), null != t3.t && t3.hasOwnProperty("t") && (n2.t = l.onnx.TensorProto.toObject(t3.t, e2)), null != t3.g && t3.hasOwnProperty("g") && (n2.g = l.onnx.GraphProto.toObject(t3.g, e2)), t3.floats && t3.floats.length) {
              n2.floats = [];
              for (var i2 = 0; i2 < t3.floats.length; ++i2) n2.floats[i2] = e2.json && !isFinite(t3.floats[i2]) ? String(t3.floats[i2]) : t3.floats[i2];
            }
            if (t3.ints && t3.ints.length) for (n2.ints = [], i2 = 0; i2 < t3.ints.length; ++i2) "number" == typeof t3.ints[i2] ? n2.ints[i2] = e2.longs === String ? String(t3.ints[i2]) : t3.ints[i2] : n2.ints[i2] = e2.longs === String ? c.Long.prototype.toString.call(t3.ints[i2]) : e2.longs === Number ? new c.LongBits(t3.ints[i2].low >>> 0, t3.ints[i2].high >>> 0).toNumber() : t3.ints[i2];
            if (t3.strings && t3.strings.length) for (n2.strings = [], i2 = 0; i2 < t3.strings.length; ++i2) n2.strings[i2] = e2.bytes === String ? c.base64.encode(t3.strings[i2], 0, t3.strings[i2].length) : e2.bytes === Array ? Array.prototype.slice.call(t3.strings[i2]) : t3.strings[i2];
            if (t3.tensors && t3.tensors.length) for (n2.tensors = [], i2 = 0; i2 < t3.tensors.length; ++i2) n2.tensors[i2] = l.onnx.TensorProto.toObject(t3.tensors[i2], e2);
            if (t3.graphs && t3.graphs.length) for (n2.graphs = [], i2 = 0; i2 < t3.graphs.length; ++i2) n2.graphs[i2] = l.onnx.GraphProto.toObject(t3.graphs[i2], e2);
            return null != t3.docString && t3.hasOwnProperty("docString") && (n2.docString = t3.docString), null != t3.type && t3.hasOwnProperty("type") && (n2.type = e2.enums === String ? l.onnx.AttributeProto.AttributeType[t3.type] : t3.type), null != t3.refAttrName && t3.hasOwnProperty("refAttrName") && (n2.refAttrName = t3.refAttrName), n2;
          }, t2.prototype.toJSON = function() {
            return this.constructor.toObject(this, a.util.toJSONOptions);
          }, t2.AttributeType = function() {
            var t3 = {}, e2 = Object.create(t3);
            return e2[t3[0] = "UNDEFINED"] = 0, e2[t3[1] = "FLOAT"] = 1, e2[t3[2] = "INT"] = 2, e2[t3[3] = "STRING"] = 3, e2[t3[4] = "TENSOR"] = 4, e2[t3[5] = "GRAPH"] = 5, e2[t3[6] = "FLOATS"] = 6, e2[t3[7] = "INTS"] = 7, e2[t3[8] = "STRINGS"] = 8, e2[t3[9] = "TENSORS"] = 9, e2[t3[10] = "GRAPHS"] = 10, e2;
          }(), t2;
        }(), o.ValueInfoProto = function() {
          function t2(t3) {
            if (t3) for (var e2 = Object.keys(t3), n2 = 0; n2 < e2.length; ++n2) null != t3[e2[n2]] && (this[e2[n2]] = t3[e2[n2]]);
          }
          return t2.prototype.name = "", t2.prototype.type = null, t2.prototype.docString = "", t2.create = function(e2) {
            return new t2(e2);
          }, t2.encode = function(t3, e2) {
            return e2 || (e2 = u.create()), null != t3.name && t3.hasOwnProperty("name") && e2.uint32(10).string(t3.name), null != t3.type && t3.hasOwnProperty("type") && l.onnx.TypeProto.encode(t3.type, e2.uint32(18).fork()).ldelim(), null != t3.docString && t3.hasOwnProperty("docString") && e2.uint32(26).string(t3.docString), e2;
          }, t2.encodeDelimited = function(t3, e2) {
            return this.encode(t3, e2).ldelim();
          }, t2.decode = function(t3, e2) {
            t3 instanceof s || (t3 = s.create(t3));
            for (var n2 = void 0 === e2 ? t3.len : t3.pos + e2, r2 = new l.onnx.ValueInfoProto(); t3.pos < n2; ) {
              var i2 = t3.uint32();
              switch (i2 >>> 3) {
                case 1:
                  r2.name = t3.string();
                  break;
                case 2:
                  r2.type = l.onnx.TypeProto.decode(t3, t3.uint32());
                  break;
                case 3:
                  r2.docString = t3.string();
                  break;
                default:
                  t3.skipType(7 & i2);
              }
            }
            return r2;
          }, t2.decodeDelimited = function(t3) {
            return t3 instanceof s || (t3 = new s(t3)), this.decode(t3, t3.uint32());
          }, t2.verify = function(t3) {
            if ("object" != typeof t3 || null === t3) return "object expected";
            if (null != t3.name && t3.hasOwnProperty("name") && !c.isString(t3.name)) return "name: string expected";
            if (null != t3.type && t3.hasOwnProperty("type")) {
              var e2 = l.onnx.TypeProto.verify(t3.type);
              if (e2) return "type." + e2;
            }
            return null != t3.docString && t3.hasOwnProperty("docString") && !c.isString(t3.docString) ? "docString: string expected" : null;
          }, t2.fromObject = function(t3) {
            if (t3 instanceof l.onnx.ValueInfoProto) return t3;
            var e2 = new l.onnx.ValueInfoProto();
            if (null != t3.name && (e2.name = String(t3.name)), null != t3.type) {
              if ("object" != typeof t3.type) throw TypeError(".onnx.ValueInfoProto.type: object expected");
              e2.type = l.onnx.TypeProto.fromObject(t3.type);
            }
            return null != t3.docString && (e2.docString = String(t3.docString)), e2;
          }, t2.toObject = function(t3, e2) {
            e2 || (e2 = {});
            var n2 = {};
            return e2.defaults && (n2.name = "", n2.type = null, n2.docString = ""), null != t3.name && t3.hasOwnProperty("name") && (n2.name = t3.name), null != t3.type && t3.hasOwnProperty("type") && (n2.type = l.onnx.TypeProto.toObject(t3.type, e2)), null != t3.docString && t3.hasOwnProperty("docString") && (n2.docString = t3.docString), n2;
          }, t2.prototype.toJSON = function() {
            return this.constructor.toObject(this, a.util.toJSONOptions);
          }, t2;
        }(), o.NodeProto = function() {
          function t2(t3) {
            if (this.input = [], this.output = [], this.attribute = [], t3) for (var e2 = Object.keys(t3), n2 = 0; n2 < e2.length; ++n2) null != t3[e2[n2]] && (this[e2[n2]] = t3[e2[n2]]);
          }
          return t2.prototype.input = c.emptyArray, t2.prototype.output = c.emptyArray, t2.prototype.name = "", t2.prototype.opType = "", t2.prototype.domain = "", t2.prototype.attribute = c.emptyArray, t2.prototype.docString = "", t2.create = function(e2) {
            return new t2(e2);
          }, t2.encode = function(t3, e2) {
            if (e2 || (e2 = u.create()), null != t3.input && t3.input.length) for (var n2 = 0; n2 < t3.input.length; ++n2) e2.uint32(10).string(t3.input[n2]);
            if (null != t3.output && t3.output.length) for (n2 = 0; n2 < t3.output.length; ++n2) e2.uint32(18).string(t3.output[n2]);
            if (null != t3.name && t3.hasOwnProperty("name") && e2.uint32(26).string(t3.name), null != t3.opType && t3.hasOwnProperty("opType") && e2.uint32(34).string(t3.opType), null != t3.attribute && t3.attribute.length) for (n2 = 0; n2 < t3.attribute.length; ++n2) l.onnx.AttributeProto.encode(t3.attribute[n2], e2.uint32(42).fork()).ldelim();
            return null != t3.docString && t3.hasOwnProperty("docString") && e2.uint32(50).string(t3.docString), null != t3.domain && t3.hasOwnProperty("domain") && e2.uint32(58).string(t3.domain), e2;
          }, t2.encodeDelimited = function(t3, e2) {
            return this.encode(t3, e2).ldelim();
          }, t2.decode = function(t3, e2) {
            t3 instanceof s || (t3 = s.create(t3));
            for (var n2 = void 0 === e2 ? t3.len : t3.pos + e2, r2 = new l.onnx.NodeProto(); t3.pos < n2; ) {
              var i2 = t3.uint32();
              switch (i2 >>> 3) {
                case 1:
                  r2.input && r2.input.length || (r2.input = []), r2.input.push(t3.string());
                  break;
                case 2:
                  r2.output && r2.output.length || (r2.output = []), r2.output.push(t3.string());
                  break;
                case 3:
                  r2.name = t3.string();
                  break;
                case 4:
                  r2.opType = t3.string();
                  break;
                case 7:
                  r2.domain = t3.string();
                  break;
                case 5:
                  r2.attribute && r2.attribute.length || (r2.attribute = []), r2.attribute.push(l.onnx.AttributeProto.decode(t3, t3.uint32()));
                  break;
                case 6:
                  r2.docString = t3.string();
                  break;
                default:
                  t3.skipType(7 & i2);
              }
            }
            return r2;
          }, t2.decodeDelimited = function(t3) {
            return t3 instanceof s || (t3 = new s(t3)), this.decode(t3, t3.uint32());
          }, t2.verify = function(t3) {
            if ("object" != typeof t3 || null === t3) return "object expected";
            if (null != t3.input && t3.hasOwnProperty("input")) {
              if (!Array.isArray(t3.input)) return "input: array expected";
              for (var e2 = 0; e2 < t3.input.length; ++e2) if (!c.isString(t3.input[e2])) return "input: string[] expected";
            }
            if (null != t3.output && t3.hasOwnProperty("output")) {
              if (!Array.isArray(t3.output)) return "output: array expected";
              for (e2 = 0; e2 < t3.output.length; ++e2) if (!c.isString(t3.output[e2])) return "output: string[] expected";
            }
            if (null != t3.name && t3.hasOwnProperty("name") && !c.isString(t3.name)) return "name: string expected";
            if (null != t3.opType && t3.hasOwnProperty("opType") && !c.isString(t3.opType)) return "opType: string expected";
            if (null != t3.domain && t3.hasOwnProperty("domain") && !c.isString(t3.domain)) return "domain: string expected";
            if (null != t3.attribute && t3.hasOwnProperty("attribute")) {
              if (!Array.isArray(t3.attribute)) return "attribute: array expected";
              for (e2 = 0; e2 < t3.attribute.length; ++e2) {
                var n2 = l.onnx.AttributeProto.verify(t3.attribute[e2]);
                if (n2) return "attribute." + n2;
              }
            }
            return null != t3.docString && t3.hasOwnProperty("docString") && !c.isString(t3.docString) ? "docString: string expected" : null;
          }, t2.fromObject = function(t3) {
            if (t3 instanceof l.onnx.NodeProto) return t3;
            var e2 = new l.onnx.NodeProto();
            if (t3.input) {
              if (!Array.isArray(t3.input)) throw TypeError(".onnx.NodeProto.input: array expected");
              e2.input = [];
              for (var n2 = 0; n2 < t3.input.length; ++n2) e2.input[n2] = String(t3.input[n2]);
            }
            if (t3.output) {
              if (!Array.isArray(t3.output)) throw TypeError(".onnx.NodeProto.output: array expected");
              for (e2.output = [], n2 = 0; n2 < t3.output.length; ++n2) e2.output[n2] = String(t3.output[n2]);
            }
            if (null != t3.name && (e2.name = String(t3.name)), null != t3.opType && (e2.opType = String(t3.opType)), null != t3.domain && (e2.domain = String(t3.domain)), t3.attribute) {
              if (!Array.isArray(t3.attribute)) throw TypeError(".onnx.NodeProto.attribute: array expected");
              for (e2.attribute = [], n2 = 0; n2 < t3.attribute.length; ++n2) {
                if ("object" != typeof t3.attribute[n2]) throw TypeError(".onnx.NodeProto.attribute: object expected");
                e2.attribute[n2] = l.onnx.AttributeProto.fromObject(t3.attribute[n2]);
              }
            }
            return null != t3.docString && (e2.docString = String(t3.docString)), e2;
          }, t2.toObject = function(t3, e2) {
            e2 || (e2 = {});
            var n2 = {};
            if ((e2.arrays || e2.defaults) && (n2.input = [], n2.output = [], n2.attribute = []), e2.defaults && (n2.name = "", n2.opType = "", n2.docString = "", n2.domain = ""), t3.input && t3.input.length) {
              n2.input = [];
              for (var r2 = 0; r2 < t3.input.length; ++r2) n2.input[r2] = t3.input[r2];
            }
            if (t3.output && t3.output.length) for (n2.output = [], r2 = 0; r2 < t3.output.length; ++r2) n2.output[r2] = t3.output[r2];
            if (null != t3.name && t3.hasOwnProperty("name") && (n2.name = t3.name), null != t3.opType && t3.hasOwnProperty("opType") && (n2.opType = t3.opType), t3.attribute && t3.attribute.length) for (n2.attribute = [], r2 = 0; r2 < t3.attribute.length; ++r2) n2.attribute[r2] = l.onnx.AttributeProto.toObject(t3.attribute[r2], e2);
            return null != t3.docString && t3.hasOwnProperty("docString") && (n2.docString = t3.docString), null != t3.domain && t3.hasOwnProperty("domain") && (n2.domain = t3.domain), n2;
          }, t2.prototype.toJSON = function() {
            return this.constructor.toObject(this, a.util.toJSONOptions);
          }, t2;
        }(), o.ModelProto = function() {
          function t2(t3) {
            if (this.opsetImport = [], this.metadataProps = [], t3) for (var e2 = Object.keys(t3), n2 = 0; n2 < e2.length; ++n2) null != t3[e2[n2]] && (this[e2[n2]] = t3[e2[n2]]);
          }
          return t2.prototype.irVersion = c.Long ? c.Long.fromBits(0, 0, false) : 0, t2.prototype.opsetImport = c.emptyArray, t2.prototype.producerName = "", t2.prototype.producerVersion = "", t2.prototype.domain = "", t2.prototype.modelVersion = c.Long ? c.Long.fromBits(0, 0, false) : 0, t2.prototype.docString = "", t2.prototype.graph = null, t2.prototype.metadataProps = c.emptyArray, t2.create = function(e2) {
            return new t2(e2);
          }, t2.encode = function(t3, e2) {
            if (e2 || (e2 = u.create()), null != t3.irVersion && t3.hasOwnProperty("irVersion") && e2.uint32(8).int64(t3.irVersion), null != t3.producerName && t3.hasOwnProperty("producerName") && e2.uint32(18).string(t3.producerName), null != t3.producerVersion && t3.hasOwnProperty("producerVersion") && e2.uint32(26).string(t3.producerVersion), null != t3.domain && t3.hasOwnProperty("domain") && e2.uint32(34).string(t3.domain), null != t3.modelVersion && t3.hasOwnProperty("modelVersion") && e2.uint32(40).int64(t3.modelVersion), null != t3.docString && t3.hasOwnProperty("docString") && e2.uint32(50).string(t3.docString), null != t3.graph && t3.hasOwnProperty("graph") && l.onnx.GraphProto.encode(t3.graph, e2.uint32(58).fork()).ldelim(), null != t3.opsetImport && t3.opsetImport.length) for (var n2 = 0; n2 < t3.opsetImport.length; ++n2) l.onnx.OperatorSetIdProto.encode(t3.opsetImport[n2], e2.uint32(66).fork()).ldelim();
            if (null != t3.metadataProps && t3.metadataProps.length) for (n2 = 0; n2 < t3.metadataProps.length; ++n2) l.onnx.StringStringEntryProto.encode(t3.metadataProps[n2], e2.uint32(114).fork()).ldelim();
            return e2;
          }, t2.encodeDelimited = function(t3, e2) {
            return this.encode(t3, e2).ldelim();
          }, t2.decode = function(t3, e2) {
            t3 instanceof s || (t3 = s.create(t3));
            for (var n2 = void 0 === e2 ? t3.len : t3.pos + e2, r2 = new l.onnx.ModelProto(); t3.pos < n2; ) {
              var i2 = t3.uint32();
              switch (i2 >>> 3) {
                case 1:
                  r2.irVersion = t3.int64();
                  break;
                case 8:
                  r2.opsetImport && r2.opsetImport.length || (r2.opsetImport = []), r2.opsetImport.push(l.onnx.OperatorSetIdProto.decode(t3, t3.uint32()));
                  break;
                case 2:
                  r2.producerName = t3.string();
                  break;
                case 3:
                  r2.producerVersion = t3.string();
                  break;
                case 4:
                  r2.domain = t3.string();
                  break;
                case 5:
                  r2.modelVersion = t3.int64();
                  break;
                case 6:
                  r2.docString = t3.string();
                  break;
                case 7:
                  r2.graph = l.onnx.GraphProto.decode(t3, t3.uint32());
                  break;
                case 14:
                  r2.metadataProps && r2.metadataProps.length || (r2.metadataProps = []), r2.metadataProps.push(l.onnx.StringStringEntryProto.decode(t3, t3.uint32()));
                  break;
                default:
                  t3.skipType(7 & i2);
              }
            }
            return r2;
          }, t2.decodeDelimited = function(t3) {
            return t3 instanceof s || (t3 = new s(t3)), this.decode(t3, t3.uint32());
          }, t2.verify = function(t3) {
            if ("object" != typeof t3 || null === t3) return "object expected";
            if (null != t3.irVersion && t3.hasOwnProperty("irVersion") && !(c.isInteger(t3.irVersion) || t3.irVersion && c.isInteger(t3.irVersion.low) && c.isInteger(t3.irVersion.high))) return "irVersion: integer|Long expected";
            if (null != t3.opsetImport && t3.hasOwnProperty("opsetImport")) {
              if (!Array.isArray(t3.opsetImport)) return "opsetImport: array expected";
              for (var e2 = 0; e2 < t3.opsetImport.length; ++e2) if (n2 = l.onnx.OperatorSetIdProto.verify(t3.opsetImport[e2])) return "opsetImport." + n2;
            }
            if (null != t3.producerName && t3.hasOwnProperty("producerName") && !c.isString(t3.producerName)) return "producerName: string expected";
            if (null != t3.producerVersion && t3.hasOwnProperty("producerVersion") && !c.isString(t3.producerVersion)) return "producerVersion: string expected";
            if (null != t3.domain && t3.hasOwnProperty("domain") && !c.isString(t3.domain)) return "domain: string expected";
            if (null != t3.modelVersion && t3.hasOwnProperty("modelVersion") && !(c.isInteger(t3.modelVersion) || t3.modelVersion && c.isInteger(t3.modelVersion.low) && c.isInteger(t3.modelVersion.high))) return "modelVersion: integer|Long expected";
            if (null != t3.docString && t3.hasOwnProperty("docString") && !c.isString(t3.docString)) return "docString: string expected";
            if (null != t3.graph && t3.hasOwnProperty("graph") && (n2 = l.onnx.GraphProto.verify(t3.graph))) return "graph." + n2;
            if (null != t3.metadataProps && t3.hasOwnProperty("metadataProps")) {
              if (!Array.isArray(t3.metadataProps)) return "metadataProps: array expected";
              for (e2 = 0; e2 < t3.metadataProps.length; ++e2) {
                var n2;
                if (n2 = l.onnx.StringStringEntryProto.verify(t3.metadataProps[e2])) return "metadataProps." + n2;
              }
            }
            return null;
          }, t2.fromObject = function(t3) {
            if (t3 instanceof l.onnx.ModelProto) return t3;
            var e2 = new l.onnx.ModelProto();
            if (null != t3.irVersion && (c.Long ? (e2.irVersion = c.Long.fromValue(t3.irVersion)).unsigned = false : "string" == typeof t3.irVersion ? e2.irVersion = parseInt(t3.irVersion, 10) : "number" == typeof t3.irVersion ? e2.irVersion = t3.irVersion : "object" == typeof t3.irVersion && (e2.irVersion = new c.LongBits(t3.irVersion.low >>> 0, t3.irVersion.high >>> 0).toNumber())), t3.opsetImport) {
              if (!Array.isArray(t3.opsetImport)) throw TypeError(".onnx.ModelProto.opsetImport: array expected");
              e2.opsetImport = [];
              for (var n2 = 0; n2 < t3.opsetImport.length; ++n2) {
                if ("object" != typeof t3.opsetImport[n2]) throw TypeError(".onnx.ModelProto.opsetImport: object expected");
                e2.opsetImport[n2] = l.onnx.OperatorSetIdProto.fromObject(t3.opsetImport[n2]);
              }
            }
            if (null != t3.producerName && (e2.producerName = String(t3.producerName)), null != t3.producerVersion && (e2.producerVersion = String(t3.producerVersion)), null != t3.domain && (e2.domain = String(t3.domain)), null != t3.modelVersion && (c.Long ? (e2.modelVersion = c.Long.fromValue(t3.modelVersion)).unsigned = false : "string" == typeof t3.modelVersion ? e2.modelVersion = parseInt(t3.modelVersion, 10) : "number" == typeof t3.modelVersion ? e2.modelVersion = t3.modelVersion : "object" == typeof t3.modelVersion && (e2.modelVersion = new c.LongBits(t3.modelVersion.low >>> 0, t3.modelVersion.high >>> 0).toNumber())), null != t3.docString && (e2.docString = String(t3.docString)), null != t3.graph) {
              if ("object" != typeof t3.graph) throw TypeError(".onnx.ModelProto.graph: object expected");
              e2.graph = l.onnx.GraphProto.fromObject(t3.graph);
            }
            if (t3.metadataProps) {
              if (!Array.isArray(t3.metadataProps)) throw TypeError(".onnx.ModelProto.metadataProps: array expected");
              for (e2.metadataProps = [], n2 = 0; n2 < t3.metadataProps.length; ++n2) {
                if ("object" != typeof t3.metadataProps[n2]) throw TypeError(".onnx.ModelProto.metadataProps: object expected");
                e2.metadataProps[n2] = l.onnx.StringStringEntryProto.fromObject(t3.metadataProps[n2]);
              }
            }
            return e2;
          }, t2.toObject = function(t3, e2) {
            e2 || (e2 = {});
            var n2 = {};
            if ((e2.arrays || e2.defaults) && (n2.opsetImport = [], n2.metadataProps = []), e2.defaults) {
              if (c.Long) {
                var r2 = new c.Long(0, 0, false);
                n2.irVersion = e2.longs === String ? r2.toString() : e2.longs === Number ? r2.toNumber() : r2;
              } else n2.irVersion = e2.longs === String ? "0" : 0;
              n2.producerName = "", n2.producerVersion = "", n2.domain = "", c.Long ? (r2 = new c.Long(0, 0, false), n2.modelVersion = e2.longs === String ? r2.toString() : e2.longs === Number ? r2.toNumber() : r2) : n2.modelVersion = e2.longs === String ? "0" : 0, n2.docString = "", n2.graph = null;
            }
            if (null != t3.irVersion && t3.hasOwnProperty("irVersion") && ("number" == typeof t3.irVersion ? n2.irVersion = e2.longs === String ? String(t3.irVersion) : t3.irVersion : n2.irVersion = e2.longs === String ? c.Long.prototype.toString.call(t3.irVersion) : e2.longs === Number ? new c.LongBits(t3.irVersion.low >>> 0, t3.irVersion.high >>> 0).toNumber() : t3.irVersion), null != t3.producerName && t3.hasOwnProperty("producerName") && (n2.producerName = t3.producerName), null != t3.producerVersion && t3.hasOwnProperty("producerVersion") && (n2.producerVersion = t3.producerVersion), null != t3.domain && t3.hasOwnProperty("domain") && (n2.domain = t3.domain), null != t3.modelVersion && t3.hasOwnProperty("modelVersion") && ("number" == typeof t3.modelVersion ? n2.modelVersion = e2.longs === String ? String(t3.modelVersion) : t3.modelVersion : n2.modelVersion = e2.longs === String ? c.Long.prototype.toString.call(t3.modelVersion) : e2.longs === Number ? new c.LongBits(t3.modelVersion.low >>> 0, t3.modelVersion.high >>> 0).toNumber() : t3.modelVersion), null != t3.docString && t3.hasOwnProperty("docString") && (n2.docString = t3.docString), null != t3.graph && t3.hasOwnProperty("graph") && (n2.graph = l.onnx.GraphProto.toObject(t3.graph, e2)), t3.opsetImport && t3.opsetImport.length) {
              n2.opsetImport = [];
              for (var i2 = 0; i2 < t3.opsetImport.length; ++i2) n2.opsetImport[i2] = l.onnx.OperatorSetIdProto.toObject(t3.opsetImport[i2], e2);
            }
            if (t3.metadataProps && t3.metadataProps.length) for (n2.metadataProps = [], i2 = 0; i2 < t3.metadataProps.length; ++i2) n2.metadataProps[i2] = l.onnx.StringStringEntryProto.toObject(t3.metadataProps[i2], e2);
            return n2;
          }, t2.prototype.toJSON = function() {
            return this.constructor.toObject(this, a.util.toJSONOptions);
          }, t2;
        }(), o.StringStringEntryProto = function() {
          function t2(t3) {
            if (t3) for (var e2 = Object.keys(t3), n2 = 0; n2 < e2.length; ++n2) null != t3[e2[n2]] && (this[e2[n2]] = t3[e2[n2]]);
          }
          return t2.prototype.key = "", t2.prototype.value = "", t2.create = function(e2) {
            return new t2(e2);
          }, t2.encode = function(t3, e2) {
            return e2 || (e2 = u.create()), null != t3.key && t3.hasOwnProperty("key") && e2.uint32(10).string(t3.key), null != t3.value && t3.hasOwnProperty("value") && e2.uint32(18).string(t3.value), e2;
          }, t2.encodeDelimited = function(t3, e2) {
            return this.encode(t3, e2).ldelim();
          }, t2.decode = function(t3, e2) {
            t3 instanceof s || (t3 = s.create(t3));
            for (var n2 = void 0 === e2 ? t3.len : t3.pos + e2, r2 = new l.onnx.StringStringEntryProto(); t3.pos < n2; ) {
              var i2 = t3.uint32();
              switch (i2 >>> 3) {
                case 1:
                  r2.key = t3.string();
                  break;
                case 2:
                  r2.value = t3.string();
                  break;
                default:
                  t3.skipType(7 & i2);
              }
            }
            return r2;
          }, t2.decodeDelimited = function(t3) {
            return t3 instanceof s || (t3 = new s(t3)), this.decode(t3, t3.uint32());
          }, t2.verify = function(t3) {
            return "object" != typeof t3 || null === t3 ? "object expected" : null != t3.key && t3.hasOwnProperty("key") && !c.isString(t3.key) ? "key: string expected" : null != t3.value && t3.hasOwnProperty("value") && !c.isString(t3.value) ? "value: string expected" : null;
          }, t2.fromObject = function(t3) {
            if (t3 instanceof l.onnx.StringStringEntryProto) return t3;
            var e2 = new l.onnx.StringStringEntryProto();
            return null != t3.key && (e2.key = String(t3.key)), null != t3.value && (e2.value = String(t3.value)), e2;
          }, t2.toObject = function(t3, e2) {
            e2 || (e2 = {});
            var n2 = {};
            return e2.defaults && (n2.key = "", n2.value = ""), null != t3.key && t3.hasOwnProperty("key") && (n2.key = t3.key), null != t3.value && t3.hasOwnProperty("value") && (n2.value = t3.value), n2;
          }, t2.prototype.toJSON = function() {
            return this.constructor.toObject(this, a.util.toJSONOptions);
          }, t2;
        }(), o.TensorAnnotation = function() {
          function t2(t3) {
            if (this.quantParameterTensorNames = [], t3) for (var e2 = Object.keys(t3), n2 = 0; n2 < e2.length; ++n2) null != t3[e2[n2]] && (this[e2[n2]] = t3[e2[n2]]);
          }
          return t2.prototype.tensorName = "", t2.prototype.quantParameterTensorNames = c.emptyArray, t2.create = function(e2) {
            return new t2(e2);
          }, t2.encode = function(t3, e2) {
            if (e2 || (e2 = u.create()), null != t3.tensorName && t3.hasOwnProperty("tensorName") && e2.uint32(10).string(t3.tensorName), null != t3.quantParameterTensorNames && t3.quantParameterTensorNames.length) for (var n2 = 0; n2 < t3.quantParameterTensorNames.length; ++n2) l.onnx.StringStringEntryProto.encode(t3.quantParameterTensorNames[n2], e2.uint32(18).fork()).ldelim();
            return e2;
          }, t2.encodeDelimited = function(t3, e2) {
            return this.encode(t3, e2).ldelim();
          }, t2.decode = function(t3, e2) {
            t3 instanceof s || (t3 = s.create(t3));
            for (var n2 = void 0 === e2 ? t3.len : t3.pos + e2, r2 = new l.onnx.TensorAnnotation(); t3.pos < n2; ) {
              var i2 = t3.uint32();
              switch (i2 >>> 3) {
                case 1:
                  r2.tensorName = t3.string();
                  break;
                case 2:
                  r2.quantParameterTensorNames && r2.quantParameterTensorNames.length || (r2.quantParameterTensorNames = []), r2.quantParameterTensorNames.push(l.onnx.StringStringEntryProto.decode(t3, t3.uint32()));
                  break;
                default:
                  t3.skipType(7 & i2);
              }
            }
            return r2;
          }, t2.decodeDelimited = function(t3) {
            return t3 instanceof s || (t3 = new s(t3)), this.decode(t3, t3.uint32());
          }, t2.verify = function(t3) {
            if ("object" != typeof t3 || null === t3) return "object expected";
            if (null != t3.tensorName && t3.hasOwnProperty("tensorName") && !c.isString(t3.tensorName)) return "tensorName: string expected";
            if (null != t3.quantParameterTensorNames && t3.hasOwnProperty("quantParameterTensorNames")) {
              if (!Array.isArray(t3.quantParameterTensorNames)) return "quantParameterTensorNames: array expected";
              for (var e2 = 0; e2 < t3.quantParameterTensorNames.length; ++e2) {
                var n2 = l.onnx.StringStringEntryProto.verify(t3.quantParameterTensorNames[e2]);
                if (n2) return "quantParameterTensorNames." + n2;
              }
            }
            return null;
          }, t2.fromObject = function(t3) {
            if (t3 instanceof l.onnx.TensorAnnotation) return t3;
            var e2 = new l.onnx.TensorAnnotation();
            if (null != t3.tensorName && (e2.tensorName = String(t3.tensorName)), t3.quantParameterTensorNames) {
              if (!Array.isArray(t3.quantParameterTensorNames)) throw TypeError(".onnx.TensorAnnotation.quantParameterTensorNames: array expected");
              e2.quantParameterTensorNames = [];
              for (var n2 = 0; n2 < t3.quantParameterTensorNames.length; ++n2) {
                if ("object" != typeof t3.quantParameterTensorNames[n2]) throw TypeError(".onnx.TensorAnnotation.quantParameterTensorNames: object expected");
                e2.quantParameterTensorNames[n2] = l.onnx.StringStringEntryProto.fromObject(t3.quantParameterTensorNames[n2]);
              }
            }
            return e2;
          }, t2.toObject = function(t3, e2) {
            e2 || (e2 = {});
            var n2 = {};
            if ((e2.arrays || e2.defaults) && (n2.quantParameterTensorNames = []), e2.defaults && (n2.tensorName = ""), null != t3.tensorName && t3.hasOwnProperty("tensorName") && (n2.tensorName = t3.tensorName), t3.quantParameterTensorNames && t3.quantParameterTensorNames.length) {
              n2.quantParameterTensorNames = [];
              for (var r2 = 0; r2 < t3.quantParameterTensorNames.length; ++r2) n2.quantParameterTensorNames[r2] = l.onnx.StringStringEntryProto.toObject(t3.quantParameterTensorNames[r2], e2);
            }
            return n2;
          }, t2.prototype.toJSON = function() {
            return this.constructor.toObject(this, a.util.toJSONOptions);
          }, t2;
        }(), o.GraphProto = function() {
          function t2(t3) {
            if (this.node = [], this.initializer = [], this.input = [], this.output = [], this.valueInfo = [], this.quantizationAnnotation = [], t3) for (var e2 = Object.keys(t3), n2 = 0; n2 < e2.length; ++n2) null != t3[e2[n2]] && (this[e2[n2]] = t3[e2[n2]]);
          }
          return t2.prototype.node = c.emptyArray, t2.prototype.name = "", t2.prototype.initializer = c.emptyArray, t2.prototype.docString = "", t2.prototype.input = c.emptyArray, t2.prototype.output = c.emptyArray, t2.prototype.valueInfo = c.emptyArray, t2.prototype.quantizationAnnotation = c.emptyArray, t2.create = function(e2) {
            return new t2(e2);
          }, t2.encode = function(t3, e2) {
            if (e2 || (e2 = u.create()), null != t3.node && t3.node.length) for (var n2 = 0; n2 < t3.node.length; ++n2) l.onnx.NodeProto.encode(t3.node[n2], e2.uint32(10).fork()).ldelim();
            if (null != t3.name && t3.hasOwnProperty("name") && e2.uint32(18).string(t3.name), null != t3.initializer && t3.initializer.length) for (n2 = 0; n2 < t3.initializer.length; ++n2) l.onnx.TensorProto.encode(t3.initializer[n2], e2.uint32(42).fork()).ldelim();
            if (null != t3.docString && t3.hasOwnProperty("docString") && e2.uint32(82).string(t3.docString), null != t3.input && t3.input.length) for (n2 = 0; n2 < t3.input.length; ++n2) l.onnx.ValueInfoProto.encode(t3.input[n2], e2.uint32(90).fork()).ldelim();
            if (null != t3.output && t3.output.length) for (n2 = 0; n2 < t3.output.length; ++n2) l.onnx.ValueInfoProto.encode(t3.output[n2], e2.uint32(98).fork()).ldelim();
            if (null != t3.valueInfo && t3.valueInfo.length) for (n2 = 0; n2 < t3.valueInfo.length; ++n2) l.onnx.ValueInfoProto.encode(t3.valueInfo[n2], e2.uint32(106).fork()).ldelim();
            if (null != t3.quantizationAnnotation && t3.quantizationAnnotation.length) for (n2 = 0; n2 < t3.quantizationAnnotation.length; ++n2) l.onnx.TensorAnnotation.encode(t3.quantizationAnnotation[n2], e2.uint32(114).fork()).ldelim();
            return e2;
          }, t2.encodeDelimited = function(t3, e2) {
            return this.encode(t3, e2).ldelim();
          }, t2.decode = function(t3, e2) {
            t3 instanceof s || (t3 = s.create(t3));
            for (var n2 = void 0 === e2 ? t3.len : t3.pos + e2, r2 = new l.onnx.GraphProto(); t3.pos < n2; ) {
              var i2 = t3.uint32();
              switch (i2 >>> 3) {
                case 1:
                  r2.node && r2.node.length || (r2.node = []), r2.node.push(l.onnx.NodeProto.decode(t3, t3.uint32()));
                  break;
                case 2:
                  r2.name = t3.string();
                  break;
                case 5:
                  r2.initializer && r2.initializer.length || (r2.initializer = []), r2.initializer.push(l.onnx.TensorProto.decode(t3, t3.uint32()));
                  break;
                case 10:
                  r2.docString = t3.string();
                  break;
                case 11:
                  r2.input && r2.input.length || (r2.input = []), r2.input.push(l.onnx.ValueInfoProto.decode(t3, t3.uint32()));
                  break;
                case 12:
                  r2.output && r2.output.length || (r2.output = []), r2.output.push(l.onnx.ValueInfoProto.decode(t3, t3.uint32()));
                  break;
                case 13:
                  r2.valueInfo && r2.valueInfo.length || (r2.valueInfo = []), r2.valueInfo.push(l.onnx.ValueInfoProto.decode(t3, t3.uint32()));
                  break;
                case 14:
                  r2.quantizationAnnotation && r2.quantizationAnnotation.length || (r2.quantizationAnnotation = []), r2.quantizationAnnotation.push(l.onnx.TensorAnnotation.decode(t3, t3.uint32()));
                  break;
                default:
                  t3.skipType(7 & i2);
              }
            }
            return r2;
          }, t2.decodeDelimited = function(t3) {
            return t3 instanceof s || (t3 = new s(t3)), this.decode(t3, t3.uint32());
          }, t2.verify = function(t3) {
            if ("object" != typeof t3 || null === t3) return "object expected";
            if (null != t3.node && t3.hasOwnProperty("node")) {
              if (!Array.isArray(t3.node)) return "node: array expected";
              for (var e2 = 0; e2 < t3.node.length; ++e2) if (n2 = l.onnx.NodeProto.verify(t3.node[e2])) return "node." + n2;
            }
            if (null != t3.name && t3.hasOwnProperty("name") && !c.isString(t3.name)) return "name: string expected";
            if (null != t3.initializer && t3.hasOwnProperty("initializer")) {
              if (!Array.isArray(t3.initializer)) return "initializer: array expected";
              for (e2 = 0; e2 < t3.initializer.length; ++e2) if (n2 = l.onnx.TensorProto.verify(t3.initializer[e2])) return "initializer." + n2;
            }
            if (null != t3.docString && t3.hasOwnProperty("docString") && !c.isString(t3.docString)) return "docString: string expected";
            if (null != t3.input && t3.hasOwnProperty("input")) {
              if (!Array.isArray(t3.input)) return "input: array expected";
              for (e2 = 0; e2 < t3.input.length; ++e2) if (n2 = l.onnx.ValueInfoProto.verify(t3.input[e2])) return "input." + n2;
            }
            if (null != t3.output && t3.hasOwnProperty("output")) {
              if (!Array.isArray(t3.output)) return "output: array expected";
              for (e2 = 0; e2 < t3.output.length; ++e2) if (n2 = l.onnx.ValueInfoProto.verify(t3.output[e2])) return "output." + n2;
            }
            if (null != t3.valueInfo && t3.hasOwnProperty("valueInfo")) {
              if (!Array.isArray(t3.valueInfo)) return "valueInfo: array expected";
              for (e2 = 0; e2 < t3.valueInfo.length; ++e2) if (n2 = l.onnx.ValueInfoProto.verify(t3.valueInfo[e2])) return "valueInfo." + n2;
            }
            if (null != t3.quantizationAnnotation && t3.hasOwnProperty("quantizationAnnotation")) {
              if (!Array.isArray(t3.quantizationAnnotation)) return "quantizationAnnotation: array expected";
              for (e2 = 0; e2 < t3.quantizationAnnotation.length; ++e2) {
                var n2;
                if (n2 = l.onnx.TensorAnnotation.verify(t3.quantizationAnnotation[e2])) return "quantizationAnnotation." + n2;
              }
            }
            return null;
          }, t2.fromObject = function(t3) {
            if (t3 instanceof l.onnx.GraphProto) return t3;
            var e2 = new l.onnx.GraphProto();
            if (t3.node) {
              if (!Array.isArray(t3.node)) throw TypeError(".onnx.GraphProto.node: array expected");
              e2.node = [];
              for (var n2 = 0; n2 < t3.node.length; ++n2) {
                if ("object" != typeof t3.node[n2]) throw TypeError(".onnx.GraphProto.node: object expected");
                e2.node[n2] = l.onnx.NodeProto.fromObject(t3.node[n2]);
              }
            }
            if (null != t3.name && (e2.name = String(t3.name)), t3.initializer) {
              if (!Array.isArray(t3.initializer)) throw TypeError(".onnx.GraphProto.initializer: array expected");
              for (e2.initializer = [], n2 = 0; n2 < t3.initializer.length; ++n2) {
                if ("object" != typeof t3.initializer[n2]) throw TypeError(".onnx.GraphProto.initializer: object expected");
                e2.initializer[n2] = l.onnx.TensorProto.fromObject(t3.initializer[n2]);
              }
            }
            if (null != t3.docString && (e2.docString = String(t3.docString)), t3.input) {
              if (!Array.isArray(t3.input)) throw TypeError(".onnx.GraphProto.input: array expected");
              for (e2.input = [], n2 = 0; n2 < t3.input.length; ++n2) {
                if ("object" != typeof t3.input[n2]) throw TypeError(".onnx.GraphProto.input: object expected");
                e2.input[n2] = l.onnx.ValueInfoProto.fromObject(t3.input[n2]);
              }
            }
            if (t3.output) {
              if (!Array.isArray(t3.output)) throw TypeError(".onnx.GraphProto.output: array expected");
              for (e2.output = [], n2 = 0; n2 < t3.output.length; ++n2) {
                if ("object" != typeof t3.output[n2]) throw TypeError(".onnx.GraphProto.output: object expected");
                e2.output[n2] = l.onnx.ValueInfoProto.fromObject(t3.output[n2]);
              }
            }
            if (t3.valueInfo) {
              if (!Array.isArray(t3.valueInfo)) throw TypeError(".onnx.GraphProto.valueInfo: array expected");
              for (e2.valueInfo = [], n2 = 0; n2 < t3.valueInfo.length; ++n2) {
                if ("object" != typeof t3.valueInfo[n2]) throw TypeError(".onnx.GraphProto.valueInfo: object expected");
                e2.valueInfo[n2] = l.onnx.ValueInfoProto.fromObject(t3.valueInfo[n2]);
              }
            }
            if (t3.quantizationAnnotation) {
              if (!Array.isArray(t3.quantizationAnnotation)) throw TypeError(".onnx.GraphProto.quantizationAnnotation: array expected");
              for (e2.quantizationAnnotation = [], n2 = 0; n2 < t3.quantizationAnnotation.length; ++n2) {
                if ("object" != typeof t3.quantizationAnnotation[n2]) throw TypeError(".onnx.GraphProto.quantizationAnnotation: object expected");
                e2.quantizationAnnotation[n2] = l.onnx.TensorAnnotation.fromObject(t3.quantizationAnnotation[n2]);
              }
            }
            return e2;
          }, t2.toObject = function(t3, e2) {
            e2 || (e2 = {});
            var n2 = {};
            if ((e2.arrays || e2.defaults) && (n2.node = [], n2.initializer = [], n2.input = [], n2.output = [], n2.valueInfo = [], n2.quantizationAnnotation = []), e2.defaults && (n2.name = "", n2.docString = ""), t3.node && t3.node.length) {
              n2.node = [];
              for (var r2 = 0; r2 < t3.node.length; ++r2) n2.node[r2] = l.onnx.NodeProto.toObject(t3.node[r2], e2);
            }
            if (null != t3.name && t3.hasOwnProperty("name") && (n2.name = t3.name), t3.initializer && t3.initializer.length) for (n2.initializer = [], r2 = 0; r2 < t3.initializer.length; ++r2) n2.initializer[r2] = l.onnx.TensorProto.toObject(t3.initializer[r2], e2);
            if (null != t3.docString && t3.hasOwnProperty("docString") && (n2.docString = t3.docString), t3.input && t3.input.length) for (n2.input = [], r2 = 0; r2 < t3.input.length; ++r2) n2.input[r2] = l.onnx.ValueInfoProto.toObject(t3.input[r2], e2);
            if (t3.output && t3.output.length) for (n2.output = [], r2 = 0; r2 < t3.output.length; ++r2) n2.output[r2] = l.onnx.ValueInfoProto.toObject(t3.output[r2], e2);
            if (t3.valueInfo && t3.valueInfo.length) for (n2.valueInfo = [], r2 = 0; r2 < t3.valueInfo.length; ++r2) n2.valueInfo[r2] = l.onnx.ValueInfoProto.toObject(t3.valueInfo[r2], e2);
            if (t3.quantizationAnnotation && t3.quantizationAnnotation.length) for (n2.quantizationAnnotation = [], r2 = 0; r2 < t3.quantizationAnnotation.length; ++r2) n2.quantizationAnnotation[r2] = l.onnx.TensorAnnotation.toObject(t3.quantizationAnnotation[r2], e2);
            return n2;
          }, t2.prototype.toJSON = function() {
            return this.constructor.toObject(this, a.util.toJSONOptions);
          }, t2;
        }(), o.TensorProto = function() {
          function t2(t3) {
            if (this.dims = [], this.floatData = [], this.int32Data = [], this.stringData = [], this.int64Data = [], this.externalData = [], this.doubleData = [], this.uint64Data = [], t3) for (var e2 = Object.keys(t3), n2 = 0; n2 < e2.length; ++n2) null != t3[e2[n2]] && (this[e2[n2]] = t3[e2[n2]]);
          }
          return t2.prototype.dims = c.emptyArray, t2.prototype.dataType = 0, t2.prototype.segment = null, t2.prototype.floatData = c.emptyArray, t2.prototype.int32Data = c.emptyArray, t2.prototype.stringData = c.emptyArray, t2.prototype.int64Data = c.emptyArray, t2.prototype.name = "", t2.prototype.docString = "", t2.prototype.rawData = c.newBuffer([]), t2.prototype.externalData = c.emptyArray, t2.prototype.dataLocation = 0, t2.prototype.doubleData = c.emptyArray, t2.prototype.uint64Data = c.emptyArray, t2.create = function(e2) {
            return new t2(e2);
          }, t2.encode = function(t3, e2) {
            if (e2 || (e2 = u.create()), null != t3.dims && t3.dims.length) {
              e2.uint32(10).fork();
              for (var n2 = 0; n2 < t3.dims.length; ++n2) e2.int64(t3.dims[n2]);
              e2.ldelim();
            }
            if (null != t3.dataType && t3.hasOwnProperty("dataType") && e2.uint32(16).int32(t3.dataType), null != t3.segment && t3.hasOwnProperty("segment") && l.onnx.TensorProto.Segment.encode(t3.segment, e2.uint32(26).fork()).ldelim(), null != t3.floatData && t3.floatData.length) {
              for (e2.uint32(34).fork(), n2 = 0; n2 < t3.floatData.length; ++n2) e2.float(t3.floatData[n2]);
              e2.ldelim();
            }
            if (null != t3.int32Data && t3.int32Data.length) {
              for (e2.uint32(42).fork(), n2 = 0; n2 < t3.int32Data.length; ++n2) e2.int32(t3.int32Data[n2]);
              e2.ldelim();
            }
            if (null != t3.stringData && t3.stringData.length) for (n2 = 0; n2 < t3.stringData.length; ++n2) e2.uint32(50).bytes(t3.stringData[n2]);
            if (null != t3.int64Data && t3.int64Data.length) {
              for (e2.uint32(58).fork(), n2 = 0; n2 < t3.int64Data.length; ++n2) e2.int64(t3.int64Data[n2]);
              e2.ldelim();
            }
            if (null != t3.name && t3.hasOwnProperty("name") && e2.uint32(66).string(t3.name), null != t3.rawData && t3.hasOwnProperty("rawData") && e2.uint32(74).bytes(t3.rawData), null != t3.doubleData && t3.doubleData.length) {
              for (e2.uint32(82).fork(), n2 = 0; n2 < t3.doubleData.length; ++n2) e2.double(t3.doubleData[n2]);
              e2.ldelim();
            }
            if (null != t3.uint64Data && t3.uint64Data.length) {
              for (e2.uint32(90).fork(), n2 = 0; n2 < t3.uint64Data.length; ++n2) e2.uint64(t3.uint64Data[n2]);
              e2.ldelim();
            }
            if (null != t3.docString && t3.hasOwnProperty("docString") && e2.uint32(98).string(t3.docString), null != t3.externalData && t3.externalData.length) for (n2 = 0; n2 < t3.externalData.length; ++n2) l.onnx.StringStringEntryProto.encode(t3.externalData[n2], e2.uint32(106).fork()).ldelim();
            return null != t3.dataLocation && t3.hasOwnProperty("dataLocation") && e2.uint32(112).int32(t3.dataLocation), e2;
          }, t2.encodeDelimited = function(t3, e2) {
            return this.encode(t3, e2).ldelim();
          }, t2.decode = function(t3, e2) {
            t3 instanceof s || (t3 = s.create(t3));
            for (var n2 = void 0 === e2 ? t3.len : t3.pos + e2, r2 = new l.onnx.TensorProto(); t3.pos < n2; ) {
              var i2 = t3.uint32();
              switch (i2 >>> 3) {
                case 1:
                  if (r2.dims && r2.dims.length || (r2.dims = []), 2 == (7 & i2)) for (var o2 = t3.uint32() + t3.pos; t3.pos < o2; ) r2.dims.push(t3.int64());
                  else r2.dims.push(t3.int64());
                  break;
                case 2:
                  r2.dataType = t3.int32();
                  break;
                case 3:
                  r2.segment = l.onnx.TensorProto.Segment.decode(t3, t3.uint32());
                  break;
                case 4:
                  if (r2.floatData && r2.floatData.length || (r2.floatData = []), 2 == (7 & i2)) for (o2 = t3.uint32() + t3.pos; t3.pos < o2; ) r2.floatData.push(t3.float());
                  else r2.floatData.push(t3.float());
                  break;
                case 5:
                  if (r2.int32Data && r2.int32Data.length || (r2.int32Data = []), 2 == (7 & i2)) for (o2 = t3.uint32() + t3.pos; t3.pos < o2; ) r2.int32Data.push(t3.int32());
                  else r2.int32Data.push(t3.int32());
                  break;
                case 6:
                  r2.stringData && r2.stringData.length || (r2.stringData = []), r2.stringData.push(t3.bytes());
                  break;
                case 7:
                  if (r2.int64Data && r2.int64Data.length || (r2.int64Data = []), 2 == (7 & i2)) for (o2 = t3.uint32() + t3.pos; t3.pos < o2; ) r2.int64Data.push(t3.int64());
                  else r2.int64Data.push(t3.int64());
                  break;
                case 8:
                  r2.name = t3.string();
                  break;
                case 12:
                  r2.docString = t3.string();
                  break;
                case 9:
                  r2.rawData = t3.bytes();
                  break;
                case 13:
                  r2.externalData && r2.externalData.length || (r2.externalData = []), r2.externalData.push(l.onnx.StringStringEntryProto.decode(t3, t3.uint32()));
                  break;
                case 14:
                  r2.dataLocation = t3.int32();
                  break;
                case 10:
                  if (r2.doubleData && r2.doubleData.length || (r2.doubleData = []), 2 == (7 & i2)) for (o2 = t3.uint32() + t3.pos; t3.pos < o2; ) r2.doubleData.push(t3.double());
                  else r2.doubleData.push(t3.double());
                  break;
                case 11:
                  if (r2.uint64Data && r2.uint64Data.length || (r2.uint64Data = []), 2 == (7 & i2)) for (o2 = t3.uint32() + t3.pos; t3.pos < o2; ) r2.uint64Data.push(t3.uint64());
                  else r2.uint64Data.push(t3.uint64());
                  break;
                default:
                  t3.skipType(7 & i2);
              }
            }
            return r2;
          }, t2.decodeDelimited = function(t3) {
            return t3 instanceof s || (t3 = new s(t3)), this.decode(t3, t3.uint32());
          }, t2.verify = function(t3) {
            if ("object" != typeof t3 || null === t3) return "object expected";
            if (null != t3.dims && t3.hasOwnProperty("dims")) {
              if (!Array.isArray(t3.dims)) return "dims: array expected";
              for (var e2 = 0; e2 < t3.dims.length; ++e2) if (!(c.isInteger(t3.dims[e2]) || t3.dims[e2] && c.isInteger(t3.dims[e2].low) && c.isInteger(t3.dims[e2].high))) return "dims: integer|Long[] expected";
            }
            if (null != t3.dataType && t3.hasOwnProperty("dataType") && !c.isInteger(t3.dataType)) return "dataType: integer expected";
            if (null != t3.segment && t3.hasOwnProperty("segment") && (n2 = l.onnx.TensorProto.Segment.verify(t3.segment))) return "segment." + n2;
            if (null != t3.floatData && t3.hasOwnProperty("floatData")) {
              if (!Array.isArray(t3.floatData)) return "floatData: array expected";
              for (e2 = 0; e2 < t3.floatData.length; ++e2) if ("number" != typeof t3.floatData[e2]) return "floatData: number[] expected";
            }
            if (null != t3.int32Data && t3.hasOwnProperty("int32Data")) {
              if (!Array.isArray(t3.int32Data)) return "int32Data: array expected";
              for (e2 = 0; e2 < t3.int32Data.length; ++e2) if (!c.isInteger(t3.int32Data[e2])) return "int32Data: integer[] expected";
            }
            if (null != t3.stringData && t3.hasOwnProperty("stringData")) {
              if (!Array.isArray(t3.stringData)) return "stringData: array expected";
              for (e2 = 0; e2 < t3.stringData.length; ++e2) if (!(t3.stringData[e2] && "number" == typeof t3.stringData[e2].length || c.isString(t3.stringData[e2]))) return "stringData: buffer[] expected";
            }
            if (null != t3.int64Data && t3.hasOwnProperty("int64Data")) {
              if (!Array.isArray(t3.int64Data)) return "int64Data: array expected";
              for (e2 = 0; e2 < t3.int64Data.length; ++e2) if (!(c.isInteger(t3.int64Data[e2]) || t3.int64Data[e2] && c.isInteger(t3.int64Data[e2].low) && c.isInteger(t3.int64Data[e2].high))) return "int64Data: integer|Long[] expected";
            }
            if (null != t3.name && t3.hasOwnProperty("name") && !c.isString(t3.name)) return "name: string expected";
            if (null != t3.docString && t3.hasOwnProperty("docString") && !c.isString(t3.docString)) return "docString: string expected";
            if (null != t3.rawData && t3.hasOwnProperty("rawData") && !(t3.rawData && "number" == typeof t3.rawData.length || c.isString(t3.rawData))) return "rawData: buffer expected";
            if (null != t3.externalData && t3.hasOwnProperty("externalData")) {
              if (!Array.isArray(t3.externalData)) return "externalData: array expected";
              for (e2 = 0; e2 < t3.externalData.length; ++e2) {
                var n2;
                if (n2 = l.onnx.StringStringEntryProto.verify(t3.externalData[e2])) return "externalData." + n2;
              }
            }
            if (null != t3.dataLocation && t3.hasOwnProperty("dataLocation")) switch (t3.dataLocation) {
              default:
                return "dataLocation: enum value expected";
              case 0:
              case 1:
            }
            if (null != t3.doubleData && t3.hasOwnProperty("doubleData")) {
              if (!Array.isArray(t3.doubleData)) return "doubleData: array expected";
              for (e2 = 0; e2 < t3.doubleData.length; ++e2) if ("number" != typeof t3.doubleData[e2]) return "doubleData: number[] expected";
            }
            if (null != t3.uint64Data && t3.hasOwnProperty("uint64Data")) {
              if (!Array.isArray(t3.uint64Data)) return "uint64Data: array expected";
              for (e2 = 0; e2 < t3.uint64Data.length; ++e2) if (!(c.isInteger(t3.uint64Data[e2]) || t3.uint64Data[e2] && c.isInteger(t3.uint64Data[e2].low) && c.isInteger(t3.uint64Data[e2].high))) return "uint64Data: integer|Long[] expected";
            }
            return null;
          }, t2.fromObject = function(t3) {
            if (t3 instanceof l.onnx.TensorProto) return t3;
            var e2 = new l.onnx.TensorProto();
            if (t3.dims) {
              if (!Array.isArray(t3.dims)) throw TypeError(".onnx.TensorProto.dims: array expected");
              e2.dims = [];
              for (var n2 = 0; n2 < t3.dims.length; ++n2) c.Long ? (e2.dims[n2] = c.Long.fromValue(t3.dims[n2])).unsigned = false : "string" == typeof t3.dims[n2] ? e2.dims[n2] = parseInt(t3.dims[n2], 10) : "number" == typeof t3.dims[n2] ? e2.dims[n2] = t3.dims[n2] : "object" == typeof t3.dims[n2] && (e2.dims[n2] = new c.LongBits(t3.dims[n2].low >>> 0, t3.dims[n2].high >>> 0).toNumber());
            }
            if (null != t3.dataType && (e2.dataType = 0 | t3.dataType), null != t3.segment) {
              if ("object" != typeof t3.segment) throw TypeError(".onnx.TensorProto.segment: object expected");
              e2.segment = l.onnx.TensorProto.Segment.fromObject(t3.segment);
            }
            if (t3.floatData) {
              if (!Array.isArray(t3.floatData)) throw TypeError(".onnx.TensorProto.floatData: array expected");
              for (e2.floatData = [], n2 = 0; n2 < t3.floatData.length; ++n2) e2.floatData[n2] = Number(t3.floatData[n2]);
            }
            if (t3.int32Data) {
              if (!Array.isArray(t3.int32Data)) throw TypeError(".onnx.TensorProto.int32Data: array expected");
              for (e2.int32Data = [], n2 = 0; n2 < t3.int32Data.length; ++n2) e2.int32Data[n2] = 0 | t3.int32Data[n2];
            }
            if (t3.stringData) {
              if (!Array.isArray(t3.stringData)) throw TypeError(".onnx.TensorProto.stringData: array expected");
              for (e2.stringData = [], n2 = 0; n2 < t3.stringData.length; ++n2) "string" == typeof t3.stringData[n2] ? c.base64.decode(t3.stringData[n2], e2.stringData[n2] = c.newBuffer(c.base64.length(t3.stringData[n2])), 0) : t3.stringData[n2].length && (e2.stringData[n2] = t3.stringData[n2]);
            }
            if (t3.int64Data) {
              if (!Array.isArray(t3.int64Data)) throw TypeError(".onnx.TensorProto.int64Data: array expected");
              for (e2.int64Data = [], n2 = 0; n2 < t3.int64Data.length; ++n2) c.Long ? (e2.int64Data[n2] = c.Long.fromValue(t3.int64Data[n2])).unsigned = false : "string" == typeof t3.int64Data[n2] ? e2.int64Data[n2] = parseInt(t3.int64Data[n2], 10) : "number" == typeof t3.int64Data[n2] ? e2.int64Data[n2] = t3.int64Data[n2] : "object" == typeof t3.int64Data[n2] && (e2.int64Data[n2] = new c.LongBits(t3.int64Data[n2].low >>> 0, t3.int64Data[n2].high >>> 0).toNumber());
            }
            if (null != t3.name && (e2.name = String(t3.name)), null != t3.docString && (e2.docString = String(t3.docString)), null != t3.rawData && ("string" == typeof t3.rawData ? c.base64.decode(t3.rawData, e2.rawData = c.newBuffer(c.base64.length(t3.rawData)), 0) : t3.rawData.length && (e2.rawData = t3.rawData)), t3.externalData) {
              if (!Array.isArray(t3.externalData)) throw TypeError(".onnx.TensorProto.externalData: array expected");
              for (e2.externalData = [], n2 = 0; n2 < t3.externalData.length; ++n2) {
                if ("object" != typeof t3.externalData[n2]) throw TypeError(".onnx.TensorProto.externalData: object expected");
                e2.externalData[n2] = l.onnx.StringStringEntryProto.fromObject(t3.externalData[n2]);
              }
            }
            switch (t3.dataLocation) {
              case "DEFAULT":
              case 0:
                e2.dataLocation = 0;
                break;
              case "EXTERNAL":
              case 1:
                e2.dataLocation = 1;
            }
            if (t3.doubleData) {
              if (!Array.isArray(t3.doubleData)) throw TypeError(".onnx.TensorProto.doubleData: array expected");
              for (e2.doubleData = [], n2 = 0; n2 < t3.doubleData.length; ++n2) e2.doubleData[n2] = Number(t3.doubleData[n2]);
            }
            if (t3.uint64Data) {
              if (!Array.isArray(t3.uint64Data)) throw TypeError(".onnx.TensorProto.uint64Data: array expected");
              for (e2.uint64Data = [], n2 = 0; n2 < t3.uint64Data.length; ++n2) c.Long ? (e2.uint64Data[n2] = c.Long.fromValue(t3.uint64Data[n2])).unsigned = true : "string" == typeof t3.uint64Data[n2] ? e2.uint64Data[n2] = parseInt(t3.uint64Data[n2], 10) : "number" == typeof t3.uint64Data[n2] ? e2.uint64Data[n2] = t3.uint64Data[n2] : "object" == typeof t3.uint64Data[n2] && (e2.uint64Data[n2] = new c.LongBits(t3.uint64Data[n2].low >>> 0, t3.uint64Data[n2].high >>> 0).toNumber(true));
            }
            return e2;
          }, t2.toObject = function(t3, e2) {
            e2 || (e2 = {});
            var n2 = {};
            if ((e2.arrays || e2.defaults) && (n2.dims = [], n2.floatData = [], n2.int32Data = [], n2.stringData = [], n2.int64Data = [], n2.doubleData = [], n2.uint64Data = [], n2.externalData = []), e2.defaults && (n2.dataType = 0, n2.segment = null, n2.name = "", e2.bytes === String ? n2.rawData = "" : (n2.rawData = [], e2.bytes !== Array && (n2.rawData = c.newBuffer(n2.rawData))), n2.docString = "", n2.dataLocation = e2.enums === String ? "DEFAULT" : 0), t3.dims && t3.dims.length) {
              n2.dims = [];
              for (var r2 = 0; r2 < t3.dims.length; ++r2) "number" == typeof t3.dims[r2] ? n2.dims[r2] = e2.longs === String ? String(t3.dims[r2]) : t3.dims[r2] : n2.dims[r2] = e2.longs === String ? c.Long.prototype.toString.call(t3.dims[r2]) : e2.longs === Number ? new c.LongBits(t3.dims[r2].low >>> 0, t3.dims[r2].high >>> 0).toNumber() : t3.dims[r2];
            }
            if (null != t3.dataType && t3.hasOwnProperty("dataType") && (n2.dataType = t3.dataType), null != t3.segment && t3.hasOwnProperty("segment") && (n2.segment = l.onnx.TensorProto.Segment.toObject(t3.segment, e2)), t3.floatData && t3.floatData.length) for (n2.floatData = [], r2 = 0; r2 < t3.floatData.length; ++r2) n2.floatData[r2] = e2.json && !isFinite(t3.floatData[r2]) ? String(t3.floatData[r2]) : t3.floatData[r2];
            if (t3.int32Data && t3.int32Data.length) for (n2.int32Data = [], r2 = 0; r2 < t3.int32Data.length; ++r2) n2.int32Data[r2] = t3.int32Data[r2];
            if (t3.stringData && t3.stringData.length) for (n2.stringData = [], r2 = 0; r2 < t3.stringData.length; ++r2) n2.stringData[r2] = e2.bytes === String ? c.base64.encode(t3.stringData[r2], 0, t3.stringData[r2].length) : e2.bytes === Array ? Array.prototype.slice.call(t3.stringData[r2]) : t3.stringData[r2];
            if (t3.int64Data && t3.int64Data.length) for (n2.int64Data = [], r2 = 0; r2 < t3.int64Data.length; ++r2) "number" == typeof t3.int64Data[r2] ? n2.int64Data[r2] = e2.longs === String ? String(t3.int64Data[r2]) : t3.int64Data[r2] : n2.int64Data[r2] = e2.longs === String ? c.Long.prototype.toString.call(t3.int64Data[r2]) : e2.longs === Number ? new c.LongBits(t3.int64Data[r2].low >>> 0, t3.int64Data[r2].high >>> 0).toNumber() : t3.int64Data[r2];
            if (null != t3.name && t3.hasOwnProperty("name") && (n2.name = t3.name), null != t3.rawData && t3.hasOwnProperty("rawData") && (n2.rawData = e2.bytes === String ? c.base64.encode(t3.rawData, 0, t3.rawData.length) : e2.bytes === Array ? Array.prototype.slice.call(t3.rawData) : t3.rawData), t3.doubleData && t3.doubleData.length) for (n2.doubleData = [], r2 = 0; r2 < t3.doubleData.length; ++r2) n2.doubleData[r2] = e2.json && !isFinite(t3.doubleData[r2]) ? String(t3.doubleData[r2]) : t3.doubleData[r2];
            if (t3.uint64Data && t3.uint64Data.length) for (n2.uint64Data = [], r2 = 0; r2 < t3.uint64Data.length; ++r2) "number" == typeof t3.uint64Data[r2] ? n2.uint64Data[r2] = e2.longs === String ? String(t3.uint64Data[r2]) : t3.uint64Data[r2] : n2.uint64Data[r2] = e2.longs === String ? c.Long.prototype.toString.call(t3.uint64Data[r2]) : e2.longs === Number ? new c.LongBits(t3.uint64Data[r2].low >>> 0, t3.uint64Data[r2].high >>> 0).toNumber(true) : t3.uint64Data[r2];
            if (null != t3.docString && t3.hasOwnProperty("docString") && (n2.docString = t3.docString), t3.externalData && t3.externalData.length) for (n2.externalData = [], r2 = 0; r2 < t3.externalData.length; ++r2) n2.externalData[r2] = l.onnx.StringStringEntryProto.toObject(t3.externalData[r2], e2);
            return null != t3.dataLocation && t3.hasOwnProperty("dataLocation") && (n2.dataLocation = e2.enums === String ? l.onnx.TensorProto.DataLocation[t3.dataLocation] : t3.dataLocation), n2;
          }, t2.prototype.toJSON = function() {
            return this.constructor.toObject(this, a.util.toJSONOptions);
          }, t2.DataType = function() {
            var t3 = {}, e2 = Object.create(t3);
            return e2[t3[0] = "UNDEFINED"] = 0, e2[t3[1] = "FLOAT"] = 1, e2[t3[2] = "UINT8"] = 2, e2[t3[3] = "INT8"] = 3, e2[t3[4] = "UINT16"] = 4, e2[t3[5] = "INT16"] = 5, e2[t3[6] = "INT32"] = 6, e2[t3[7] = "INT64"] = 7, e2[t3[8] = "STRING"] = 8, e2[t3[9] = "BOOL"] = 9, e2[t3[10] = "FLOAT16"] = 10, e2[t3[11] = "DOUBLE"] = 11, e2[t3[12] = "UINT32"] = 12, e2[t3[13] = "UINT64"] = 13, e2[t3[14] = "COMPLEX64"] = 14, e2[t3[15] = "COMPLEX128"] = 15, e2[t3[16] = "BFLOAT16"] = 16, e2;
          }(), t2.Segment = function() {
            function t3(t4) {
              if (t4) for (var e2 = Object.keys(t4), n2 = 0; n2 < e2.length; ++n2) null != t4[e2[n2]] && (this[e2[n2]] = t4[e2[n2]]);
            }
            return t3.prototype.begin = c.Long ? c.Long.fromBits(0, 0, false) : 0, t3.prototype.end = c.Long ? c.Long.fromBits(0, 0, false) : 0, t3.create = function(e2) {
              return new t3(e2);
            }, t3.encode = function(t4, e2) {
              return e2 || (e2 = u.create()), null != t4.begin && t4.hasOwnProperty("begin") && e2.uint32(8).int64(t4.begin), null != t4.end && t4.hasOwnProperty("end") && e2.uint32(16).int64(t4.end), e2;
            }, t3.encodeDelimited = function(t4, e2) {
              return this.encode(t4, e2).ldelim();
            }, t3.decode = function(t4, e2) {
              t4 instanceof s || (t4 = s.create(t4));
              for (var n2 = void 0 === e2 ? t4.len : t4.pos + e2, r2 = new l.onnx.TensorProto.Segment(); t4.pos < n2; ) {
                var i2 = t4.uint32();
                switch (i2 >>> 3) {
                  case 1:
                    r2.begin = t4.int64();
                    break;
                  case 2:
                    r2.end = t4.int64();
                    break;
                  default:
                    t4.skipType(7 & i2);
                }
              }
              return r2;
            }, t3.decodeDelimited = function(t4) {
              return t4 instanceof s || (t4 = new s(t4)), this.decode(t4, t4.uint32());
            }, t3.verify = function(t4) {
              return "object" != typeof t4 || null === t4 ? "object expected" : null != t4.begin && t4.hasOwnProperty("begin") && !(c.isInteger(t4.begin) || t4.begin && c.isInteger(t4.begin.low) && c.isInteger(t4.begin.high)) ? "begin: integer|Long expected" : null != t4.end && t4.hasOwnProperty("end") && !(c.isInteger(t4.end) || t4.end && c.isInteger(t4.end.low) && c.isInteger(t4.end.high)) ? "end: integer|Long expected" : null;
            }, t3.fromObject = function(t4) {
              if (t4 instanceof l.onnx.TensorProto.Segment) return t4;
              var e2 = new l.onnx.TensorProto.Segment();
              return null != t4.begin && (c.Long ? (e2.begin = c.Long.fromValue(t4.begin)).unsigned = false : "string" == typeof t4.begin ? e2.begin = parseInt(t4.begin, 10) : "number" == typeof t4.begin ? e2.begin = t4.begin : "object" == typeof t4.begin && (e2.begin = new c.LongBits(t4.begin.low >>> 0, t4.begin.high >>> 0).toNumber())), null != t4.end && (c.Long ? (e2.end = c.Long.fromValue(t4.end)).unsigned = false : "string" == typeof t4.end ? e2.end = parseInt(t4.end, 10) : "number" == typeof t4.end ? e2.end = t4.end : "object" == typeof t4.end && (e2.end = new c.LongBits(t4.end.low >>> 0, t4.end.high >>> 0).toNumber())), e2;
            }, t3.toObject = function(t4, e2) {
              e2 || (e2 = {});
              var n2 = {};
              if (e2.defaults) {
                if (c.Long) {
                  var r2 = new c.Long(0, 0, false);
                  n2.begin = e2.longs === String ? r2.toString() : e2.longs === Number ? r2.toNumber() : r2;
                } else n2.begin = e2.longs === String ? "0" : 0;
                c.Long ? (r2 = new c.Long(0, 0, false), n2.end = e2.longs === String ? r2.toString() : e2.longs === Number ? r2.toNumber() : r2) : n2.end = e2.longs === String ? "0" : 0;
              }
              return null != t4.begin && t4.hasOwnProperty("begin") && ("number" == typeof t4.begin ? n2.begin = e2.longs === String ? String(t4.begin) : t4.begin : n2.begin = e2.longs === String ? c.Long.prototype.toString.call(t4.begin) : e2.longs === Number ? new c.LongBits(t4.begin.low >>> 0, t4.begin.high >>> 0).toNumber() : t4.begin), null != t4.end && t4.hasOwnProperty("end") && ("number" == typeof t4.end ? n2.end = e2.longs === String ? String(t4.end) : t4.end : n2.end = e2.longs === String ? c.Long.prototype.toString.call(t4.end) : e2.longs === Number ? new c.LongBits(t4.end.low >>> 0, t4.end.high >>> 0).toNumber() : t4.end), n2;
            }, t3.prototype.toJSON = function() {
              return this.constructor.toObject(this, a.util.toJSONOptions);
            }, t3;
          }(), t2.DataLocation = function() {
            var t3 = {}, e2 = Object.create(t3);
            return e2[t3[0] = "DEFAULT"] = 0, e2[t3[1] = "EXTERNAL"] = 1, e2;
          }(), t2;
        }(), o.TensorShapeProto = function() {
          function t2(t3) {
            if (this.dim = [], t3) for (var e2 = Object.keys(t3), n2 = 0; n2 < e2.length; ++n2) null != t3[e2[n2]] && (this[e2[n2]] = t3[e2[n2]]);
          }
          return t2.prototype.dim = c.emptyArray, t2.create = function(e2) {
            return new t2(e2);
          }, t2.encode = function(t3, e2) {
            if (e2 || (e2 = u.create()), null != t3.dim && t3.dim.length) for (var n2 = 0; n2 < t3.dim.length; ++n2) l.onnx.TensorShapeProto.Dimension.encode(t3.dim[n2], e2.uint32(10).fork()).ldelim();
            return e2;
          }, t2.encodeDelimited = function(t3, e2) {
            return this.encode(t3, e2).ldelim();
          }, t2.decode = function(t3, e2) {
            t3 instanceof s || (t3 = s.create(t3));
            for (var n2 = void 0 === e2 ? t3.len : t3.pos + e2, r2 = new l.onnx.TensorShapeProto(); t3.pos < n2; ) {
              var i2 = t3.uint32();
              i2 >>> 3 == 1 ? (r2.dim && r2.dim.length || (r2.dim = []), r2.dim.push(l.onnx.TensorShapeProto.Dimension.decode(t3, t3.uint32()))) : t3.skipType(7 & i2);
            }
            return r2;
          }, t2.decodeDelimited = function(t3) {
            return t3 instanceof s || (t3 = new s(t3)), this.decode(t3, t3.uint32());
          }, t2.verify = function(t3) {
            if ("object" != typeof t3 || null === t3) return "object expected";
            if (null != t3.dim && t3.hasOwnProperty("dim")) {
              if (!Array.isArray(t3.dim)) return "dim: array expected";
              for (var e2 = 0; e2 < t3.dim.length; ++e2) {
                var n2 = l.onnx.TensorShapeProto.Dimension.verify(t3.dim[e2]);
                if (n2) return "dim." + n2;
              }
            }
            return null;
          }, t2.fromObject = function(t3) {
            if (t3 instanceof l.onnx.TensorShapeProto) return t3;
            var e2 = new l.onnx.TensorShapeProto();
            if (t3.dim) {
              if (!Array.isArray(t3.dim)) throw TypeError(".onnx.TensorShapeProto.dim: array expected");
              e2.dim = [];
              for (var n2 = 0; n2 < t3.dim.length; ++n2) {
                if ("object" != typeof t3.dim[n2]) throw TypeError(".onnx.TensorShapeProto.dim: object expected");
                e2.dim[n2] = l.onnx.TensorShapeProto.Dimension.fromObject(t3.dim[n2]);
              }
            }
            return e2;
          }, t2.toObject = function(t3, e2) {
            e2 || (e2 = {});
            var n2 = {};
            if ((e2.arrays || e2.defaults) && (n2.dim = []), t3.dim && t3.dim.length) {
              n2.dim = [];
              for (var r2 = 0; r2 < t3.dim.length; ++r2) n2.dim[r2] = l.onnx.TensorShapeProto.Dimension.toObject(t3.dim[r2], e2);
            }
            return n2;
          }, t2.prototype.toJSON = function() {
            return this.constructor.toObject(this, a.util.toJSONOptions);
          }, t2.Dimension = function() {
            function t3(t4) {
              if (t4) for (var e3 = Object.keys(t4), n2 = 0; n2 < e3.length; ++n2) null != t4[e3[n2]] && (this[e3[n2]] = t4[e3[n2]]);
            }
            var e2;
            return t3.prototype.dimValue = c.Long ? c.Long.fromBits(0, 0, false) : 0, t3.prototype.dimParam = "", t3.prototype.denotation = "", Object.defineProperty(t3.prototype, "value", { get: c.oneOfGetter(e2 = ["dimValue", "dimParam"]), set: c.oneOfSetter(e2) }), t3.create = function(e3) {
              return new t3(e3);
            }, t3.encode = function(t4, e3) {
              return e3 || (e3 = u.create()), null != t4.dimValue && t4.hasOwnProperty("dimValue") && e3.uint32(8).int64(t4.dimValue), null != t4.dimParam && t4.hasOwnProperty("dimParam") && e3.uint32(18).string(t4.dimParam), null != t4.denotation && t4.hasOwnProperty("denotation") && e3.uint32(26).string(t4.denotation), e3;
            }, t3.encodeDelimited = function(t4, e3) {
              return this.encode(t4, e3).ldelim();
            }, t3.decode = function(t4, e3) {
              t4 instanceof s || (t4 = s.create(t4));
              for (var n2 = void 0 === e3 ? t4.len : t4.pos + e3, r2 = new l.onnx.TensorShapeProto.Dimension(); t4.pos < n2; ) {
                var i2 = t4.uint32();
                switch (i2 >>> 3) {
                  case 1:
                    r2.dimValue = t4.int64();
                    break;
                  case 2:
                    r2.dimParam = t4.string();
                    break;
                  case 3:
                    r2.denotation = t4.string();
                    break;
                  default:
                    t4.skipType(7 & i2);
                }
              }
              return r2;
            }, t3.decodeDelimited = function(t4) {
              return t4 instanceof s || (t4 = new s(t4)), this.decode(t4, t4.uint32());
            }, t3.verify = function(t4) {
              if ("object" != typeof t4 || null === t4) return "object expected";
              var e3 = {};
              if (null != t4.dimValue && t4.hasOwnProperty("dimValue") && (e3.value = 1, !(c.isInteger(t4.dimValue) || t4.dimValue && c.isInteger(t4.dimValue.low) && c.isInteger(t4.dimValue.high)))) return "dimValue: integer|Long expected";
              if (null != t4.dimParam && t4.hasOwnProperty("dimParam")) {
                if (1 === e3.value) return "value: multiple values";
                if (e3.value = 1, !c.isString(t4.dimParam)) return "dimParam: string expected";
              }
              return null != t4.denotation && t4.hasOwnProperty("denotation") && !c.isString(t4.denotation) ? "denotation: string expected" : null;
            }, t3.fromObject = function(t4) {
              if (t4 instanceof l.onnx.TensorShapeProto.Dimension) return t4;
              var e3 = new l.onnx.TensorShapeProto.Dimension();
              return null != t4.dimValue && (c.Long ? (e3.dimValue = c.Long.fromValue(t4.dimValue)).unsigned = false : "string" == typeof t4.dimValue ? e3.dimValue = parseInt(t4.dimValue, 10) : "number" == typeof t4.dimValue ? e3.dimValue = t4.dimValue : "object" == typeof t4.dimValue && (e3.dimValue = new c.LongBits(t4.dimValue.low >>> 0, t4.dimValue.high >>> 0).toNumber())), null != t4.dimParam && (e3.dimParam = String(t4.dimParam)), null != t4.denotation && (e3.denotation = String(t4.denotation)), e3;
            }, t3.toObject = function(t4, e3) {
              e3 || (e3 = {});
              var n2 = {};
              return e3.defaults && (n2.denotation = ""), null != t4.dimValue && t4.hasOwnProperty("dimValue") && ("number" == typeof t4.dimValue ? n2.dimValue = e3.longs === String ? String(t4.dimValue) : t4.dimValue : n2.dimValue = e3.longs === String ? c.Long.prototype.toString.call(t4.dimValue) : e3.longs === Number ? new c.LongBits(t4.dimValue.low >>> 0, t4.dimValue.high >>> 0).toNumber() : t4.dimValue, e3.oneofs && (n2.value = "dimValue")), null != t4.dimParam && t4.hasOwnProperty("dimParam") && (n2.dimParam = t4.dimParam, e3.oneofs && (n2.value = "dimParam")), null != t4.denotation && t4.hasOwnProperty("denotation") && (n2.denotation = t4.denotation), n2;
            }, t3.prototype.toJSON = function() {
              return this.constructor.toObject(this, a.util.toJSONOptions);
            }, t3;
          }(), t2;
        }(), o.TypeProto = function() {
          function t2(t3) {
            if (t3) for (var e3 = Object.keys(t3), n2 = 0; n2 < e3.length; ++n2) null != t3[e3[n2]] && (this[e3[n2]] = t3[e3[n2]]);
          }
          var e2;
          return t2.prototype.tensorType = null, t2.prototype.denotation = "", Object.defineProperty(t2.prototype, "value", { get: c.oneOfGetter(e2 = ["tensorType"]), set: c.oneOfSetter(e2) }), t2.create = function(e3) {
            return new t2(e3);
          }, t2.encode = function(t3, e3) {
            return e3 || (e3 = u.create()), null != t3.tensorType && t3.hasOwnProperty("tensorType") && l.onnx.TypeProto.Tensor.encode(t3.tensorType, e3.uint32(10).fork()).ldelim(), null != t3.denotation && t3.hasOwnProperty("denotation") && e3.uint32(50).string(t3.denotation), e3;
          }, t2.encodeDelimited = function(t3, e3) {
            return this.encode(t3, e3).ldelim();
          }, t2.decode = function(t3, e3) {
            t3 instanceof s || (t3 = s.create(t3));
            for (var n2 = void 0 === e3 ? t3.len : t3.pos + e3, r2 = new l.onnx.TypeProto(); t3.pos < n2; ) {
              var i2 = t3.uint32();
              switch (i2 >>> 3) {
                case 1:
                  r2.tensorType = l.onnx.TypeProto.Tensor.decode(t3, t3.uint32());
                  break;
                case 6:
                  r2.denotation = t3.string();
                  break;
                default:
                  t3.skipType(7 & i2);
              }
            }
            return r2;
          }, t2.decodeDelimited = function(t3) {
            return t3 instanceof s || (t3 = new s(t3)), this.decode(t3, t3.uint32());
          }, t2.verify = function(t3) {
            if ("object" != typeof t3 || null === t3) return "object expected";
            if (null != t3.tensorType && t3.hasOwnProperty("tensorType")) {
              var e3 = l.onnx.TypeProto.Tensor.verify(t3.tensorType);
              if (e3) return "tensorType." + e3;
            }
            return null != t3.denotation && t3.hasOwnProperty("denotation") && !c.isString(t3.denotation) ? "denotation: string expected" : null;
          }, t2.fromObject = function(t3) {
            if (t3 instanceof l.onnx.TypeProto) return t3;
            var e3 = new l.onnx.TypeProto();
            if (null != t3.tensorType) {
              if ("object" != typeof t3.tensorType) throw TypeError(".onnx.TypeProto.tensorType: object expected");
              e3.tensorType = l.onnx.TypeProto.Tensor.fromObject(t3.tensorType);
            }
            return null != t3.denotation && (e3.denotation = String(t3.denotation)), e3;
          }, t2.toObject = function(t3, e3) {
            e3 || (e3 = {});
            var n2 = {};
            return e3.defaults && (n2.denotation = ""), null != t3.tensorType && t3.hasOwnProperty("tensorType") && (n2.tensorType = l.onnx.TypeProto.Tensor.toObject(t3.tensorType, e3), e3.oneofs && (n2.value = "tensorType")), null != t3.denotation && t3.hasOwnProperty("denotation") && (n2.denotation = t3.denotation), n2;
          }, t2.prototype.toJSON = function() {
            return this.constructor.toObject(this, a.util.toJSONOptions);
          }, t2.Tensor = function() {
            function t3(t4) {
              if (t4) for (var e3 = Object.keys(t4), n2 = 0; n2 < e3.length; ++n2) null != t4[e3[n2]] && (this[e3[n2]] = t4[e3[n2]]);
            }
            return t3.prototype.elemType = 0, t3.prototype.shape = null, t3.create = function(e3) {
              return new t3(e3);
            }, t3.encode = function(t4, e3) {
              return e3 || (e3 = u.create()), null != t4.elemType && t4.hasOwnProperty("elemType") && e3.uint32(8).int32(t4.elemType), null != t4.shape && t4.hasOwnProperty("shape") && l.onnx.TensorShapeProto.encode(t4.shape, e3.uint32(18).fork()).ldelim(), e3;
            }, t3.encodeDelimited = function(t4, e3) {
              return this.encode(t4, e3).ldelim();
            }, t3.decode = function(t4, e3) {
              t4 instanceof s || (t4 = s.create(t4));
              for (var n2 = void 0 === e3 ? t4.len : t4.pos + e3, r2 = new l.onnx.TypeProto.Tensor(); t4.pos < n2; ) {
                var i2 = t4.uint32();
                switch (i2 >>> 3) {
                  case 1:
                    r2.elemType = t4.int32();
                    break;
                  case 2:
                    r2.shape = l.onnx.TensorShapeProto.decode(t4, t4.uint32());
                    break;
                  default:
                    t4.skipType(7 & i2);
                }
              }
              return r2;
            }, t3.decodeDelimited = function(t4) {
              return t4 instanceof s || (t4 = new s(t4)), this.decode(t4, t4.uint32());
            }, t3.verify = function(t4) {
              if ("object" != typeof t4 || null === t4) return "object expected";
              if (null != t4.elemType && t4.hasOwnProperty("elemType") && !c.isInteger(t4.elemType)) return "elemType: integer expected";
              if (null != t4.shape && t4.hasOwnProperty("shape")) {
                var e3 = l.onnx.TensorShapeProto.verify(t4.shape);
                if (e3) return "shape." + e3;
              }
              return null;
            }, t3.fromObject = function(t4) {
              if (t4 instanceof l.onnx.TypeProto.Tensor) return t4;
              var e3 = new l.onnx.TypeProto.Tensor();
              if (null != t4.elemType && (e3.elemType = 0 | t4.elemType), null != t4.shape) {
                if ("object" != typeof t4.shape) throw TypeError(".onnx.TypeProto.Tensor.shape: object expected");
                e3.shape = l.onnx.TensorShapeProto.fromObject(t4.shape);
              }
              return e3;
            }, t3.toObject = function(t4, e3) {
              e3 || (e3 = {});
              var n2 = {};
              return e3.defaults && (n2.elemType = 0, n2.shape = null), null != t4.elemType && t4.hasOwnProperty("elemType") && (n2.elemType = t4.elemType), null != t4.shape && t4.hasOwnProperty("shape") && (n2.shape = l.onnx.TensorShapeProto.toObject(t4.shape, e3)), n2;
            }, t3.prototype.toJSON = function() {
              return this.constructor.toObject(this, a.util.toJSONOptions);
            }, t3;
          }(), t2;
        }(), o.OperatorSetIdProto = function() {
          function t2(t3) {
            if (t3) for (var e2 = Object.keys(t3), n2 = 0; n2 < e2.length; ++n2) null != t3[e2[n2]] && (this[e2[n2]] = t3[e2[n2]]);
          }
          return t2.prototype.domain = "", t2.prototype.version = c.Long ? c.Long.fromBits(0, 0, false) : 0, t2.create = function(e2) {
            return new t2(e2);
          }, t2.encode = function(t3, e2) {
            return e2 || (e2 = u.create()), null != t3.domain && t3.hasOwnProperty("domain") && e2.uint32(10).string(t3.domain), null != t3.version && t3.hasOwnProperty("version") && e2.uint32(16).int64(t3.version), e2;
          }, t2.encodeDelimited = function(t3, e2) {
            return this.encode(t3, e2).ldelim();
          }, t2.decode = function(t3, e2) {
            t3 instanceof s || (t3 = s.create(t3));
            for (var n2 = void 0 === e2 ? t3.len : t3.pos + e2, r2 = new l.onnx.OperatorSetIdProto(); t3.pos < n2; ) {
              var i2 = t3.uint32();
              switch (i2 >>> 3) {
                case 1:
                  r2.domain = t3.string();
                  break;
                case 2:
                  r2.version = t3.int64();
                  break;
                default:
                  t3.skipType(7 & i2);
              }
            }
            return r2;
          }, t2.decodeDelimited = function(t3) {
            return t3 instanceof s || (t3 = new s(t3)), this.decode(t3, t3.uint32());
          }, t2.verify = function(t3) {
            return "object" != typeof t3 || null === t3 ? "object expected" : null != t3.domain && t3.hasOwnProperty("domain") && !c.isString(t3.domain) ? "domain: string expected" : null != t3.version && t3.hasOwnProperty("version") && !(c.isInteger(t3.version) || t3.version && c.isInteger(t3.version.low) && c.isInteger(t3.version.high)) ? "version: integer|Long expected" : null;
          }, t2.fromObject = function(t3) {
            if (t3 instanceof l.onnx.OperatorSetIdProto) return t3;
            var e2 = new l.onnx.OperatorSetIdProto();
            return null != t3.domain && (e2.domain = String(t3.domain)), null != t3.version && (c.Long ? (e2.version = c.Long.fromValue(t3.version)).unsigned = false : "string" == typeof t3.version ? e2.version = parseInt(t3.version, 10) : "number" == typeof t3.version ? e2.version = t3.version : "object" == typeof t3.version && (e2.version = new c.LongBits(t3.version.low >>> 0, t3.version.high >>> 0).toNumber())), e2;
          }, t2.toObject = function(t3, e2) {
            e2 || (e2 = {});
            var n2 = {};
            if (e2.defaults) if (n2.domain = "", c.Long) {
              var r2 = new c.Long(0, 0, false);
              n2.version = e2.longs === String ? r2.toString() : e2.longs === Number ? r2.toNumber() : r2;
            } else n2.version = e2.longs === String ? "0" : 0;
            return null != t3.domain && t3.hasOwnProperty("domain") && (n2.domain = t3.domain), null != t3.version && t3.hasOwnProperty("version") && ("number" == typeof t3.version ? n2.version = e2.longs === String ? String(t3.version) : t3.version : n2.version = e2.longs === String ? c.Long.prototype.toString.call(t3.version) : e2.longs === Number ? new c.LongBits(t3.version.low >>> 0, t3.version.high >>> 0).toNumber() : t3.version), n2;
          }, t2.prototype.toJSON = function() {
            return this.constructor.toObject(this, a.util.toJSONOptions);
          }, t2;
        }(), o), t.exports = l;
      }, 2100: (t, e, n) => {
        t.exports = n(9482);
      }, 9482: (t, e, n) => {
        var r = e;
        function i() {
          r.util._configure(), r.Writer._configure(r.BufferWriter), r.Reader._configure(r.BufferReader);
        }
        r.build = "minimal", r.Writer = n(1173), r.BufferWriter = n(3155), r.Reader = n(1408), r.BufferReader = n(593), r.util = n(9693), r.rpc = n(5994), r.roots = n(5054), r.configure = i, i();
      }, 1408: (t, e, n) => {
        t.exports = u;
        var r, i = n(9693), o = i.LongBits, a = i.utf8;
        function s(t2, e2) {
          return RangeError("index out of range: " + t2.pos + " + " + (e2 || 1) + " > " + t2.len);
        }
        function u(t2) {
          this.buf = t2, this.pos = 0, this.len = t2.length;
        }
        var c, l = "undefined" != typeof Uint8Array ? function(t2) {
          if (t2 instanceof Uint8Array || Array.isArray(t2)) return new u(t2);
          throw Error("illegal buffer");
        } : function(t2) {
          if (Array.isArray(t2)) return new u(t2);
          throw Error("illegal buffer");
        }, p = function() {
          return i.Buffer ? function(t2) {
            return (u.create = function(t3) {
              return i.Buffer.isBuffer(t3) ? new r(t3) : l(t3);
            })(t2);
          } : l;
        };
        function f() {
          var t2 = new o(0, 0), e2 = 0;
          if (!(this.len - this.pos > 4)) {
            for (; e2 < 3; ++e2) {
              if (this.pos >= this.len) throw s(this);
              if (t2.lo = (t2.lo | (127 & this.buf[this.pos]) << 7 * e2) >>> 0, this.buf[this.pos++] < 128) return t2;
            }
            return t2.lo = (t2.lo | (127 & this.buf[this.pos++]) << 7 * e2) >>> 0, t2;
          }
          for (; e2 < 4; ++e2) if (t2.lo = (t2.lo | (127 & this.buf[this.pos]) << 7 * e2) >>> 0, this.buf[this.pos++] < 128) return t2;
          if (t2.lo = (t2.lo | (127 & this.buf[this.pos]) << 28) >>> 0, t2.hi = (t2.hi | (127 & this.buf[this.pos]) >> 4) >>> 0, this.buf[this.pos++] < 128) return t2;
          if (e2 = 0, this.len - this.pos > 4) {
            for (; e2 < 5; ++e2) if (t2.hi = (t2.hi | (127 & this.buf[this.pos]) << 7 * e2 + 3) >>> 0, this.buf[this.pos++] < 128) return t2;
          } else for (; e2 < 5; ++e2) {
            if (this.pos >= this.len) throw s(this);
            if (t2.hi = (t2.hi | (127 & this.buf[this.pos]) << 7 * e2 + 3) >>> 0, this.buf[this.pos++] < 128) return t2;
          }
          throw Error("invalid varint encoding");
        }
        function d(t2, e2) {
          return (t2[e2 - 4] | t2[e2 - 3] << 8 | t2[e2 - 2] << 16 | t2[e2 - 1] << 24) >>> 0;
        }
        function h() {
          if (this.pos + 8 > this.len) throw s(this, 8);
          return new o(d(this.buf, this.pos += 4), d(this.buf, this.pos += 4));
        }
        u.create = p(), u.prototype._slice = i.Array.prototype.subarray || i.Array.prototype.slice, u.prototype.uint32 = (c = 4294967295, function() {
          if (c = (127 & this.buf[this.pos]) >>> 0, this.buf[this.pos++] < 128) return c;
          if (c = (c | (127 & this.buf[this.pos]) << 7) >>> 0, this.buf[this.pos++] < 128) return c;
          if (c = (c | (127 & this.buf[this.pos]) << 14) >>> 0, this.buf[this.pos++] < 128) return c;
          if (c = (c | (127 & this.buf[this.pos]) << 21) >>> 0, this.buf[this.pos++] < 128) return c;
          if (c = (c | (15 & this.buf[this.pos]) << 28) >>> 0, this.buf[this.pos++] < 128) return c;
          if ((this.pos += 5) > this.len) throw this.pos = this.len, s(this, 10);
          return c;
        }), u.prototype.int32 = function() {
          return 0 | this.uint32();
        }, u.prototype.sint32 = function() {
          var t2 = this.uint32();
          return t2 >>> 1 ^ -(1 & t2) | 0;
        }, u.prototype.bool = function() {
          return 0 !== this.uint32();
        }, u.prototype.fixed32 = function() {
          if (this.pos + 4 > this.len) throw s(this, 4);
          return d(this.buf, this.pos += 4);
        }, u.prototype.sfixed32 = function() {
          if (this.pos + 4 > this.len) throw s(this, 4);
          return 0 | d(this.buf, this.pos += 4);
        }, u.prototype.float = function() {
          if (this.pos + 4 > this.len) throw s(this, 4);
          var t2 = i.float.readFloatLE(this.buf, this.pos);
          return this.pos += 4, t2;
        }, u.prototype.double = function() {
          if (this.pos + 8 > this.len) throw s(this, 4);
          var t2 = i.float.readDoubleLE(this.buf, this.pos);
          return this.pos += 8, t2;
        }, u.prototype.bytes = function() {
          var t2 = this.uint32(), e2 = this.pos, n2 = this.pos + t2;
          if (n2 > this.len) throw s(this, t2);
          return this.pos += t2, Array.isArray(this.buf) ? this.buf.slice(e2, n2) : e2 === n2 ? new this.buf.constructor(0) : this._slice.call(this.buf, e2, n2);
        }, u.prototype.string = function() {
          var t2 = this.bytes();
          return a.read(t2, 0, t2.length);
        }, u.prototype.skip = function(t2) {
          if ("number" == typeof t2) {
            if (this.pos + t2 > this.len) throw s(this, t2);
            this.pos += t2;
          } else do {
            if (this.pos >= this.len) throw s(this);
          } while (128 & this.buf[this.pos++]);
          return this;
        }, u.prototype.skipType = function(t2) {
          switch (t2) {
            case 0:
              this.skip();
              break;
            case 1:
              this.skip(8);
              break;
            case 2:
              this.skip(this.uint32());
              break;
            case 3:
              for (; 4 != (t2 = 7 & this.uint32()); ) this.skipType(t2);
              break;
            case 5:
              this.skip(4);
              break;
            default:
              throw Error("invalid wire type " + t2 + " at offset " + this.pos);
          }
          return this;
        }, u._configure = function(t2) {
          r = t2, u.create = p(), r._configure();
          var e2 = i.Long ? "toLong" : "toNumber";
          i.merge(u.prototype, { int64: function() {
            return f.call(this)[e2](false);
          }, uint64: function() {
            return f.call(this)[e2](true);
          }, sint64: function() {
            return f.call(this).zzDecode()[e2](false);
          }, fixed64: function() {
            return h.call(this)[e2](true);
          }, sfixed64: function() {
            return h.call(this)[e2](false);
          } });
        };
      }, 593: (t, e, n) => {
        t.exports = o;
        var r = n(1408);
        (o.prototype = Object.create(r.prototype)).constructor = o;
        var i = n(9693);
        function o(t2) {
          r.call(this, t2);
        }
        o._configure = function() {
          i.Buffer && (o.prototype._slice = i.Buffer.prototype.slice);
        }, o.prototype.string = function() {
          var t2 = this.uint32();
          return this.buf.utf8Slice ? this.buf.utf8Slice(this.pos, this.pos = Math.min(this.pos + t2, this.len)) : this.buf.toString("utf-8", this.pos, this.pos = Math.min(this.pos + t2, this.len));
        }, o._configure();
      }, 5054: (t) => {
        t.exports = {};
      }, 5994: (t, e, n) => {
        e.Service = n(7948);
      }, 7948: (t, e, n) => {
        t.exports = i;
        var r = n(9693);
        function i(t2, e2, n2) {
          if ("function" != typeof t2) throw TypeError("rpcImpl must be a function");
          r.EventEmitter.call(this), this.rpcImpl = t2, this.requestDelimited = Boolean(e2), this.responseDelimited = Boolean(n2);
        }
        (i.prototype = Object.create(r.EventEmitter.prototype)).constructor = i, i.prototype.rpcCall = function t2(e2, n2, i2, o, a) {
          if (!o) throw TypeError("request must be specified");
          var s = this;
          if (!a) return r.asPromise(t2, s, e2, n2, i2, o);
          if (s.rpcImpl) try {
            return s.rpcImpl(e2, n2[s.requestDelimited ? "encodeDelimited" : "encode"](o).finish(), function(t3, n3) {
              if (t3) return s.emit("error", t3, e2), a(t3);
              if (null !== n3) {
                if (!(n3 instanceof i2)) try {
                  n3 = i2[s.responseDelimited ? "decodeDelimited" : "decode"](n3);
                } catch (t4) {
                  return s.emit("error", t4, e2), a(t4);
                }
                return s.emit("data", n3, e2), a(null, n3);
              }
              s.end(true);
            });
          } catch (t3) {
            return s.emit("error", t3, e2), void setTimeout(function() {
              a(t3);
            }, 0);
          }
          else setTimeout(function() {
            a(Error("already ended"));
          }, 0);
        }, i.prototype.end = function(t2) {
          return this.rpcImpl && (t2 || this.rpcImpl(null, null, null), this.rpcImpl = null, this.emit("end").off()), this;
        };
      }, 1945: (t, e, n) => {
        t.exports = i;
        var r = n(9693);
        function i(t2, e2) {
          this.lo = t2 >>> 0, this.hi = e2 >>> 0;
        }
        var o = i.zero = new i(0, 0);
        o.toNumber = function() {
          return 0;
        }, o.zzEncode = o.zzDecode = function() {
          return this;
        }, o.length = function() {
          return 1;
        };
        var a = i.zeroHash = "\0\0\0\0\0\0\0\0";
        i.fromNumber = function(t2) {
          if (0 === t2) return o;
          var e2 = t2 < 0;
          e2 && (t2 = -t2);
          var n2 = t2 >>> 0, r2 = (t2 - n2) / 4294967296 >>> 0;
          return e2 && (r2 = ~r2 >>> 0, n2 = ~n2 >>> 0, ++n2 > 4294967295 && (n2 = 0, ++r2 > 4294967295 && (r2 = 0))), new i(n2, r2);
        }, i.from = function(t2) {
          if ("number" == typeof t2) return i.fromNumber(t2);
          if (r.isString(t2)) {
            if (!r.Long) return i.fromNumber(parseInt(t2, 10));
            t2 = r.Long.fromString(t2);
          }
          return t2.low || t2.high ? new i(t2.low >>> 0, t2.high >>> 0) : o;
        }, i.prototype.toNumber = function(t2) {
          if (!t2 && this.hi >>> 31) {
            var e2 = 1 + ~this.lo >>> 0, n2 = ~this.hi >>> 0;
            return e2 || (n2 = n2 + 1 >>> 0), -(e2 + 4294967296 * n2);
          }
          return this.lo + 4294967296 * this.hi;
        }, i.prototype.toLong = function(t2) {
          return r.Long ? new r.Long(0 | this.lo, 0 | this.hi, Boolean(t2)) : { low: 0 | this.lo, high: 0 | this.hi, unsigned: Boolean(t2) };
        };
        var s = String.prototype.charCodeAt;
        i.fromHash = function(t2) {
          return t2 === a ? o : new i((s.call(t2, 0) | s.call(t2, 1) << 8 | s.call(t2, 2) << 16 | s.call(t2, 3) << 24) >>> 0, (s.call(t2, 4) | s.call(t2, 5) << 8 | s.call(t2, 6) << 16 | s.call(t2, 7) << 24) >>> 0);
        }, i.prototype.toHash = function() {
          return String.fromCharCode(255 & this.lo, this.lo >>> 8 & 255, this.lo >>> 16 & 255, this.lo >>> 24, 255 & this.hi, this.hi >>> 8 & 255, this.hi >>> 16 & 255, this.hi >>> 24);
        }, i.prototype.zzEncode = function() {
          var t2 = this.hi >> 31;
          return this.hi = ((this.hi << 1 | this.lo >>> 31) ^ t2) >>> 0, this.lo = (this.lo << 1 ^ t2) >>> 0, this;
        }, i.prototype.zzDecode = function() {
          var t2 = -(1 & this.lo);
          return this.lo = ((this.lo >>> 1 | this.hi << 31) ^ t2) >>> 0, this.hi = (this.hi >>> 1 ^ t2) >>> 0, this;
        }, i.prototype.length = function() {
          var t2 = this.lo, e2 = (this.lo >>> 28 | this.hi << 4) >>> 0, n2 = this.hi >>> 24;
          return 0 === n2 ? 0 === e2 ? t2 < 16384 ? t2 < 128 ? 1 : 2 : t2 < 2097152 ? 3 : 4 : e2 < 16384 ? e2 < 128 ? 5 : 6 : e2 < 2097152 ? 7 : 8 : n2 < 128 ? 9 : 10;
        };
      }, 9693: function(t, e, n) {
        var r = e;
        function i(t2, e2, n2) {
          for (var r2 = Object.keys(e2), i2 = 0; i2 < r2.length; ++i2) void 0 !== t2[r2[i2]] && n2 || (t2[r2[i2]] = e2[r2[i2]]);
          return t2;
        }
        function o(t2) {
          function e2(t3, n2) {
            if (!(this instanceof e2)) return new e2(t3, n2);
            Object.defineProperty(this, "message", { get: function() {
              return t3;
            } }), Error.captureStackTrace ? Error.captureStackTrace(this, e2) : Object.defineProperty(this, "stack", { value: new Error().stack || "" }), n2 && i(this, n2);
          }
          return (e2.prototype = Object.create(Error.prototype)).constructor = e2, Object.defineProperty(e2.prototype, "name", { get: function() {
            return t2;
          } }), e2.prototype.toString = function() {
            return this.name + ": " + this.message;
          }, e2;
        }
        r.asPromise = n(4537), r.base64 = n(7419), r.EventEmitter = n(9211), r.float = n(945), r.inquire = n(7199), r.utf8 = n(4997), r.pool = n(6662), r.LongBits = n(1945), r.isNode = Boolean(void 0 !== n.g && n.g && n.g.process && n.g.process.versions && n.g.process.versions.node), r.global = r.isNode && n.g || "undefined" != typeof window && window || "undefined" != typeof self && self || this, r.emptyArray = Object.freeze ? Object.freeze([]) : [], r.emptyObject = Object.freeze ? Object.freeze({}) : {}, r.isInteger = Number.isInteger || function(t2) {
          return "number" == typeof t2 && isFinite(t2) && Math.floor(t2) === t2;
        }, r.isString = function(t2) {
          return "string" == typeof t2 || t2 instanceof String;
        }, r.isObject = function(t2) {
          return t2 && "object" == typeof t2;
        }, r.isset = r.isSet = function(t2, e2) {
          var n2 = t2[e2];
          return !(null == n2 || !t2.hasOwnProperty(e2)) && ("object" != typeof n2 || (Array.isArray(n2) ? n2.length : Object.keys(n2).length) > 0);
        }, r.Buffer = function() {
          try {
            var t2 = r.inquire("buffer").Buffer;
            return t2.prototype.utf8Write ? t2 : null;
          } catch (t3) {
            return null;
          }
        }(), r._Buffer_from = null, r._Buffer_allocUnsafe = null, r.newBuffer = function(t2) {
          return "number" == typeof t2 ? r.Buffer ? r._Buffer_allocUnsafe(t2) : new r.Array(t2) : r.Buffer ? r._Buffer_from(t2) : "undefined" == typeof Uint8Array ? t2 : new Uint8Array(t2);
        }, r.Array = "undefined" != typeof Uint8Array ? Uint8Array : Array, r.Long = r.global.dcodeIO && r.global.dcodeIO.Long || r.global.Long || r.inquire("long"), r.key2Re = /^true|false|0|1$/, r.key32Re = /^-?(?:0|[1-9][0-9]*)$/, r.key64Re = /^(?:[\\x00-\\xff]{8}|-?(?:0|[1-9][0-9]*))$/, r.longToHash = function(t2) {
          return t2 ? r.LongBits.from(t2).toHash() : r.LongBits.zeroHash;
        }, r.longFromHash = function(t2, e2) {
          var n2 = r.LongBits.fromHash(t2);
          return r.Long ? r.Long.fromBits(n2.lo, n2.hi, e2) : n2.toNumber(Boolean(e2));
        }, r.merge = i, r.lcFirst = function(t2) {
          return t2.charAt(0).toLowerCase() + t2.substring(1);
        }, r.newError = o, r.ProtocolError = o("ProtocolError"), r.oneOfGetter = function(t2) {
          for (var e2 = {}, n2 = 0; n2 < t2.length; ++n2) e2[t2[n2]] = 1;
          return function() {
            for (var t3 = Object.keys(this), n3 = t3.length - 1; n3 > -1; --n3) if (1 === e2[t3[n3]] && void 0 !== this[t3[n3]] && null !== this[t3[n3]]) return t3[n3];
          };
        }, r.oneOfSetter = function(t2) {
          return function(e2) {
            for (var n2 = 0; n2 < t2.length; ++n2) t2[n2] !== e2 && delete this[t2[n2]];
          };
        }, r.toJSONOptions = { longs: String, enums: String, bytes: String, json: true }, r._configure = function() {
          var t2 = r.Buffer;
          t2 ? (r._Buffer_from = t2.from !== Uint8Array.from && t2.from || function(e2, n2) {
            return new t2(e2, n2);
          }, r._Buffer_allocUnsafe = t2.allocUnsafe || function(e2) {
            return new t2(e2);
          }) : r._Buffer_from = r._Buffer_allocUnsafe = null;
        };
      }, 1173: (t, e, n) => {
        t.exports = p;
        var r, i = n(9693), o = i.LongBits, a = i.base64, s = i.utf8;
        function u(t2, e2, n2) {
          this.fn = t2, this.len = e2, this.next = void 0, this.val = n2;
        }
        function c() {
        }
        function l(t2) {
          this.head = t2.head, this.tail = t2.tail, this.len = t2.len, this.next = t2.states;
        }
        function p() {
          this.len = 0, this.head = new u(c, 0, 0), this.tail = this.head, this.states = null;
        }
        var f = function() {
          return i.Buffer ? function() {
            return (p.create = function() {
              return new r();
            })();
          } : function() {
            return new p();
          };
        };
        function d(t2, e2, n2) {
          e2[n2] = 255 & t2;
        }
        function h(t2, e2) {
          this.len = t2, this.next = void 0, this.val = e2;
        }
        function g(t2, e2, n2) {
          for (; t2.hi; ) e2[n2++] = 127 & t2.lo | 128, t2.lo = (t2.lo >>> 7 | t2.hi << 25) >>> 0, t2.hi >>>= 7;
          for (; t2.lo > 127; ) e2[n2++] = 127 & t2.lo | 128, t2.lo = t2.lo >>> 7;
          e2[n2++] = t2.lo;
        }
        function b(t2, e2, n2) {
          e2[n2] = 255 & t2, e2[n2 + 1] = t2 >>> 8 & 255, e2[n2 + 2] = t2 >>> 16 & 255, e2[n2 + 3] = t2 >>> 24;
        }
        p.create = f(), p.alloc = function(t2) {
          return new i.Array(t2);
        }, i.Array !== Array && (p.alloc = i.pool(p.alloc, i.Array.prototype.subarray)), p.prototype._push = function(t2, e2, n2) {
          return this.tail = this.tail.next = new u(t2, e2, n2), this.len += e2, this;
        }, h.prototype = Object.create(u.prototype), h.prototype.fn = function(t2, e2, n2) {
          for (; t2 > 127; ) e2[n2++] = 127 & t2 | 128, t2 >>>= 7;
          e2[n2] = t2;
        }, p.prototype.uint32 = function(t2) {
          return this.len += (this.tail = this.tail.next = new h((t2 >>>= 0) < 128 ? 1 : t2 < 16384 ? 2 : t2 < 2097152 ? 3 : t2 < 268435456 ? 4 : 5, t2)).len, this;
        }, p.prototype.int32 = function(t2) {
          return t2 < 0 ? this._push(g, 10, o.fromNumber(t2)) : this.uint32(t2);
        }, p.prototype.sint32 = function(t2) {
          return this.uint32((t2 << 1 ^ t2 >> 31) >>> 0);
        }, p.prototype.uint64 = function(t2) {
          var e2 = o.from(t2);
          return this._push(g, e2.length(), e2);
        }, p.prototype.int64 = p.prototype.uint64, p.prototype.sint64 = function(t2) {
          var e2 = o.from(t2).zzEncode();
          return this._push(g, e2.length(), e2);
        }, p.prototype.bool = function(t2) {
          return this._push(d, 1, t2 ? 1 : 0);
        }, p.prototype.fixed32 = function(t2) {
          return this._push(b, 4, t2 >>> 0);
        }, p.prototype.sfixed32 = p.prototype.fixed32, p.prototype.fixed64 = function(t2) {
          var e2 = o.from(t2);
          return this._push(b, 4, e2.lo)._push(b, 4, e2.hi);
        }, p.prototype.sfixed64 = p.prototype.fixed64, p.prototype.float = function(t2) {
          return this._push(i.float.writeFloatLE, 4, t2);
        }, p.prototype.double = function(t2) {
          return this._push(i.float.writeDoubleLE, 8, t2);
        };
        var m = i.Array.prototype.set ? function(t2, e2, n2) {
          e2.set(t2, n2);
        } : function(t2, e2, n2) {
          for (var r2 = 0; r2 < t2.length; ++r2) e2[n2 + r2] = t2[r2];
        };
        p.prototype.bytes = function(t2) {
          var e2 = t2.length >>> 0;
          if (!e2) return this._push(d, 1, 0);
          if (i.isString(t2)) {
            var n2 = p.alloc(e2 = a.length(t2));
            a.decode(t2, n2, 0), t2 = n2;
          }
          return this.uint32(e2)._push(m, e2, t2);
        }, p.prototype.string = function(t2) {
          var e2 = s.length(t2);
          return e2 ? this.uint32(e2)._push(s.write, e2, t2) : this._push(d, 1, 0);
        }, p.prototype.fork = function() {
          return this.states = new l(this), this.head = this.tail = new u(c, 0, 0), this.len = 0, this;
        }, p.prototype.reset = function() {
          return this.states ? (this.head = this.states.head, this.tail = this.states.tail, this.len = this.states.len, this.states = this.states.next) : (this.head = this.tail = new u(c, 0, 0), this.len = 0), this;
        }, p.prototype.ldelim = function() {
          var t2 = this.head, e2 = this.tail, n2 = this.len;
          return this.reset().uint32(n2), n2 && (this.tail.next = t2.next, this.tail = e2, this.len += n2), this;
        }, p.prototype.finish = function() {
          for (var t2 = this.head.next, e2 = this.constructor.alloc(this.len), n2 = 0; t2; ) t2.fn(t2.val, e2, n2), n2 += t2.len, t2 = t2.next;
          return e2;
        }, p._configure = function(t2) {
          r = t2, p.create = f(), r._configure();
        };
      }, 3155: (t, e, n) => {
        t.exports = o;
        var r = n(1173);
        (o.prototype = Object.create(r.prototype)).constructor = o;
        var i = n(9693);
        function o() {
          r.call(this);
        }
        function a(t2, e2, n2) {
          t2.length < 40 ? i.utf8.write(t2, e2, n2) : e2.utf8Write ? e2.utf8Write(t2, n2) : e2.write(t2, n2);
        }
        o._configure = function() {
          o.alloc = i._Buffer_allocUnsafe, o.writeBytesBuffer = i.Buffer && i.Buffer.prototype instanceof Uint8Array && "set" === i.Buffer.prototype.set.name ? function(t2, e2, n2) {
            e2.set(t2, n2);
          } : function(t2, e2, n2) {
            if (t2.copy) t2.copy(e2, n2, 0, t2.length);
            else for (var r2 = 0; r2 < t2.length; ) e2[n2++] = t2[r2++];
          };
        }, o.prototype.bytes = function(t2) {
          i.isString(t2) && (t2 = i._Buffer_from(t2, "base64"));
          var e2 = t2.length >>> 0;
          return this.uint32(e2), e2 && this._push(o.writeBytesBuffer, e2, t2), this;
        }, o.prototype.string = function(t2) {
          var e2 = i.Buffer.byteLength(t2);
          return this.uint32(e2), e2 && this._push(a, e2, t2), this;
        }, o._configure();
      }, 7714: (t, e, n) => {
        e.R = void 0;
        const r = n(6919), i = n(7448);
        e.R = new class {
          init() {
            return __async(this, null, function* () {
            });
          }
          createSessionHandler(t2, e2) {
            return __async(this, null, function* () {
              const n2 = new r.Session(e2);
              return yield n2.loadModel(t2), new i.OnnxjsSessionHandler(n2);
            });
          }
        }();
      }, 4200: (t, e, n) => {
        e.c8 = e.rX = void 0;
        const r = n(1670), i = n(5381), o = n(2157), a = n(2306);
        e.rX = () => {
          if (("number" != typeof r.env.wasm.initTimeout || r.env.wasm.initTimeout < 0) && (r.env.wasm.initTimeout = 0), "boolean" != typeof r.env.wasm.simd && (r.env.wasm.simd = true), "boolean" != typeof r.env.wasm.proxy && (r.env.wasm.proxy = false), "number" != typeof r.env.wasm.numThreads || !Number.isInteger(r.env.wasm.numThreads) || r.env.wasm.numThreads <= 0) {
            const t2 = "undefined" == typeof navigator ? (0, i.cpus)().length : navigator.hardwareConcurrency;
            r.env.wasm.numThreads = Math.min(4, Math.ceil((t2 || 1) / 2));
          }
        }, e.c8 = new class {
          init() {
            return __async(this, null, function* () {
              (0, e.rX)(), yield (0, o.initWasm)();
            });
          }
          createSessionHandler(t2, e2) {
            return __async(this, null, function* () {
              const n2 = new a.OnnxruntimeWebAssemblySessionHandler();
              return yield n2.loadModel(t2, e2), Promise.resolve(n2);
            });
          }
        }();
      }, 6018: function(t, e, n) {
        var r = this && this.__createBinding || (Object.create ? function(t2, e2, n2, r2) {
          void 0 === r2 && (r2 = n2);
          var i2 = Object.getOwnPropertyDescriptor(e2, n2);
          i2 && !("get" in i2 ? !e2.__esModule : i2.writable || i2.configurable) || (i2 = { enumerable: true, get: function() {
            return e2[n2];
          } }), Object.defineProperty(t2, r2, i2);
        } : function(t2, e2, n2, r2) {
          void 0 === r2 && (r2 = n2), t2[r2] = e2[n2];
        }), i = this && this.__exportStar || function(t2, e2) {
          for (var n2 in t2) "default" === n2 || Object.prototype.hasOwnProperty.call(e2, n2) || r(e2, t2, n2);
        };
        Object.defineProperty(e, "__esModule", { value: true }), i(n(1670), e);
        const o = n(1670);
        {
          const t2 = n(7714).R;
          (0, o.registerBackend)("webgl", t2, -10);
        }
        {
          const t2 = n(4200).c8;
          (0, o.registerBackend)("cpu", t2, 10), (0, o.registerBackend)("wasm", t2, 10), (0, o.registerBackend)("xnnpack", t2, 9);
        }
      }, 246: (t, e) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.createAttributeWithCacheKey = void 0;
        class n {
          constructor(t2) {
            Object.assign(this, t2);
          }
          get cacheKey() {
            return this._cacheKey || (this._cacheKey = Object.getOwnPropertyNames(this).sort().map((t2) => `${this[t2]}`).join(";")), this._cacheKey;
          }
        }
        e.createAttributeWithCacheKey = (t2) => new n(t2);
      }, 7778: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.Attribute = void 0;
        const r = n(1446), i = n(9395), o = n(9162), a = n(2517);
        var s = i.onnxruntime.experimental.fbs;
        class u {
          constructor(t2) {
            if (this._attributes = /* @__PURE__ */ new Map(), null != t2) {
              for (const e2 of t2) e2 instanceof r.onnx.AttributeProto ? this._attributes.set(e2.name, [u.getValue(e2), u.getType(e2)]) : e2 instanceof s.Attribute && this._attributes.set(e2.name(), [u.getValue(e2), u.getType(e2)]);
              if (this._attributes.size < t2.length) throw new Error("duplicated attribute names");
            }
          }
          set(t2, e2, n2) {
            this._attributes.set(t2, [n2, e2]);
          }
          delete(t2) {
            this._attributes.delete(t2);
          }
          getFloat(t2, e2) {
            return this.get(t2, "float", e2);
          }
          getInt(t2, e2) {
            return this.get(t2, "int", e2);
          }
          getString(t2, e2) {
            return this.get(t2, "string", e2);
          }
          getTensor(t2, e2) {
            return this.get(t2, "tensor", e2);
          }
          getFloats(t2, e2) {
            return this.get(t2, "floats", e2);
          }
          getInts(t2, e2) {
            return this.get(t2, "ints", e2);
          }
          getStrings(t2, e2) {
            return this.get(t2, "strings", e2);
          }
          getTensors(t2, e2) {
            return this.get(t2, "tensors", e2);
          }
          get(t2, e2, n2) {
            const r2 = this._attributes.get(t2);
            if (void 0 === r2) {
              if (void 0 !== n2) return n2;
              throw new Error(`required attribute not found: ${t2}`);
            }
            if (r2[1] !== e2) throw new Error(`type mismatch: expected ${e2} but got ${r2[1]}`);
            return r2[0];
          }
          static getType(t2) {
            const e2 = t2 instanceof r.onnx.AttributeProto ? t2.type : t2.type();
            switch (e2) {
              case r.onnx.AttributeProto.AttributeType.FLOAT:
                return "float";
              case r.onnx.AttributeProto.AttributeType.INT:
                return "int";
              case r.onnx.AttributeProto.AttributeType.STRING:
                return "string";
              case r.onnx.AttributeProto.AttributeType.TENSOR:
                return "tensor";
              case r.onnx.AttributeProto.AttributeType.FLOATS:
                return "floats";
              case r.onnx.AttributeProto.AttributeType.INTS:
                return "ints";
              case r.onnx.AttributeProto.AttributeType.STRINGS:
                return "strings";
              case r.onnx.AttributeProto.AttributeType.TENSORS:
                return "tensors";
              default:
                throw new Error(`attribute type is not supported yet: ${r.onnx.AttributeProto.AttributeType[e2]}`);
            }
          }
          static getValue(t2) {
            const e2 = t2 instanceof r.onnx.AttributeProto ? t2.type : t2.type();
            if (e2 === r.onnx.AttributeProto.AttributeType.GRAPH || e2 === r.onnx.AttributeProto.AttributeType.GRAPHS) throw new Error("graph attribute is not supported yet");
            const n2 = this.getValueNoCheck(t2);
            if (e2 === r.onnx.AttributeProto.AttributeType.INT && a.LongUtil.isLong(n2)) return a.LongUtil.longToNumber(n2);
            if (e2 === r.onnx.AttributeProto.AttributeType.INTS) {
              const t3 = n2, e3 = new Array(t3.length);
              for (let n3 = 0; n3 < t3.length; n3++) {
                const r2 = t3[n3];
                e3[n3] = a.LongUtil.longToNumber(r2);
              }
              return e3;
            }
            if (e2 === r.onnx.AttributeProto.AttributeType.TENSOR) return t2 instanceof r.onnx.AttributeProto ? o.Tensor.fromProto(n2) : o.Tensor.fromOrtTensor(n2);
            if (e2 === r.onnx.AttributeProto.AttributeType.TENSORS) {
              if (t2 instanceof r.onnx.AttributeProto) return n2.map((t3) => o.Tensor.fromProto(t3));
              if (t2 instanceof s.Attribute) return n2.map((t3) => o.Tensor.fromOrtTensor(t3));
            }
            if (e2 === r.onnx.AttributeProto.AttributeType.STRING && t2 instanceof r.onnx.AttributeProto) {
              const t3 = n2;
              return (0, a.decodeUtf8String)(t3);
            }
            return e2 === r.onnx.AttributeProto.AttributeType.STRINGS && t2 instanceof r.onnx.AttributeProto ? n2.map(a.decodeUtf8String) : n2;
          }
          static getValueNoCheck(t2) {
            return t2 instanceof r.onnx.AttributeProto ? this.getValueNoCheckFromOnnxFormat(t2) : this.getValueNoCheckFromOrtFormat(t2);
          }
          static getValueNoCheckFromOnnxFormat(t2) {
            switch (t2.type) {
              case r.onnx.AttributeProto.AttributeType.FLOAT:
                return t2.f;
              case r.onnx.AttributeProto.AttributeType.INT:
                return t2.i;
              case r.onnx.AttributeProto.AttributeType.STRING:
                return t2.s;
              case r.onnx.AttributeProto.AttributeType.TENSOR:
                return t2.t;
              case r.onnx.AttributeProto.AttributeType.GRAPH:
                return t2.g;
              case r.onnx.AttributeProto.AttributeType.FLOATS:
                return t2.floats;
              case r.onnx.AttributeProto.AttributeType.INTS:
                return t2.ints;
              case r.onnx.AttributeProto.AttributeType.STRINGS:
                return t2.strings;
              case r.onnx.AttributeProto.AttributeType.TENSORS:
                return t2.tensors;
              case r.onnx.AttributeProto.AttributeType.GRAPHS:
                return t2.graphs;
              default:
                throw new Error(`unsupported attribute type: ${r.onnx.AttributeProto.AttributeType[t2.type]}`);
            }
          }
          static getValueNoCheckFromOrtFormat(t2) {
            switch (t2.type()) {
              case s.AttributeType.FLOAT:
                return t2.f();
              case s.AttributeType.INT:
                return t2.i();
              case s.AttributeType.STRING:
                return t2.s();
              case s.AttributeType.TENSOR:
                return t2.t();
              case s.AttributeType.GRAPH:
                return t2.g();
              case s.AttributeType.FLOATS:
                return t2.floatsArray();
              case s.AttributeType.INTS: {
                const e2 = [];
                for (let n2 = 0; n2 < t2.intsLength(); n2++) e2.push(t2.ints(n2));
                return e2;
              }
              case s.AttributeType.STRINGS: {
                const e2 = [];
                for (let n2 = 0; n2 < t2.stringsLength(); n2++) e2.push(t2.strings(n2));
                return e2;
              }
              case s.AttributeType.TENSORS: {
                const e2 = [];
                for (let n2 = 0; n2 < t2.tensorsLength(); n2++) e2.push(t2.tensors(n2));
                return e2;
              }
              default:
                throw new Error(`unsupported attribute type: ${s.AttributeType[t2.type()]}`);
            }
          }
        }
        e.Attribute = u;
      }, 7091: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.resolveBackend = e.backend = void 0;
        const r = n(5038), i = /* @__PURE__ */ new Map();
        function o(t2) {
          return __async(this, null, function* () {
            const n2 = e.backend;
            if (void 0 !== n2[t2] && function(t3) {
              const e2 = t3;
              return "initialize" in e2 && "function" == typeof e2.initialize && "createSessionHandler" in e2 && "function" == typeof e2.createSessionHandler && "dispose" in e2 && "function" == typeof e2.dispose;
            }(n2[t2])) {
              const e2 = n2[t2];
              let r2 = e2.initialize();
              if ("object" == typeof r2 && "then" in r2 && (r2 = yield r2), r2) return i.set(t2, e2), e2;
            }
          });
        }
        e.backend = { webgl: new r.WebGLBackend() }, e.resolveBackend = function t2(e2) {
          return __async(this, null, function* () {
            if (!e2) return t2(["webgl"]);
            {
              const t3 = "string" == typeof e2 ? [e2] : e2;
              for (const e3 of t3) {
                const t4 = i.get(e3);
                if (t4) return t4;
                const n2 = yield o(e3);
                if (n2) return n2;
              }
            }
            throw new Error("no available backend to use");
          });
        };
      }, 5038: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.WebGLBackend = void 0;
        const r = n(1670), i = n(6231), o = n(6416), a = n(7305);
        e.WebGLBackend = class {
          get contextId() {
            return r.env.webgl.contextId;
          }
          set contextId(t2) {
            r.env.webgl.contextId = t2;
          }
          get matmulMaxBatchSize() {
            return r.env.webgl.matmulMaxBatchSize;
          }
          set matmulMaxBatchSize(t2) {
            r.env.webgl.matmulMaxBatchSize = t2;
          }
          get textureCacheMode() {
            return r.env.webgl.textureCacheMode;
          }
          set textureCacheMode(t2) {
            r.env.webgl.textureCacheMode = t2;
          }
          get pack() {
            return r.env.webgl.pack;
          }
          set pack(t2) {
            r.env.webgl.pack = t2;
          }
          get async() {
            return r.env.webgl.async;
          }
          set async(t2) {
            r.env.webgl.async = t2;
          }
          initialize() {
            try {
              return this.glContext = (0, a.createWebGLContext)(this.contextId), "number" != typeof this.matmulMaxBatchSize && (this.matmulMaxBatchSize = 16), "string" != typeof this.textureCacheMode && (this.textureCacheMode = "full"), "boolean" != typeof this.pack && (this.pack = false), "boolean" != typeof this.async && (this.async = false), i.Logger.setWithEnv(r.env), i.Logger.verbose("WebGLBackend", `Created WebGLContext: ${typeof this.glContext} with matmulMaxBatchSize: ${this.matmulMaxBatchSize}; textureCacheMode: ${this.textureCacheMode}; pack: ${this.pack}; async: ${this.async}.`), true;
            } catch (t2) {
              return i.Logger.warning("WebGLBackend", `Unable to initialize WebGLBackend. ${t2}`), false;
            }
          }
          createSessionHandler(t2) {
            return new o.WebGLSessionHandler(this, t2);
          }
          dispose() {
            this.glContext.dispose();
          }
        };
      }, 5107: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.CoordsGlslLib = void 0;
        const r = n(2517), i = n(8520), o = n(5060), a = n(7859), s = n(9390);
        class u extends i.GlslLib {
          constructor(t2) {
            super(t2);
          }
          getFunctions() {
            return Object.assign(Object.assign(Object.assign(Object.assign(Object.assign(Object.assign(Object.assign({}, this.offsetToCoords()), this.coordsToOffset()), this.toVec()), this.valueFrom()), this.getCommonUtilFuncs()), this.getInputsSamplingSnippets()), this.getOutputSamplingSnippet());
          }
          getCustomTypes() {
            return {};
          }
          offsetToCoords() {
            return { offsetToCoords: new i.GlslLibRoutine("\n      vec2 offsetToCoords(int offset, int width, int height) {\n        int t = offset / width;\n        int s = offset - t*width;\n        vec2 coords = (vec2(s,t) + vec2(0.5,0.5)) / vec2(width, height);\n        return coords;\n      }\n      ") };
          }
          coordsToOffset() {
            return { coordsToOffset: new i.GlslLibRoutine("\n      int coordsToOffset(vec2 coords, int width, int height) {\n        float s = coords.s * float(width);\n        float t = coords.t * float(height);\n        int offset = int(t) * width + int(s);\n        return offset;\n      }\n      ") };
          }
          getOutputSamplingSnippet() {
            const t2 = this.context.outputTextureLayout;
            return t2.isPacked ? this.getPackedOutputSamplingSnippet(t2) : this.getUnpackedOutputSamplingSnippet(t2);
          }
          getPackedOutputSamplingSnippet(t2) {
            const e2 = t2.unpackedShape, n2 = [t2.width, t2.height], r2 = {}, a2 = "getOutputCoords";
            switch (e2.length) {
              case 0:
                r2[a2] = this.getOutputScalarCoords();
                break;
              case 1:
                r2[a2] = this.getOutputPacked1DCoords(e2, n2);
                break;
              case 2:
                r2[a2] = this.getOutputPacked2DCoords(e2, n2);
                break;
              case 3:
                r2[a2] = this.getOutputPacked3DCoords(e2, n2);
                break;
              default:
                r2[a2] = this.getOutputPackedNDCoords(e2, n2);
            }
            const s2 = `
      void setOutput(vec4 val) {
        ${(0, o.getGlsl)(this.context.glContext.version).output} = val;
      }
    `;
            return r2.floatTextureSetRGBA = new i.GlslLibRoutine(s2), r2;
          }
          getUnpackedOutputSamplingSnippet(t2) {
            const e2 = t2.unpackedShape, n2 = [t2.width, t2.height], r2 = {}, a2 = "getOutputCoords";
            switch (e2.length) {
              case 0:
                r2[a2] = this.getOutputScalarCoords();
                break;
              case 1:
                r2[a2] = this.getOutputUnpacked1DCoords(e2, n2);
                break;
              case 2:
                r2[a2] = this.getOutputUnpacked2DCoords(e2, n2);
                break;
              case 3:
                r2[a2] = this.getOutputUnpacked3DCoords(e2, n2);
                break;
              case 4:
                r2[a2] = this.getOutputUnpacked4DCoords(e2, n2);
                break;
              case 5:
                r2[a2] = this.getOutputUnpacked5DCoords(e2, n2);
                break;
              case 6:
                r2[a2] = this.getOutputUnpacked6DCoords(e2, n2);
                break;
              default:
                throw new Error(`Unsupported output dimensionality: ${e2.length}`);
            }
            const s2 = `
        void setOutput(float val) {
          ${(0, o.getGlsl)(this.context.glContext.version).output} = vec4(val, 0, 0, 0);
        }
    `;
            return r2.floatTextureSetR = new i.GlslLibRoutine(s2), r2;
          }
          getOutputScalarCoords() {
            return new i.GlslLibRoutine("\n      int getOutputCoords() {\n        return 0;\n      }\n    ");
          }
          getOutputPacked1DCoords(t2, e2) {
            const n2 = e2;
            let r2 = "";
            return 1 === n2[0] ? (r2 = `
          int getOutputCoords() {
            return 2 * int(TexCoords.y * ${n2[1]}.0);
          }
        `, new i.GlslLibRoutine(r2)) : 1 === n2[1] ? (r2 = `
          int getOutputCoords() {
            return 2 * int(TexCoords.x * ${n2[0]}.0);
          }
        `, new i.GlslLibRoutine(r2)) : (r2 = `
        int getOutputCoords() {
          ivec2 resTexRC = ivec2(TexCoords.xy *
                                 vec2(${n2[0]}, ${n2[1]}));
          return 2 * (resTexRC.y * ${n2[0]} + resTexRC.x);
        }
      `, new i.GlslLibRoutine(r2));
          }
          getOutputPacked2DCoords(t2, e2) {
            let n2 = "";
            if (r.ArrayUtil.arraysEqual(t2, e2)) return n2 = `
        ivec2 getOutputCoords() {
          return 2 * ivec2(TexCoords.xy * vec2(${e2[0]}, ${e2[1]}));
        }
      `, new i.GlslLibRoutine(n2);
            const o2 = e2, a2 = Math.ceil(t2[1] / 2);
            return n2 = `
        ivec2 getOutputCoords() {
          ivec2 resTexRC = ivec2(TexCoords.xy *
                                vec2(${o2[0]}, ${o2[1]}));

          int index = resTexRC.y * ${o2[0]} + resTexRC.x;

          // reverse r and c order for packed texture
          int r = imod(index, ${a2}) * 2;
          int c = 2 * (index / ${a2});

          return ivec2(r, c);
        }
      `, new i.GlslLibRoutine(n2);
          }
          getOutputPacked3DCoords(t2, e2) {
            const n2 = [e2[0], e2[1]], r2 = Math.ceil(t2[2] / 2), o2 = r2 * Math.ceil(t2[1] / 2), a2 = `
        ivec3 getOutputCoords() {
          ivec2 resTexRC = ivec2(TexCoords.xy *
                                vec2(${n2[0]}, ${n2[1]}));
          int index = resTexRC.y * ${n2[0]} + resTexRC.x;

          int b = index / ${o2};
          index -= b * ${o2};

          // reverse r and c order for packed texture
          int r = imod(index, ${r2}) * 2;
          int c = 2 * (index / ${r2});

          return ivec3(b, r, c);
        }
      `;
            return new i.GlslLibRoutine(a2);
          }
          getOutputPackedNDCoords(t2, e2) {
            const n2 = [e2[0], e2[1]], r2 = Math.ceil(t2[t2.length - 1] / 2), o2 = r2 * Math.ceil(t2[t2.length - 2] / 2);
            let a2 = o2, s2 = "", u2 = "b, r, c";
            for (let e3 = 2; e3 < t2.length - 1; e3++) a2 *= t2[t2.length - e3 - 1], s2 = `
      int b${e3} = index / ${a2};
      index -= b${e3} * ${a2};
    ` + s2, u2 = `b${e3}, ` + u2;
            const c = `
      ivec${t2.length} getOutputCoords() {
        ivec2 resTexRC = ivec2(TexCoords.xy *
                              vec2(${n2[0]}, ${n2[1]}));
        int index = resTexRC.y * ${n2[0]} + resTexRC.x;

        ${s2}

        int b = index / ${o2};
        index -= b * ${o2};

        // reverse r and c order for packed texture
        int r = imod(index, ${r2}) * 2;
        int c = 2 * (index / ${r2});

        return ivec${t2.length}(${u2});
      }
    `;
            return new i.GlslLibRoutine(c);
          }
          getOutputUnpacked1DCoords(t2, e2) {
            const n2 = `
        int getOutputCoords() {
          ivec2 resTexRC = ivec2(TexCoords.xy *
                                vec2(${e2[0]}, ${e2[1]}));
          return resTexRC.y * ${e2[0]} + resTexRC.x;
        }
      `;
            return new i.GlslLibRoutine(n2);
          }
          getOutputUnpacked2DCoords(t2, e2) {
            const n2 = `
        ivec2 getOutputCoords() {
          ivec2 resTexRC = ivec2(TexCoords.xy *
                                vec2(${e2[0]}, ${e2[1]}));
          int index = resTexRC.y * ${e2[0]} + resTexRC.x;
          int r = index / ${t2[1]};
          int c = index - r * ${t2[1]};
          return ivec2(r, c);
        }
      `;
            return new i.GlslLibRoutine(n2);
          }
          getOutputUnpacked3DCoords(t2, e2) {
            let n2 = "";
            const r2 = t2.length;
            let o2 = null;
            r2 < 2 && (o2 = []), o2 = new Array(r2 - 1), o2[r2 - 2] = t2[r2 - 1];
            for (let e3 = r2 - 3; e3 >= 0; --e3) o2[e3] = o2[e3 + 1] * t2[e3 + 1];
            const a2 = ["r", "c", "d"], s2 = o2.map((t3, e3) => `int ${a2[e3]} = index / ${t3}; ${e3 === o2.length - 1 ? `int ${a2[e3 + 1]} = index - ${a2[e3]} * ${t3}` : `index -= ${a2[e3]} * ${t3}`};`).join("");
            return n2 = `
        ivec3 getOutputCoords() {
          ivec2 resTexRC = ivec2(TexCoords.xy *
                                vec2(${e2[0]}, ${e2[1]}));
          int index = resTexRC.y * ${e2[0]} + resTexRC.x;
          ${s2}
          return ivec3(r, c, d);
        }
      `, new i.GlslLibRoutine(n2);
          }
          getOutputUnpacked4DCoords(t2, e2) {
            let n2 = "";
            const r2 = t2.length;
            let o2 = null;
            r2 < 2 && (o2 = []), o2 = new Array(r2 - 1), o2[r2 - 2] = t2[r2 - 1];
            for (let e3 = r2 - 3; e3 >= 0; --e3) o2[e3] = o2[e3 + 1] * t2[e3 + 1];
            const a2 = ["r", "c", "d", "d2"], s2 = o2.map((t3, e3) => `int ${a2[e3]} = index / ${t3}; ${e3 === o2.length - 1 ? `int ${a2[e3 + 1]} = index - ${a2[e3]} * ${t3}` : `index -= ${a2[e3]} * ${t3}`};`).join("");
            return n2 = `
      ivec4 getOutputCoords() {
          ivec2 resTexRC = ivec2(TexCoords.xy *
                                vec2(${e2[0]}, ${e2[1]}));
          int index = resTexRC.y * ${e2[0]} + resTexRC.x;
          ${s2}
          return ivec4(r, c, d, d2);
        }
      `, new i.GlslLibRoutine(n2);
          }
          getOutputUnpacked5DCoords(t2, e2) {
            let n2 = "";
            const r2 = t2.length;
            let o2 = null;
            r2 < 2 && (o2 = []), o2 = new Array(r2 - 1), o2[r2 - 2] = t2[r2 - 1];
            for (let e3 = r2 - 3; e3 >= 0; --e3) o2[e3] = o2[e3 + 1] * t2[e3 + 1];
            const a2 = ["r", "c", "d", "d2", "d3"], s2 = o2.map((t3, e3) => `int ${a2[e3]} = index / ${t3}; ${e3 === o2.length - 1 ? `int ${a2[e3 + 1]} = index - ${a2[e3]} * ${t3}` : `index -= ${a2[e3]} * ${t3}`};`).join("");
            return n2 = `
      ivec5 getOutputCoords() {
          ivec2 resTexRC = ivec2(TexCoords.xy *
                                vec2(${e2[0]}, ${e2[1]}));
          int index = resTexRC.y * ${e2[0]} + resTexRC.x;
          ${s2}
          return ivec5(r, c, d, d2, d3);
        }
      `, new i.GlslLibRoutine(n2);
          }
          getOutputUnpacked6DCoords(t2, e2) {
            let n2 = "";
            const r2 = t2.length;
            let o2 = null;
            r2 < 2 && (o2 = []), o2 = new Array(r2 - 1), o2[r2 - 2] = t2[r2 - 1];
            for (let e3 = r2 - 3; e3 >= 0; --e3) o2[e3] = o2[e3 + 1] * t2[e3 + 1];
            const a2 = ["r", "c", "d", "d2", "d3", "d4"], s2 = o2.map((t3, e3) => `int ${a2[e3]} = index / ${t3}; ${e3 === o2.length - 1 ? `int ${a2[e3 + 1]} = index - ${a2[e3]} * ${t3}` : `index -= ${a2[e3]} * ${t3}`};`).join("");
            return n2 = `
     ivec6 getOutputCoords() {
         ivec2 resTexRC = ivec2(TexCoords.xy *
                               vec2(${e2[0]}, ${e2[1]}));
         int index = resTexRC.y * ${e2[0]} + resTexRC.x;
         ${s2}
         return ivec6(r, c, d, d2, d3, d4);
       }
     `, new i.GlslLibRoutine(n2);
          }
          getCommonUtilFuncs() {
            const t2 = {};
            let e2 = "uvFromFlat";
            t2[e2] = new i.GlslLibRoutine("\n    vec2 uvFromFlat(int texNumR, int texNumC, int index) {\n      int texC = index / texNumR;\n      int texR = index - texC * texNumR;\n      // TODO: swap texR, texC order in following function so row is corresponding to u and column is corresponding to\n      //       v.\n      return (vec2(texR, texC) + halfCR) / vec2(texNumR, texNumC);\n    }\n    "), e2 = "packedUVfrom1D", t2[e2] = new i.GlslLibRoutine("\n      vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n        int texelIndex = index / 2;\n        int texR = texelIndex / texNumC;\n        int texC = texelIndex - texR * texNumC;\n        return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n      }\n      "), e2 = "packedUVfrom2D", t2[e2] = new i.GlslLibRoutine("\n      vec2 packedUVfrom2D(int texNumR, int texNumC, int texelsInLogicalRow, int row, int col) {\n        int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n        int texR = texelIndex / texNumC;\n        int texC = texelIndex - texR * texNumC;\n        return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n      }\n      "), e2 = "packedUVfrom3D", t2[e2] = new i.GlslLibRoutine("\n      vec2 packedUVfrom3D(int texNumR, int texNumC,\n          int texelsInBatch, int texelsInLogicalRow, int b,\n          int row, int col) {\n        int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n        int texR = index / texNumC;\n        int texC = index - texR * texNumC;\n        return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n      }\n      "), e2 = "sampleTexture";
            const n2 = (0, o.getGlsl)(this.context.glContext.version);
            return t2[e2] = new i.GlslLibRoutine(`
        float sampleTexture(sampler2D textureSampler, vec2 uv) {
            return ${n2.texture2D}(textureSampler, uv).r;
        }`), t2;
          }
          getInputsSamplingSnippets() {
            const t2 = {}, e2 = this.context.outputTextureLayout;
            return this.context.programInfo.inputNames.forEach((n2, r2) => {
              const i2 = this.context.inputTextureLayouts[r2], o2 = (0, s.generateShaderFuncNameFromInputSamplerName)(n2);
              i2.isPacked ? t2[o2] = this.getPackedSamplerFromInput(o2, n2, i2) : t2[o2] = this.getUnpackedSamplerFromInput(o2, n2, i2);
              const a2 = (0, s.generateShaderFuncNameFromInputSamplerNameAtOutCoords)(n2);
              i2.unpackedShape.length <= e2.unpackedShape.length && (i2.isPacked ? t2[a2] = this.getPackedSamplerAtOutputCoords(a2, i2, e2, n2) : t2[a2] = this.getUnpackedSamplerAtOutputCoords(a2, i2, e2, n2));
            }), t2;
          }
          getPackedSamplerAtOutputCoords(t2, e2, n2, o2) {
            const a2 = e2.unpackedShape, u2 = n2.unpackedShape, c = o2, l = (0, s.generateShaderFuncNameFromInputSamplerName)(c), p = a2.length, f = u2.length, d = r.BroadcastUtil.getBroadcastDims(a2, u2), h = (0, s.getCoordsDataType)(f), g = f - p;
            let b;
            const m = (0, s.getGlChannels)();
            b = 0 === p ? "" : f < 2 && d.length >= 1 ? "coords = 0;" : d.map((t3) => `coords.${m[t3 + g]} = 0;`).join("\n");
            let y = "";
            y = f < 2 && p > 0 ? "coords" : a2.map((t3, e3) => `coords.${m[e3 + g]}`).join(", ");
            let _ = "return outputValue;";
            const v = 1 === r.ShapeUtil.size(a2), w = 1 === r.ShapeUtil.size(u2);
            if (1 !== p || v || w) {
              if (v && !w) _ = 1 === f ? "\n          return vec4(outputValue.x, outputValue.x, 0., 0.);\n        " : "\n          return vec4(outputValue.x);\n        ";
              else if (d.length) {
                const t3 = p - 2, e3 = p - 1;
                d.indexOf(t3) > -1 && d.indexOf(e3) > -1 ? _ = "return vec4(outputValue.x);" : d.indexOf(t3) > -1 ? _ = "return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);" : d.indexOf(e3) > -1 && (_ = "return vec4(outputValue.xx, outputValue.zz);");
              }
            } else _ = "\n        return vec4(outputValue.xy, outputValue.xy);\n      ";
            const x = `
      vec4 ${t2}() {
        ${h} coords = getOutputCoords();
        
        int lastDim = coords.${m[f - 1]};
        coords.${m[f - 1]} = coords.${m[f - 2]};
        coords.${m[f - 2]} = lastDim;
      
        ${b}
        vec4 outputValue = ${l}(${y});
        ${_}
      }
    `;
            return new i.GlslLibRoutine(x, ["coordinates.getOutputCoords"]);
          }
          getUnpackedSamplerAtOutputCoords(t2, e2, n2, o2) {
            const a2 = [n2.width, n2.height], u2 = [e2.width, e2.height], c = e2.unpackedShape.length, l = n2.unpackedShape.length, p = e2.unpackedShape, f = n2.unpackedShape, d = (0, s.generateShaderFuncNameFromInputSamplerName)(o2);
            if (c === l && r.ArrayUtil.arraysEqual(u2, a2)) {
              const e3 = `
          float ${t2}() {
            return sampleTexture(${o2}, TexCoords);
          }
        `;
              return new i.GlslLibRoutine(e3, ["coordinates.sampleTexture"]);
            }
            const h = (0, s.getCoordsDataType)(l), g = r.BroadcastUtil.getBroadcastDims(p, f), b = l - c;
            let m;
            const y = (0, s.getGlChannels)();
            m = 0 === c ? "" : l < 2 && g.length >= 1 ? "coords = 0;" : g.map((t3) => `coords.${y[t3 + b]} = 0;`).join("\n");
            let _ = "";
            _ = l < 2 && c > 0 ? "coords" : e2.unpackedShape.map((t3, e3) => `coords.${y[e3 + b]}`).join(", ");
            const v = `
        float ${t2}() {
          ${h} coords = getOutputCoords();
          ${m}
          return ${d}(${_});
        }
      `;
            return new i.GlslLibRoutine(v, ["coordinates.getOutputCoords"]);
          }
          getPackedSamplerFromInput(t2, e2, n2) {
            switch (n2.unpackedShape.length) {
              case 0:
                return this.getPackedSamplerScalar(t2, e2);
              case 1:
                return this.getPackedSampler1D(t2, e2, n2);
              case 2:
                return this.getPackedSampler2D(t2, e2, n2);
              case 3:
                return this.getPackedSampler3D(t2, e2, n2);
              default:
                return this.getPackedSamplerND(t2, e2, n2);
            }
          }
          getUnpackedSamplerFromInput(t2, e2, n2) {
            const r2 = n2.unpackedShape;
            switch (r2.length) {
              case 0:
                return this.getUnpackedSamplerScalar(t2, e2, n2);
              case 1:
                return this.getUnpackedSampler1D(t2, e2, n2);
              case 2:
                return this.getUnpackedSampler2D(t2, e2, n2);
              case 3:
                return this.getUnpackedSampler3D(t2, e2, n2);
              case 4:
                return this.getUnpackedSampler4D(t2, e2, n2);
              case 5:
                return this.getUnpackedSampler5D(t2, e2, n2);
              case 6:
                return this.getUnpackedSampler6D(t2, e2, n2);
              default:
                throw new Error(`Unsupported dimension ${r2.length}-D`);
            }
          }
          getPackedSamplerScalar(t2, e2) {
            const n2 = `
          vec4 ${t2}() {
            return ${(0, o.getGlsl)(this.context.glContext.version).texture2D}(${e2}, halfCR);
          }
        `;
            return new i.GlslLibRoutine(n2);
          }
          getPackedSampler1D(t2, e2, n2) {
            const r2 = [n2.width, n2.height], a2 = [r2[1], r2[0]], s2 = (0, o.getGlsl)(this.context.glContext.version), u2 = `vec4 ${t2}(int index) {
      vec2 uv = packedUVfrom1D(
      ${a2[0]}, ${a2[1]}, index);
      return ${s2.texture2D}(${e2}, uv);
    }`;
            return new i.GlslLibRoutine(u2, ["coordinates.packedUVfrom1D"]);
          }
          getPackedSampler2D(t2, e2, n2) {
            const a2 = n2.unpackedShape, s2 = [n2.width, n2.height], u2 = (0, o.getGlsl)(this.context.glContext.version), c = s2[0], l = s2[1];
            if (null != s2 && r.ArrayUtil.arraysEqual(a2, s2)) {
              const n3 = `vec4 ${t2}(int row, int col) {
        vec2 uv = (vec2(col, row) + halfCR) / vec2(${l}.0, ${c}.0);
        return ${u2.texture2D}(${e2}, uv);
      }`;
              return new i.GlslLibRoutine(n3);
            }
            const p = s2, f = Math.ceil(a2[1] / 2), d = `vec4 ${t2}(int row, int col) {
      vec2 uv = packedUVfrom2D(${p[1]}, ${p[0]}, ${f}, row, col);
      return ${u2.texture2D}(${e2}, uv);
    }`;
            return new i.GlslLibRoutine(d, ["coordinates.packedUVfrom2D"]);
          }
          getPackedSampler3D(t2, e2, n2) {
            const r2 = n2.unpackedShape, a2 = [n2.width, n2.height], u2 = [a2[0], a2[1]], c = (0, o.getGlsl)(this.context.glContext.version);
            if (1 === r2[0]) {
              const o2 = r2.slice(1), a3 = [1, 2], u3 = (0, s.squeezeInputShape)(r2, o2), c2 = ["b", "row", "col"], l2 = JSON.parse(JSON.stringify(n2));
              l2.unpackedShape = u3;
              const p2 = this.getPackedSamplerFromInput(t2, e2, l2), f2 = `${p2.routineBody}
      vec4 ${t2}(int b, int row, int col) {
        return ${t2}(${(0, s.getSqueezedParams)(c2, a3)});
      } `;
              return new i.GlslLibRoutine(f2, p2.dependencies);
            }
            const l = u2[0], p = u2[1], f = Math.ceil(r2[2] / 2), d = `vec4 ${t2}(int b, int row, int col) {
      vec2 uv = packedUVfrom3D(
        ${p}, ${l}, ${f * Math.ceil(r2[1] / 2)}, ${f}, b, row, col);
      return ${c.texture2D}(${e2}, uv);}`;
            return new i.GlslLibRoutine(d, ["coordinates.packedUVfrom3D"]);
          }
          getPackedSamplerND(t2, e2, n2) {
            const r2 = n2.unpackedShape, a2 = r2.length, s2 = [n2.width, n2.height], u2 = (0, o.getGlsl)(this.context.glContext.version), c = [s2[0], s2[1]], l = c[1], p = c[0], f = Math.ceil(r2[a2 - 1] / 2);
            let d = f * Math.ceil(r2[a2 - 2] / 2), h = "int b, int row, int col", g = `b * ${d} + (row / 2) * ${f} + (col / 2)`;
            for (let t3 = 2; t3 < a2 - 1; t3++) h = `int b${t3}, ` + h, d *= r2[a2 - t3 - 1], g = `b${t3} * ${d} + ` + g;
            const b = `vec4 ${t2}(${h}) {
      int index = ${g};
      int texR = index / ${p};
      int texC = index - texR * ${p};
      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}, ${l});
      return ${u2.texture2D}(${e2}, uv);
    }`;
            return new i.GlslLibRoutine(b);
          }
          getUnpackedSamplerScalar(t2, e2, n2) {
            const [r2, o2] = [n2.width, n2.height];
            if (1 === r2 && 1 === o2) {
              const n3 = `
          float ${t2}() {
            return sampleTexture(${e2}, halfCR);
          }
        `;
              return new i.GlslLibRoutine(n3, ["coordinates.sampleTexture"]);
            }
            const a2 = `
        float ${t2}() {
          int offset_${e2} = coordsToOffset(TexCoords, ${r2}, ${o2});
          vec2 uv = uvFromFlat(${r2}, ${o2}, offset_${e2});
          return sampleTexture(${e2}, uv);
        }
      `;
            return new i.GlslLibRoutine(a2, ["coordinates.uvFromFlat", "coordinates.sampleTexture", "coordinates.coordsToOffset"]);
          }
          getUnpackedSampler1D(t2, e2, n2) {
            const r2 = n2.width, o2 = n2.height;
            if (1 === o2 && 1 === r2) {
              const n3 = `
        float ${t2}(int index) {
          return sampleTexture(${e2}, halfCR);
        }
      `;
              return new i.GlslLibRoutine(n3, ["coordinates.sampleTexture"]);
            }
            if (1 === o2) {
              const n3 = `
          float ${t2}(int index) {
            vec2 uv = vec2((float(index) + 0.5) / ${r2}.0, 0.5);
            return sampleTexture(${e2}, uv);
          }
        `;
              return new i.GlslLibRoutine(n3, ["coordinates.sampleTexture"]);
            }
            if (1 === r2) {
              const n3 = `
          float ${t2}(int index) {
            vec2 uv = vec2(0.5, (float(index) + 0.5) / ${o2}.0);
            return sampleTexture(${e2}, uv);
          }
        `;
              return new i.GlslLibRoutine(n3, ["coordinates.sampleTexture"]);
            }
            const a2 = `
        float ${t2}(int index) {
          vec2 uv = uvFromFlat(${r2}, ${o2}, index);
          return sampleTexture(${e2}, uv);
        }
      `;
            return new i.GlslLibRoutine(a2, ["coordinates.uvFromFlat", "coordinates.sampleTexture"]);
          }
          getUnpackedSampler2D(t2, e2, n2) {
            const o2 = n2.unpackedShape, u2 = [n2.height, n2.width];
            if (null != u2 && r.ArrayUtil.arraysEqual(o2, u2)) {
              const n3 = `
          float ${t2}(int row, int col) {
            vec2 uv = (vec2(row, col) + halfCR) / vec2(${u2[1]}.0, ${u2[0]}.0);
            return sampleTexture(${e2}, uv);
          }
        `;
              return new i.GlslLibRoutine(n3, ["coordinates.sampleTexture"]);
            }
            const { newShape: c, keptDims: l } = (0, a.squeezeShape)(o2), p = c;
            if (p.length < o2.length) {
              const r2 = (0, s.squeezeInputShape)(o2, p), a2 = JSON.parse(JSON.stringify(n2));
              a2.unpackedShape = r2;
              const u3 = ["col", "row"], c2 = `
          ${this.getUnpackedSamplerFromInput(t2, e2, a2).routineBody}
          float ${t2}(int row, int col) {
            return ${t2}(${(0, s.getSqueezedParams)(u3, l)});
          }
        `;
              return new i.GlslLibRoutine(c2, ["coordinates.sampleTexture"]);
            }
            const f = u2[1], d = u2[0];
            if (1 === d) {
              const n3 = `
          float ${t2}(int row, int col) {
            int offset_${e2} = coordsToOffset(TexCoords, ${f}, ${d});
            float index = dot(vec3(row, col, offset_${e2}), vec3(${o2[1]}, 1, 1));
            vec2 uv = vec2(0.5, (index + 0.5) / ${f}.0);
            return sampleTexture(${e2}, uv);
          }
        `;
              return new i.GlslLibRoutine(n3, ["coordinates.sampleTexture", "coordinates.coordsToOffset"]);
            }
            if (1 === f) {
              const n3 = `
          float ${t2}(int row, int col) {
            int offset_${e2} = coordsToOffset(TexCoords, ${f}, ${d});
            float index = dot(vec3(row, col, offset_${e2}), vec3(${o2[1]}, 1, 1));
            vec2 uv = vec2((index + 0.5) / ${d}.0, 0.5);
            return sampleTexture(${e2}, uv);
          }
        `;
              return new i.GlslLibRoutine(n3, ["coordinates.sampleTexture", "coordinates.coordsToOffset"]);
            }
            const h = `
        float ${t2}(int row, int col) {
          int index = col * ${o2[1]} + row;
          vec2 uv = uvFromFlat(${f}, ${d}, index);
          return sampleTexture(${e2}, uv);
        }
      `;
            return new i.GlslLibRoutine(h, ["coordinates.uvFromFlat", "coordinates.sampleTexture", "coordinates.coordsToOffset"]);
          }
          getUnpackedSampler3D(t2, e2, n2) {
            const r2 = n2.unpackedShape, o2 = r2[1] * r2[2], u2 = r2[2], { newShape: c, keptDims: l } = (0, a.squeezeShape)(r2), p = c;
            if (p.length < r2.length) {
              const o3 = (0, s.squeezeInputShape)(r2, p), a2 = ["batch", "col", "row"], u3 = JSON.parse(JSON.stringify(n2));
              u3.unpackedShape = o3;
              const c2 = this.getUnpackedSamplerFromInput(t2, e2, u3), f2 = l.reverse(), d = `
          ${c2.routineBody}
          float ${t2}(int batch, int row, int col) {
            return ${t2}(${(0, s.getSqueezedParams)(a2, f2)});
          }
        `;
              return new i.GlslLibRoutine(d, c2.dependencies);
            }
            const f = `
          float ${t2}(int depth, int row, int col) {
            // Explicitly use integer operations as dot() only works on floats.
            int index = depth * ${o2} + col * ${u2} + row;
            vec2 uv = uvFromFlat(${n2.width}, ${n2.height}, index);
            return sampleTexture(${e2}, uv);
          }
      `;
            return new i.GlslLibRoutine(f, ["coordinates.uvFromFlat", "coordinates.sampleTexture", "coordinates.coordsToOffset"]);
          }
          getUnpackedSampler4D(t2, e2, n2) {
            const r2 = n2.unpackedShape, o2 = r2[3], a2 = r2[2] * o2, s2 = `
        float ${t2}(int row, int col, int depth, int depth2) {
          int index = row * ${r2[1] * a2} + col * ${a2} +
              depth2 * ${o2} + depth;
          vec2 uv = uvFromFlat(${n2.width}, ${n2.height}, index);
          return sampleTexture(${e2}, uv);
        }
      `;
            return new i.GlslLibRoutine(s2, ["coordinates.uvFromFlat", "coordinates.sampleTexture"]);
          }
          getUnpackedSampler5D(t2, e2, n2) {
            const r2 = n2.unpackedShape, o2 = r2[4], u2 = r2[3] * o2, c = r2[2] * u2, l = r2[1] * c, { newShape: p, keptDims: f } = (0, a.squeezeShape)(r2);
            if (p.length < r2.length) {
              const o3 = (0, s.squeezeInputShape)(r2, p), a2 = ["row", "col", "depth", "depth2", "depth3"], u3 = JSON.parse(JSON.stringify(n2));
              u3.unpackedShape = o3;
              const c2 = `
          ${this.getUnpackedSamplerFromInput(t2, e2, u3).routineBody}
          float ${t2}(int row, int col, int depth, int depth2, int depth3) {
            return ${t2}(${(0, s.getSqueezedParams)(a2, f)});
          }
        `;
              return new i.GlslLibRoutine(c2, ["coordinates.sampleTexture", "coordinates.uvFromFlat"]);
            }
            const d = `
        float ${t2}(int row, int col, int depth, int depth2, int depth3) {
          int index = row * ${l} + col * ${c} + depth * ${u2} +
          depth3 * ${o2} + depth2;
          vec2 uv = uvFromFlat(${n2.width}, ${n2.height}, index);
          return sampleTexture(${e2}, uv);
        }
      `;
            return new i.GlslLibRoutine(d, ["coordinates.sampleTexture", "coordinates.uvFromFlat"]);
          }
          getUnpackedSampler6D(t2, e2, n2) {
            const r2 = n2.unpackedShape, o2 = r2[5], u2 = r2[4] * o2, c = r2[3] * u2, l = r2[2] * c, p = r2[1] * l, { newShape: f, keptDims: d } = (0, a.squeezeShape)(r2);
            if (f.length < r2.length) {
              const o3 = (0, s.squeezeInputShape)(r2, f), a2 = ["row", "col", "depth", "depth2", "depth3", "depth4"], u3 = JSON.parse(JSON.stringify(n2));
              u3.unpackedShape = o3;
              const c2 = `
            ${this.getUnpackedSamplerFromInput(t2, e2, u3).routineBody}
            float ${t2}(int row, int col, int depth,
              int depth2, int depth3, int depth4) {
              return ${t2}(${(0, s.getSqueezedParams)(a2, d)});
            }
          `;
              return new i.GlslLibRoutine(c2, ["coordinates.sampleTexture", "coordinates.uvFromFlat"]);
            }
            const h = `
          float ${t2}(int row, int col, int depth,
            int depth2, int depth3, int depth4) {
            int index = row * ${p} + col * ${l} + depth * ${c} +
            depth2 * ${u2} + depth3 * ${o2} + depth4;
            vec2 uv = uvFromFlat(${n2.width}, ${n2.height}, index);
            return sampleTexture(${e2}, uv);
          }
        `;
            return new i.GlslLibRoutine(h, ["coordinates.uvFromFlat", "coordinates.sampleTexture", "coordinates.coordsToOffset"]);
          }
          toVec() {
            const t2 = this.context.outputTextureLayout, e2 = t2.shape.length, n2 = t2.strides, r2 = t2.width, o2 = t2.height, a2 = [];
            for (let t3 = 0; t3 < e2 - 1; ++t3) a2.push(`
        c[${t3}] = offset / ${n2[t3]};`), a2.push(`
        offset -= c[${t3}] * ${n2[t3]};`);
            a2.push(`
        c[${e2 - 1}] = offset;`);
            const s2 = `
      void toVec(vec2 texCoords, out int c[${e2}]) {
        int offset = coordsToOffset(texCoords, ${r2}, ${o2});
        ${a2.join("")}
      }
      void toVec(int offset, out int c[${e2}]) {
        ${a2.join("")}
      }
    `;
            return { toVec: new i.GlslLibRoutine(s2, ["coordinates.coordsToOffset"]) };
          }
          valueFrom() {
            const t2 = {};
            return this.context.programInfo.inputNames.forEach((e2, n2) => {
              const r2 = this.context.inputTextureLayouts[n2], o2 = (r2.unpackedShape.length > 0 ? r2.unpackedShape : r2.shape).length;
              let a2 = `_${e2}`;
              t2[a2] = new i.GlslLibRoutine(this.getValueFromSingle(e2, o2, r2.width, r2.height, false), [`shapeUtils.indicesToOffset${a2}`, "coordinates.offsetToCoords", "fragcolor.getColorAsFloat"]), a2 += "_T", t2[a2] = new i.GlslLibRoutine(this.getValueFromSingle(e2, o2, r2.width, r2.height, true), [`shapeUtils.indicesToOffset${a2}`, "coordinates.offsetToCoords", "fragcolor.getColorAsFloat"]);
            }), t2;
          }
          getValueFromSingle(t2, e2, n2, r2, i2) {
            let a2 = `_${t2}`;
            return i2 && (a2 += "_T"), `
        float ${a2}(int m[${e2}]) {
          int offset = indicesToOffset${a2}(m);
          vec2 coords = offsetToCoords(offset, ${n2}, ${r2});
          float value = getColorAsFloat(${(0, o.getGlsl)(this.context.glContext.version).texture2D}(${t2}, coords));
          return value;
        }
        `;
          }
          getPackedValueFrom(t2, e2, n2, r2, i2) {
            let a2 = `_${t2}_Pack`;
            return i2 && (a2 += "_T"), `
        vec4 ${a2}(int m[${e2}]) {
          int offset = indicesToOffset_${t2}(m);
          vec2 coords = offsetToCoords(offset, ${n2}, ${r2});
          return ${(0, o.getGlsl)(this.context.glContext.version).texture2D}(${t2}, coords);
        }
        `;
          }
        }
        e.CoordsGlslLib = u;
      }, 8520: (t, e) => {
        var n;
        Object.defineProperty(e, "__esModule", { value: true }), e.TopologicalSortGlslRoutines = e.GlslLibRoutineNode = e.GlslLibRoutine = e.GlslLib = e.GlslContext = e.FunctionType = void 0, (n = e.FunctionType || (e.FunctionType = {}))[n.ValueBased = 0] = "ValueBased", n[n.Positional = 1] = "Positional", e.GlslContext = class {
          constructor(t2, e2, n2, r) {
            this.glContext = t2, this.programInfo = e2, this.inputTextureLayouts = n2, this.outputTextureLayout = r;
          }
        }, e.GlslLib = class {
          constructor(t2) {
            this.context = t2;
          }
        }, e.GlslLibRoutine = class {
          constructor(t2, e2) {
            this.routineBody = t2, this.dependencies = e2;
          }
        }, e.GlslLibRoutineNode = class {
          constructor(t2, e2, n2) {
            this.name = t2, this.dependencies = n2 || [], e2 && (this.routineBody = e2);
          }
          addDependency(t2) {
            t2 && this.dependencies.push(t2);
          }
        }, e.TopologicalSortGlslRoutines = class {
          static returnOrderedNodes(t2) {
            if (!t2 || 0 === t2.length) return [];
            if (1 === t2.length) return t2;
            const e2 = /* @__PURE__ */ new Set(), n2 = /* @__PURE__ */ new Set(), r = new Array();
            return this.createOrderedNodes(t2, e2, n2, r), r;
          }
          static createOrderedNodes(t2, e2, n2, r) {
            for (let i = 0; i < t2.length; ++i) this.dfsTraverse(t2[i], e2, n2, r);
          }
          static dfsTraverse(t2, e2, n2, r) {
            if (!t2 || n2.has(t2.name)) return;
            if (e2.has(t2.name)) throw new Error("Cyclic dependency detected. Can't topologically sort routines needed for shader.");
            e2.add(t2.name);
            const i = t2.dependencies;
            if (i && i.length > 0) for (let t3 = 0; t3 < i.length; ++t3) this.dfsTraverse(i[t3], e2, n2, r);
            r.push(t2), n2.add(t2.name), e2.delete(t2.name);
          }
        };
      }, 7341: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.EncodingGlslLib = void 0;
        const r = n(8520);
        class i extends r.GlslLib {
          constructor(t2) {
            super(t2);
          }
          getFunctions() {
            return Object.assign(Object.assign({}, this.encodeFloat32()), this.decodeFloat32());
          }
          getCustomTypes() {
            return {};
          }
          encodeFloat32() {
            return { encode: new r.GlslLibRoutine("highp vec4 encode(highp float f) {\n        return vec4(f, 0.0, 0.0, 0.0);\n      }\n        ") };
          }
          decodeFloat32() {
            return { decode: new r.GlslLibRoutine("highp float decode(highp vec4 rgba) {\n        return rgba.r;\n      }\n        ") };
          }
          encodeUint8() {
            const t2 = i.isLittleEndian() ? "rgba.rgba=rgba.abgr;" : "";
            return { encode: new r.GlslLibRoutine(`
      highp vec4 encode(highp float f) {
        highp float F = abs(f);
        highp float Sign = step(0.0,-f);
        highp float Exponent = floor(log2(F));
        highp float Mantissa = (exp2(- Exponent) * F);
        Exponent = floor(log2(F) + 127.0) + floor(log2(Mantissa));
        highp vec4 rgba;
        rgba[0] = 128.0 * Sign  + floor(Exponent*exp2(-1.0));
        rgba[1] = 128.0 * mod(Exponent,2.0) + mod(floor(Mantissa*128.0),128.0);
        rgba[2] = floor(mod(floor(Mantissa*exp2(23.0 -8.0)),exp2(8.0)));
        rgba[3] = floor(exp2(23.0)*mod(Mantissa,exp2(-15.0)));
        ${t2}
        rgba = rgba / 255.0; // values need to be normalized to [0,1]
        return rgba;
    }
        `) };
          }
          decodeUint8() {
            const t2 = i.isLittleEndian() ? "rgba.rgba=rgba.abgr;" : "";
            return { decode: new r.GlslLibRoutine(`
        highp float decode(highp vec4 rgba) {
          rgba = rgba * 255.0; // values need to be de-normalized from [0,1] to [0,255]
          ${t2}
          highp float Sign = 1.0 - step(128.0,rgba[0])*2.0;
          highp float Exponent = 2.0 * mod(rgba[0],128.0) + step(128.0,rgba[1]) - 127.0;
          highp float Mantissa = mod(rgba[1],128.0)*65536.0 + rgba[2]*256.0 +rgba[3] + float(0x800000);
          highp float Result =  Sign * exp2(Exponent) * (Mantissa * exp2(-23.0 ));
          return Result;
      }
        `) };
          }
          static isLittleEndian() {
            const t2 = new ArrayBuffer(4), e2 = new Uint32Array(t2), n2 = new Uint8Array(t2);
            if (e2[0] = 3735928559, 239 === n2[0]) return true;
            if (222 === n2[0]) return false;
            throw new Error("unknown endianness");
          }
        }
        e.EncodingGlslLib = i;
      }, 9894: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.FragColorGlslLib = void 0;
        const r = n(8520), i = n(5060);
        class o extends r.GlslLib {
          constructor(t2) {
            super(t2);
          }
          getFunctions() {
            return Object.assign(Object.assign({}, this.setFragColor()), this.getColorAsFloat());
          }
          getCustomTypes() {
            return {};
          }
          setFragColor() {
            const t2 = (0, i.getGlsl)(this.context.glContext.version);
            return { setFragColor: new r.GlslLibRoutine(`
        void setFragColor(float value) {
            ${t2.output} = encode(value);
        }
        `, ["encoding.encode"]) };
          }
          getColorAsFloat() {
            return { getColorAsFloat: new r.GlslLibRoutine("\n        float getColorAsFloat(vec4 color) {\n            return decode(color);\n        }\n        ", ["encoding.decode"]) };
          }
        }
        e.FragColorGlslLib = o;
      }, 2848: (t, e) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.replaceInlines = void 0;
        const n = /@inline[\s\n\r]+(\w+)[\s\n\r]+([0-9a-zA-Z_]+)\s*\(([^)]*)\)\s*{(([^}]|[\n\r])*)}/gm;
        e.replaceInlines = function(t2) {
          const e2 = {};
          let r;
          for (; null !== (r = n.exec(t2)); ) {
            const t3 = r[3].split(",").map((t4) => {
              const e3 = t4.trim().split(" ");
              return e3 && 2 === e3.length ? { type: e3[0], name: e3[1] } : null;
            }).filter((t4) => null !== t4);
            e2[r[2]] = { params: t3, body: r[4] };
          }
          for (const n2 in e2) {
            const i = "(\\w+)?\\s+([_0-9a-zA-Z]+)\\s+=\\s+__FUNC__\\((.*)\\)\\s*;".replace("__FUNC__", n2), o = new RegExp(i, "gm");
            for (; null !== (r = o.exec(t2)); ) {
              const i2 = r[1], o2 = r[2], a = r[3].split(","), s = i2 ? `${i2} ${o2};` : "";
              let u = e2[n2].body, c = "";
              e2[n2].params.forEach((t3, e3) => {
                t3 && (c += `${t3.type} ${t3.name} = ${a[e3]};
`);
              }), u = `${c}
 ${u}`, u = u.replace("return", `${o2} = `);
              const l = `
      ${s}
      {
        ${u}
      }
      `;
              t2 = t2.replace(r[0], l);
            }
          }
          return t2.replace(n, "");
        };
      }, 8879: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.GlslPreprocessor = void 0;
        const r = n(8520), i = n(2848), o = n(5483), a = n(5060);
        e.GlslPreprocessor = class {
          constructor(t2, e2, n2, i2) {
            this.libs = {}, this.glslLibRoutineDependencyGraph = {}, this.context = new r.GlslContext(t2, e2, n2, i2), Object.keys(o.glslRegistry).forEach((t3) => {
              const e3 = new o.glslRegistry[t3](this.context);
              this.libs[t3] = e3;
            });
            const a2 = this.glslLibRoutineDependencyGraph;
            for (const t3 in this.libs) {
              const e3 = this.libs[t3].getFunctions();
              for (const n3 in e3) {
                const i3 = t3 + "." + n3;
                let o2;
                a2[i3] ? (o2 = a2[i3], o2.routineBody = e3[n3].routineBody) : (o2 = new r.GlslLibRoutineNode(i3, e3[n3].routineBody), a2[i3] = o2);
                const s = e3[n3].dependencies;
                if (s) for (let t4 = 0; t4 < s.length; ++t4) if (a2[s[t4]]) o2.addDependency(a2[s[t4]]);
                else {
                  const e4 = new r.GlslLibRoutineNode(s[t4]);
                  a2[s[t4]] = e4, o2.addDependency(e4);
                }
              }
            }
          }
          preprocess() {
            const t2 = this.context.programInfo;
            let e2 = t2.shaderSource;
            return this.context.programInfo.hasMain || (e2 = `${e2}
      ${(0, a.getDefaultFragShaderMain)(this.context.glContext.version, this.context.outputTextureLayout.shape.length)}`), e2 = (0, i.replaceInlines)(e2), `${(0, a.getFragShaderPreamble)(this.context.glContext.version)}
    ${this.getUniforms(t2.inputNames, t2.variables)}
    ${this.getImports(e2)}
    ${e2}`;
          }
          getImports(t2) {
            const e2 = this.selectGlslLibRoutinesToBeIncluded(t2);
            if (0 === e2.length) return "";
            let n2 = "";
            for (let t3 = 0; t3 < e2.length; ++t3) {
              if (!e2[t3].routineBody) throw new Error(`Missing body for the Glsl Library routine: ${e2[t3].name}`);
              n2 += e2[t3].routineBody + "\n";
            }
            return n2;
          }
          selectGlslLibRoutinesToBeIncluded(t2) {
            const e2 = [];
            return Object.keys(this.glslLibRoutineDependencyGraph).forEach((n2) => {
              const r2 = n2.split(".")[1];
              -1 !== t2.indexOf(r2) && e2.push(this.glslLibRoutineDependencyGraph[n2]);
            }), r.TopologicalSortGlslRoutines.returnOrderedNodes(e2);
          }
          getUniforms(t2, e2) {
            const n2 = [];
            if (t2) for (const e3 of t2) n2.push(`uniform sampler2D ${e3};`);
            if (e2) for (const t3 of e2) n2.push(`uniform ${t3.type} ${t3.name}${t3.arrayLength ? `[${t3.arrayLength}]` : ""};`);
            return n2.join("\n");
          }
        };
      }, 5483: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.glslRegistry = void 0;
        const r = n(5107), i = n(7341), o = n(9894), a = n(2655), s = n(3891);
        e.glslRegistry = { encoding: i.EncodingGlslLib, fragcolor: o.FragColorGlslLib, vec: s.VecGlslLib, shapeUtils: a.ShapeUtilsGlslLib, coordinates: r.CoordsGlslLib };
      }, 2655: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.ShapeUtilsGlslLib = void 0;
        const r = n(8520);
        class i extends r.GlslLib {
          constructor(t2) {
            super(t2);
          }
          getFunctions() {
            return Object.assign(Object.assign(Object.assign(Object.assign(Object.assign({}, this.bcastIndex()), this.bcastMatmulIndex()), this.offsetToIndices()), this.indicesToOffset()), this.incrementIndices());
          }
          getCustomTypes() {
            return {};
          }
          bcastIndex() {
            const t2 = this.context.outputTextureLayout.shape.length, e2 = {};
            return this.context.programInfo.inputNames.forEach((n2, i2) => {
              const o = this.context.inputTextureLayouts[i2].unpackedShape;
              if (o.length <= t2) {
                const i3 = o.length, a = t2 - i3, s = `bcastIndices_${n2}`;
                let u = "";
                for (let t3 = 0; t3 < i3; ++t3) u += `
          realIndices[${t3}] = int( mod(float(bcastedIndices[${a + t3}]), ${o[t3]}.0) );
          `;
                const c = `
        void ${s} (int bcastedIndices[${t2}], out int realIndices[${i3}]) {
          ${u}
        }
        `;
                e2[s] = new r.GlslLibRoutine(c);
              }
            }), e2;
          }
          bcastMatmulIndex() {
            const t2 = this.context.outputTextureLayout.shape.length, e2 = {};
            return this.context.programInfo.inputNames.forEach((n2, i2) => {
              const o = this.context.inputTextureLayouts[i2].shape;
              if (!(o.length < 2 || o.length > t2)) {
                const i3 = o.length, a = t2 - i3, s = `bcastMatmulIndices_${n2}`;
                let u = "";
                for (let t3 = 0; t3 < i3 - 2; ++t3) u += `
          realIndices[${t3}] = int( mod(float(bcastedIndices[${a + t3}]), ${o[t3]}.0) );
          `;
                const c = `
        void ${s}(int bcastedIndices[${t2}], out int realIndices[${i3}]) {
          ${u}
          realIndices[${i3 - 1}] = bcastedIndices[${t2 - 1}];
          realIndices[${i3 - 2}] = bcastedIndices[${t2 - 2}];
        }
        `;
                e2[s] = new r.GlslLibRoutine(c);
              }
            }), e2;
          }
          indicesToOffset() {
            const t2 = {};
            return this.context.programInfo.inputNames.forEach((e2, n2) => {
              const o = this.context.inputTextureLayouts[n2].shape, a = this.context.inputTextureLayouts[n2].strides, s = o.length;
              let u = `indicesToOffset_${e2}`;
              t2[u] = new r.GlslLibRoutine(i.indexToOffsetSingle(u, s, a)), u = `indicesToOffset_${e2}_T`, t2[u] = new r.GlslLibRoutine(i.indexToOffsetSingle(u, s, a.slice().reverse()));
            }), t2;
          }
          static indexToOffsetSingle(t2, e2, n2) {
            let r2 = "";
            for (let t3 = e2 - 1; t3 >= 0; --t3) r2 += `
        offset += indices[${t3}] * ${n2[t3]};
        `;
            return `
      int ${t2}(int indices[${e2}]) {
        int offset = 0;
        ${r2}
        return offset;
      }
      `;
          }
          offsetToIndices() {
            const t2 = {};
            return this.context.programInfo.inputNames.forEach((e2, n2) => {
              const o = this.context.inputTextureLayouts[n2].shape, a = this.context.inputTextureLayouts[n2].strides, s = o.length;
              let u = `offsetToIndices_${e2}`;
              t2[u] = new r.GlslLibRoutine(i.offsetToIndicesSingle(u, s, a)), u = `offsetToIndices_${e2}_T`, t2[u] = new r.GlslLibRoutine(i.offsetToIndicesSingle(u, s, a.slice().reverse()));
            }), t2;
          }
          static offsetToIndicesSingle(t2, e2, n2) {
            const r2 = [];
            for (let t3 = 0; t3 < e2 - 1; ++t3) r2.push(`
      indices[${t3}] = offset / ${n2[t3]};`), r2.push(`
        offset -= indices[${t3}] * ${n2[t3]};`);
            return r2.push(`
      indices[${e2 - 1}] = offset;`), `
      void ${t2}(int offset, out int indices[${e2}]) {
        ${r2.join("")}
      }
      `;
          }
          incrementIndices() {
            const t2 = {};
            return this.context.programInfo.inputNames.forEach((e2, n2) => {
              const i2 = this.context.inputTextureLayouts[n2].shape, o = i2.length, a = `incrementIndices_${e2}`;
              let s = "";
              for (let t3 = 0; t3 < o; ++t3) s += `
        shape[${t3}] = ${i2[t3]};`;
              const u = `
        void ${a}(int axis, out int indices[${o}]) {
          int shape[${o}];
          ${s};
          for(int i = ${o} -1 ; i >= 0; --i) {
            if(i > axis) continue;
            indices[i] += 1;
            if(indices[i] < shape[i]) {
              break;
            }
            indices[i] = 0;
          }
        }
        `;
              t2[a] = new r.GlslLibRoutine(u);
            }), t2;
          }
        }
        e.ShapeUtilsGlslLib = i;
      }, 5060: (t, e) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.getDefaultFragShaderMain = e.getFragShaderPreamble = e.getVertexShaderSource = e.getGlsl = void 0;
        const n = { version: "", attribute: "attribute", varyingVertex: "varying", varyingFrag: "varying", texture2D: "texture2D", output: "gl_FragColor", outputDeclaration: "" }, r = { version: "#version 300 es", attribute: "in", varyingVertex: "out", varyingFrag: "in", texture2D: "texture", output: "outputColor", outputDeclaration: "out vec4 outputColor;" };
        function i(t2) {
          return 1 === t2 ? n : r;
        }
        e.getGlsl = i, e.getVertexShaderSource = function(t2) {
          const e2 = i(t2);
          return `${e2.version}
      precision highp float;
      ${e2.attribute} vec3 position;
      ${e2.attribute} vec2 textureCoord;

      ${e2.varyingVertex} vec2 TexCoords;

      void main()
      {
          gl_Position = vec4(position, 1.0);
          TexCoords = textureCoord;
      }`;
        }, e.getFragShaderPreamble = function(t2) {
          const e2 = i(t2);
          return `${e2.version}
    precision highp float;
    precision highp int;
    precision highp sampler2D;
    ${e2.varyingFrag} vec2 TexCoords;
    ${e2.outputDeclaration}
    const vec2 halfCR = vec2(0.5, 0.5);

    // Custom vector types to handle higher dimenalities.
    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;
    };

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

    `;
        }, e.getDefaultFragShaderMain = function(t2, e2) {
          return `
  void main() {
    int indices[${e2}];
    toVec(TexCoords, indices);
    vec4 result = vec4(process(indices));
    ${i(t2).output} = result;
  }
  `;
        };
      }, 3891: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.VecGlslLib = void 0;
        const r = n(8520);
        class i extends r.GlslLib {
          constructor(t2) {
            super(t2);
          }
          getCustomTypes() {
            return {};
          }
          getFunctions() {
            return Object.assign(Object.assign(Object.assign(Object.assign({}, this.binaryVecFunctions()), this.copyVec()), this.setVecItem()), this.getVecItem());
          }
          binaryVecFunctions() {
            const t2 = this.context.outputTextureLayout.shape.length, e2 = { add: "+=", sub: "-=", mul: "*=", div: "/=" }, n2 = {};
            for (const i2 in e2) {
              const o = `${i2}Vec`;
              let a = "";
              for (let n3 = 0; n3 < t2; ++n3) a += `
          dest[${n3}] ${e2[i2]} src[${n3}];
          `;
              const s = `
        void ${o}(int src[${t2}], out int dest[${t2}]) {
          ${a}
        }
        `;
              n2[o] = new r.GlslLibRoutine(s);
            }
            return n2;
          }
          copyVec() {
            const t2 = this.context.outputTextureLayout.shape.length;
            let e2 = "";
            for (let n3 = 0; n3 < t2; ++n3) e2 += `
        dest[${n3}] = src[${n3}];
        `;
            const n2 = `
      void copyVec(int src[${t2}], out int dest[${t2}]) {
        ${e2}
      }
      `;
            return { copyVec: new r.GlslLibRoutine(n2) };
          }
          setVecItem() {
            const t2 = this.context.outputTextureLayout.shape.length;
            let e2 = `
        if(index < 0)
            index =${t2} + index;
        if (index == 0)
            m[0] = value;
        `;
            for (let n3 = 1; n3 < t2 - 1; ++n3) e2 += `
        else if (index == ${n3})
            m[${n3}] = value;
            `;
            e2 += `
        else
            m[${t2 - 1}] = value;
        `;
            const n2 = `
      void setVecItem(out int m[${t2}], int index, int value) {
        ${e2}
      }
        `;
            return { setVecItem: new r.GlslLibRoutine(n2) };
          }
          getVecItem() {
            const t2 = this.context.outputTextureLayout.shape.length;
            let e2 = `
        if(index < 0)
            index = ${t2} + index;
        if (index == 0)
            return m[0];
      `;
            for (let n3 = 1; n3 < t2 - 1; ++n3) e2 += `
        else if (index == ${n3})
            return m[${n3}];
      `;
            e2 += `
        else
            return m[${t2 - 1}];
        `;
            const n2 = `
      int getVecItem(int m[${t2}], int index) {
        ${e2}
      }
    `;
            return { getVecItem: new r.GlslLibRoutine(n2) };
          }
        }
        e.VecGlslLib = i;
      }, 8316: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.WebGLInferenceHandler = void 0;
        const r = n(6231), i = n(9162), o = n(2517), a = n(2403), s = n(7019), u = n(8710), c = n(5611), l = n(4057), p = n(2039);
        e.WebGLInferenceHandler = class {
          constructor(t2) {
            this.session = t2, this.packedTextureDataCache = /* @__PURE__ */ new Map(), this.unpackedTextureDataCache = /* @__PURE__ */ new Map();
          }
          calculateTextureWidthAndHeight(t2, e2) {
            return (0, l.calculateTextureWidthAndHeight)(this.session.layoutStrategy, t2, e2);
          }
          executeProgram(t2, e2) {
            if (e2.length < t2.inputNames.length) throw new Error(`Input size mustn't be less than ${t2.inputNames.length}.`);
            if (t2.inputNames.length !== t2.inputTypes.length) throw new Error("input names size does not match input types");
            const n2 = [];
            for (let r3 = 0; r3 < t2.inputNames.length; ++r3) n2[r3] = this.getOrCreateTextureData(e2[r3], t2.inputTypes[r3]);
            const r2 = ((t3, e3) => {
              const n3 = e3.map((t4) => `${t4.unpackedShape.join(",")};${t4.width}x${t4.height}`).join("_");
              let r3 = t3.name;
              return t3.cacheHint && (r3 += "[" + t3.cacheHint + "]"), r3 += ":" + n3, r3;
            })(t2, n2);
            let i2 = this.session.programManager.getArtifact(r2);
            const o2 = i2 ? i2.programInfo : "function" == typeof t2.get ? t2.get() : t2, a2 = (0, l.createTextureLayoutFromTextureType)(this.session.layoutStrategy, o2.output.dims, o2.output.textureType), s2 = this.createTextureData(a2, o2.output.type);
            return i2 || (i2 = this.session.programManager.build(o2, n2, s2), this.session.programManager.setArtifact(r2, i2)), this.runProgram(i2, n2, s2), s2;
          }
          run(t2, e2) {
            return this.executeProgram(t2, e2).tensor;
          }
          runProgram(t2, e2, n2) {
            for (let n3 = 0; n3 < e2.length; ++n3) if (!!e2[n3].isPacked != (t2.programInfo.inputTypes[n3] === p.TextureType.packed)) throw new Error(`input[${n3}] property packed inconsistent`);
            if (!!n2.isPacked != (t2.programInfo.output.textureType === p.TextureType.packed)) throw new Error("output property packed inconsistent");
            this.session.programManager.run(t2, e2, n2);
          }
          getOrCreateTextureData(t2, e2) {
            let n2 = this.getTextureData(t2.dataId, e2 === p.TextureType.packed);
            if (!n2 && (n2 = this.getTextureData(t2.dataId, e2 !== p.TextureType.packed), n2)) return e2 === p.TextureType.packed ? this.pack(n2) : this.unpack(n2);
            if (!n2) {
              const r2 = (0, l.createTextureLayoutFromTextureType)(this.session.layoutStrategy, t2.dims, e2);
              if (e2 === p.TextureType.packedLastDimension) {
                const n3 = 1, r3 = 4, i2 = t2.dims;
                if (4 === i2.length) {
                  const o2 = [i2[0], Math.ceil(i2[1] * i2[2] * i2[3] / r3)], a2 = (0, l.createTextureLayoutFromTextureType)(this.session.layoutStrategy, o2, e2);
                  let s2 = t2.numberData;
                  if (i2[1] * i2[2] * i2[3] % r3 != 0) {
                    const e3 = i2[0], o3 = i2[1] * i2[2] * i2[3], a3 = Math.ceil(o3 * n3 / r3) * r3;
                    s2 = new Float32Array(e3 * a3);
                    for (let r4 = 0; r4 < e3; ++r4) {
                      const e4 = r4 * o3, i3 = r4 * a3 + r4 % n3 * o3;
                      s2.set(t2.numberData.subarray(e4, e4 + o3), i3);
                    }
                  }
                  return this.createTextureData(a2, t2.type, s2, t2, 1);
                }
              }
              if (e2 === p.TextureType.packed) {
                const e3 = (0, l.createTextureLayoutFromShape)(this.session.layoutStrategy, t2.dims, 1, [], { reverseWH: true }), r3 = this.createTextureData(e3, t2.type, t2.numberData, t2, 1);
                n2 = this.pack(r3);
              } else n2 = this.createTextureData(r2, t2.type, t2.numberData, t2, 1);
            }
            return n2;
          }
          createTextureDataFromLayoutBindTensor(t2, e2, n2, r2) {
            return this.createTextureData(t2, e2, n2, r2, 1);
          }
          createTextureData(t2, e2, n2, i2, o2) {
            r.Logger.verbose("InferenceHandler", `Creating TextureData: layout:[${JSON.stringify(t2)}]`);
            const a2 = this.session.textureManager.createTextureFromLayout(e2, t2, n2, o2);
            return this.createTextureDataFromTexture(t2, e2, a2, i2);
          }
          reshapeUnpacked(t2, e2) {
            const n2 = this.getOrCreateTextureData(t2, p.TextureType.unpacked), r2 = { channels: n2.channels, height: n2.height, width: n2.width, shape: 0 !== e2.length ? e2 : [1], strides: o.ShapeUtil.computeStrides(e2), unpackedShape: e2 };
            return this.createTextureDataFromTexture(r2, t2.type, n2.texture).tensor;
          }
          reshapePacked(t2, e2) {
            const n2 = this.getOrCreateTextureData(t2, p.TextureType.packed);
            if ((0, s.isReshapeCheap)(t2.dims, e2)) {
              const r3 = { channels: n2.channels, height: n2.height, width: n2.width, shape: 0 !== e2.length ? e2 : [1], strides: o.ShapeUtil.computeStrides(e2), unpackedShape: e2, isPacked: true };
              return this.createTextureDataFromTexture(r3, t2.type, n2.texture).tensor;
            }
            const r2 = (0, s.processDims3D)(t2.dims), i2 = (0, s.processDims3D)(e2), a2 = this.reshapePacked(t2, r2), u2 = this.run((0, s.createPackedReshape3DProgramInfoLoader)(this, a2, i2), [a2]);
            return this.reshapePacked(u2, e2);
          }
          cast(t2, e2) {
            const n2 = this.getOrCreateTextureData(t2, p.TextureType.unpacked);
            return this.createTextureDataFromTexture(n2, e2, n2.texture).tensor;
          }
          createTextureDataFromTexture(t2, e2, n2, r2, o2) {
            const a2 = Object.assign(Object.assign({}, t2), { tensor: r2 || new i.Tensor(t2.unpackedShape, e2, (t3) => this.readTexture(a2), (t3) => __async(this, null, function* () {
              return this.readTextureAsync(a2);
            }), void 0, o2), texture: n2 });
            return this.setTextureData(a2.tensor.dataId, a2, t2.isPacked), a2;
          }
          getTextureData(t2, e2 = false) {
            return this.session.isInitializer(t2) ? this.session.getTextureData(t2, e2) : e2 ? this.packedTextureDataCache.get(t2) : this.unpackedTextureDataCache.get(t2);
          }
          setTextureData(t2, e2, n2 = false) {
            this.session.isInitializer(t2) ? this.session.setTextureData(t2, e2, n2) : (n2 ? this.packedTextureDataCache : this.unpackedTextureDataCache).set(t2, e2);
          }
          isTextureLayoutCached(t2, e2 = false) {
            return !!this.getTextureData(t2.dataId, e2);
          }
          dispose() {
            this.session.textureManager.clearActiveTextures(), this.packedTextureDataCache.forEach((t2) => this.session.textureManager.releaseTexture(t2)), this.packedTextureDataCache = /* @__PURE__ */ new Map(), this.unpackedTextureDataCache.forEach((t2) => this.session.textureManager.releaseTexture(t2)), this.unpackedTextureDataCache = /* @__PURE__ */ new Map();
          }
          readTexture(t2) {
            return t2.isPacked ? this.readTexture(this.unpack(t2)) : this.session.backend.glContext.isFloat32DownloadSupported ? this.session.textureManager.readTexture(t2, t2.tensor.type, t2.channels) : this.session.textureManager.readUint8TextureAsFloat((0, u.encodeAsUint8)(this, t2));
          }
          readTextureAsync(t2) {
            return __async(this, null, function* () {
              return t2.isPacked ? this.readTextureAsync(this.unpack(t2)) : this.session.backend.glContext.isFloat32DownloadSupported ? this.session.textureManager.readTextureAsync(t2, t2.tensor.type, t2.channels) : this.session.textureManager.readUint8TextureAsFloat((0, u.encodeAsUint8)(this, t2));
            });
          }
          pack(t2) {
            return this.executeProgram((0, a.createPackProgramInfoLoader)(this, t2.tensor), [t2.tensor]);
          }
          unpack(t2) {
            return this.executeProgram((0, c.createUnpackProgramInfoLoader)(this, t2.tensor), [t2.tensor]);
          }
        };
      }, 1640: function(t, e, n) {
        var r = this && this.__createBinding || (Object.create ? function(t2, e2, n2, r2) {
          void 0 === r2 && (r2 = n2);
          var i2 = Object.getOwnPropertyDescriptor(e2, n2);
          i2 && !("get" in i2 ? !e2.__esModule : i2.writable || i2.configurable) || (i2 = { enumerable: true, get: function() {
            return e2[n2];
          } }), Object.defineProperty(t2, r2, i2);
        } : function(t2, e2, n2, r2) {
          void 0 === r2 && (r2 = n2), t2[r2] = e2[n2];
        }), i = this && this.__setModuleDefault || (Object.create ? function(t2, e2) {
          Object.defineProperty(t2, "default", { enumerable: true, value: e2 });
        } : function(t2, e2) {
          t2.default = e2;
        }), o = this && this.__importStar || function(t2) {
          if (t2 && t2.__esModule) return t2;
          var e2 = {};
          if (null != t2) for (var n2 in t2) "default" !== n2 && Object.prototype.hasOwnProperty.call(t2, n2) && r(e2, t2, n2);
          return i(e2, t2), e2;
        };
        Object.defineProperty(e, "__esModule", { value: true }), e.WEBGL_OP_RESOLVE_RULES = void 0;
        const a = n(2898), s = o(n(7839)), u = n(4196), c = n(2069), l = n(8138), p = n(9663), f = n(5193), d = n(7992), h = n(1253), g = n(4776), b = n(6572), m = n(3346), y = n(5623), _ = n(2870), v = n(2143), w = n(4939), x = n(718), T = n(2268), S = n(8117), O = n(2278), A = n(5524), E = n(5975), I = n(3933), P = n(6558), D = n(5723), $ = n(3738), k = o(n(4909)), C = n(8428), F = n(9793);
        e.WEBGL_OP_RESOLVE_RULES = [["Abs", "", "6+", k.abs], ["Acos", "", "7+", k.acos], ["Add", "", "7+", s.add], ["And", "", "7+", s.and], ["Asin", "", "7+", k.asin], ["Atan", "", "7+", k.atan], ["AveragePool", "", "7+", v.averagePool, v.parseAveragePoolAttributes], ["BatchNormalization", "", "7+", a.batchNormalization, a.parseBatchNormalizationAttributes], ["Cast", "", "6+", u.cast, u.parseCastAttributes], ["Ceil", "", "6+", k.ceil], ["Clip", "", "6-10", k.clip, k.parseClipAttributes], ["Clip", "", "11+", k.clipV11], ["Concat", "", "4+", c.concat, c.parseConcatAttributes], ["Conv", "", "1+", l.conv, l.parseConvAttributes], ["ConvTranspose", "", "1+", p.convTranspose, p.parseConvTransposeAttributes], ["Cos", "", "7+", k.cos], ["Div", "", "7+", s.div], ["Dropout", "", "7+", k.identity], ["DepthToSpace", "", "1+", f.depthToSpace, f.parseDepthToSpaceAttributes], ["Equal", "", "7+", s.equal], ["Elu", "", "6+", k.elu, k.parseEluAttributes], ["Exp", "", "6+", k.exp], ["Flatten", "", "1+", d.flatten, d.parseFlattenAttributes], ["Floor", "", "6+", k.floor], ["FusedConv", "com.microsoft", "1+", l.conv, l.parseConvAttributes], ["Gather", "", "1+", h.gather, h.parseGatherAttributes], ["Gemm", "", "7-10", g.gemm, g.parseGemmAttributesV7], ["Gemm", "", "11+", g.gemm, g.parseGemmAttributesV11], ["GlobalAveragePool", "", "1+", v.globalAveragePool, v.parseGlobalAveragePoolAttributes], ["GlobalMaxPool", "", "1+", v.globalMaxPool], ["Greater", "", "7+", s.greater], ["Identity", "", "1+", k.identity], ["ImageScaler", "", "1+", b.imageScaler, b.parseImageScalerAttributes], ["InstanceNormalization", "", "6+", m.instanceNormalization, m.parseInstanceNormalizationAttributes], ["LeakyRelu", "", "6+", k.leakyRelu, k.parseLeakyReluAttributes], ["Less", "", "7+", s.less], ["Log", "", "6+", k.log], ["MatMul", "", "1+", y.matMul, y.parseMatMulAttributes], ["MaxPool", "", "1+", v.maxPool, v.parseMaxPoolAttributes], ["Mul", "", "7+", s.mul], ["Neg", "", "6+", k.neg], ["Not", "", "1+", k.not], ["Or", "", "7+", s.or], ["Pad", "", "2-10", _.padV2, _.parsePadAttributesV2], ["Pad", "", "11+", _.padV11, _.parsePadAttributesV11], ["Pow", "", "7+", s.pow], ["PRelu", "", "7+", s.pRelu], ["ReduceLogSum", "", "1+", w.reduceLogSum, w.parseReduceAttributes], ["ReduceMax", "", "1+", w.reduceMax, w.parseReduceAttributes], ["ReduceMean", "", "1+", w.reduceMean, w.parseReduceAttributes], ["ReduceMin", "", "1+", w.reduceMin, w.parseReduceAttributes], ["ReduceProd", "", "1+", w.reduceProd, w.parseReduceAttributes], ["ReduceSum", "", "1-12", w.reduceSum, w.parseReduceAttributes], ["ReduceSumSquare", "", "1+", w.reduceLogSumSquare, w.parseReduceAttributes], ["Relu", "", "6+", k.relu], ["Reshape", "", "5+", x.reshape], ["Resize", "", "10", T.resize, T.parseResizeAttributesV10], ["Resize", "", "11+", T.resize, T.parseResizeAttributesV11], ["Shape", "", "1+", S.shape], ["Sigmoid", "", "6+", k.sigmoid], ["Sin", "", "7+", k.sin], ["Slice", "", "10+", O.sliceV10], ["Slice", "", "1-9", O.slice, O.parseSliceAttributes], ["Softmax", "", "1-12", A.softmax, A.parseSoftmaxAttributes], ["Softmax", "", "13+", A.softmaxV13, A.parseSoftmaxAttributesV13], ["Split", "", "2-12", E.split, E.parseSplitAttributes], ["Sqrt", "", "6+", k.sqrt], ["Squeeze", "", "1-12", I.squeeze, I.parseSqueezeAttributes], ["Squeeze", "", "13+", I.squeezeV13], ["Sub", "", "7+", s.sub], ["Sum", "", "6+", P.sum], ["Tan", "", "7+", k.tan], ["Tanh", "", "6+", k.tanh], ["Tile", "", "6+", D.tile], ["Transpose", "", "1+", $.transpose, $.parseTransposeAttributes], ["Upsample", "", "7-8", F.upsample, F.parseUpsampleAttributesV7], ["Upsample", "", "9", F.upsample, F.parseUpsampleAttributesV9], ["Unsqueeze", "", "1-12", C.unsqueeze, C.parseUnsqueezeAttributes], ["Unsqueeze", "", "13+", C.unsqueezeV13], ["Xor", "", "7+", s.xor]];
      }, 2898: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.parseBatchNormalizationAttributes = e.batchNormalization = void 0;
        const r = n(246), i = n(5060), o = n(2039), a = { name: "BatchNormalization", inputNames: ["A", "Scale", "B", "Mean", "Variance"], inputTypes: [o.TextureType.unpacked, o.TextureType.unpacked, o.TextureType.unpacked, o.TextureType.unpacked, o.TextureType.unpacked] };
        e.batchNormalization = (t2, e2, n2) => (u(e2), [t2.run(Object.assign(Object.assign({}, a), { cacheHint: n2.cacheKey, get: () => s(t2, e2, n2) }), e2)]), e.parseBatchNormalizationAttributes = (t2) => {
          const e2 = t2.attributes.getFloat("epsilon", 1e-5), n2 = t2.attributes.getFloat("momentum", 0.9), i2 = t2.attributes.getInt("spatial", 1);
          return (0, r.createAttributeWithCacheKey)({ epsilon: e2, momentum: n2, spatial: i2 });
        };
        const s = (t2, e2, n2) => {
          const r2 = (0, i.getGlsl)(t2.session.backend.glContext.version), s2 = e2[0].dims.length, [u2, c] = t2.calculateTextureWidthAndHeight(e2[1].dims, o.TextureType.unpacked), l = `
  float process(int[${s2}] indices) {
    vec2 position = offsetToCoords(indices[1], ${u2}, ${c});
    float scale = getColorAsFloat(${r2.texture2D}(Scale, position));
    float mean = getColorAsFloat(${r2.texture2D}(Mean, position));
    float variance = getColorAsFloat(${r2.texture2D}(Variance, position));
    float b = getColorAsFloat(${r2.texture2D}(B, position));

    return scale * ( (_A(indices) - mean) / sqrt(variance + float(${n2.epsilon})) ) + b;
  }`;
          return Object.assign(Object.assign({}, a), { output: { dims: e2[0].dims, type: e2[0].type, textureType: o.TextureType.unpacked }, shaderSource: l });
        }, u = (t2) => {
          if (!t2 || 5 !== t2.length) throw new Error("BatchNormalization requires 5 inputs.");
          const e2 = t2[0], n2 = t2[1], r2 = t2[2], i2 = t2[3], o2 = t2[4];
          if (e2.dims.length < 3 || 1 !== n2.dims.length || 1 !== r2.dims.length || 1 !== i2.dims.length || 1 !== o2.dims.length) throw new Error("invalid input shape.");
          if (n2.dims[0] !== e2.dims[1] || r2.dims[0] !== e2.dims[1] || i2.dims[0] !== e2.dims[1] || o2.dims[0] !== e2.dims[1]) throw new Error("invalid input shape.");
          if ("float32" !== e2.type && "float64" !== e2.type || "float32" !== n2.type && "float64" !== n2.type || "float32" !== r2.type && "float64" !== r2.type || "float32" !== i2.type && "float64" !== i2.type || "float32" !== o2.type && "float64" !== o2.type) throw new Error("invalid input tensor types.");
        };
      }, 7839: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.xor = e.sub = e.pRelu = e.pow = e.or = e.mul = e.less = e.greater = e.equal = e.div = e.and = e.add = e.glslPRelu = e.glslPow = e.glslXor = e.glslOr = e.glslAnd = e.glslLess = e.glslGreater = e.glslEqual = e.glslSub = e.glslMul = e.glslDiv = e.glslAdd = void 0;
        const r = n(2517), i = n(8520), o = n(5060), a = n(2039);
        function s() {
          const t2 = "add_";
          return { body: `
  float ${t2}(float a, float b) {
    return a + b;
  }
  vec4 ${t2}(vec4 v1, vec4 v2) {
    return v1 + v2;
  }
  `, name: t2, type: i.FunctionType.ValueBased };
        }
        function u() {
          const t2 = "div_";
          return { body: `
  float ${t2}(float a, float b) {
    return a / b;
  }
  vec4 ${t2}(vec4 v1, vec4 v2) {
    return v1 / v2;
  }
  `, name: t2, type: i.FunctionType.ValueBased };
        }
        function c() {
          const t2 = "mul_";
          return { body: `
  float ${t2}(float a, float b) {
    return a * b;
  }
  vec4 ${t2}(vec4 v1, vec4 v2) {
    return v1 * v2;
  }
  `, name: t2, type: i.FunctionType.ValueBased };
        }
        function l() {
          const t2 = "sub_";
          return { body: `
  float ${t2}(float a, float b) {
    return a - b;
  }
  vec4 ${t2}(vec4 v1, vec4 v2) {
    return v1 - v2;
  }
  `, name: t2, type: i.FunctionType.ValueBased };
        }
        function p() {
          const t2 = "equal_";
          return { body: `
  float ${t2}(float a, float b) {
    return float(a == b);
  }
  vec4 ${t2}(vec4 v1, vec4 v2) {
    return vec4(equal(v1, v2));
  }
  `, name: t2, type: i.FunctionType.ValueBased };
        }
        function f() {
          const t2 = "greater_";
          return { body: `
  float ${t2}(float a, float b) {
    return float(a > b);
  }
  vec4 ${t2}(vec4 v1, vec4 v2) {
    return vec4( v1.r > v2.r ,
      v1.g > v2.g,
      v1.b > v2.b,
      v1.a > v2.a );
  }
  `, name: t2, type: i.FunctionType.ValueBased };
        }
        function d() {
          const t2 = "less_";
          return { body: `
  float ${t2}(float a, float b) {
    return float(a < b);
  }
  vec4 ${t2}(vec4 v1, vec4 v2) {
    return vec4( v1.r < v2.r ,
                v1.g < v2.g,
                v1.b < v2.b,
                v1.a < v2.a );
  }
  `, name: t2, type: i.FunctionType.ValueBased };
        }
        function h() {
          const t2 = "and_";
          return { body: `
  float ${t2}(float a, float b) {
    return float( bool(a) && bool(b) );
  }
  vec4 ${t2}(vec4 v1, vec4 v2) {
    bvec4 b1 = bvec4(v1);
    bvec4 b2 = bvec4(v2);
    return vec4( b1.r && b2.r ,
                b1.g && b2.g,
                b1.b && b2.b,
                b1.a && b2.a );
  }
  `, name: t2, type: i.FunctionType.ValueBased };
        }
        function g() {
          const t2 = "or_";
          return { body: `
  float ${t2}(float a, float b) {
    return float( bool(a) || bool(b) );
  }
  vec4 ${t2}(vec4 v1, vec4 v2) {
    bvec4 b1 = bvec4(v1);
    bvec4 b2 = bvec4(v2);
    return vec4( b1.r || b2.r ,
                b1.g || b2.g,
                b1.b || b2.b,
                b1.a || b2.a );
  }
  `, name: t2, type: i.FunctionType.ValueBased };
        }
        function b() {
          const t2 = "xor_";
          return { body: `
  float ${t2}(float a, float b) {
    return float( bool(a) ^^ bool(b) );
  }
  vec4 ${t2}(vec4 v1, vec4 v2) {
    bvec4 b1 = bvec4(v1);
    bvec4 b2 = bvec4(v2);
    return vec4( b1.r ^^ b2.r ,
                b1.g ^^ b2.g,
                b1.b ^^ b2.b,
                b1.a ^^ b2.a );
  }
  `, name: t2, type: i.FunctionType.ValueBased };
        }
        function m() {
          return function(t2) {
            const e2 = `${t2}_`;
            return { body: `
  float ${e2}(float a, float b) {
    return ${t2}(a, b);
  }
  vec4 ${e2}(vec4 v1, vec4 v2) {
    return ${t2}(v1, v2);
  }
  `, name: e2, type: i.FunctionType.ValueBased };
          }("pow");
        }
        function y() {
          const t2 = "prelu_";
          return { body: `
  float ${t2}(float a, float b) {
    return a < 0.0 ? a * b: a;
  }
  vec4 ${t2}(vec4 v1, vec4 v2) {
    return vec4(
      v1.r < 0.0 ? v1.r * v2.r: v1.r,
      v1.g < 0.0 ? v1.g * v2.g: v1.g,
      v1.b < 0.0 ? v1.b * v2.b: v1.b,
      v1.a < 0.0 ? v1.a * v2.a: v1.a
      );
  }
  `, name: t2, type: i.FunctionType.ValueBased };
        }
        e.glslAdd = s, e.glslDiv = u, e.glslMul = c, e.glslSub = l, e.glslEqual = p, e.glslGreater = f, e.glslLess = d, e.glslAnd = h, e.glslOr = g, e.glslXor = b, e.glslPow = m, e.glslPRelu = y;
        const _ = (t2, e2, n2, r2 = e2[0].type, i2) => {
          const o2 = t2.session.pack ? a.TextureType.packed : a.TextureType.unpacked;
          return { name: n2.name, inputNames: ["A", "B"], inputTypes: [o2, o2], cacheHint: i2, get: () => v(t2, e2, n2, r2) };
        }, v = (t2, e2, n2, i2 = e2[0].type) => {
          const s2 = t2.session.pack ? a.TextureType.packed : a.TextureType.unpacked, u2 = !r.ShapeUtil.areEqual(e2[0].dims, e2[1].dims);
          let c2 = e2[0].dims;
          const l2 = t2.session.pack;
          if (u2) {
            const a2 = r.BroadcastUtil.calcShape(e2[0].dims, e2[1].dims, false);
            if (!a2) throw new Error("Can't perform binary op on the given tensors");
            c2 = a2;
            const u3 = c2.length, p3 = 0 !== e2[0].dims.length ? e2[0].dims.length : 1, f3 = 0 !== e2[1].dims.length ? e2[1].dims.length : 1, d2 = 0 !== e2[0].dims.length ? "bcastIndices_A(indices, aindices);" : "aindices[0] = 0;", h2 = 0 !== e2[1].dims.length ? "bcastIndices_B(indices, bindices);" : "bindices[0] = 0;", g2 = (0, o.getGlsl)(t2.session.backend.glContext.version), b2 = l2 ? `
      ${n2.body}
      void main() {
        vec4 a = getAAtOutCoords();
        vec4 b = getBAtOutCoords();
        vec4 result = ${n2.name}(a, b);
        ${g2.output} = result;
      }` : `
      ${n2.body}
      float process(int indices[${u3}]) {
        int aindices[${p3}];
        int bindices[${f3}];
        ${d2}
        ${h2}
        return ${n2.name}(_A(aindices), _B(bindices));
      }`;
            return { name: n2.name, inputNames: ["A", "B"], inputTypes: [s2, s2], output: { dims: c2, type: i2, textureType: s2 }, shaderSource: b2, hasMain: l2 };
          }
          const p2 = (0, o.getGlsl)(t2.session.backend.glContext.version), f2 = `
    ${n2.body}
    void main() {
      vec4 v1 = ${p2.texture2D}(A, TexCoords);
      vec4 v2 = ${p2.texture2D}(B, TexCoords);
      vec4 result = ${n2.name}(v1, v2);
      ${p2.output} = result;
    }
    `;
          return { name: n2.name, inputNames: ["A", "B"], inputTypes: [s2, s2], output: { dims: e2[0].dims, type: i2, textureType: s2 }, shaderSource: f2, hasMain: true };
        };
        e.add = (t2, e2) => [t2.run(_(t2, e2, s()), e2)], e.and = (t2, e2) => [t2.run(_(t2, e2, h(), "bool"), e2)], e.div = (t2, e2) => [t2.run(_(t2, e2, u()), e2)], e.equal = (t2, e2) => [t2.run(_(t2, e2, p(), "bool"), e2)], e.greater = (t2, e2) => [t2.run(_(t2, e2, f(), "bool"), e2)], e.less = (t2, e2) => [t2.run(_(t2, e2, d(), "bool"), e2)], e.mul = (t2, e2) => [t2.run(_(t2, e2, c()), e2)], e.or = (t2, e2) => [t2.run(_(t2, e2, g(), "bool"), e2)], e.pow = (t2, e2) => [t2.run(_(t2, e2, m()), e2)], e.pRelu = (t2, e2) => [t2.run(_(t2, e2, y()), e2)], e.sub = (t2, e2) => [t2.run(_(t2, e2, l()), e2)], e.xor = (t2, e2) => [t2.run(_(t2, e2, b(), "bool"), e2)];
      }, 4196: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.parseCastAttributes = e.cast = void 0;
        const r = n(2517);
        e.cast = (t2, e2, n2) => (i(e2), [t2.cast(e2[0], n2)]), e.parseCastAttributes = (t2) => r.ProtoUtil.tensorDataTypeFromProto(t2.attributes.getInt("to"));
        const i = (t2) => {
          if (!t2 || 1 !== t2.length) throw new Error("Cast requires 1 input.");
          if ("string" === t2[0].type) throw new Error("Invalid input type.");
        };
      }, 1163: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.createPackedConcatProgramInfoLoader = void 0;
        const r = n(5060), i = n(2039), o = n(9390), a = n(2827);
        e.createPackedConcatProgramInfoLoader = (t2, e2, n2) => {
          const u = (c = e2.length, l = n2.cacheKey, { name: "Concat (packed)", inputNames: Array.from({ length: c }, (t3, e3) => `X${e3}`), inputTypes: Array(c).fill(i.TextureType.packed), cacheHint: l });
          var c, l;
          return Object.assign(Object.assign({}, u), { get: () => ((t3, e3, n3, u2) => {
            const c2 = n3[0].dims.slice();
            if (u2 >= c2.length || u2 < -1 * c2.length) throw new Error("axis specified for concat doesn't match input dimensionality");
            u2 < 0 && (u2 = c2.length + u2);
            const l2 = c2.slice(0);
            for (let t4 = 1; t4 < n3.length; t4++) {
              const e4 = n3[t4].dims.slice();
              for (let t5 = 0; t5 < c2.length; t5++) if (t5 === u2) l2[u2] += e4[t5];
              else if (c2[t5] !== e4[t5]) throw new Error("non concat dimensions must match");
            }
            const p = l2.length, f = (0, a.getChannels)("coords", p), d = (0, o.getCoordsDataType)(p), h = (0, a.unpackFromChannel)(), g = n3.map((t4) => t4.dims), b = (0, o.getGlChannels)(p), m = new Array(g.length - 1);
            m[0] = g[0][u2];
            for (let t4 = 1; t4 < m.length; t4++) m[t4] = m[t4 - 1] + g[t4][u2];
            const y = b[u2], _ = b.slice(-2), v = b.join();
            let w = `if (${y} < ${m[0]}) {
        return getChannel(
            getX0(${v}), vec2(${_.join()}));
        }`;
            for (let t4 = 1; t4 < m.length; t4++) {
              const e4 = m[t4 - 1];
              w += `
            if (${y} < ${m[t4]}  && ${y} >= ${m[t4 - 1]}) {
              return getChannel(
                getX${t4}(${s(b, y, e4)}),
                vec2(${s(_, y, e4)}));
            }`;
            }
            const x = m.length, T = m[m.length - 1];
            w += `
            return getChannel(
              getX${x}(${s(b, y, T)}),
              vec2(${s(_, y, T)}));`;
            const S = (0, r.getGlsl)(t3.session.backend.glContext.version), O = `
          ${h}
          float getValue(${b.map((t4) => "int " + t4)}) {
            ${w}
          }

          void main() {
            ${d} coords = getOutputCoords();
            int lastDim = coords.${b[p - 1]};
            coords.${b[p - 1]} = coords.${b[p - 2]};
            coords.${b[p - 2]} = lastDim;

            vec4 result = vec4(getValue(${f}), 0., 0., 0.);

            ${f[p - 1]} = ${f[p - 1]} + 1;
            if (${f[p - 1]} < ${l2[p - 1]}) {
              result.g = getValue(${f});
            }

            ${f[p - 2]} = ${f[p - 2]} + 1;
            if (${f[p - 2]} < ${l2[p - 2]}) {
              result.a = getValue(${f});
            }

            ${f[p - 1]} = ${f[p - 1]} - 1;
            if (${f[p - 2]} < ${l2[p - 2]} &&
                ${f[p - 1]} < ${l2[p - 1]}) {
              result.b = getValue(${f});
            }
            ${S.output} = result;
          }
        `;
            return Object.assign(Object.assign({}, e3), { output: { dims: l2, type: n3[0].type, textureType: i.TextureType.packed }, shaderSource: O, hasMain: true });
          })(t2, u, e2, n2.axis) });
        };
        const s = (t2, e2, n2) => {
          const r2 = t2.indexOf(e2);
          return t2.map((t3, e3) => e3 === r2 ? `${t3} - ${n2}` : t3).join();
        };
      }, 2069: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.parseConcatAttributes = e.concat = void 0;
        const r = n(246), i = n(2039), o = n(1163);
        e.concat = (t2, e2, n2) => (p(e2), t2.session.pack && e2[0].dims.length > 1 ? [t2.run((0, o.createPackedConcatProgramInfoLoader)(t2, e2, n2), e2)] : [t2.run(a(t2, e2, n2), e2)]);
        const a = (t2, e2, n2) => {
          const r2 = (o2 = e2.length, a2 = n2.cacheKey, { name: "Concat", inputNames: Array.from({ length: o2 }, (t3, e3) => `X${e3}`), inputTypes: Array(o2).fill(i.TextureType.unpacked), cacheHint: a2 });
          var o2, a2;
          return Object.assign(Object.assign({}, r2), { get: () => ((t3, e3, n3, r3) => {
            const o3 = n3[0].dims.slice();
            if (r3 >= o3.length || r3 < -1 * o3.length) throw new Error("axis specified for concat doesn't match input dimensionality");
            r3 < 0 && (r3 = o3.length + r3);
            const a3 = o3.slice(0);
            for (let t4 = 1; t4 < n3.length; t4++) {
              const e4 = n3[t4].dims.slice();
              for (let t5 = 0; t5 < o3.length; t5++) if (t5 === r3) a3[r3] += e4[t5];
              else if (o3[t5] !== e4[t5]) throw new Error("non concat dimensions must match");
            }
            const p2 = a3.length, f = new Array(n3.length);
            let d = 0;
            for (let t4 = 0; t4 < f.length; ++t4) d += n3[t4].dims[r3], f[t4] = d;
            let h = "";
            h = n3.length < 5 ? s(f) : u(f);
            const g = `
        ${c(n3.length, p2)}
        ${l(f)}
        ${h}
        float process(int indices[${p2}]) {
          int textureIndex = getTextureWhereDataResides (indices[${r3}]);

          if(textureIndex != 0) {
            indices[${r3}] = indices[${r3}] - int(getSizeInConcatAxisValueFromIndex(textureIndex-int(1)));
          }

          return fetchDataFromCorrectTexture(textureIndex, indices);
        }`;
            return Object.assign(Object.assign({}, e3), { output: { dims: a3, type: n3[0].type, textureType: i.TextureType.unpacked }, shaderSource: g });
          })(0, r2, e2, n2.axis) });
        }, s = (t2) => `int getTextureWhereDataResides(int index) {
      ${t2.map((t3, e2) => `if(index<${t3}) {return ${e2};}
`).join("")}
    }`, u = (t2) => s(t2), c = (t2, e2) => {
          const n2 = [`float fetchDataFromCorrectTexture(int textureIndex, int indices[${e2}]) {`];
          for (let e3 = 0; e3 < t2; ++e3) 0 === e3 ? n2.push(`	if (textureIndex == ${e3}) { return _X${e3}(indices); }`) : e3 === t2 - 1 ? n2.push(`	else { return _X${e3}(indices); }`) : n2.push(`	else if (textureIndex == ${e3}) { return _X${e3}(indices); }`);
          return n2.push("	}"), n2.join("\n");
        }, l = (t2) => {
          const e2 = ["int getSizeInConcatAxisValueFromIndex(int index) {"];
          for (let n2 = 0; n2 < t2.length; ++n2) 0 === n2 ? e2.push(`	if (index == ${n2}) { return ${t2[n2]}; }`) : n2 === t2.length - 1 ? e2.push(`	else { return ${t2[n2]}; }`) : e2.push(`	else if (index == ${n2}) { return ${t2[n2]}; }`);
          return e2.push("	}"), e2.join("\n");
        };
        e.parseConcatAttributes = (t2) => (0, r.createAttributeWithCacheKey)({ axis: t2.attributes.getInt("axis") });
        const p = (t2) => {
          if (!t2 || t2.length < 1) throw new Error("too few inputs");
          const e2 = t2[0].type, n2 = t2[0].dims.length;
          if ("string" === e2) throw new Error("string tensor is not supported yet");
          for (const r2 of t2) {
            if (r2.type !== e2) throw new Error("input tensors should be one type");
            if (r2.dims.length !== n2) throw new Error("input tensors should have the same shape");
          }
        };
      }, 4770: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.createUnpackedGroupedConvProgramInfoLoader = void 0;
        const r = n(6231), i = n(5060), o = n(2039), a = n(8138), s = n(2823);
        e.createUnpackedGroupedConvProgramInfoLoader = (t2, e2, n2) => {
          const u = (c = e2.length > 2, l = n2.cacheKey, { name: "GroupedConv", inputNames: c ? ["X", "W", "Bias"] : ["X", "W"], inputTypes: c ? [o.TextureType.unpacked, o.TextureType.unpacked, o.TextureType.unpacked] : [o.TextureType.unpacked, o.TextureType.unpacked], cacheHint: l });
          var c, l;
          return Object.assign(Object.assign({}, u), { get: () => ((t3, e3, n3, u2) => {
            const c2 = e3.length > 2 ? "value += getBias(output_channel);" : "", l2 = e3[0].dims.slice(), p = e3[1].dims.slice(), f = p[0] / u2.group;
            r.Logger.verbose("GroupedConv", `autpPad:${u2.autoPad}, dilations:${u2.dilations}, group:${u2.group}, kernelShape:${u2.kernelShape}, pads:${u2.pads}, strides:${u2.strides}`);
            const d = (0, a.calculateOutputShape)(l2, p, u2.dilations, u2.pads, u2.strides), h = (0, i.getGlsl)(t3.session.backend.glContext.version), { activationFunction: g, applyActivation: b } = (0, s.getActivationSnippet)(u2), m = `
  const ivec2 strides = ivec2(${u2.strides[0]}, ${u2.strides[1]});
  const ivec2 pads = ivec2(${u2.pads[0]}, ${u2.pads[1]});
  ${g}
  void main() {
    ivec4 coords = getOutputCoords();
    int batch = coords.x;
    int output_channel = coords.y;
    ivec2 xRCCorner = coords.zw * strides - pads;
    int group_id = output_channel / ${f};

    float value = 0.0;
    for (int wInChannel = 0; wInChannel < ${p[1]}; wInChannel++) {
      int input_channel = group_id * ${p[1]} + wInChannel;
      for (int wHeight = 0; wHeight < ${p[2]}; wHeight++) {
        int xHeight = xRCCorner.x + wHeight * ${u2.dilations[0]};

        if (xHeight < 0 || xHeight >= ${l2[2]}) {
          continue;
        }

        for (int wWidth = 0; wWidth < ${p[3]}; wWidth++) {
          int xWidth = xRCCorner.y + wWidth * ${u2.dilations[1]};
          if (xWidth < 0 || xWidth >= ${l2[3]}) {
            continue;
          }

          float xVal = getX(batch, input_channel, xWidth, xHeight);
          float wVal = getW(output_channel, wInChannel, wWidth, wHeight);
          value += xVal*wVal;
        }
      }
    }
    ${c2}
    ${b}
    ${h.output} = vec4(value, .0, .0, .0);
  }
`;
            return Object.assign(Object.assign({}, n3), { output: { dims: d, type: e3[0].type, textureType: o.TextureType.unpacked }, shaderSource: m, hasMain: true });
          })(t2, e2, u, n2) });
        };
      }, 1386: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.conv2DPacked = e.conv2DPackedPointwise = void 0;
        const r = n(8138), i = n(8555), o = n(708);
        e.conv2DPackedPointwise = (t2, e2, n2) => {
          const i2 = e2[0].dims, a = e2[1].dims, s = (0, r.calculateOutputShape)(i2, a, n2.dilations, n2.pads, n2.strides), u = t2.reshapePacked(e2[0], [i2[1], i2[2] * i2[3]]), c = t2.reshapePacked(e2[1], [a[0], a[1]]), l = e2.length > 2 ? [c, u, e2[2]] : [c, u], p = t2.run((0, o.createPackedMatmulProgramInfoLoader)(t2, l, n2), l);
          return t2.reshapePacked(p, s);
        }, e.conv2DPacked = (t2, e2, n2) => {
          const a = e2[0].dims, s = e2[1].dims, u = (0, r.calculateOutputShape)(a, s, n2.dilations, n2.pads, n2.strides), c = t2.run((0, i.createPackedIm2ColProgramInfoLoader)(t2, e2[0], e2[1], u, n2), [e2[0]]), l = t2.reshapePacked(e2[1], [s[0], s[1] * s[2] * s[3]]), p = 3 === e2.length ? [l, c, e2[2]] : [l, c], f = t2.run((0, o.createPackedMatmulProgramInfoLoader)(t2, p, n2), p);
          return t2.reshapePacked(f, u);
        };
      }, 9663: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.parseConvTransposeAttributes = e.convTranspose = void 0;
        const r = n(246), i = n(5060), o = n(2039), a = n(2823), s = (t2, e2, n2, r2, i2, o2) => (t2 - 1) * e2 + n2 + (r2 - 1) * i2 + 1 - o2, u = (t2, e2, n2, r2, i2) => {
          const o2 = Math.floor(t2 / 2);
          "SAME_UPPER" === e2 ? (n2[r2] = o2, n2[i2] = t2 - o2) : "SAME_LOWER" === e2 && (n2[r2] = t2 - o2, n2[i2] = o2);
        };
        e.convTranspose = (t2, e2, n2) => (f(e2, n2), c(t2, e2, n2));
        const c = (t2, e2, n2) => {
          const r2 = p(n2, e2);
          return [l(t2, e2, r2)];
        }, l = (t2, e2, n2) => t2.run(((t3, e3, n3) => {
          const r2 = (s2 = e3.length > 2, u2 = n3.cacheKey, { name: "ConvTranspose", inputNames: s2 ? ["X", "W", "B"] : ["X", "W"], inputTypes: s2 ? [o.TextureType.unpacked, o.TextureType.unpacked, o.TextureType.unpacked] : [o.TextureType.unpacked, o.TextureType.unpacked], cacheHint: u2 });
          var s2, u2;
          return Object.assign(Object.assign({}, r2), { get: () => ((t4, e4, n4, r3) => {
            const s3 = e4.length > 2 ? "getB(output_channel)" : "0.0", u3 = e4[0].dims, c2 = e4[1].dims, l2 = c2[1], p2 = c2[0] / r3.group, f2 = [e4[0].dims[0], e4[1].dims[1] * r3.group, ...r3.outputShape], d = (0, i.getGlsl)(t4.session.backend.glContext.version), { activationFunction: h, applyActivation: g } = (0, a.getActivationSnippet)(r3), b = `
  const ivec2 strides = ivec2(${r3.strides[0]}, ${r3.strides[1]});
  const ivec2 pads = ivec2(${r3.pads[0]}, ${r3.pads[1]});
  ${h}
  void main() {
    ivec4 coords = getOutputCoords();
    int batch = coords.x;
    int output_channel = coords.y;

    ivec2 loc = coords.zw + pads;

    int group_id = output_channel / ${l2};
    int wOutChannel = output_channel - group_id * ${l2};

    float value = ${s3};
    for (int inChannelOffset = 0; inChannelOffset < ${p2}; inChannelOffset++) {
      int input_channel = group_id * ${p2} + inChannelOffset;
      for (int wWOff = 0; wWOff < ${c2[2]}; wWOff++) {
        for (int wHOff = 0; wHOff < ${c2[3]}; wHOff++) {
          ivec2 wOff = ivec2(wWOff * ${r3.dilations[0]}, wHOff * ${r3.dilations[1]});
          ivec2 wLoc = loc - wOff;
          ivec2 wLocIn = wLoc / strides;
          if (
            wLocIn * strides == wLoc &&
            wLocIn.x >= 0 && wLocIn.x < ${u3[2]} &&
            wLocIn.y >= 0 && wLocIn.y < ${u3[3]}
          ) {
            float xVal = getX(batch, input_channel, wLocIn.y, wLocIn.x);
            float wVal = getW(input_channel, wOutChannel, wHOff, wWOff);
            value += xVal * wVal;
          }
        }
      }
    }
    ${g}
    ${d.output} = vec4(value, .0, .0, .0);
  }
`;
            return Object.assign(Object.assign({}, n4), { output: { dims: f2, type: e4[0].type, textureType: o.TextureType.unpacked }, shaderSource: b, hasMain: true });
          })(t3, e3, r2, n3) });
        })(t2, e2, n2), e2), p = (t2, e2) => {
          const n2 = t2.kernelShape.slice();
          if (0 === t2.kernelShape.length) for (let t3 = 2; t3 < e2[1].dims.length; ++t3) n2.push(e2[1].dims[t3]);
          const r2 = t2.pads.slice(), i2 = t2.outputShape.slice();
          ((t3, e3, n3, r3, i3, o3, a2, c2) => {
            const l2 = t3.length - 2, p2 = 0 === c2.length;
            for (let f2 = 0; f2 < l2; ++f2) {
              const d = p2 ? t3[f2 + 2] * o3[f2] : c2[f2], h = s(t3[f2 + 2], o3[f2], i3[f2], e3[f2], n3[f2], d);
              u(h, r3, i3, f2, f2 + l2), p2 && c2.push(o3[f2] * (t3[f2 + 2] - 1) + a2[f2] + (e3[f2] - 1) * n3[f2] + 1 - i3[f2] - i3[f2 + l2]);
            }
          })(e2[0].dims, n2, t2.dilations, t2.autoPad, r2, t2.strides, t2.outputPadding, i2);
          const o2 = Object.assign({}, t2);
          return Object.assign(o2, { kernelShape: n2, pads: r2, outputShape: i2, cacheKey: t2.cacheKey }), o2;
        };
        e.parseConvTransposeAttributes = (t2) => {
          const e2 = t2.attributes, n2 = (0, a.parseInternalActivationAttributes)(e2), i2 = e2.getString("auto_pad", "NOTSET"), o2 = e2.getInts("dilations", [1, 1]), s2 = e2.getInt("group", 1), u2 = e2.getInts("kernel_shape", []), c2 = e2.getInts("output_padding", [0, 0]), l2 = e2.getInts("output_shape", []), p2 = e2.getInts("pads", [0, 0, 0, 0]), f2 = e2.getInts("strides", [1, 1]);
          return (0, r.createAttributeWithCacheKey)(Object.assign({ autoPad: i2, dilations: o2, group: s2, kernelShape: u2, outputPadding: c2, outputShape: l2, pads: p2, strides: f2 }, n2));
        };
        const f = (t2, e2) => {
          if (!t2 || 2 !== t2.length && 3 !== t2.length) throw new Error("Conv requires 2 or 3 inputs");
          if (4 !== t2[0].dims.length || 4 !== t2[1].dims.length) throw new Error("currently only support 2-dimensional conv");
          if (t2[0].dims[1] !== t2[1].dims[0]) throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");
          const n2 = t2[1].dims[1] * e2.group;
          if (3 === t2.length && (1 !== t2[2].dims.length || t2[2].dims[0] !== n2)) throw new Error("invalid bias");
          const r2 = t2[0].dims.length - 2;
          if (e2.dilations.length !== r2) throw new Error(`dilations should be ${r2}D`);
          if (e2.strides.length !== r2) throw new Error(`strides should be ${r2}D`);
          if (e2.pads.length !== 2 * r2) throw new Error(`pads should be ${2 * r2}D`);
          if (e2.outputPadding.length !== r2) throw new Error(`output_padding should be ${r2}D`);
          if (0 !== e2.kernelShape.length && e2.kernelShape.length !== t2[1].dims.length - 2) throw new Error("invalid kernel shape");
          if (0 !== e2.outputShape.length && e2.outputShape.length !== t2[0].dims.length - 2) throw new Error("invalid output shape");
          if ("float32" !== t2[0].type || "float32" !== t2[1].type) throw new Error("ConvTranspose input(X,W) should be float tensor");
          if (3 === t2.length && "float32" !== t2[2].type) throw new Error("ConvTranspose input(bias) should be float tensor");
        };
      }, 8138: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.parseConvAttributes = e.conv = e.calculateOutputShape = void 0;
        const r = n(246), i = n(2517), o = n(4770), a = n(1386), s = n(9828), u = n(2823), c = n(3248), l = n(5623);
        e.calculateOutputShape = (t2, e2, n2, r2, i2) => {
          const o2 = t2[0], a2 = t2.slice(2), s2 = a2.length, u2 = e2[0], c2 = e2.slice(2).map((t3, e3) => t3 + (t3 - 1) * (n2[e3] - 1)), l2 = a2.map((t3, e3) => t3 + r2[e3] + r2[e3 + s2]).map((t3, e3) => Math.floor((t3 - c2[e3] + i2[e3]) / i2[e3]));
          return [o2, u2].concat(...l2);
        }, e.conv = (t2, e2, n2) => (g(e2, n2), p(t2, e2, n2));
        const p = (t2, e2, n2) => {
          const r2 = h(n2, e2), i2 = t2.session.pack, s2 = 1 === r2.kernelShape[0] && 1 === r2.kernelShape[1];
          return r2.group > 1 ? [t2.run((0, o.createUnpackedGroupedConvProgramInfoLoader)(t2, e2, r2), e2)] : s2 && i2 ? [f(t2, e2, r2)] : i2 && 4 === e2[0].dims.length && 1 === e2[0].dims[0] && !s2 ? [(0, a.conv2DPacked)(t2, e2, r2)] : [d(t2, e2, r2)];
        }, f = (t2, n2, r2) => {
          const i2 = n2[0].dims, o2 = n2[1].dims, a2 = (0, e.calculateOutputShape)(i2, o2, r2.dilations, r2.pads, r2.strides), s2 = t2.reshapeUnpacked(n2[0], [i2[1], i2[2] * i2[3]]), u2 = t2.reshapeUnpacked(n2[1], [o2[0], o2[1]]), c2 = n2.length > 2 ? [u2, s2, n2[2]] : [u2, s2], p2 = t2.run((0, l.createMatmulProgramInfoLoader)(c2, r2), c2);
          return t2.reshapeUnpacked(p2, a2);
        }, d = (t2, n2, r2) => {
          const i2 = n2[0].dims, o2 = n2[1].dims, a2 = (0, e.calculateOutputShape)(i2, o2, r2.dilations, r2.pads, r2.strides), u2 = t2.run((0, c.createIm2ColProgramInfoLoader)(t2, n2[0], n2[1], a2, r2), [n2[0]]), l2 = 3 === n2.length ? [u2, n2[1], n2[2]] : [u2, n2[1]];
          return t2.run((0, s.createDotProductProgramInfoLoader)(t2, n2, a2, r2), l2);
        }, h = (t2, e2) => {
          const n2 = t2.kernelShape.slice();
          if (0 === t2.kernelShape.length) for (let t3 = 2; t3 < e2[1].dims.length; ++t3) n2.push(e2[1].dims[t3]);
          const r2 = t2.pads.slice();
          i.PoolConvUtil.adjustPadsBasedOnAutoPad(e2[0].dims, t2.strides, t2.dilations, n2, r2, t2.autoPad);
          const o2 = Object.assign({}, t2);
          return Object.assign(o2, { kernelShape: n2, pads: r2, cacheKey: t2.cacheKey }), o2;
        };
        e.parseConvAttributes = (t2) => {
          const e2 = t2.attributes, n2 = (0, u.parseInternalActivationAttributes)(e2), i2 = e2.getString("auto_pad", "NOTSET"), o2 = e2.getInts("dilations", [1, 1]), a2 = e2.getInt("group", 1), s2 = e2.getInts("kernel_shape", []), c2 = e2.getInts("pads", [0, 0, 0, 0]), l2 = e2.getInts("strides", [1, 1]);
          return (0, r.createAttributeWithCacheKey)(Object.assign({ autoPad: i2, dilations: o2, group: a2, kernelShape: s2, pads: c2, strides: l2 }, n2));
        };
        const g = (t2, e2) => {
          if (!t2 || 2 !== t2.length && 3 !== t2.length) throw new Error("Conv requires 2 or 3 inputs");
          if (4 !== t2[0].dims.length || 4 !== t2[1].dims.length) throw new Error("currently only support 2-dimensional conv");
          if (t2[0].dims[1] !== t2[1].dims[1] * e2.group) throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");
          if (3 === t2.length && (1 !== t2[2].dims.length || t2[1].dims[0] !== t2[2].dims[0])) throw new Error("invalid bias");
          const n2 = t2[0].dims.length - 2;
          if (e2.dilations.length !== n2) throw new Error(`dilations should be ${n2}D`);
          if (e2.strides.length !== n2) throw new Error(`strides should be ${n2}D`);
          if (e2.pads.length !== 2 * n2) throw new Error(`pads should be ${2 * n2}D`);
          if (0 !== e2.kernelShape.length && e2.kernelShape.length !== t2[1].dims.length - 2) throw new Error("invalid kernel shape");
          if ("float32" !== t2[0].type || "float32" !== t2[1].type) throw new Error("Conv input(X,W) should be float tensor");
          if (3 === t2.length && "float32" !== t2[2].type) throw new Error("Conv input(bias) should be float tensor");
        };
      }, 5193: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.parseDepthToSpaceAttributes = e.depthToSpace = void 0;
        const r = n(3738);
        e.depthToSpace = (t2, e2, n2) => {
          i(e2);
          const o = n2.blocksize, a = o * o, s = "DCR" === n2.mode ? [0, 3, 4, 1, 5, 2] : [0, 1, 4, 2, 5, 3], u = "DCR" === n2.mode ? [e2[0].dims[0], o, o, e2[0].dims[1] / a, e2[0].dims[2], e2[0].dims[3]] : [e2[0].dims[0], e2[0].dims[1] / a, o, o, e2[0].dims[2], e2[0].dims[3]], c = t2.reshapeUnpacked(e2[0], u), l = { perm: s, cacheKey: `${s}` }, [p] = (0, r.transpose)(t2, [c], l), f = [e2[0].dims[0], e2[0].dims[1] / a, e2[0].dims[2] * o, e2[0].dims[3] * o];
          return [t2.reshapeUnpacked(p, f)];
        }, e.parseDepthToSpaceAttributes = (t2) => {
          const e2 = t2.attributes.getInt("blocksize");
          if (e2 < 1) throw new Error(`blocksize must be >= 1, but got : ${e2} for DepthToSpace`);
          const n2 = t2.attributes.getString("mode", "DCR");
          if ("DCR" !== n2 && "CRD" !== n2) throw new Error(`unrecognized mode: ${n2} for DepthToSpace`);
          return { mode: n2, blocksize: e2 };
        };
        const i = (t2) => {
          if (1 !== t2.length) throw new Error(`DepthToSpace expect 1 inputs, but got ${t2.length}`);
          if ("string" === t2[0].type || 4 !== t2[0].dims.length) throw new TypeError("DepthToSpace input should be a 4-D numeric tensor");
        };
      }, 9828: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.createDotProductProgramInfoLoader = void 0;
        const r = n(2517), i = n(5060), o = n(2039), a = n(2823), s = n(3248);
        e.createDotProductProgramInfoLoader = (t2, e2, n2, u) => {
          const c = ((t3, e3) => ({ name: "ConvDotProduct", inputNames: t3 ? ["Im2Col", "K", "B"] : ["Im2Col", "K"], inputTypes: t3 ? [o.TextureType.unpacked, o.TextureType.packedLastDimension, o.TextureType.unpacked] : [o.TextureType.unpacked, o.TextureType.packedLastDimension], cacheKey: e3.activationCacheKey }))(e2.length > 2, u);
          return Object.assign(Object.assign({}, c), { get: () => ((t3, e3, n3, u2, c2) => {
            const l = n3[0].dims, p = n3[1].dims, f = [p[0], Math.ceil(l[1] * p[2] * p[3] / 4)], d = (0, s.calculateIm2ColDims)(l, p, u2), [h, g] = t3.calculateTextureWidthAndHeight(f, o.TextureType.packedLastDimension), b = r.ShapeUtil.computeStrides(d), [m, y] = t3.calculateTextureWidthAndHeight(d, o.TextureType.packedLastDimension), _ = u2.length, v = n3.length < 3 ? "0.0" : "_B(b)", w = Math.ceil(l[1] * p[2] * p[3] / 4), { activationFunction: x, applyActivation: T } = (0, a.getActivationSnippet)(c2), S = (0, i.getGlsl)(t3.session.backend.glContext.version), O = `
${x}
float process(int indices[${_}]) {
  int b[1];
  b[0] = indices[1];
  int im2col[4];
  im2col[0] = indices[0];
  im2col[1] = indices[2];
  im2col[2] = indices[3];
  int im2colOffset = im2col[0] * ${b[0]} + im2col[1] * ${b[1]} + im2col[2] * ${b[2]};
  int kernelOffset = indices[1] * ${f[1]};
  float value = ${v};
  for (int i = 0; i < ${w}; ++i) {
    vec2 im2colCoords = offsetToCoords(im2colOffset, ${m}, ${y});
    vec2 kernelCoords = offsetToCoords(kernelOffset, ${h}, ${g});
    value += dot(${S.texture2D}(Im2Col, im2colCoords), ${S.texture2D}(K, kernelCoords));
    ++im2colOffset;
    ++kernelOffset;
  }
  ${T}
  return value;
}`;
            return Object.assign(Object.assign({}, e3), { output: { dims: u2, type: n3[0].type, textureType: o.TextureType.unpacked }, shaderSource: O });
          })(t2, c, e2, n2, u) });
        };
      }, 7992: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.parseFlattenAttributes = e.flatten = void 0;
        const r = n(2517);
        e.flatten = (t2, e2, n2) => {
          i(e2, n2);
          const o = r.ShapeUtil.flattenShape(e2[0].dims, n2);
          return [t2.reshapeUnpacked(e2[0], o)];
        }, e.parseFlattenAttributes = (t2) => t2.attributes.getInt("axis", 1);
        const i = (t2, e2) => {
          if (!t2 || 1 !== t2.length) throw new Error("Flatten requires 1 input.");
          const n2 = t2[0].dims.length;
          if (0 === n2) throw new Error("scalar tensor is not supported.");
          if (e2 < -n2 || e2 > n2) throw new Error("Invalid axis");
          if ("string" === t2[0].type) throw new Error("string tensor is not supported.");
        };
      }, 2823: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.parseInternalActivationAttributes = e.getActivationSnippet = void 0;
        const r = n(2517), i = n(4909);
        e.getActivationSnippet = function(t2) {
          let e2;
          switch (t2.activation) {
            case "Relu":
              e2 = (0, i.glslRelu)();
              break;
            case "Sigmoid":
              e2 = (0, i.glslSigmoid)();
              break;
            case "Clip":
              e2 = (0, i.glslClip)(t2.clipMin, t2.clipMax);
              break;
            default:
              return { activationFunction: "", applyActivation: "" };
          }
          const n2 = e2.name;
          return { activationFunction: e2.body, applyActivation: `value = ${n2}_(value);` };
        }, e.parseInternalActivationAttributes = (t2) => {
          const e2 = t2.getString("activation", "");
          if ("Clip" === e2) {
            const [n2, i2] = t2.getFloats("activation_params", [r.MIN_CLIP, r.MAX_CLIP]);
            return { activation: e2, clipMax: i2, clipMin: n2, activationCacheKey: `${e2}:${n2},${i2}` };
          }
          return { activation: e2, activationCacheKey: e2 };
        };
      }, 1253: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.parseGatherAttributes = e.gather = void 0;
        const r = n(246), i = n(782), o = n(2517), a = n(2039);
        e.gather = (t2, e2, n2) => (c(e2, n2.axis), [t2.run(u(t2, e2, n2), e2)]), e.parseGatherAttributes = (t2) => (0, r.createAttributeWithCacheKey)({ axis: t2.attributes.getInt("axis", 0) });
        const s = { name: "Gather", inputNames: ["A", "B"], inputTypes: [a.TextureType.unpacked, a.TextureType.unpacked] }, u = (t2, e2, n2) => {
          const r2 = Object.assign(Object.assign({}, s), { cacheHint: n2.cacheKey });
          return Object.assign(Object.assign({}, r2), { get: () => ((t3, e3, n3, r3) => {
            const i2 = n3[0].dims.slice(), s2 = n3[1].dims.slice(), u2 = new Array(i2.length + s2.length - 1);
            r3 = o.ShapeUtil.normalizeAxis(r3, i2.length);
            const c2 = [];
            for (let t4 = 0; t4 < u2.length; t4++) t4 < r3 ? (u2[t4] = i2[t4], c2.push(`inputIdx[${t4}] = outputIdx[${t4}];`)) : t4 < r3 + s2.length ? (u2[t4] = s2[t4 - r3], c2.push(`indexDataIdx[${t4 - r3}] = outputIdx[${t4}];`)) : (u2[t4] = i2[t4 - s2.length + 1], c2.push(`inputIdx[${t4 - s2.length + 1}] = outputIdx[${t4}];`));
            const l = `
      float process(int outputIdx[${u2.length || 1}]) {
        int inputIdx[${i2.length}];
        int indexDataIdx[${s2.length || 1}];
        indexDataIdx[0] = 0;
        ${c2.join("\n        ")}
        int idx = int(_B(indexDataIdx));
        inputIdx[${r3}] = idx < 0 ? idx + ${i2[r3]} : idx;
        return _A(inputIdx);
      }`;
            return Object.assign(Object.assign({}, e3), { output: { dims: u2, type: n3[0].type, textureType: a.TextureType.unpacked }, shaderSource: l });
          })(0, r2, e2, n2.axis) });
        }, c = (t2, e2) => {
          if (!t2 || 2 !== t2.length) throw new Error("Gather requires 2 inputs.");
          const n2 = t2[0].dims.length;
          if (n2 < 1) throw new Error("Invalid input shape.");
          if (e2 < -n2 || e2 > n2 - 1) throw new Error("Invalid axis.");
          if (-1 === i.NUMBER_TYPES.indexOf(t2[0].type)) throw new Error("Invaid input type.");
          if ("int32" !== t2[1].type && "int16" !== t2[1].type) throw new Error("Invaid input type.");
        };
      }, 4776: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.parseGemmAttributesV11 = e.parseGemmAttributesV7 = e.gemm = void 0;
        const r = n(246), i = n(2517), o = n(2039);
        e.gemm = (t2, e2, n2) => (c(e2, n2), [t2.run(s(e2, n2), e2)]);
        const a = (t2, e2) => {
          const n2 = 0 !== t2.attributes.getInt("transA", 0), i2 = 0 !== t2.attributes.getInt("transB", 0), o2 = t2.attributes.getFloat("alpha", 1), a2 = t2.attributes.getFloat("beta", 1);
          return (0, r.createAttributeWithCacheKey)({ transA: n2, transB: i2, alpha: o2, beta: a2, isOptionalC: e2 });
        };
        e.parseGemmAttributesV7 = (t2) => a(t2, false), e.parseGemmAttributesV11 = (t2) => a(t2, true);
        const s = (t2, e2) => {
          const n2 = { name: "Gemm", inputNames: 3 === t2.length ? ["A", "B", "C"] : ["A", "B"], inputTypes: 3 === t2.length ? [o.TextureType.unpacked, o.TextureType.unpacked, o.TextureType.unpacked] : [o.TextureType.unpacked, o.TextureType.unpacked], key: e2.cacheKey };
          return Object.assign(Object.assign({}, n2), { get: () => u(n2, t2, e2) });
        }, u = (t2, e2, n2) => {
          const r2 = e2[0].dims.slice(), a2 = e2[1].dims.slice(), [s2, u2] = i.GemmUtil.getShapeOfGemmResult(r2, n2.transA, a2, n2.transB, 3 === e2.length ? e2[2].dims : void 0), c2 = [s2, u2];
          if (!c2) throw new Error("Can't use gemm on the given tensors");
          let l = r2[r2.length - 1], p = "";
          n2.transA && (l = r2[0]), n2.transA && n2.transB ? p = "value += _A_T(a) * _B_T(b);" : n2.transA && !n2.transB ? p = "value += _A_T(a) * _B(b);" : !n2.transA && n2.transB ? p = "value += _A(a) * _B_T(b);" : n2.transA || n2.transB || (p = "value += _A(a) * _B(b);");
          const f = c2.length, d = `
      float process(int indices[${f}]) {
          int a[${f}];
          int b[${f}];
          ${3 === e2.length ? `int c[${e2[2].dims.length}];` : ""}

          copyVec(indices, a);
          copyVec(indices, b);
          ${3 === e2.length ? "bcastIndices_C(indices, c);" : ""}

          float value = 0.0;
          for (int k=0; k<${l}; ++k) {
              a[${f - 1}] = k;
              b[${f - 2}] = k;
              ${p}
          }

          value = value * alpha;
          ${3 === e2.length ? "value += beta * _C(c);" : ""}
          return value;
      }`;
          return Object.assign(Object.assign({}, t2), { output: { dims: c2, type: e2[0].type, textureType: o.TextureType.unpacked }, variables: [{ name: "alpha", type: "float", data: n2.alpha }, { name: "beta", type: "float", data: n2.beta }], shaderSource: d });
        }, c = (t2, e2) => {
          if (!t2) throw new Error("Input is missing");
          if (e2.isOptionalC && (t2.length < 2 || t2.length > 3)) throw new Error("Invaid input shape.");
          if (!e2.isOptionalC && 3 !== t2.length) throw new Error("Gemm requires 3 inputs");
          if (3 === t2.length && 1 !== t2[2].dims.length && 2 !== t2[2].dims.length) throw new Error("Invalid input shape of C");
          if ("float32" !== t2[0].type && "float64" !== t2[0].type || "float32" !== t2[1].type && "float64" !== t2[1].type || 3 === t2.length && "float32" !== t2[2].type && "float64" !== t2[2].type) throw new Error("Invalid input type.");
          if (t2[0].type !== t2[1].type || 3 === t2.length && t2[0].type !== t2[2].type) throw new Error("Input types are mismatched");
        };
      }, 8555: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.createPackedIm2ColProgramInfoLoader = void 0;
        const r = n(5060), i = n(2039), o = n(2827);
        e.createPackedIm2ColProgramInfoLoader = (t2, e2, n2, a, s) => {
          const u = (c = s.cacheKey, { name: "Im2Col (packed)", inputNames: ["A"], inputTypes: [i.TextureType.packed], cacheHint: c });
          var c;
          return Object.assign(Object.assign({}, u), { get: () => ((t3, e3, n3, a2, s2, u2) => {
            const c2 = n3.dims, l = a2.dims, p = s2.length, f = [l[1] * l[2] * l[3], s2[2] * s2[3]], d = l[2] * l[3], h = (0, o.unpackFromChannel)(), g = (0, r.getGlsl)(t3.session.backend.glContext.version);
            let b = "";
            for (let t4 = 0; t4 <= 1; t4++) for (let e4 = 0; e4 <= 1; e4++) b += `
            blockIndex = rc.x + ${e4};
            pos = rc.y + ${t4};

            if(blockIndex < ${f[1]} && pos < ${f[0]}) {
              offsetY = int(blockIndex / (${s2[p - 1]})) * ${u2.strides[0]} -
                ${u2.pads[0]};
              d0 = offsetY + ${u2.dilations[0]} * (imod(pos, ${d}) / ${l[2]});

              if(d0 < ${c2[2]} && d0 >= 0) {
                offsetX = imod(blockIndex, ${s2[p - 1]}) * ${u2.strides[1]} -
                  ${u2.pads[1]};
                d1 = offsetX + ${u2.dilations[1]} * imod(imod(pos, ${d}), ${l[2]});

                if(d1 < ${c2[3]} && d1 >= 0) {

                  ch = int(float(pos)/ ${d}.);
                    innerDims = vec2(d0, d1);
                    result[${2 * t4 + e4}] = getChannel(
                      getA(0, ch, int(innerDims.x),
                      int(innerDims.y)), innerDims);
                }
              }
            }

          `;
            const m = `
      ${h}

      void main() {
        ivec2 rc = getOutputCoords();
          vec4 result = vec4(0.0);
          int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
          vec2 innerDims;
          ${b}
          ${g.output} = result;
      }
            `;
            return Object.assign(Object.assign({}, e3), { output: { dims: f, type: n3.type, textureType: i.TextureType.packed }, shaderSource: m, hasMain: true });
          })(t2, u, e2, n2, a, s) });
        };
      }, 3248: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.calculateIm2ColDims = e.createIm2ColProgramInfoLoader = void 0;
        const r = n(2039);
        e.createIm2ColProgramInfoLoader = (t2, n2, i, o, a) => {
          const s = (u = a.cacheKey, { name: "Im2Col", inputNames: ["X"], inputTypes: [r.TextureType.unpacked], cacheHint: u });
          var u;
          return Object.assign(Object.assign({}, s), { get: () => ((t3, n3, i2, o2, a2, s2) => {
            const u2 = i2.dims, c = o2.dims, l = a2.length, p = (0, e.calculateIm2ColDims)(u2, c, a2, 4), f = `
        const int XC = ${u2[1]};
        const int XH = ${u2[2]};
        const int XW = ${u2[3]};
        const int KH = ${s2.kernelShape[0]};
        const int KW = ${s2.kernelShape[1]};
        const int dilationH = ${s2.dilations[0]};
        const int dilationW = ${s2.dilations[1]};
        const int strideH = ${s2.strides[0]};
        const int strideW = ${s2.strides[1]};
        const int padH = ${s2.pads[0]};
        const int padW = ${s2.pads[1]};
        const int KHKW = KH*KW;
        const int XCKHKW = XC * KHKW;
        const int outputChannels = 4;
        vec4 process(int indices[${l}]) {
          int b  = indices[0]; // batch size
          int oh = indices[1] * strideH - padH; //output height
          int ow = indices[2] * strideW - padW; //output width
          int p = indices[3] * outputChannels; //patch
          vec4 value = vec4(0.0);
          for(int i=0; i < outputChannels; ++i) {
            if(p < XCKHKW) {
              int patchC = p / KHKW;
              int patchH = (p - patchC*KHKW) / KW;
              int patchW = (p - patchC*KHKW) - patchH * KW;
              int xh2 = oh + patchH * dilationH;
              int xw2 = ow + patchW * dilationW;
              int x[${u2.length}];
              x[0] = b;
              x[1] = patchC;
              x[2] = xh2;
              x[3] = xw2;
              if(xh2 >= 0 &&
                  xh2 < XH &&
                  xw2 >= 0 &&
                  xw2 < XW) {
                value[i] = _X(x);
              }
            }
            ++p;
          }
          return value;
        }
        `;
            return Object.assign(Object.assign({}, n3), { output: { dims: p, type: i2.type, textureType: r.TextureType.packedLastDimension }, shaderSource: f });
          })(0, s, n2, i, o, a) });
        }, e.calculateIm2ColDims = (t2, e2, n2, r2 = 4) => [n2[0], n2[2], n2[3], Math.ceil(t2[1] * e2[2] * e2[3] / r2)];
      }, 6572: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.parseImageScalerAttributes = e.imageScaler = void 0;
        const r = n(246), i = n(2039);
        e.imageScaler = (t2, e2, n2) => (u(e2), [t2.run(a(t2, e2, n2), e2)]), e.parseImageScalerAttributes = (t2) => {
          const e2 = t2.attributes.getFloat("scale"), n2 = t2.attributes.getFloats("bias");
          return (0, r.createAttributeWithCacheKey)({ scale: e2, bias: n2 });
        };
        const o = { name: "ImageScaler", inputNames: ["X"], inputTypes: [i.TextureType.unpacked] }, a = (t2, e2, n2) => {
          const r2 = Object.assign(Object.assign({}, o), { cacheHint: n2.cacheKey });
          return Object.assign(Object.assign({}, r2), { get: () => ((t3, e3, n3, r3) => {
            const o2 = n3[0].dims.slice(), a2 = o2.length, u2 = `
      ${s(r3.bias.length)}
      float process(int indices[${a2}]) {
        return _X(indices) * scale + getBias(bias, indices[1]);
      }`;
            return Object.assign(Object.assign({}, e3), { output: { dims: o2, type: n3[0].type, textureType: i.TextureType.unpacked }, variables: [{ name: "bias", type: "float", arrayLength: r3.bias.length, data: r3.bias }, { name: "scale", type: "float", data: r3.scale }], shaderSource: u2 });
          })(0, r2, e2, n2) });
        }, s = (t2) => {
          const e2 = [`float getBias(float bias[${t2}], int channel) {`];
          for (let n2 = 0; n2 < t2; ++n2) 0 === n2 ? e2.push(`	if (channel == ${n2}) { return bias[${n2}]; }`) : n2 === t2 - 1 ? e2.push(`	else { return bias[${n2}]; }`) : e2.push(`	else if (channel == ${n2}) { return bias[${n2}]; }`);
          return e2.push("	}"), e2.join("\n");
        }, u = (t2) => {
          if (!t2 || 1 !== t2.length) throw new Error("ImageScaler requires 1 input.");
          if (4 !== t2[0].dims.length) throw new Error("Invalid input shape.");
          if ("float32" !== t2[0].type && "float64" !== t2[0].type) throw new Error("Invalid input type.");
        };
      }, 3346: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.parseInstanceNormalizationAttributes = e.instanceNormalization = void 0;
        const r = n(5060), i = n(2039);
        e.instanceNormalization = (t2, e2, n2) => {
          c(e2);
          const r2 = t2.run(a(e2[0]), e2);
          return [t2.run(u(t2, e2[0], n2, r2.dims), [e2[0], r2, e2[1], e2[2]])];
        }, e.parseInstanceNormalizationAttributes = (t2) => t2.attributes.getFloat("epsilon", 1e-5);
        const o = { name: "InstanceNormalization_MeanAndVariance", inputNames: ["X"], inputTypes: [i.TextureType.unpacked] }, a = (t2) => Object.assign(Object.assign({}, o), { get: () => ((t3, e2) => {
          const n2 = e2.dims.slice(), r2 = n2[1], o2 = n2[2] * n2[3], a2 = [n2[0], r2], s2 = `
      vec4 process(int[2] indices) {
        vec4 v = vec4(0.0);
        int a[4];
        a[0] = indices[0];
        a[1] = indices[1];
        float temp = 0.0;
        for(int a2=0; a2<${n2[2]}; a2++) {
          a[2] = a2;
          for(int a3=0; a3<${n2[3]}; a3++) {
            a[3] = a3;
            float x = _X(a);
            temp += x;
          }
        }
        float mean = temp / float(${o2});
        temp = 0.0;
        for(int a2=0; a2<${n2[2]}; a2++) {
          a[2] = a2;
          for(int a3=0; a3<${n2[3]}; a3++) {
            a[3] = a3;
            float x = _X(a);
            temp += (x - mean) * (x - mean);
          }
        }
        v.r = mean;
        v.g = temp / float(${o2});

        return v;
      }`;
          return Object.assign(Object.assign({}, t3), { output: { dims: a2, type: e2.type, textureType: i.TextureType.packedLastDimension }, shaderSource: s2 });
        })(o, t2) }), s = { name: "InstanceNormalization_ComputeOutput", inputNames: ["X", "MeanAndVariance", "Scale", "B"], inputTypes: [i.TextureType.unpacked, i.TextureType.packedLastDimension, i.TextureType.unpacked, i.TextureType.unpacked] }, u = (t2, e2, n2, o2) => {
          const a2 = Object.assign(Object.assign({}, s), { cacheHint: `${n2}` });
          return Object.assign(Object.assign({}, a2), { get: () => ((t3, e3, n3, o3, a3) => {
            const s2 = (0, r.getGlsl)(t3.session.backend.glContext.version), [u2, c2] = t3.calculateTextureWidthAndHeight(a3, i.TextureType.packedLastDimension), [l, p] = [u2 / 4, c2], f = `
      vec4 get_MeanAndVariance(int[2] mv) {
        int offset = indicesToOffset_MeanAndVariance(mv);
        vec2 coords = offsetToCoords(offset, ${l}, ${p});
        return ${s2.texture2D}(MeanAndVariance, coords);
      }

      float process(int[4] indices) {
        int mv[2];
        mv[0] = indices[0];
        mv[1] = indices[1];
        vec4 mean_and_variance = get_MeanAndVariance(mv);
        float mean = mean_and_variance.r;
        float variance = mean_and_variance.g;

        int sb[1];
        sb[0] = indices[1];
        float scale = _Scale(sb);
        float b = _B(sb);

        return scale * (_X(indices) - mean) / sqrt(variance + epsilon) + b;
      }`;
            return Object.assign(Object.assign({}, e3), { output: { dims: n3.dims, type: n3.type, textureType: i.TextureType.unpacked }, variables: [{ name: "epsilon", type: "float", data: o3 }], shaderSource: f });
          })(t2, a2, e2, n2, o2) });
        }, c = (t2) => {
          if (!t2 || 3 !== t2.length) throw new Error("InstanceNormalization requires 3 inputs.");
          const e2 = t2[0], n2 = t2[1], r2 = t2[2];
          if (e2.dims.length < 3 || 1 !== n2.dims.length || 1 !== r2.dims.length) throw new Error("Invalid input shape.");
          if (n2.dims[0] !== e2.dims[1] || r2.dims[0] !== e2.dims[1]) throw new Error("Input shapes are mismatched.");
          if ("float32" !== e2.type && "float64" !== e2.type || "float32" !== n2.type && "float64" !== n2.type || "float32" !== r2.type && "float64" !== r2.type) throw new Error("Invalid input type.");
          if (4 !== t2[0].dims.length) throw new Error("Only support 4-D input shape.");
        };
      }, 708: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.createPackedMatmulProgramInfoLoader = void 0;
        const r = n(2517), i = n(5060), o = n(2039), a = n(9390), s = n(2823), u = n(5623);
        e.createPackedMatmulProgramInfoLoader = (t2, e2, n2) => {
          const c = (l = e2.length > 2, p = n2.activationCacheKey, { name: "MatMul (packed)", inputNames: l ? ["A", "B", "Bias"] : ["A", "B"], inputTypes: l ? [o.TextureType.packed, o.TextureType.packed, o.TextureType.packed] : [o.TextureType.packed, o.TextureType.packed], cacheHint: p });
          var l, p;
          return Object.assign(Object.assign({}, c), { get: () => ((t3, e3, n3, c2) => {
            const l2 = n3.length > 2, p2 = l2 ? "value += getBiasForMatmul();" : "", f = n3[0].dims, d = n3[1].dims, h = r.BroadcastUtil.calcShape(f, d, true), g = !r.ShapeUtil.areEqual(n3[0].dims, n3[1].dims);
            if (!h) throw new Error("Can't use matmul on the given tensors");
            const b = f[f.length - 1], m = Math.ceil(b / 2), y = f.length, _ = d.length, v = (0, i.getGlsl)(t3.session.backend.glContext.version), w = (0, a.getCoordsDataType)(h.length), x = h.length, T = (0, a.getGlChannels)(), { activationFunction: S, applyActivation: O } = (0, s.getActivationSnippet)(c2), A = l2 ? `${(0, u.getBiasForMatmul)(w, T, n3[2].dims, h, true)}` : "", E = g ? `${function(t4, e4, n4, i2) {
              let o2 = [], a2 = [];
              const s2 = n4[0].dims, u2 = n4[1].dims, c3 = s2.length, l3 = u2.length, p3 = i2.length, f2 = p3 - c3, d2 = p3 - l3;
              o2 = s2.map((t5, n5) => `coords.${e4[n5 + f2]}`), o2[c3 - 1] = "i*2", o2.join(", "), a2 = u2.map((t5, n5) => `coords.${e4[n5 + d2]}`), a2[l3 - 2] = "i*2", a2.join(", ");
              const h2 = r.BroadcastUtil.getBroadcastDims(s2, i2), g2 = r.BroadcastUtil.getBroadcastDims(u2, i2), b2 = h2.map((t5) => `coords.${e4[t5 + f2]} = 0;`).join("\n"), m2 = g2.map((t5) => `coords.${e4[t5 + d2]} = 0;`).join("\n"), y2 = `int lastDim = coords.${e4[p3 - 1]};
  coords.${e4[p3 - 1]} = coords.${e4[p3 - 2]};
  coords.${e4[p3 - 2]} = lastDim;`;
              return `
vec4 getAAtOutCoordsMatmul(int i) {
  ${t4} coords = getOutputCoords();
  ${y2}
  ${b2}
  vec4 outputValue = getA(${o2});
  return outputValue;
}

vec4 getBAtOutCoordsMatmul(int i) {
  ${t4} coords = getOutputCoords();
  ${y2}
  ${m2}
  vec4 outputValue = getB(${a2});
  return outputValue;
}`;
            }(w, T, n3, h)}` : "", I = g ? "getAAtOutCoordsMatmul(i)" : `getA(${function(t4, e4) {
              let n4 = "";
              for (let r2 = 0; r2 < e4 - 2; r2++) n4 += `rc.${t4[r2]}, `;
              return n4 += `rc.${t4[e4 - 2]}, i*2`, n4;
            }(T, y)})`, P = g ? "getBAtOutCoordsMatmul(i)" : `getB(${function(t4, e4) {
              let n4 = "";
              for (let r2 = 0; r2 < e4 - 2; r2++) n4 += `rc.${t4[r2]}, `;
              return n4 += `i*2, rc.${t4[e4 - 1]}`, n4;
            }(T, _)})`, D = `
            ${E}
            ${A}
            ${S}
            void main() {
              ${g ? "" : `${w} rc =
          getOutputCoords(); int lastDim = rc.${T[x - 1]}; rc.${T[x - 1]} =
          rc.${T[x - 2]}; rc.${T[x - 2]} = lastDim;
      `}

              vec4 value = vec4(0);
              for (int i = 0; i < ${m}; i++) {
                vec4 a = ${I};
                vec4 b = ${P};

                value += (a.rrbb * b.rgrg);
                value += (a.ggaa * b.baba);
              }
              ${p2}
              ${O}
              ${v.output} = value;
            }`;
            return Object.assign(Object.assign({}, e3), { output: { dims: h, type: n3[0].type, textureType: o.TextureType.packed }, shaderSource: D, hasMain: true });
          })(t2, c, e2, n2) });
        };
      }, 5623: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.getBiasForMatmul = e.createMatmulProgramInfoLoader = e.parseMatMulAttributes = e.matMul = void 0;
        const r = n(2517), i = n(2039), o = n(9390), a = n(2823), s = n(708);
        function u(t2, e2) {
          const n2 = (s2 = t2.length > 2, u2 = e2.activationCacheKey, { name: "MatMul", inputNames: s2 ? ["A", "B", "Bias"] : ["A", "B"], inputTypes: s2 ? [i.TextureType.unpacked, i.TextureType.unpacked, i.TextureType.unpacked] : [i.TextureType.unpacked, i.TextureType.unpacked], cacheHint: u2 });
          var s2, u2;
          return Object.assign(Object.assign({}, n2), { get: () => function(t3, e3, n3) {
            const s3 = e3[0].dims, u3 = e3[1].dims, c2 = r.BroadcastUtil.calcShape(s3, u3, true);
            if (!c2) throw new Error("Can't use matmul on the given tensors");
            const p = (0, o.getCoordsDataType)(c2.length), f = (0, o.getGlChannels)(), { activationFunction: d, applyActivation: h } = (0, a.getActivationSnippet)(n3), g = e3.length > 2, b = g ? "value += getBiasForMatmul();" : "", m = g ? `${l(p, f, e3[2].dims, c2, false)}` : "", y = c2.length, _ = s3.length, v = u3.length, w = `
    ${d}
    ${m}
    float process(int indices[${y}]) {
        int a[${_}];
        int b[${v}];
        bcastMatmulIndices_A(indices, a);
        bcastMatmulIndices_B(indices, b);

        float value;
        for (int k=0; k<${s3[s3.length - 1]}; ++k) {
            a[${_ - 1}] = k;
            b[${v - 2}] = k;
            value += _A(a) * _B(b);
        }
        ${b}
        ${h}
        return value;
    }`;
            return Object.assign(Object.assign({}, t3), { output: { dims: c2, type: e3[0].type, textureType: i.TextureType.unpacked }, shaderSource: w });
          }(n2, t2, e2) });
        }
        e.matMul = (t2, e2, n2) => (c(e2), t2.session.pack ? [t2.run((0, s.createPackedMatmulProgramInfoLoader)(t2, e2, n2), e2)] : [t2.run(u(e2, n2), e2)]), e.parseMatMulAttributes = (t2) => (0, a.parseInternalActivationAttributes)(t2.attributes), e.createMatmulProgramInfoLoader = u;
        const c = (t2) => {
          if (!t2 || 2 !== t2.length) throw new Error("MatMul requires 2 inputs.");
          if (t2[0].dims[t2[0].dims.length - 1] !== t2[1].dims[t2[1].dims.length - 2]) throw new Error("shared dimension does not match.");
          if ("float32" !== t2[0].type && "float64" !== t2[0].type || "float32" !== t2[1].type && "float64" !== t2[1].type) throw new Error("inputs should be float type");
          if (t2[0].type !== t2[1].type) throw new Error("inputs types should match");
        };
        function l(t2, e2, n2, i2, o2) {
          let a2 = "";
          const s2 = n2.length, u2 = i2.length, c2 = u2 - s2;
          a2 = u2 < 2 && s2 > 0 ? "coords" : n2.map((t3, n3) => `coords.${e2[n3 + c2]}`).join(", ");
          const l2 = r.BroadcastUtil.getBroadcastDims(n2, i2).map((t3) => `coords.${e2[t3 + c2]} = 0;`).join("\n");
          let p = "vec4(outputValue.xx, outputValue.yy)";
          return 1 === r.ShapeUtil.size(n2) && (p = "vec4(outputValue.x)"), o2 ? `
vec4 getBiasForMatmul() {
  ${t2} coords = getOutputCoords();
  ${l2}
  vec4 outputValue = getBias(${a2});
  return ${p};
}` : `
float getBiasForMatmul() {
  ${t2} coords = getOutputCoords();
  ${l2}
  return getBias(coords.x);
}`;
        }
        e.getBiasForMatmul = l;
      }, 2403: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.createPackProgramInfoLoader = void 0;
        const r = n(5060), i = n(2039), o = n(9390), a = n(2827), s = { name: "pack", inputNames: ["A"], inputTypes: [i.TextureType.unpackedReversed] };
        e.createPackProgramInfoLoader = (t2, e2) => Object.assign(Object.assign({}, s), { get: () => ((t3, e3) => {
          const n2 = (0, r.getGlsl)(t3.session.backend.glContext.version), u = e3.dims, c = u.length, l = e3.dims.length, p = (0, o.getCoordsDataType)(l), f = (0, a.getChannels)("rc", l), d = (h = l, g = f, b = u[u.length - 2], m = u[u.length - 1], 0 === h || 1 === h ? "" : `
    int r = ${g[h - 2]};
    int c = ${g[h - 1]};
    int rp1 = ${g[h - 2]} + 1;
    int cp1 = ${g[h - 1]} + 1;
    bool rEdge = rp1 >= ${m};
    bool cEdge = cp1 >= ${b};
    `);
          var h, g, b, m;
          let y;
          y = 0 === c ? [1, 1] : 1 === c ? [u[0], 1] : [u[l - 1], u[l - 2]];
          const _ = function(t4, e4, n3) {
            if (0 === t4) return "false";
            if (1 === t4) return `rc > ${e4[0]}`;
            let r2 = "";
            for (let i2 = t4 - 2; i2 < t4; i2++) r2 += `${n3[i2]} >= ${e4[i2 - t4 + 2]}`, i2 < t4 - 1 && (r2 += "||");
            return r2;
          }(l, y, f), v = function(t4, e4) {
            const n3 = t4.length;
            if (0 === n3) return "getA(), 0, 0, 0";
            if (1 === n3) return `getA(rc),
            rc + 1 >= ${t4[0]} ? 0. : getA(rc + 1),
            0, 0`;
            let r2 = "";
            if (n3 > 2) for (let t5 = 0; t5 < n3 - 2; ++t5) r2 += `${e4[t5]},`;
            return `getA(${r2}r, c),
          rEdge ? 0. : getA(${r2}rp1, c),
          cEdge ? 0. : getA(${r2}r, cp1),
          rEdge || cEdge ? 0. : getA(${r2}rp1, cp1)`;
          }(u, f), w = `
        void main() {
          ${p} rc = getOutputCoords();

          if(${_}) {
            ${n2.output} = vec4(0);
          } else {
            ${d}

            ${n2.output} = vec4(${v});
          }
        }
      `;
          return Object.assign(Object.assign({}, s), { hasMain: true, output: { dims: e3.dims, type: e3.type, textureType: i.TextureType.packed }, shaderSource: w });
        })(t2, e2) });
      }, 2827: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.unpackFromChannel = e.getChannels = e.getVecChannels = void 0;
        const r = n(9390);
        function i(t2, e2) {
          return (0, r.getGlChannels)(e2).map((e3) => `${t2}.${e3}`);
        }
        e.getVecChannels = i, e.getChannels = function(t2, e2) {
          return 1 === e2 ? [t2] : i(t2, e2);
        }, e.unpackFromChannel = function() {
          return "\n    float getChannel(vec4 frag, int dim) {\n      int modCoord = imod(dim, 2);\n      return modCoord == 0 ? frag.r : frag.g;\n    }\n\n    float getChannel(vec4 frag, vec2 innerDims) {\n      vec2 modCoord = mod(innerDims, 2.);\n      return modCoord.x == 0. ?\n        (modCoord.y == 0. ? frag.r : frag.g) :\n        (modCoord.y == 0. ? frag.b : frag.a);\n    }\n  ";
        };
      }, 2870: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.parsePadAttributesV11 = e.padV11 = e.parsePadAttributesV2 = e.padV2 = void 0;
        const r = n(246), i = n(2517), o = n(5060), a = n(2039), s = { name: "Pad", inputNames: ["A"], inputTypes: [a.TextureType.unpacked] };
        e.padV2 = (t2, e2, n2) => (l(e2), [t2.run(Object.assign(Object.assign({}, s), { cacheHint: n2.cacheKey, get: () => c(t2, e2[0], n2) }), e2)]), e.parsePadAttributesV2 = (t2) => {
          const e2 = t2.attributes.getString("mode", "constant"), n2 = t2.attributes.getFloat("value", 0), i2 = t2.attributes.getInts("pads");
          return (0, r.createAttributeWithCacheKey)({ mode: e2, value: n2, pads: i2 });
        }, e.padV11 = (t2, n2, r2) => {
          p(n2);
          const i2 = u(t2, n2, r2);
          return (0, e.padV2)(t2, [n2[0]], i2);
        }, e.parsePadAttributesV11 = (t2) => t2.attributes.getString("mode", "constant");
        const u = (t2, e2, n2) => {
          if (!t2.session.isInitializer(e2[1].dataId) || e2.length >= 3 && !t2.session.isInitializer(e2[2].dataId)) throw new Error("dynamic pad attributes are not allowed");
          const i2 = Array.from(e2[1].integerData), o2 = e2.length >= 3 ? e2[2].floatData[0] : 0;
          return (0, r.createAttributeWithCacheKey)({ mode: n2, pads: i2, value: o2 });
        }, c = (t2, e2, n2) => {
          const r2 = i.ShapeUtil.padShape(e2.dims.slice(), n2.pads), o2 = r2.length, s2 = `
      ${f(t2, e2, n2)}
      float process(int[${o2}] indices) {
          return padA(indices);
      }`;
          return { name: "Pad", inputNames: ["A"], inputTypes: [a.TextureType.unpacked], output: { dims: r2, type: e2.type, textureType: a.TextureType.unpacked }, shaderSource: s2 };
        }, l = (t2) => {
          if (!t2 || 1 !== t2.length) throw new Error("Pad requires 1 input");
          if ("float32" !== t2[0].type && "float64" !== t2[0].type) throw new Error("Invalid input type.");
        }, p = (t2) => {
          if (!t2 || 2 !== t2.length && 3 !== t2.length) throw new Error("Pad requires 2 or 3 inputs");
          if ("int32" !== t2[1].type) throw new Error("Invalid input type.");
          if (t2.length >= 3 && "string" === t2[2].type) throw new Error("Invalid input type.");
        }, f = (t2, e2, n2) => {
          const r2 = (0, o.getGlsl)(t2.session.backend.glContext.version), [s2, u2] = t2.calculateTextureWidthAndHeight(e2.dims, a.TextureType.unpacked), c2 = i.ShapeUtil.computeStrides(e2.dims);
          switch (n2.mode) {
            case "constant":
              return d(r2, e2.dims, c2, s2, u2, n2.pads, n2.value);
            case "reflect":
              return h(r2, e2.dims, c2, s2, u2, n2.pads);
            case "edge":
              return g(r2, e2.dims, c2, s2, u2, n2.pads);
            default:
              throw new Error("Invalid mode");
          }
        }, d = (t2, e2, n2, r2, i2, o2, a2) => {
          const s2 = e2.length;
          let u2 = "";
          for (let t3 = s2 - 1; t3 >= 0; --t3) u2 += `
        k = m[${t3}] - ${o2[t3]};
        if (k < 0)  return constant;
        if (k >= ${e2[t3]}) return constant;
        offset += k * ${n2[t3]};
        `;
          return `
      float padA(int m[${s2}]) {
        const float constant = float(${a2});
        int offset = 0;
        int k = 0;
        ${u2}
        vec2 coords = offsetToCoords(offset, ${r2}, ${i2});
        float value = getColorAsFloat(${t2.texture2D}(A, coords));
        return value;
      }
      `;
        }, h = (t2, e2, n2, r2, i2, o2) => {
          const a2 = e2.length;
          let s2 = "";
          for (let t3 = a2 - 1; t3 >= 0; --t3) s2 += `
        k = m[${t3}] - ${o2[t3]};
        if (k < 0) { k = -k; }
        {
          const int _2n_1 = ${2 * (e2[t3] - 1)};
          k = int( mod( float(k), float(_2n_1) ) ) ;
          if(k >= ${e2[t3]}) { k = _2n_1 - k; }
        }
        offset += k * ${n2[t3]};
        `;
          return `
      float padA(int m[${a2}]) {
        int offset = 0;
        int k = 0;
        ${s2}
        vec2 coords = offsetToCoords(offset, ${r2}, ${i2});
        float value = getColorAsFloat(${t2.texture2D}(A, coords));
        return value;
      }
      `;
        }, g = (t2, e2, n2, r2, i2, o2) => {
          const a2 = e2.length;
          let s2 = "";
          for (let t3 = a2 - 1; t3 >= 0; --t3) s2 += `
        k = m[${t3}] - ${o2[t3]};
        if (k < 0)  k = 0;
        if (k >= ${e2[t3]}) k = ${e2[t3] - 1};
        offset += k * ${n2[t3]};
      `;
          return `
      float padA(int m[${a2}]) {
        int offset = 0;
        int k = 0;
        ${s2}
        vec2 coords = offsetToCoords(offset, ${r2}, ${i2});
        float value = getColorAsFloat(${t2.texture2D}(A, coords));
        return value;
      }
      `;
        };
      }, 2143: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.globalMaxPool = e.parseMaxPoolAttributes = e.maxPool = e.parseGlobalAveragePoolAttributes = e.globalAveragePool = e.parseAveragePoolAttributes = e.averagePool = void 0;
        const r = n(246), i = n(2517), o = n(2039);
        e.averagePool = (t2, e2, n2) => {
          p(e2);
          const r2 = { name: "AveragePool", inputNames: ["X"], inputTypes: [o.TextureType.unpacked], cacheHint: n2.cacheKey };
          return [t2.run(Object.assign(Object.assign({}, r2), { get: () => a(e2, r2, false, n2) }), e2)];
        }, e.parseAveragePoolAttributes = (t2) => {
          const e2 = t2.attributes.getString("auto_pad", "NOTSET"), n2 = t2.attributes.getInt("ceil_mode", 0), i2 = 0 !== t2.attributes.getInt("count_include_pad", 0), o2 = t2.attributes.getInts("kernel_shape"), a2 = t2.attributes.getInts("strides", []), s2 = t2.attributes.getInts("pads", []);
          if (0 !== n2) throw new Error("using ceil() in shape computation is not yet supported for AveragePool");
          return (0, r.createAttributeWithCacheKey)({ autoPad: e2, ceilMode: n2, countIncludePad: i2, kernelShape: o2, strides: a2, pads: s2 });
        };
        const a = (t2, e2, n2, r2) => {
          const [a2, s2] = u(t2, r2, n2), c2 = i.ShapeUtil.size(a2.kernelShape);
          let l2 = "";
          a2.countIncludePad ? l2 += `value /= float(${c2});` : l2 += `value /= float(${c2} - pad);`;
          const p2 = `
        ${f(t2[0].dims, a2, "value += _X(x);", l2, "0.0")}
      `;
          return Object.assign(Object.assign({}, e2), { output: { dims: s2, type: t2[0].type, textureType: o.TextureType.unpacked }, shaderSource: p2 });
        };
        e.globalAveragePool = (t2, e2, n2) => {
          p(e2);
          const r2 = { name: "GlobalAveragePool", inputNames: ["X"], inputTypes: [o.TextureType.unpacked], cacheHint: `${n2.countIncludePad}` };
          return [t2.run(Object.assign(Object.assign({}, r2), { get: () => a(e2, r2, true, n2) }), e2)];
        }, e.parseGlobalAveragePoolAttributes = (t2) => {
          const e2 = 0 !== t2.attributes.getInt("count_include_pad", 0);
          return (0, r.createAttributeWithCacheKey)({ autoPad: "", ceilMode: 0, countIncludePad: e2, kernelShape: [], strides: [], pads: [] });
        }, e.maxPool = (t2, e2, n2) => {
          p(e2);
          const r2 = { name: "MaxPool", inputNames: ["X"], inputTypes: [o.TextureType.unpacked], cacheHint: n2.cacheKey };
          return [t2.run(Object.assign(Object.assign({}, r2), { get: () => s(e2, r2, false, n2) }), e2)];
        }, e.parseMaxPoolAttributes = (t2) => {
          const e2 = t2.attributes.getString("auto_pad", "NOTSET"), n2 = t2.attributes.getInt("ceil_mode", 0), i2 = t2.attributes.getInts("kernel_shape"), o2 = t2.attributes.getInts("strides", []), a2 = t2.attributes.getInts("pads", []), s2 = t2.attributes.getInt("storage_order", 0), u2 = t2.attributes.getInts("dilations", []);
          if (0 !== s2) throw new Error("column major storage order is not yet supported for MaxPool");
          if (0 !== n2) throw new Error("using ceil() in shape computation is not yet supported for MaxPool");
          return (0, r.createAttributeWithCacheKey)({ autoPad: e2, ceilMode: n2, countIncludePad: false, kernelShape: i2, strides: o2, pads: a2, storageOrder: s2, dilations: u2 });
        };
        const s = (t2, e2, n2, r2) => {
          const [i2, a2] = u(t2, r2, n2), s2 = `
      ${f(t2[0].dims, i2, "\n      value = max(_X(x), value);\n    ", "", "-1e5")}
    `;
          return Object.assign(Object.assign({}, e2), { output: { dims: a2, type: t2[0].type, textureType: o.TextureType.unpacked }, shaderSource: s2 });
        }, u = (t2, e2, n2) => {
          const r2 = t2[0].dims.slice(), o2 = Object.hasOwnProperty.call(e2, "dilations"), a2 = e2.kernelShape.slice(), s2 = e2.strides.slice(), u2 = o2 ? e2.dilations.slice() : [], c2 = e2.pads.slice();
          i.PoolConvUtil.adjustPoolAttributes(n2, r2, a2, s2, u2, c2);
          const l2 = i.PoolConvUtil.computePoolOutputShape(n2, r2, s2, u2, a2, c2, e2.autoPad), p2 = Object.assign({}, e2);
          return o2 ? Object.assign(p2, { kernelShape: a2, strides: s2, pads: c2, dilations: u2, cacheKey: e2.cacheKey }) : Object.assign(p2, { kernelShape: a2, strides: s2, pads: c2, cacheKey: e2.cacheKey }), [p2, l2];
        }, c = { autoPad: "", ceilMode: 0, countIncludePad: false, kernelShape: [], strides: [], pads: [], storageOrder: 0, dilations: [], cacheKey: "" }, l = { name: "GlobalMaxPool", inputNames: ["X"], inputTypes: [o.TextureType.unpacked] };
        e.globalMaxPool = (t2, e2) => (p(e2), [t2.run(Object.assign(Object.assign({}, l), { get: () => s(e2, l, true, c) }), e2)]);
        const p = (t2) => {
          if (!t2 || 1 !== t2.length) throw new Error("Pool ops requires 1 input.");
          if ("float32" !== t2[0].type && "float64" !== t2[0].type) throw new Error("Invalid input type.");
        }, f = (t2, e2, n2, r2, o2) => {
          const a2 = t2.length;
          if (e2.kernelShape.length <= 2) {
            const i2 = e2.kernelShape[e2.kernelShape.length - 1], s2 = e2.strides[e2.strides.length - 1], u2 = e2.pads[e2.pads.length / 2 - 1], c2 = e2.pads[e2.pads.length - 1], l2 = t2[a2 - 1];
            let p2 = "", f2 = "", d2 = "";
            if (p2 = u2 + c2 !== 0 ? `
          for (int i = 0; i < ${i2}; i++) {
            x[${a2} - 1] = indices[${a2} - 1] * ${s2} - ${u2} + i;
            if (x[${a2} - 1] < 0 || x[${a2} - 1] >= ${l2}) {
              pad++;
              continue;
            }
            ${n2}
          }` : `
          for (int i = 0; i < ${i2}; i++) {
            x[${a2} - 1] = indices[${a2} - 1] * ${s2} - ${u2} + i;
            ${n2}
          }`, 2 === e2.kernelShape.length) {
              const n3 = e2.kernelShape[e2.kernelShape.length - 2], r3 = e2.strides[e2.strides.length - 2], o3 = e2.pads[e2.pads.length / 2 - 2], s3 = e2.pads[e2.pads.length - 2], u3 = t2[a2 - 2];
              f2 = o3 + s3 !== 0 ? `
            for (int j = 0; j < ${n3}; j++) {
              x[${a2} - 2] = indices[${a2} - 2] * ${r3} - ${o3} + j;
              if (x[${a2} - 2] < 0 || x[${a2} - 2] >= ${u3}) {
                pad+= ${i2};
                continue;
              }
          ` : `
            for (int j = 0; j < ${n3}; j++) {
              x[${a2} - 2] = indices[${a2} - 2] * ${r3} - ${o3} + j;
            `, d2 = "\n          }\n        ";
            }
            return `
        float process(int indices[${a2}]) {
          int x[${a2}];
          copyVec(indices, x);

          float value = ${o2};
          int pad = 0;
          ${f2}
          ${p2}
          ${d2}
          ${r2}
          return value;
        }
      `;
          }
          {
            const s2 = i.ShapeUtil.size(e2.kernelShape), u2 = i.ShapeUtil.computeStrides(e2.kernelShape), c2 = u2.length, l2 = e2.pads.length, p2 = h(c2), f2 = d(t2, "inputDims"), g = d(e2.pads, "pads"), b = d(u2, "kernelStrides"), m = d(e2.strides, "strides");
            let y = "";
            return y = e2.pads.reduce((t3, e3) => t3 + e3) ? `
            if (x[j] >= inputDims[j] || x[j] < 0) {
              pad++;
              isPad = true;
              break;
            }
          }
          if (!isPad) {
            ${n2}
          }` : `
          }
          ${n2}
        `, `
        ${p2}
        float process(int indices[${a2}]) {
          int x[${a2}];
          copyVec(indices, x);
          int offset[${c2}];
          int pads[${l2}];
          int inputDims[${a2}];
          int kernelStrides[${c2}];
          int strides[${c2}];
          ${g}
          ${f2}
          ${m}
          ${b}

          float value = ${o2};
          int pad = 0;
          bool isPad = false;
          for (int i = 0; i < ${s2}; i++) {
            offsetToIndices(i, kernelStrides, offset);
            isPad = false;
            for (int j = ${a2} - ${c2}; j < ${a2}; j++) {
              x[j] = indices[j] * strides[j - ${a2} + ${c2}]
                + offset[j - ${a2} + ${c2}] - pads[j - 2];
              ${y}
          }
          ${r2}

          return value;
        }
      `;
          }
        }, d = (t2, e2) => {
          let n2 = "";
          for (let r2 = 0; r2 < t2.length; r2++) n2 += `
      ${e2}[${r2}] = ${t2[r2]};
    `;
          return n2;
        }, h = (t2) => `
  void offsetToIndices(int offset, int[${t2}] strides, out int[${t2}] indices) {
    if (${t2} == 0) {
      return;
    }
    for (int i = 0; i < ${t2} - 1; ++i) {
      indices[i] = offset / strides[i];
      offset -= indices[i] * strides[i];
    }
    indices[${t2} - 1] = offset;
  }`;
      }, 4939: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.reduceLogSumSquare = e.reduceLogSum = e.reduceProd = e.reduceMin = e.reduceMax = e.reduceMean = e.reduceSum = e.parseReduceAttributes = void 0;
        const r = n(246), i = n(782), o = n(2517), a = n(2039), s = (t2, e2, n2, r2, i2) => {
          c(e2);
          const o2 = { name: r2, inputNames: ["A"], inputTypes: [a.TextureType.unpacked] };
          return [t2.run(Object.assign(Object.assign({}, o2), { cacheHint: n2.cacheKey, get: () => u(t2, e2, n2, r2, i2, o2) }), e2)];
        };
        e.parseReduceAttributes = (t2) => {
          const e2 = t2.attributes.getInts("axes", []), n2 = 1 === t2.attributes.getInt("keepdims", 1);
          return (0, r.createAttributeWithCacheKey)({ axes: e2, keepDims: n2 });
        };
        const u = (t2, e2, n2, r2, i2, s2) => {
          const u2 = [], c2 = e2[0].dims.length || 1, l = [], p = o.ShapeUtil.normalizeAxes(n2.axes, e2[0].dims.length), f = i2(e2, p);
          let d = f[1];
          for (let t3 = 0; t3 < e2[0].dims.length; t3++) p.indexOf(t3) >= 0 || 0 === p.length ? (n2.keepDims && u2.push(1), d = `
          for(int j${t3} = 0; j${t3} < ${e2[0].dims[t3]}; j${t3}++) {
            inputIdx[${t3}] = j${t3};
            ${d}
          }`) : (l.push(`inputIdx[${t3}] = outputIdx[${u2.length}];`), u2.push(e2[0].dims[t3]));
          const h = `
      float process(int outputIdx[${u2.length || 1}]) {
        float value;                 // final result
        int inputIdx[${c2}];      // addressing input data
        ${l.join("\n")}
        ${f[0]}       // init ops for reduce max/min
        ${d}
        ${f[2]}       // final computation for reduce mean
        return value;
      }`;
          return Object.assign(Object.assign({}, s2), { output: { dims: u2, type: e2[0].type, textureType: a.TextureType.unpacked }, shaderSource: h });
        }, c = (t2) => {
          if (!t2 || 1 !== t2.length) throw new Error("Reduce op requires 1 input.");
          if (-1 === i.NUMBER_TYPES.indexOf(t2[0].type)) throw new Error("Invalid input type.");
        };
        e.reduceSum = (t2, e2, n2) => s(t2, e2, n2, "ReduceSum", () => ["value = 0.0;", "value += _A(inputIdx);", ""]), e.reduceMean = (t2, e2, n2) => s(t2, e2, n2, "ReduceMean", (t3, e3) => {
          let n3 = 1;
          for (let r2 = 0; r2 < t3[0].dims.length; r2++) (e3.indexOf(r2) >= 0 || 0 === e3.length) && (n3 *= t3[0].dims[r2]);
          return ["value = 0.0;", "value += _A(inputIdx);", `value /= ${n3}.;`];
        }), e.reduceMax = (t2, e2, n2) => s(t2, e2, n2, "ReduceMax", (t3, e3) => {
          const n3 = [];
          for (let r2 = 0; r2 < t3[0].dims.length; r2++) (e3.indexOf(r2) >= 0 || 0 === e3.length) && n3.push(`inputIdx[${r2}] = 0;`);
          return [`${n3.join("\n")}
value = _A(inputIdx);`, "value = max(value, _A(inputIdx));", ""];
        }), e.reduceMin = (t2, e2, n2) => s(t2, e2, n2, "ReduceMin", (t3, e3) => {
          const n3 = [];
          for (let r2 = 0; r2 < t3[0].dims.length; r2++) (e3.indexOf(r2) >= 0 || 0 === e3.length) && n3.push(`inputIdx[${r2}] = 0;`);
          return [`${n3.join("\n")}
value = _A(inputIdx);`, "value = min(value, _A(inputIdx));", ""];
        }), e.reduceProd = (t2, e2, n2) => s(t2, e2, n2, "ReduceProd", () => ["value = 1.0;", "value *= _A(inputIdx);", ""]), e.reduceLogSum = (t2, e2, n2) => s(t2, e2, n2, "ReduceLogSum", () => ["value = 0.0;", "value += _A(inputIdx);", "value = log(value);"]), e.reduceLogSumSquare = (t2, e2, n2) => s(t2, e2, n2, "ReduceLogSumSquare", () => ["float t; value = 0.0;", "t = _A(inputIdx); value += t * t;", ""]);
      }, 7019: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.isReshapeCheap = e.processDims3D = e.createPackedReshape3DProgramInfoLoader = void 0;
        const r = n(2517), i = n(5060), o = n(2039), a = n(2827);
        e.createPackedReshape3DProgramInfoLoader = (t2, e2, n2) => {
          const s = ((t3) => ({ name: "Reshape (packed)", inputTypes: [o.TextureType.packed], inputNames: ["A"], cacheHint: `${t3}` }))(n2);
          return Object.assign(Object.assign({}, s), { get: () => ((t3, e3, n3, s2) => {
            const u = e3.dims, c = s2;
            let l = "";
            for (let t4 = 0; t4 < 4; t4++) {
              let e4 = "";
              switch (t4) {
                case 0:
                  e4 = "outputCoords = rc;";
                  break;
                case 1:
                  e4 = "outputCoords = ivec3(rc.x, rc.y+1, rc.z);";
                  break;
                case 2:
                  e4 = "outputCoords = ivec3(rc.x, rc.y, rc.z+1);";
                  break;
                case 3:
                  e4 = "outputCoords = ivec3(rc.x, rc.y+1, rc.z+1);";
                  break;
                default:
                  throw new Error();
              }
              l += `
        ${e4}
        ${t4 > 0 ? "if(outputCoords.y < rows && outputCoords.z < cols){" : ""}
          int flattenedIndex = getFlattenedIndex(outputCoords);

          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flattenedIndex);
          vec2 innerDims = vec2(float(inputRC.y),float(inputRC.z));

          result[${t4}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), innerDims);

        ${t4 > 0 ? "}" : ""}
      `;
            }
            const p = (0, i.getGlsl)(t3.session.backend.glContext.version), f = `
      ${function(t4) {
              const e4 = r.ShapeUtil.computeStrides(t4), n4 = ["b", "r", "c"], i2 = "index";
              return `
    ivec3 inputCoordsFromReshapedOutCoords(int index) {
      ${e4.map((t5, r2) => `int ${n4[r2]} = ${i2} / ${t5}; ${r2 === e4.length - 1 ? `int ${n4[r2 + 1]} = ${i2} - ${n4[r2]} * ${t5}` : `index -= ${n4[r2]} * ${t5}`};`).join("")}
      return ivec3(b, r, c);
    }
  `;
            }(u)}
      ${function(t4) {
              const e4 = r.ShapeUtil.computeStrides(t4);
              return `
  int getFlattenedIndex(ivec3 coords) {
    // reverse y, z order
    return coords.x * ${e4[0]} + coords.z * ${e4[1]} + coords.y;
  }
`;
            }(c)}
      ${(0, a.unpackFromChannel)()}

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

        vec4 result = vec4(0.0);

        ivec3 outputCoords;
        int rows = ${c[2]};
        int cols = ${c[1]};

        ${l}
        ${p.output} = result;
      }
    `;
            return Object.assign(Object.assign({}, n3), { output: { dims: c, type: e3.type, textureType: o.TextureType.packed }, shaderSource: f, hasMain: true });
          })(t2, e2, s, n2) });
        }, e.processDims3D = function(t2) {
          if (0 === t2.length) return [1, 1, 1];
          let e2 = 1;
          for (let n2 = 0; n2 < t2.length - 2; ++n2) e2 *= t2[n2];
          return [e2, t2.length > 1 ? t2[t2.length - 2] : 1, t2[t2.length - 1]];
        }, e.isReshapeCheap = function(t2, e2) {
          let n2 = false;
          return n2 = 0 === t2.length || 0 === e2.length || (t2.length < 2 || e2.length < 2 ? t2[t2.length - 1] === e2[e2.length - 1] : t2[t2.length - 1] === e2[e2.length - 1] && t2[t2.length - 2] === e2[e2.length - 2]), n2;
        };
      }, 718: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.reshape = void 0;
        const r = n(2517);
        e.reshape = (t2, e2) => {
          const n2 = r.ShapeUtil.calculateReshapedDims(e2[0].dims, e2[1].integerData);
          return t2.session.pack ? [t2.reshapePacked(e2[0], n2)] : [t2.reshapeUnpacked(e2[0], n2)];
        };
      }, 2268: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.parseResizeAttributesV11 = e.parseResizeAttributesV10 = e.resize = void 0;
        const r = n(5060), i = n(2039), o = n(9390), a = n(2827), s = n(9793), u = { name: "Resize", inputNames: ["A"], inputTypes: [i.TextureType.packed] };
        e.resize = (t2, e2, n2) => ((0, s.validateInputs)(e2, n2), [t2.run(Object.assign(Object.assign({}, u), { cacheHint: n2.cacheKey, get: () => c(t2, e2, n2) }), e2)]), e.parseResizeAttributesV10 = (t2) => (0, s.parseUpsampleAttributes)(t2, 10), e.parseResizeAttributesV11 = (t2) => (0, s.parseUpsampleAttributes)(t2, 11);
        const c = (t2, e2, n2) => {
          const s2 = (0, r.getGlsl)(t2.session.backend.glContext.version), [c2, p2] = l(e2, n2);
          if (c2.every((t3) => 1 === t3) && "tf_crop_and_resize" !== n2.coordinateTransformMode) return Object.assign(Object.assign({}, u), { output: { dims: p2, type: e2[0].type, textureType: i.TextureType.packed }, hasMain: true, shaderSource: `void main() {
                    vec4 v = ${s2.texture2D}(X, TexCoords);
                    ${s2.output} = v;
                }` });
          const f2 = p2.length;
          if (f2 < 2) throw new Error(`output dimension should be at least 2, but got ${f2}`);
          const d = p2[f2 - 2], h = p2[f2 - 1], g = e2[0].dims;
          if (f2 !== g.length) throw new Error(`output dimension should match input ${g.length}, but got ${f2}`);
          const b = g[f2 - 2], m = g[f2 - 1], y = c2[f2 - 2], _ = c2[f2 - 1];
          let v = "";
          if ("linear" !== n2.mode) throw new Error(`resize (packed) does not support mode: '${n2.mode}'`);
          switch (n2.coordinateTransformMode) {
            case "asymmetric":
              v = "\n                    vec4 getSourceFracIndex(ivec4 coords) {\n                        return vec4(coords) / scaleWHWH;\n                    }\n                ";
              break;
            case "half_pixel":
              v = "\n                    vec4 getSourceFracIndex(ivec4 coords) {\n                        return (vec4(coords) + 0.5) / scaleWHWH - 0.5;\n                    }\n                ";
              break;
            case "pytorch_half_pixel":
              v = `
                    vec4 getSourceFracIndex(ivec4 coords) {
                        vec4 fcoords = vec4(coords);
                        return vec4(
                            ${h}.0 > 1.0 ? (fcoords.x + 0.5) / scaleWHWH.x - 0.5 : 0.0,
                            ${d}.0 > 1.0 ? (fcoords.y + 0.5) / scaleWHWH.y - 0.5 : 0.0,
                            ${h}.0 > 1.0 ? (fcoords.z + 0.5) / scaleWHWH.z - 0.5 : 0.0,
                            ${d}.0 > 1.0 ? (fcoords.w + 0.5) / scaleWHWH.w - 0.5 : 0.0
                          );
                    }
                `;
              break;
            case "align_corners":
              v = `
                    vec4 getSourceFracIndex(ivec4 coords) {
                        vec4 resized = vec4(${h}.0 - 1.0, ${d}.0 - 1.0, ${h}.0 - 1.0,
                            ${d}.0 - 1.0);
                        vec4 original = vec4(${m}.0 - 1.0, ${b}.0 - 1.0, ${m}.0 - 1.0,
                            ${b}.0 - 1.0);
                        vec4 new_scale = original / resized;
                        return vec4(coords) * new_scale;
                    }
                `;
              break;
            default:
              throw new Error(`resize (packed) does not support coordinateTransformMode:                                 '${n2.coordinateTransformMode}'`);
          }
          const w = (0, o.getCoordsDataType)(f2), x = `
            const vec2 inputWH = vec2(${b}.0, ${m}.0);
            const vec4 scaleWHWH = vec4(float(${y}), float(${_}), float(${y}), float(${_}));
            ${(0, a.unpackFromChannel)()}
            ${v}
            float getAValue(int x10, int r, int c, int d) {
                return getChannel(getA(x10, r, c, d), vec2(c, d));
            }
            void main() {
                ${w} rc = getOutputCoords();

                int batch = rc[0];
                int depth = rc[1];

                // retrieve the 4 coordinates that is used in the 4 packed output values.
                ivec4 coords = ivec4(rc.wz, rc.w + 1, rc.z + 1);

                // calculate the source index in fraction
                vec4 sourceFrac = getSourceFracIndex(coords);

                // get the lower and upper bound of the 4 values that will be packed into one texel.
                ivec4 x00 = ivec4(max(sourceFrac.xy, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.xy)));
                ivec4 x01 = ivec4(max(sourceFrac.xw, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.xw)));
                ivec4 x10 = ivec4(max(sourceFrac.zy, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.zy)));
                ivec4 x11 = ivec4(max(sourceFrac.zw, vec2(0.0)), min(inputWH - 1.0, ceil(sourceFrac.zw)));

                bool hasNextRow = rc.w < ${d - 1};
                bool hasNextCol = rc.z < ${h - 1};

                // pack x00, x01, x10, x11's top-left corner into one vec4 structure
                vec4 topLeft = vec4(
                    getAValue(batch, depth, x00.x, x00.y),
                    hasNextCol ? getAValue(batch, depth, x01.x, x01.y) : 0.0,
                    hasNextRow ? getAValue(batch, depth, x10.x, x10.y) : 0.0,
                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.y) : 0.0);

                // pack x00, x01, x10, x11's top-right corner into one vec4 structure
                vec4 topRight = vec4(
                    getAValue(batch, depth, x00.x, x00.w),
                    hasNextCol ? getAValue(batch, depth, x01.x, x01.w) : 0.0,
                    hasNextRow ? getAValue(batch, depth, x10.x, x10.w) : 0.0,
                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.x, x11.w) : 0.0);

                // pack x00, x01, x10, x11's bottom-left corner into one vec4 structure
                vec4 bottomLeft = vec4(
                    getAValue(batch, depth, x00.z, x00.y),
                    hasNextCol ? getAValue(batch, depth, x01.z, x01.y) : 0.0,
                    hasNextRow ? getAValue(batch, depth, x10.z, x10.y) : 0.0,
                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.y) : 0.0);

                // pack x00, x01, x10, x11's bottom-right corner into one vec4 structure
                vec4 bottomRight = vec4(
                    getAValue(batch, depth, x00.z, x00.w),
                    hasNextCol ? getAValue(batch, depth, x01.z, x01.w) : 0.0,
                    hasNextRow ? getAValue(batch, depth, x10.z, x10.w) : 0.0,
                    (hasNextRow && hasNextCol) ? getAValue(batch, depth, x11.z, x11.w) : 0.0);

                // calculate the interpolation fraction on u and v direction
                vec4 frac = vec4(sourceFrac) - floor(sourceFrac);
                vec4 clampFrac = clamp(frac, vec4(0.0), vec4(1.0));

                vec4 top = mix(topLeft, topRight, clampFrac.ywyw);
                vec4 bottom = mix(bottomLeft, bottomRight, clampFrac.ywyw);
                vec4 newValue = mix(top, bottom, clampFrac.xxzz);

                ${s2.output} = vec4(newValue);
            }
        `;
          return Object.assign(Object.assign({}, u), { output: { dims: p2, type: e2[0].type, textureType: i.TextureType.packed }, hasMain: true, shaderSource: x });
        }, l = (t2, e2) => {
          const n2 = t2[0].dims;
          let r2, i2 = e2.scales;
          if (0 === i2.length) {
            const o3 = t2[e2.scalesInputIdx];
            if (o3 && 0 !== o3.size) {
              if (t2[e2.sizesInputIdx]) throw new Error("Only one of scales or sizes must be provided as input.");
              i2 = p(o3, e2.mode, e2.isResize);
            } else {
              const o4 = t2[e2.sizesInputIdx];
              if (!o4 || 0 === o4.size) throw new Error("Either scales or sizes MUST be provided as input.");
              r2 = Array.from(o4.integerData), i2 = f(r2, n2, e2.mode, e2.isResize);
            }
          } else if (t2[e2.sizesInputIdx]) throw new Error("Only one of scales or sizes must be provided as input.");
          const o2 = r2 || n2.map((t3, e3) => Math.floor(t3 * i2[e3]));
          return [i2, o2];
        }, p = (t2, e2, n2) => {
          const r2 = Array.from(t2.floatData);
          return (0, s.scalesValidation)(r2, e2, n2), r2;
        }, f = (t2, e2, n2, r2) => {
          const i2 = e2.length, o2 = new Array(i2);
          for (let n3 = 0, r3 = i2; n3 < r3; n3++) if (0 === e2[n3]) {
            if (0 !== t2[n3]) throw new Error("Input dim is zero but required output dim is non-zero.");
            o2[n3] = 1;
          } else o2[n3] = t2[n3] / e2[n3];
          return (0, s.scalesValidation)(o2, n2, r2), o2;
        };
      }, 8117: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.shape = void 0;
        const r = n(9162);
        e.shape = (t2, e2) => (i(e2), [new r.Tensor([e2[0].dims.length], "int32", void 0, void 0, new Int32Array(e2[0].dims))]);
        const i = (t2) => {
          if (!t2 || 1 !== t2.length) throw new Error("Shape requires 1 input.");
        };
      }, 2278: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.sliceV10 = e.parseSliceAttributes = e.slice = void 0;
        const r = n(246), i = n(782), o = n(2517), a = n(2039), s = { name: "Slice", inputNames: ["A"], inputTypes: [a.TextureType.unpacked] };
        e.slice = (t2, e2, n2) => (c(e2), [t2.run(Object.assign(Object.assign({}, s), { cacheHint: n2.cacheKey, get: () => u(t2, e2[0], n2) }), e2)]), e.parseSliceAttributes = (t2) => {
          const e2 = t2.attributes.getInts("starts"), n2 = t2.attributes.getInts("ends"), i2 = t2.attributes.getInts("axes", []);
          return (0, r.createAttributeWithCacheKey)({ starts: e2, ends: n2, axes: i2 });
        };
        const u = (t2, e2, n2) => {
          const r2 = 0 === n2.axes.length ? e2.dims.slice(0).map((t3, e3) => e3) : n2.axes, i2 = o.ShapeUtil.normalizeAxes(r2, e2.dims.length), u2 = n2.starts.map((t3, n3) => t3 > e2.dims[i2[n3]] - 1 ? e2.dims[i2[n3]] : o.ShapeUtil.normalizeAxis(t3, e2.dims[i2[n3]])), c2 = n2.ends.map((t3, n3) => t3 > e2.dims[i2[n3]] - 1 ? e2.dims[i2[n3]] : o.ShapeUtil.normalizeAxis(t3, e2.dims[i2[n3]])), l2 = e2.dims.slice(), p2 = [];
          for (let t3 = 0; t3 < i2.length; t3++) l2[i2[t3]] = c2[t3] - u2[t3], u2[t3] > 0 && p2.push(`outputIdx[${i2[t3]}] += ${u2[t3]};`);
          const f = `
      float process(int outputIdx[${l2.length}]) {
        ${p2.join("\n      ")}
        return _A(outputIdx);
      }`;
          return Object.assign(Object.assign({}, s), { output: { dims: l2, type: e2.type, textureType: a.TextureType.unpacked }, shaderSource: f });
        }, c = (t2) => {
          if (!t2 || 1 !== t2.length) throw new Error("Slice requires 1 input.");
          if (-1 === i.NUMBER_TYPES.indexOf(t2[0].type)) throw new Error("Invalid input type.");
        };
        e.sliceV10 = (t2, e2) => {
          p(e2);
          const n2 = l(t2, e2);
          return [t2.run(Object.assign(Object.assign({}, s), { cacheHint: n2.cacheKey, get: () => u(t2, e2[0], n2) }), [e2[0]])];
        };
        const l = (t2, e2) => {
          if (!t2.session.isInitializer(e2[1].dataId) || !t2.session.isInitializer(e2[2].dataId) || e2.length >= 4 && !t2.session.isInitializer(e2[3].dataId) || e2.length >= 5 && !t2.session.isInitializer(e2[4].dataId)) throw new Error("dynamic slice attributes are not allowed");
          if (e2.length >= 5 && e2[4].integerData.some((t3) => 1 !== t3)) throw new Error("currently non-1 steps is not supported for Slice");
          const n2 = Array.from(e2[1].integerData), r2 = Array.from(e2[2].integerData), i2 = e2.length >= 4 ? Array.from(e2[3].integerData) : [];
          return { starts: n2, ends: r2, axes: i2, cacheKey: `${i2};${n2};${r2}` };
        }, p = (t2) => {
          if (!t2 || t2.length < 3 || t2.length > 5) throw new Error("Invalid input number.");
          if ("int32" !== t2[1].type || 1 !== t2[1].dims.length) throw new Error("Invalid input type.");
          if ("int32" !== t2[2].type || 1 !== t2[2].dims.length) throw new Error("Invalid input type.");
          if (t2.length >= 4 && ("int32" !== t2[3].type || 1 !== t2[3].dims.length)) throw new Error("Invalid input type.");
          if (t2.length >= 5 && ("int32" !== t2[4].type || 1 !== t2[4].dims.length)) throw new Error("Invalid input type.");
        };
      }, 5524: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.softmaxV13 = e.parseSoftmaxAttributesV13 = e.parseSoftmaxAttributes = e.softmax = void 0;
        const r = n(246), i = n(2517), o = n(5060), a = n(2039), s = n(3738), u = { name: "SoftmaxComputeMax", inputNames: ["A"], inputTypes: [a.TextureType.unpacked] }, c = { name: "SoftmaxComputeScale", inputNames: ["A", "Max"], inputTypes: [a.TextureType.unpacked, a.TextureType.unpacked] }, l = { name: "SoftMax", inputNames: ["A", "Max", "Norm"], inputTypes: [a.TextureType.unpacked, a.TextureType.unpacked, a.TextureType.unpacked] };
        e.softmax = (t2, e2, n2) => {
          g(e2);
          const r2 = e2[0].dims.slice(), o2 = i.ShapeUtil.normalizeAxis(n2.axis, r2.length), a2 = i.ShapeUtil.sizeToDimension(r2, o2), s2 = i.ShapeUtil.sizeFromDimension(r2, o2);
          return p(t2, e2, n2, a2, s2);
        }, e.parseSoftmaxAttributes = (t2) => (0, r.createAttributeWithCacheKey)({ axis: t2.attributes.getInt("axis", 1) }), e.parseSoftmaxAttributesV13 = (t2) => (0, r.createAttributeWithCacheKey)({ axis: t2.attributes.getInt("axis", -1) }), e.softmaxV13 = (t2, e2, n2) => {
          g(e2);
          const o2 = e2[0].dims.slice(), a2 = i.ShapeUtil.normalizeAxis(n2.axis, o2.length), u2 = o2.length, c2 = a2 !== u2 - 1, l2 = [];
          let f2, d2 = [], h2 = [];
          c2 && (d2 = Array.from({ length: u2 }).map((t3, e3) => e3), d2[a2] = u2 - 1, d2[u2 - 1] = a2, d2.map((t3) => l2.push(o2[t3])), f2 = (0, r.createAttributeWithCacheKey)({ perm: d2 }), h2 = (0, s.transpose)(t2, e2, f2));
          const b = c2 ? i.ShapeUtil.sizeToDimension(l2, u2 - 1) : i.ShapeUtil.sizeToDimension(o2, u2 - 1), m = c2 ? i.ShapeUtil.sizeFromDimension(l2, u2 - 1) : i.ShapeUtil.sizeFromDimension(o2, u2 - 1), y = p(t2, c2 ? h2 : e2, n2, b, m);
          return c2 ? (0, s.transpose)(t2, y, f2) : y;
        };
        const p = (t2, e2, n2, r2, i2) => {
          const o2 = f(t2, e2[0], r2, i2, [r2]), a2 = t2.run(Object.assign(Object.assign({}, u), { cacheHint: n2.cacheKey, get: () => o2 }), e2), s2 = d(t2, e2[0], r2, i2, o2.output.dims, [r2]), p2 = t2.run(Object.assign(Object.assign({}, c), { cacheHint: n2.cacheKey, get: () => s2 }), [e2[0], a2]), g2 = h(t2, e2[0], r2, i2, o2.output.dims, s2.output.dims);
          return [t2.run(Object.assign(Object.assign({}, l), { cacheHint: n2.cacheKey, get: () => g2 }), [e2[0], a2, p2])];
        }, f = (t2, e2, n2, r2, i2) => {
          const [s2, c2] = t2.calculateTextureWidthAndHeight(e2.dims, a.TextureType.unpacked), l2 = i2.length;
          if (n2 < 1 || r2 < 1) throw new Error("Logical row count N and feature count D must be greater than or equal to 1");
          if (1 !== i2.length) throw new Error("Dimensionality of the output should be 1");
          if (i2[0] !== n2) throw new Error("Shape of the output should be equal to logical row count");
          const p2 = (0, o.getGlsl)(t2.session.backend.glContext.version), f2 = `
      float process(int[${l2}] indices) {
        int logical_row_start_offset = indices[0] * ${r2};

        float max = getColorAsFloat(${p2.texture2D}(A, offsetToCoords(logical_row_start_offset, ${s2},
        ${c2} )));
        for(int i=1; i<${r2}; ++i)
        {
          float current = getColorAsFloat(${p2.texture2D}(A, offsetToCoords(logical_row_start_offset + i,
            ${s2}, ${c2})));
          if(current > max)
          max = current;
        }

        return max;
      }`;
          return Object.assign(Object.assign({}, u), { output: { dims: i2, type: e2.type, textureType: a.TextureType.unpacked }, shaderSource: f2 });
        }, d = (t2, e2, n2, r2, i2, s2) => {
          const [u2, l2] = t2.calculateTextureWidthAndHeight(e2.dims, a.TextureType.unpacked), p2 = s2.length;
          if (n2 < 1 || r2 < 1) throw new Error("Logical row count N and feature count D must be greater than or equal to 1");
          if (1 !== s2.length) throw new Error("Dimensionality of the output should be 1");
          if (s2[0] !== n2) throw new Error("Shape of the output should be equal to logical row count");
          if (1 !== i2.length) throw new Error("Dimensionality of the intermediate results should be 1");
          if (i2[0] !== n2) throw new Error("Shape of the intermediate results should be equal to logical row count");
          const f2 = `
      float process(int[${p2}] indices) {
        int logical_row_start_offset = indices[0] * ${r2};

        float norm_factor = 0.0;
        float max = _Max(indices);
        for(int i=0; i<${r2}; ++i)
        {
          norm_factor += exp(getColorAsFloat(${(0, o.getGlsl)(t2.session.backend.glContext.version).texture2D}(A, offsetToCoords(logical_row_start_offset + i,
            ${u2}, ${l2}))) - max);
        }

        return norm_factor;
      }`;
          return Object.assign(Object.assign({}, c), { output: { dims: s2, type: e2.type, textureType: a.TextureType.unpacked }, shaderSource: f2 });
        }, h = (t2, e2, n2, r2, i2, o2) => {
          const [s2, u2] = t2.calculateTextureWidthAndHeight(e2.dims, a.TextureType.unpacked), c2 = e2.dims.length;
          if (n2 < 1 || r2 < 1) throw new Error("Logical row count N and feature count D must be greater than or equal to 1");
          if (1 !== i2.length || 1 !== o2.length) throw new Error("Dimensionality of the intermediate results should be 1");
          if (i2[0] !== n2 || o2[0] !== n2) throw new Error("Shape of the intermediate results should be equal to logical row count");
          const p2 = `
      float process(int[${c2}] indices) {

      // get offset of current logical tensor index from the 2-D texture coordinates (TexCoords)
      int offset = coordsToOffset(TexCoords, ${s2}, ${u2});

      //determine the logical row for this index
      int logical_row_index[1];
      logical_row_index[0] = offset / ${r2};

      float norm_factor = _Norm(logical_row_index);

      // avoid possible division by 0
      // if norm_facor is 0, all elements are zero
      // if so, return 0
      if(norm_factor == 0.0)
        return 0.0;

      return exp(_A(indices) - _Max(logical_row_index)) / norm_factor;
    }`;
          return Object.assign(Object.assign({}, l), { output: { dims: e2.dims, type: e2.type, textureType: a.TextureType.unpacked }, shaderSource: p2 });
        }, g = (t2) => {
          if (!t2 || 1 !== t2.length) throw new Error("Softmax requires 1 input.");
          if ("float32" !== t2[0].type && "float64" !== t2[0].type) throw new Error("Invalid input type");
        };
      }, 5975: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.parseSplitAttributes = e.split = void 0;
        const r = n(246), i = n(2517), o = n(2039), a = { name: "Split", inputNames: ["A"], inputTypes: [o.TextureType.unpacked] };
        e.split = (t2, e2, n2) => {
          c(e2);
          const r2 = i.ShapeUtil.normalizeAxis(n2.axis, e2[0].dims.length), o2 = s(t2, e2, r2, n2), l = [];
          for (let i2 = 0; i2 < o2; ++i2) l.push(t2.run(Object.assign(Object.assign({}, a), { cacheHint: `${n2.cacheKey};${i2}`, get: () => u(t2, e2[0], n2, r2, i2) }), e2));
          return l;
        }, e.parseSplitAttributes = (t2) => {
          const e2 = t2.attributes.getInt("axis", 0), n2 = t2.attributes.getInts("split", []), i2 = t2.outputs.length;
          return (0, r.createAttributeWithCacheKey)({ axis: e2, split: n2, numOutputs: i2 });
        };
        const s = (t2, e2, n2, r2) => {
          const [, o2] = i.SplitUtil.splitShape(e2[0].dims, n2, r2.split, r2.numOutputs);
          return o2.length;
        }, u = (t2, e2, n2, r2, s2) => {
          const [u2, c2] = i.SplitUtil.splitShape(e2.dims, r2, n2.split, n2.numOutputs), l = c2[s2], p = u2[s2], f = `
      float process(int indices[${p.length}]) {
        indices[${r2}] += ${l};
        return _A(indices);
      }
    `;
          return Object.assign(Object.assign({}, a), { cacheHint: `${n2.cacheKey}:${s2}`, output: { dims: p, type: e2.type, textureType: o.TextureType.unpacked }, shaderSource: f });
        }, c = (t2) => {
          if (!t2 || 1 !== t2.length) throw new Error("Split requires one input.");
          if ("int8" !== t2[0].type && "uint8" !== t2[0].type && "int16" !== t2[0].type && "uint16" !== t2[0].type && "int32" !== t2[0].type && "uint32" !== t2[0].type && "float32" !== t2[0].type && "float64" !== t2[0].type && "bool" !== t2[0].type) throw new Error("Invalid input type.");
        };
      }, 3933: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.parseSqueezeAttributes = e.squeezeV13 = e.squeeze = void 0;
        const r = n(2517);
        e.squeeze = (t2, e2, n2) => {
          i(e2);
          const o2 = r.ShapeUtil.squeezeShape(e2[0].dims, n2);
          return [t2.reshapeUnpacked(e2[0], o2)];
        }, e.squeezeV13 = (t2, n2) => (o(n2), (0, e.squeeze)(t2, [n2[0]], Array.from(n2[1].integerData))), e.parseSqueezeAttributes = (t2) => t2.attributes.getInts("axes");
        const i = (t2) => {
          if (!t2 || 1 !== t2.length) throw new Error("Squeeze requires 1 input.");
          if ("string" === t2[0].type) throw new Error("invalid input tensor types.");
        }, o = (t2) => {
          if (!t2 || 2 !== t2.length) throw new Error("Squeeze requires 2 inputs.");
          if ("int32" !== t2[1].type) throw new Error("Invalid input type.");
        };
      }, 6558: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.sum = void 0;
        const r = n(5060), i = n(2039);
        e.sum = (t2, e2) => {
          a(e2);
          const n2 = { name: "Sum", inputNames: e2.map((t3, e3) => `X${e3}`), inputTypes: new Array(e2.length).fill(i.TextureType.unpacked) };
          return [t2.run(Object.assign(Object.assign({}, n2), { get: () => o(t2, e2, n2) }), e2)];
        };
        const o = (t2, e2, n2) => {
          const o2 = (0, r.getGlsl)(t2.session.backend.glContext.version), a2 = e2[0].dims.slice(), s = `
      void main() {
        vec4 result = ${e2.map((t3, e3) => `${o2.texture2D}(X${e3},TexCoords)`).join(" + ")};
        ${o2.output} = result;
      }
    `;
          return Object.assign(Object.assign({}, n2), { output: { dims: a2, type: e2[0].type, textureType: i.TextureType.unpacked }, hasMain: true, shaderSource: s });
        }, a = (t2) => {
          if (!t2 || 0 === t2.length) throw new Error("Sum requires inputs.");
          const e2 = t2[0].dims.length;
          for (let n2 = 1; n2 < t2.length; n2++) {
            if (e2 !== t2[n2].dims.length) throw new Error("Input shapes are mismatched.");
            for (let r2 = 0; r2 < e2; r2++) if (t2[0].dims[r2] !== t2[n2].dims[r2]) throw new Error("Input shapes are not matched.");
          }
          if ("float32" !== t2[0].type && "float64" !== t2[0].type) throw new Error("Invalid input type.");
          for (let e3 = 1; e3 < t2.length; e3++) if (t2[0].type !== t2[e3].type) throw new Error("Input types are not matched.");
        };
      }, 5723: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.tile = void 0;
        const r = n(782), i = n(2039);
        e.tile = (t2, e2) => {
          a(e2);
          const n2 = { name: "Tile", inputNames: ["A"], inputTypes: [i.TextureType.unpacked] };
          return [t2.run(Object.assign(Object.assign({}, n2), { get: () => o(t2, e2, n2) }), e2)];
        };
        const o = (t2, e2, n2) => {
          const r2 = e2[0].dims.slice(), o2 = new Array(r2.length), a2 = [];
          for (let t3 = 0; t3 < r2.length; t3++) o2[t3] = r2[t3] * e2[1].numberData[t3], a2.push(`inputIdx[${t3}] = int(mod(float(outputIdx[${t3}]), ${r2[t3]}.));`);
          const s = o2.length, u = `
      float process(int outputIdx[${s}]) {
        int inputIdx[${s}];
        ${a2.join("\n")}
        return _A(inputIdx);
      }
    `;
          return Object.assign(Object.assign({}, n2), { output: { dims: o2, type: e2[0].type, textureType: i.TextureType.unpacked }, shaderSource: u });
        }, a = (t2) => {
          if (!t2 || 2 !== t2.length) throw new Error("Tile requires 2 input.");
          if (1 !== t2[1].dims.length) throw new Error("The second input shape must 1 dimension.");
          if (t2[1].dims[0] !== t2[0].dims.length) throw new Error("Invalid input shape.");
          if (-1 === r.NUMBER_TYPES.indexOf(t2[0].type)) throw new Error("Invalid input type.");
          if ("int32" !== t2[1].type && "int16" !== t2[1].type) throw new Error("Invalid repeat type.");
        };
      }, 3738: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.parseTransposeAttributes = e.transpose = void 0;
        const r = n(246), i = n(2517), o = n(2039), a = { name: "Transpose", inputNames: ["A"], inputTypes: [o.TextureType.unpacked] };
        e.transpose = (t2, e2, n2) => (p(e2), [t2.run(Object.assign(Object.assign({}, a), { cacheHint: n2.cacheKey, get: () => s(t2, e2[0], n2.perm) }), e2)]), e.parseTransposeAttributes = (t2) => (0, r.createAttributeWithCacheKey)({ perm: t2.attributes.getInts("perm", []) });
        const s = (t2, e2, n2) => {
          const r2 = e2.dims;
          n2 = u(r2, n2);
          const i2 = c(r2, n2), s2 = r2.length, p2 = `
      ${l("perm", n2, s2)}
      float process(int indices[${s2}]) {
        int a[${s2}];
        perm(a, indices);
        return _A(a);
      }`;
          return Object.assign(Object.assign({}, a), { output: { dims: i2, type: e2.type, textureType: o.TextureType.unpacked }, shaderSource: p2 });
        }, u = (t2, e2) => (e2 && e2.length !== t2.length && (e2 = [...t2.keys()].reverse()), e2), c = (t2, e2) => (e2 = u(t2, e2), i.ShapeUtil.sortBasedOnPerm(t2, e2)), l = (t2, e2, n2) => {
          const r2 = [];
          r2.push(`void ${t2}(out int a[${n2}], int src[${n2}]) {`);
          for (let t3 = 0; t3 < n2; ++t3) r2.push(`	a[${e2[t3]}]=src[${t3}];`);
          return r2.push("	}"), r2.join("\n");
        }, p = (t2) => {
          if (!t2 || 1 !== t2.length) throw new Error("Transpose requires 1 input.");
          if ("float32" !== t2[0].type && "float64" !== t2[0].type) throw new Error("input should be float tensor");
        };
      }, 8710: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.encodeAsUint8 = void 0;
        const r = n(5060), i = n(2039);
        e.encodeAsUint8 = (t2, e2) => {
          const n2 = e2.shape, o = (0, r.getGlsl)(t2.session.backend.glContext.version), a = `
    const float FLOAT_MAX = 1.70141184e38;
    const float FLOAT_MIN = 1.17549435e-38;

    bool isNaN(float val) {
      return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;
    }

    highp vec4 encodeAsUint8(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;
    }

    void main() {
      float value = ${o.texture2D}(X,TexCoords).r;
      ${o.output} = encodeAsUint8(value);
    }`, s = { name: "Uint8Encode", inputTypes: [i.TextureType.unpacked], inputNames: ["X"], output: { dims: n2, type: e2.tensor.type, textureType: i.TextureType.downloadUint8AsFloat }, shaderSource: a, hasMain: true };
          return t2.executeProgram(s, [e2.tensor]);
        };
      }, 4909: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.tanh = e.tan = e.sqrt = e.sin = e.sigmoid = e.relu = e.not = e.neg = e.log = e.parseLeakyReluAttributes = e.leakyRelu = e.identity = e.floor = e.exp = e.parseEluAttributes = e.elu = e.cos = e.ceil = e.clipV11 = e.parseClipAttributes = e.clip = e.atan = e.asin = e.acos = e.abs = e.glslTanh = e.glslTan = e.glslSqrt = e.glslSigmoid = e.glslRelu = e.glslSin = e.glslNot = e.glslNeg = e.glslLog = e.glslLeakyRelu = e.glslIdentity = e.glslClip = e.glslFloor = e.glslExp = e.glslElu = e.glslCos = e.glslCeil = e.glslAtan = e.glslAsin = e.glslAcos = e.glslAbs = void 0;
        const r = n(246), i = n(2517), o = n(8520), a = n(5060), s = n(2039);
        function u() {
          return P("abs");
        }
        function c() {
          return P("acos");
        }
        function l() {
          return P("asin");
        }
        function p() {
          return P("atan");
        }
        function f() {
          return P("ceil");
        }
        function d() {
          return P("cos");
        }
        function h(t2) {
          const e2 = "elu";
          return { body: `
  const float alpha = float(${t2});

  float ${e2}_(float a) {
    return a >= 0.0 ? a: (exp(a) - 1.0) * alpha;
  }
  vec4 ${e2}_(vec4 v) {
    return vec4(${e2}_(v.x), ${e2}_(v.y), ${e2}_(v.z), ${e2}_(v.w));
  }
  `, name: e2, type: o.FunctionType.ValueBased };
        }
        function g() {
          return P("exp");
        }
        function b() {
          return P("floor");
        }
        function m(t2, e2) {
          const n2 = "clip";
          return { body: `
  const float min = float(${t2});
  const float max = float(${e2});

  float ${n2}_(float a) {
    return clamp(a, min, max);
  }
  vec4 ${n2}_(vec4 v) {
    return clamp(v, min, max);
  }
  `, name: n2, type: o.FunctionType.ValueBased };
        }
        function y() {
          const t2 = "indentity";
          return { body: `
  float ${t2}_(float a) {
    return a;
  }
  vec4 ${t2}_(vec4 v) {
    return v;
  }
  `, name: t2, type: o.FunctionType.ValueBased };
        }
        function _(t2) {
          const e2 = "leakyRelu";
          return { body: `
  const float alpha = float(${t2});

  float ${e2}_(float a) {
    return a < 0.0 ? a * alpha : a;
  }
  vec4 ${e2}_(vec4 v) {
    return vec4(${e2}_(v.x), ${e2}_(v.y), ${e2}_(v.z), ${e2}_(v.w));
  }
  `, name: e2, type: o.FunctionType.ValueBased };
        }
        function v() {
          return P("log");
        }
        function w() {
          const t2 = "neg";
          return { body: `
  float ${t2}_(float a) {
    return -a;
  }
  vec4 ${t2}_(vec4 v) {
    return -v;
  }
  `, name: t2, type: o.FunctionType.ValueBased };
        }
        function x() {
          const t2 = "not";
          return { body: `
  float ${t2}_(float a) {
    return float( ! bool(a) );
  }
  bool ${t2}_(bool a) {
    return !a;
  }
  vec4 ${t2}_(vec4 v) {
    return vec4(!bool(v.x), !bool(v.y), !bool(v.z), !bool(v.w));
  }
  bvec4 ${t2}_(bvec4 v) {
    return bvec4(!v.x, !v.y, !v.z, !v.w);
  }
  `, name: t2, type: o.FunctionType.ValueBased };
        }
        function T() {
          return P("sin");
        }
        function S() {
          const t2 = "relu";
          return { body: `
  float ${t2}_(float a) {
    return max( a, 0.0 );
  }
  vec4 ${t2}_(vec4 v) {
    return max( v, 0.0 );
  }
  `, name: t2, type: o.FunctionType.ValueBased };
        }
        function O() {
          const t2 = "sigmoid";
          return { body: `
  float ${t2}_(float a) {
    return 1.0 / (1.0 + exp(-a));
  }
  vec4 ${t2}_(vec4 v) {
    return 1.0 / (1.0 + exp(-v));
  }
  `, name: t2, type: o.FunctionType.ValueBased };
        }
        function A() {
          return P("sqrt");
        }
        function E() {
          return P("tan");
        }
        function I() {
          const t2 = "tanh";
          return { body: `
  float ${t2}_(float a) {
    a = clamp(a, -10., 10.);
    a = exp(2.*a);
    return (a - 1.) / (a + 1.);
  }
  vec4 ${t2}_(vec4 v) {
    v = clamp(v, -10., 10.);
    v = exp(2.*v);
    return (v - 1.) / (v + 1.);
  }
  `, name: t2, type: o.FunctionType.ValueBased };
        }
        function P(t2) {
          return { body: `
  float ${t2}_(float a) {
    return ${t2}(a);
  }
  vec4 ${t2}_(vec4 v) {
    return ${t2}(v);
  }
  `, name: t2, type: o.FunctionType.ValueBased };
        }
        e.glslAbs = u, e.glslAcos = c, e.glslAsin = l, e.glslAtan = p, e.glslCeil = f, e.glslCos = d, e.glslElu = h, e.glslExp = g, e.glslFloor = b, e.glslClip = m, e.glslIdentity = y, e.glslLeakyRelu = _, e.glslLog = v, e.glslNeg = w, e.glslNot = x, e.glslSin = T, e.glslRelu = S, e.glslSigmoid = O, e.glslSqrt = A, e.glslTan = E, e.glslTanh = I;
        const D = (t2, e2, n2, r2) => {
          const i2 = t2.session.pack ? s.TextureType.packed : s.TextureType.unpacked, o2 = { name: n2.name, inputTypes: [i2], inputNames: ["A"], cacheHint: r2 };
          return Object.assign(Object.assign({}, o2), { get: () => ((t3, e3, n3, r3) => {
            const i3 = t3.session.pack ? s.TextureType.packed : s.TextureType.unpacked, o3 = (0, a.getGlsl)(t3.session.backend.glContext.version);
            return Object.assign(Object.assign({}, e3), { output: { dims: n3.dims, type: n3.type, textureType: i3 }, shaderSource: `
     ${r3.body}
     void main() {
       vec4 v = ${o3.texture2D}(A, TexCoords);
       v = ${r3.name}_(v);
       ${o3.output} = v;
     }
     `, hasMain: true });
          })(t2, o2, e2, n2) });
        };
        e.abs = (t2, e2) => [t2.run(D(t2, e2[0], u()), e2)], e.acos = (t2, e2) => [t2.run(D(t2, e2[0], c()), e2)], e.asin = (t2, e2) => [t2.run(D(t2, e2[0], l()), e2)], e.atan = (t2, e2) => [t2.run(D(t2, e2[0], p()), e2)], e.clip = (t2, e2, n2) => [t2.run(D(t2, e2[0], m(n2.min, n2.max), n2.cacheKey), e2)], e.parseClipAttributes = (t2) => (0, r.createAttributeWithCacheKey)({ min: t2.attributes.getFloat("min", i.MIN_CLIP), max: t2.attributes.getFloat("max", i.MAX_CLIP) }), e.clipV11 = (t2, n2) => {
          const r2 = $(t2, n2);
          return (0, e.clip)(t2, [n2[0]], r2);
        };
        const $ = (t2, e2) => {
          if (e2.length >= 3 && (!t2.session.isInitializer(e2[1].dataId) || !t2.session.isInitializer(e2[2].dataId))) throw new Error("dynamic clip attributes are not allowed");
          const n2 = e2.length >= 3 ? e2[1].numberData[0] : i.MIN_CLIP, o2 = e2.length >= 3 ? e2[2].numberData[0] : i.MAX_CLIP;
          return (0, r.createAttributeWithCacheKey)({ min: n2, max: o2 });
        };
        e.ceil = (t2, e2) => [t2.run(D(t2, e2[0], f()), e2)], e.cos = (t2, e2) => [t2.run(D(t2, e2[0], d()), e2)], e.elu = (t2, e2, n2) => [t2.run(D(t2, e2[0], h(n2.alpha), n2.cacheKey), e2)], e.parseEluAttributes = (t2) => (0, r.createAttributeWithCacheKey)({ alpha: t2.attributes.getFloat("alpha", 1) }), e.exp = (t2, e2) => [t2.run(D(t2, e2[0], g()), e2)], e.floor = (t2, e2) => [t2.run(D(t2, e2[0], b()), e2)], e.identity = (t2, e2) => [t2.run(D(t2, e2[0], y()), e2)], e.leakyRelu = (t2, e2, n2) => [t2.run(D(t2, e2[0], _(n2.alpha), n2.cacheKey), e2)], e.parseLeakyReluAttributes = (t2) => (0, r.createAttributeWithCacheKey)({ alpha: t2.attributes.getFloat("alpha", 0.01) }), e.log = (t2, e2) => [t2.run(D(t2, e2[0], v()), e2)], e.neg = (t2, e2) => [t2.run(D(t2, e2[0], w()), e2)], e.not = (t2, e2) => [t2.run(D(t2, e2[0], x()), e2)], e.relu = (t2, e2) => [t2.run(D(t2, e2[0], S()), e2)], e.sigmoid = (t2, e2) => [t2.run(D(t2, e2[0], O()), e2)], e.sin = (t2, e2) => [t2.run(D(t2, e2[0], T()), e2)], e.sqrt = (t2, e2) => [t2.run(D(t2, e2[0], A()), e2)], e.tan = (t2, e2) => [t2.run(D(t2, e2[0], E()), e2)], e.tanh = (t2, e2) => [t2.run(D(t2, e2[0], I()), e2)];
      }, 5611: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.createUnpackProgramInfoLoader = e.createUnpackProgramInfo = void 0;
        const r = n(5060), i = n(2039), o = n(9390), a = n(2827), s = { name: "unpack", inputNames: ["A"], inputTypes: [i.TextureType.packed] };
        e.createUnpackProgramInfo = (t2, e2) => {
          const n2 = e2.dims.length, u = (0, a.getChannels)("rc", n2), c = u.slice(-2), l = (0, o.getCoordsDataType)(n2), p = (0, a.unpackFromChannel)(), f = 0 === e2.dims.length ? "" : function(t3, e3) {
            if (1 === t3) return "rc";
            let n3 = "";
            for (let r2 = 0; r2 < t3; r2++) n3 += e3[r2], r2 < t3 - 1 && (n3 += ",");
            return n3;
          }(n2, u), d = n2 <= 1 ? "rc" : `vec2(${c.join(",")})`, h = `
    ${p}
    void main() {
      ${l} rc = getOutputCoords();

       // Sample the texture with the coords to get the rgba channel value.
       vec4 packedInput = getA(${f});

       ${(0, r.getGlsl)(t2.session.backend.glContext.version).output} = vec4(getChannel(packedInput, ${d}), 0, 0, 0);
     }
   `;
          return Object.assign(Object.assign({}, s), { hasMain: true, output: { dims: e2.dims, type: e2.type, textureType: i.TextureType.unpacked }, shaderSource: h });
        }, e.createUnpackProgramInfoLoader = (t2, n2) => Object.assign(Object.assign({}, s), { get: () => (0, e.createUnpackProgramInfo)(t2, n2) });
      }, 8428: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.parseUnsqueezeAttributes = e.unsqueezeV13 = e.unsqueeze = void 0;
        const r = n(2517);
        e.unsqueeze = (t2, e2, n2) => {
          i(e2);
          const o2 = r.ShapeUtil.unsqueezeShape(e2[0].dims, n2);
          return [t2.reshapeUnpacked(e2[0], o2)];
        }, e.unsqueezeV13 = (t2, n2) => (o(n2), (0, e.unsqueeze)(t2, [n2[0]], Array.from(n2[1].integerData))), e.parseUnsqueezeAttributes = (t2) => t2.attributes.getInts("axes");
        const i = (t2) => {
          if (!t2 || 1 !== t2.length) throw new Error("Unsqueeze requires 1 input.");
          if ("string" === t2[0].type) throw new Error("invalid input tensor types.");
        }, o = (t2) => {
          if (!t2 || 2 !== t2.length) throw new Error("Unsqueeze requires 2 inputs.");
          if ("int32" !== t2[1].type) throw new Error("Invalid input type.");
        };
      }, 9793: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.scalesValidation = e.validateInputs = e.parseUpsampleAttributes = e.parseUpsampleAttributesV9 = e.parseUpsampleAttributesV7 = e.upsample = void 0;
        const r = n(246), i = n(5060), o = n(2039), a = { name: "Upsample", inputNames: ["X"], inputTypes: [o.TextureType.unpacked] };
        e.upsample = (t2, n2, r2) => ((0, e.validateInputs)(n2, r2), [t2.run(Object.assign(Object.assign({}, a), { cacheHint: r2.cacheKey, get: () => s(t2, n2, r2) }), n2)]), e.parseUpsampleAttributesV7 = (t2) => (0, e.parseUpsampleAttributes)(t2, 7), e.parseUpsampleAttributesV9 = (t2) => (0, e.parseUpsampleAttributes)(t2, 9), e.parseUpsampleAttributes = (t2, n2) => {
          const i2 = n2 >= 10, o2 = t2.attributes.getString("mode", "nearest");
          if ("nearest" !== o2 && "linear" !== o2 && (n2 < 11 || "cubic" !== o2)) throw new Error(`unrecognized mode: ${o2}`);
          let a2 = [];
          n2 < 9 && (a2 = t2.attributes.getFloats("scales"), (0, e.scalesValidation)(a2, o2, i2));
          const s2 = t2.attributes.getFloat("extrapolation_value", 0), u = n2 > 10 ? t2.attributes.getString("coordinate_transformation_mode", "half_pixel") : "asymmetric";
          if (-1 === ["asymmetric", "pytorch_half_pixel", "tf_half_pixel_for_nn", "align_corners", "tf_crop_and_resize", "half_pixel"].indexOf(u)) throw new Error(`coordinate_transform_mode '${u}' is not supported`);
          const c = "tf_crop_and_resize" === u, l = c, p = "nearest" === o2 && n2 >= 11 ? t2.attributes.getString("nearest_mode", "round_prefer_floor") : "";
          if (-1 === ["round_prefer_floor", "round_prefer_ceil", "floor", "ceil", ""].indexOf(p)) throw new Error(`nearest_mode '${p}' is not supported`);
          const f = t2.attributes.getFloat("cubic_coeff_a", -0.75), d = 0 !== t2.attributes.getInt("exclude_outside", 0);
          if (d && "cubic" !== o2) throw new Error("exclude_outside can be set to 1 only when mode is CUBIC.");
          const h = n2 < 11 || "nearest" === o2 && "asymmetric" === u && "floor" === p;
          let g = 0, b = 0, m = 0;
          return n2 > 10 ? t2.inputs.length > 2 ? (g = 1, b = 2, m = 3) : (b = 1, m = 2) : 9 === n2 && (b = 1), (0, r.createAttributeWithCacheKey)({ opset: n2, isResize: i2, mode: o2, scales: a2, extrapolationValue: s2, coordinateTransformMode: u, useExtrapolation: l, needRoiInput: c, nearestMode: p, cubicCoefficientA: f, excludeOutside: d, useNearest2xOptimization: h, roiInputIdx: g, scalesInputIdx: b, sizesInputIdx: m });
        };
        const s = (t2, e2, n2) => {
          const r2 = (0, i.getGlsl)(t2.session.backend.glContext.version), [s2, u] = t2.calculateTextureWidthAndHeight(e2[0].dims, o.TextureType.unpacked), c = e2[0].dims.map((t3, e3) => Math.floor(t3 * n2.scales[e3])), [l, p] = t2.calculateTextureWidthAndHeight(c, o.TextureType.unpacked), f = c.length, d = new Array(f), h = new Array(f);
          let g = `
      int output_pitches[${f}];
      int input_pitches[${f}];
      `;
          for (let t3 = f - 1; t3 >= 0; t3--) d[t3] = t3 === f - 1 ? 1 : d[t3 + 1] * c[t3 + 1], h[t3] = t3 === f - 1 ? 1 : h[t3 + 1] * e2[0].dims[t3 + 1], g += `
        output_pitches[${t3}] = ${d[t3]};
        input_pitches[${t3}] = ${h[t3]};
        `;
          const b = `
      float getInputFloat(int index) {
        vec2 coords = offsetToCoords(index, ${s2}, ${u});
        float value = getColorAsFloat(${r2.texture2D}(X, coords));
        return value;
      }
      `, m = "nearest" === n2.mode ? `
    ${b}
    float process(int indices[${f}]) {
      int input_index = 0;
      int output_index = coordsToOffset(TexCoords, ${l}, ${p});

      ${g}

      int d, m;
      for (int dim = 0; dim < ${f}; ++dim) {
        d = output_index / output_pitches[dim];
        m = output_index - d * output_pitches[dim];
        output_index = m;

        if (scales[dim] != 1 && d > 0) {
          int d2 = d / scales[dim];
          m = d - d2 * scales[dim];
          d = d2;
        }
        input_index += input_pitches[dim] * d;
      }

      return getInputFloat(input_index);
    }` : 4 === f ? `
    ${b}
    float process(int indices[4]) {
      int input_index = 0;
      int output_index = coordsToOffset(TexCoords, ${l}, ${p});

      ${g}

      int m;
      int index_of_dim0, index_of_dim1, index_of_dim2, index_of_dim3;
      index_of_dim0 = output_index / output_pitches[0];
      m = output_index - index_of_dim0 * output_pitches[0];
      index_of_dim1 = m / output_pitches[1];
      m = m - index_of_dim1 * output_pitches[1];
      index_of_dim2 = m / output_pitches[2];
      m = m - index_of_dim2 * output_pitches[2];
      index_of_dim3 = m;

      int index_of_input_dim2, index_of_input_dim3, x_offset, y_offset;
      index_of_input_dim2 = index_of_dim2 / scales[2];
      y_offset = index_of_dim2 - index_of_input_dim2 * scales[2];
      index_of_input_dim3 = index_of_dim3 / scales[3];
      x_offset = index_of_dim3 - index_of_input_dim3 * scales[3];

      input_index = index_of_dim0 * input_pitches[0] +
            index_of_dim1 * input_pitches[1] +
            index_of_input_dim2 * input_pitches[2] +
            index_of_input_dim3;

      float x00 = getInputFloat(input_index);
      float x10, x01, x11;

      bool end_of_dim2 = false;
      if (index_of_input_dim2 == (${e2[0].dims[2]} - 1)) {
        // It's the end in dimension 2
        x01 = x00;
        end_of_dim2 = true;
      } else {
        x01 = getInputFloat(input_index + input_pitches[2]);
      }

      if (index_of_input_dim3 == (input_pitches[2] - 1)) {
        // It's the end in dimension 3
        x10 = x00;
        x11 = x01;
      }
      else {
        x10 = getInputFloat(input_index + 1);
        x11 = end_of_dim2 ? x10 : getInputFloat(input_index + input_pitches[2] + 1);
      }

      float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[2]);
      float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[2]);
      return y0 + float(x_offset) * (y1 - y0) / float(scales[3]);
    }` : `
    ${b}
    float process(int indices[2]) {
      int input_index = 0;
      int output_index = coordsToOffset(TexCoords, ${l}, ${p});

      ${g}

      int m;
      int index_of_dim0, index_of_dim1;
      index_of_dim0 = output_index / output_pitches[0];
      m = output_index - index_of_dim0 * output_pitches[0];
      index_of_dim1 = m;

      int index_of_input_dim0, index_of_input_dim1, x_offset, y_offset;
      index_of_input_dim0 = index_of_dim0 / scales[0];
      y_offset = index_of_dim0 - index_of_input_dim0 * scales[0];
      index_of_input_dim1 = index_of_dim1 / scales[1];
      x_offset = index_of_dim1 - index_of_input_dim1 * scales[1];

      input_index = index_of_input_dim0 * input_pitches[0] + index_of_input_dim1;

      float x00 = getInputFloat(input_index);
      float x10, x01, x11;

      bool end_of_dim0 = false;
      if (index_of_input_dim0 == (${e2[0].dims[0]} - 1)) {
        // It's the end in dimension 0
        x01 = x00;
        end_of_dim0 = true;
      } else {
        x01 = getInputFloat(input_index + input_pitches[0]);
      }

      if (index_of_input_dim1 == (input_pitches[0] - 1)) {
        // It's the end in dimension 1
        x10 = x00;
        x11 = x01;
      }
      else {
        x10 = getInputFloat(input_index + 1);
        x11 = end_of_dim0 ? x10 : getInputFloat(input_index + input_pitches[0] + 1);
      }

      float y0 = x00 + float(y_offset) * (x01 - x00) / float(scales[0]);
      float y1 = x10 + float(y_offset) * (x11 - x10) / float(scales[0]);
      return y0 + float(x_offset) * (y1 - y0) / float(scales[1]);
    }`;
          return Object.assign(Object.assign({}, a), { output: { dims: c, type: e2[0].type, textureType: o.TextureType.unpacked }, shaderSource: m, variables: [{ name: "scales", type: "int", arrayLength: n2.scales.length, data: n2.scales.map((t3) => Math.ceil(t3)) }] });
        };
        e.validateInputs = (t2, e2) => {
          if (!t2 || e2.opset < 9 && 1 !== t2.length || e2.opset >= 9 && e2.opset < 11 && 2 !== t2.length || e2.opset >= 11 && t2.length < 2) throw new Error("invalid inputs.");
          if (e2.scales.length > 0 && t2[0].dims.length !== e2.scales.length) throw new Error("Invalid input shape.");
          if ("string" === t2[0].type) throw new Error("Invalid input tensor types.");
        }, e.scalesValidation = (t2, e2, n2) => {
          if (n2) {
            for (const e3 of t2) if (e3 <= 0) throw new Error("Scale value should be greater than 0.");
          } else for (const e3 of t2) if (e3 < 1) throw new Error("Scale value should be greater than or equal to 1.");
          if (!("linear" !== e2 && "cubic" !== e2 || 2 === t2.length || 4 === t2.length && 1 === t2[0] && 1 === t2[1])) throw new Error(`'Linear' mode and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic')         or 4-D inputs with the corresponding outermost 2 scale values being 1         in the ${n2 ? "Resize" : "Upsample"} opeartor.`);
        };
      }, 1958: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.ProgramManager = void 0;
        const r = n(1670), i = n(6231), o = n(8879), a = n(5060);
        e.ProgramManager = class {
          constructor(t2, e2, n2) {
            this.profiler = t2, this.glContext = e2, this.textureLayoutStrategy = n2, this.repo = /* @__PURE__ */ new Map(), this.attributesBound = false;
          }
          getArtifact(t2) {
            return this.repo.get(t2);
          }
          setArtifact(t2, e2) {
            this.repo.set(t2, e2);
          }
          run(t2, e2, n2) {
            var r2;
            this.profiler.event("op", `ProgramManager.run ${null !== (r2 = t2.programInfo.name) && void 0 !== r2 ? r2 : "unknown kernel"}`, () => {
              var r3;
              const o2 = this.glContext.gl, a2 = t2.program;
              o2.useProgram(a2);
              try {
                this.bindOutput(n2), this.attributesBound || this.bindAttributes(t2.attribLocations), this.bindUniforms(t2.uniformLocations, null !== (r3 = t2.programInfo.variables) && void 0 !== r3 ? r3 : [], e2);
              } catch (e3) {
                throw i.Logger.error("ProgramManager", t2.programInfo.shaderSource), e3;
              }
              this.profiler.event("backend", "GlContext.draw()", () => {
                this.glContext.draw();
              });
            }, this.glContext);
          }
          dispose() {
            this.vertexShader && this.glContext.deleteShader(this.vertexShader), this.repo.forEach((t2) => this.glContext.deleteProgram(t2.program));
          }
          build(t2, e2, n2) {
            return this.profiler.event("backend", "ProgramManager.build", () => {
              const r2 = new o.GlslPreprocessor(this.glContext, t2, e2, n2), i2 = r2.preprocess(), a2 = this.compile(i2);
              return { programInfo: t2, program: a2, uniformLocations: this.getUniformLocations(a2, r2.context.programInfo.inputNames, r2.context.programInfo.variables), attribLocations: this.getAttribLocations(a2) };
            });
          }
          compile(t2) {
            if (!this.vertexShader) {
              i.Logger.verbose("ProrgramManager", "Compiling and caching Vertex shader for the first time");
              const t3 = (0, a.getVertexShaderSource)(this.glContext.version);
              this.vertexShader = this.glContext.compileShader(t3, this.glContext.gl.VERTEX_SHADER);
            }
            r.env.debug && i.Logger.verbose("ProrgramManager", `FragShader:
${t2}
`);
            const e2 = this.glContext.compileShader(t2, this.glContext.gl.FRAGMENT_SHADER), n2 = this.glContext.createProgram(this.vertexShader, e2);
            return this.glContext.deleteShader(e2), n2;
          }
          bindOutput(t2) {
            const e2 = t2.width, n2 = t2.height;
            i.Logger.verbose("ProrgramManager", `Binding output texture to Framebuffer: w/h=${e2}/${n2}, shape=${t2.shape}, type=${t2.tensor.type}`), this.glContext.attachFramebuffer(t2.texture, e2, n2);
          }
          bindAttributes(t2) {
            const e2 = t2.position, n2 = t2.textureCoord;
            this.glContext.setVertexAttributes(e2, n2), this.attributesBound = true;
          }
          bindUniforms(t2, e2, n2) {
            var r2;
            const i2 = this.glContext.gl;
            let o2 = 0;
            for (const { name: a2, type: s, location: u, arrayLength: c } of t2) {
              const t3 = null === (r2 = e2.find((t4) => t4.name === a2)) || void 0 === r2 ? void 0 : r2.data;
              if ("sampler2D" !== s && !t3) throw new Error(`variable '${a2}' does not have data defined in program info`);
              switch (s) {
                case "sampler2D":
                  this.bindTexture(n2[o2], u, o2), o2++;
                  break;
                case "float":
                  c ? i2.uniform1fv(u, t3) : i2.uniform1f(u, t3);
                  break;
                case "int":
                  c ? i2.uniform1iv(u, t3) : i2.uniform1i(u, t3);
                  break;
                default:
                  throw new Error(`Uniform not implemented: ${s}`);
              }
            }
          }
          bindTexture(t2, e2, n2) {
            this.glContext.bindTextureToUniform(t2.texture, n2, e2);
          }
          getAttribLocations(t2) {
            return { position: this.getAttribLocation(t2, "position"), textureCoord: this.getAttribLocation(t2, "textureCoord") };
          }
          getUniformLocations(t2, e2, n2) {
            const r2 = [];
            if (e2) for (const n3 of e2) r2.push({ name: n3, type: "sampler2D", location: this.getUniformLocation(t2, n3) });
            if (n2) for (const e3 of n2) r2.push(Object.assign(Object.assign({}, e3), { location: this.getUniformLocation(t2, e3.name) }));
            return r2;
          }
          getUniformLocation(t2, e2) {
            const n2 = this.glContext.gl.getUniformLocation(t2, e2);
            if (null === n2) throw new Error(`Uniform ${e2} not found.`);
            return n2;
          }
          getAttribLocation(t2, e2) {
            return this.glContext.gl.getAttribLocation(t2, e2);
          }
        };
      }, 6416: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.WebGLSessionHandler = void 0;
        const r = n(6231), i = n(1047), o = n(8316), a = n(1640), s = n(1958), u = n(7859), c = n(5702);
        e.WebGLSessionHandler = class {
          constructor(t2, e2) {
            this.backend = t2, this.context = e2, this.layoutStrategy = new u.PreferLogicalStrategy(t2.glContext.maxTextureSize), this.programManager = new s.ProgramManager(this.context.profiler, t2.glContext, this.layoutStrategy), this.textureManager = new c.TextureManager(t2.glContext, this.layoutStrategy, this.context.profiler, { reuseTextures: "full" === t2.textureCacheMode }), this.packedTextureDataCache = /* @__PURE__ */ new Map(), this.unpackedTextureDataCache = /* @__PURE__ */ new Map(), this.pack = t2.pack, this.pack2unpackMap = /* @__PURE__ */ new Map(), this.unpack2packMap = /* @__PURE__ */ new Map();
          }
          createInferenceHandler() {
            return new o.WebGLInferenceHandler(this);
          }
          onGraphInitialized(t2) {
            const e2 = t2.getValues().filter((t3) => -1 === t3.from && t3.tensor).map((t3) => t3.tensor.dataId);
            this.initializers = new Set(e2);
          }
          isInitializer(t2) {
            return !!this.initializers && this.initializers.has(t2);
          }
          addInitializer(t2) {
            this.initializers.add(t2);
          }
          getTextureData(t2, e2) {
            return e2 ? this.packedTextureDataCache.get(t2) : this.unpackedTextureDataCache.get(t2);
          }
          setTextureData(t2, e2, n2 = false) {
            r.Logger.verbose("WebGLSessionHandler", "Storing Texture data in cache"), n2 ? this.packedTextureDataCache.set(t2, e2) : this.unpackedTextureDataCache.set(t2, e2);
          }
          dispose() {
            this.programManager.dispose(), this.textureManager.clearActiveTextures(), this.packedTextureDataCache.forEach((t2) => this.textureManager.releaseTexture(t2, true)), this.packedTextureDataCache = /* @__PURE__ */ new Map(), this.unpackedTextureDataCache.forEach((t2) => this.textureManager.releaseTexture(t2, true)), this.unpackedTextureDataCache = /* @__PURE__ */ new Map();
          }
          resolve(t2, e2, n2) {
            const r2 = (0, i.resolveOperator)(t2, e2, a.WEBGL_OP_RESOLVE_RULES);
            return { impl: r2.opImpl, context: r2.opInit ? r2.opInit(t2, n2) : t2 };
          }
        };
      }, 7769: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.Uint8DataEncoder = e.RGBAFloatDataEncoder = e.RedFloat32DataEncoder = void 0;
        const r = n(6231);
        e.RedFloat32DataEncoder = class {
          constructor(t2, e2 = 1) {
            if (1 === e2) this.internalFormat = t2.R32F, this.format = t2.RED, this.textureType = t2.FLOAT, this.channelSize = e2;
            else {
              if (4 !== e2) throw new Error(`Invalid number of channels: ${e2}`);
              this.internalFormat = t2.RGBA32F, this.format = t2.RGBA, this.textureType = t2.FLOAT, this.channelSize = e2;
            }
          }
          encode(t2, e2) {
            let n2, i;
            return t2.constructor !== Float32Array && (r.Logger.warning("Encoder", "data was not of type Float32; creating new Float32Array"), i = new Float32Array(t2)), e2 * this.channelSize > t2.length ? (r.Logger.warning("Encoder", "Source data too small. Allocating larger array"), i = t2, n2 = this.allocate(e2 * this.channelSize), i.forEach((t3, e3) => n2[e3] = t3)) : (i = t2, n2 = i), n2;
          }
          allocate(t2) {
            return new Float32Array(4 * t2);
          }
          decode(t2, e2) {
            return 1 === this.channelSize ? t2.filter((t3, e3) => e3 % 4 == 0).subarray(0, e2) : t2.subarray(0, e2);
          }
        }, e.RGBAFloatDataEncoder = class {
          constructor(t2, e2 = 1, n2) {
            if (1 !== e2 && 4 !== e2) throw new Error(`Invalid number of channels: ${e2}`);
            this.internalFormat = t2.RGBA, this.format = t2.RGBA, this.channelSize = e2, this.textureType = n2 || t2.FLOAT;
          }
          encode(t2, e2) {
            let n2 = t2;
            return 1 === this.channelSize && (r.Logger.verbose("Encoder", "Exploding into a larger array"), n2 = this.allocate(e2), t2.forEach((t3, e3) => n2[4 * e3] = t3)), n2;
          }
          allocate(t2) {
            return new Float32Array(4 * t2);
          }
          decode(t2, e2) {
            return 1 === this.channelSize ? t2.filter((t3, e3) => e3 % 4 == 0).subarray(0, e2) : t2.subarray(0, e2);
          }
        }, e.Uint8DataEncoder = class {
          constructor(t2, e2 = 1) {
            if (this.channelSize = 4, 1 === e2) this.internalFormat = t2.ALPHA, this.format = t2.ALPHA, this.textureType = t2.UNSIGNED_BYTE, this.channelSize = e2;
            else {
              if (4 !== e2) throw new Error(`Invalid number of channels: ${e2}`);
              this.internalFormat = t2.RGBA, this.format = t2.RGBA, this.textureType = t2.UNSIGNED_BYTE, this.channelSize = e2;
            }
          }
          encode(t2, e2) {
            return new Uint8Array(t2.buffer, t2.byteOffset, t2.byteLength);
          }
          allocate(t2) {
            return new Uint8Array(t2 * this.channelSize);
          }
          decode(t2, e2) {
            if (t2 instanceof Uint8Array) return t2.subarray(0, e2);
            throw new Error(`Invalid array type: ${t2.constructor}`);
          }
        };
      }, 7859: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.getBatchDim = e.sizeToSquarishShape = e.getRowsCols = e.sizeFromShape = e.isInt = e.parseAxisParam = e.squeezeShape = e.PreferLogicalStrategy = e.AlwaysKeepOriginalSizeStrategy = void 0;
        const r = n(6231), i = n(2517);
        function o(t2, e2) {
          const n2 = [], r2 = [], i2 = null != e2 && Array.isArray(e2) && 0 === e2.length, o2 = null == e2 || i2 ? null : a(e2, t2).sort();
          let s2 = 0;
          for (let e3 = 0; e3 < t2.length; ++e3) {
            if (null != o2) {
              if (o2[s2] === e3 && 1 !== t2[e3]) throw new Error(`Can't squeeze axis ${e3} since its dim '${t2[e3]}' is not 1`);
              (null == o2[s2] || o2[s2] > e3) && 1 === t2[e3] && (n2.push(t2[e3]), r2.push(e3)), o2[s2] <= e3 && s2++;
            }
            1 !== t2[e3] && (n2.push(t2[e3]), r2.push(e3));
          }
          return { newShape: n2, keptDims: r2 };
        }
        function a(t2, e2) {
          const n2 = e2.length;
          return t2 = null == t2 ? e2.map((t3, e3) => e3) : [].concat(t2), (0, i.assert)(t2.every((t3) => t3 >= -n2 && t3 < n2), () => `All values in axis param must be in range [-${n2}, ${n2}) but got axis ${t2}`), (0, i.assert)(t2.every(s), () => `All values in axis param must be integers but got axis ${t2}`), t2.map((t3) => t3 < 0 ? n2 + t3 : t3);
        }
        function s(t2) {
          return t2 % 1 == 0;
        }
        function u(t2) {
          if (0 === t2.length) return 1;
          let e2 = t2[0];
          for (let n2 = 1; n2 < t2.length; n2++) e2 *= t2[n2];
          return e2;
        }
        function c(t2) {
          const e2 = Math.ceil(Math.sqrt(t2));
          return [e2, Math.ceil(t2 / e2)];
        }
        e.AlwaysKeepOriginalSizeStrategy = class {
          constructor(t2) {
            this.maxTextureSize = t2;
          }
          computeTextureWH(t2, e2) {
            if (0 === t2.length) return [1, 1];
            const n2 = this.maxTextureSize;
            if (e2 && void 0 !== e2.breakAxis) {
              const i3 = e2.breakAxis >= t2.length ? 1 : t2.slice(e2.breakAxis).reduce((t3, e3) => t3 * e3), o3 = e2.breakAxis <= 0 ? 1 : t2.slice(0, e2.breakAxis).reduce((t3, e3) => t3 * e3);
              if (!(i3 > n2 || o3 > n2)) return [i3, o3];
              r.Logger.verbose("TextureLayout", `Given width/height preferences were unattainable: shape:${t2}, breakAxis:${e2.breakAxis}`);
            }
            const i2 = t2.reduce((t3, e3) => t3 * e3);
            let o2 = Math.floor(Math.sqrt(i2));
            for (; o2 < n2 && o2 < i2 && i2 % o2 != 0; o2++) ;
            if (o2 >= n2 || i2 % o2 != 0) throw new Error(`The given dimensions are outside this GPU's boundaries: ${t2}`);
            return [o2, i2 / o2];
          }
        }, e.PreferLogicalStrategy = class {
          constructor(t2) {
            this.maxTextureSize = t2;
          }
          computeTextureWH(t2, e2) {
            const n2 = this.computeTexture(t2, e2);
            return e2 && e2.isPacked && (n2[0] /= 2, n2[1] /= 2), e2 && e2.reverseWH ? [n2[1], n2[0]] : n2;
          }
          computeTexture(t2, e2) {
            const n2 = e2 && e2.isPacked;
            if (0 === t2.length) return n2 ? [2, 2] : [1, 1];
            let i2 = this.maxTextureSize;
            if (e2 && void 0 !== e2.breakAxis) {
              const n3 = e2.breakAxis >= t2.length ? 1 : t2.slice(e2.breakAxis).reduce((t3, e3) => t3 * e3), o2 = e2.breakAxis <= 0 ? 1 : t2.slice(0, e2.breakAxis).reduce((t3, e3) => t3 * e3);
              if (!(n3 > i2 || o2 > i2)) return [n3, o2];
              r.Logger.verbose("TextureLayout", `Given width/height preferences were unattainable: shape:${t2}, breakAxis:${e2.breakAxis}`);
            }
            let a2 = t2.slice(0);
            if (n2 && (i2 *= 2, a2 = a2.map((t3, e3) => e3 >= a2.length - 2 ? a2[e3] % 2 == 0 ? a2[e3] : a2[e3] + 1 : a2[e3]), 1 === a2.length && (a2 = [2, a2[0]])), 2 !== a2.length) {
              const t3 = o(a2);
              a2 = t3.newShape;
            }
            const s2 = u(a2);
            return a2.length <= 1 && s2 <= i2 ? [1, s2] : 2 === a2.length && a2[0] <= i2 && a2[1] <= i2 ? a2 : 3 === a2.length && a2[0] * a2[1] <= i2 && a2[2] <= i2 ? [a2[0] * a2[1], a2[2]] : 3 === a2.length && a2[0] <= i2 && a2[1] * a2[2] <= i2 ? [a2[0], a2[1] * a2[2]] : 4 === a2.length && a2[0] * a2[1] * a2[2] <= i2 && a2[3] <= i2 ? [a2[0] * a2[1] * a2[2], a2[3]] : 4 === a2.length && a2[0] <= i2 && a2[1] * a2[2] * a2[3] <= i2 ? [a2[0], a2[1] * a2[2] * a2[3]] : n2 ? c(s2 / 4).map((t3) => 2 * t3) : c(s2);
          }
        }, e.squeezeShape = o, e.parseAxisParam = a, e.isInt = s, e.sizeFromShape = u, e.getRowsCols = function(t2) {
          if (0 === t2.length) throw Error("Cannot get rows and columns of an empty shape array.");
          return [t2.length > 1 ? t2[t2.length - 2] : 1, t2[t2.length - 1]];
        }, e.sizeToSquarishShape = c, e.getBatchDim = function(t2, e2 = 2) {
          return u(t2.slice(0, t2.length - e2));
        };
      }, 4057: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.createTextureLayoutFromShape = e.calculateTextureWidthAndHeight = e.createTextureLayoutFromTextureType = void 0;
        const r = n(2517), i = n(2039);
        e.createTextureLayoutFromTextureType = (t2, n2, r2) => {
          const o = r2 === i.TextureType.unpacked || r2 === i.TextureType.unpackedReversed ? 1 : 4, a = r2 === i.TextureType.packed, s = r2 === i.TextureType.unpackedReversed || r2 === i.TextureType.packed, u = r2 === i.TextureType.packedLastDimension ? n2.length - 1 : void 0, c = r2 === i.TextureType.packedLastDimension ? n2.map((t3, e2) => e2 === n2.length - 1 ? 4 * t3 : t3) : void 0;
          return (0, e.createTextureLayoutFromShape)(t2, n2, o, c, { isPacked: a, reverseWH: s, breakAxis: u });
        }, e.calculateTextureWidthAndHeight = (t2, n2, r2) => {
          const i2 = (0, e.createTextureLayoutFromTextureType)(t2, n2, r2);
          return [i2.width, i2.height];
        }, e.createTextureLayoutFromShape = (t2, e2, n2 = 1, i2, o) => {
          const a = !(!o || !o.isPacked), [s, u] = t2.computeTextureWH(a && i2 || e2, o), c = e2.length;
          let l = e2.slice(0);
          if (0 === c && (l = [1]), 1 === n2) i2 = e2;
          else if (a) {
            if (4 !== n2) throw new Error("a packed texture must be 4-channel");
            i2 = e2, c > 0 && (l[c - 1] = Math.ceil(l[c - 1] / 2)), c > 1 && (l[c - 2] = Math.ceil(l[c - 2] / 2));
          } else if (!i2) throw new Error("Unpacked shape is needed when using channels > 1");
          return { width: s, height: u, channels: n2, isPacked: a, shape: l, strides: r.ShapeUtil.computeStrides(l), unpackedShape: i2, reversedWH: o && o.reverseWH };
        };
      }, 5702: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.TextureManager = void 0;
        const r = n(6231);
        e.TextureManager = class {
          constructor(t2, e2, n2, r2) {
            this.glContext = t2, this.layoutStrategy = e2, this.profiler = n2, this.config = r2, this.pendingRead = /* @__PURE__ */ new Map(), r2.reuseTextures && (this.inUseTextures = /* @__PURE__ */ new Map(), this.idleTextures = /* @__PURE__ */ new Map(), this.textureLookup = /* @__PURE__ */ new Map());
          }
          createTextureFromLayout(t2, e2, n2, i) {
            const o = this.toEncoderType(t2), a = this.glContext.getEncoder(o, e2.channels || 1, i);
            if (e2.isPacked && 1 === i) throw new Error("not implemented");
            const s = e2.width, u = e2.height;
            let c, l;
            if (this.config.reuseTextures) {
              c = `${s}x${u}_${a.format}_${a.internalFormat}_${a.textureType}`, l = this.inUseTextures.get(c), l || (l = [], this.inUseTextures.set(c, l));
              const e3 = this.idleTextures.get(c);
              if (e3 && e3.length > 0) {
                const r2 = e3.pop();
                return l.push(r2), 1 === i && this.glContext.updateTexture(r2, s, u, a, this.toTextureData(t2, n2)), r2;
              }
            }
            r.Logger.verbose("TextureManager", `Creating new texture of size ${e2.width}x${e2.height}`);
            const p = this.glContext.allocateTexture(s, u, a, this.toTextureData(t2, n2));
            return this.config.reuseTextures && (l.push(p), this.textureLookup.set(p, c)), p;
          }
          readTexture(t2, e2, n2) {
            return n2 || (n2 = 1), this.profiler.event("backend", "TextureManager.readTexture", () => {
              const r2 = t2.shape.reduce((t3, e3) => t3 * e3) * n2, i = this.glContext.readTexture(t2.texture, t2.width, t2.height, r2, this.toEncoderType(e2), n2);
              return this.toTensorData(e2, i);
            });
          }
          readTextureAsync(t2, e2, n2) {
            return __async(this, null, function* () {
              const r2 = t2.tensor.dataId;
              if (n2 || (n2 = 1), this.pendingRead.has(r2)) {
                const t3 = this.pendingRead.get(r2);
                return new Promise((e3) => null == t3 ? void 0 : t3.push(e3));
              }
              return this.profiler.event("backend", "TextureManager.readTextureAsync", () => __async(this, null, function* () {
                this.pendingRead.set(r2, []);
                const i = t2.shape.reduce((t3, e3) => t3 * e3) * n2;
                yield this.glContext.createAndWaitForFence();
                const o = this.glContext.readTexture(t2.texture, t2.width, t2.height, i, this.toEncoderType(e2), n2), a = this.toTensorData(e2, o), s = this.pendingRead.get(r2);
                return this.pendingRead.delete(r2), null == s || s.forEach((t3) => t3(a)), a;
              }));
            });
          }
          readUint8TextureAsFloat(t2) {
            return this.profiler.event("backend", "TextureManager.readUint8TextureAsFloat", () => {
              const e2 = t2.shape.reduce((t3, e3) => t3 * e3), n2 = this.glContext.readTexture(t2.texture, t2.width, t2.height, 4 * e2, "byte", 4);
              return new Float32Array(n2.buffer, n2.byteOffset, e2);
            });
          }
          releaseTexture(t2, e2) {
            let n2;
            if (this.config.reuseTextures && (n2 = this.textureLookup.get(t2.texture), n2)) {
              e2 && this.textureLookup.delete(n2);
              const r2 = this.inUseTextures.get(n2);
              if (r2) {
                const e3 = r2.indexOf(t2.texture);
                if (-1 !== e3) {
                  r2.splice(e3, 1);
                  let i = this.idleTextures.get(n2);
                  i || (i = [], this.idleTextures.set(n2, i)), i.push(t2.texture);
                }
              }
            }
            n2 && !e2 || (r.Logger.verbose("TextureManager", `Deleting texture of size ${t2.width}x${t2.height}`), this.glContext.deleteTexture(t2.texture));
          }
          toTensorData(t2, e2) {
            switch (t2) {
              case "int16":
                return e2 instanceof Int16Array ? e2 : Int16Array.from(e2);
              case "int32":
                return e2 instanceof Int32Array ? e2 : Int32Array.from(e2);
              case "int8":
                return e2 instanceof Int8Array ? e2 : Int8Array.from(e2);
              case "uint16":
                return e2 instanceof Uint16Array ? e2 : Uint16Array.from(e2);
              case "uint32":
                return e2 instanceof Uint32Array ? e2 : Uint32Array.from(e2);
              case "uint8":
              case "bool":
                return e2 instanceof Uint8Array ? e2 : Uint8Array.from(e2);
              case "float32":
                return e2 instanceof Float32Array ? e2 : Float32Array.from(e2);
              case "float64":
                return e2 instanceof Float64Array ? e2 : Float64Array.from(e2);
              default:
                throw new Error(`TensorData type ${t2} is not supported`);
            }
          }
          toTextureData(t2, e2) {
            if (e2) return e2 instanceof Float32Array ? e2 : new Float32Array(e2);
          }
          toEncoderType(t2) {
            return "float";
          }
          clearActiveTextures() {
            this.glContext.clearActiveTextures();
          }
        };
      }, 2039: (t, e) => {
        var n;
        Object.defineProperty(e, "__esModule", { value: true }), e.TextureType = void 0, (n = e.TextureType || (e.TextureType = {}))[n.unpacked = 0] = "unpacked", n[n.unpackedReversed = 1] = "unpackedReversed", n[n.packed = 2] = "packed", n[n.downloadUint8AsFloat = 3] = "downloadUint8AsFloat", n[n.packedLastDimension = 4] = "packedLastDimension";
      }, 9390: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.getGlChannels = e.getCoordsDataType = e.getSqueezedParams = e.squeezeInputShape = e.generateShaderFuncNameFromInputSamplerNameAtOutCoords = e.generateShaderFuncNameFromInputSamplerName = e.repeatedTry = e.getPackedShape = void 0;
        const r = n(2517);
        e.getPackedShape = function(t2) {
          const e2 = t2.length;
          return t2.slice(0, e2 - 1).concat(t2[e2 - 1] / 4);
        }, e.repeatedTry = function(t2, e2 = (t3) => 0, n2) {
          return __async(this, null, function* () {
            return new Promise((r2, i) => {
              let o = 0;
              const a = () => {
                if (t2()) return void r2();
                o++;
                const s = e2(o);
                null != n2 && o >= n2 ? i() : setTimeout(a, s);
              };
              a();
            });
          });
        }, e.generateShaderFuncNameFromInputSamplerName = function(t2) {
          return (0, r.assert)(void 0 !== t2 && 0 !== t2.length, () => "empty string found for sampler name"), "get" + t2.charAt(0).toUpperCase() + t2.slice(1);
        }, e.generateShaderFuncNameFromInputSamplerNameAtOutCoords = function(t2) {
          return (0, r.assert)(void 0 !== t2 && 0 !== t2.length, () => "empty string found for sampler name"), "get" + t2.charAt(0).toUpperCase() + t2.slice(1) + "AtOutCoords";
        }, e.squeezeInputShape = function(t2, e2) {
          let n2 = JSON.parse(JSON.stringify(t2));
          return n2 = e2, n2;
        }, e.getSqueezedParams = function(t2, e2) {
          return e2.map((e3) => t2[e3]).join(", ");
        }, e.getCoordsDataType = function(t2) {
          if (t2 <= 1) return "int";
          if (2 === t2) return "ivec2";
          if (3 === t2) return "ivec3";
          if (4 === t2) return "ivec4";
          if (5 === t2) return "ivec5";
          if (6 === t2) return "ivec6";
          throw Error(`GPU for rank ${t2} is not yet supported`);
        }, e.getGlChannels = function(t2 = 6) {
          return ["x", "y", "z", "w", "u", "v"].slice(0, t2);
        };
      }, 7305: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.createNewWebGLContext = e.createWebGLContext = void 0;
        const r = n(6231), i = n(1713), o = {};
        function a(t2) {
          const e2 = function() {
            if ("undefined" == typeof document) {
              if ("undefined" == typeof OffscreenCanvas) throw new TypeError("failed to create canvas: OffscreenCanvas is not supported");
              return new OffscreenCanvas(1, 1);
            }
            const t3 = document.createElement("canvas");
            return t3.width = 1, t3.height = 1, t3;
          }();
          let n2;
          const o2 = { alpha: false, depth: false, antialias: false, stencil: false, preserveDrawingBuffer: false, premultipliedAlpha: false, failIfMajorPerformanceCaveat: false };
          if ((!t2 || "webgl2" === t2) && (n2 = e2.getContext("webgl2", o2), n2)) try {
            return new i.WebGLContext(n2, 2);
          } catch (t3) {
            r.Logger.warning("GlContextFactory", `failed to create WebGLContext using contextId 'webgl2'. Error: ${t3}`);
          }
          if ((!t2 || "webgl" === t2) && (n2 = e2.getContext("webgl", o2) || e2.getContext("experimental-webgl", o2), n2)) try {
            return new i.WebGLContext(n2, 1);
          } catch (t3) {
            r.Logger.warning("GlContextFactory", `failed to create WebGLContext using contextId 'webgl' or 'experimental-webgl'. Error: ${t3}`);
          }
          throw new Error("WebGL is not supported");
        }
        e.createWebGLContext = function t2(e2) {
          let n2;
          e2 && "webgl2" !== e2 || !("webgl2" in o) ? e2 && "webgl" !== e2 || !("webgl" in o) || (n2 = o.webgl) : n2 = o.webgl2, n2 = n2 || a(e2), e2 = e2 || 1 === n2.version ? "webgl" : "webgl2";
          const r2 = n2.gl;
          return o[e2] = n2, r2.isContextLost() ? (delete o[e2], t2(e2)) : (r2.disable(r2.DEPTH_TEST), r2.disable(r2.STENCIL_TEST), r2.disable(r2.BLEND), r2.disable(r2.DITHER), r2.disable(r2.POLYGON_OFFSET_FILL), r2.disable(r2.SAMPLE_COVERAGE), r2.enable(r2.SCISSOR_TEST), r2.enable(r2.CULL_FACE), r2.cullFace(r2.BACK), n2);
        }, e.createNewWebGLContext = a;
      }, 1713: function(t, e, n) {
        var r = this && this.__createBinding || (Object.create ? function(t2, e2, n2, r2) {
          void 0 === r2 && (r2 = n2);
          var i2 = Object.getOwnPropertyDescriptor(e2, n2);
          i2 && !("get" in i2 ? !e2.__esModule : i2.writable || i2.configurable) || (i2 = { enumerable: true, get: function() {
            return e2[n2];
          } }), Object.defineProperty(t2, r2, i2);
        } : function(t2, e2, n2, r2) {
          void 0 === r2 && (r2 = n2), t2[r2] = e2[n2];
        }), i = this && this.__setModuleDefault || (Object.create ? function(t2, e2) {
          Object.defineProperty(t2, "default", { enumerable: true, value: e2 });
        } : function(t2, e2) {
          t2.default = e2;
        }), o = this && this.__importStar || function(t2) {
          if (t2 && t2.__esModule) return t2;
          var e2 = {};
          if (null != t2) for (var n2 in t2) "default" !== n2 && Object.prototype.hasOwnProperty.call(t2, n2) && r(e2, t2, n2);
          return i(e2, t2), e2;
        };
        Object.defineProperty(e, "__esModule", { value: true }), e.WebGLContext = e.linearSearchLastTrue = void 0;
        const a = n(1670), s = o(n(7769)), u = n(9390);
        function c(t2) {
          let e2 = 0;
          for (; e2 < t2.length && t2[e2](); ++e2) ;
          return e2 - 1;
        }
        e.linearSearchLastTrue = c, e.WebGLContext = class {
          constructor(t2, e2) {
            this.frameBufferBound = false, this.itemsToPoll = [], this.gl = t2, this.version = e2, this.getExtensions(), this.vertexbuffer = this.createVertexbuffer(), this.framebuffer = this.createFramebuffer(), this.queryVitalParameters();
          }
          allocateTexture(t2, e2, n2, r2) {
            const i2 = this.gl, o2 = i2.createTexture();
            i2.bindTexture(i2.TEXTURE_2D, o2), i2.texParameteri(i2.TEXTURE_2D, i2.TEXTURE_MIN_FILTER, i2.NEAREST), i2.texParameteri(i2.TEXTURE_2D, i2.TEXTURE_MAG_FILTER, i2.NEAREST), i2.texParameteri(i2.TEXTURE_2D, i2.TEXTURE_WRAP_S, i2.CLAMP_TO_EDGE), i2.texParameteri(i2.TEXTURE_2D, i2.TEXTURE_WRAP_T, i2.CLAMP_TO_EDGE);
            const a2 = r2 ? n2.encode(r2, t2 * e2) : null;
            return i2.texImage2D(i2.TEXTURE_2D, 0, n2.internalFormat, t2, e2, 0, n2.format, n2.textureType, a2), this.checkError(), o2;
          }
          updateTexture(t2, e2, n2, r2, i2) {
            const o2 = this.gl;
            o2.bindTexture(o2.TEXTURE_2D, t2);
            const a2 = r2.encode(i2, e2 * n2);
            o2.texSubImage2D(o2.TEXTURE_2D, 0, 0, 0, e2, n2, r2.format, r2.textureType, a2), this.checkError();
          }
          attachFramebuffer(t2, e2, n2) {
            const r2 = this.gl;
            r2.bindTexture(r2.TEXTURE_2D, t2), r2.bindFramebuffer(r2.FRAMEBUFFER, this.framebuffer), r2.framebufferTexture2D(r2.FRAMEBUFFER, r2.COLOR_ATTACHMENT0, r2.TEXTURE_2D, t2, 0), this.checkError(), r2.viewport(0, 0, e2, n2), r2.scissor(0, 0, e2, n2);
          }
          readTexture(t2, e2, n2, r2, i2, o2) {
            const a2 = this.gl;
            o2 || (o2 = 1), this.frameBufferBound || this.attachFramebuffer(t2, e2, n2);
            const s2 = this.getEncoder(i2, o2), u2 = s2.allocate(e2 * n2);
            return a2.bindTexture(a2.TEXTURE_2D, t2), a2.framebufferTexture2D(a2.FRAMEBUFFER, a2.COLOR_ATTACHMENT0, a2.TEXTURE_2D, t2, 0), a2.readPixels(0, 0, e2, n2, a2.RGBA, s2.textureType, u2), this.checkError(), s2.decode(u2, r2);
          }
          isFramebufferReady() {
            return true;
          }
          getActiveTexture() {
            const t2 = this.gl;
            return "TEXTURE" + (t2.getParameter(this.gl.ACTIVE_TEXTURE) - t2.TEXTURE0);
          }
          getTextureBinding() {
            return this.gl.getParameter(this.gl.TEXTURE_BINDING_2D);
          }
          getFramebufferBinding() {
            return this.gl.getParameter(this.gl.FRAMEBUFFER_BINDING);
          }
          setVertexAttributes(t2, e2) {
            const n2 = this.gl;
            n2.vertexAttribPointer(t2, 3, n2.FLOAT, false, 20, 0), n2.enableVertexAttribArray(t2), -1 !== e2 && (n2.vertexAttribPointer(e2, 2, n2.FLOAT, false, 20, 12), n2.enableVertexAttribArray(e2)), this.checkError();
          }
          createProgram(t2, e2) {
            const n2 = this.gl, r2 = n2.createProgram();
            return n2.attachShader(r2, t2), n2.attachShader(r2, e2), n2.linkProgram(r2), r2;
          }
          compileShader(t2, e2) {
            const n2 = this.gl, r2 = n2.createShader(e2);
            if (!r2) throw new Error(`createShader() returned null with type ${e2}`);
            if (n2.shaderSource(r2, t2), n2.compileShader(r2), false === n2.getShaderParameter(r2, n2.COMPILE_STATUS)) throw new Error(`Failed to compile shader: ${n2.getShaderInfoLog(r2)}
Shader source:
${t2}`);
            return r2;
          }
          deleteShader(t2) {
            this.gl.deleteShader(t2);
          }
          bindTextureToUniform(t2, e2, n2) {
            const r2 = this.gl;
            r2.activeTexture(r2.TEXTURE0 + e2), this.checkError(), r2.bindTexture(r2.TEXTURE_2D, t2), this.checkError(), r2.uniform1i(n2, e2), this.checkError();
          }
          draw() {
            this.gl.drawArrays(this.gl.TRIANGLE_STRIP, 0, 4), this.checkError();
          }
          checkError() {
            if (a.env.debug) {
              const t2 = this.gl, e2 = t2.getError();
              let n2 = "";
              switch (e2) {
                case t2.NO_ERROR:
                  return;
                case t2.INVALID_ENUM:
                  n2 = "INVALID_ENUM";
                  break;
                case t2.INVALID_VALUE:
                  n2 = "INVALID_VALUE";
                  break;
                case t2.INVALID_OPERATION:
                  n2 = "INVALID_OPERATION";
                  break;
                case t2.INVALID_FRAMEBUFFER_OPERATION:
                  n2 = "INVALID_FRAMEBUFFER_OPERATION";
                  break;
                case t2.OUT_OF_MEMORY:
                  n2 = "OUT_OF_MEMORY";
                  break;
                case t2.CONTEXT_LOST_WEBGL:
                  n2 = "CONTEXT_LOST_WEBGL";
                  break;
                default:
                  n2 = `Unknown WebGL Error: ${e2.toString(16)}`;
              }
              throw new Error(n2);
            }
          }
          deleteTexture(t2) {
            this.gl.deleteTexture(t2);
          }
          deleteProgram(t2) {
            this.gl.deleteProgram(t2);
          }
          getEncoder(t2, e2, n2 = 0) {
            if (2 === this.version) return new s.RedFloat32DataEncoder(this.gl, e2);
            switch (t2) {
              case "float":
                return 1 === n2 || this.isRenderFloat32Supported ? new s.RGBAFloatDataEncoder(this.gl, e2) : new s.RGBAFloatDataEncoder(this.gl, e2, this.textureHalfFloatExtension.HALF_FLOAT_OES);
              case "int":
                throw new Error("not implemented");
              case "byte":
                return new s.Uint8DataEncoder(this.gl, e2);
              default:
                throw new Error(`Invalid dataType: ${t2}`);
            }
          }
          clearActiveTextures() {
            const t2 = this.gl;
            for (let e2 = 0; e2 < this.maxTextureImageUnits; ++e2) t2.activeTexture(t2.TEXTURE0 + e2), t2.bindTexture(t2.TEXTURE_2D, null);
          }
          dispose() {
            if (this.disposed) return;
            const t2 = this.gl;
            t2.bindFramebuffer(t2.FRAMEBUFFER, null), t2.deleteFramebuffer(this.framebuffer), t2.bindBuffer(t2.ARRAY_BUFFER, null), t2.deleteBuffer(this.vertexbuffer), t2.bindBuffer(t2.ELEMENT_ARRAY_BUFFER, null), t2.finish(), this.disposed = true;
          }
          createDefaultGeometry() {
            return new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);
          }
          createVertexbuffer() {
            const t2 = this.gl, e2 = t2.createBuffer();
            if (!e2) throw new Error("createBuffer() returned null");
            const n2 = this.createDefaultGeometry();
            return t2.bindBuffer(t2.ARRAY_BUFFER, e2), t2.bufferData(t2.ARRAY_BUFFER, n2, t2.STATIC_DRAW), this.checkError(), e2;
          }
          createFramebuffer() {
            const t2 = this.gl.createFramebuffer();
            if (!t2) throw new Error("createFramebuffer returned null");
            return t2;
          }
          queryVitalParameters() {
            const t2 = this.gl;
            if (this.isFloatTextureAttachableToFrameBuffer = this.checkFloatTextureAttachableToFrameBuffer(), this.isRenderFloat32Supported = this.checkRenderFloat32(), this.isFloat32DownloadSupported = this.checkFloat32Download(), 1 === this.version && !this.textureHalfFloatExtension && !this.isRenderFloat32Supported) throw new Error("both float32 and float16 TextureType are not supported");
            this.isBlendSupported = !this.isRenderFloat32Supported || this.checkFloat32Blend(), this.maxTextureSize = t2.getParameter(t2.MAX_TEXTURE_SIZE), this.maxTextureImageUnits = t2.getParameter(t2.MAX_TEXTURE_IMAGE_UNITS), this.version;
          }
          getExtensions() {
            2 === this.version ? (this.colorBufferFloatExtension = this.gl.getExtension("EXT_color_buffer_float"), this.disjointTimerQueryWebgl2Extension = this.gl.getExtension("EXT_disjoint_timer_query_webgl2")) : (this.textureFloatExtension = this.gl.getExtension("OES_texture_float"), this.textureHalfFloatExtension = this.gl.getExtension("OES_texture_half_float"));
          }
          checkFloatTextureAttachableToFrameBuffer() {
            const t2 = this.gl, e2 = t2.createTexture();
            t2.bindTexture(t2.TEXTURE_2D, e2);
            const n2 = 2 === this.version ? t2.RGBA32F : t2.RGBA;
            t2.texImage2D(t2.TEXTURE_2D, 0, n2, 1, 1, 0, t2.RGBA, t2.FLOAT, null);
            const r2 = t2.createFramebuffer();
            t2.bindFramebuffer(t2.FRAMEBUFFER, r2), t2.framebufferTexture2D(t2.FRAMEBUFFER, t2.COLOR_ATTACHMENT0, t2.TEXTURE_2D, e2, 0);
            const i2 = t2.checkFramebufferStatus(t2.FRAMEBUFFER) === t2.FRAMEBUFFER_COMPLETE;
            return t2.bindTexture(t2.TEXTURE_2D, null), t2.bindFramebuffer(t2.FRAMEBUFFER, null), t2.deleteTexture(e2), t2.deleteFramebuffer(r2), i2;
          }
          checkRenderFloat32() {
            if (2 === this.version) {
              if (!this.colorBufferFloatExtension) return false;
            } else if (!this.textureFloatExtension) return false;
            return this.isFloatTextureAttachableToFrameBuffer;
          }
          checkFloat32Download() {
            if (2 === this.version) {
              if (!this.colorBufferFloatExtension) return false;
            } else {
              if (!this.textureFloatExtension) return false;
              if (!this.gl.getExtension("WEBGL_color_buffer_float")) return false;
            }
            return this.isFloatTextureAttachableToFrameBuffer;
          }
          checkFloat32Blend() {
            const t2 = this.gl;
            let e2, n2, r2, i2, o2;
            try {
              e2 = t2.createTexture(), n2 = t2.createFramebuffer(), t2.bindTexture(t2.TEXTURE_2D, e2);
              const a2 = 2 === this.version ? t2.RGBA32F : t2.RGBA;
              return t2.texImage2D(t2.TEXTURE_2D, 0, a2, 1, 1, 0, t2.RGBA, t2.FLOAT, null), t2.bindFramebuffer(t2.FRAMEBUFFER, n2), t2.framebufferTexture2D(t2.FRAMEBUFFER, t2.COLOR_ATTACHMENT0, t2.TEXTURE_2D, e2, 0), t2.enable(t2.BLEND), r2 = t2.createShader(t2.VERTEX_SHADER), !!r2 && (t2.shaderSource(r2, "void main(){}"), t2.compileShader(r2), i2 = t2.createShader(t2.FRAGMENT_SHADER), !!i2 && (t2.shaderSource(i2, "precision highp float;void main(){gl_FragColor=vec4(0.5);}"), t2.compileShader(i2), o2 = t2.createProgram(), !!o2 && (t2.attachShader(o2, r2), t2.attachShader(o2, i2), t2.linkProgram(o2), t2.useProgram(o2), t2.drawArrays(t2.POINTS, 0, 1), t2.getError() === t2.NO_ERROR)));
            } finally {
              t2.disable(t2.BLEND), o2 && t2.deleteProgram(o2), r2 && t2.deleteShader(r2), i2 && t2.deleteShader(i2), n2 && (t2.bindFramebuffer(t2.FRAMEBUFFER, null), t2.deleteFramebuffer(n2)), e2 && (t2.bindTexture(t2.TEXTURE_2D, null), t2.deleteTexture(e2));
            }
          }
          beginTimer() {
            if (2 === this.version && this.disjointTimerQueryWebgl2Extension) {
              const t2 = this.gl, e2 = this.disjointTimerQueryWebgl2Extension, n2 = t2.createQuery();
              return t2.beginQuery(e2.TIME_ELAPSED_EXT, n2), n2;
            }
            throw new Error("WebGL1 profiling currently not supported.");
          }
          endTimer() {
            if (2 !== this.version || !this.disjointTimerQueryWebgl2Extension) throw new Error("WebGL1 profiling currently not supported");
            {
              const t2 = this.gl, e2 = this.disjointTimerQueryWebgl2Extension;
              t2.endQuery(e2.TIME_ELAPSED_EXT);
            }
          }
          isTimerResultAvailable(t2) {
            let e2 = false, n2 = false;
            if (2 !== this.version || !this.disjointTimerQueryWebgl2Extension) throw new Error("WebGL1 profiling currently not supported");
            {
              const r2 = this.gl, i2 = this.disjointTimerQueryWebgl2Extension;
              e2 = r2.getQueryParameter(t2, r2.QUERY_RESULT_AVAILABLE), n2 = r2.getParameter(i2.GPU_DISJOINT_EXT);
            }
            return e2 && !n2;
          }
          getTimerResult(t2) {
            let e2 = 0;
            if (2 !== this.version) throw new Error("WebGL1 profiling currently not supported");
            {
              const n2 = this.gl;
              e2 = n2.getQueryParameter(t2, n2.QUERY_RESULT), n2.deleteQuery(t2);
            }
            return e2 / 1e6;
          }
          waitForQueryAndGetTime(t2) {
            return __async(this, null, function* () {
              return yield (0, u.repeatedTry)(() => this.isTimerResultAvailable(t2)), this.getTimerResult(t2);
            });
          }
          createAndWaitForFence() {
            return __async(this, null, function* () {
              const t2 = this.createFence(this.gl);
              return this.pollFence(t2);
            });
          }
          createFence(t2) {
            let e2;
            const n2 = t2, r2 = n2.fenceSync(n2.SYNC_GPU_COMMANDS_COMPLETE, 0);
            return t2.flush(), e2 = null === r2 ? () => true : () => {
              const t3 = n2.clientWaitSync(r2, 0, 0);
              return t3 === n2.ALREADY_SIGNALED || t3 === n2.CONDITION_SATISFIED;
            }, { query: r2, isFencePassed: e2 };
          }
          pollFence(t2) {
            return __async(this, null, function* () {
              return new Promise((e2) => {
                this.addItemToPoll(() => t2.isFencePassed(), () => e2());
              });
            });
          }
          pollItems() {
            const t2 = c(this.itemsToPoll.map((t3) => t3.isDoneFn));
            for (let e2 = 0; e2 <= t2; ++e2) {
              const { resolveFn: t3 } = this.itemsToPoll[e2];
              t3();
            }
            this.itemsToPoll = this.itemsToPoll.slice(t2 + 1);
          }
          addItemToPoll(t2, e2) {
            return __async(this, null, function* () {
              this.itemsToPoll.push({ isDoneFn: t2, resolveFn: e2 }), this.itemsToPoll.length > 1 || (yield (0, u.repeatedTry)(() => (this.pollItems(), 0 === this.itemsToPoll.length)));
            });
          }
        };
      }, 1036: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.ExecutionPlan = void 0;
        const r = n(6231);
        class i {
          constructor(t2, e2) {
            this.op = t2, this.node = e2;
          }
        }
        e.ExecutionPlan = class {
          constructor(t2, e2, n2) {
            this.graph = t2, this.profiler = n2, this.initialize(e2);
          }
          initialize(t2) {
            this.profiler.event("session", "ExecutionPlan.initialize", () => {
              const e2 = this.graph.getNodes();
              if (e2.length !== t2.length) throw new Error("The size of nodes and OPs do not match.");
              this._ops = t2.map((t3, n2) => new i(t3, e2[n2])), this.reset(), this._starter = [], this._ops.forEach((t3, e3) => {
                let n2 = true;
                for (const e4 of t3.node.inputs) if (!this._values[e4] && -1 === this.graph.getInputIndices().indexOf(e4)) {
                  n2 = false;
                  break;
                }
                n2 && this._starter.push(e3);
              });
            });
          }
          reset() {
            this._values = this.graph.getValues().map((t2) => t2.tensor);
          }
          execute(t2, e2) {
            return __async(this, null, function* () {
              return this.profiler.event("session", "ExecutionPlan.execute", () => __async(this, null, function* () {
                this.reset();
                const n2 = t2.createInferenceHandler(), i2 = this.graph.getInputIndices();
                if (e2.length !== i2.length) throw new Error(`number of input tensors don't match the number of inputs to the model: actual: ${e2.length} expected: ${i2.length}`);
                e2.forEach((t3, e3) => {
                  const n3 = i2[e3];
                  this._values[n3] = t3;
                });
                const o = this._starter.slice(0), a = this.graph.getValues(), s = this.graph.getNodes();
                let u = 0;
                for (; u < o.length; ) {
                  const t3 = o[u++], e3 = this._ops[t3], i3 = e3.node.inputs.map((t4) => this._values[t4]);
                  if (-1 !== i3.indexOf(void 0)) throw new Error(`unresolved input detected: op: ${e3.node}`);
                  const c2 = i3;
                  r.Logger.verbose("ExecPlan", `Runing op:${e3.node.name} (${c2.map((t4, n3) => `'${e3.node.inputs[n3]}': ${t4.type}[${t4.dims.join(",")}]`).join(", ")})`);
                  const l = yield this.profiler.event("node", e3.node.name, () => __async(this, null, function* () {
                    return e3.op.impl(n2, c2, e3.op.context);
                  }));
                  if (l.length !== e3.node.outputs.length) throw new Error("the size of output does not match model definition.");
                  l.forEach((t4, n3) => {
                    const r2 = e3.node.outputs[n3];
                    if (this._values[r2]) throw new Error(`output [${r2}] already has value: op:${e3.node.name}`);
                    this._values[r2] = t4;
                  });
                  const p = /* @__PURE__ */ new Set();
                  l.forEach((t4, n3) => {
                    const r2 = e3.node.outputs[n3];
                    for (const t5 of a[r2].to) {
                      const e4 = s[t5];
                      let n4 = true;
                      for (const t6 of e4.inputs) if (!this._values[t6]) {
                        n4 = false;
                        break;
                      }
                      n4 && p.add(t5);
                    }
                  }), o.push(...p);
                }
                const c = [];
                for (let t3 = 0; t3 < this.graph.getOutputIndices().length; t3++) {
                  const e3 = this.graph.getOutputIndices()[t3], n3 = this._values[e3];
                  if (void 0 === n3) throw new Error(`required output [${e3}] does not have value`);
                  0 === e3 ? yield n3.getData() : n3.data, c.push(n3);
                }
                return r.Logger.verbose("ExecPlan", "disposing of inferenceHandler"), n2.dispose(), c;
              }));
            });
          }
        };
      }, 7070: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.Graph = void 0;
        const r = n(1446), i = n(7778), o = n(9395), a = n(9162), s = n(2517);
        var u = o.onnxruntime.experimental.fbs;
        e.Graph = { from: (t2, e2) => new p(t2, e2) };
        class c {
          constructor(t2) {
            this._from = void 0, this._to = [], this.tensor = void 0, this.type = void 0, t2 && (this.type = s.ProtoUtil.tensorValueTypeFromProto(t2.type.tensorType));
          }
          get from() {
            return this._from;
          }
          get to() {
            return this._to;
          }
        }
        class l {
          constructor(t2, e2) {
            t2 instanceof r.onnx.NodeProto ? (this.name = t2.name, this.opType = t2.opType, this.attributes = new i.Attribute(t2.attribute)) : t2 instanceof u.Node && (this.name = null != e2 ? e2 : t2.name(), this.opType = t2.opType(), this.attributes = new i.Attribute(s.ProtoUtil.tensorAttributesFromORTFormat(t2))), this.inputs = [], this.outputs = [], this.executeNode = true;
          }
        }
        class p {
          constructor(t2, e2) {
            if (!t2) throw new TypeError("graph is empty");
            this.buildGraph(t2), this.transformGraph(e2), this.checkIsAcyclic();
          }
          getInputIndices() {
            return this._allInputIndices;
          }
          getInputNames() {
            return this._allInputNames;
          }
          getOutputIndices() {
            return this._allOutputIndices;
          }
          getOutputNames() {
            return this._allOutputNames;
          }
          getValues() {
            return this._allData;
          }
          getNodes() {
            return this._nodes;
          }
          buildGraph(t2) {
            if (t2 instanceof r.onnx.GraphProto) this.buildGraphFromOnnxFormat(t2);
            else {
              if (!(t2 instanceof u.Graph)) throw new TypeError("Graph type is not supported.");
              this.buildGraphFromOrtFormat(t2);
            }
          }
          buildGraphFromOnnxFormat(t2) {
            const e2 = /* @__PURE__ */ new Map();
            this._allData = [], this._allInputIndices = [], this._allInputNames = [], this._allOutputIndices = [], this._allOutputNames = [], this._nodes = [];
            const n2 = /* @__PURE__ */ new Map();
            if (!t2.input) throw new Error("missing information in graph: input");
            const r2 = [];
            for (const n3 of t2.input) {
              if (e2.has(n3.name)) throw new Error(`duplicated input name: ${n3.name}`);
              const t3 = this._allData.push(new c(n3)) - 1;
              e2.set(n3.name, t3), r2.push(n3.name);
            }
            if (!t2.initializer) throw new Error("missing information in graph: initializer");
            for (const n3 of t2.initializer) {
              let t3 = e2.get(n3.name);
              if (void 0 === t3) {
                const r3 = new c();
                r3.type = { shape: { dims: s.ProtoUtil.tensorDimsFromProto(n3.dims) }, tensorType: s.ProtoUtil.tensorDataTypeFromProto(n3.dataType) }, t3 = this._allData.push(r3) - 1, e2.set(n3.name, t3);
              }
              this._allData[t3]._from = -1, this._allData[t3].tensor = a.Tensor.fromProto(n3);
            }
            for (let t3 = 0; t3 < this._allData.length; t3++) this._allData[t3].tensor || (this._allInputIndices.push(t3), this._allInputNames.push(r2[t3]));
            if (!t2.output) throw new Error("missing information in graph: output");
            for (const n3 of t2.output) {
              if (e2.has(n3.name)) throw new Error(`duplicated output name: ${n3.name}`);
              const t3 = this._allData.push(new c(n3)) - 1;
              e2.set(n3.name, t3), this._allOutputIndices.push(t3), this._allOutputNames.push(n3.name);
            }
            if (!t2.node) throw new Error("missing information in graph: node");
            for (const e3 of t2.node) {
              if (!e3.name) for (let t4 = 0; ; t4++) {
                const r3 = `unnamed_${e3.opType}_${t4}`;
                if (!n2.has(r3)) {
                  e3.name = r3;
                  break;
                }
              }
              if (n2.has(e3.name)) throw new Error(`duplicated node name: ${e3.name}`);
              const t3 = this._nodes.push(new l(e3)) - 1;
              n2.set(e3.name, t3);
            }
            for (let n3 = 0; n3 < this._nodes.length; n3++) {
              const r3 = this._nodes[n3], i2 = t2.node[n3];
              if (!i2.output) throw new Error(`missing output for node: ${i2.name}`);
              for (const t3 of i2.output) {
                let o2 = e2.get(t3);
                if (void 0 === o2 && (o2 = this._allData.push(new c()) - 1, e2.set(t3, o2)), r3.outputs.push(o2), void 0 !== this._allData[o2]._from) throw new Error(`multiple nodes output to one data value: ${o2}`);
                if (this._allData[o2]._from = n3, "Constant" === i2.opType) {
                  if (!i2.attribute || 1 !== i2.attribute.length || !i2.attribute[0].t) throw new Error("missing attributes or missing tensor value in attributes for this Constant operator");
                  if (!i2.output || 1 !== i2.output.length) throw new Error("missing output or incorrect number of outputs for this Constant operator");
                  r3.outputs.pop(), r3.executeNode = false, this._allData[o2]._from = -1, this._allData[o2].tensor = a.Tensor.fromProto(i2.attribute[0].t);
                }
              }
            }
            for (let n3 = 0; n3 < this._nodes.length; n3++) {
              const r3 = this._nodes[n3], i2 = t2.node[n3];
              if (!i2.input) throw new Error(`missing input for node: ${i2.name}`);
              for (const t3 of i2.input) {
                const o2 = e2.get(t3);
                if (void 0 === o2) {
                  if ("" === t3 && 3 === i2.input.length && "Resize" === i2.opType) continue;
                  throw new Error(`unrecognized input '${t3}' for node: ${i2.name}`);
                }
                r3.inputs.push(o2), this._allData[o2]._to.push(n3);
              }
            }
            return true;
          }
          buildGraphFromOrtFormat(t2) {
            var e2, n2, r2;
            const i2 = /* @__PURE__ */ new Map();
            this._allData = [], this._allInputIndices = [], this._allInputNames = [], this._allOutputIndices = [], this._allOutputNames = [], this._nodes = [];
            const o2 = /* @__PURE__ */ new Map(), p2 = [];
            for (let o3 = 0; o3 < t2.inputsLength(); o3++) {
              const a2 = t2.inputs(o3);
              if (i2.has(a2)) throw new Error(`duplicated input name: ${a2}`);
              for (let o4 = 0; o4 < t2.nodeArgsLength(); o4++) if ((null === (e2 = t2.nodeArgs(o4)) || void 0 === e2 ? void 0 : e2.name()) === a2) {
                const e3 = new c();
                if ((null === (r2 = null === (n2 = t2.nodeArgs(o4)) || void 0 === n2 ? void 0 : n2.type()) || void 0 === r2 ? void 0 : r2.valueType()) !== u.TypeInfoValue.tensor_type) throw new Error("Unexpected value type for the nodeArg.");
                const l2 = t2.nodeArgs(o4).type().value(new u.TensorTypeAndShape()), f = s.ProtoUtil.tensorDataTypeFromProto(l2.elemType()), d = l2.shape(), h = [];
                for (let t3 = 0; t3 < d.dimLength(); t3++) h.push(s.LongUtil.longToNumber(d.dim(t3).value().dimValue()));
                e3.type = { shape: { dims: h }, tensorType: f };
                const g = this._allData.push(e3) - 1;
                i2.set(a2, g), p2.push(a2);
              }
            }
            for (let e3 = 0; e3 < t2.initializersLength(); e3++) {
              const n3 = t2.initializers(e3);
              let r3 = i2.get(n3.name());
              if (void 0 === r3) {
                const t3 = new c(), e4 = s.ProtoUtil.tensorDimsFromORTFormat(n3), o3 = s.ProtoUtil.tensorDataTypeFromProto(n3.dataType());
                t3.type = { shape: { dims: e4 }, tensorType: o3 }, r3 = this._allData.push(t3) - 1, i2.set(n3.name(), r3);
              }
              this._allData[r3]._from = -1, this._allData[r3].tensor = a.Tensor.fromOrtTensor(n3);
            }
            for (let t3 = 0; t3 < this._allData.length; t3++) this._allData[t3].tensor || (this._allInputIndices.push(t3), this._allInputNames.push(p2[t3]));
            for (let e3 = 0; e3 < t2.outputsLength(); e3++) {
              const n3 = t2.outputs(e3);
              if (i2.has(n3)) throw new Error(`duplicated output name: ${n3}`);
              const r3 = this._allData.push(new c()) - 1;
              i2.set(n3, r3), this._allOutputIndices.push(r3), this._allOutputNames.push(n3);
            }
            if (!t2.nodes) throw new Error("missing information in graph: node");
            for (let e3 = 0; e3 < t2.nodesLength(); e3++) {
              const n3 = t2.nodes(e3);
              let r3 = n3.name();
              if (!r3) for (let t3 = 0; r3 = `unnamed_${n3.opType()}_${t3}`, o2.has(r3); t3++) ;
              if (o2.has(r3)) throw new Error(`duplicated node name: ${r3}`);
              const i3 = this._nodes.push(new l(n3, r3)) - 1;
              o2.set(r3, i3);
            }
            for (let e3 = 0; e3 < this._nodes.length; e3++) {
              const n3 = this._nodes[e3], r3 = t2.nodes(e3);
              if (null == r3) throw new Error(`No node exists at index ${e3}`);
              if (0 === (null == r3 ? void 0 : r3.outputsLength())) throw new Error(`missing output for node: ${r3.name}`);
              for (let t3 = 0; t3 < (null == r3 ? void 0 : r3.outputsLength()); t3++) {
                const o3 = null == r3 ? void 0 : r3.outputs(t3);
                let s2 = i2.get(o3);
                if (void 0 === s2 && (s2 = this._allData.push(new c()) - 1, i2.set(o3, s2)), n3.outputs.push(s2), void 0 !== this._allData[s2]._from) throw new Error(`multiple nodes output to one data value: ${s2}`);
                if (this._allData[s2]._from = e3, "Constant" === r3.opType()) {
                  if (1 !== r3.attributesLength() || !r3.attributes(0).t()) throw new Error("missing attributes or missing tensor value in attributes for this Constant operator");
                  if (1 !== r3.outputsLength()) throw new Error("missing output or incorrect number of outputs for this Constant operator");
                  n3.outputs.pop(), n3.executeNode = false, this._allData[s2]._from = -1, this._allData[s2].tensor = a.Tensor.fromOrtTensor(r3.attributes(0).t());
                }
              }
            }
            for (let e3 = 0; e3 < this._nodes.length; e3++) {
              const n3 = this._nodes[e3], r3 = t2.nodes(e3);
              if (0 === r3.inputsLength()) throw new Error(`missing input for node: ${r3.name}`);
              for (let t3 = 0; t3 < r3.inputsLength(); t3++) {
                const o3 = r3.inputs(t3), a2 = i2.get(o3);
                if (void 0 === a2) throw new Error(`unrecognized input '${o3}' for node: ${r3.name()}`);
                n3.inputs.push(a2), this._allData[a2]._to.push(e3);
              }
            }
          }
          checkIsAcyclic() {
            const t2 = /* @__PURE__ */ new Set();
            this._allInputIndices.forEach((e3) => {
              this._allData[e3]._to.forEach((e4) => {
                t2.add(e4);
              });
            });
            const e2 = Array.from(t2), n2 = new Array(this._nodes.length).fill("white");
            for (; e2.length > 0; ) {
              const t3 = e2.pop();
              "gray" === n2[t3] ? n2[t3] = "black" : (e2.push(t3), n2[t3] = "gray", this._nodes[t3].outputs.forEach((r2) => {
                const i2 = this._allData[r2];
                if (void 0 !== i2.tensor) throw new Error("node outputs should not be initialized");
                if (i2._from !== t3) throw new Error("from property of the Value object doesn't match index of Node being processed");
                i2._to.forEach((t4) => {
                  if ("gray" === n2[t4]) throw new Error("model graph is cyclic");
                  "white" === n2[t4] && e2.push(t4);
                });
              }));
            }
          }
          transformGraph(t2) {
            this.removeAllIdentityNodes(), this.removeAllDropoutNodes(), this.fuseConvActivationNodes(), t2 && t2.transformGraph(this), this.finalizeGraph();
          }
          finalizeGraph() {
            let t2 = 0;
            for (let e2 = 0; e2 < this._nodes.length; e2++) this._nodes[e2].executeNode ? t2 > 0 && (this._nodes[e2].inputs.forEach((n2) => {
              const r2 = this._allData[n2]._to.indexOf(e2 + t2);
              -1 !== r2 && (this._allData[n2]._to[r2] = e2);
            }), this._nodes[e2].outputs.forEach((n2) => {
              this._allData[n2]._from && this._allData[n2]._from === e2 + t2 && (this._allData[n2]._from = e2);
            })) : (t2++, this._nodes[e2].outputs.forEach((t3) => {
              this._allData[t3]._from = -2;
            }), this._nodes.splice(e2, 1), e2--);
            t2 = 0;
            for (let e2 = 0; e2 < this._allData.length; e2++) if (-2 !== this._allData[e2].from || -1 !== this._allOutputIndices.indexOf(e2 + t2)) {
              if (t2 > 0) {
                let n2 = -1;
                void 0 !== this._allData[e2].from && -1 !== this._allData[e2].from ? (n2 = this._nodes[this._allData[e2].from].outputs.indexOf(e2 + t2), -1 !== n2 && (this._nodes[this._allData[e2].from].outputs[n2] = e2)) : (n2 = this._allInputIndices.indexOf(e2 + t2), -1 !== n2 && (this._allInputIndices[n2] = e2)), this._allData[e2].to.forEach((r2) => {
                  n2 = this._nodes[r2].inputs.indexOf(e2 + t2), -1 !== n2 && (this._nodes[r2].inputs[n2] = e2);
                }), 0 === this._allData[e2].to.length && (n2 = this._allOutputIndices.indexOf(e2 + t2), -1 !== n2 && (this._allOutputIndices[n2] = e2));
              }
            } else t2++, this._allData.splice(e2, 1), e2--;
          }
          deleteNode(t2) {
            const e2 = this._nodes[t2];
            if (e2.outputs.length > 1) {
              for (let t3 = 1; t3 < e2.outputs.length; t3++) if (this._allData[e2.outputs[t3]].to.length > 0) throw new Error("Node deletion with more than one output connected to other nodes is not supported. ");
            }
            e2.executeNode = false;
            const n2 = e2.inputs[0], r2 = e2.outputs[0], i2 = this._allData[r2].to, o2 = this._allData[n2].to.indexOf(t2);
            if (-1 === o2) throw new Error("The Value object doesn't have the current Node in it's 'to' property ");
            this._allData[n2].to.splice(o2, 1), this._allData[r2]._to = [];
            const a2 = this._allOutputIndices.indexOf(r2);
            if (-1 !== a2 && (this._allOutputIndices[a2] = n2), i2 && i2.length > 0) for (const t3 of i2) {
              const e3 = this._nodes[t3].inputs.indexOf(r2);
              if (-1 === e3) throw new Error("The Node object doesn't have the output Value in it's 'inputs' property ");
              this._nodes[t3].inputs[e3] = n2, this._allData[n2].to.push(t3);
            }
          }
          removeAllDropoutNodes() {
            let t2 = 0;
            for (const e2 of this._nodes) {
              if ("Dropout" === e2.opType) {
                if (1 !== e2.inputs.length) throw new Error("Dropout nodes should only contain one input. ");
                if (1 !== e2.outputs.length && 2 !== e2.outputs.length) throw new Error("Dropout nodes should contain either 1 or 2 output(s)");
                if (2 === e2.outputs.length && 0 !== this._allData[e2.outputs[1]]._to.length) throw new Error("Dropout nodes's second output should not be referenced by other nodes");
                this.deleteNode(t2);
              }
              t2++;
            }
          }
          removeAllIdentityNodes() {
            let t2 = 0;
            for (const e2 of this._nodes) "Identity" === e2.opType && this.deleteNode(t2), t2++;
          }
          isActivation(t2) {
            switch (t2.opType) {
              case "Relu":
              case "Sigmoid":
              case "Clip":
                return true;
              default:
                return false;
            }
          }
          fuseConvActivationNodes() {
            for (const t2 of this._nodes) if ("Conv" === t2.opType) {
              const e2 = this._allData[t2.outputs[0]]._to;
              if (1 === e2.length && this.isActivation(this._nodes[e2[0]])) {
                const n2 = this._nodes[e2[0]];
                if ("Clip" === n2.opType) if (1 === n2.inputs.length) try {
                  t2.attributes.set("activation_params", "floats", [n2.attributes.getFloat("min"), n2.attributes.getFloat("max")]);
                } catch (e3) {
                  t2.attributes.set("activation_params", "floats", [s.MIN_CLIP, s.MAX_CLIP]);
                }
                else {
                  if (!(n2.inputs.length >= 3 && void 0 !== this._allData[n2.inputs[1]].tensor && void 0 !== this._allData[n2.inputs[2]].tensor)) continue;
                  t2.attributes.set("activation_params", "floats", [this._allData[n2.inputs[1]].tensor.floatData[0], this._allData[n2.inputs[2]].tensor.floatData[0]]);
                }
                t2.attributes.set("activation", "string", n2.opType), this.deleteNode(e2[0]);
              }
            }
          }
        }
      }, 6231: (t, e) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.now = e.Profiler = e.Logger = void 0;
        const n = { verbose: 1e3, info: 2e3, warning: 4e3, error: 5e3, fatal: 6e3 }, r = { none: new class {
          log(t2, e2, n2) {
          }
        }(), console: new class {
          log(t2, e2, n2) {
            console.log(`${this.color(t2)} ${n2 ? "\x1B[35m" + n2 + "\x1B[0m " : ""}${e2}`);
          }
          color(t2) {
            switch (t2) {
              case "verbose":
                return "\x1B[34;40mv\x1B[0m";
              case "info":
                return "\x1B[32mi\x1B[0m";
              case "warning":
                return "\x1B[30;43mw\x1B[0m";
              case "error":
                return "\x1B[31;40me\x1B[0m";
              case "fatal":
                return "\x1B[101mf\x1B[0m";
              default:
                throw new Error(`unsupported severity: ${t2}`);
            }
          }
        }() }, i = { provider: "console", minimalSeverity: "warning", logDateTime: true, logSourceLocation: false };
        let o = { "": i };
        function a(t2, e2, n2, r2) {
          if (void 0 === e2) return i2 = t2, { verbose: a.verbose.bind(null, i2), info: a.info.bind(null, i2), warning: a.warning.bind(null, i2), error: a.error.bind(null, i2), fatal: a.fatal.bind(null, i2) };
          if (void 0 === n2) s(t2, e2);
          else if ("number" == typeof n2 && void 0 === r2) s(t2, e2);
          else if ("string" == typeof n2 && void 0 === r2) s(t2, n2, 0, e2);
          else {
            if ("string" != typeof n2 || "number" != typeof r2) throw new TypeError("input is valid");
            s(t2, n2, 0, e2);
          }
          var i2;
        }
        function s(t2, e2, i2, a2) {
          const s2 = o[a2 || ""] || o[""];
          n[t2] < n[s2.minimalSeverity] || (s2.logDateTime && (e2 = `${(/* @__PURE__ */ new Date()).toISOString()}|${e2}`), s2.logSourceLocation, r[s2.provider].log(t2, e2, a2));
        }
        !function(t2) {
          function e2(t3) {
            o = {}, n2("", t3 || {});
          }
          function n2(t3, n3) {
            if ("*" === t3) e2(n3);
            else {
              const e3 = o[t3] || i;
              o[t3] = { provider: n3.provider || e3.provider, minimalSeverity: n3.minimalSeverity || e3.minimalSeverity, logDateTime: void 0 === n3.logDateTime ? e3.logDateTime : n3.logDateTime, logSourceLocation: void 0 === n3.logSourceLocation ? e3.logSourceLocation : n3.logSourceLocation };
            }
          }
          t2.verbose = function(e3, n3) {
            t2("verbose", e3, n3);
          }, t2.info = function(e3, n3) {
            t2("info", e3, n3);
          }, t2.warning = function(e3, n3) {
            t2("warning", e3, n3);
          }, t2.error = function(e3, n3) {
            t2("error", e3, n3);
          }, t2.fatal = function(e3, n3) {
            t2("fatal", e3, n3);
          }, t2.reset = e2, t2.set = n2, t2.setWithEnv = function(t3) {
            const e3 = {};
            t3.logLevel && (e3.minimalSeverity = t3.logLevel), n2("", e3);
          };
        }(a || (a = {})), e.Logger = a;
        class u {
          constructor(t2, e2, n2, r2, i2, o2) {
            this.category = t2, this.name = e2, this.startTime = n2, this.endCallback = r2, this.timer = i2, this.ctx = o2;
          }
          end() {
            return this.endCallback(this);
          }
          checkTimer() {
            return __async(this, null, function* () {
              if (void 0 === this.ctx || void 0 === this.timer) throw new Error("No webgl timer found");
              return this.ctx.endTimer(), this.ctx.waitForQueryAndGetTime(this.timer);
            });
          }
        }
        class c {
          constructor(t2, e2, n2, r2) {
            this.category = t2, this.name = e2, this.startTime = n2, this.endTime = r2;
          }
        }
        e.Profiler = class {
          static create(t2) {
            return void 0 === t2 ? new this() : new this(t2.maxNumberEvents, t2.flushBatchSize, t2.flushIntervalInMilliseconds);
          }
          constructor(t2, e2, n2) {
            this._started = false, this._flushPointer = 0, this._started = false, this._maxNumberEvents = void 0 === t2 ? 1e4 : t2, this._flushBatchSize = void 0 === e2 ? 10 : e2, this._flushIntervalInMilliseconds = void 0 === n2 ? 5e3 : n2;
          }
          start() {
            this._started = true, this._timingEvents = [], this._flushTime = (0, e.now)(), this._flushPointer = 0;
          }
          stop() {
            for (this._started = false; this._flushPointer < this._timingEvents.length; this._flushPointer++) this.logOneEvent(this._timingEvents[this._flushPointer]);
          }
          event(t2, e2, n2, r2) {
            const i2 = this._started ? this.begin(t2, e2, r2) : void 0;
            let o2 = false;
            const a2 = n2();
            if (a2 && "function" == typeof a2.then) return o2 = true, new Promise((t3, e3) => {
              a2.then((e4) => __async(this, null, function* () {
                i2 && (yield i2.end()), t3(e4);
              }), (t4) => __async(this, null, function* () {
                i2 && (yield i2.end()), e3(t4);
              }));
            });
            if (!o2 && i2) {
              const t3 = i2.end();
              if (t3 && "function" == typeof t3.then) return new Promise((e3, n3) => {
                t3.then(() => {
                  e3(a2);
                }, (t4) => {
                  n3(t4);
                });
              });
            }
            return a2;
          }
          begin(t2, n2, r2) {
            if (!this._started) throw new Error("profiler is not started yet");
            if (void 0 === r2) {
              const r3 = (0, e.now)();
              return this.flush(r3), new u(t2, n2, r3, (t3) => this.endSync(t3));
            }
            {
              const e2 = r2.beginTimer();
              return new u(t2, n2, 0, (t3) => __async(this, null, function* () {
                return this.end(t3);
              }), e2, r2);
            }
          }
          end(t2) {
            return __async(this, null, function* () {
              const e2 = yield t2.checkTimer();
              this._timingEvents.length < this._maxNumberEvents && (this._timingEvents.push(new c(t2.category, t2.name, t2.startTime, e2)), this.flush(e2));
            });
          }
          endSync(t2) {
            const n2 = (0, e.now)();
            this._timingEvents.length < this._maxNumberEvents && (this._timingEvents.push(new c(t2.category, t2.name, t2.startTime, n2)), this.flush(n2));
          }
          logOneEvent(t2) {
            e.Logger.verbose(`Profiler.${t2.category}`, `${(t2.endTime - t2.startTime).toFixed(2)}ms on event '${t2.name}' at ${t2.endTime.toFixed(2)}`);
          }
          flush(t2) {
            if (this._timingEvents.length - this._flushPointer >= this._flushBatchSize || t2 - this._flushTime >= this._flushIntervalInMilliseconds) {
              for (const t3 = this._flushPointer; this._flushPointer < t3 + this._flushBatchSize && this._flushPointer < this._timingEvents.length; this._flushPointer++) this.logOneEvent(this._timingEvents[this._flushPointer]);
              this._flushTime = (0, e.now)();
            }
          }
          get started() {
            return this._started;
          }
        }, e.now = "undefined" != typeof performance && performance.now ? () => performance.now() : Date.now;
      }, 2644: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.Model = void 0;
        const r = n(5686), i = n(1446), o = n(7070), a = n(9395), s = n(2517);
        var u = a.onnxruntime.experimental.fbs;
        e.Model = class {
          constructor() {
          }
          load(t2, e2, n2) {
            if (!n2) try {
              return void this.loadFromOnnxFormat(t2, e2);
            } catch (t3) {
              if (void 0 !== n2) throw t3;
            }
            this.loadFromOrtFormat(t2, e2);
          }
          loadFromOnnxFormat(t2, e2) {
            const n2 = i.onnx.ModelProto.decode(t2);
            if (s.LongUtil.longToNumber(n2.irVersion) < 3) throw new Error("only support ONNX model with IR_VERSION>=3");
            this._opsets = n2.opsetImport.map((t3) => ({ domain: t3.domain, version: s.LongUtil.longToNumber(t3.version) })), this._graph = o.Graph.from(n2.graph, e2);
          }
          loadFromOrtFormat(t2, e2) {
            const n2 = new r.flatbuffers.ByteBuffer(t2), i2 = u.InferenceSession.getRootAsInferenceSession(n2).model();
            if (s.LongUtil.longToNumber(i2.irVersion()) < 3) throw new Error("only support ONNX model with IR_VERSION>=3");
            this._opsets = [];
            for (let t3 = 0; t3 < i2.opsetImportLength(); t3++) {
              const e3 = i2.opsetImport(t3);
              this._opsets.push({ domain: null == e3 ? void 0 : e3.domain(), version: s.LongUtil.longToNumber(e3.version()) });
            }
            this._graph = o.Graph.from(i2.graph(), e2);
          }
          get graph() {
            return this._graph;
          }
          get opsets() {
            return this._opsets;
          }
        };
      }, 782: (t, e) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.FLOAT_TYPES = e.INT_TYPES = e.NUMBER_TYPES = void 0, e.NUMBER_TYPES = ["float32", "float64", "int32", "int16", "int8", "uint16", "uint32", "uint8"], e.INT_TYPES = ["int32", "int16", "int8", "uint16", "uint32", "uint8"], e.FLOAT_TYPES = ["float32", "float64"];
      }, 1047: (t, e) => {
        function n(t2, e2) {
          if (e2.endsWith("+")) {
            const n2 = Number.parseInt(e2.substring(0, e2.length - 1), 10);
            return !isNaN(n2) && n2 <= t2;
          }
          if (2 === e2.split("-").length) {
            const n2 = e2.split("-"), r = Number.parseInt(n2[0], 10), i = Number.parseInt(n2[1], 10);
            return !isNaN(r) && !isNaN(i) && r <= t2 && t2 <= i;
          }
          return Number.parseInt(e2, 10) === t2;
        }
        Object.defineProperty(e, "__esModule", { value: true }), e.resolveOperator = void 0, e.resolveOperator = function(t2, e2, r) {
          for (const i of r) {
            const r2 = i[0], o = i[1], a = i[2], s = i[3], u = i[4];
            if (t2.opType === r2) {
              for (const t3 of e2) if ((t3.domain === o || "ai.onnx" === t3.domain && "" === o) && n(t3.version, a)) return { opImpl: s, opInit: u };
            }
          }
          throw new TypeError(`cannot resolve operator '${t2.opType}' with opsets: ${e2.map((t3) => `${t3.domain || "ai.onnx"} v${t3.version}`).join(", ")}`);
        };
      }, 9395: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.onnxruntime = void 0;
        const r = n(5686);
        var i, o;
        i = e.onnxruntime || (e.onnxruntime = {}), function(t2) {
          !function(t3) {
            t3[t3.UNDEFINED = 0] = "UNDEFINED", t3[t3.FLOAT = 1] = "FLOAT", t3[t3.INT = 2] = "INT", t3[t3.STRING = 3] = "STRING", t3[t3.TENSOR = 4] = "TENSOR", t3[t3.GRAPH = 5] = "GRAPH", t3[t3.FLOATS = 6] = "FLOATS", t3[t3.INTS = 7] = "INTS", t3[t3.STRINGS = 8] = "STRINGS", t3[t3.TENSORS = 9] = "TENSORS", t3[t3.GRAPHS = 10] = "GRAPHS", t3[t3.SPARSE_TENSOR = 11] = "SPARSE_TENSOR", t3[t3.SPARSE_TENSORS = 12] = "SPARSE_TENSORS";
          }(t2.AttributeType || (t2.AttributeType = {}));
        }((o = i.experimental || (i.experimental = {})).fbs || (o.fbs = {})), function(t2) {
          !function(t3) {
            !function(t4) {
              !function(t5) {
                t5[t5.UNKNOWN = 0] = "UNKNOWN", t5[t5.VALUE = 1] = "VALUE", t5[t5.PARAM = 2] = "PARAM";
              }(t4.DimensionValueType || (t4.DimensionValueType = {}));
            }(t3.fbs || (t3.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(t3) {
            !function(t4) {
              !function(t5) {
                t5[t5.UNDEFINED = 0] = "UNDEFINED", t5[t5.FLOAT = 1] = "FLOAT", t5[t5.UINT8 = 2] = "UINT8", t5[t5.INT8 = 3] = "INT8", t5[t5.UINT16 = 4] = "UINT16", t5[t5.INT16 = 5] = "INT16", t5[t5.INT32 = 6] = "INT32", t5[t5.INT64 = 7] = "INT64", t5[t5.STRING = 8] = "STRING", t5[t5.BOOL = 9] = "BOOL", t5[t5.FLOAT16 = 10] = "FLOAT16", t5[t5.DOUBLE = 11] = "DOUBLE", t5[t5.UINT32 = 12] = "UINT32", t5[t5.UINT64 = 13] = "UINT64", t5[t5.COMPLEX64 = 14] = "COMPLEX64", t5[t5.COMPLEX128 = 15] = "COMPLEX128", t5[t5.BFLOAT16 = 16] = "BFLOAT16";
              }(t4.TensorDataType || (t4.TensorDataType = {}));
            }(t3.fbs || (t3.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(t3) {
            !function(t4) {
              !function(t5) {
                t5[t5.Primitive = 0] = "Primitive", t5[t5.Fused = 1] = "Fused";
              }(t4.NodeType || (t4.NodeType = {}));
            }(t3.fbs || (t3.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(t3) {
            !function(t4) {
              !function(t5) {
                t5[t5.NONE = 0] = "NONE", t5[t5.tensor_type = 1] = "tensor_type", t5[t5.sequence_type = 2] = "sequence_type", t5[t5.map_type = 3] = "map_type";
              }(t4.TypeInfoValue || (t4.TypeInfoValue = {}));
            }(t3.fbs || (t3.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(e2) {
            !function(e3) {
              class n2 {
                constructor() {
                  this.bb = null, this.bb_pos = 0;
                }
                __init(t3, e4) {
                  return this.bb_pos = t3, this.bb = e4, this;
                }
                static getRootAsShape(t3, e4) {
                  return (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                static getSizePrefixedRootAsShape(t3, e4) {
                  return t3.setPosition(t3.position() + r.flatbuffers.SIZE_PREFIX_LENGTH), (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                dim(e4, n3) {
                  let r2 = this.bb.__offset(this.bb_pos, 4);
                  return r2 ? (n3 || new t2.experimental.fbs.Dimension()).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos + r2) + 4 * e4), this.bb) : null;
                }
                dimLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 4);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                static startShape(t3) {
                  t3.startObject(1);
                }
                static addDim(t3, e4) {
                  t3.addFieldOffset(0, e4, 0);
                }
                static createDimVector(t3, e4) {
                  t3.startVector(4, e4.length, 4);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addOffset(e4[n3]);
                  return t3.endVector();
                }
                static startDimVector(t3, e4) {
                  t3.startVector(4, e4, 4);
                }
                static endShape(t3) {
                  return t3.endObject();
                }
                static createShape(t3, e4) {
                  return n2.startShape(t3), n2.addDim(t3, e4), n2.endShape(t3);
                }
              }
              e3.Shape = n2;
            }(e2.fbs || (e2.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(e2) {
            !function(e3) {
              class n2 {
                constructor() {
                  this.bb = null, this.bb_pos = 0;
                }
                __init(t3, e4) {
                  return this.bb_pos = t3, this.bb = e4, this;
                }
                static getRootAsDimension(t3, e4) {
                  return (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                static getSizePrefixedRootAsDimension(t3, e4) {
                  return t3.setPosition(t3.position() + r.flatbuffers.SIZE_PREFIX_LENGTH), (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                value(e4) {
                  let n3 = this.bb.__offset(this.bb_pos, 4);
                  return n3 ? (e4 || new t2.experimental.fbs.DimensionValue()).__init(this.bb.__indirect(this.bb_pos + n3), this.bb) : null;
                }
                denotation(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 6);
                  return e4 ? this.bb.__string(this.bb_pos + e4, t3) : null;
                }
                static startDimension(t3) {
                  t3.startObject(2);
                }
                static addValue(t3, e4) {
                  t3.addFieldOffset(0, e4, 0);
                }
                static addDenotation(t3, e4) {
                  t3.addFieldOffset(1, e4, 0);
                }
                static endDimension(t3) {
                  return t3.endObject();
                }
                static createDimension(t3, e4, r2) {
                  return n2.startDimension(t3), n2.addValue(t3, e4), n2.addDenotation(t3, r2), n2.endDimension(t3);
                }
              }
              e3.Dimension = n2;
            }(e2.fbs || (e2.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(e2) {
            !function(e3) {
              class n2 {
                constructor() {
                  this.bb = null, this.bb_pos = 0;
                }
                __init(t3, e4) {
                  return this.bb_pos = t3, this.bb = e4, this;
                }
                static getRootAsDimensionValue(t3, e4) {
                  return (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                static getSizePrefixedRootAsDimensionValue(t3, e4) {
                  return t3.setPosition(t3.position() + r.flatbuffers.SIZE_PREFIX_LENGTH), (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                dimType() {
                  let e4 = this.bb.__offset(this.bb_pos, 4);
                  return e4 ? this.bb.readInt8(this.bb_pos + e4) : t2.experimental.fbs.DimensionValueType.UNKNOWN;
                }
                dimValue() {
                  let t3 = this.bb.__offset(this.bb_pos, 6);
                  return t3 ? this.bb.readInt64(this.bb_pos + t3) : this.bb.createLong(0, 0);
                }
                dimParam(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 8);
                  return e4 ? this.bb.__string(this.bb_pos + e4, t3) : null;
                }
                static startDimensionValue(t3) {
                  t3.startObject(3);
                }
                static addDimType(e4, n3) {
                  e4.addFieldInt8(0, n3, t2.experimental.fbs.DimensionValueType.UNKNOWN);
                }
                static addDimValue(t3, e4) {
                  t3.addFieldInt64(1, e4, t3.createLong(0, 0));
                }
                static addDimParam(t3, e4) {
                  t3.addFieldOffset(2, e4, 0);
                }
                static endDimensionValue(t3) {
                  return t3.endObject();
                }
                static createDimensionValue(t3, e4, r2, i2) {
                  return n2.startDimensionValue(t3), n2.addDimType(t3, e4), n2.addDimValue(t3, r2), n2.addDimParam(t3, i2), n2.endDimensionValue(t3);
                }
              }
              e3.DimensionValue = n2;
            }(e2.fbs || (e2.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(e2) {
            !function(e3) {
              class n2 {
                constructor() {
                  this.bb = null, this.bb_pos = 0;
                }
                __init(t3, e4) {
                  return this.bb_pos = t3, this.bb = e4, this;
                }
                static getRootAsTensorTypeAndShape(t3, e4) {
                  return (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                static getSizePrefixedRootAsTensorTypeAndShape(t3, e4) {
                  return t3.setPosition(t3.position() + r.flatbuffers.SIZE_PREFIX_LENGTH), (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                elemType() {
                  let e4 = this.bb.__offset(this.bb_pos, 4);
                  return e4 ? this.bb.readInt32(this.bb_pos + e4) : t2.experimental.fbs.TensorDataType.UNDEFINED;
                }
                shape(e4) {
                  let n3 = this.bb.__offset(this.bb_pos, 6);
                  return n3 ? (e4 || new t2.experimental.fbs.Shape()).__init(this.bb.__indirect(this.bb_pos + n3), this.bb) : null;
                }
                static startTensorTypeAndShape(t3) {
                  t3.startObject(2);
                }
                static addElemType(e4, n3) {
                  e4.addFieldInt32(0, n3, t2.experimental.fbs.TensorDataType.UNDEFINED);
                }
                static addShape(t3, e4) {
                  t3.addFieldOffset(1, e4, 0);
                }
                static endTensorTypeAndShape(t3) {
                  return t3.endObject();
                }
                static createTensorTypeAndShape(t3, e4, r2) {
                  return n2.startTensorTypeAndShape(t3), n2.addElemType(t3, e4), n2.addShape(t3, r2), n2.endTensorTypeAndShape(t3);
                }
              }
              e3.TensorTypeAndShape = n2;
            }(e2.fbs || (e2.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(e2) {
            !function(e3) {
              class n2 {
                constructor() {
                  this.bb = null, this.bb_pos = 0;
                }
                __init(t3, e4) {
                  return this.bb_pos = t3, this.bb = e4, this;
                }
                static getRootAsMapType(t3, e4) {
                  return (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                static getSizePrefixedRootAsMapType(t3, e4) {
                  return t3.setPosition(t3.position() + r.flatbuffers.SIZE_PREFIX_LENGTH), (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                keyType() {
                  let e4 = this.bb.__offset(this.bb_pos, 4);
                  return e4 ? this.bb.readInt32(this.bb_pos + e4) : t2.experimental.fbs.TensorDataType.UNDEFINED;
                }
                valueType(e4) {
                  let n3 = this.bb.__offset(this.bb_pos, 6);
                  return n3 ? (e4 || new t2.experimental.fbs.TypeInfo()).__init(this.bb.__indirect(this.bb_pos + n3), this.bb) : null;
                }
                static startMapType(t3) {
                  t3.startObject(2);
                }
                static addKeyType(e4, n3) {
                  e4.addFieldInt32(0, n3, t2.experimental.fbs.TensorDataType.UNDEFINED);
                }
                static addValueType(t3, e4) {
                  t3.addFieldOffset(1, e4, 0);
                }
                static endMapType(t3) {
                  return t3.endObject();
                }
                static createMapType(t3, e4, r2) {
                  return n2.startMapType(t3), n2.addKeyType(t3, e4), n2.addValueType(t3, r2), n2.endMapType(t3);
                }
              }
              e3.MapType = n2;
            }(e2.fbs || (e2.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(e2) {
            !function(e3) {
              class n2 {
                constructor() {
                  this.bb = null, this.bb_pos = 0;
                }
                __init(t3, e4) {
                  return this.bb_pos = t3, this.bb = e4, this;
                }
                static getRootAsSequenceType(t3, e4) {
                  return (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                static getSizePrefixedRootAsSequenceType(t3, e4) {
                  return t3.setPosition(t3.position() + r.flatbuffers.SIZE_PREFIX_LENGTH), (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                elemType(e4) {
                  let n3 = this.bb.__offset(this.bb_pos, 4);
                  return n3 ? (e4 || new t2.experimental.fbs.TypeInfo()).__init(this.bb.__indirect(this.bb_pos + n3), this.bb) : null;
                }
                static startSequenceType(t3) {
                  t3.startObject(1);
                }
                static addElemType(t3, e4) {
                  t3.addFieldOffset(0, e4, 0);
                }
                static endSequenceType(t3) {
                  return t3.endObject();
                }
                static createSequenceType(t3, e4) {
                  return n2.startSequenceType(t3), n2.addElemType(t3, e4), n2.endSequenceType(t3);
                }
              }
              e3.SequenceType = n2;
            }(e2.fbs || (e2.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(t3) {
            (t3.fbs || (t3.fbs = {})).EdgeEnd = class {
              constructor() {
                this.bb = null, this.bb_pos = 0;
              }
              __init(t4, e2) {
                return this.bb_pos = t4, this.bb = e2, this;
              }
              nodeIndex() {
                return this.bb.readUint32(this.bb_pos);
              }
              srcArgIndex() {
                return this.bb.readInt32(this.bb_pos + 4);
              }
              dstArgIndex() {
                return this.bb.readInt32(this.bb_pos + 8);
              }
              static createEdgeEnd(t4, e2, n2, r2) {
                return t4.prep(4, 12), t4.writeInt32(r2), t4.writeInt32(n2), t4.writeInt32(e2), t4.offset();
              }
            };
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(e2) {
            !function(e3) {
              class n2 {
                constructor() {
                  this.bb = null, this.bb_pos = 0;
                }
                __init(t3, e4) {
                  return this.bb_pos = t3, this.bb = e4, this;
                }
                static getRootAsNodeEdge(t3, e4) {
                  return (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                static getSizePrefixedRootAsNodeEdge(t3, e4) {
                  return t3.setPosition(t3.position() + r.flatbuffers.SIZE_PREFIX_LENGTH), (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                nodeIndex() {
                  let t3 = this.bb.__offset(this.bb_pos, 4);
                  return t3 ? this.bb.readUint32(this.bb_pos + t3) : 0;
                }
                inputEdges(e4, n3) {
                  let r2 = this.bb.__offset(this.bb_pos, 6);
                  return r2 ? (n3 || new t2.experimental.fbs.EdgeEnd()).__init(this.bb.__vector(this.bb_pos + r2) + 12 * e4, this.bb) : null;
                }
                inputEdgesLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 6);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                outputEdges(e4, n3) {
                  let r2 = this.bb.__offset(this.bb_pos, 8);
                  return r2 ? (n3 || new t2.experimental.fbs.EdgeEnd()).__init(this.bb.__vector(this.bb_pos + r2) + 12 * e4, this.bb) : null;
                }
                outputEdgesLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 8);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                static startNodeEdge(t3) {
                  t3.startObject(3);
                }
                static addNodeIndex(t3, e4) {
                  t3.addFieldInt32(0, e4, 0);
                }
                static addInputEdges(t3, e4) {
                  t3.addFieldOffset(1, e4, 0);
                }
                static startInputEdgesVector(t3, e4) {
                  t3.startVector(12, e4, 4);
                }
                static addOutputEdges(t3, e4) {
                  t3.addFieldOffset(2, e4, 0);
                }
                static startOutputEdgesVector(t3, e4) {
                  t3.startVector(12, e4, 4);
                }
                static endNodeEdge(t3) {
                  return t3.endObject();
                }
                static createNodeEdge(t3, e4, r2, i2) {
                  return n2.startNodeEdge(t3), n2.addNodeIndex(t3, e4), n2.addInputEdges(t3, r2), n2.addOutputEdges(t3, i2), n2.endNodeEdge(t3);
                }
              }
              e3.NodeEdge = n2;
            }(e2.fbs || (e2.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(e2) {
            !function(e3) {
              class n2 {
                constructor() {
                  this.bb = null, this.bb_pos = 0;
                }
                __init(t3, e4) {
                  return this.bb_pos = t3, this.bb = e4, this;
                }
                static getRootAsNode(t3, e4) {
                  return (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                static getSizePrefixedRootAsNode(t3, e4) {
                  return t3.setPosition(t3.position() + r.flatbuffers.SIZE_PREFIX_LENGTH), (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                name(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 4);
                  return e4 ? this.bb.__string(this.bb_pos + e4, t3) : null;
                }
                docString(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 6);
                  return e4 ? this.bb.__string(this.bb_pos + e4, t3) : null;
                }
                domain(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 8);
                  return e4 ? this.bb.__string(this.bb_pos + e4, t3) : null;
                }
                sinceVersion() {
                  let t3 = this.bb.__offset(this.bb_pos, 10);
                  return t3 ? this.bb.readInt32(this.bb_pos + t3) : 0;
                }
                index() {
                  let t3 = this.bb.__offset(this.bb_pos, 12);
                  return t3 ? this.bb.readUint32(this.bb_pos + t3) : 0;
                }
                opType(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 14);
                  return e4 ? this.bb.__string(this.bb_pos + e4, t3) : null;
                }
                type() {
                  let e4 = this.bb.__offset(this.bb_pos, 16);
                  return e4 ? this.bb.readInt32(this.bb_pos + e4) : t2.experimental.fbs.NodeType.Primitive;
                }
                executionProviderType(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 18);
                  return e4 ? this.bb.__string(this.bb_pos + e4, t3) : null;
                }
                inputs(t3, e4) {
                  let n3 = this.bb.__offset(this.bb_pos, 20);
                  return n3 ? this.bb.__string(this.bb.__vector(this.bb_pos + n3) + 4 * t3, e4) : null;
                }
                inputsLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 20);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                outputs(t3, e4) {
                  let n3 = this.bb.__offset(this.bb_pos, 22);
                  return n3 ? this.bb.__string(this.bb.__vector(this.bb_pos + n3) + 4 * t3, e4) : null;
                }
                outputsLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 22);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                attributes(e4, n3) {
                  let r2 = this.bb.__offset(this.bb_pos, 24);
                  return r2 ? (n3 || new t2.experimental.fbs.Attribute()).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos + r2) + 4 * e4), this.bb) : null;
                }
                attributesLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 24);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                inputArgCounts(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 26);
                  return e4 ? this.bb.readInt32(this.bb.__vector(this.bb_pos + e4) + 4 * t3) : 0;
                }
                inputArgCountsLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 26);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                inputArgCountsArray() {
                  let t3 = this.bb.__offset(this.bb_pos, 26);
                  return t3 ? new Int32Array(this.bb.bytes().buffer, this.bb.bytes().byteOffset + this.bb.__vector(this.bb_pos + t3), this.bb.__vector_len(this.bb_pos + t3)) : null;
                }
                implicitInputs(t3, e4) {
                  let n3 = this.bb.__offset(this.bb_pos, 28);
                  return n3 ? this.bb.__string(this.bb.__vector(this.bb_pos + n3) + 4 * t3, e4) : null;
                }
                implicitInputsLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 28);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                static startNode(t3) {
                  t3.startObject(13);
                }
                static addName(t3, e4) {
                  t3.addFieldOffset(0, e4, 0);
                }
                static addDocString(t3, e4) {
                  t3.addFieldOffset(1, e4, 0);
                }
                static addDomain(t3, e4) {
                  t3.addFieldOffset(2, e4, 0);
                }
                static addSinceVersion(t3, e4) {
                  t3.addFieldInt32(3, e4, 0);
                }
                static addIndex(t3, e4) {
                  t3.addFieldInt32(4, e4, 0);
                }
                static addOpType(t3, e4) {
                  t3.addFieldOffset(5, e4, 0);
                }
                static addType(e4, n3) {
                  e4.addFieldInt32(6, n3, t2.experimental.fbs.NodeType.Primitive);
                }
                static addExecutionProviderType(t3, e4) {
                  t3.addFieldOffset(7, e4, 0);
                }
                static addInputs(t3, e4) {
                  t3.addFieldOffset(8, e4, 0);
                }
                static createInputsVector(t3, e4) {
                  t3.startVector(4, e4.length, 4);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addOffset(e4[n3]);
                  return t3.endVector();
                }
                static startInputsVector(t3, e4) {
                  t3.startVector(4, e4, 4);
                }
                static addOutputs(t3, e4) {
                  t3.addFieldOffset(9, e4, 0);
                }
                static createOutputsVector(t3, e4) {
                  t3.startVector(4, e4.length, 4);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addOffset(e4[n3]);
                  return t3.endVector();
                }
                static startOutputsVector(t3, e4) {
                  t3.startVector(4, e4, 4);
                }
                static addAttributes(t3, e4) {
                  t3.addFieldOffset(10, e4, 0);
                }
                static createAttributesVector(t3, e4) {
                  t3.startVector(4, e4.length, 4);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addOffset(e4[n3]);
                  return t3.endVector();
                }
                static startAttributesVector(t3, e4) {
                  t3.startVector(4, e4, 4);
                }
                static addInputArgCounts(t3, e4) {
                  t3.addFieldOffset(11, e4, 0);
                }
                static createInputArgCountsVector(t3, e4) {
                  t3.startVector(4, e4.length, 4);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addInt32(e4[n3]);
                  return t3.endVector();
                }
                static startInputArgCountsVector(t3, e4) {
                  t3.startVector(4, e4, 4);
                }
                static addImplicitInputs(t3, e4) {
                  t3.addFieldOffset(12, e4, 0);
                }
                static createImplicitInputsVector(t3, e4) {
                  t3.startVector(4, e4.length, 4);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addOffset(e4[n3]);
                  return t3.endVector();
                }
                static startImplicitInputsVector(t3, e4) {
                  t3.startVector(4, e4, 4);
                }
                static endNode(t3) {
                  return t3.endObject();
                }
                static createNode(t3, e4, r2, i2, o2, a, s, u, c, l, p, f, d, h) {
                  return n2.startNode(t3), n2.addName(t3, e4), n2.addDocString(t3, r2), n2.addDomain(t3, i2), n2.addSinceVersion(t3, o2), n2.addIndex(t3, a), n2.addOpType(t3, s), n2.addType(t3, u), n2.addExecutionProviderType(t3, c), n2.addInputs(t3, l), n2.addOutputs(t3, p), n2.addAttributes(t3, f), n2.addInputArgCounts(t3, d), n2.addImplicitInputs(t3, h), n2.endNode(t3);
                }
              }
              e3.Node = n2;
            }(e2.fbs || (e2.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(e2) {
            !function(e3) {
              class n2 {
                constructor() {
                  this.bb = null, this.bb_pos = 0;
                }
                __init(t3, e4) {
                  return this.bb_pos = t3, this.bb = e4, this;
                }
                static getRootAsValueInfo(t3, e4) {
                  return (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                static getSizePrefixedRootAsValueInfo(t3, e4) {
                  return t3.setPosition(t3.position() + r.flatbuffers.SIZE_PREFIX_LENGTH), (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                name(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 4);
                  return e4 ? this.bb.__string(this.bb_pos + e4, t3) : null;
                }
                docString(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 6);
                  return e4 ? this.bb.__string(this.bb_pos + e4, t3) : null;
                }
                type(e4) {
                  let n3 = this.bb.__offset(this.bb_pos, 8);
                  return n3 ? (e4 || new t2.experimental.fbs.TypeInfo()).__init(this.bb.__indirect(this.bb_pos + n3), this.bb) : null;
                }
                static startValueInfo(t3) {
                  t3.startObject(3);
                }
                static addName(t3, e4) {
                  t3.addFieldOffset(0, e4, 0);
                }
                static addDocString(t3, e4) {
                  t3.addFieldOffset(1, e4, 0);
                }
                static addType(t3, e4) {
                  t3.addFieldOffset(2, e4, 0);
                }
                static endValueInfo(t3) {
                  return t3.endObject();
                }
                static createValueInfo(t3, e4, r2, i2) {
                  return n2.startValueInfo(t3), n2.addName(t3, e4), n2.addDocString(t3, r2), n2.addType(t3, i2), n2.endValueInfo(t3);
                }
              }
              e3.ValueInfo = n2;
            }(e2.fbs || (e2.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(e2) {
            !function(e3) {
              class n2 {
                constructor() {
                  this.bb = null, this.bb_pos = 0;
                }
                __init(t3, e4) {
                  return this.bb_pos = t3, this.bb = e4, this;
                }
                static getRootAsTypeInfo(t3, e4) {
                  return (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                static getSizePrefixedRootAsTypeInfo(t3, e4) {
                  return t3.setPosition(t3.position() + r.flatbuffers.SIZE_PREFIX_LENGTH), (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                denotation(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 4);
                  return e4 ? this.bb.__string(this.bb_pos + e4, t3) : null;
                }
                valueType() {
                  let e4 = this.bb.__offset(this.bb_pos, 6);
                  return e4 ? this.bb.readUint8(this.bb_pos + e4) : t2.experimental.fbs.TypeInfoValue.NONE;
                }
                value(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 8);
                  return e4 ? this.bb.__union(t3, this.bb_pos + e4) : null;
                }
                static startTypeInfo(t3) {
                  t3.startObject(3);
                }
                static addDenotation(t3, e4) {
                  t3.addFieldOffset(0, e4, 0);
                }
                static addValueType(e4, n3) {
                  e4.addFieldInt8(1, n3, t2.experimental.fbs.TypeInfoValue.NONE);
                }
                static addValue(t3, e4) {
                  t3.addFieldOffset(2, e4, 0);
                }
                static endTypeInfo(t3) {
                  return t3.endObject();
                }
                static createTypeInfo(t3, e4, r2, i2) {
                  return n2.startTypeInfo(t3), n2.addDenotation(t3, e4), n2.addValueType(t3, r2), n2.addValue(t3, i2), n2.endTypeInfo(t3);
                }
              }
              e3.TypeInfo = n2;
            }(e2.fbs || (e2.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(t3) {
            !function(t4) {
              class e2 {
                constructor() {
                  this.bb = null, this.bb_pos = 0;
                }
                __init(t5, e3) {
                  return this.bb_pos = t5, this.bb = e3, this;
                }
                static getRootAsOperatorSetId(t5, n2) {
                  return (n2 || new e2()).__init(t5.readInt32(t5.position()) + t5.position(), t5);
                }
                static getSizePrefixedRootAsOperatorSetId(t5, n2) {
                  return t5.setPosition(t5.position() + r.flatbuffers.SIZE_PREFIX_LENGTH), (n2 || new e2()).__init(t5.readInt32(t5.position()) + t5.position(), t5);
                }
                domain(t5) {
                  let e3 = this.bb.__offset(this.bb_pos, 4);
                  return e3 ? this.bb.__string(this.bb_pos + e3, t5) : null;
                }
                version() {
                  let t5 = this.bb.__offset(this.bb_pos, 6);
                  return t5 ? this.bb.readInt64(this.bb_pos + t5) : this.bb.createLong(0, 0);
                }
                static startOperatorSetId(t5) {
                  t5.startObject(2);
                }
                static addDomain(t5, e3) {
                  t5.addFieldOffset(0, e3, 0);
                }
                static addVersion(t5, e3) {
                  t5.addFieldInt64(1, e3, t5.createLong(0, 0));
                }
                static endOperatorSetId(t5) {
                  return t5.endObject();
                }
                static createOperatorSetId(t5, n2, r2) {
                  return e2.startOperatorSetId(t5), e2.addDomain(t5, n2), e2.addVersion(t5, r2), e2.endOperatorSetId(t5);
                }
              }
              t4.OperatorSetId = e2;
            }(t3.fbs || (t3.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(e2) {
            !function(e3) {
              class n2 {
                constructor() {
                  this.bb = null, this.bb_pos = 0;
                }
                __init(t3, e4) {
                  return this.bb_pos = t3, this.bb = e4, this;
                }
                static getRootAsTensor(t3, e4) {
                  return (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                static getSizePrefixedRootAsTensor(t3, e4) {
                  return t3.setPosition(t3.position() + r.flatbuffers.SIZE_PREFIX_LENGTH), (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                name(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 4);
                  return e4 ? this.bb.__string(this.bb_pos + e4, t3) : null;
                }
                docString(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 6);
                  return e4 ? this.bb.__string(this.bb_pos + e4, t3) : null;
                }
                dims(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 8);
                  return e4 ? this.bb.readInt64(this.bb.__vector(this.bb_pos + e4) + 8 * t3) : this.bb.createLong(0, 0);
                }
                dimsLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 8);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                dataType() {
                  let e4 = this.bb.__offset(this.bb_pos, 10);
                  return e4 ? this.bb.readInt32(this.bb_pos + e4) : t2.experimental.fbs.TensorDataType.UNDEFINED;
                }
                rawData(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 12);
                  return e4 ? this.bb.readUint8(this.bb.__vector(this.bb_pos + e4) + t3) : 0;
                }
                rawDataLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 12);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                rawDataArray() {
                  let t3 = this.bb.__offset(this.bb_pos, 12);
                  return t3 ? new Uint8Array(this.bb.bytes().buffer, this.bb.bytes().byteOffset + this.bb.__vector(this.bb_pos + t3), this.bb.__vector_len(this.bb_pos + t3)) : null;
                }
                stringData(t3, e4) {
                  let n3 = this.bb.__offset(this.bb_pos, 14);
                  return n3 ? this.bb.__string(this.bb.__vector(this.bb_pos + n3) + 4 * t3, e4) : null;
                }
                stringDataLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 14);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                static startTensor(t3) {
                  t3.startObject(6);
                }
                static addName(t3, e4) {
                  t3.addFieldOffset(0, e4, 0);
                }
                static addDocString(t3, e4) {
                  t3.addFieldOffset(1, e4, 0);
                }
                static addDims(t3, e4) {
                  t3.addFieldOffset(2, e4, 0);
                }
                static createDimsVector(t3, e4) {
                  t3.startVector(8, e4.length, 8);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addInt64(e4[n3]);
                  return t3.endVector();
                }
                static startDimsVector(t3, e4) {
                  t3.startVector(8, e4, 8);
                }
                static addDataType(e4, n3) {
                  e4.addFieldInt32(3, n3, t2.experimental.fbs.TensorDataType.UNDEFINED);
                }
                static addRawData(t3, e4) {
                  t3.addFieldOffset(4, e4, 0);
                }
                static createRawDataVector(t3, e4) {
                  t3.startVector(1, e4.length, 1);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addInt8(e4[n3]);
                  return t3.endVector();
                }
                static startRawDataVector(t3, e4) {
                  t3.startVector(1, e4, 1);
                }
                static addStringData(t3, e4) {
                  t3.addFieldOffset(5, e4, 0);
                }
                static createStringDataVector(t3, e4) {
                  t3.startVector(4, e4.length, 4);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addOffset(e4[n3]);
                  return t3.endVector();
                }
                static startStringDataVector(t3, e4) {
                  t3.startVector(4, e4, 4);
                }
                static endTensor(t3) {
                  return t3.endObject();
                }
                static createTensor(t3, e4, r2, i2, o2, a, s) {
                  return n2.startTensor(t3), n2.addName(t3, e4), n2.addDocString(t3, r2), n2.addDims(t3, i2), n2.addDataType(t3, o2), n2.addRawData(t3, a), n2.addStringData(t3, s), n2.endTensor(t3);
                }
              }
              e3.Tensor = n2;
            }(e2.fbs || (e2.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(e2) {
            !function(e3) {
              class n2 {
                constructor() {
                  this.bb = null, this.bb_pos = 0;
                }
                __init(t3, e4) {
                  return this.bb_pos = t3, this.bb = e4, this;
                }
                static getRootAsSparseTensor(t3, e4) {
                  return (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                static getSizePrefixedRootAsSparseTensor(t3, e4) {
                  return t3.setPosition(t3.position() + r.flatbuffers.SIZE_PREFIX_LENGTH), (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                values(e4) {
                  let n3 = this.bb.__offset(this.bb_pos, 4);
                  return n3 ? (e4 || new t2.experimental.fbs.Tensor()).__init(this.bb.__indirect(this.bb_pos + n3), this.bb) : null;
                }
                indices(e4) {
                  let n3 = this.bb.__offset(this.bb_pos, 6);
                  return n3 ? (e4 || new t2.experimental.fbs.Tensor()).__init(this.bb.__indirect(this.bb_pos + n3), this.bb) : null;
                }
                dims(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 8);
                  return e4 ? this.bb.readInt64(this.bb.__vector(this.bb_pos + e4) + 8 * t3) : this.bb.createLong(0, 0);
                }
                dimsLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 8);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                static startSparseTensor(t3) {
                  t3.startObject(3);
                }
                static addValues(t3, e4) {
                  t3.addFieldOffset(0, e4, 0);
                }
                static addIndices(t3, e4) {
                  t3.addFieldOffset(1, e4, 0);
                }
                static addDims(t3, e4) {
                  t3.addFieldOffset(2, e4, 0);
                }
                static createDimsVector(t3, e4) {
                  t3.startVector(8, e4.length, 8);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addInt64(e4[n3]);
                  return t3.endVector();
                }
                static startDimsVector(t3, e4) {
                  t3.startVector(8, e4, 8);
                }
                static endSparseTensor(t3) {
                  return t3.endObject();
                }
                static createSparseTensor(t3, e4, r2, i2) {
                  return n2.startSparseTensor(t3), n2.addValues(t3, e4), n2.addIndices(t3, r2), n2.addDims(t3, i2), n2.endSparseTensor(t3);
                }
              }
              e3.SparseTensor = n2;
            }(e2.fbs || (e2.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(e2) {
            !function(e3) {
              class n2 {
                constructor() {
                  this.bb = null, this.bb_pos = 0;
                }
                __init(t3, e4) {
                  return this.bb_pos = t3, this.bb = e4, this;
                }
                static getRootAsAttribute(t3, e4) {
                  return (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                static getSizePrefixedRootAsAttribute(t3, e4) {
                  return t3.setPosition(t3.position() + r.flatbuffers.SIZE_PREFIX_LENGTH), (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                name(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 4);
                  return e4 ? this.bb.__string(this.bb_pos + e4, t3) : null;
                }
                docString(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 6);
                  return e4 ? this.bb.__string(this.bb_pos + e4, t3) : null;
                }
                type() {
                  let e4 = this.bb.__offset(this.bb_pos, 8);
                  return e4 ? this.bb.readInt32(this.bb_pos + e4) : t2.experimental.fbs.AttributeType.UNDEFINED;
                }
                f() {
                  let t3 = this.bb.__offset(this.bb_pos, 10);
                  return t3 ? this.bb.readFloat32(this.bb_pos + t3) : 0;
                }
                i() {
                  let t3 = this.bb.__offset(this.bb_pos, 12);
                  return t3 ? this.bb.readInt64(this.bb_pos + t3) : this.bb.createLong(0, 0);
                }
                s(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 14);
                  return e4 ? this.bb.__string(this.bb_pos + e4, t3) : null;
                }
                t(e4) {
                  let n3 = this.bb.__offset(this.bb_pos, 16);
                  return n3 ? (e4 || new t2.experimental.fbs.Tensor()).__init(this.bb.__indirect(this.bb_pos + n3), this.bb) : null;
                }
                g(e4) {
                  let n3 = this.bb.__offset(this.bb_pos, 18);
                  return n3 ? (e4 || new t2.experimental.fbs.Graph()).__init(this.bb.__indirect(this.bb_pos + n3), this.bb) : null;
                }
                floats(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 20);
                  return e4 ? this.bb.readFloat32(this.bb.__vector(this.bb_pos + e4) + 4 * t3) : 0;
                }
                floatsLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 20);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                floatsArray() {
                  let t3 = this.bb.__offset(this.bb_pos, 20);
                  return t3 ? new Float32Array(this.bb.bytes().buffer, this.bb.bytes().byteOffset + this.bb.__vector(this.bb_pos + t3), this.bb.__vector_len(this.bb_pos + t3)) : null;
                }
                ints(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 22);
                  return e4 ? this.bb.readInt64(this.bb.__vector(this.bb_pos + e4) + 8 * t3) : this.bb.createLong(0, 0);
                }
                intsLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 22);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                strings(t3, e4) {
                  let n3 = this.bb.__offset(this.bb_pos, 24);
                  return n3 ? this.bb.__string(this.bb.__vector(this.bb_pos + n3) + 4 * t3, e4) : null;
                }
                stringsLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 24);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                tensors(e4, n3) {
                  let r2 = this.bb.__offset(this.bb_pos, 26);
                  return r2 ? (n3 || new t2.experimental.fbs.Tensor()).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos + r2) + 4 * e4), this.bb) : null;
                }
                tensorsLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 26);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                graphs(e4, n3) {
                  let r2 = this.bb.__offset(this.bb_pos, 28);
                  return r2 ? (n3 || new t2.experimental.fbs.Graph()).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos + r2) + 4 * e4), this.bb) : null;
                }
                graphsLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 28);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                static startAttribute(t3) {
                  t3.startObject(13);
                }
                static addName(t3, e4) {
                  t3.addFieldOffset(0, e4, 0);
                }
                static addDocString(t3, e4) {
                  t3.addFieldOffset(1, e4, 0);
                }
                static addType(e4, n3) {
                  e4.addFieldInt32(2, n3, t2.experimental.fbs.AttributeType.UNDEFINED);
                }
                static addF(t3, e4) {
                  t3.addFieldFloat32(3, e4, 0);
                }
                static addI(t3, e4) {
                  t3.addFieldInt64(4, e4, t3.createLong(0, 0));
                }
                static addS(t3, e4) {
                  t3.addFieldOffset(5, e4, 0);
                }
                static addT(t3, e4) {
                  t3.addFieldOffset(6, e4, 0);
                }
                static addG(t3, e4) {
                  t3.addFieldOffset(7, e4, 0);
                }
                static addFloats(t3, e4) {
                  t3.addFieldOffset(8, e4, 0);
                }
                static createFloatsVector(t3, e4) {
                  t3.startVector(4, e4.length, 4);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addFloat32(e4[n3]);
                  return t3.endVector();
                }
                static startFloatsVector(t3, e4) {
                  t3.startVector(4, e4, 4);
                }
                static addInts(t3, e4) {
                  t3.addFieldOffset(9, e4, 0);
                }
                static createIntsVector(t3, e4) {
                  t3.startVector(8, e4.length, 8);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addInt64(e4[n3]);
                  return t3.endVector();
                }
                static startIntsVector(t3, e4) {
                  t3.startVector(8, e4, 8);
                }
                static addStrings(t3, e4) {
                  t3.addFieldOffset(10, e4, 0);
                }
                static createStringsVector(t3, e4) {
                  t3.startVector(4, e4.length, 4);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addOffset(e4[n3]);
                  return t3.endVector();
                }
                static startStringsVector(t3, e4) {
                  t3.startVector(4, e4, 4);
                }
                static addTensors(t3, e4) {
                  t3.addFieldOffset(11, e4, 0);
                }
                static createTensorsVector(t3, e4) {
                  t3.startVector(4, e4.length, 4);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addOffset(e4[n3]);
                  return t3.endVector();
                }
                static startTensorsVector(t3, e4) {
                  t3.startVector(4, e4, 4);
                }
                static addGraphs(t3, e4) {
                  t3.addFieldOffset(12, e4, 0);
                }
                static createGraphsVector(t3, e4) {
                  t3.startVector(4, e4.length, 4);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addOffset(e4[n3]);
                  return t3.endVector();
                }
                static startGraphsVector(t3, e4) {
                  t3.startVector(4, e4, 4);
                }
                static endAttribute(t3) {
                  return t3.endObject();
                }
                static createAttribute(t3, e4, r2, i2, o2, a, s, u, c, l, p, f, d, h) {
                  return n2.startAttribute(t3), n2.addName(t3, e4), n2.addDocString(t3, r2), n2.addType(t3, i2), n2.addF(t3, o2), n2.addI(t3, a), n2.addS(t3, s), n2.addT(t3, u), n2.addG(t3, c), n2.addFloats(t3, l), n2.addInts(t3, p), n2.addStrings(t3, f), n2.addTensors(t3, d), n2.addGraphs(t3, h), n2.endAttribute(t3);
                }
              }
              e3.Attribute = n2;
            }(e2.fbs || (e2.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(e2) {
            !function(e3) {
              class n2 {
                constructor() {
                  this.bb = null, this.bb_pos = 0;
                }
                __init(t3, e4) {
                  return this.bb_pos = t3, this.bb = e4, this;
                }
                static getRootAsGraph(t3, e4) {
                  return (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                static getSizePrefixedRootAsGraph(t3, e4) {
                  return t3.setPosition(t3.position() + r.flatbuffers.SIZE_PREFIX_LENGTH), (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                initializers(e4, n3) {
                  let r2 = this.bb.__offset(this.bb_pos, 4);
                  return r2 ? (n3 || new t2.experimental.fbs.Tensor()).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos + r2) + 4 * e4), this.bb) : null;
                }
                initializersLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 4);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                nodeArgs(e4, n3) {
                  let r2 = this.bb.__offset(this.bb_pos, 6);
                  return r2 ? (n3 || new t2.experimental.fbs.ValueInfo()).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos + r2) + 4 * e4), this.bb) : null;
                }
                nodeArgsLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 6);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                nodes(e4, n3) {
                  let r2 = this.bb.__offset(this.bb_pos, 8);
                  return r2 ? (n3 || new t2.experimental.fbs.Node()).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos + r2) + 4 * e4), this.bb) : null;
                }
                nodesLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 8);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                maxNodeIndex() {
                  let t3 = this.bb.__offset(this.bb_pos, 10);
                  return t3 ? this.bb.readUint32(this.bb_pos + t3) : 0;
                }
                nodeEdges(e4, n3) {
                  let r2 = this.bb.__offset(this.bb_pos, 12);
                  return r2 ? (n3 || new t2.experimental.fbs.NodeEdge()).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos + r2) + 4 * e4), this.bb) : null;
                }
                nodeEdgesLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 12);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                inputs(t3, e4) {
                  let n3 = this.bb.__offset(this.bb_pos, 14);
                  return n3 ? this.bb.__string(this.bb.__vector(this.bb_pos + n3) + 4 * t3, e4) : null;
                }
                inputsLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 14);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                outputs(t3, e4) {
                  let n3 = this.bb.__offset(this.bb_pos, 16);
                  return n3 ? this.bb.__string(this.bb.__vector(this.bb_pos + n3) + 4 * t3, e4) : null;
                }
                outputsLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 16);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                sparseInitializers(e4, n3) {
                  let r2 = this.bb.__offset(this.bb_pos, 18);
                  return r2 ? (n3 || new t2.experimental.fbs.SparseTensor()).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos + r2) + 4 * e4), this.bb) : null;
                }
                sparseInitializersLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 18);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                static startGraph(t3) {
                  t3.startObject(8);
                }
                static addInitializers(t3, e4) {
                  t3.addFieldOffset(0, e4, 0);
                }
                static createInitializersVector(t3, e4) {
                  t3.startVector(4, e4.length, 4);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addOffset(e4[n3]);
                  return t3.endVector();
                }
                static startInitializersVector(t3, e4) {
                  t3.startVector(4, e4, 4);
                }
                static addNodeArgs(t3, e4) {
                  t3.addFieldOffset(1, e4, 0);
                }
                static createNodeArgsVector(t3, e4) {
                  t3.startVector(4, e4.length, 4);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addOffset(e4[n3]);
                  return t3.endVector();
                }
                static startNodeArgsVector(t3, e4) {
                  t3.startVector(4, e4, 4);
                }
                static addNodes(t3, e4) {
                  t3.addFieldOffset(2, e4, 0);
                }
                static createNodesVector(t3, e4) {
                  t3.startVector(4, e4.length, 4);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addOffset(e4[n3]);
                  return t3.endVector();
                }
                static startNodesVector(t3, e4) {
                  t3.startVector(4, e4, 4);
                }
                static addMaxNodeIndex(t3, e4) {
                  t3.addFieldInt32(3, e4, 0);
                }
                static addNodeEdges(t3, e4) {
                  t3.addFieldOffset(4, e4, 0);
                }
                static createNodeEdgesVector(t3, e4) {
                  t3.startVector(4, e4.length, 4);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addOffset(e4[n3]);
                  return t3.endVector();
                }
                static startNodeEdgesVector(t3, e4) {
                  t3.startVector(4, e4, 4);
                }
                static addInputs(t3, e4) {
                  t3.addFieldOffset(5, e4, 0);
                }
                static createInputsVector(t3, e4) {
                  t3.startVector(4, e4.length, 4);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addOffset(e4[n3]);
                  return t3.endVector();
                }
                static startInputsVector(t3, e4) {
                  t3.startVector(4, e4, 4);
                }
                static addOutputs(t3, e4) {
                  t3.addFieldOffset(6, e4, 0);
                }
                static createOutputsVector(t3, e4) {
                  t3.startVector(4, e4.length, 4);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addOffset(e4[n3]);
                  return t3.endVector();
                }
                static startOutputsVector(t3, e4) {
                  t3.startVector(4, e4, 4);
                }
                static addSparseInitializers(t3, e4) {
                  t3.addFieldOffset(7, e4, 0);
                }
                static createSparseInitializersVector(t3, e4) {
                  t3.startVector(4, e4.length, 4);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addOffset(e4[n3]);
                  return t3.endVector();
                }
                static startSparseInitializersVector(t3, e4) {
                  t3.startVector(4, e4, 4);
                }
                static endGraph(t3) {
                  return t3.endObject();
                }
                static createGraph(t3, e4, r2, i2, o2, a, s, u, c) {
                  return n2.startGraph(t3), n2.addInitializers(t3, e4), n2.addNodeArgs(t3, r2), n2.addNodes(t3, i2), n2.addMaxNodeIndex(t3, o2), n2.addNodeEdges(t3, a), n2.addInputs(t3, s), n2.addOutputs(t3, u), n2.addSparseInitializers(t3, c), n2.endGraph(t3);
                }
              }
              e3.Graph = n2;
            }(e2.fbs || (e2.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(e2) {
            !function(e3) {
              class n2 {
                constructor() {
                  this.bb = null, this.bb_pos = 0;
                }
                __init(t3, e4) {
                  return this.bb_pos = t3, this.bb = e4, this;
                }
                static getRootAsModel(t3, e4) {
                  return (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                static getSizePrefixedRootAsModel(t3, e4) {
                  return t3.setPosition(t3.position() + r.flatbuffers.SIZE_PREFIX_LENGTH), (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                irVersion() {
                  let t3 = this.bb.__offset(this.bb_pos, 4);
                  return t3 ? this.bb.readInt64(this.bb_pos + t3) : this.bb.createLong(0, 0);
                }
                opsetImport(e4, n3) {
                  let r2 = this.bb.__offset(this.bb_pos, 6);
                  return r2 ? (n3 || new t2.experimental.fbs.OperatorSetId()).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos + r2) + 4 * e4), this.bb) : null;
                }
                opsetImportLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 6);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                producerName(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 8);
                  return e4 ? this.bb.__string(this.bb_pos + e4, t3) : null;
                }
                producerVersion(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 10);
                  return e4 ? this.bb.__string(this.bb_pos + e4, t3) : null;
                }
                domain(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 12);
                  return e4 ? this.bb.__string(this.bb_pos + e4, t3) : null;
                }
                modelVersion() {
                  let t3 = this.bb.__offset(this.bb_pos, 14);
                  return t3 ? this.bb.readInt64(this.bb_pos + t3) : this.bb.createLong(0, 0);
                }
                docString(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 16);
                  return e4 ? this.bb.__string(this.bb_pos + e4, t3) : null;
                }
                graph(e4) {
                  let n3 = this.bb.__offset(this.bb_pos, 18);
                  return n3 ? (e4 || new t2.experimental.fbs.Graph()).__init(this.bb.__indirect(this.bb_pos + n3), this.bb) : null;
                }
                graphDocString(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 20);
                  return e4 ? this.bb.__string(this.bb_pos + e4, t3) : null;
                }
                static startModel(t3) {
                  t3.startObject(9);
                }
                static addIrVersion(t3, e4) {
                  t3.addFieldInt64(0, e4, t3.createLong(0, 0));
                }
                static addOpsetImport(t3, e4) {
                  t3.addFieldOffset(1, e4, 0);
                }
                static createOpsetImportVector(t3, e4) {
                  t3.startVector(4, e4.length, 4);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addOffset(e4[n3]);
                  return t3.endVector();
                }
                static startOpsetImportVector(t3, e4) {
                  t3.startVector(4, e4, 4);
                }
                static addProducerName(t3, e4) {
                  t3.addFieldOffset(2, e4, 0);
                }
                static addProducerVersion(t3, e4) {
                  t3.addFieldOffset(3, e4, 0);
                }
                static addDomain(t3, e4) {
                  t3.addFieldOffset(4, e4, 0);
                }
                static addModelVersion(t3, e4) {
                  t3.addFieldInt64(5, e4, t3.createLong(0, 0));
                }
                static addDocString(t3, e4) {
                  t3.addFieldOffset(6, e4, 0);
                }
                static addGraph(t3, e4) {
                  t3.addFieldOffset(7, e4, 0);
                }
                static addGraphDocString(t3, e4) {
                  t3.addFieldOffset(8, e4, 0);
                }
                static endModel(t3) {
                  return t3.endObject();
                }
                static createModel(t3, e4, r2, i2, o2, a, s, u, c, l) {
                  return n2.startModel(t3), n2.addIrVersion(t3, e4), n2.addOpsetImport(t3, r2), n2.addProducerName(t3, i2), n2.addProducerVersion(t3, o2), n2.addDomain(t3, a), n2.addModelVersion(t3, s), n2.addDocString(t3, u), n2.addGraph(t3, c), n2.addGraphDocString(t3, l), n2.endModel(t3);
                }
              }
              e3.Model = n2;
            }(e2.fbs || (e2.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(t3) {
            !function(t4) {
              class e2 {
                constructor() {
                  this.bb = null, this.bb_pos = 0;
                }
                __init(t5, e3) {
                  return this.bb_pos = t5, this.bb = e3, this;
                }
                static getRootAsKernelCreateInfos(t5, n2) {
                  return (n2 || new e2()).__init(t5.readInt32(t5.position()) + t5.position(), t5);
                }
                static getSizePrefixedRootAsKernelCreateInfos(t5, n2) {
                  return t5.setPosition(t5.position() + r.flatbuffers.SIZE_PREFIX_LENGTH), (n2 || new e2()).__init(t5.readInt32(t5.position()) + t5.position(), t5);
                }
                nodeIndices(t5) {
                  let e3 = this.bb.__offset(this.bb_pos, 4);
                  return e3 ? this.bb.readUint32(this.bb.__vector(this.bb_pos + e3) + 4 * t5) : 0;
                }
                nodeIndicesLength() {
                  let t5 = this.bb.__offset(this.bb_pos, 4);
                  return t5 ? this.bb.__vector_len(this.bb_pos + t5) : 0;
                }
                nodeIndicesArray() {
                  let t5 = this.bb.__offset(this.bb_pos, 4);
                  return t5 ? new Uint32Array(this.bb.bytes().buffer, this.bb.bytes().byteOffset + this.bb.__vector(this.bb_pos + t5), this.bb.__vector_len(this.bb_pos + t5)) : null;
                }
                kernelDefHashes(t5) {
                  let e3 = this.bb.__offset(this.bb_pos, 6);
                  return e3 ? this.bb.readUint64(this.bb.__vector(this.bb_pos + e3) + 8 * t5) : this.bb.createLong(0, 0);
                }
                kernelDefHashesLength() {
                  let t5 = this.bb.__offset(this.bb_pos, 6);
                  return t5 ? this.bb.__vector_len(this.bb_pos + t5) : 0;
                }
                static startKernelCreateInfos(t5) {
                  t5.startObject(2);
                }
                static addNodeIndices(t5, e3) {
                  t5.addFieldOffset(0, e3, 0);
                }
                static createNodeIndicesVector(t5, e3) {
                  t5.startVector(4, e3.length, 4);
                  for (let n2 = e3.length - 1; n2 >= 0; n2--) t5.addInt32(e3[n2]);
                  return t5.endVector();
                }
                static startNodeIndicesVector(t5, e3) {
                  t5.startVector(4, e3, 4);
                }
                static addKernelDefHashes(t5, e3) {
                  t5.addFieldOffset(1, e3, 0);
                }
                static createKernelDefHashesVector(t5, e3) {
                  t5.startVector(8, e3.length, 8);
                  for (let n2 = e3.length - 1; n2 >= 0; n2--) t5.addInt64(e3[n2]);
                  return t5.endVector();
                }
                static startKernelDefHashesVector(t5, e3) {
                  t5.startVector(8, e3, 8);
                }
                static endKernelCreateInfos(t5) {
                  return t5.endObject();
                }
                static createKernelCreateInfos(t5, n2, r2) {
                  return e2.startKernelCreateInfos(t5), e2.addNodeIndices(t5, n2), e2.addKernelDefHashes(t5, r2), e2.endKernelCreateInfos(t5);
                }
              }
              t4.KernelCreateInfos = e2;
            }(t3.fbs || (t3.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(e2) {
            !function(e3) {
              class n2 {
                constructor() {
                  this.bb = null, this.bb_pos = 0;
                }
                __init(t3, e4) {
                  return this.bb_pos = t3, this.bb = e4, this;
                }
                static getRootAsSubGraphSessionState(t3, e4) {
                  return (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                static getSizePrefixedRootAsSubGraphSessionState(t3, e4) {
                  return t3.setPosition(t3.position() + r.flatbuffers.SIZE_PREFIX_LENGTH), (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                graphId(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 4);
                  return e4 ? this.bb.__string(this.bb_pos + e4, t3) : null;
                }
                sessionState(e4) {
                  let n3 = this.bb.__offset(this.bb_pos, 6);
                  return n3 ? (e4 || new t2.experimental.fbs.SessionState()).__init(this.bb.__indirect(this.bb_pos + n3), this.bb) : null;
                }
                static startSubGraphSessionState(t3) {
                  t3.startObject(2);
                }
                static addGraphId(t3, e4) {
                  t3.addFieldOffset(0, e4, 0);
                }
                static addSessionState(t3, e4) {
                  t3.addFieldOffset(1, e4, 0);
                }
                static endSubGraphSessionState(t3) {
                  let e4 = t3.endObject();
                  return t3.requiredField(e4, 4), e4;
                }
                static createSubGraphSessionState(t3, e4, r2) {
                  return n2.startSubGraphSessionState(t3), n2.addGraphId(t3, e4), n2.addSessionState(t3, r2), n2.endSubGraphSessionState(t3);
                }
              }
              e3.SubGraphSessionState = n2;
            }(e2.fbs || (e2.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(e2) {
            !function(e3) {
              class n2 {
                constructor() {
                  this.bb = null, this.bb_pos = 0;
                }
                __init(t3, e4) {
                  return this.bb_pos = t3, this.bb = e4, this;
                }
                static getRootAsSessionState(t3, e4) {
                  return (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                static getSizePrefixedRootAsSessionState(t3, e4) {
                  return t3.setPosition(t3.position() + r.flatbuffers.SIZE_PREFIX_LENGTH), (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                kernels(e4) {
                  let n3 = this.bb.__offset(this.bb_pos, 4);
                  return n3 ? (e4 || new t2.experimental.fbs.KernelCreateInfos()).__init(this.bb.__indirect(this.bb_pos + n3), this.bb) : null;
                }
                subGraphSessionStates(e4, n3) {
                  let r2 = this.bb.__offset(this.bb_pos, 6);
                  return r2 ? (n3 || new t2.experimental.fbs.SubGraphSessionState()).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos + r2) + 4 * e4), this.bb) : null;
                }
                subGraphSessionStatesLength() {
                  let t3 = this.bb.__offset(this.bb_pos, 6);
                  return t3 ? this.bb.__vector_len(this.bb_pos + t3) : 0;
                }
                static startSessionState(t3) {
                  t3.startObject(2);
                }
                static addKernels(t3, e4) {
                  t3.addFieldOffset(0, e4, 0);
                }
                static addSubGraphSessionStates(t3, e4) {
                  t3.addFieldOffset(1, e4, 0);
                }
                static createSubGraphSessionStatesVector(t3, e4) {
                  t3.startVector(4, e4.length, 4);
                  for (let n3 = e4.length - 1; n3 >= 0; n3--) t3.addOffset(e4[n3]);
                  return t3.endVector();
                }
                static startSubGraphSessionStatesVector(t3, e4) {
                  t3.startVector(4, e4, 4);
                }
                static endSessionState(t3) {
                  return t3.endObject();
                }
                static createSessionState(t3, e4, r2) {
                  return n2.startSessionState(t3), n2.addKernels(t3, e4), n2.addSubGraphSessionStates(t3, r2), n2.endSessionState(t3);
                }
              }
              e3.SessionState = n2;
            }(e2.fbs || (e2.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {})), function(t2) {
          !function(e2) {
            !function(e3) {
              class n2 {
                constructor() {
                  this.bb = null, this.bb_pos = 0;
                }
                __init(t3, e4) {
                  return this.bb_pos = t3, this.bb = e4, this;
                }
                static getRootAsInferenceSession(t3, e4) {
                  return (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                static getSizePrefixedRootAsInferenceSession(t3, e4) {
                  return t3.setPosition(t3.position() + r.flatbuffers.SIZE_PREFIX_LENGTH), (e4 || new n2()).__init(t3.readInt32(t3.position()) + t3.position(), t3);
                }
                static bufferHasIdentifier(t3) {
                  return t3.__has_identifier("ORTM");
                }
                ortVersion(t3) {
                  let e4 = this.bb.__offset(this.bb_pos, 4);
                  return e4 ? this.bb.__string(this.bb_pos + e4, t3) : null;
                }
                model(e4) {
                  let n3 = this.bb.__offset(this.bb_pos, 6);
                  return n3 ? (e4 || new t2.experimental.fbs.Model()).__init(this.bb.__indirect(this.bb_pos + n3), this.bb) : null;
                }
                sessionState(e4) {
                  let n3 = this.bb.__offset(this.bb_pos, 8);
                  return n3 ? (e4 || new t2.experimental.fbs.SessionState()).__init(this.bb.__indirect(this.bb_pos + n3), this.bb) : null;
                }
                static startInferenceSession(t3) {
                  t3.startObject(3);
                }
                static addOrtVersion(t3, e4) {
                  t3.addFieldOffset(0, e4, 0);
                }
                static addModel(t3, e4) {
                  t3.addFieldOffset(1, e4, 0);
                }
                static addSessionState(t3, e4) {
                  t3.addFieldOffset(2, e4, 0);
                }
                static endInferenceSession(t3) {
                  return t3.endObject();
                }
                static finishInferenceSessionBuffer(t3, e4) {
                  t3.finish(e4, "ORTM");
                }
                static finishSizePrefixedInferenceSessionBuffer(t3, e4) {
                  t3.finish(e4, "ORTM", true);
                }
                static createInferenceSession(t3, e4, r2, i2) {
                  return n2.startInferenceSession(t3), n2.addOrtVersion(t3, e4), n2.addModel(t3, r2), n2.addSessionState(t3, i2), n2.endInferenceSession(t3);
                }
              }
              e3.InferenceSession = n2;
            }(e2.fbs || (e2.fbs = {}));
          }(t2.experimental || (t2.experimental = {}));
        }(e.onnxruntime || (e.onnxruntime = {}));
      }, 7448: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.OnnxjsSessionHandler = void 0;
        const r = n(1670), i = n(9162);
        e.OnnxjsSessionHandler = class {
          constructor(t2) {
            this.session = t2, this.inputNames = this.session.inputNames, this.outputNames = this.session.outputNames;
          }
          dispose() {
            return __async(this, null, function* () {
            });
          }
          run(t2, e2, n2) {
            return __async(this, null, function* () {
              const o = /* @__PURE__ */ new Map();
              for (const e3 in t2) if (Object.hasOwnProperty.call(t2, e3)) {
                const n3 = t2[e3];
                o.set(e3, new i.Tensor(n3.dims, n3.type, void 0, void 0, n3.data));
              }
              const a = yield this.session.run(o), s = {};
              return a.forEach((t3, e3) => {
                s[e3] = new r.Tensor(t3.type, t3.data, t3.dims);
              }), s;
            });
          }
          startProfiling() {
            this.session.startProfiling();
          }
          endProfiling() {
            this.session.endProfiling();
          }
        };
      }, 6919: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.Session = void 0;
        const r = n(7067), i = n(1296), o = n(7091), a = n(1036), s = n(6231), u = n(2644);
        e.Session = class {
          constructor(t2 = {}) {
            this._initialized = false, this.backendHint = t2.backendHint, this.profiler = s.Profiler.create(t2.profiler), this.context = { profiler: this.profiler, graphInputTypes: [], graphInputDims: [] };
          }
          get inputNames() {
            return this._model.graph.getInputNames();
          }
          get outputNames() {
            return this._model.graph.getOutputNames();
          }
          startProfiling() {
            this.profiler.start();
          }
          endProfiling() {
            this.profiler.stop();
          }
          loadModel(t2, e2, n2) {
            return __async(this, null, function* () {
              yield this.profiler.event("session", "Session.loadModel", () => __async(this, null, function* () {
                const a2 = yield (0, o.resolveBackend)(this.backendHint);
                if (this.sessionHandler = a2.createSessionHandler(this.context), this._model = new u.Model(), "string" == typeof t2) {
                  const e3 = t2.endsWith(".ort");
                  if ("undefined" == typeof fetch) {
                    const n3 = yield (0, i.promisify)(r.readFile)(t2);
                    this.initialize(n3, e3);
                  } else {
                    const n3 = yield fetch(t2), r2 = yield n3.arrayBuffer();
                    this.initialize(new Uint8Array(r2), e3);
                  }
                } else if (ArrayBuffer.isView(t2)) this.initialize(t2);
                else {
                  const r2 = new Uint8Array(t2, e2 || 0, n2 || t2.byteLength);
                  this.initialize(r2);
                }
              }));
            });
          }
          initialize(t2, e2) {
            if (this._initialized) throw new Error("already initialized");
            this.profiler.event("session", "Session.initialize", () => {
              const n2 = this.sessionHandler.transformGraph ? this.sessionHandler : void 0;
              this._model.load(t2, n2, e2), this.sessionHandler.onGraphInitialized && this.sessionHandler.onGraphInitialized(this._model.graph), this.initializeOps(this._model.graph), this._executionPlan = new a.ExecutionPlan(this._model.graph, this._ops, this.profiler);
            }), this._initialized = true;
          }
          run(t2) {
            return __async(this, null, function* () {
              if (!this._initialized) throw new Error("session not initialized yet");
              return this.profiler.event("session", "Session.run", () => __async(this, null, function* () {
                const e2 = this.normalizeAndValidateInputs(t2), n2 = yield this._executionPlan.execute(this.sessionHandler, e2);
                return this.createOutput(n2);
              }));
            });
          }
          normalizeAndValidateInputs(t2) {
            const e2 = this._model.graph.getInputNames();
            if (Array.isArray(t2)) {
              if (t2.length !== e2.length) throw new Error(`incorrect input array length: expected ${e2.length} but got ${t2.length}`);
            } else {
              if (t2.size !== e2.length) throw new Error(`incorrect input map size: expected ${e2.length} but got ${t2.size}`);
              const n2 = new Array(t2.size);
              let r2 = 0;
              for (let i2 = 0; i2 < e2.length; ++i2) {
                const o2 = t2.get(e2[i2]);
                if (!o2) throw new Error(`missing input tensor for: '${name}'`);
                n2[r2++] = o2;
              }
              t2 = n2;
            }
            if (this.context.graphInputTypes && 0 !== this.context.graphInputTypes.length && this.context.graphInputDims && 0 !== this.context.graphInputDims.length) this.validateInputTensorDims(this.context.graphInputDims, t2, false);
            else {
              const e3 = this._model.graph.getInputIndices(), n2 = this._model.graph.getValues(), r2 = new Array(e3.length);
              for (let i2 = 0; i2 < e3.length; ++i2) {
                const o2 = n2[e3[i2]];
                r2[i2] = o2.type.shape.dims, this.context.graphInputTypes.push(o2.type.tensorType), this.context.graphInputDims.push(t2[i2].dims);
              }
              this.validateInputTensorDims(r2, t2, true);
            }
            return this.validateInputTensorTypes(this.context.graphInputTypes, t2), t2;
          }
          validateInputTensorTypes(t2, e2) {
            for (let n2 = 0; n2 < e2.length; n2++) {
              const r2 = t2[n2], i2 = e2[n2].type;
              if (r2 !== i2) throw new Error(`input tensor[${n2}] check failed: expected type '${r2}' but got ${i2}`);
            }
          }
          validateInputTensorDims(t2, e2, n2) {
            for (let r2 = 0; r2 < e2.length; r2++) {
              const i2 = t2[r2], o2 = e2[r2].dims;
              if (!this.compareTensorDims(i2, o2, n2)) throw new Error(`input tensor[${r2}] check failed: expected shape '[${i2.join(",")}]' but got [${o2.join(",")}]`);
            }
          }
          compareTensorDims(t2, e2, n2) {
            if (t2.length !== e2.length) return false;
            for (let r2 = 0; r2 < t2.length; ++r2) if (t2[r2] !== e2[r2] && (!n2 || 0 !== t2[r2])) return false;
            return true;
          }
          createOutput(t2) {
            const e2 = this._model.graph.getOutputNames();
            if (t2.length !== e2.length) throw new Error("expected number of outputs do not match number of generated outputs");
            const n2 = /* @__PURE__ */ new Map();
            for (let r2 = 0; r2 < e2.length; ++r2) n2.set(e2[r2], t2[r2]);
            return n2;
          }
          initializeOps(t2) {
            const e2 = t2.getNodes();
            this._ops = new Array(e2.length);
            for (let n2 = 0; n2 < e2.length; n2++) this._ops[n2] = this.sessionHandler.resolve(e2[n2], this._model.opsets, t2);
          }
        };
      }, 9162: function(t, e, n) {
        var r = this && this.__importDefault || function(t2) {
          return t2 && t2.__esModule ? t2 : { default: t2 };
        };
        Object.defineProperty(e, "__esModule", { value: true }), e.Tensor = void 0;
        const i = n(3442), o = r(n(3720)), a = n(1446), s = n(9395), u = n(2517);
        var c = s.onnxruntime.experimental.fbs;
        class l {
          get data() {
            if (void 0 === this.cache) {
              const t2 = this.dataProvider(this.dataId);
              if (t2.length !== this.size) throw new Error("Length of data provided by the Data Provider is inconsistent with the dims of this Tensor.");
              this.cache = t2;
            }
            return this.cache;
          }
          get stringData() {
            if ("string" !== this.type) throw new TypeError("data type is not string");
            return this.data;
          }
          get integerData() {
            switch (this.type) {
              case "uint8":
              case "int8":
              case "uint16":
              case "int16":
              case "int32":
              case "uint32":
              case "bool":
                return this.data;
              default:
                throw new TypeError("data type is not integer (uint8, int8, uint16, int16, int32, uint32, bool)");
            }
          }
          get floatData() {
            switch (this.type) {
              case "float32":
              case "float64":
                return this.data;
              default:
                throw new TypeError("data type is not float (float32, float64)");
            }
          }
          get numberData() {
            if ("string" !== this.type) return this.data;
            throw new TypeError("type cannot be non-number (string)");
          }
          get(t2) {
            return this.data[u.ShapeUtil.indicesToOffset(t2, this.strides)];
          }
          set(t2, e2) {
            this.data[u.ShapeUtil.indicesToOffset(t2, this.strides)] = e2;
          }
          getData() {
            return __async(this, null, function* () {
              return void 0 === this.cache && (this.cache = yield this.asyncDataProvider(this.dataId)), this.cache;
            });
          }
          get strides() {
            return this._strides || (this._strides = u.ShapeUtil.computeStrides(this.dims)), this._strides;
          }
          constructor(t2, e2, n2, r2, o2, a2 = i.Guid.create()) {
            this.dims = t2, this.type = e2, this.dataProvider = n2, this.asyncDataProvider = r2, this.cache = o2, this.dataId = a2, this.size = u.ShapeUtil.validateDimsAndCalcSize(t2);
            const s2 = this.size, c2 = void 0 === n2 && void 0 === r2 && void 0 === o2;
            if (void 0 !== o2 && o2.length !== s2) throw new RangeError("Input dims doesn't match data length.");
            if ("string" === e2) {
              if (!(void 0 === o2 || Array.isArray(o2) && o2.every((t3) => "string" == typeof t3))) throw new TypeError("cache should be a string array");
              c2 && (this.cache = new Array(s2));
            } else {
              if (void 0 !== o2) {
                const t3 = f(e2);
                if (!(o2 instanceof t3)) throw new TypeError(`cache should be type ${t3.name}`);
              }
              if (c2) {
                const t3 = new ArrayBuffer(s2 * function(t4) {
                  switch (t4) {
                    case "bool":
                    case "int8":
                    case "uint8":
                      return 1;
                    case "int16":
                    case "uint16":
                      return 2;
                    case "int32":
                    case "uint32":
                    case "float32":
                      return 4;
                    case "float64":
                      return 8;
                    default:
                      throw new Error(`cannot calculate sizeof() on type ${t4}`);
                  }
                }(e2));
                this.cache = function(t4, e3) {
                  return new (f(e3))(t4);
                }(t3, e2);
              }
            }
          }
          static fromProto(t2) {
            if (!t2) throw new Error("cannot construct Value from an empty tensor");
            const e2 = u.ProtoUtil.tensorDataTypeFromProto(t2.dataType), n2 = u.ProtoUtil.tensorDimsFromProto(t2.dims), r2 = new l(n2, e2);
            if ("string" === e2) t2.stringData.forEach((t3, e3) => {
              r2.data[e3] = (0, u.decodeUtf8String)(t3);
            });
            else if (t2.rawData && "number" == typeof t2.rawData.byteLength && t2.rawData.byteLength > 0) {
              const e3 = r2.data, n3 = new DataView(t2.rawData.buffer, t2.rawData.byteOffset, t2.rawData.byteLength), i2 = p(t2.dataType), o2 = t2.rawData.byteLength / i2;
              if (t2.rawData.byteLength % i2 != 0) throw new Error("invalid buffer length");
              if (e3.length !== o2) throw new Error("buffer length mismatch");
              for (let r3 = 0; r3 < o2; r3++) {
                const o3 = h(n3, t2.dataType, r3 * i2);
                e3[r3] = o3;
              }
            } else {
              let e3;
              switch (t2.dataType) {
                case a.onnx.TensorProto.DataType.FLOAT:
                  e3 = t2.floatData;
                  break;
                case a.onnx.TensorProto.DataType.INT32:
                case a.onnx.TensorProto.DataType.INT16:
                case a.onnx.TensorProto.DataType.UINT16:
                case a.onnx.TensorProto.DataType.INT8:
                case a.onnx.TensorProto.DataType.UINT8:
                case a.onnx.TensorProto.DataType.BOOL:
                  e3 = t2.int32Data;
                  break;
                case a.onnx.TensorProto.DataType.INT64:
                  e3 = t2.int64Data;
                  break;
                case a.onnx.TensorProto.DataType.DOUBLE:
                  e3 = t2.doubleData;
                  break;
                case a.onnx.TensorProto.DataType.UINT32:
                case a.onnx.TensorProto.DataType.UINT64:
                  e3 = t2.uint64Data;
                  break;
                default:
                  throw new Error("unspecific error");
              }
              if (null == e3) throw new Error("failed to populate data from a tensorproto value");
              const n3 = r2.data;
              if (n3.length !== e3.length) throw new Error("array length mismatch");
              for (let r3 = 0; r3 < e3.length; r3++) {
                const i2 = e3[r3];
                o.default.isLong(i2) ? n3[r3] = d(i2, t2.dataType) : n3[r3] = i2;
              }
            }
            return r2;
          }
          static fromData(t2, e2, n2) {
            return new l(e2, n2, void 0, void 0, t2);
          }
          static fromOrtTensor(t2) {
            if (!t2) throw new Error("cannot construct Value from an empty tensor");
            const e2 = u.ProtoUtil.tensorDimsFromORTFormat(t2), n2 = u.ProtoUtil.tensorDataTypeFromProto(t2.dataType()), r2 = new l(e2, n2);
            if ("string" === n2) for (let e3 = 0; e3 < t2.stringDataLength(); e3++) r2.data[e3] = t2.stringData(e3);
            else if (t2.rawDataArray() && "number" == typeof t2.rawDataLength() && t2.rawDataLength() > 0) {
              const e3 = r2.data, n3 = new DataView(t2.rawDataArray().buffer, t2.rawDataArray().byteOffset, t2.rawDataLength()), i2 = p(t2.dataType()), o2 = t2.rawDataLength() / i2;
              if (t2.rawDataLength() % i2 != 0) throw new Error("invalid buffer length");
              if (e3.length !== o2) throw new Error("buffer length mismatch");
              for (let r3 = 0; r3 < o2; r3++) {
                const o3 = h(n3, t2.dataType(), r3 * i2);
                e3[r3] = o3;
              }
            }
            return r2;
          }
        }
        function p(t2) {
          switch (t2) {
            case a.onnx.TensorProto.DataType.UINT8:
            case a.onnx.TensorProto.DataType.INT8:
            case a.onnx.TensorProto.DataType.BOOL:
              return 1;
            case a.onnx.TensorProto.DataType.UINT16:
            case a.onnx.TensorProto.DataType.INT16:
              return 2;
            case a.onnx.TensorProto.DataType.FLOAT:
            case a.onnx.TensorProto.DataType.INT32:
            case a.onnx.TensorProto.DataType.UINT32:
              return 4;
            case a.onnx.TensorProto.DataType.INT64:
            case a.onnx.TensorProto.DataType.DOUBLE:
            case a.onnx.TensorProto.DataType.UINT64:
              return 8;
            default:
              throw new Error(`cannot calculate sizeof() on type ${a.onnx.TensorProto.DataType[t2]}`);
          }
        }
        function f(t2) {
          switch (t2) {
            case "bool":
            case "uint8":
              return Uint8Array;
            case "int8":
              return Int8Array;
            case "int16":
              return Int16Array;
            case "uint16":
              return Uint16Array;
            case "int32":
              return Int32Array;
            case "uint32":
              return Uint32Array;
            case "float32":
              return Float32Array;
            case "float64":
              return Float64Array;
            default:
              throw new Error("unspecified error");
          }
        }
        function d(t2, e2) {
          if (e2 === a.onnx.TensorProto.DataType.INT64 || e2 === c.TensorDataType.INT64) {
            if (t2.greaterThanOrEqual(2147483648) || t2.lessThan(-2147483648)) throw new TypeError("int64 is not supported");
          } else {
            if (e2 !== a.onnx.TensorProto.DataType.UINT32 && e2 !== c.TensorDataType.UINT32 && e2 !== a.onnx.TensorProto.DataType.UINT64 && e2 !== c.TensorDataType.UINT64) throw new TypeError(`not a LONG type: ${a.onnx.TensorProto.DataType[e2]}`);
            if (t2.greaterThanOrEqual(4294967296) || t2.lessThan(0)) throw new TypeError("uint64 is not supported");
          }
          return t2.toNumber();
        }
        function h(t2, e2, n2) {
          switch (e2) {
            case a.onnx.TensorProto.DataType.BOOL:
            case a.onnx.TensorProto.DataType.UINT8:
              return t2.getUint8(n2);
            case a.onnx.TensorProto.DataType.INT8:
              return t2.getInt8(n2);
            case a.onnx.TensorProto.DataType.UINT16:
              return t2.getUint16(n2, true);
            case a.onnx.TensorProto.DataType.INT16:
              return t2.getInt16(n2, true);
            case a.onnx.TensorProto.DataType.FLOAT:
              return t2.getFloat32(n2, true);
            case a.onnx.TensorProto.DataType.INT32:
              return t2.getInt32(n2, true);
            case a.onnx.TensorProto.DataType.UINT32:
              return t2.getUint32(n2, true);
            case a.onnx.TensorProto.DataType.INT64:
              return d(o.default.fromBits(t2.getUint32(n2, true), t2.getUint32(n2 + 4, true), false), e2);
            case a.onnx.TensorProto.DataType.DOUBLE:
              return t2.getFloat64(n2, true);
            case a.onnx.TensorProto.DataType.UINT64:
              return d(o.default.fromBits(t2.getUint32(n2, true), t2.getUint32(n2 + 4, true), true), e2);
            default:
              throw new Error(`cannot read from DataView for type ${a.onnx.TensorProto.DataType[e2]}`);
          }
        }
        e.Tensor = l;
      }, 2517: function(t, e, n) {
        var r = this && this.__importDefault || function(t2) {
          return t2 && t2.__esModule ? t2 : { default: t2 };
        };
        Object.defineProperty(e, "__esModule", { value: true }), e.decodeUtf8String = e.MAX_CLIP = e.MIN_CLIP = e.PoolConvUtil = e.ReduceUtil = e.SplitUtil = e.MathUtil = e.ShapeUtil = e.LongUtil = e.ProtoUtil = e.GemmUtil = e.arrayCopyHelper = e.BroadcastUtil = e.MatMulUtil = e.ArrayUtil = e.assert = e.checkInputsShape = void 0;
        const i = n(5686), o = r(n(3720)), a = n(1446), s = n(9162);
        e.checkInputsShape = function(t2, ...e2) {
          if (!t2 || t2.length !== e2.length) return false;
          for (let n2 = 0; n2 < t2.length; n2++) if (!t2[n2].dims || t2[n2].dims.length !== e2[n2]) return false;
          return true;
        }, e.assert = function(t2, e2) {
          if (!t2) throw new Error("string" == typeof e2 ? e2 : e2());
        }, e.ArrayUtil = class {
          static arraysEqual(t2, e2) {
            if (t2.length !== e2.length) return false;
            for (let n2 = 0; n2 < t2.length; n2++) if (t2[n2] !== e2[n2]) return false;
            return true;
          }
        };
        class u {
          static preprocessInputShapes(t2, e2) {
            return [1 === t2.length ? [1, t2[0]] : t2, 1 === e2.length ? [e2[0], 1] : e2];
          }
          static postprocessOutputShape(t2, e2, n2) {
            1 === e2 && t2.splice(t2.length - 2, 1), 1 === n2 && t2.pop();
          }
          static calcMatMulShape(t2, e2) {
            return t2[1] !== e2[0] ? void 0 : [t2[0], e2[1]];
          }
        }
        e.MatMulUtil = u;
        class c {
          static calcShape(t2, e2, n2 = false) {
            const r2 = t2.length, i2 = e2.length;
            if (0 === r2) return e2;
            if (0 === i2) return t2;
            const o2 = Math.max(t2.length, e2.length), a2 = new Array(o2);
            if (n2) {
              if (r2 < 2 || i2 < 2) return;
              const n3 = u.calcMatMulShape([t2[r2 - 2], t2[r2 - 1]], [e2[i2 - 2], e2[i2 - 1]]);
              if (void 0 === n3) return;
              [a2[o2 - 2], a2[o2 - 1]] = n3;
            }
            for (let s2 = n2 ? 3 : 1; s2 <= o2; s2++) {
              const n3 = r2 - s2 < 0 ? 1 : t2[r2 - s2], u2 = i2 - s2 < 0 ? 1 : e2[i2 - s2];
              if (n3 !== u2 && n3 > 1 && u2 > 1) return;
              a2[o2 - s2] = Math.max(n3, u2);
            }
            return a2;
          }
          static index(t2, e2) {
            const n2 = new Array(e2.length);
            return c.fillIndex(t2, e2, n2), n2;
          }
          static fillIndex(t2, e2, n2) {
            const r2 = t2.length - e2.length;
            for (let i2 = 0; i2 < e2.length; i2++) n2[i2] = t2[r2 + i2] % e2[i2];
          }
          static calc(t2, e2, n2, r2, i2) {
            const o2 = c.calcShape(t2.dims, e2.dims);
            if (o2) {
              if (r2 && !f.areEqual(o2, t2.dims)) return;
              const a2 = f.size(o2), u2 = r2 ? t2 : new s.Tensor(o2, i2 || t2.type);
              if (0 === o2.length) u2.set([], n2(t2.get([]), e2.get([])));
              else {
                const r3 = new Array(o2.length), i3 = new Array(t2.dims.length), s2 = new Array(e2.dims.length);
                let l2, p2 = 0, f2 = 0, d2 = false, h2 = false;
                0 === t2.dims.length && (p2 = t2.get([]), d2 = true), 0 === e2.dims.length && (f2 = e2.get([]), h2 = true);
                for (let g2 = 0; g2 < a2; g2++) {
                  l2 = g2;
                  for (let t3 = o2.length - 1; t3 >= 0; t3--) r3[t3] = l2 % o2[t3], l2 = Math.floor(l2 / o2[t3]);
                  d2 || (c.fillIndex(r3, t2.dims, i3), p2 = t2.get(i3)), h2 || (c.fillIndex(r3, e2.dims, s2), f2 = e2.get(s2)), u2.set(r3, n2(p2, f2));
                }
              }
              return u2;
            }
          }
          static isValidBroadcast(t2, e2) {
            const n2 = t2.length, r2 = e2.length;
            if (n2 > r2) return false;
            for (let i2 = 1; i2 <= n2; i2++) if (1 !== t2[n2 - i2] && t2[n2 - i2] !== e2[r2 - i2]) return false;
            return true;
          }
          static getBroadcastDims(t2, e2) {
            const n2 = t2.length, r2 = [];
            for (let i2 = 0; i2 < n2; i2++) {
              const o2 = n2 - 1 - i2, a2 = t2[o2] || 1;
              (e2[e2.length - 1 - i2] || 1) > 1 && 1 === a2 && r2.unshift(o2);
            }
            return r2;
          }
        }
        e.BroadcastUtil = c, e.arrayCopyHelper = function(t2, e2, n2, r2, i2) {
          if (r2 < 0 || r2 >= e2.length) throw new Error("sourceIndex out of bounds");
          if (n2 < 0 || n2 >= t2.length) throw new Error("targetIndex out of bounds");
          if (r2 + i2 > e2.length) throw new Error("source indices to be copied are outside bounds");
          if (n2 + i2 > t2.length) throw new Error("target array is too small to hold result");
          for (let o2 = 0; o2 < i2; o2++) t2[n2 + o2] = e2[r2 + o2];
        }, e.GemmUtil = class {
          static getShapeOfGemmResult(t2, e2, n2, r2, i2) {
            if (2 !== t2.length || 2 !== n2.length) throw new Error("shape need to be of size 2");
            let o2, a2, s2;
            e2 ? (o2 = t2[1], a2 = t2[0]) : (o2 = t2[0], a2 = t2[1]);
            let u2 = -1;
            if (r2 ? (s2 = n2[0], u2 = 1) : (s2 = n2[1], u2 = 0), n2[u2] !== a2) throw new Error("dimension mismatch");
            if (o2 <= 0 || s2 <= 0 || a2 <= 0) throw new Error("invalid shape specified");
            if (i2 && !c.isValidBroadcast(i2, [o2, s2])) throw new Error("gemm: invalid bias shape for broadcast");
            return [o2, s2, a2];
          }
        };
        class l {
          static tensorDataTypeFromProto(t2) {
            switch (t2) {
              case a.onnx.TensorProto.DataType.INT8:
                return "int8";
              case a.onnx.TensorProto.DataType.UINT8:
                return "uint8";
              case a.onnx.TensorProto.DataType.BOOL:
                return "bool";
              case a.onnx.TensorProto.DataType.INT16:
                return "int16";
              case a.onnx.TensorProto.DataType.UINT16:
                return "uint16";
              case a.onnx.TensorProto.DataType.INT32:
                return "int32";
              case a.onnx.TensorProto.DataType.UINT32:
                return "uint32";
              case a.onnx.TensorProto.DataType.FLOAT:
                return "float32";
              case a.onnx.TensorProto.DataType.DOUBLE:
                return "float64";
              case a.onnx.TensorProto.DataType.STRING:
                return "string";
              case a.onnx.TensorProto.DataType.INT64:
                return "int32";
              case a.onnx.TensorProto.DataType.UINT64:
                return "uint32";
              default:
                throw new Error(`unsupported data type: ${a.onnx.TensorProto.DataType[t2]}`);
            }
          }
          static tensorDataTypeStringToEnum(t2) {
            switch (t2) {
              case "int8":
                return a.onnx.TensorProto.DataType.INT8;
              case "uint8":
                return a.onnx.TensorProto.DataType.UINT8;
              case "bool":
                return a.onnx.TensorProto.DataType.BOOL;
              case "int16":
                return a.onnx.TensorProto.DataType.INT16;
              case "uint16":
                return a.onnx.TensorProto.DataType.UINT16;
              case "int32":
                return a.onnx.TensorProto.DataType.INT32;
              case "uint32":
                return a.onnx.TensorProto.DataType.UINT32;
              case "float32":
                return a.onnx.TensorProto.DataType.FLOAT;
              case "float64":
                return a.onnx.TensorProto.DataType.DOUBLE;
              case "string":
                return a.onnx.TensorProto.DataType.STRING;
              case "int64":
                return a.onnx.TensorProto.DataType.INT64;
              case "uint64":
                return a.onnx.TensorProto.DataType.UINT64;
              default:
                throw new Error(`unsupported data type: ${t2}`);
            }
          }
          static tensorDimsFromProto(t2) {
            return t2.map((t3) => o.default.isLong(t3) ? t3.toNumber() : t3);
          }
          static tensorValueTypeFromProto(t2) {
            return { tensorType: l.tensorDataTypeFromProto(t2.elemType), shape: { dims: l.tensorDimsFromProto(t2.shape.dim.map((t3) => t3.dimValue)) } };
          }
          static tensorDimsFromORTFormat(t2) {
            const e2 = [];
            for (let n2 = 0; n2 < t2.dimsLength(); n2++) e2.push(p.longToNumber(t2.dims(n2)));
            return e2;
          }
          static tensorAttributesFromORTFormat(t2) {
            const e2 = [];
            for (let n2 = 0; n2 < t2.attributesLength(); n2++) e2.push(t2.attributes(n2));
            return e2;
          }
        }
        e.ProtoUtil = l;
        class p {
          static longToNumber(t2, e2) {
            return o.default.isLong(t2) ? t2.toNumber() : t2 instanceof i.flatbuffers.Long ? o.default.fromValue({ low: t2.low, high: t2.high, unsigned: null != e2 && e2 }).toNumber() : t2;
          }
          static isLong(t2) {
            return o.default.isLong(t2) || t2 instanceof i.flatbuffers.Long;
          }
        }
        e.LongUtil = p;
        class f {
          static size(t2) {
            return f.getSizeFromDimensionRange(t2, 0, t2.length);
          }
          static sizeFromDimension(t2, e2) {
            if (e2 < 0 || e2 > t2.length) throw new Error(`invalid dimension of ${e2} for sizeFromDimension as Tensor has ${t2.length} dimensions.`);
            return f.getSizeFromDimensionRange(t2, e2, t2.length);
          }
          static sizeToDimension(t2, e2) {
            if (e2 < 0 || e2 > t2.length) throw new Error(`invalid dimension of ${e2} for sizeToDimension as Tensor has ${t2.length} dimensions.`);
            return f.getSizeFromDimensionRange(t2, 0, e2);
          }
          static getSizeFromDimensionRange(t2, e2, n2) {
            let r2 = 1;
            for (let i2 = e2; i2 < n2; i2++) {
              if (t2[i2] <= 0) throw new Error("cannot get valid size from specified dimension range. Most likely the range contains 0 or negative values in them.");
              r2 *= t2[i2];
            }
            return r2;
          }
          static computeStrides(t2) {
            const e2 = t2.length;
            if (0 === e2) return [];
            if (1 === e2) return [1];
            const n2 = new Array(e2);
            n2[e2 - 1] = 1, n2[e2 - 2] = t2[e2 - 1];
            for (let r2 = e2 - 3; r2 >= 0; --r2) n2[r2] = n2[r2 + 1] * t2[r2 + 1];
            return n2;
          }
          static transpose(t2) {
            return t2.slice().reverse();
          }
          static indicesToOffset(t2, e2, n2) {
            void 0 === n2 && (n2 = t2.length);
            let r2 = 0;
            for (let i2 = 0; i2 < n2; ++i2) r2 += e2[i2] * t2[i2];
            return r2;
          }
          static offsetToIndices(t2, e2) {
            const n2 = e2.length;
            if (0 === n2) return [];
            if (1 === n2) return [t2 * e2[0]];
            const r2 = new Array(e2.length);
            for (let n3 = 0; n3 < r2.length - 1; ++n3) r2[n3] = Math.floor(t2 / e2[n3]), t2 -= r2[n3] * e2[n3];
            return r2[r2.length - 1] = t2, r2;
          }
          static normalizeAxis(t2, e2) {
            if (t2 < -e2 && t2 >= e2) throw new Error("unsupported axis for this operation.");
            return t2 < 0 ? t2 + e2 : t2;
          }
          static normalizeAxes(t2, e2) {
            return t2.map((t3) => this.normalizeAxis(t3, e2));
          }
          static incrementIndex(t2, e2, n2) {
            if (0 === e2.length || 0 === t2.length) throw new Error("Index incrementing unsupported for scalar Tensor");
            if (void 0 === n2) n2 = e2.length;
            else if (n2 <= 0 || n2 > e2.length) throw new Error("Incorrect axis to increment on");
            for (let r2 = n2 - 1; r2 >= 0 && (t2[r2]++, !(t2[r2] < e2[r2])); --r2) t2[r2] = 0;
          }
          static calculateReshapedDims(t2, e2) {
            if (0 === e2.length) {
              if (0 === t2.length || 1 === f.size(t2)) return [];
              throw new Error("cannot reshape to a scalar Tensor");
            }
            const n2 = e2.length, r2 = new Array(n2);
            let i2 = -1, o2 = 1;
            for (let a3 = 0; a3 < n2; a3++) {
              if (e2[a3] < -1) throw new Error("a dimension in shape hints cannot be less than -1");
              if (-1 === e2[a3]) {
                if (-1 !== i2) throw new Error("at most one dimension in shape hints can be -1");
                i2 = a3;
              } else {
                if (0 === e2[a3]) {
                  if (a3 >= t2.length) throw new Error("the dimension with value zero exceeds the dimension size of the input tensor");
                  r2[a3] = t2[a3];
                } else r2[a3] = e2[a3];
                o2 *= r2[a3];
              }
            }
            const a2 = f.size(t2);
            if (-1 !== i2) {
              if (a2 % o2 != 0) throw new Error(`the input tensor cannot be reshaped to the requested shape. Input shape: [${t2}] Output shape: [${e2}]`);
              r2[i2] = a2 / o2;
            } else if (o2 !== a2) throw new Error("reshapedDims and originalDims don't have matching sizes");
            return r2;
          }
          static sortBasedOnPerm(t2, e2) {
            return e2 ? e2.map((e3) => t2[e3]) : t2.slice().reverse();
          }
          static padShape(t2, e2) {
            const n2 = t2.length;
            return t2.map((t3, r2) => t3 + e2[r2] + e2[r2 + n2]);
          }
          static areEqual(t2, e2) {
            return t2.length === e2.length && t2.every((t3, n2) => t3 === e2[n2]);
          }
          static validateDimsAndCalcSize(t2) {
            if (t2.length > 6) throw new TypeError("Only rank 0 to 6 is supported for tensor shape.");
            let e2 = 1;
            for (const n2 of t2) {
              if (!Number.isInteger(n2)) throw new TypeError(`Invalid shape: ${n2} is not an integer`);
              if (n2 < 0 || n2 > 2147483647) throw new TypeError(`Invalid shape: length ${n2} is not allowed`);
              e2 *= n2;
            }
            return e2;
          }
          static flattenShape(t2, e2) {
            e2 < 0 && (e2 += t2.length);
            const n2 = t2.reduce((t3, e3) => t3 * e3, 1), r2 = t2.slice(e2).reduce((t3, e3) => t3 * e3, 1);
            return [n2 / r2, r2];
          }
          static squeezeShape(t2, e2) {
            const n2 = new Array();
            e2 = f.normalizeAxes(e2, t2.length);
            for (let r2 = 0; r2 < t2.length; r2++) {
              const i2 = e2.indexOf(r2) >= 0;
              if (i2 && 1 !== t2[r2]) throw new Error("squeeze an axis of size different than 1");
              (0 === e2.length && t2[r2] > 1 || e2.length > 0 && !i2) && n2.push(t2[r2]);
            }
            return n2;
          }
          static unsqueezeShape(t2, e2) {
            const n2 = new Array(t2.length + e2.length);
            n2.fill(0);
            for (let t3 = 0; t3 < e2.length; t3++) {
              const r3 = f.normalizeAxis(e2[t3], n2.length);
              if (r3 >= n2.length) throw new Error("'axes' has an out of range axis");
              if (0 !== n2[r3]) throw new Error("'axes' has a duplicate axis");
              n2[r3] = 1;
            }
            let r2 = 0;
            for (let e3 = 0; e3 < n2.length; e3++) 0 === n2[e3] && (n2[e3] = t2[r2++]);
            if (r2 !== t2.length) throw new Error("the unsqueezed dimension could not be established");
            return n2;
          }
        }
        e.ShapeUtil = f, e.MathUtil = class {
          static sqr(t2, e2, n2, r2, i2) {
            if (r2 < 0 || r2 >= e2.length) throw new Error("sourceIndex out of bounds");
            if (n2 < 0 || n2 >= t2.length) throw new Error("targetIndex out of bounds");
            if (r2 + i2 > e2.length) throw new Error("source indices to be copied are outside bounds");
            if (n2 + i2 > t2.length) throw new Error("target array is too small to hold result");
            for (let o2 = 0; o2 < i2; o2++) t2[n2 + o2] += Math.pow(e2[r2 + o2], 2);
          }
          static axpy(t2, e2, n2, r2, i2, o2) {
            if (r2 < 0 || r2 >= e2.length) throw new Error("sourceIndex out of bounds");
            if (n2 < 0 || n2 >= t2.length) throw new Error("targetIndex out of bounds");
            if (r2 + i2 > e2.length) throw new Error("source indices to be copied are outside bounds");
            if (n2 + i2 > t2.length) throw new Error("target array is too small to hold result");
            for (let a2 = 0; a2 < i2; a2++) t2[n2 + a2] += o2 * e2[r2 + a2];
          }
          static powx(t2, e2, n2, r2, i2, o2) {
            if (r2 < 0 || r2 >= e2.length) throw new Error("sourceIndex out of bounds");
            if (n2 < 0 || n2 >= t2.length) throw new Error("targetIndex out of bounds");
            if (r2 + i2 > e2.length) throw new Error("source indices to be copied are outside bounds");
            if (n2 + i2 > t2.length) throw new Error("target array is too small to hold result");
            for (let a2 = 0; a2 < i2; a2++) t2[n2 + a2] = Math.pow(e2[r2 + a2], o2);
          }
          static mul(t2, e2, n2, r2, i2) {
            if (r2 < 0 || r2 >= e2.length) throw new Error("sourceIndex out of bounds");
            if (n2 < 0 || n2 >= t2.length) throw new Error("targetIndex out of bounds");
            if (r2 + i2 > e2.length) throw new Error("source indices to be copied are outside bounds");
            if (n2 + i2 > t2.length) throw new Error("target array is too small to hold result");
            for (let o2 = 0; o2 < i2; o2++) t2[n2 + o2] = e2[r2 + o2] * t2[n2 + o2];
          }
        };
        class d {
          static splitShape(t2, e2, n2, r2) {
            if (0 === n2.length) {
              if (!r2) throw new Error("need to know number of outputs when the 'split' attribute is not specified");
              d.determineSplit(t2[e2], r2, n2);
            }
            const i2 = [], o2 = [0];
            for (let r3 = 0; r3 < n2.length; ++r3) {
              0 !== r3 && o2.push(o2[r3 - 1] + n2[r3 - 1]);
              const a2 = t2.slice();
              a2[e2] = n2[r3], i2.push(a2);
            }
            return [i2, o2];
          }
          static determineSplit(t2, e2, n2) {
            if (t2 % e2 != 0) throw new Error("cannot split tensor to equal sized parts");
            for (let r2 = 0; r2 < e2; ++r2) n2.push(t2 / e2);
          }
        }
        e.SplitUtil = d;
        class h {
          static calcReduce(t2, e2, n2, r2, i2) {
            const o2 = t2.dims.slice(0);
            0 === e2.length && o2.forEach((t3, n3) => e2.push(n3));
            const a2 = h.calcReduceShape(o2, e2, true), u2 = f.size(a2), l2 = new s.Tensor(a2, t2.type), p2 = f.computeStrides(a2), d2 = f.computeStrides(o2), g2 = new Array(o2.length);
            for (let n3 = 0; n3 < u2; n3++) {
              const a3 = f.offsetToIndices(n3, p2);
              c.fillIndex(a3, o2, g2), l2.set(a3, h.calcReduceByAxis(t2.numberData, e2, o2, 0, f.indicesToOffset(g2, d2), r2, i2));
            }
            return n2 ? l2 : new s.Tensor(h.calcReduceShape(o2, e2, n2), l2.type, void 0, void 0, l2.data, l2.dataId);
          }
          static calcReduceByAxis(t2, e2, n2, r2, i2, o2, a2) {
            let s2 = 0;
            if (r2 >= e2.length) return o2(t2[i2]);
            const u2 = e2[r2], c2 = u2 >= n2.length ? 1 : f.size(n2.slice(u2 + 1));
            for (let l2 = 0; l2 < n2[u2]; l2++) s2 = 0 === l2 ? h.calcReduceByAxis(t2, e2, n2, r2 + 1, i2, o2, a2) : a2(s2, h.calcReduceByAxis(t2, e2, n2, r2 + 1, i2, o2, a2)), i2 += c2;
            return s2;
          }
          static calcReduceShape(t2, e2, n2) {
            const r2 = t2.slice();
            for (let t3 = 0; t3 < e2.length; t3++) r2[e2[t3]] = n2 ? 1 : 0;
            return r2.filter((t3) => 0 !== t3);
          }
        }
        e.ReduceUtil = h;
        class g {
          static adjustPoolAttributes(t2, e2, n2, r2, i2, o2) {
            if (!t2 && n2.length !== e2.length - 2) throw new Error("length of specified kernel shapes should be 2 less than length of input dimensions");
            if (t2) for (let t3 = 0; t3 < e2.length - 2; t3++) t3 >= n2.length ? n2.push(e2[t3 + 2]) : n2[t3] = e2[t3 + 2];
            for (let t3 = 0; t3 < n2.length; t3++) if (t3 < r2.length) {
              if (r2[t3] < 0) throw new Error("strides should be greater than or equal to 1");
            } else r2.push(1);
            for (let t3 = 0; t3 < n2.length; t3++) if (t3 < i2.length) {
              if (i2[t3] < 0) throw new Error("dilations should be greater than or equal to 1");
            } else i2.push(1);
            for (let t3 = 0; t3 < 2 * n2.length; t3++) if (t3 < o2.length) {
              if (o2[t3] < 0) throw new Error("pad should be greater than or equal to 1");
            } else o2.push(0);
            for (let t3 = 0; t3 < n2.length; t3++) {
              if (n2[t3] <= 0) throw new Error("kernel shapes need to be greater than 0");
              if (o2[t3] >= n2[t3] || o2[t3 + n2.length] >= n2[t3]) throw new Error("pads should be smaller than kernel");
            }
          }
          static adjustPadsBasedOnAutoPad(t2, e2, n2, r2, i2, o2) {
            if (o2) {
              if (i2.length !== 2 * (t2.length - 2)) throw new Error("length of pads should be twice the length of data dimensions");
              if (e2.length !== t2.length - 2) throw new Error("length of strides should be the length of data dimensions");
              if (r2.length !== t2.length - 2) throw new Error("length of kernel shapes should be the length of data dimensions");
              for (let a2 = 0; a2 < t2.length - 2; a2++) g.adjustPadAndReturnShape(t2[a2 + 2], e2[a2], n2[a2], r2[a2], i2, a2, a2 + t2.length - 2, o2);
            }
          }
          static computePoolOutputShape(t2, e2, n2, r2, i2, o2, a2) {
            if (e2.length <= 0) throw new Error("input shape must be of size greater than 0");
            const s2 = [e2[0], e2[1]];
            return g.computeShapeHelper(t2, e2, s2, n2, r2, i2, o2, a2), s2;
          }
          static computeConvOutputShape(t2, e2, n2, r2, i2, o2, a2) {
            if (t2.length <= 0 || e2.length <= 0) throw new Error("invalid input tensor dims or invalid filter tensor dims");
            const s2 = [t2[0], e2[0]];
            return g.computeShapeHelper(false, t2, s2, n2, r2, i2, o2, a2), s2;
          }
          static computeShapeHelper(t2, e2, n2, r2, i2, o2, a2, s2) {
            if (t2) for (let t3 = 0; t3 < e2.length - 2; t3++) n2.push(1);
            else for (let t3 = 0; t3 < e2.length - 2; t3++) n2.push(g.adjustPadAndReturnShape(e2[t3 + 2], r2[t3], i2[t3], o2[t3], a2, t3, t3 + e2.length - 2, s2));
          }
          static adjustPadAndReturnShape(t2, e2, n2, r2, i2, o2, a2, s2) {
            const u2 = n2 * (r2 - 1) + 1;
            if (!s2 || "NOTSET" === s2) return Math.floor((t2 + i2[o2] + i2[a2] - u2) / e2 + 1);
            switch (s2) {
              case "VALID":
                return i2[o2] = 0, i2[a2] = 0, Math.floor((t2 - u2) / e2 + 1);
              case "SAME_LOWER":
              case "SAME_UPPER":
                if (1 !== n2) throw new Error("Dilation not supported for SAME_UPPER or SAME_LOWER");
                {
                  const n3 = ((t2 + e2 - 1) / e2 - 1) * e2 + r2 - t2;
                  return i2[o2] = "SAME_LOWER" === s2 ? Math.floor((n3 + 1) / 2) : Math.floor(n3 / 2), i2[a2] = n3 - i2[o2], Math.floor((t2 + n3 - r2) / e2 + 1);
                }
              default:
                throw new Error("Unsupported AutoPad type");
            }
          }
        }
        e.PoolConvUtil = g, e.MIN_CLIP = -34028234663852886e22, e.MAX_CLIP = 34028234663852886e22, e.decodeUtf8String = function(t2) {
          return new TextDecoder().decode(t2);
        };
      }, 7967: (t, e) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.iterateExtraOptions = void 0, e.iterateExtraOptions = (t2, n, r, i) => {
          if ("object" == typeof t2 && null !== t2) {
            if (r.has(t2)) throw new Error("Circular reference in options");
            r.add(t2);
          }
          Object.entries(t2).forEach(([t3, o]) => {
            const a = n ? n + t3 : t3;
            if ("object" == typeof o) (0, e.iterateExtraOptions)(o, a + ".", r, i);
            else if ("string" == typeof o || "number" == typeof o) i(a, o.toString());
            else {
              if ("boolean" != typeof o) throw new Error("Can't handle extra config type: " + typeof o);
              i(a, o ? "1" : "0");
            }
          });
        };
      }, 2157: function(t, e, n) {
        var r, i = this && this.__createBinding || (Object.create ? function(t2, e2, n2, r2) {
          void 0 === r2 && (r2 = n2);
          var i2 = Object.getOwnPropertyDescriptor(e2, n2);
          i2 && !("get" in i2 ? !e2.__esModule : i2.writable || i2.configurable) || (i2 = { enumerable: true, get: function() {
            return e2[n2];
          } }), Object.defineProperty(t2, r2, i2);
        } : function(t2, e2, n2, r2) {
          void 0 === r2 && (r2 = n2), t2[r2] = e2[n2];
        }), o = this && this.__setModuleDefault || (Object.create ? function(t2, e2) {
          Object.defineProperty(t2, "default", { enumerable: true, value: e2 });
        } : function(t2, e2) {
          t2.default = e2;
        }), a = this && this.__importStar || function(t2) {
          if (t2 && t2.__esModule) return t2;
          var e2 = {};
          if (null != t2) for (var n2 in t2) "default" !== n2 && Object.prototype.hasOwnProperty.call(t2, n2) && i(e2, t2, n2);
          return o(e2, t2), e2;
        };
        Object.defineProperty(e, "__esModule", { value: true }), e.endProfiling = e.run = e.releaseSession = e.createSession = e.createSessionFinalize = e.createSessionAllocate = e.initOrt = e.initWasm = void 0;
        const s = n(1670), u = a(n(349)), c = n(6361), l = () => !!s.env.wasm.proxy && "undefined" != typeof document;
        let p, f, d, h = false, g = false, b = false;
        const m = [], y = [], _ = [], v = [], w = [], x = [], T = () => {
          if (h || !g || b || !p) throw new Error("worker not ready");
        }, S = (t2) => {
          switch (t2.data.type) {
            case "init-wasm":
              h = false, t2.data.err ? (b = true, f[1](t2.data.err)) : (g = true, f[0]());
              break;
            case "init-ort":
              t2.data.err ? d[1](t2.data.err) : d[0]();
              break;
            case "create_allocate":
              t2.data.err ? m.shift()[1](t2.data.err) : m.shift()[0](t2.data.out);
              break;
            case "create_finalize":
              t2.data.err ? y.shift()[1](t2.data.err) : y.shift()[0](t2.data.out);
              break;
            case "create":
              t2.data.err ? _.shift()[1](t2.data.err) : _.shift()[0](t2.data.out);
              break;
            case "release":
              t2.data.err ? v.shift()[1](t2.data.err) : v.shift()[0]();
              break;
            case "run":
              t2.data.err ? w.shift()[1](t2.data.err) : w.shift()[0](t2.data.out);
              break;
            case "end-profiling":
              t2.data.err ? x.shift()[1](t2.data.err) : x.shift()[0]();
          }
        }, O = "undefined" != typeof document ? null === (r = null === document || void 0 === document ? void 0 : document.currentScript) || void 0 === r ? void 0 : r.src : void 0;
        e.initWasm = () => __async(this, null, function* () {
          if (l()) {
            if (g) return;
            if (h) throw new Error("multiple calls to 'initWasm()' detected.");
            if (b) throw new Error("previous call to 'initWasm()' failed.");
            return h = true, void 0 === s.env.wasm.wasmPaths && O && 0 !== O.indexOf("blob:") && (s.env.wasm.wasmPaths = O.substr(0, +O.lastIndexOf("/") + 1)), new Promise((t2, e2) => {
              null == p || p.terminate(), p = n(9710).Z(), p.onmessage = S, f = [t2, e2];
              const r2 = { type: "init-wasm", in: s.env.wasm };
              p.postMessage(r2);
            });
          }
          return (0, c.initializeWebAssembly)(s.env.wasm);
        }), e.initOrt = (t2, e2) => __async(this, null, function* () {
          if (l()) return T(), new Promise((n2, r2) => {
            d = [n2, r2];
            const i2 = { type: "init-ort", in: { numThreads: t2, loggingLevel: e2 } };
            p.postMessage(i2);
          });
          u.initOrt(t2, e2);
        }), e.createSessionAllocate = (t2) => __async(this, null, function* () {
          return l() ? (T(), new Promise((e2, n2) => {
            m.push([e2, n2]);
            const r2 = { type: "create_allocate", in: { model: t2 } };
            p.postMessage(r2, [t2.buffer]);
          })) : u.createSessionAllocate(t2);
        }), e.createSessionFinalize = (t2, e2) => __async(this, null, function* () {
          return l() ? (T(), new Promise((n2, r2) => {
            y.push([n2, r2]);
            const i2 = { type: "create_finalize", in: { modeldata: t2, options: e2 } };
            p.postMessage(i2);
          })) : u.createSessionFinalize(t2, e2);
        }), e.createSession = (t2, e2) => __async(this, null, function* () {
          return l() ? (T(), new Promise((n2, r2) => {
            _.push([n2, r2]);
            const i2 = { type: "create", in: { model: t2, options: e2 } };
            p.postMessage(i2, [t2.buffer]);
          })) : u.createSession(t2, e2);
        }), e.releaseSession = (t2) => __async(this, null, function* () {
          if (l()) return T(), new Promise((e2, n2) => {
            v.push([e2, n2]);
            const r2 = { type: "release", in: t2 };
            p.postMessage(r2);
          });
          u.releaseSession(t2);
        }), e.run = (t2, e2, n2, r2, i2) => __async(this, null, function* () {
          return l() ? (T(), new Promise((o2, a2) => {
            w.push([o2, a2]);
            const s2 = { type: "run", in: { sessionId: t2, inputIndices: e2, inputs: n2, outputIndices: r2, options: i2 } };
            p.postMessage(s2, u.extractTransferableBuffers(n2));
          })) : u.run(t2, e2, n2, r2, i2);
        }), e.endProfiling = (t2) => __async(this, null, function* () {
          if (l()) return T(), new Promise((e2, n2) => {
            x.push([e2, n2]);
            const r2 = { type: "end-profiling", in: t2 };
            p.postMessage(r2);
          });
          u.endProfiling(t2);
        });
      }, 586: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.setRunOptions = void 0;
        const r = n(7967), i = n(4983), o = n(6361);
        e.setRunOptions = (t2) => {
          const e2 = (0, o.getInstance)();
          let n2 = 0;
          const a = [], s = t2 || {};
          try {
            if (void 0 === (null == t2 ? void 0 : t2.logSeverityLevel)) s.logSeverityLevel = 2;
            else if ("number" != typeof t2.logSeverityLevel || !Number.isInteger(t2.logSeverityLevel) || t2.logSeverityLevel < 0 || t2.logSeverityLevel > 4) throw new Error(`log serverity level is not valid: ${t2.logSeverityLevel}`);
            if (void 0 === (null == t2 ? void 0 : t2.logVerbosityLevel)) s.logVerbosityLevel = 0;
            else if ("number" != typeof t2.logVerbosityLevel || !Number.isInteger(t2.logVerbosityLevel)) throw new Error(`log verbosity level is not valid: ${t2.logVerbosityLevel}`);
            void 0 === (null == t2 ? void 0 : t2.terminate) && (s.terminate = false);
            let o2 = 0;
            if (void 0 !== (null == t2 ? void 0 : t2.tag) && (o2 = (0, i.allocWasmString)(t2.tag, a)), n2 = e2._OrtCreateRunOptions(s.logSeverityLevel, s.logVerbosityLevel, !!s.terminate, o2), 0 === n2) throw new Error("Can't create run options");
            return void 0 !== (null == t2 ? void 0 : t2.extra) && (0, r.iterateExtraOptions)(t2.extra, "", /* @__PURE__ */ new WeakSet(), (t3, r2) => {
              const o3 = (0, i.allocWasmString)(t3, a), s2 = (0, i.allocWasmString)(r2, a);
              if (0 !== e2._OrtAddRunConfigEntry(n2, o3, s2)) throw new Error(`Can't set a run config entry: ${t3} - ${r2}`);
            }), [n2, a];
          } catch (t3) {
            throw 0 !== n2 && e2._OrtReleaseRunOptions(n2), a.forEach(e2._free), t3;
          }
        };
      }, 2306: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.OnnxruntimeWebAssemblySessionHandler = void 0;
        const r = n(2806), i = n(1670), o = n(2850), a = n(2157);
        let s;
        e.OnnxruntimeWebAssemblySessionHandler = class {
          createSessionAllocate(t2) {
            return __async(this, null, function* () {
              const e2 = yield fetch(t2), n2 = yield e2.arrayBuffer();
              return (0, a.createSessionAllocate)(new Uint8Array(n2));
            });
          }
          loadModel(t2, e2) {
            return __async(this, null, function* () {
              if (s || (yield (0, a.initOrt)(i.env.wasm.numThreads, ((t3) => {
                switch (t3) {
                  case "verbose":
                    return 0;
                  case "info":
                    return 1;
                  case "warning":
                    return 2;
                  case "error":
                    return 3;
                  case "fatal":
                    return 4;
                  default:
                    throw new Error(`unsupported logging level: ${t3}`);
                }
              })(i.env.logLevel)), s = true), "string" == typeof t2) if ("undefined" == typeof fetch) {
                const n2 = yield (0, o.promisify)(r.readFile)(t2);
                [this.sessionId, this.inputNames, this.outputNames] = yield (0, a.createSession)(n2, e2);
              } else {
                const n2 = yield this.createSessionAllocate(t2);
                [this.sessionId, this.inputNames, this.outputNames] = yield (0, a.createSessionFinalize)(n2, e2);
              }
              else [this.sessionId, this.inputNames, this.outputNames] = yield (0, a.createSession)(t2, e2);
            });
          }
          dispose() {
            return __async(this, null, function* () {
              return (0, a.releaseSession)(this.sessionId);
            });
          }
          run(t2, e2, n2) {
            return __async(this, null, function* () {
              const r2 = [], o2 = [];
              Object.entries(t2).forEach((t3) => {
                const e3 = t3[0], n3 = t3[1], i2 = this.inputNames.indexOf(e3);
                if (-1 === i2) throw new Error(`invalid input '${e3}'`);
                r2.push(n3), o2.push(i2);
              });
              const s2 = [];
              Object.entries(e2).forEach((t3) => {
                const e3 = t3[0], n3 = this.outputNames.indexOf(e3);
                if (-1 === n3) throw new Error(`invalid output '${e3}'`);
                s2.push(n3);
              });
              const u = yield (0, a.run)(this.sessionId, o2, r2.map((t3) => [t3.type, t3.dims, t3.data]), s2, n2), c = {};
              for (let t3 = 0; t3 < u.length; t3++) c[this.outputNames[s2[t3]]] = new i.Tensor(u[t3][0], u[t3][2], u[t3][1]);
              return c;
            });
          }
          startProfiling() {
          }
          endProfiling() {
            (0, a.endProfiling)(this.sessionId);
          }
        };
      }, 4919: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.setSessionOptions = void 0;
        const r = n(7967), i = n(4983), o = n(6361);
        e.setSessionOptions = (t2) => {
          const e2 = (0, o.getInstance)();
          let n2 = 0;
          const a = [], s = t2 || {};
          ((t3) => {
            t3.extra || (t3.extra = {}), t3.extra.session || (t3.extra.session = {});
            const e3 = t3.extra.session;
            e3.use_ort_model_bytes_directly || (e3.use_ort_model_bytes_directly = "1");
          })(s);
          try {
            void 0 === (null == t2 ? void 0 : t2.graphOptimizationLevel) && (s.graphOptimizationLevel = "all");
            const u = ((t3) => {
              switch (t3) {
                case "disabled":
                  return 0;
                case "basic":
                  return 1;
                case "extended":
                  return 2;
                case "all":
                  return 99;
                default:
                  throw new Error(`unsupported graph optimization level: ${t3}`);
              }
            })(s.graphOptimizationLevel);
            void 0 === (null == t2 ? void 0 : t2.enableCpuMemArena) && (s.enableCpuMemArena = true), void 0 === (null == t2 ? void 0 : t2.enableMemPattern) && (s.enableMemPattern = true), void 0 === (null == t2 ? void 0 : t2.executionMode) && (s.executionMode = "sequential");
            const c = ((t3) => {
              switch (t3) {
                case "sequential":
                  return 0;
                case "parallel":
                  return 1;
                default:
                  throw new Error(`unsupported execution mode: ${t3}`);
              }
            })(s.executionMode);
            let l = 0;
            if (void 0 !== (null == t2 ? void 0 : t2.logId) && (l = (0, i.allocWasmString)(t2.logId, a)), void 0 === (null == t2 ? void 0 : t2.logSeverityLevel)) s.logSeverityLevel = 2;
            else if ("number" != typeof t2.logSeverityLevel || !Number.isInteger(t2.logSeverityLevel) || t2.logSeverityLevel < 0 || t2.logSeverityLevel > 4) throw new Error(`log serverity level is not valid: ${t2.logSeverityLevel}`);
            if (void 0 === (null == t2 ? void 0 : t2.logVerbosityLevel)) s.logVerbosityLevel = 0;
            else if ("number" != typeof t2.logVerbosityLevel || !Number.isInteger(t2.logVerbosityLevel)) throw new Error(`log verbosity level is not valid: ${t2.logVerbosityLevel}`);
            if (void 0 === (null == t2 ? void 0 : t2.enableProfiling) && (s.enableProfiling = false), n2 = e2._OrtCreateSessionOptions(u, !!s.enableCpuMemArena, !!s.enableMemPattern, c, !!s.enableProfiling, 0, l, s.logSeverityLevel, s.logVerbosityLevel), 0 === n2) throw new Error("Can't create session options");
            return (null == t2 ? void 0 : t2.executionProviders) && ((t3, e3, n3) => {
              for (const r2 of e3) {
                let e4 = "string" == typeof r2 ? r2 : r2.name;
                switch (e4) {
                  case "xnnpack":
                    e4 = "XNNPACK";
                    break;
                  case "wasm":
                  case "cpu":
                    continue;
                  default:
                    throw new Error(`not supported EP: ${e4}`);
                }
                const a2 = (0, i.allocWasmString)(e4, n3);
                if (0 !== (0, o.getInstance)()._OrtAppendExecutionProvider(t3, a2)) throw new Error(`Can't append execution provider: ${e4}`);
              }
            })(n2, t2.executionProviders, a), void 0 !== (null == t2 ? void 0 : t2.extra) && (0, r.iterateExtraOptions)(t2.extra, "", /* @__PURE__ */ new WeakSet(), (t3, r2) => {
              const o2 = (0, i.allocWasmString)(t3, a), s2 = (0, i.allocWasmString)(r2, a);
              if (0 !== e2._OrtAddSessionConfigEntry(n2, o2, s2)) throw new Error(`Can't set a session config entry: ${t3} - ${r2}`);
            }), [n2, a];
          } catch (t3) {
            throw 0 !== n2 && e2._OrtReleaseSessionOptions(n2), a.forEach(e2._free), t3;
          }
        };
      }, 4983: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.allocWasmString = void 0;
        const r = n(6361);
        e.allocWasmString = (t2, e2) => {
          const n2 = (0, r.getInstance)(), i = n2.lengthBytesUTF8(t2) + 1, o = n2._malloc(i);
          return n2.stringToUTF8(t2, o, i), e2.push(o), o;
        };
      }, 349: (t, e, n) => {
        Object.defineProperty(e, "__esModule", { value: true }), e.extractTransferableBuffers = e.endProfiling = e.run = e.releaseSession = e.createSession = e.createSessionFinalize = e.createSessionAllocate = e.initOrt = void 0;
        const r = n(586), i = n(4919), o = n(4983), a = n(6361);
        e.initOrt = (t2, e2) => {
          const n2 = (0, a.getInstance)()._OrtInit(t2, e2);
          if (0 !== n2) throw new Error(`Can't initialize onnxruntime. error code = ${n2}`);
        };
        const s = /* @__PURE__ */ new Map();
        e.createSessionAllocate = (t2) => {
          const e2 = (0, a.getInstance)(), n2 = e2._malloc(t2.byteLength);
          return e2.HEAPU8.set(t2, n2), [n2, t2.byteLength];
        }, e.createSessionFinalize = (t2, e2) => {
          const n2 = (0, a.getInstance)();
          let r2 = 0, o2 = 0, u2 = [];
          try {
            if ([o2, u2] = (0, i.setSessionOptions)(e2), r2 = n2._OrtCreateSession(t2[0], t2[1], o2), 0 === r2) throw new Error("Can't create a session");
          } finally {
            n2._free(t2[0]), n2._OrtReleaseSessionOptions(o2), u2.forEach(n2._free);
          }
          const c2 = n2._OrtGetInputCount(r2), l2 = n2._OrtGetOutputCount(r2), p = [], f = [], d = [], h = [];
          for (let t3 = 0; t3 < c2; t3++) {
            const e3 = n2._OrtGetInputName(r2, t3);
            if (0 === e3) throw new Error("Can't get an input name");
            f.push(e3), p.push(n2.UTF8ToString(e3));
          }
          for (let t3 = 0; t3 < l2; t3++) {
            const e3 = n2._OrtGetOutputName(r2, t3);
            if (0 === e3) throw new Error("Can't get an output name");
            h.push(e3), d.push(n2.UTF8ToString(e3));
          }
          return s.set(r2, [r2, f, h]), [r2, p, d];
        }, e.createSession = (t2, n2) => {
          const r2 = (0, e.createSessionAllocate)(t2);
          return (0, e.createSessionFinalize)(r2, n2);
        }, e.releaseSession = (t2) => {
          const e2 = (0, a.getInstance)(), n2 = s.get(t2);
          if (!n2) throw new Error("invalid session id");
          const r2 = n2[0], i2 = n2[1], o2 = n2[2];
          i2.forEach(e2._OrtFree), o2.forEach(e2._OrtFree), e2._OrtReleaseSession(r2), s.delete(t2);
        };
        const u = (t2) => {
          switch (t2) {
            case "int8":
              return 3;
            case "uint8":
              return 2;
            case "bool":
              return 9;
            case "int16":
              return 5;
            case "uint16":
              return 4;
            case "int32":
              return 6;
            case "uint32":
              return 12;
            case "float32":
              return 1;
            case "float64":
              return 11;
            case "string":
              return 8;
            case "int64":
              return 7;
            case "uint64":
              return 13;
            default:
              throw new Error(`unsupported data type: ${t2}`);
          }
        }, c = (t2) => {
          switch (t2) {
            case 3:
              return "int8";
            case 2:
              return "uint8";
            case 9:
              return "bool";
            case 5:
              return "int16";
            case 4:
              return "uint16";
            case 6:
              return "int32";
            case 12:
              return "uint32";
            case 1:
              return "float32";
            case 11:
              return "float64";
            case 8:
              return "string";
            case 7:
              return "int64";
            case 13:
              return "uint64";
            default:
              throw new Error(`unsupported data type: ${t2}`);
          }
        }, l = (t2) => {
          switch (t2) {
            case "float32":
              return Float32Array;
            case "uint8":
            case "bool":
              return Uint8Array;
            case "int8":
              return Int8Array;
            case "uint16":
              return Uint16Array;
            case "int16":
              return Int16Array;
            case "int32":
              return Int32Array;
            case "float64":
              return Float64Array;
            case "uint32":
              return Uint32Array;
            case "int64":
              return BigInt64Array;
            case "uint64":
              return BigUint64Array;
            default:
              throw new Error(`unsupported type: ${t2}`);
          }
        };
        e.run = (t2, e2, n2, i2, p) => {
          const f = (0, a.getInstance)(), d = s.get(t2);
          if (!d) throw new Error("invalid session id");
          const h = d[0], g = d[1], b = d[2], m = e2.length, y = i2.length;
          let _ = 0, v = [];
          const w = [], x = [];
          try {
            [_, v] = (0, r.setRunOptions)(p);
            for (let t4 = 0; t4 < m; t4++) {
              const e3 = n2[t4][0], r2 = n2[t4][1], i3 = n2[t4][2];
              let a3, s3;
              if (Array.isArray(i3)) {
                s3 = 4 * i3.length, a3 = f._malloc(s3), x.push(a3);
                let t5 = a3 / 4;
                for (let e4 = 0; e4 < i3.length; e4++) {
                  if ("string" != typeof i3[e4]) throw new TypeError(`tensor data at index ${e4} is not a string`);
                  f.HEAPU32[t5++] = (0, o.allocWasmString)(i3[e4], x);
                }
              } else s3 = i3.byteLength, a3 = f._malloc(s3), x.push(a3), f.HEAPU8.set(new Uint8Array(i3.buffer, i3.byteOffset, s3), a3);
              const c2 = f.stackSave(), l2 = f.stackAlloc(4 * r2.length);
              try {
                let t5 = l2 / 4;
                r2.forEach((e4) => f.HEAP32[t5++] = e4);
                const n3 = f._OrtCreateTensor(u(e3), a3, s3, l2, r2.length);
                if (0 === n3) throw new Error("Can't create a tensor");
                w.push(n3);
              } finally {
                f.stackRestore(c2);
              }
            }
            const t3 = f.stackSave(), a2 = f.stackAlloc(4 * m), s2 = f.stackAlloc(4 * m), d2 = f.stackAlloc(4 * y), T = f.stackAlloc(4 * y);
            try {
              let n3 = a2 / 4, r2 = s2 / 4, o2 = d2 / 4, u2 = T / 4;
              for (let t4 = 0; t4 < m; t4++) f.HEAPU32[n3++] = w[t4], f.HEAPU32[r2++] = g[e2[t4]];
              for (let t4 = 0; t4 < y; t4++) f.HEAPU32[o2++] = 0, f.HEAPU32[u2++] = b[i2[t4]];
              let p2 = f._OrtRun(h, s2, a2, m, T, y, d2, _);
              const v2 = [];
              if (0 === p2) for (let t4 = 0; t4 < y; t4++) {
                const e3 = f.HEAPU32[d2 / 4 + t4], n4 = f.stackSave(), r3 = f.stackAlloc(16);
                let i3, o3 = 0;
                try {
                  if (p2 = f._OrtGetTensorData(e3, r3, r3 + 4, r3 + 8, r3 + 12), 0 !== p2) throw new Error(`Can't access output tensor data. error code = ${p2}`);
                  let t5 = r3 / 4;
                  const a3 = f.HEAPU32[t5++];
                  o3 = f.HEAPU32[t5++];
                  const s3 = f.HEAPU32[t5++], u3 = f.HEAPU32[t5++], d3 = [];
                  for (let t6 = 0; t6 < u3; t6++) d3.push(f.HEAPU32[s3 / 4 + t6]);
                  f._OrtFree(s3);
                  const h2 = 0 === d3.length ? 1 : d3.reduce((t6, e4) => t6 * e4);
                  if (i3 = c(a3), "string" === i3) {
                    const t6 = [];
                    let e4 = o3 / 4;
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e.ExitStatus))throw t;e.keepRuntimeAlive()||e.__emscripten_thread_exit(t.status)}}}else"cancel"===t.data.cmd?e._pthread_self()&&e.__emscripten_thread_exit(-1):"setimmediate"===t.data.target||("processProxyingQueue"===t.data.cmd?s?e.executeNotifiedProxyingQueue(t.data.queue):n.push(t.data.queue):(i("worker.js received unknown command "+t.data.cmd),i(t.data)))}catch(t){throw i("worker.js onmessage() captured an uncaught exception: "+t),t&&t.stack&&i(t.stack),e.__emscripten_thread_crashed&&e.__emscripten_thread_crashed(),t}};\\n\'},384:()=>{},993:()=>{},908:()=>{},953:()=>{},925:()=>{},449:()=>{}},e={};function n(r){var a=e[r];if(void 0!==a)return a.exports;var i=e[r]={exports:{}};return t[r].call(i.exports,i,i.exports,n),i.exports}n.g=function(){if("object"==typeof globalThis)return globalThis;try{return this||new Function("return this")()}catch(t){if("object"==typeof window)return window}}(),(()=>{"use strict";const t=n(349),e=n(361);self.onmessage=n=>{switch(n.data.type){case"init-wasm":(0,e.initializeWebAssembly)(n.data.in).then((()=>postMessage({type:"init-wasm"})),(t=>postMessage({type:"init-wasm",err:t})));break;case"init-ort":try{const{numThreads:e,loggingLevel:r}=n.data.in;(0,t.initOrt)(e,r),postMessage({type:"init-ort"})}catch(t){postMessage({type:"init-ort",err:t})}break;case"create_allocate":try{const{model:e}=n.data.in,r=(0,t.createSessionAllocate)(e);postMessage({type:"create_allocate",out:r})}catch(t){postMessage({type:"create_allocate",err:t})}break;case"create_finalize":try{const{modeldata:e,options:r}=n.data.in,a=(0,t.createSessionFinalize)(e,r);postMessage({type:"create_finalize",out:a})}catch(t){postMessage({type:"create_finalize",err:t})}break;case"create":try{const{model:e,options:r}=n.data.in,a=(0,t.createSession)(e,r);postMessage({type:"create",out:a})}catch(t){postMessage({type:"create",err:t})}break;case"release":try{const e=n.data.in;(0,t.releaseSession)(e),postMessage({type:"release"})}catch(t){postMessage({type:"release",err:t})}break;case"run":try{const{sessionId:e,inputIndices:r,inputs:a,outputIndices:i,options:o}=n.data.in,u=(0,t.run)(e,r,a,i,o);postMessage({type:"run",out:u},(0,t.extractTransferableBuffers)(u))}catch(t){postMessage({type:"run",err:t})}break;case"end-profiling":try{const e=n.data.in;(0,t.endProfiling)(e),postMessage({type:"end-profiling"})}catch(t){postMessage({type:"end-profiling",err:t})}}}})()})();\n', "Worker", void 0, void 0);
        }
      }, 477: (t) => {
        t.exports = function(t2, e, n, r) {
          var i = self || window;
          try {
            try {
              var o;
              try {
                o = new i.Blob([t2]);
              } catch (e2) {
                (o = new (i.BlobBuilder || i.WebKitBlobBuilder || i.MozBlobBuilder || i.MSBlobBuilder)()).append(t2), o = o.getBlob();
              }
              var a = i.URL || i.webkitURL, s = a.createObjectURL(o), u = new i[e](s, n);
              return a.revokeObjectURL(s), u;
            } catch (r2) {
              return new i[e]("data:application/javascript,".concat(encodeURIComponent(t2)), n);
            }
          } catch (t3) {
            if (!r) throw Error("Inline worker is not supported");
            return new i[e](r, n);
          }
        };
      }, 4154: (t) => {
        t.exports = '"use strict";var e={},t="object"==typeof process&&"object"==typeof process.versions&&"string"==typeof process.versions.node;if(t){var r=require("worker_threads"),a=r.parentPort;a.on("message",(e=>onmessage({data:e})));var o=require("fs");Object.assign(global,{self:global,require:require,Module:e,location:{href:__filename},Worker:r.Worker,importScripts:function(e){(0,eval)(o.readFileSync(e,"utf8"))},postMessage:function(e){a.postMessage(e)},performance:global.performance||{now:function(){return Date.now()}}})}var s=!1,n=[],i=function(){var e=Array.prototype.slice.call(arguments).join(" ");t?o.writeSync(2,e+"\\n"):console.error(e)};self.alert=function(){var t=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:t,threadId:e._pthread_self()})},e.instantiateWasm=(t,r)=>{var a=new WebAssembly.Instance(e.wasmModule,t);return r(a),e.wasmModule=null,a.exports},self.onunhandledrejection=e=>{throw e.reason??e},self.onmessage=t=>{try{if("load"===t.data.cmd){if(e.wasmModule=t.data.wasmModule,e.wasmMemory=t.data.wasmMemory,e.buffer=e.wasmMemory.buffer,e.ENVIRONMENT_IS_PTHREAD=!0,"string"==typeof t.data.urlOrBlob)importScripts(t.data.urlOrBlob);else{var r=URL.createObjectURL(t.data.urlOrBlob);importScripts(r),URL.revokeObjectURL(r)}ortWasmThreaded(e).then((function(t){e=t}))}else if("run"===t.data.cmd){e.__performance_now_clock_drift=performance.now()-t.data.time,e.__emscripten_thread_init(t.data.pthread_ptr,0,0,1),e.establishStackSpace(),e.PThread.receiveObjectTransfer(t.data),e.PThread.threadInitTLS(),s||(n.forEach((t=>{e.executeNotifiedProxyingQueue(t)})),n=[],s=!0);try{e.invokeEntryPoint(t.data.start_routine,t.data.arg)}catch(t){if("unwind"!=t){if(!(t instanceof e.ExitStatus))throw t;e.keepRuntimeAlive()||e.__emscripten_thread_exit(t.status)}}}else"cancel"===t.data.cmd?e._pthread_self()&&e.__emscripten_thread_exit(-1):"setimmediate"===t.data.target||("processProxyingQueue"===t.data.cmd?s?e.executeNotifiedProxyingQueue(t.data.queue):n.push(t.data.queue):(i("worker.js received unknown command "+t.data.cmd),i(t.data)))}catch(t){throw i("worker.js onmessage() captured an uncaught exception: "+t),t&&t.stack&&i(t.stack),e.__emscripten_thread_crashed&&e.__emscripten_thread_crashed(),t}};\n';
      }, 1670: (t) => {
        t.exports = __WEBPACK_EXTERNAL_MODULE__1670__;
      }, 7067: () => {
      }, 1296: () => {
      }, 1384: () => {
      }, 3993: () => {
      }, 908: () => {
      }, 6953: () => {
      }, 9925: () => {
      }, 2806: () => {
      }, 6449: () => {
      }, 2850: () => {
      }, 5381: () => {
      }, 5686: (t, e, n) => {
        n.r(e), n.d(e, { flatbuffers: () => r });
        var r = {};
        r.Offset, r.Table, r.SIZEOF_SHORT = 2, r.SIZEOF_INT = 4, r.FILE_IDENTIFIER_LENGTH = 4, r.SIZE_PREFIX_LENGTH = 4, r.Encoding = { UTF8_BYTES: 1, UTF16_STRING: 2 }, r.int32 = new Int32Array(2), r.float32 = new Float32Array(r.int32.buffer), r.float64 = new Float64Array(r.int32.buffer), r.isLittleEndian = 1 === new Uint16Array(new Uint8Array([1, 0]).buffer)[0], r.Long = function(t2, e2) {
          this.low = 0 | t2, this.high = 0 | e2;
        }, r.Long.create = function(t2, e2) {
          return 0 == t2 && 0 == e2 ? r.Long.ZERO : new r.Long(t2, e2);
        }, r.Long.prototype.toFloat64 = function() {
          return (this.low >>> 0) + 4294967296 * this.high;
        }, r.Long.prototype.equals = function(t2) {
          return this.low == t2.low && this.high == t2.high;
        }, r.Long.ZERO = new r.Long(0, 0), r.Builder = function(t2) {
          if (t2) e2 = t2;
          else var e2 = 1024;
          this.bb = r.ByteBuffer.allocate(e2), this.space = e2, this.minalign = 1, this.vtable = null, this.vtable_in_use = 0, this.isNested = false, this.object_start = 0, this.vtables = [], this.vector_num_elems = 0, this.force_defaults = false;
        }, r.Builder.prototype.clear = function() {
          this.bb.clear(), this.space = this.bb.capacity(), this.minalign = 1, this.vtable = null, this.vtable_in_use = 0, this.isNested = false, this.object_start = 0, this.vtables = [], this.vector_num_elems = 0, this.force_defaults = false;
        }, r.Builder.prototype.forceDefaults = function(t2) {
          this.force_defaults = t2;
        }, r.Builder.prototype.dataBuffer = function() {
          return this.bb;
        }, r.Builder.prototype.asUint8Array = function() {
          return this.bb.bytes().subarray(this.bb.position(), this.bb.position() + this.offset());
        }, r.Builder.prototype.prep = function(t2, e2) {
          t2 > this.minalign && (this.minalign = t2);
          for (var n2 = 1 + ~(this.bb.capacity() - this.space + e2) & t2 - 1; this.space < n2 + t2 + e2; ) {
            var i = this.bb.capacity();
            this.bb = r.Builder.growByteBuffer(this.bb), this.space += this.bb.capacity() - i;
          }
          this.pad(n2);
        }, r.Builder.prototype.pad = function(t2) {
          for (var e2 = 0; e2 < t2; e2++) this.bb.writeInt8(--this.space, 0);
        }, r.Builder.prototype.writeInt8 = function(t2) {
          this.bb.writeInt8(this.space -= 1, t2);
        }, r.Builder.prototype.writeInt16 = function(t2) {
          this.bb.writeInt16(this.space -= 2, t2);
        }, r.Builder.prototype.writeInt32 = function(t2) {
          this.bb.writeInt32(this.space -= 4, t2);
        }, r.Builder.prototype.writeInt64 = function(t2) {
          this.bb.writeInt64(this.space -= 8, t2);
        }, r.Builder.prototype.writeFloat32 = function(t2) {
          this.bb.writeFloat32(this.space -= 4, t2);
        }, r.Builder.prototype.writeFloat64 = function(t2) {
          this.bb.writeFloat64(this.space -= 8, t2);
        }, r.Builder.prototype.addInt8 = function(t2) {
          this.prep(1, 0), this.writeInt8(t2);
        }, r.Builder.prototype.addInt16 = function(t2) {
          this.prep(2, 0), this.writeInt16(t2);
        }, r.Builder.prototype.addInt32 = function(t2) {
          this.prep(4, 0), this.writeInt32(t2);
        }, r.Builder.prototype.addInt64 = function(t2) {
          this.prep(8, 0), this.writeInt64(t2);
        }, r.Builder.prototype.addFloat32 = function(t2) {
          this.prep(4, 0), this.writeFloat32(t2);
        }, r.Builder.prototype.addFloat64 = function(t2) {
          this.prep(8, 0), this.writeFloat64(t2);
        }, r.Builder.prototype.addFieldInt8 = function(t2, e2, n2) {
          (this.force_defaults || e2 != n2) && (this.addInt8(e2), this.slot(t2));
        }, r.Builder.prototype.addFieldInt16 = function(t2, e2, n2) {
          (this.force_defaults || e2 != n2) && (this.addInt16(e2), this.slot(t2));
        }, r.Builder.prototype.addFieldInt32 = function(t2, e2, n2) {
          (this.force_defaults || e2 != n2) && (this.addInt32(e2), this.slot(t2));
        }, r.Builder.prototype.addFieldInt64 = function(t2, e2, n2) {
          !this.force_defaults && e2.equals(n2) || (this.addInt64(e2), this.slot(t2));
        }, r.Builder.prototype.addFieldFloat32 = function(t2, e2, n2) {
          (this.force_defaults || e2 != n2) && (this.addFloat32(e2), this.slot(t2));
        }, r.Builder.prototype.addFieldFloat64 = function(t2, e2, n2) {
          (this.force_defaults || e2 != n2) && (this.addFloat64(e2), this.slot(t2));
        }, r.Builder.prototype.addFieldOffset = function(t2, e2, n2) {
          (this.force_defaults || e2 != n2) && (this.addOffset(e2), this.slot(t2));
        }, r.Builder.prototype.addFieldStruct = function(t2, e2, n2) {
          e2 != n2 && (this.nested(e2), this.slot(t2));
        }, r.Builder.prototype.nested = function(t2) {
          if (t2 != this.offset()) throw new Error("FlatBuffers: struct must be serialized inline.");
        }, r.Builder.prototype.notNested = function() {
          if (this.isNested) throw new Error("FlatBuffers: object serialization must not be nested.");
        }, r.Builder.prototype.slot = function(t2) {
          this.vtable[t2] = this.offset();
        }, r.Builder.prototype.offset = function() {
          return this.bb.capacity() - this.space;
        }, r.Builder.growByteBuffer = function(t2) {
          var e2 = t2.capacity();
          if (3221225472 & e2) throw new Error("FlatBuffers: cannot grow buffer beyond 2 gigabytes.");
          var n2 = e2 << 1, i = r.ByteBuffer.allocate(n2);
          return i.setPosition(n2 - e2), i.bytes().set(t2.bytes(), n2 - e2), i;
        }, r.Builder.prototype.addOffset = function(t2) {
          this.prep(r.SIZEOF_INT, 0), this.writeInt32(this.offset() - t2 + r.SIZEOF_INT);
        }, r.Builder.prototype.startObject = function(t2) {
          this.notNested(), null == this.vtable && (this.vtable = []), this.vtable_in_use = t2;
          for (var e2 = 0; e2 < t2; e2++) this.vtable[e2] = 0;
          this.isNested = true, this.object_start = this.offset();
        }, r.Builder.prototype.endObject = function() {
          if (null == this.vtable || !this.isNested) throw new Error("FlatBuffers: endObject called without startObject");
          this.addInt32(0);
          for (var t2 = this.offset(), e2 = this.vtable_in_use - 1; e2 >= 0 && 0 == this.vtable[e2]; e2--) ;
          for (var n2 = e2 + 1; e2 >= 0; e2--) this.addInt16(0 != this.vtable[e2] ? t2 - this.vtable[e2] : 0);
          this.addInt16(t2 - this.object_start);
          var i = (n2 + 2) * r.SIZEOF_SHORT;
          this.addInt16(i);
          var o = 0, a = this.space;
          t: for (e2 = 0; e2 < this.vtables.length; e2++) {
            var s = this.bb.capacity() - this.vtables[e2];
            if (i == this.bb.readInt16(s)) {
              for (var u = r.SIZEOF_SHORT; u < i; u += r.SIZEOF_SHORT) if (this.bb.readInt16(a + u) != this.bb.readInt16(s + u)) continue t;
              o = this.vtables[e2];
              break;
            }
          }
          return o ? (this.space = this.bb.capacity() - t2, this.bb.writeInt32(this.space, o - t2)) : (this.vtables.push(this.offset()), this.bb.writeInt32(this.bb.capacity() - t2, this.offset() - t2)), this.isNested = false, t2;
        }, r.Builder.prototype.finish = function(t2, e2, n2) {
          var i = n2 ? r.SIZE_PREFIX_LENGTH : 0;
          if (e2) {
            var o = e2;
            if (this.prep(this.minalign, r.SIZEOF_INT + r.FILE_IDENTIFIER_LENGTH + i), o.length != r.FILE_IDENTIFIER_LENGTH) throw new Error("FlatBuffers: file identifier must be length " + r.FILE_IDENTIFIER_LENGTH);
            for (var a = r.FILE_IDENTIFIER_LENGTH - 1; a >= 0; a--) this.writeInt8(o.charCodeAt(a));
          }
          this.prep(this.minalign, r.SIZEOF_INT + i), this.addOffset(t2), i && this.addInt32(this.bb.capacity() - this.space), this.bb.setPosition(this.space);
        }, r.Builder.prototype.finishSizePrefixed = function(t2, e2) {
          this.finish(t2, e2, true);
        }, r.Builder.prototype.requiredField = function(t2, e2) {
          var n2 = this.bb.capacity() - t2, r2 = n2 - this.bb.readInt32(n2);
          if (0 == this.bb.readInt16(r2 + e2)) throw new Error("FlatBuffers: field " + e2 + " must be set");
        }, r.Builder.prototype.startVector = function(t2, e2, n2) {
          this.notNested(), this.vector_num_elems = e2, this.prep(r.SIZEOF_INT, t2 * e2), this.prep(n2, t2 * e2);
        }, r.Builder.prototype.endVector = function() {
          return this.writeInt32(this.vector_num_elems), this.offset();
        }, r.Builder.prototype.createString = function(t2) {
          if (t2 instanceof Uint8Array) var e2 = t2;
          else {
            e2 = [];
            for (var n2 = 0; n2 < t2.length; ) {
              var r2, i = t2.charCodeAt(n2++);
              (r2 = i < 55296 || i >= 56320 ? i : (i << 10) + t2.charCodeAt(n2++) + -56613888) < 128 ? e2.push(r2) : (r2 < 2048 ? e2.push(r2 >> 6 & 31 | 192) : (r2 < 65536 ? e2.push(r2 >> 12 & 15 | 224) : e2.push(r2 >> 18 & 7 | 240, r2 >> 12 & 63 | 128), e2.push(r2 >> 6 & 63 | 128)), e2.push(63 & r2 | 128));
            }
          }
          this.addInt8(0), this.startVector(1, e2.length, 1), this.bb.setPosition(this.space -= e2.length), n2 = 0;
          for (var o = this.space, a = this.bb.bytes(); n2 < e2.length; n2++) a[o++] = e2[n2];
          return this.endVector();
        }, r.Builder.prototype.createLong = function(t2, e2) {
          return r.Long.create(t2, e2);
        }, r.ByteBuffer = function(t2) {
          this.bytes_ = t2, this.position_ = 0;
        }, r.ByteBuffer.allocate = function(t2) {
          return new r.ByteBuffer(new Uint8Array(t2));
        }, r.ByteBuffer.prototype.clear = function() {
          this.position_ = 0;
        }, r.ByteBuffer.prototype.bytes = function() {
          return this.bytes_;
        }, r.ByteBuffer.prototype.position = function() {
          return this.position_;
        }, r.ByteBuffer.prototype.setPosition = function(t2) {
          this.position_ = t2;
        }, r.ByteBuffer.prototype.capacity = function() {
          return this.bytes_.length;
        }, r.ByteBuffer.prototype.readInt8 = function(t2) {
          return this.readUint8(t2) << 24 >> 24;
        }, r.ByteBuffer.prototype.readUint8 = function(t2) {
          return this.bytes_[t2];
        }, r.ByteBuffer.prototype.readInt16 = function(t2) {
          return this.readUint16(t2) << 16 >> 16;
        }, r.ByteBuffer.prototype.readUint16 = function(t2) {
          return this.bytes_[t2] | this.bytes_[t2 + 1] << 8;
        }, r.ByteBuffer.prototype.readInt32 = function(t2) {
          return this.bytes_[t2] | this.bytes_[t2 + 1] << 8 | this.bytes_[t2 + 2] << 16 | this.bytes_[t2 + 3] << 24;
        }, r.ByteBuffer.prototype.readUint32 = function(t2) {
          return this.readInt32(t2) >>> 0;
        }, r.ByteBuffer.prototype.readInt64 = function(t2) {
          return new r.Long(this.readInt32(t2), this.readInt32(t2 + 4));
        }, r.ByteBuffer.prototype.readUint64 = function(t2) {
          return new r.Long(this.readUint32(t2), this.readUint32(t2 + 4));
        }, r.ByteBuffer.prototype.readFloat32 = function(t2) {
          return r.int32[0] = this.readInt32(t2), r.float32[0];
        }, r.ByteBuffer.prototype.readFloat64 = function(t2) {
          return r.int32[r.isLittleEndian ? 0 : 1] = this.readInt32(t2), r.int32[r.isLittleEndian ? 1 : 0] = this.readInt32(t2 + 4), r.float64[0];
        }, r.ByteBuffer.prototype.writeInt8 = function(t2, e2) {
          this.bytes_[t2] = e2;
        }, r.ByteBuffer.prototype.writeUint8 = function(t2, e2) {
          this.bytes_[t2] = e2;
        }, r.ByteBuffer.prototype.writeInt16 = function(t2, e2) {
          this.bytes_[t2] = e2, this.bytes_[t2 + 1] = e2 >> 8;
        }, r.ByteBuffer.prototype.writeUint16 = function(t2, e2) {
          this.bytes_[t2] = e2, this.bytes_[t2 + 1] = e2 >> 8;
        }, r.ByteBuffer.prototype.writeInt32 = function(t2, e2) {
          this.bytes_[t2] = e2, this.bytes_[t2 + 1] = e2 >> 8, this.bytes_[t2 + 2] = e2 >> 16, this.bytes_[t2 + 3] = e2 >> 24;
        }, r.ByteBuffer.prototype.writeUint32 = function(t2, e2) {
          this.bytes_[t2] = e2, this.bytes_[t2 + 1] = e2 >> 8, this.bytes_[t2 + 2] = e2 >> 16, this.bytes_[t2 + 3] = e2 >> 24;
        }, r.ByteBuffer.prototype.writeInt64 = function(t2, e2) {
          this.writeInt32(t2, e2.low), this.writeInt32(t2 + 4, e2.high);
        }, r.ByteBuffer.prototype.writeUint64 = function(t2, e2) {
          this.writeUint32(t2, e2.low), this.writeUint32(t2 + 4, e2.high);
        }, r.ByteBuffer.prototype.writeFloat32 = function(t2, e2) {
          r.float32[0] = e2, this.writeInt32(t2, r.int32[0]);
        }, r.ByteBuffer.prototype.writeFloat64 = function(t2, e2) {
          r.float64[0] = e2, this.writeInt32(t2, r.int32[r.isLittleEndian ? 0 : 1]), this.writeInt32(t2 + 4, r.int32[r.isLittleEndian ? 1 : 0]);
        }, r.ByteBuffer.prototype.getBufferIdentifier = function() {
          if (this.bytes_.length < this.position_ + r.SIZEOF_INT + r.FILE_IDENTIFIER_LENGTH) throw new Error("FlatBuffers: ByteBuffer is too short to contain an identifier.");
          for (var t2 = "", e2 = 0; e2 < r.FILE_IDENTIFIER_LENGTH; e2++) t2 += String.fromCharCode(this.readInt8(this.position_ + r.SIZEOF_INT + e2));
          return t2;
        }, r.ByteBuffer.prototype.__offset = function(t2, e2) {
          var n2 = t2 - this.readInt32(t2);
          return e2 < this.readInt16(n2) ? this.readInt16(n2 + e2) : 0;
        }, r.ByteBuffer.prototype.__union = function(t2, e2) {
          return t2.bb_pos = e2 + this.readInt32(e2), t2.bb = this, t2;
        }, r.ByteBuffer.prototype.__string = function(t2, e2) {
          t2 += this.readInt32(t2);
          var n2 = this.readInt32(t2), i = "", o = 0;
          if (t2 += r.SIZEOF_INT, e2 === r.Encoding.UTF8_BYTES) return this.bytes_.subarray(t2, t2 + n2);
          for (; o < n2; ) {
            var a, s = this.readUint8(t2 + o++);
            if (s < 192) a = s;
            else {
              var u = this.readUint8(t2 + o++);
              if (s < 224) a = (31 & s) << 6 | 63 & u;
              else {
                var c = this.readUint8(t2 + o++);
                a = s < 240 ? (15 & s) << 12 | (63 & u) << 6 | 63 & c : (7 & s) << 18 | (63 & u) << 12 | (63 & c) << 6 | 63 & this.readUint8(t2 + o++);
              }
            }
            a < 65536 ? i += String.fromCharCode(a) : (a -= 65536, i += String.fromCharCode(55296 + (a >> 10), 56320 + (1023 & a)));
          }
          return i;
        }, r.ByteBuffer.prototype.__indirect = function(t2) {
          return t2 + this.readInt32(t2);
        }, r.ByteBuffer.prototype.__vector = function(t2) {
          return t2 + this.readInt32(t2) + r.SIZEOF_INT;
        }, r.ByteBuffer.prototype.__vector_len = function(t2) {
          return this.readInt32(t2 + this.readInt32(t2));
        }, r.ByteBuffer.prototype.__has_identifier = function(t2) {
          if (t2.length != r.FILE_IDENTIFIER_LENGTH) throw new Error("FlatBuffers: file identifier must be length " + r.FILE_IDENTIFIER_LENGTH);
          for (var e2 = 0; e2 < r.FILE_IDENTIFIER_LENGTH; e2++) if (t2.charCodeAt(e2) != this.readInt8(this.position_ + r.SIZEOF_INT + e2)) return false;
          return true;
        }, r.ByteBuffer.prototype.createLong = function(t2, e2) {
          return r.Long.create(t2, e2);
        };
      } }, __webpack_module_cache__ = {};
      function __webpack_require__(t) {
        var e = __webpack_module_cache__[t];
        if (void 0 !== e) return e.exports;
        var n = __webpack_module_cache__[t] = { exports: {} };
        return __webpack_modules__[t].call(n.exports, n, n.exports, __webpack_require__), n.exports;
      }
      __webpack_require__.n = (t) => {
        var e = t && t.__esModule ? () => t.default : () => t;
        return __webpack_require__.d(e, { a: e }), e;
      }, __webpack_require__.d = (t, e) => {
        for (var n in e) __webpack_require__.o(e, n) && !__webpack_require__.o(t, n) && Object.defineProperty(t, n, { enumerable: true, get: e[n] });
      }, __webpack_require__.g = function() {
        if ("object" == typeof globalThis) return globalThis;
        try {
          return this || new Function("return this")();
        } catch (t) {
          if ("object" == typeof window) return window;
        }
      }(), __webpack_require__.o = (t, e) => Object.prototype.hasOwnProperty.call(t, e), __webpack_require__.r = (t) => {
        "undefined" != typeof Symbol && Symbol.toStringTag && Object.defineProperty(t, Symbol.toStringTag, { value: "Module" }), Object.defineProperty(t, "__esModule", { value: true });
      };
      var __webpack_exports__ = __webpack_require__(6018);
      return __webpack_exports__;
    })());
  })(ortWeb_min$1);
  return ortWeb_min$1.exports;
}
var ortWeb_minExports = requireOrtWeb_min();
const ortWeb_min = /* @__PURE__ */ getDefaultExportFromCjs(ortWeb_minExports);
const ONNX_WEB = /* @__PURE__ */ _mergeNamespaces({
  __proto__: null,
  default: ortWeb_min
}, [ortWeb_minExports]);
let ONNX;
const executionProviders = [
  // 'webgpu',
  "wasm"
];
if (typeof process !== "undefined" && ((_a = process == null ? void 0 : process.release) == null ? void 0 : _a.name) === "node") {
  ONNX = sharp != null ? sharp : ONNX_NODE;
  executionProviders.unshift("cpu");
} else {
  ONNX = ortWeb_min != null ? ortWeb_min : ONNX_WEB;
  const isIOS = typeof navigator !== "undefined" && /iP(hone|od|ad).+16_4.+AppleWebKit/.test(navigator.userAgent);
  if (isIOS) {
    ONNX.env.wasm.simd = false;
  }
}
const { env: onnx_env } = ONNX;
const VERSION = "2.17.2";
const WEB_CACHE_AVAILABLE = typeof self !== "undefined" && "caches" in self;
const FS_AVAILABLE = !isEmpty(sharp);
const PATH_AVAILABLE = !isEmpty(sharp);
const RUNNING_LOCALLY = FS_AVAILABLE && PATH_AVAILABLE;
const __dirname = RUNNING_LOCALLY ? sharp.dirname(sharp.dirname(sharp.fileURLToPath(self.location.href))) : "./";
const DEFAULT_CACHE_DIR = RUNNING_LOCALLY ? sharp.join(__dirname, "/.cache/") : null;
const DEFAULT_LOCAL_MODEL_PATH = "/models/";
const localModelPath = RUNNING_LOCALLY ? sharp.join(__dirname, DEFAULT_LOCAL_MODEL_PATH) : DEFAULT_LOCAL_MODEL_PATH;
if (onnx_env == null ? void 0 : onnx_env.wasm) {
  onnx_env.wasm.wasmPaths = RUNNING_LOCALLY ? sharp.join(__dirname, "/dist/") : `https://cdn.jsdelivr.net/npm/@xenova/transformers@${VERSION}/dist/`;
}
const env$1 = {
  /////////////////// Backends settings ///////////////////
  backends: {
    // onnxruntime-web/onnxruntime-node
    onnx: onnx_env,
    // TensorFlow.js
    tfjs: {}
  },
  __dirname,
  version: VERSION,
  /////////////////// Model settings ///////////////////
  allowRemoteModels: true,
  remoteHost: "https://huggingface.co/",
  remotePathTemplate: "{model}/resolve/{revision}/",
  allowLocalModels: true,
  localModelPath,
  useFS: FS_AVAILABLE,
  /////////////////// Cache settings ///////////////////
  useBrowserCache: WEB_CACHE_AVAILABLE,
  useFSCache: FS_AVAILABLE,
  cacheDir: DEFAULT_CACHE_DIR,
  useCustomCache: false,
  customCache: null
  //////////////////////////////////////////////////////
};
function isEmpty(obj) {
  return Object.keys(obj).length === 0;
}
var define_process_env_default = {};
class FileResponse {
  /**
   * Creates a new `FileResponse` object.
   * @param {string|URL} filePath
   */
  constructor(filePath) {
    /**
     * Mapping from file extensions to MIME types.
     */
    __publicField(this, "_CONTENT_TYPE_MAP", {
      "txt": "text/plain",
      "html": "text/html",
      "css": "text/css",
      "js": "text/javascript",
      "json": "application/json",
      "png": "image/png",
      "jpg": "image/jpeg",
      "jpeg": "image/jpeg",
      "gif": "image/gif"
    });
    this.filePath = filePath;
    this.headers = new Headers();
    this.exists = sharp.existsSync(filePath);
    if (this.exists) {
      this.status = 200;
      this.statusText = "OK";
      let stats = sharp.statSync(filePath);
      this.headers.set("content-length", stats.size.toString());
      this.updateContentType();
      let self2 = this;
      this.body = new ReadableStream({
        start(controller) {
          self2.arrayBuffer().then((buffer) => {
            controller.enqueue(new Uint8Array(buffer));
            controller.close();
          });
        }
      });
    } else {
      this.status = 404;
      this.statusText = "Not Found";
      this.body = null;
    }
  }
  /**
   * Updates the 'content-type' header property of the response based on the extension of
   * the file specified by the filePath property of the current object.
   * @returns {void}
   */
  updateContentType() {
    var _a2;
    const extension = this.filePath.toString().split(".").pop().toLowerCase();
    this.headers.set("content-type", (_a2 = this._CONTENT_TYPE_MAP[extension]) != null ? _a2 : "application/octet-stream");
  }
  /**
   * Clone the current FileResponse object.
   * @returns {FileResponse} A new FileResponse object with the same properties as the current object.
   */
  clone() {
    let response = new FileResponse(this.filePath);
    response.exists = this.exists;
    response.status = this.status;
    response.statusText = this.statusText;
    response.headers = new Headers(this.headers);
    return response;
  }
  /**
   * Reads the contents of the file specified by the filePath property and returns a Promise that
   * resolves with an ArrayBuffer containing the file's contents.
   * @returns {Promise<ArrayBuffer>} A Promise that resolves with an ArrayBuffer containing the file's contents.
   * @throws {Error} If the file cannot be read.
   */
  arrayBuffer() {
    return __async(this, null, function* () {
      const data = yield sharp.promises.readFile(this.filePath);
      return data.buffer;
    });
  }
  /**
   * Reads the contents of the file specified by the filePath property and returns a Promise that
   * resolves with a Blob containing the file's contents.
   * @returns {Promise<Blob>} A Promise that resolves with a Blob containing the file's contents.
   * @throws {Error} If the file cannot be read.
   */
  blob() {
    return __async(this, null, function* () {
      const data = yield sharp.promises.readFile(this.filePath);
      return new Blob([data], { type: this.headers.get("content-type") });
    });
  }
  /**
   * Reads the contents of the file specified by the filePath property and returns a Promise that
   * resolves with a string containing the file's contents.
   * @returns {Promise<string>} A Promise that resolves with a string containing the file's contents.
   * @throws {Error} If the file cannot be read.
   */
  text() {
    return __async(this, null, function* () {
      const data = yield sharp.promises.readFile(this.filePath, "utf8");
      return data;
    });
  }
  /**
   * Reads the contents of the file specified by the filePath property and returns a Promise that
   * resolves with a parsed JavaScript object containing the file's contents.
   * 
   * @returns {Promise<Object>} A Promise that resolves with a parsed JavaScript object containing the file's contents.
   * @throws {Error} If the file cannot be read.
   */
  json() {
    return __async(this, null, function* () {
      return JSON.parse(yield this.text());
    });
  }
}
function isValidUrl(string, protocols = null, validHosts = null) {
  let url;
  try {
    url = new URL(string);
  } catch (_) {
    return false;
  }
  if (protocols && !protocols.includes(url.protocol)) {
    return false;
  }
  if (validHosts && !validHosts.includes(url.hostname)) {
    return false;
  }
  return true;
}
function getFile(urlOrPath) {
  return __async(this, null, function* () {
    var _a2, _b;
    if (env$1.useFS && !isValidUrl(urlOrPath, ["http:", "https:", "blob:"])) {
      return new FileResponse(urlOrPath);
    } else if (typeof process !== "undefined" && ((_a2 = process == null ? void 0 : process.release) == null ? void 0 : _a2.name) === "node") {
      const IS_CI = !!(define_process_env_default == null ? void 0 : define_process_env_default.TESTING_REMOTELY);
      const version = env$1.version;
      const headers = new Headers();
      headers.set("User-Agent", `transformers.js/${version}; is_ci/${IS_CI};`);
      const isHFURL = isValidUrl(urlOrPath, ["http:", "https:"], ["huggingface.co", "hf.co"]);
      if (isHFURL) {
        const token = (_b = define_process_env_default == null ? void 0 : define_process_env_default.HF_TOKEN) != null ? _b : define_process_env_default == null ? void 0 : define_process_env_default.HF_ACCESS_TOKEN;
        if (token) {
          headers.set("Authorization", `Bearer ${token}`);
        }
      }
      return fetch(urlOrPath, { headers });
    } else {
      return fetch(urlOrPath);
    }
  });
}
const ERROR_MAPPING = {
  // 4xx errors (https://developer.mozilla.org/en-US/docs/Web/HTTP/Status#client_error_responses)
  400: "Bad request error occurred while trying to load file",
  401: "Unauthorized access to file",
  403: "Forbidden access to file",
  404: "Could not locate file",
  408: "Request timeout error occurred while trying to load file",
  // 5xx errors (https://developer.mozilla.org/en-US/docs/Web/HTTP/Status#server_error_responses)
  500: "Internal server error error occurred while trying to load file",
  502: "Bad gateway error occurred while trying to load file",
  503: "Service unavailable error occurred while trying to load file",
  504: "Gateway timeout error occurred while trying to load file"
};
function handleError(status, remoteURL, fatal) {
  var _a2;
  if (!fatal) {
    return null;
  }
  const message = (_a2 = ERROR_MAPPING[status]) != null ? _a2 : `Error (${status}) occurred while trying to load file`;
  throw Error(`${message}: "${remoteURL}".`);
}
class FileCache {
  /**
   * Instantiate a `FileCache` object.
   * @param {string} path 
   */
  constructor(path2) {
    this.path = path2;
  }
  /**
   * Checks whether the given request is in the cache.
   * @param {string} request 
   * @returns {Promise<FileResponse | undefined>}
   */
  match(request) {
    return __async(this, null, function* () {
      let filePath = sharp.join(this.path, request);
      let file = new FileResponse(filePath);
      if (file.exists) {
        return file;
      } else {
        return void 0;
      }
    });
  }
  /**
   * Adds the given response to the cache.
   * @param {string} request 
   * @param {Response|FileResponse} response 
   * @returns {Promise<void>}
   */
  put(request, response) {
    return __async(this, null, function* () {
      const buffer = Buffer.from(yield response.arrayBuffer());
      let outputPath = sharp.join(this.path, request);
      try {
        yield sharp.promises.mkdir(sharp.dirname(outputPath), { recursive: true });
        yield sharp.promises.writeFile(outputPath, buffer);
      } catch (err) {
        console.warn("An error occurred while writing the file to cache:", err);
      }
    });
  }
  // TODO add the rest?
  // addAll(requests: RequestInfo[]): Promise<void>;
  // delete(request: RequestInfo | URL, options?: CacheQueryOptions): Promise<boolean>;
  // keys(request?: RequestInfo | URL, options?: CacheQueryOptions): Promise<ReadonlyArray<Request>>;
  // match(request: RequestInfo | URL, options?: CacheQueryOptions): Promise<Response | undefined>;
  // matchAll(request?: RequestInfo | URL, options?: CacheQueryOptions): Promise<ReadonlyArray<Response>>;
}
function tryCache(cache, ...names) {
  return __async(this, null, function* () {
    for (let name2 of names) {
      try {
        let result = yield cache.match(name2);
        if (result) return result;
      } catch (e) {
        continue;
      }
    }
    return void 0;
  });
}
function getModelFile(_0, _1) {
  return __async(this, arguments, function* (path_or_repo_id, filename, fatal = true, options = {}) {
    var _a2, _b;
    if (!env$1.allowLocalModels) {
      if (options.local_files_only) {
        throw Error("Invalid configuration detected: local models are disabled (`env.allowLocalModels=false`) but you have requested to only use local models (`local_files_only=true`).");
      } else if (!env$1.allowRemoteModels) {
        throw Error("Invalid configuration detected: both local and remote models are disabled. Fix by setting `env.allowLocalModels` or `env.allowRemoteModels` to `true`.");
      }
    }
    dispatchCallback(options.progress_callback, {
      status: "initiate",
      name: path_or_repo_id,
      file: filename
    });
    let cache;
    if (!cache && env$1.useBrowserCache) {
      if (typeof caches === "undefined") {
        throw Error("Browser cache is not available in this environment.");
      }
      try {
        cache = yield caches.open("transformers-cache");
      } catch (e) {
        console.warn("An error occurred while opening the browser cache:", e);
      }
    }
    if (!cache && env$1.useFSCache) {
      cache = new FileCache((_a2 = options.cache_dir) != null ? _a2 : env$1.cacheDir);
    }
    const revision = (_b = options.revision) != null ? _b : "main";
    let requestURL = pathJoin(path_or_repo_id, filename);
    let localPath = pathJoin(env$1.localModelPath, requestURL);
    let remoteURL = pathJoin(
      env$1.remoteHost,
      env$1.remotePathTemplate.replaceAll("{model}", path_or_repo_id).replaceAll("{revision}", encodeURIComponent(revision)),
      filename
    );
    let fsCacheKey = revision === "main" ? requestURL : pathJoin(path_or_repo_id, revision, filename);
    let cacheKey;
    let proposedCacheKey = cache instanceof FileCache ? fsCacheKey : remoteURL;
    let toCacheResponse = false;
    let response;
    if (cache) {
      response = yield tryCache(cache, localPath, proposedCacheKey);
    }
    const cacheHit = response !== void 0;
    if (response === void 0) {
      if (env$1.allowLocalModels) {
        const isURL = isValidUrl(requestURL, ["http:", "https:"]);
        if (!isURL) {
          try {
            response = yield getFile(localPath);
            cacheKey = localPath;
          } catch (e) {
            console.warn(`Unable to load from local path "${localPath}": "${e}"`);
          }
        } else if (options.local_files_only) {
          throw new Error(`\`local_files_only=true\`, but attempted to load a remote file from: ${requestURL}.`);
        } else if (!env$1.allowRemoteModels) {
          throw new Error(`\`env.allowRemoteModels=false\`, but attempted to load a remote file from: ${requestURL}.`);
        }
      }
      if (response === void 0 || response.status === 404) {
        if (options.local_files_only || !env$1.allowRemoteModels) {
          if (fatal) {
            throw Error(`\`local_files_only=true\` or \`env.allowRemoteModels=false\` and file was not found locally at "${localPath}".`);
          } else {
            return null;
          }
        }
        response = yield getFile(remoteURL);
        if (response.status !== 200) {
          return handleError(response.status, remoteURL, fatal);
        }
        cacheKey = proposedCacheKey;
      }
      toCacheResponse = cache && typeof Response !== "undefined" && response instanceof Response && response.status === 200;
    }
    dispatchCallback(options.progress_callback, {
      status: "download",
      name: path_or_repo_id,
      file: filename
    });
    const progressInfo = {
      status: "progress",
      name: path_or_repo_id,
      file: filename
    };
    let buffer;
    if (!options.progress_callback) {
      buffer = new Uint8Array(yield response.arrayBuffer());
    } else if (cacheHit && typeof navigator !== "undefined" && /firefox/i.test(navigator.userAgent)) {
      buffer = new Uint8Array(yield response.arrayBuffer());
      dispatchCallback(options.progress_callback, __spreadProps(__spreadValues({}, progressInfo), {
        progress: 100,
        loaded: buffer.length,
        total: buffer.length
      }));
    } else {
      buffer = yield readResponse(response, (data) => {
        dispatchCallback(options.progress_callback, __spreadValues(__spreadValues({}, progressInfo), data));
      });
    }
    if (
      // Only cache web responses
      // i.e., do not cache FileResponses (prevents duplication)
      toCacheResponse && cacheKey && // Check again whether request is in cache. If not, we add the response to the cache
      (yield cache.match(cacheKey)) === void 0
    ) {
      yield cache.put(cacheKey, new Response(buffer, {
        headers: response.headers
      })).catch((err) => {
        console.warn(`Unable to add response to browser cache: ${err}.`);
      });
    }
    dispatchCallback(options.progress_callback, {
      status: "done",
      name: path_or_repo_id,
      file: filename
    });
    return buffer;
  });
}
function getModelJSON(_0, _1) {
  return __async(this, arguments, function* (modelPath, fileName, fatal = true, options = {}) {
    let buffer = yield getModelFile(modelPath, fileName, fatal, options);
    if (buffer === null) {
      return {};
    }
    let decoder = new TextDecoder("utf-8");
    let jsonData = decoder.decode(buffer);
    return JSON.parse(jsonData);
  });
}
function readResponse(response, progress_callback) {
  return __async(this, null, function* () {
    const contentLength = response.headers.get("Content-Length");
    if (contentLength === null) {
      console.warn("Unable to determine content-length from response headers. Will expand buffer when needed.");
    }
    let total = parseInt(contentLength != null ? contentLength : "0");
    let buffer = new Uint8Array(total);
    let loaded = 0;
    const reader = response.body.getReader();
    function read() {
      return __async(this, null, function* () {
        const { done, value } = yield reader.read();
        if (done) return;
        let newLoaded = loaded + value.length;
        if (newLoaded > total) {
          total = newLoaded;
          let newBuffer = new Uint8Array(total);
          newBuffer.set(buffer);
          buffer = newBuffer;
        }
        buffer.set(value, loaded);
        loaded = newLoaded;
        const progress = loaded / total * 100;
        progress_callback({
          progress,
          loaded,
          total
        });
        return read();
      });
    }
    yield read();
    return buffer;
  });
}
function pathJoin(...parts) {
  parts = parts.map((part, index) => {
    if (index) {
      part = part.replace(new RegExp("^/"), "");
    }
    if (index !== parts.length - 1) {
      part = part.replace(new RegExp("/$"), "");
    }
    return part;
  });
  return parts.join("/");
}
function permute_data(array, dims, axes) {
  const shape = new Array(axes.length);
  const stride = new Array(axes.length);
  for (let i = axes.length - 1, s = 1; i >= 0; --i) {
    stride[i] = s;
    shape[i] = dims[axes[i]];
    s *= shape[i];
  }
  const invStride = axes.map((_, i) => stride[axes.indexOf(i)]);
  const permutedData = new array.constructor(array.length);
  for (let i = 0; i < array.length; ++i) {
    let newIndex = 0;
    for (let j = dims.length - 1, k = i; j >= 0; --j) {
      newIndex += k % dims[j] * invStride[j];
      k = Math.floor(k / dims[j]);
    }
    permutedData[newIndex] = array[i];
  }
  return [permutedData, shape];
}
function softmax(arr) {
  const maxVal = max(arr)[0];
  const exps = arr.map((x) => Math.exp(x - maxVal));
  const sumExps = exps.reduce((acc, val) => acc + val, 0);
  const softmaxArr = exps.map((x) => x / sumExps);
  return (
    /** @type {T} */
    softmaxArr
  );
}
function log_softmax(arr) {
  const softmaxArr = softmax(arr);
  const logSoftmaxArr = softmaxArr.map((x) => Math.log(x));
  return (
    /** @type {T} */
    logSoftmaxArr
  );
}
function getTopItems(items, top_k = 0) {
  items = Array.from(items).map((x, i) => [i, x]).sort((a, b) => b[1] - a[1]);
  if (top_k !== null && top_k > 0) {
    items = items.slice(0, top_k);
  }
  return items;
}
function min(arr) {
  if (arr.length === 0) throw Error("Array must not be empty");
  let min2 = arr[0];
  let indexOfMin = 0;
  for (let i = 1; i < arr.length; ++i) {
    if (arr[i] < min2) {
      min2 = arr[i];
      indexOfMin = i;
    }
  }
  return [min2, indexOfMin];
}
function max(arr) {
  if (arr.length === 0) throw Error("Array must not be empty");
  let max2 = arr[0];
  let indexOfMax = 0;
  for (let i = 1; i < arr.length; ++i) {
    if (arr[i] > max2) {
      max2 = arr[i];
      indexOfMax = i;
    }
  }
  return [Number(max2), indexOfMax];
}
function medianFilter(data, windowSize) {
  if (windowSize % 2 === 0 || windowSize <= 0) {
    throw new Error("Window size must be a positive odd number");
  }
  const outputArray = new data.constructor(data.length);
  const buffer = new data.constructor(windowSize);
  const halfWindowSize = Math.floor(windowSize / 2);
  for (let i = 0; i < data.length; ++i) {
    let valuesIndex = 0;
    for (let j = -halfWindowSize; j <= halfWindowSize; ++j) {
      let index = i + j;
      if (index < 0) {
        index = Math.abs(index);
      } else if (index >= data.length) {
        index = 2 * (data.length - 1) - index;
      }
      buffer[valuesIndex++] = data[index];
    }
    buffer.sort();
    outputArray[i] = buffer[halfWindowSize];
  }
  return outputArray;
}
function round(num, decimals) {
  const pow = Math.pow(10, decimals);
  return Math.round(num * pow) / pow;
}
const DataTypeMap = Object.freeze({
  float32: Float32Array,
  float64: Float64Array,
  string: Array,
  // string[]
  int8: Int8Array,
  uint8: Uint8Array,
  int16: Int16Array,
  uint16: Uint16Array,
  int32: Int32Array,
  uint32: Uint32Array,
  int64: BigInt64Array,
  uint64: BigUint64Array,
  bool: Uint8Array
});
const ONNXTensor$1 = ONNX.Tensor;
class Tensor {
  /**
   * Create a new Tensor or copy an existing Tensor.
   * @param {[DataType, DataArray, number[]]|[import('onnxruntime-common').Tensor]} args
   */
  constructor(...args) {
    /** @type {number[]} Dimensions of the tensor. */
    __publicField(this, "dims");
    /** @type {DataType} Type of the tensor. */
    __publicField(this, "type");
    /** @type {DataArray} The data stored in the tensor. */
    __publicField(this, "data");
    /** @type {number} The number of elements in the tensor. */
    __publicField(this, "size");
    if (args[0] instanceof ONNXTensor$1) {
      Object.assign(this, args[0]);
    } else {
      Object.assign(this, new ONNXTensor$1(
        /** @type {DataType} */
        args[0],
        /** @type {Exclude<import('./maths.js').AnyTypedArray, Uint8ClampedArray>} */
        args[1],
        args[2]
      ));
    }
    return new Proxy(this, {
      get: (obj, key) => {
        if (typeof key === "string") {
          let index = Number(key);
          if (Number.isInteger(index)) {
            return obj._getitem(index);
          }
        }
        return obj[key];
      },
      set: (obj, key, value) => {
        return obj[key] = value;
      }
    });
  }
  /**
   * Returns an iterator object for iterating over the tensor data in row-major order.
   * If the tensor has more than one dimension, the iterator will yield subarrays.
   * @returns {Iterator} An iterator object for iterating over the tensor data in row-major order.
   */
  *[Symbol.iterator]() {
    const [iterLength, ...iterDims] = this.dims;
    if (iterDims.length > 0) {
      const iterSize = iterDims.reduce((a, b) => a * b);
      for (let i = 0; i < iterLength; ++i) {
        yield this._subarray(i, iterSize, iterDims);
      }
    } else {
      yield* __yieldStar(this.data);
    }
  }
  /**
   * Index into a Tensor object.
   * @param {number} index The index to access.
   * @returns {Tensor} The data at the specified index.
   */
  _getitem(index) {
    const [iterLength, ...iterDims] = this.dims;
    index = safeIndex(index, iterLength);
    if (iterDims.length > 0) {
      const iterSize = iterDims.reduce((a, b) => a * b);
      return this._subarray(index, iterSize, iterDims);
    } else {
      return new Tensor(this.type, [this.data[index]], iterDims);
    }
  }
  /**
   * @param {number|bigint} item The item to search for in the tensor
   * @returns {number} The index of the first occurrence of item in the tensor data.
   */
  indexOf(item) {
    for (let index = 0; index < this.data.length; ++index) {
      if (this.data[index] == item) {
        return index;
      }
    }
    return -1;
  }
  /**
   * @param {number} index 
   * @param {number} iterSize 
   * @param {any} iterDims 
   * @returns {Tensor}
   */
  _subarray(index, iterSize, iterDims) {
    const o1 = index * iterSize;
    const o2 = (index + 1) * iterSize;
    const data = "subarray" in this.data ? this.data.subarray(o1, o2) : this.data.slice(o1, o2);
    return new Tensor(this.type, data, iterDims);
  }
  /**
   * Returns the value of this tensor as a standard JavaScript Number. This only works
   * for tensors with one element. For other cases, see `Tensor.tolist()`.
   * @returns {number|bigint} The value of this tensor as a standard JavaScript Number.
   * @throws {Error} If the tensor has more than one element.
   */
  item() {
    if (this.data.length !== 1) {
      throw new Error(`a Tensor with ${this.data.length} elements cannot be converted to Scalar`);
    }
    return this.data[0];
  }
  /**
   * Convert tensor data to a n-dimensional JS list
   * @returns {Array}
   */
  tolist() {
    return reshape(this.data, this.dims);
  }
  /**
   * Return a new Tensor with the sigmoid function applied to each element.
   * @returns {Tensor} The tensor with the sigmoid function applied.
   */
  sigmoid() {
    return this.clone().sigmoid_();
  }
  /**
   * Applies the sigmoid function to the tensor in place.
   * @returns {Tensor} Returns `this`.
   */
  sigmoid_() {
    for (let i = 0; i < this.data.length; ++i) {
      this.data[i] = 1 / (1 + Math.exp(-this.data[i]));
    }
    return this;
  }
  /**
   * Return a new Tensor with every element multiplied by a constant.
   * @param {number} val The value to multiply by.
   * @returns {Tensor} The new tensor.
   */
  mul(val) {
    return this.clone().mul_(val);
  }
  /**
   * Multiply the tensor by a constant in place.
   * @param {number} val The value to multiply by.
   * @returns {Tensor} Returns `this`.
   */
  mul_(val) {
    for (let i = 0; i < this.data.length; ++i) {
      this.data[i] *= val;
    }
    return this;
  }
  /**
   * Return a new Tensor with every element added by a constant.
   * @param {number} val The value to add by.
   * @returns {Tensor} The new tensor.
   */
  add(val) {
    return this.clone().add_(val);
  }
  /**
   * Add the tensor by a constant in place.
   * @param {number} val The value to add by.
   * @returns {Tensor} Returns `this`.
   */
  add_(val) {
    for (let i = 0; i < this.data.length; ++i) {
      this.data[i] += val;
    }
    return this;
  }
  clone() {
    return new Tensor(this.type, this.data.slice(), this.dims.slice());
  }
  slice(...slices) {
    let newTensorDims = [];
    let newOffsets = [];
    for (let sliceIndex = 0; sliceIndex < this.dims.length; ++sliceIndex) {
      let slice2 = slices[sliceIndex];
      if (slice2 === null || slice2 === void 0) {
        newOffsets.push([0, this.dims[sliceIndex]]);
        newTensorDims.push(this.dims[sliceIndex]);
      } else if (typeof slice2 === "number") {
        slice2 = safeIndex(slice2, this.dims[sliceIndex], sliceIndex);
        newOffsets.push([slice2, slice2 + 1]);
      } else if (Array.isArray(slice2) && slice2.length === 2) {
        if (slice2[0] > slice2[1]) {
          throw new Error(`Invalid slice: ${slice2}`);
        }
        let offsets = [
          Math.max(slice2[0], 0),
          Math.min(slice2[1], this.dims[sliceIndex])
        ];
        newOffsets.push(offsets);
        newTensorDims.push(offsets[1] - offsets[0]);
      } else {
        throw new Error(`Invalid slice: ${slice2}`);
      }
    }
    let newDims = newOffsets.map(([start, end]) => end - start);
    let newBufferSize = newDims.reduce((a, b) => a * b);
    let data = new this.data.constructor(newBufferSize);
    const stride = this.stride();
    for (let i = 0; i < newBufferSize; ++i) {
      let originalIndex = 0;
      for (let j = newDims.length - 1, num = i; j >= 0; --j) {
        const size = newDims[j];
        originalIndex += (num % size + newOffsets[j][0]) * stride[j];
        num = Math.floor(num / size);
      }
      data[i] = this.data[originalIndex];
    }
    return new Tensor(this.type, data, newTensorDims);
  }
  /**
   * Return a permuted version of this Tensor, according to the provided dimensions.
   * @param  {...number} dims Dimensions to permute.
   * @returns {Tensor} The permuted tensor.
   */
  permute(...dims) {
    return permute(this, dims);
  }
  // TODO: implement transpose. For now (backwards compatibility), it's just an alias for permute()
  transpose(...dims) {
    return this.permute(...dims);
  }
  // TODO add .max() and .min() methods
  /**
   * Returns the sum of each row of the input tensor in the given dimension dim.
   * 
   * @param {number} [dim=null] The dimension or dimensions to reduce. If `null`, all dimensions are reduced.
   * @param {boolean} keepdim Whether the output tensor has `dim` retained or not.
   * @returns The summed tensor
   */
  sum(dim = null, keepdim = false) {
    return this.norm(1, dim, keepdim);
  }
  /**
   * Returns the matrix norm or vector norm of a given tensor.
   * @param {number|string} [p='fro'] The order of norm
   * @param {number} [dim=null] Specifies which dimension of the tensor to calculate the norm across.
   * If dim is None, the norm will be calculated across all dimensions of input.
   * @param {boolean} [keepdim=false] Whether the output tensors have dim retained or not.
   * @returns {Tensor} The norm of the tensor.
   */
  norm(p = "fro", dim = null, keepdim = false) {
    if (p === "fro") {
      p = 2;
    } else if (typeof p === "string") {
      throw Error(`Unsupported norm: ${p}`);
    }
    if (dim === null) {
      let val = __pow(this.data.reduce((a, b) => a + __pow(b, p), 0), 1 / p);
      return new Tensor(this.type, [val], []);
    }
    dim = safeIndex(dim, this.dims.length);
    const resultDims = this.dims.slice();
    resultDims[dim] = 1;
    const result = new this.data.constructor(this.data.length / this.dims[dim]);
    for (let i = 0; i < this.data.length; ++i) {
      let resultIndex = 0;
      for (let j = this.dims.length - 1, num = i, resultMultiplier = 1; j >= 0; --j) {
        const size = this.dims[j];
        if (j !== dim) {
          const index = num % size;
          resultIndex += index * resultMultiplier;
          resultMultiplier *= resultDims[j];
        }
        num = Math.floor(num / size);
      }
      result[resultIndex] += __pow(this.data[i], p);
    }
    if (p !== 1) {
      for (let i = 0; i < result.length; ++i) {
        result[i] = __pow(result[i], 1 / p);
      }
    }
    if (!keepdim) {
      resultDims.splice(dim, 1);
    }
    return new Tensor(this.type, result, resultDims);
  }
  /**
   * Performs `L_p` normalization of inputs over specified dimension. Operates in place.
   * @param {number} [p=2] The exponent value in the norm formulation
   * @param {number} [dim=1] The dimension to reduce
   * @returns {Tensor} `this` for operation chaining.
   */
  normalize_(p = 2, dim = 1) {
    dim = safeIndex(dim, this.dims.length);
    const norm = this.norm(p, dim, true);
    for (let i = 0; i < this.data.length; ++i) {
      let resultIndex = 0;
      for (let j = this.dims.length - 1, num = i, resultMultiplier = 1; j >= 0; --j) {
        const size = this.dims[j];
        if (j !== dim) {
          const index = num % size;
          resultIndex += index * resultMultiplier;
          resultMultiplier *= this.dims[j];
        }
        num = Math.floor(num / size);
      }
      this.data[i] /= norm.data[resultIndex];
    }
    return this;
  }
  /**
   * Performs `L_p` normalization of inputs over specified dimension.
   * @param {number} [p=2] The exponent value in the norm formulation
   * @param {number} [dim=1] The dimension to reduce
   * @returns {Tensor} The normalized tensor.
   */
  normalize(p = 2, dim = 1) {
    return this.clone().normalize_(p, dim);
  }
  /**
   * Compute and return the stride of this tensor.
   * Stride is the jump necessary to go from one element to the next one in the specified dimension dim.
   * @returns {number[]} The stride of this tensor.
   */
  stride() {
    return dimsToStride(this.dims);
  }
  /**
   * Returns a tensor with all specified dimensions of input of size 1 removed.
   * 
   * NOTE: The returned tensor shares the storage with the input tensor, so changing the contents of one will change the contents of the other.
   * If you would like a copy, use `tensor.clone()` before squeezing.
   * 
   * @param {number} [dim=null] If given, the input will be squeezed only in the specified dimensions.
   * @returns The squeezed tensor
   */
  squeeze(dim = null) {
    return new Tensor(
      this.type,
      this.data,
      calc_squeeze_dims(this.dims, dim)
    );
  }
  /**
   * In-place version of @see {@link Tensor.squeeze}
   */
  squeeze_(dim = null) {
    this.dims = calc_squeeze_dims(this.dims, dim);
    return this;
  }
  /**
   * Returns a new tensor with a dimension of size one inserted at the specified position.
   * 
   * NOTE: The returned tensor shares the same underlying data with this tensor.
   * 
   * @param {number} dim The index at which to insert the singleton dimension
   * @returns The unsqueezed tensor
   */
  unsqueeze(dim = null) {
    return new Tensor(
      this.type,
      this.data,
      calc_unsqueeze_dims(this.dims, dim)
    );
  }
  /**
   * In-place version of @see {@link Tensor.unsqueeze}
   */
  unsqueeze_(dim = null) {
    this.dims = calc_unsqueeze_dims(this.dims, dim);
    return this;
  }
  /**
   * In-place version of @see {@link Tensor.flatten}
   */
  flatten_(start_dim = 0, end_dim = -1) {
    end_dim = (end_dim + this.dims.length) % this.dims.length;
    let dimsToKeepBefore = this.dims.slice(0, start_dim);
    let dimsToFlatten = this.dims.slice(start_dim, end_dim + 1);
    let dimsToKeepAfter = this.dims.slice(end_dim + 1);
    this.dims = [...dimsToKeepBefore, dimsToFlatten.reduce((a, b) => a * b, 1), ...dimsToKeepAfter];
    return this;
  }
  /**
   * Flattens input by reshaping it into a one-dimensional tensor.
   * If `start_dim` or `end_dim` are passed, only dimensions starting with `start_dim`
   * and ending with `end_dim` are flattened. The order of elements in input is unchanged.
   * @param {number} start_dim the first dim to flatten
   * @param {number} end_dim the last dim to flatten
   * @returns The flattened tensor.
   */
  flatten(start_dim = 0, end_dim = -1) {
    return this.clone().flatten_(start_dim, end_dim);
  }
  /**
   * Returns a new tensor with the same data as the `self` tensor but of a different `shape`.
   * @param  {...number} dims the desired size
   * @returns {Tensor} The tensor with the same data but different shape
   */
  view(...dims) {
    let inferredIndex = -1;
    for (let i = 0; i < dims.length; ++i) {
      if (dims[i] === -1) {
        if (inferredIndex !== -1) {
          throw new Error("Only one dimension can be inferred");
        }
        inferredIndex = i;
      }
    }
    if (inferredIndex !== -1) {
      const productOther = dims.reduce((product, curr, index) => {
        return index !== inferredIndex ? product * curr : product;
      }, 1);
      dims[inferredIndex] = this.data.length / productOther;
    }
    return new Tensor(this.type, this.data, dims);
  }
  neg_() {
    for (let i = 0; i < this.data.length; ++i) {
      this.data[i] = -this.data[i];
    }
    return this;
  }
  neg() {
    return this.clone().neg_();
  }
  /**
   * In-place version of @see {@link Tensor.clamp}
   */
  clamp_(min2, max2) {
    for (let i = 0; i < this.data.length; ++i) {
      this.data[i] = Math.min(Math.max(this.data[i], min2), max2);
    }
    return this;
  }
  /**
   * Clamps all elements in input into the range [ min, max ]
   * @param {number} min lower-bound of the range to be clamped to
   * @param {number} max upper-bound of the range to be clamped to
   * @returns the output tensor.
   */
  clamp(min2, max2) {
    return this.clone().clamp_(min2, max2);
  }
  /**
   * In-place version of @see {@link Tensor.round}
   */
  round_() {
    for (let i = 0; i < this.data.length; ++i) {
      this.data[i] = Math.round(this.data[i]);
    }
    return this;
  }
  /**
   * Rounds elements of input to the nearest integer.
   * @returns the output tensor.
   */
  round() {
    return this.clone().round_();
  }
  /**
   * Performs Tensor dtype conversion.
   * @param {DataType} type The desired data type.
   * @returns {Tensor} The converted tensor.
   */
  to(type) {
    if (this.type === type) return this;
    if (!DataTypeMap.hasOwnProperty(type)) {
      throw new Error(`Unsupported type: ${type}`);
    }
    return new Tensor(type, DataTypeMap[type].from(this.data), this.dims);
  }
}
function reshape(data, dimensions) {
  const totalElements = data.length;
  const dimensionSize = dimensions.reduce((a, b) => a * b);
  if (totalElements !== dimensionSize) {
    throw Error(`cannot reshape array of size ${totalElements} into shape (${dimensions})`);
  }
  let reshapedArray = data;
  for (let i = dimensions.length - 1; i >= 0; i--) {
    reshapedArray = reshapedArray.reduce((acc, val) => {
      let lastArray = acc[acc.length - 1];
      if (lastArray.length < dimensions[i]) {
        lastArray.push(val);
      } else {
        acc.push([val]);
      }
      return acc;
    }, [[]]);
  }
  return reshapedArray[0];
}
function permute(tensor, axes) {
  const [permutedData, shape] = permute_data(tensor.data, tensor.dims, axes);
  return new Tensor(tensor.type, permutedData, shape);
}
function calc_squeeze_dims(dims, dim) {
  dims = dims.slice();
  if (dim === null) {
    dims = dims.filter((d) => d !== 1);
  } else if (typeof dim === "number") {
    if (dims[dim] === 1) {
      dims.splice(dim, 1);
    }
  } else if (Array.isArray(dim)) {
    dims = dims.filter((x, i) => {
      return x !== 1 || !dim.includes(i);
    });
  }
  return dims;
}
function calc_unsqueeze_dims(dims, dim) {
  dim = safeIndex(dim, dims.length + 1);
  dims = dims.slice();
  dims.splice(dim, 0, 1);
  return dims;
}
function safeIndex(index, size, dimension = null) {
  if (index < -size || index >= size) {
    throw new Error(`IndexError: index ${index} is out of bounds for dimension${dimension === null ? "" : " " + dimension} with size ${size}`);
  }
  if (index < 0) {
    index = (index % size + size) % size;
  }
  return index;
}
function cat(tensors, dim = 0) {
  dim = safeIndex(dim, tensors[0].dims.length);
  const resultDims = tensors[0].dims.slice();
  resultDims[dim] = tensors.reduce((a, b) => a + b.dims[dim], 0);
  const resultSize = resultDims.reduce((a, b) => a * b, 1);
  const result = new tensors[0].data.constructor(resultSize);
  const resultType = tensors[0].type;
  if (dim === 0) {
    let offset = 0;
    for (let t of tensors) {
      result.set(t.data, offset);
      offset += t.data.length;
    }
  } else {
    let currentDim = 0;
    for (let t = 0; t < tensors.length; ++t) {
      let tensor = tensors[t];
      for (let i = 0; i < tensor.data.length; ++i) {
        let resultIndex = 0;
        for (let j = tensor.dims.length - 1, num = i, resultMultiplier = 1; j >= 0; --j) {
          const size = tensor.dims[j];
          let index = num % size;
          if (j === dim) {
            index += currentDim;
          }
          resultIndex += index * resultMultiplier;
          resultMultiplier *= resultDims[j];
          num = Math.floor(num / size);
        }
        result[resultIndex] = tensor.data[i];
      }
      currentDim += tensor.dims[dim];
    }
  }
  return new Tensor(resultType, result, resultDims);
}
function stack(tensors, dim = 0) {
  return cat(tensors.map((t) => t.unsqueeze(dim)), dim);
}
function std_mean(input, dim = null, correction = 1, keepdim = false) {
  if (dim === null) {
    const sum = input.data.reduce((a, b) => a + b, 0);
    const mean2 = sum / input.data.length;
    const std = Math.sqrt(input.data.reduce((a, b) => a + __pow(b - mean2, 2), 0) / (input.data.length - correction));
    const meanTensor2 = new Tensor(input.type, [mean2], [
      /* scalar */
    ]);
    const stdTensor2 = new Tensor(input.type, [std], [
      /* scalar */
    ]);
    return [stdTensor2, meanTensor2];
  }
  dim = safeIndex(dim, input.dims.length);
  const meanTensor = mean(input, dim, keepdim);
  const resultDims = input.dims.slice();
  resultDims[dim] = 1;
  const result = new input.data.constructor(input.data.length / input.dims[dim]);
  for (let i = 0; i < input.data.length; ++i) {
    let resultIndex = 0;
    for (let j = input.dims.length - 1, num = i, resultMultiplier = 1; j >= 0; --j) {
      const size = input.dims[j];
      if (j !== dim) {
        const index = num % size;
        resultIndex += index * resultMultiplier;
        resultMultiplier *= resultDims[j];
      }
      num = Math.floor(num / size);
    }
    result[resultIndex] += __pow(input.data[i] - meanTensor.data[resultIndex], 2);
  }
  for (let i = 0; i < result.length; ++i) {
    result[i] = Math.sqrt(result[i] / (input.dims[dim] - correction));
  }
  if (!keepdim) {
    resultDims.splice(dim, 1);
  }
  const stdTensor = new Tensor(input.type, result, resultDims);
  return [stdTensor, meanTensor];
}
function mean(input, dim = null, keepdim = false) {
  if (dim === null) {
    let val = input.data.reduce((a, b) => a + b, 0);
    return new Tensor(input.type, [val / input.data.length], [
      /* scalar */
    ]);
  }
  dim = safeIndex(dim, input.dims.length);
  const resultDims = input.dims.slice();
  resultDims[dim] = 1;
  const result = new input.data.constructor(input.data.length / input.dims[dim]);
  for (let i = 0; i < input.data.length; ++i) {
    let resultIndex = 0;
    for (let j = input.dims.length - 1, num = i, resultMultiplier = 1; j >= 0; --j) {
      const size = input.dims[j];
      if (j !== dim) {
        const index = num % size;
        resultIndex += index * resultMultiplier;
        resultMultiplier *= resultDims[j];
      }
      num = Math.floor(num / size);
    }
    result[resultIndex] += input.data[i];
  }
  if (input.dims[dim] !== 1) {
    for (let i = 0; i < result.length; ++i) {
      result[i] = result[i] / input.dims[dim];
    }
  }
  if (!keepdim) {
    resultDims.splice(dim, 1);
  }
  return new Tensor(input.type, result, resultDims);
}
function dynamicTimeWarping(matrix) {
  const [output_length, input_length] = matrix.dims;
  const outputShape = [output_length + 1, input_length + 1];
  const cost = new Tensor(
    "float32",
    new Float32Array(outputShape[0] * outputShape[1]).fill(Infinity),
    outputShape
  );
  const trace = new Tensor(
    "float32",
    new Float32Array(outputShape[0] * outputShape[1]).fill(-1),
    outputShape
  );
  cost[0].data[0] = 0;
  for (let j2 = 1; j2 < input_length + 1; ++j2) {
    for (let i2 = 1; i2 < output_length + 1; ++i2) {
      const c0 = cost[i2 - 1][j2 - 1].item();
      const c1 = cost[i2 - 1][j2].item();
      const c2 = cost[i2][j2 - 1].item();
      let c, t;
      if (c0 < c1 && c0 < c2) {
        c = c0;
        t = 0;
      } else if (c1 < c0 && c1 < c2) {
        c = c1;
        t = 1;
      } else {
        c = c2;
        t = 2;
      }
      cost[i2].data[j2] = matrix[i2 - 1][j2 - 1].item() + c;
      trace[i2].data[j2] = t;
    }
  }
  let i = output_length;
  let j = input_length;
  trace.data.fill(2, 0, outputShape[1]);
  for (let i2 = 0; i2 < outputShape[0]; ++i2) {
    trace[i2].data[0] = 1;
  }
  let text_indices = [];
  let time_indices = [];
  while (i > 0 || j > 0) {
    text_indices.push(i - 1);
    time_indices.push(j - 1);
    const t = trace[i][j].item();
    switch (t) {
      case 0:
        --i;
        --j;
        break;
      case 1:
        --i;
        break;
      case 2:
        --j;
        break;
      default:
        throw new Error(
          `Internal error in dynamic time warping. Unexpected trace[${i}, ${j}]. Please file a bug report.`
        );
    }
  }
  text_indices.reverse();
  time_indices.reverse();
  return [text_indices, time_indices];
}
function dimsToStride(dims) {
  const stride = new Array(dims.length);
  for (let i = dims.length - 1, s2 = 1; i >= 0; --i) {
    stride[i] = s2;
    s2 *= dims[i];
  }
  return stride;
}
function ones(size) {
  const numElements = size.reduce((a, b) => a * b, 1);
  return new Tensor(
    "int64",
    new BigInt64Array(numElements).fill(/* @__PURE__ */ BigInt("1")),
    size
  );
}
function ones_like(tensor) {
  return ones(tensor.dims);
}
class PriorityQueue {
  /**
   * Create a new PriorityQueue.
   * @param {Function} comparator Comparator function to determine priority. Defaults to a MaxHeap.
   */
  constructor(comparator = (a, b) => a > b) {
    this._heap = [];
    this._comparator = comparator;
  }
  /**
   * The size of the queue
   */
  get size() {
    return this._heap.length;
  }
  /**
   * Check if the queue is empty.
   * @returns {boolean} `true` if the queue is empty, `false` otherwise.
   */
  isEmpty() {
    return this.size === 0;
  }
  /**
   * Return the element with the highest priority in the queue.
   * @returns {any} The highest priority element in the queue.
   */
  peek() {
    return this._heap[0];
  }
  /**
   * Add one or more elements to the queue.
   * @param  {...any} values The values to push into the queue.
   * @returns {number} The new size of the queue.
   */
  push(...values) {
    return this.extend(values);
  }
  /**
   * Add multiple elements to the queue.
   * @param {any[]} values The values to push into the queue.
   * @returns {number} The new size of the queue.
   */
  extend(values) {
    for (const value of values) {
      this._heap.push(value);
      this._siftUp();
    }
    return this.size;
  }
  /**
   * Remove and return the element with the highest priority in the queue.
   * @returns {any} The element with the highest priority in the queue.
   */
  pop() {
    const poppedValue = this.peek();
    const bottom = this.size - 1;
    if (bottom > 0) {
      this._swap(0, bottom);
    }
    this._heap.pop();
    this._siftDown();
    return poppedValue;
  }
  /**
   * Replace the element with the highest priority in the queue with a new value.
   * @param {*} value The new value.
   * @returns {*} The replaced value.
   */
  replace(value) {
    const replacedValue = this.peek();
    this._heap[0] = value;
    this._siftDown();
    return replacedValue;
  }
  /**
   * Compute the index for the parent of the node at index `i`.
   * @param {number} i The index of the node to get the parent of.
   * @returns {number} The index of the parent node.
   * @private
   */
  _parent(i) {
    return (i + 1 >>> 1) - 1;
  }
  /**
   * Compute the index for the left child of the node at index `i`.
   * @param {number} i The index of the node to get the left child of.
   * @returns {number} The index of the left child.
   * @private
   */
  _left(i) {
    return (i << 1) + 1;
  }
  /**
   * Compute the index for the right child of the node at index `i`.
   * @param {number} i The index of the node to get the right child of.
   * @returns {number} The index of the right child.
   * @private
   */
  _right(i) {
    return i + 1 << 1;
  }
  /**
   * Check if the element at index `i` is greater than the element at index `j`.
   * @param {number} i The index of the first element to compare.
   * @param {number} j The index of the second element to compare.
   * @returns {boolean} `true` if the element at index `i` is greater than the element at index `j`, `false` otherwise.
   * @private
   */
  _greater(i, j) {
    return this._comparator(this._heap[i], this._heap[j]);
  }
  /**
   * Swap the elements at indices `i` and `j`.
   * @param {number} i The index of the first element to swap.
   * @param {number} j The index of the second element to swap.
   * @private
   */
  _swap(i, j) {
    const temp = this._heap[i];
    this._heap[i] = this._heap[j];
    this._heap[j] = temp;
  }
  /**
   * Maintain the heap property by updating positions in the heap,
   * starting at the last element and moving up the heap.
   * @private
   */
  _siftUp() {
    let node = this.size - 1;
    while (node > 0 && this._greater(node, this._parent(node))) {
      this._swap(node, this._parent(node));
      node = this._parent(node);
    }
  }
  /**
   * Maintain the heap property by updating positions in the heap,
   * starting at the first element and moving down the heap.
   * @private
   */
  _siftDown() {
    let node = 0;
    while (this._left(node) < this.size && this._greater(this._left(node), node) || this._right(node) < this.size && this._greater(this._right(node), node)) {
      const maxChild = this._right(node) < this.size && this._greater(this._right(node), this._left(node)) ? this._right(node) : this._left(node);
      this._swap(node, maxChild);
      node = maxChild;
    }
  }
}
class CharTrie {
  constructor() {
    this.root = CharTrieNode.default();
  }
  /**
   * Adds one or more `texts` to the trie.
   * @param {string[]} texts The strings to add to the trie.
   */
  extend(texts) {
    for (let text of texts) {
      this.push(text);
    }
  }
  /**
   * Adds text to the trie.
   * @param {string} text The string to add to the trie.
   */
  push(text) {
    let node = this.root;
    for (let ch of text) {
      let child = node.children.get(ch);
      if (child === void 0) {
        child = CharTrieNode.default();
        node.children.set(ch, child);
      }
      node = child;
    }
    node.isLeaf = true;
  }
  /**
   * Searches the trie for all strings with a common prefix of `text`.
   * @param {string} text The common prefix to search for.
   * @yields {string} Each string in the trie that has `text` as a prefix.
   */
  *commonPrefixSearch(text) {
    let node = this.root;
    let prefix = "";
    for (let i = 0; i < text.length && node !== void 0; ++i) {
      const ch = text[i];
      prefix += ch;
      node = node.children.get(ch);
      if (node !== void 0 && node.isLeaf) {
        yield prefix;
      }
    }
  }
}
class CharTrieNode {
  /**
   * Create a new CharTrieNode.
   * @param {boolean} isLeaf Whether the node is a leaf node or not.
   * @param {Map<string, CharTrieNode>} children A map containing the node's children, where the key is a character and the value is a `CharTrieNode`.
   */
  constructor(isLeaf, children) {
    this.isLeaf = isLeaf;
    this.children = children;
  }
  /**
   * Returns a new `CharTrieNode` instance with default values.
   * @returns {CharTrieNode} A new `CharTrieNode` instance with `isLeaf` set to `false` and an empty `children` map.
   */
  static default() {
    return new CharTrieNode(false, /* @__PURE__ */ new Map());
  }
}
class TokenLattice {
  /**
   * Creates a new TokenLattice instance.
   *
   * @param {string} sentence The input sentence to be tokenized.
   * @param {number} bosTokenId The beginning-of-sequence token ID.
   * @param {number} eosTokenId The end-of-sequence token ID.
   */
  constructor(sentence, bosTokenId, eosTokenId) {
    this.sentence = sentence;
    this.len = sentence.length;
    this.bosTokenId = bosTokenId;
    this.eosTokenId = eosTokenId;
    this.nodes = [];
    this.beginNodes = Array.from({ length: this.len + 1 }, () => []);
    this.endNodes = Array.from({ length: this.len + 1 }, () => []);
    const bos = new TokenLatticeNode(this.bosTokenId, 0, 0, 0, 0);
    const eos = new TokenLatticeNode(this.eosTokenId, 1, this.len, 0, 0);
    this.nodes.push(bos.clone());
    this.nodes.push(eos.clone());
    this.beginNodes[this.len].push(eos);
    this.endNodes[0].push(bos);
  }
  /**
   * Inserts a new token node into the token lattice.
   *
   * @param {number} pos The starting position of the token.
   * @param {number} length The length of the token.
   * @param {number} score The score of the token.
   * @param {number} tokenId The token ID of the token.
   */
  insert(pos, length, score, tokenId) {
    const nodeId = this.nodes.length;
    const node = new TokenLatticeNode(tokenId, nodeId, pos, length, score);
    this.beginNodes[pos].push(node);
    this.endNodes[pos + length].push(node);
    this.nodes.push(node);
  }
  /**
   * Implements the Viterbi algorithm to compute the most likely sequence of tokens.
   *
   * @returns {TokenLatticeNode[]} The array of nodes representing the most likely sequence of tokens.
   */
  viterbi() {
    const len = this.len;
    let pos = 0;
    while (pos <= len) {
      if (this.beginNodes[pos].length == 0) {
        return [];
      }
      for (let rnode of this.beginNodes[pos]) {
        rnode.prev = null;
        let bestScore = 0;
        let bestNode = null;
        for (let lnode of this.endNodes[pos]) {
          const score = lnode.backtraceScore + rnode.score;
          if (bestNode === null || score > bestScore) {
            bestNode = lnode.clone();
            bestScore = score;
          }
        }
        if (bestNode !== null) {
          rnode.prev = bestNode;
          rnode.backtraceScore = bestScore;
        } else {
          return [];
        }
      }
      ++pos;
    }
    const results = [];
    const root = this.beginNodes[len][0];
    const prev = root.prev;
    if (prev === null) {
      return [];
    }
    let node = prev.clone();
    while (node.prev !== null) {
      results.push(node.clone());
      const n = node.clone();
      node = n.prev.clone();
    }
    results.reverse();
    return results;
  }
  /**
   * @param {TokenLatticeNode} node
   * @returns {string} The array of nodes representing the most likely sequence of tokens.
   */
  piece(node) {
    return this.sentence.slice(node.pos, node.pos + node.length);
  }
  /**
   * @returns {Array} The array of nodes representing the most likely sequence of tokens.
   */
  tokens() {
    const nodes = this.viterbi();
    return nodes.map((x) => this.piece(x));
  }
  /**
   * @returns {Array} The array of nodes representing the most likely sequence of tokens.
   */
  tokenIds() {
    const nodes = this.viterbi();
    return nodes.map((x) => x.tokenId);
  }
}
class TokenLatticeNode {
  /**
   * Represents a node in a token lattice for a given sentence.
   * @param {number} tokenId The ID of the token associated with this node.
   * @param {number} nodeId The ID of this node.
   * @param {number} pos The starting position of the token in the sentence.
   * @param {number} length The length of the token.
   * @param {number} score The score associated with the token.
   */
  constructor(tokenId, nodeId, pos, length, score) {
    this.tokenId = tokenId;
    this.nodeId = nodeId;
    this.pos = pos;
    this.length = length;
    this.score = score;
    this.prev = null;
    this.backtraceScore = 0;
  }
  /**
   * Returns a clone of this node.
   * @returns {TokenLatticeNode} A clone of this node.
   */
  clone() {
    const n = new TokenLatticeNode(this.tokenId, this.nodeId, this.pos, this.length, this.score);
    n.prev = this.prev;
    n.backtraceScore = this.backtraceScore;
    return n;
  }
}
var TOKEN_TYPES = Object.freeze({
  Text: "Text",
  // The text between Jinja statements or expressions
  NumericLiteral: "NumericLiteral",
  // e.g., 123
  BooleanLiteral: "BooleanLiteral",
  // true or false
  StringLiteral: "StringLiteral",
  // 'string'
  Identifier: "Identifier",
  // Variables, functions, etc.
  Equals: "Equals",
  // =
  OpenParen: "OpenParen",
  // (
  CloseParen: "CloseParen",
  // )
  OpenStatement: "OpenStatement",
  // {%
  CloseStatement: "CloseStatement",
  // %}
  OpenExpression: "OpenExpression",
  // {{
  CloseExpression: "CloseExpression",
  // }}
  OpenSquareBracket: "OpenSquareBracket",
  // [
  CloseSquareBracket: "CloseSquareBracket",
  // ]
  OpenCurlyBracket: "OpenCurlyBracket",
  // {
  CloseCurlyBracket: "CloseCurlyBracket",
  // }
  Comma: "Comma",
  // ,
  Dot: "Dot",
  // .
  Colon: "Colon",
  // :
  Pipe: "Pipe",
  // |
  CallOperator: "CallOperator",
  // ()
  AdditiveBinaryOperator: "AdditiveBinaryOperator",
  // + -
  MultiplicativeBinaryOperator: "MultiplicativeBinaryOperator",
  // * / %
  ComparisonBinaryOperator: "ComparisonBinaryOperator",
  // < > <= >= == !=
  UnaryOperator: "UnaryOperator",
  // ! - +
  // Keywords
  Set: "Set",
  If: "If",
  For: "For",
  In: "In",
  Is: "Is",
  NotIn: "NotIn",
  Else: "Else",
  EndIf: "EndIf",
  ElseIf: "ElseIf",
  EndFor: "EndFor",
  And: "And",
  Or: "Or",
  Not: "UnaryOperator"
});
var KEYWORDS = Object.freeze({
  set: TOKEN_TYPES.Set,
  for: TOKEN_TYPES.For,
  in: TOKEN_TYPES.In,
  is: TOKEN_TYPES.Is,
  if: TOKEN_TYPES.If,
  else: TOKEN_TYPES.Else,
  endif: TOKEN_TYPES.EndIf,
  elif: TOKEN_TYPES.ElseIf,
  endfor: TOKEN_TYPES.EndFor,
  and: TOKEN_TYPES.And,
  or: TOKEN_TYPES.Or,
  not: TOKEN_TYPES.Not,
  "not in": TOKEN_TYPES.NotIn,
  // Literals
  true: TOKEN_TYPES.BooleanLiteral,
  false: TOKEN_TYPES.BooleanLiteral
});
var Token = class {
  /**
   * Constructs a new Token.
   * @param {string} value The raw value as seen inside the source code.
   * @param {TokenType} type The type of token.
   */
  constructor(value, type) {
    this.value = value;
    this.type = type;
  }
};
function isWord(char) {
  return /\w/.test(char);
}
function isInteger(char) {
  return /[0-9]/.test(char);
}
var ORDERED_MAPPING_TABLE = [
  // Control sequences
  ["{%", TOKEN_TYPES.OpenStatement],
  ["%}", TOKEN_TYPES.CloseStatement],
  ["{{", TOKEN_TYPES.OpenExpression],
  ["}}", TOKEN_TYPES.CloseExpression],
  // Single character tokens
  ["(", TOKEN_TYPES.OpenParen],
  [")", TOKEN_TYPES.CloseParen],
  ["{", TOKEN_TYPES.OpenCurlyBracket],
  ["}", TOKEN_TYPES.CloseCurlyBracket],
  ["[", TOKEN_TYPES.OpenSquareBracket],
  ["]", TOKEN_TYPES.CloseSquareBracket],
  [",", TOKEN_TYPES.Comma],
  [".", TOKEN_TYPES.Dot],
  [":", TOKEN_TYPES.Colon],
  ["|", TOKEN_TYPES.Pipe],
  // Comparison operators
  ["<=", TOKEN_TYPES.ComparisonBinaryOperator],
  [">=", TOKEN_TYPES.ComparisonBinaryOperator],
  ["==", TOKEN_TYPES.ComparisonBinaryOperator],
  ["!=", TOKEN_TYPES.ComparisonBinaryOperator],
  ["<", TOKEN_TYPES.ComparisonBinaryOperator],
  [">", TOKEN_TYPES.ComparisonBinaryOperator],
  // Arithmetic operators
  ["+", TOKEN_TYPES.AdditiveBinaryOperator],
  ["-", TOKEN_TYPES.AdditiveBinaryOperator],
  ["*", TOKEN_TYPES.MultiplicativeBinaryOperator],
  ["/", TOKEN_TYPES.MultiplicativeBinaryOperator],
  ["%", TOKEN_TYPES.MultiplicativeBinaryOperator],
  // Assignment operator
  ["=", TOKEN_TYPES.Equals]
];
var ESCAPE_CHARACTERS = /* @__PURE__ */ new Map([
  ["n", "\n"],
  // New line
  ["t", "	"],
  // Horizontal tab
  ["r", "\r"],
  // Carriage return
  ["b", "\b"],
  // Backspace
  ["f", "\f"],
  // Form feed
  ["v", "\v"],
  // Vertical tab
  ["'", "'"],
  // Single quote
  ['"', '"'],
  // Double quote
  ["\\", "\\"]
  // Backslash
]);
function preprocess(template, options = {}) {
  if (template.endsWith("\n")) {
    template = template.slice(0, -1);
  }
  template = template.replace(new RegExp("{#.*?#}", "gs"), "{##}");
  if (options.lstrip_blocks) {
    template = template.replace(/^[ \t]*({[#%])/gm, "$1");
  }
  if (options.trim_blocks) {
    template = template.replace(/([#%]})\n/g, "$1");
  }
  return template.replace(/{##}/g, "").replace(/-%}\s*/g, "%}").replace(/\s*{%-/g, "{%").replace(/-}}\s*/g, "}}").replace(/\s*{{-/g, "{{");
}
function tokenize(source, options = {}) {
  var _a2, _b, _c;
  const tokens = [];
  const src = preprocess(source, options);
  let cursorPosition = 0;
  const consumeWhile = (predicate) => {
    let str = "";
    while (predicate(src[cursorPosition])) {
      if (src[cursorPosition] === "\\") {
        ++cursorPosition;
        if (cursorPosition >= src.length)
          throw new SyntaxError("Unexpected end of input");
        const escaped = src[cursorPosition++];
        const unescaped = ESCAPE_CHARACTERS.get(escaped);
        if (unescaped === void 0) {
          throw new SyntaxError(`Unexpected escaped character: ${escaped}`);
        }
        str += unescaped;
        continue;
      }
      str += src[cursorPosition++];
      if (cursorPosition >= src.length)
        throw new SyntaxError("Unexpected end of input");
    }
    return str;
  };
  main:
    while (cursorPosition < src.length) {
      const lastTokenType = (_a2 = tokens.at(-1)) == null ? void 0 : _a2.type;
      if (lastTokenType === void 0 || lastTokenType === TOKEN_TYPES.CloseStatement || lastTokenType === TOKEN_TYPES.CloseExpression) {
        let text = "";
        while (cursorPosition < src.length && // Keep going until we hit the next Jinja statement or expression
        !(src[cursorPosition] === "{" && (src[cursorPosition + 1] === "%" || src[cursorPosition + 1] === "{"))) {
          text += src[cursorPosition++];
        }
        if (text.length > 0) {
          tokens.push(new Token(text, TOKEN_TYPES.Text));
          continue;
        }
      }
      consumeWhile((char2) => /\s/.test(char2));
      const char = src[cursorPosition];
      if (char === "-" || char === "+") {
        const lastTokenType2 = (_b = tokens.at(-1)) == null ? void 0 : _b.type;
        if (lastTokenType2 === TOKEN_TYPES.Text || lastTokenType2 === void 0) {
          throw new SyntaxError(`Unexpected character: ${char}`);
        }
        switch (lastTokenType2) {
          case TOKEN_TYPES.Identifier:
          case TOKEN_TYPES.NumericLiteral:
          case TOKEN_TYPES.BooleanLiteral:
          case TOKEN_TYPES.StringLiteral:
          case TOKEN_TYPES.CloseParen:
          case TOKEN_TYPES.CloseSquareBracket:
            break;
          default: {
            ++cursorPosition;
            const num = consumeWhile(isInteger);
            tokens.push(
              new Token(`${char}${num}`, num.length > 0 ? TOKEN_TYPES.NumericLiteral : TOKEN_TYPES.UnaryOperator)
            );
            continue;
          }
        }
      }
      for (const [char2, token] of ORDERED_MAPPING_TABLE) {
        const slice2 = src.slice(cursorPosition, cursorPosition + char2.length);
        if (slice2 === char2) {
          tokens.push(new Token(char2, token));
          cursorPosition += char2.length;
          continue main;
        }
      }
      if (char === "'" || char === '"') {
        ++cursorPosition;
        const str = consumeWhile((c) => c !== char);
        tokens.push(new Token(str, TOKEN_TYPES.StringLiteral));
        ++cursorPosition;
        continue;
      }
      if (isInteger(char)) {
        const num = consumeWhile(isInteger);
        tokens.push(new Token(num, TOKEN_TYPES.NumericLiteral));
        continue;
      }
      if (isWord(char)) {
        const word = consumeWhile(isWord);
        const type = Object.hasOwn(KEYWORDS, word) ? KEYWORDS[word] : TOKEN_TYPES.Identifier;
        if (type === TOKEN_TYPES.In && ((_c = tokens.at(-1)) == null ? void 0 : _c.type) === TOKEN_TYPES.Not) {
          tokens.pop();
          tokens.push(new Token("not in", TOKEN_TYPES.NotIn));
        } else {
          tokens.push(new Token(word, type));
        }
        continue;
      }
      throw new SyntaxError(`Unexpected character: ${char}`);
    }
  return tokens;
}
var Statement = class {
  constructor() {
    __publicField(this, "type", "Statement");
  }
};
var Program = class extends Statement {
  constructor(body) {
    super();
    __publicField(this, "type", "Program");
    this.body = body;
  }
};
var If = class extends Statement {
  constructor(test, body, alternate) {
    super();
    __publicField(this, "type", "If");
    this.test = test;
    this.body = body;
    this.alternate = alternate;
  }
};
var For = class extends Statement {
  constructor(loopvar, iterable, body) {
    super();
    __publicField(this, "type", "For");
    this.loopvar = loopvar;
    this.iterable = iterable;
    this.body = body;
  }
};
var SetStatement = class extends Statement {
  constructor(assignee, value) {
    super();
    __publicField(this, "type", "Set");
    this.assignee = assignee;
    this.value = value;
  }
};
var Expression = class extends Statement {
  constructor() {
    super(...arguments);
    __publicField(this, "type", "Expression");
  }
};
var MemberExpression = class extends Expression {
  constructor(object, property, computed) {
    super();
    __publicField(this, "type", "MemberExpression");
    this.object = object;
    this.property = property;
    this.computed = computed;
  }
};
var CallExpression = class extends Expression {
  constructor(callee, args) {
    super();
    __publicField(this, "type", "CallExpression");
    this.callee = callee;
    this.args = args;
  }
};
var Identifier = class extends Expression {
  /**
   * @param {string} value The name of the identifier
   */
  constructor(value) {
    super();
    __publicField(this, "type", "Identifier");
    this.value = value;
  }
};
var Literal = class extends Expression {
  constructor(value) {
    super();
    __publicField(this, "type", "Literal");
    this.value = value;
  }
};
var NumericLiteral = class extends Literal {
  constructor() {
    super(...arguments);
    __publicField(this, "type", "NumericLiteral");
  }
};
var StringLiteral = class extends Literal {
  constructor() {
    super(...arguments);
    __publicField(this, "type", "StringLiteral");
  }
};
var BooleanLiteral = class extends Literal {
  constructor() {
    super(...arguments);
    __publicField(this, "type", "BooleanLiteral");
  }
};
var ArrayLiteral = class extends Literal {
  constructor() {
    super(...arguments);
    __publicField(this, "type", "ArrayLiteral");
  }
};
var TupleLiteral = class extends Literal {
  constructor() {
    super(...arguments);
    __publicField(this, "type", "TupleLiteral");
  }
};
var ObjectLiteral = class extends Literal {
  constructor() {
    super(...arguments);
    __publicField(this, "type", "ObjectLiteral");
  }
};
var BinaryExpression = class extends Expression {
  constructor(operator, left, right) {
    super();
    __publicField(this, "type", "BinaryExpression");
    this.operator = operator;
    this.left = left;
    this.right = right;
  }
};
var FilterExpression = class extends Expression {
  constructor(operand, filter) {
    super();
    __publicField(this, "type", "FilterExpression");
    this.operand = operand;
    this.filter = filter;
  }
};
var TestExpression = class extends Expression {
  constructor(operand, negate, test) {
    super();
    __publicField(this, "type", "TestExpression");
    this.operand = operand;
    this.negate = negate;
    this.test = test;
  }
};
var UnaryExpression = class extends Expression {
  constructor(operator, argument) {
    super();
    __publicField(this, "type", "UnaryExpression");
    this.operator = operator;
    this.argument = argument;
  }
};
var SliceExpression = class extends Expression {
  constructor(start = void 0, stop = void 0, step = void 0) {
    super();
    __publicField(this, "type", "SliceExpression");
    this.start = start;
    this.stop = stop;
    this.step = step;
  }
};
var KeywordArgumentExpression = class extends Expression {
  constructor(key, value) {
    super();
    __publicField(this, "type", "KeywordArgumentExpression");
    this.key = key;
    this.value = value;
  }
};
function parse(tokens) {
  const program = new Program([]);
  let current = 0;
  function expect(type, error) {
    const prev = tokens[current++];
    if (!prev || prev.type !== type) {
      throw new Error(`Parser Error: ${error}. ${prev.type} !== ${type}.`);
    }
    return prev;
  }
  function parseAny() {
    switch (tokens[current].type) {
      case TOKEN_TYPES.Text:
        return parseText();
      case TOKEN_TYPES.OpenStatement:
        return parseJinjaStatement();
      case TOKEN_TYPES.OpenExpression:
        return parseJinjaExpression();
      default:
        throw new SyntaxError(`Unexpected token type: ${tokens[current].type}`);
    }
  }
  function not(...types) {
    return current + types.length <= tokens.length && types.some((type, i) => type !== tokens[current + i].type);
  }
  function is(...types) {
    return current + types.length <= tokens.length && types.every((type, i) => type === tokens[current + i].type);
  }
  function parseText() {
    return new StringLiteral(expect(TOKEN_TYPES.Text, "Expected text token").value);
  }
  function parseJinjaStatement() {
    expect(TOKEN_TYPES.OpenStatement, "Expected opening statement token");
    let result;
    switch (tokens[current].type) {
      case TOKEN_TYPES.Set:
        ++current;
        result = parseSetStatement();
        expect(TOKEN_TYPES.CloseStatement, "Expected closing statement token");
        break;
      case TOKEN_TYPES.If:
        ++current;
        result = parseIfStatement();
        expect(TOKEN_TYPES.OpenStatement, "Expected {% token");
        expect(TOKEN_TYPES.EndIf, "Expected endif token");
        expect(TOKEN_TYPES.CloseStatement, "Expected %} token");
        break;
      case TOKEN_TYPES.For:
        ++current;
        result = parseForStatement();
        expect(TOKEN_TYPES.OpenStatement, "Expected {% token");
        expect(TOKEN_TYPES.EndFor, "Expected endfor token");
        expect(TOKEN_TYPES.CloseStatement, "Expected %} token");
        break;
      default:
        throw new SyntaxError(`Unknown statement type: ${tokens[current].type}`);
    }
    return result;
  }
  function parseJinjaExpression() {
    expect(TOKEN_TYPES.OpenExpression, "Expected opening expression token");
    const result = parseExpression();
    expect(TOKEN_TYPES.CloseExpression, "Expected closing expression token");
    return result;
  }
  function parseSetStatement() {
    const left = parseExpression();
    if (is(TOKEN_TYPES.Equals)) {
      ++current;
      const value = parseSetStatement();
      return new SetStatement(left, value);
    }
    return left;
  }
  function parseIfStatement() {
    var _a2, _b, _c, _d, _e, _f, _g, _h;
    const test = parseExpression();
    expect(TOKEN_TYPES.CloseStatement, "Expected closing statement token");
    const body = [];
    const alternate = [];
    while (!(((_a2 = tokens[current]) == null ? void 0 : _a2.type) === TOKEN_TYPES.OpenStatement && (((_b = tokens[current + 1]) == null ? void 0 : _b.type) === TOKEN_TYPES.ElseIf || ((_c = tokens[current + 1]) == null ? void 0 : _c.type) === TOKEN_TYPES.Else || ((_d = tokens[current + 1]) == null ? void 0 : _d.type) === TOKEN_TYPES.EndIf))) {
      body.push(parseAny());
    }
    if (((_e = tokens[current]) == null ? void 0 : _e.type) === TOKEN_TYPES.OpenStatement && ((_f = tokens[current + 1]) == null ? void 0 : _f.type) !== TOKEN_TYPES.EndIf) {
      ++current;
      if (is(TOKEN_TYPES.ElseIf)) {
        expect(TOKEN_TYPES.ElseIf, "Expected elseif token");
        alternate.push(parseIfStatement());
      } else {
        expect(TOKEN_TYPES.Else, "Expected else token");
        expect(TOKEN_TYPES.CloseStatement, "Expected closing statement token");
        while (!(((_g = tokens[current]) == null ? void 0 : _g.type) === TOKEN_TYPES.OpenStatement && ((_h = tokens[current + 1]) == null ? void 0 : _h.type) === TOKEN_TYPES.EndIf)) {
          alternate.push(parseAny());
        }
      }
    }
    return new If(test, body, alternate);
  }
  function parseExpressionSequence(primary = false) {
    const fn = primary ? parsePrimaryExpression : parseExpression;
    const expressions = [fn()];
    const isTuple = is(TOKEN_TYPES.Comma);
    while (isTuple) {
      ++current;
      expressions.push(fn());
      if (!is(TOKEN_TYPES.Comma)) {
        break;
      }
    }
    return isTuple ? new TupleLiteral(expressions) : expressions[0];
  }
  function parseForStatement() {
    const loopVariable = parseExpressionSequence(true);
    if (!(loopVariable instanceof Identifier || loopVariable instanceof TupleLiteral)) {
      throw new SyntaxError(`Expected identifier/tuple for the loop variable, got ${loopVariable.type} instead`);
    }
    expect(TOKEN_TYPES.In, "Expected `in` keyword following loop variable");
    const iterable = parseExpression();
    expect(TOKEN_TYPES.CloseStatement, "Expected closing statement token");
    const body = [];
    while (not(TOKEN_TYPES.OpenStatement, TOKEN_TYPES.EndFor)) {
      body.push(parseAny());
    }
    return new For(loopVariable, iterable, body);
  }
  function parseExpression() {
    return parseTernaryExpression();
  }
  function parseTernaryExpression() {
    const a = parseLogicalOrExpression();
    if (is(TOKEN_TYPES.If)) {
      ++current;
      const predicate = parseLogicalOrExpression();
      expect(TOKEN_TYPES.Else, "Expected else token");
      const b = parseLogicalOrExpression();
      return new If(predicate, [a], [b]);
    }
    return a;
  }
  function parseLogicalOrExpression() {
    let left = parseLogicalAndExpression();
    while (is(TOKEN_TYPES.Or)) {
      const operator = tokens[current];
      ++current;
      const right = parseLogicalAndExpression();
      left = new BinaryExpression(operator, left, right);
    }
    return left;
  }
  function parseLogicalAndExpression() {
    let left = parseLogicalNegationExpression();
    while (is(TOKEN_TYPES.And)) {
      const operator = tokens[current];
      ++current;
      const right = parseLogicalNegationExpression();
      left = new BinaryExpression(operator, left, right);
    }
    return left;
  }
  function parseLogicalNegationExpression() {
    let right;
    while (is(TOKEN_TYPES.Not)) {
      const operator = tokens[current];
      ++current;
      const arg = parseLogicalNegationExpression();
      right = new UnaryExpression(operator, arg);
    }
    return right != null ? right : parseComparisonExpression();
  }
  function parseComparisonExpression() {
    let left = parseAdditiveExpression();
    while (is(TOKEN_TYPES.ComparisonBinaryOperator) || is(TOKEN_TYPES.In) || is(TOKEN_TYPES.NotIn)) {
      const operator = tokens[current];
      ++current;
      const right = parseAdditiveExpression();
      left = new BinaryExpression(operator, left, right);
    }
    return left;
  }
  function parseAdditiveExpression() {
    let left = parseMultiplicativeExpression();
    while (is(TOKEN_TYPES.AdditiveBinaryOperator)) {
      const operator = tokens[current];
      ++current;
      const right = parseMultiplicativeExpression();
      left = new BinaryExpression(operator, left, right);
    }
    return left;
  }
  function parseCallMemberExpression() {
    const member = parseMemberExpression();
    if (is(TOKEN_TYPES.OpenParen)) {
      return parseCallExpression(member);
    }
    return member;
  }
  function parseCallExpression(callee) {
    let callExpression = new CallExpression(callee, parseArgs());
    if (is(TOKEN_TYPES.OpenParen)) {
      callExpression = parseCallExpression(callExpression);
    }
    return callExpression;
  }
  function parseArgs() {
    expect(TOKEN_TYPES.OpenParen, "Expected opening parenthesis for arguments list");
    const args = parseArgumentsList();
    expect(TOKEN_TYPES.CloseParen, "Expected closing parenthesis for arguments list");
    return args;
  }
  function parseArgumentsList() {
    const args = [];
    while (!is(TOKEN_TYPES.CloseParen)) {
      let argument = parseExpression();
      if (is(TOKEN_TYPES.Equals)) {
        ++current;
        if (!(argument instanceof Identifier)) {
          throw new SyntaxError(`Expected identifier for keyword argument`);
        }
        const value = parseExpression();
        argument = new KeywordArgumentExpression(argument, value);
      }
      args.push(argument);
      if (is(TOKEN_TYPES.Comma)) {
        ++current;
      }
    }
    return args;
  }
  function parseMemberExpressionArgumentsList() {
    const slices = [];
    let isSlice = false;
    while (!is(TOKEN_TYPES.CloseSquareBracket)) {
      if (is(TOKEN_TYPES.Colon)) {
        slices.push(void 0);
        ++current;
        isSlice = true;
      } else {
        slices.push(parseExpression());
        if (is(TOKEN_TYPES.Colon)) {
          ++current;
          isSlice = true;
        }
      }
    }
    if (slices.length === 0) {
      throw new SyntaxError(`Expected at least one argument for member/slice expression`);
    }
    if (isSlice) {
      if (slices.length > 3) {
        throw new SyntaxError(`Expected 0-3 arguments for slice expression`);
      }
      return new SliceExpression(...slices);
    }
    return slices[0];
  }
  function parseMemberExpression() {
    let object = parsePrimaryExpression();
    while (is(TOKEN_TYPES.Dot) || is(TOKEN_TYPES.OpenSquareBracket)) {
      const operator = tokens[current];
      ++current;
      let property;
      const computed = operator.type !== TOKEN_TYPES.Dot;
      if (computed) {
        property = parseMemberExpressionArgumentsList();
        expect(TOKEN_TYPES.CloseSquareBracket, "Expected closing square bracket");
      } else {
        property = parsePrimaryExpression();
        if (property.type !== "Identifier") {
          throw new SyntaxError(`Expected identifier following dot operator`);
        }
      }
      object = new MemberExpression(object, property, computed);
    }
    return object;
  }
  function parseMultiplicativeExpression() {
    let left = parseTestExpression();
    while (is(TOKEN_TYPES.MultiplicativeBinaryOperator)) {
      const operator = tokens[current];
      ++current;
      const right = parseTestExpression();
      left = new BinaryExpression(operator, left, right);
    }
    return left;
  }
  function parseTestExpression() {
    let operand = parseFilterExpression();
    while (is(TOKEN_TYPES.Is)) {
      ++current;
      const negate = is(TOKEN_TYPES.Not);
      if (negate) {
        ++current;
      }
      let filter = parsePrimaryExpression();
      if (filter instanceof BooleanLiteral) {
        filter = new Identifier(filter.value.toString());
      }
      if (!(filter instanceof Identifier)) {
        throw new SyntaxError(`Expected identifier for the test`);
      }
      operand = new TestExpression(operand, negate, filter);
    }
    return operand;
  }
  function parseFilterExpression() {
    let operand = parseCallMemberExpression();
    while (is(TOKEN_TYPES.Pipe)) {
      ++current;
      let filter = parsePrimaryExpression();
      if (!(filter instanceof Identifier)) {
        throw new SyntaxError(`Expected identifier for the filter`);
      }
      if (is(TOKEN_TYPES.OpenParen)) {
        filter = parseCallExpression(filter);
      }
      operand = new FilterExpression(operand, filter);
    }
    return operand;
  }
  function parsePrimaryExpression() {
    const token = tokens[current];
    switch (token.type) {
      case TOKEN_TYPES.NumericLiteral:
        ++current;
        return new NumericLiteral(Number(token.value));
      case TOKEN_TYPES.StringLiteral:
        ++current;
        return new StringLiteral(token.value);
      case TOKEN_TYPES.BooleanLiteral:
        ++current;
        return new BooleanLiteral(token.value === "true");
      case TOKEN_TYPES.Identifier:
        ++current;
        return new Identifier(token.value);
      case TOKEN_TYPES.OpenParen: {
        ++current;
        const expression = parseExpressionSequence();
        if (tokens[current].type !== TOKEN_TYPES.CloseParen) {
          throw new SyntaxError(`Expected closing parenthesis, got ${tokens[current].type} instead`);
        }
        ++current;
        return expression;
      }
      case TOKEN_TYPES.OpenSquareBracket: {
        ++current;
        const values = [];
        while (!is(TOKEN_TYPES.CloseSquareBracket)) {
          values.push(parseExpression());
          if (is(TOKEN_TYPES.Comma)) {
            ++current;
          }
        }
        ++current;
        return new ArrayLiteral(values);
      }
      case TOKEN_TYPES.OpenCurlyBracket: {
        ++current;
        const values = /* @__PURE__ */ new Map();
        while (!is(TOKEN_TYPES.CloseCurlyBracket)) {
          const key = parseExpression();
          expect(TOKEN_TYPES.Colon, "Expected colon between key and value in object literal");
          const value = parseExpression();
          values.set(key, value);
          if (is(TOKEN_TYPES.Comma)) {
            ++current;
          }
        }
        ++current;
        return new ObjectLiteral(values);
      }
      default:
        throw new SyntaxError(`Unexpected token: ${token.type}`);
    }
  }
  while (current < tokens.length) {
    program.body.push(parseAny());
  }
  return program;
}
function range(start, stop, step = 1) {
  if (stop === void 0) {
    stop = start;
    start = 0;
  }
  const result = [];
  for (let i = start; i < stop; i += step) {
    result.push(i);
  }
  return result;
}
function slice(array, start, stop, step = 1) {
  const direction = Math.sign(step);
  if (direction >= 0) {
    start = (start != null ? start : start = 0) < 0 ? Math.max(array.length + start, 0) : Math.min(start, array.length);
    stop = (stop != null ? stop : stop = array.length) < 0 ? Math.max(array.length + stop, 0) : Math.min(stop, array.length);
  } else {
    start = (start != null ? start : start = array.length - 1) < 0 ? Math.max(array.length + start, -1) : Math.min(start, array.length - 1);
    stop = (stop != null ? stop : stop = -1) < -1 ? Math.max(array.length + stop, -1) : Math.min(stop, array.length - 1);
  }
  const result = [];
  for (let i = start; direction * i < direction * stop; i += step) {
    result.push(array[i]);
  }
  return result;
}
function titleCase(value) {
  return value.replace(/\b\w/g, (c) => c.toUpperCase());
}
var RuntimeValue = class {
  /**
   * Creates a new RuntimeValue.
   */
  constructor(value = void 0) {
    __publicField(this, "type", "RuntimeValue");
    __publicField(this, "value");
    /**
     * A collection of built-in functions for this type.
     */
    __publicField(this, "builtins", /* @__PURE__ */ new Map());
    this.value = value;
  }
  /**
   * Determines truthiness or falsiness of the runtime value.
   * This function should be overridden by subclasses if it has custom truthiness criteria.
   * @returns {BooleanValue} BooleanValue(true) if the value is truthy, BooleanValue(false) otherwise.
   */
  __bool__() {
    return new BooleanValue(!!this.value);
  }
};
var NumericValue = class extends RuntimeValue {
  constructor() {
    super(...arguments);
    __publicField(this, "type", "NumericValue");
  }
};
var StringValue = class extends RuntimeValue {
  constructor() {
    super(...arguments);
    __publicField(this, "type", "StringValue");
    __publicField(this, "builtins", /* @__PURE__ */ new Map([
      [
        "upper",
        new FunctionValue(() => {
          return new StringValue(this.value.toUpperCase());
        })
      ],
      [
        "lower",
        new FunctionValue(() => {
          return new StringValue(this.value.toLowerCase());
        })
      ],
      [
        "strip",
        new FunctionValue(() => {
          return new StringValue(this.value.trim());
        })
      ],
      [
        "title",
        new FunctionValue(() => {
          return new StringValue(titleCase(this.value));
        })
      ],
      ["length", new NumericValue(this.value.length)]
    ]));
  }
};
var BooleanValue = class extends RuntimeValue {
  constructor() {
    super(...arguments);
    __publicField(this, "type", "BooleanValue");
  }
};
var ObjectValue = class extends RuntimeValue {
  constructor() {
    super(...arguments);
    __publicField(this, "type", "ObjectValue");
    __publicField(this, "builtins", /* @__PURE__ */ new Map([
      [
        "get",
        new FunctionValue(([key, defaultValue]) => {
          var _a2, _b;
          if (!(key instanceof StringValue)) {
            throw new Error(`Object key must be a string: got ${key.type}`);
          }
          return (_b = (_a2 = this.value.get(key.value)) != null ? _a2 : defaultValue) != null ? _b : new NullValue();
        })
      ],
      [
        "items",
        new FunctionValue(() => {
          return new ArrayValue(
            Array.from(this.value.entries()).map(([key, value]) => new ArrayValue([new StringValue(key), value]))
          );
        })
      ]
    ]));
  }
  /**
   * NOTE: necessary to override since all JavaScript arrays are considered truthy,
   * while only non-empty Python arrays are consider truthy.
   *
   * e.g.,
   *  - JavaScript:  {} && 5 -> 5
   *  - Python:      {} and 5 -> {}
   */
  __bool__() {
    return new BooleanValue(this.value.size > 0);
  }
};
var ArrayValue = class extends RuntimeValue {
  constructor() {
    super(...arguments);
    __publicField(this, "type", "ArrayValue");
    __publicField(this, "builtins", /* @__PURE__ */ new Map([["length", new NumericValue(this.value.length)]]));
  }
  /**
   * NOTE: necessary to override since all JavaScript arrays are considered truthy,
   * while only non-empty Python arrays are consider truthy.
   *
   * e.g.,
   *  - JavaScript:  [] && 5 -> 5
   *  - Python:      [] and 5 -> []
   */
  __bool__() {
    return new BooleanValue(this.value.length > 0);
  }
};
var TupleValue = class extends ArrayValue {
  constructor() {
    super(...arguments);
    __publicField(this, "type", "TupleValue");
  }
};
var FunctionValue = class extends RuntimeValue {
  constructor() {
    super(...arguments);
    __publicField(this, "type", "FunctionValue");
  }
};
var NullValue = class extends RuntimeValue {
  constructor() {
    super(...arguments);
    __publicField(this, "type", "NullValue");
  }
};
var UndefinedValue = class extends RuntimeValue {
  constructor() {
    super(...arguments);
    __publicField(this, "type", "UndefinedValue");
  }
};
var Environment = class {
  constructor(parent) {
    /**
     * The variables declared in this environment.
     */
    __publicField(this, "variables", /* @__PURE__ */ new Map([
      [
        "namespace",
        new FunctionValue((args) => {
          if (args.length === 0) {
            return new ObjectValue(/* @__PURE__ */ new Map());
          }
          if (args.length !== 1 || !(args[0] instanceof ObjectValue)) {
            throw new Error("`namespace` expects either zero arguments or a single object argument");
          }
          return args[0];
        })
      ]
    ]));
    /**
     * The tests available in this environment.
     */
    __publicField(this, "tests", /* @__PURE__ */ new Map([
      ["boolean", (operand) => operand.type === "BooleanValue"],
      ["callable", (operand) => operand instanceof FunctionValue],
      [
        "odd",
        (operand) => {
          if (operand.type !== "NumericValue") {
            throw new Error(`Cannot apply test "odd" to type: ${operand.type}`);
          }
          return operand.value % 2 !== 0;
        }
      ],
      [
        "even",
        (operand) => {
          if (operand.type !== "NumericValue") {
            throw new Error(`Cannot apply test "even" to type: ${operand.type}`);
          }
          return operand.value % 2 === 0;
        }
      ],
      ["false", (operand) => operand.type === "BooleanValue" && !operand.value],
      ["true", (operand) => operand.type === "BooleanValue" && operand.value],
      ["number", (operand) => operand.type === "NumericValue"],
      ["integer", (operand) => operand.type === "NumericValue" && Number.isInteger(operand.value)],
      ["iterable", (operand) => operand instanceof ArrayValue || operand instanceof StringValue],
      [
        "lower",
        (operand) => {
          const str = operand.value;
          return operand.type === "StringValue" && str === str.toLowerCase();
        }
      ],
      [
        "upper",
        (operand) => {
          const str = operand.value;
          return operand.type === "StringValue" && str === str.toUpperCase();
        }
      ],
      ["none", (operand) => operand.type === "NullValue"],
      ["defined", (operand) => operand.type !== "UndefinedValue"],
      ["undefined", (operand) => operand.type === "UndefinedValue"],
      ["equalto", (a, b) => a.value === b.value]
    ]));
    this.parent = parent;
  }
  /**
   * Set the value of a variable in the current environment.
   */
  set(name2, value) {
    return this.declareVariable(name2, convertToRuntimeValues(value));
  }
  declareVariable(name2, value) {
    if (this.variables.has(name2)) {
      throw new SyntaxError(`Variable already declared: ${name2}`);
    }
    this.variables.set(name2, value);
    return value;
  }
  // private assignVariable(name: string, value: AnyRuntimeValue): AnyRuntimeValue {
  // 	const env = this.resolve(name);
  // 	env.variables.set(name, value);
  // 	return value;
  // }
  /**
   * Set variable in the current scope.
   * See https://jinja.palletsprojects.com/en/3.0.x/templates/#assignments for more information.
   */
  setVariable(name2, value) {
    this.variables.set(name2, value);
    return value;
  }
  /**
   * Resolve the environment in which the variable is declared.
   * @param {string} name The name of the variable.
   * @returns {Environment} The environment in which the variable is declared.
   */
  resolve(name2) {
    if (this.variables.has(name2)) {
      return this;
    }
    if (this.parent) {
      return this.parent.resolve(name2);
    }
    throw new Error(`Unknown variable: ${name2}`);
  }
  lookupVariable(name2) {
    var _a2;
    try {
      return (_a2 = this.resolve(name2).variables.get(name2)) != null ? _a2 : new UndefinedValue();
    } catch (e) {
      return new UndefinedValue();
    }
  }
};
var Interpreter = class {
  constructor(env2) {
    __publicField(this, "global");
    this.global = env2 != null ? env2 : new Environment();
  }
  /**
   * Run the program.
   */
  run(program) {
    return this.evaluate(program, this.global);
  }
  /**
   * Evaluates expressions following the binary operation type.
   */
  evaluateBinaryExpression(node, environment) {
    const left = this.evaluate(node.left, environment);
    switch (node.operator.value) {
      case "and":
        return left.__bool__().value ? this.evaluate(node.right, environment) : left;
      case "or":
        return left.__bool__().value ? left : this.evaluate(node.right, environment);
    }
    const right = this.evaluate(node.right, environment);
    switch (node.operator.value) {
      case "==":
        return new BooleanValue(left.value == right.value);
      case "!=":
        return new BooleanValue(left.value != right.value);
    }
    if (left instanceof UndefinedValue || right instanceof UndefinedValue) {
      throw new Error("Cannot perform operation on undefined values");
    } else if (left instanceof NullValue || right instanceof NullValue) {
      throw new Error("Cannot perform operation on null values");
    } else if (left instanceof NumericValue && right instanceof NumericValue) {
      switch (node.operator.value) {
        case "+":
          return new NumericValue(left.value + right.value);
        case "-":
          return new NumericValue(left.value - right.value);
        case "*":
          return new NumericValue(left.value * right.value);
        case "/":
          return new NumericValue(left.value / right.value);
        case "%":
          return new NumericValue(left.value % right.value);
        case "<":
          return new BooleanValue(left.value < right.value);
        case ">":
          return new BooleanValue(left.value > right.value);
        case ">=":
          return new BooleanValue(left.value >= right.value);
        case "<=":
          return new BooleanValue(left.value <= right.value);
      }
    } else if (left instanceof ArrayValue && right instanceof ArrayValue) {
      switch (node.operator.value) {
        case "+":
          return new ArrayValue(left.value.concat(right.value));
      }
    } else if (right instanceof ArrayValue) {
      const member = right.value.find((x) => x.value === left.value) !== void 0;
      switch (node.operator.value) {
        case "in":
          return new BooleanValue(member);
        case "not in":
          return new BooleanValue(!member);
      }
    }
    if (left instanceof StringValue || right instanceof StringValue) {
      switch (node.operator.value) {
        case "+":
          return new StringValue(left.value.toString() + right.value.toString());
      }
    }
    if (left instanceof StringValue && right instanceof StringValue) {
      switch (node.operator.value) {
        case "in":
          return new BooleanValue(right.value.includes(left.value));
        case "not in":
          return new BooleanValue(!right.value.includes(left.value));
      }
    }
    if (left instanceof StringValue && right instanceof ObjectValue) {
      switch (node.operator.value) {
        case "in":
          return new BooleanValue(right.value.has(left.value));
        case "not in":
          return new BooleanValue(!right.value.has(left.value));
      }
    }
    throw new SyntaxError(`Unknown operator "${node.operator.value}" between ${left.type} and ${right.type}`);
  }
  /**
   * Evaluates expressions following the filter operation type.
   */
  evaluateFilterExpression(node, environment) {
    const operand = this.evaluate(node.operand, environment);
    if (node.filter.type === "Identifier") {
      const filter = node.filter;
      if (operand instanceof ArrayValue) {
        switch (filter.value) {
          case "list":
            return operand;
          case "first":
            return operand.value[0];
          case "last":
            return operand.value[operand.value.length - 1];
          case "length":
            return new NumericValue(operand.value.length);
          case "reverse":
            return new ArrayValue(operand.value.reverse());
          case "sort":
            return new ArrayValue(
              operand.value.sort((a, b) => {
                if (a.type !== b.type) {
                  throw new Error(`Cannot compare different types: ${a.type} and ${b.type}`);
                }
                switch (a.type) {
                  case "NumericValue":
                    return a.value - b.value;
                  case "StringValue":
                    return a.value.localeCompare(b.value);
                  default:
                    throw new Error(`Cannot compare type: ${a.type}`);
                }
              })
            );
          default:
            throw new Error(`Unknown ArrayValue filter: ${filter.value}`);
        }
      } else if (operand instanceof StringValue) {
        switch (filter.value) {
          case "length":
            return new NumericValue(operand.value.length);
          case "upper":
            return new StringValue(operand.value.toUpperCase());
          case "lower":
            return new StringValue(operand.value.toLowerCase());
          case "title":
            return new StringValue(titleCase(operand.value));
          case "capitalize":
            return new StringValue(operand.value.charAt(0).toUpperCase() + operand.value.slice(1));
          case "trim":
            return new StringValue(operand.value.trim());
          default:
            throw new Error(`Unknown StringValue filter: ${filter.value}`);
        }
      } else if (operand instanceof NumericValue) {
        switch (filter.value) {
          case "abs":
            return new NumericValue(Math.abs(operand.value));
          default:
            throw new Error(`Unknown NumericValue filter: ${filter.value}`);
        }
      } else if (operand instanceof ObjectValue) {
        switch (filter.value) {
          case "items":
            return new ArrayValue(
              Array.from(operand.value.entries()).map(([key, value]) => new ArrayValue([new StringValue(key), value]))
            );
          case "length":
            return new NumericValue(operand.value.size);
          default:
            throw new Error(`Unknown ObjectValue filter: ${filter.value}`);
        }
      }
      throw new Error(`Cannot apply filter "${filter.value}" to type: ${operand.type}`);
    } else if (node.filter.type === "CallExpression") {
      const filter = node.filter;
      if (filter.callee.type !== "Identifier") {
        throw new Error(`Unknown filter: ${filter.callee.type}`);
      }
      const filterName = filter.callee.value;
      if (operand instanceof ArrayValue) {
        switch (filterName) {
          case "selectattr": {
            if (operand.value.some((x) => !(x instanceof ObjectValue))) {
              throw new Error("`selectattr` can only be applied to array of objects");
            }
            if (filter.args.some((x) => x.type !== "StringLiteral")) {
              throw new Error("arguments of `selectattr` must be strings");
            }
            const [attr, testName, value] = filter.args.map((x) => this.evaluate(x, environment));
            let testFunction;
            if (testName) {
              const test = environment.tests.get(testName.value);
              if (!test) {
                throw new Error(`Unknown test: ${testName.value}`);
              }
              testFunction = test;
            } else {
              testFunction = (...x) => x[0].__bool__().value;
            }
            const filtered = operand.value.filter((item) => {
              const a = item.value.get(attr.value);
              if (a) {
                return testFunction(a, value);
              }
              return false;
            });
            return new ArrayValue(filtered);
          }
        }
        throw new Error(`Unknown ArrayValue filter: ${filterName}`);
      } else {
        throw new Error(`Cannot apply filter "${filterName}" to type: ${operand.type}`);
      }
    }
    throw new Error(`Unknown filter: ${node.filter.type}`);
  }
  /**
   * Evaluates expressions following the test operation type.
   */
  evaluateTestExpression(node, environment) {
    const operand = this.evaluate(node.operand, environment);
    const test = environment.tests.get(node.test.value);
    if (!test) {
      throw new Error(`Unknown test: ${node.test.value}`);
    }
    const result = test(operand);
    return new BooleanValue(node.negate ? !result : result);
  }
  /**
   * Evaluates expressions following the unary operation type.
   */
  evaluateUnaryExpression(node, environment) {
    const argument = this.evaluate(node.argument, environment);
    switch (node.operator.value) {
      case "not":
        return new BooleanValue(!argument.value);
      default:
        throw new SyntaxError(`Unknown operator: ${node.operator.value}`);
    }
  }
  evalProgram(program, environment) {
    return this.evaluateBlock(program.body, environment);
  }
  evaluateBlock(statements, environment) {
    let result = "";
    for (const statement of statements) {
      const lastEvaluated = this.evaluate(statement, environment);
      if (lastEvaluated.type !== "NullValue" && lastEvaluated.type !== "UndefinedValue") {
        result += lastEvaluated.value;
      }
    }
    return new StringValue(result);
  }
  evaluateIdentifier(node, environment) {
    return environment.lookupVariable(node.value);
  }
  evaluateCallExpression(expr, environment) {
    const args = [];
    const kwargs = /* @__PURE__ */ new Map();
    for (const argument of expr.args) {
      if (argument.type === "KeywordArgumentExpression") {
        const kwarg = argument;
        kwargs.set(kwarg.key.value, this.evaluate(kwarg.value, environment));
      } else {
        args.push(this.evaluate(argument, environment));
      }
    }
    if (kwargs.size > 0) {
      args.push(new ObjectValue(kwargs));
    }
    const fn = this.evaluate(expr.callee, environment);
    if (fn.type !== "FunctionValue") {
      throw new Error(`Cannot call something that is not a function: got ${fn.type}`);
    }
    return fn.value(args, environment);
  }
  evaluateSliceExpression(object, expr, environment) {
    if (!(object instanceof ArrayValue || object instanceof StringValue)) {
      throw new Error("Slice object must be an array or string");
    }
    const start = this.evaluate(expr.start, environment);
    const stop = this.evaluate(expr.stop, environment);
    const step = this.evaluate(expr.step, environment);
    if (!(start instanceof NumericValue || start instanceof UndefinedValue)) {
      throw new Error("Slice start must be numeric or undefined");
    }
    if (!(stop instanceof NumericValue || stop instanceof UndefinedValue)) {
      throw new Error("Slice stop must be numeric or undefined");
    }
    if (!(step instanceof NumericValue || step instanceof UndefinedValue)) {
      throw new Error("Slice step must be numeric or undefined");
    }
    if (object instanceof ArrayValue) {
      return new ArrayValue(slice(object.value, start.value, stop.value, step.value));
    } else {
      return new StringValue(slice(Array.from(object.value), start.value, stop.value, step.value).join(""));
    }
  }
  evaluateMemberExpression(expr, environment) {
    var _a2;
    const object = this.evaluate(expr.object, environment);
    let property;
    if (expr.computed) {
      if (expr.property.type === "SliceExpression") {
        return this.evaluateSliceExpression(object, expr.property, environment);
      } else {
        property = this.evaluate(expr.property, environment);
      }
    } else {
      property = new StringValue(expr.property.value);
    }
    let value;
    if (object instanceof ObjectValue) {
      if (!(property instanceof StringValue)) {
        throw new Error(`Cannot access property with non-string: got ${property.type}`);
      }
      value = (_a2 = object.value.get(property.value)) != null ? _a2 : object.builtins.get(property.value);
    } else if (object instanceof ArrayValue || object instanceof StringValue) {
      if (property instanceof NumericValue) {
        value = object.value.at(property.value);
        if (object instanceof StringValue) {
          value = new StringValue(object.value.at(property.value));
        }
      } else if (property instanceof StringValue) {
        value = object.builtins.get(property.value);
      } else {
        throw new Error(`Cannot access property with non-string/non-number: got ${property.type}`);
      }
    } else {
      if (!(property instanceof StringValue)) {
        throw new Error(`Cannot access property with non-string: got ${property.type}`);
      }
      value = object.builtins.get(property.value);
    }
    return value instanceof RuntimeValue ? value : new UndefinedValue();
  }
  evaluateSet(node, environment) {
    const rhs = this.evaluate(node.value, environment);
    if (node.assignee.type === "Identifier") {
      const variableName = node.assignee.value;
      environment.setVariable(variableName, rhs);
    } else if (node.assignee.type === "MemberExpression") {
      const member = node.assignee;
      const object = this.evaluate(member.object, environment);
      if (!(object instanceof ObjectValue)) {
        throw new Error("Cannot assign to member of non-object");
      }
      if (member.property.type !== "Identifier") {
        throw new Error("Cannot assign to member with non-identifier property");
      }
      object.value.set(member.property.value, rhs);
    } else {
      throw new Error(`Invalid LHS inside assignment expression: ${JSON.stringify(node.assignee)}`);
    }
    return new NullValue();
  }
  evaluateIf(node, environment) {
    const test = this.evaluate(node.test, environment);
    return this.evaluateBlock(test.__bool__().value ? node.body : node.alternate, environment);
  }
  evaluateFor(node, environment) {
    const scope = new Environment(environment);
    const iterable = this.evaluate(node.iterable, scope);
    if (!(iterable instanceof ArrayValue)) {
      throw new Error(`Expected iterable type in for loop: got ${iterable.type}`);
    }
    let result = "";
    for (let i = 0; i < iterable.value.length; ++i) {
      const loop = /* @__PURE__ */ new Map([
        ["index", new NumericValue(i + 1)],
        ["index0", new NumericValue(i)],
        ["revindex", new NumericValue(iterable.value.length - i)],
        ["revindex0", new NumericValue(iterable.value.length - i - 1)],
        ["first", new BooleanValue(i === 0)],
        ["last", new BooleanValue(i === iterable.value.length - 1)],
        ["length", new NumericValue(iterable.value.length)],
        ["previtem", i > 0 ? iterable.value[i - 1] : new UndefinedValue()],
        ["nextitem", i < iterable.value.length - 1 ? iterable.value[i + 1] : new UndefinedValue()]
      ]);
      scope.setVariable("loop", new ObjectValue(loop));
      const current = iterable.value[i];
      if (node.loopvar.type === "Identifier") {
        scope.setVariable(node.loopvar.value, current);
      } else if (node.loopvar.type === "TupleLiteral") {
        const loopvar = node.loopvar;
        if (current.type !== "ArrayValue") {
          throw new Error(`Cannot unpack non-iterable type: ${current.type}`);
        }
        const c = current;
        if (loopvar.value.length !== c.value.length) {
          throw new Error(`Too ${loopvar.value.length > c.value.length ? "few" : "many"} items to unpack`);
        }
        for (let j = 0; j < loopvar.value.length; ++j) {
          if (loopvar.value[j].type !== "Identifier") {
            throw new Error(`Cannot unpack non-identifier type: ${loopvar.value[j].type}`);
          }
          scope.setVariable(loopvar.value[j].value, c.value[j]);
        }
      }
      const evaluated = this.evaluateBlock(node.body, scope);
      result += evaluated.value;
    }
    return new StringValue(result);
  }
  evaluate(statement, environment) {
    if (statement === void 0)
      return new UndefinedValue();
    switch (statement.type) {
      case "Program":
        return this.evalProgram(statement, environment);
      case "Set":
        return this.evaluateSet(statement, environment);
      case "If":
        return this.evaluateIf(statement, environment);
      case "For":
        return this.evaluateFor(statement, environment);
      case "NumericLiteral":
        return new NumericValue(Number(statement.value));
      case "StringLiteral":
        return new StringValue(statement.value);
      case "BooleanLiteral":
        return new BooleanValue(statement.value);
      case "ArrayLiteral":
        return new ArrayValue(statement.value.map((x) => this.evaluate(x, environment)));
      case "TupleLiteral":
        return new TupleValue(statement.value.map((x) => this.evaluate(x, environment)));
      case "ObjectLiteral": {
        const mapping = /* @__PURE__ */ new Map();
        for (const [key, value] of statement.value) {
          const evaluatedKey = this.evaluate(key, environment);
          if (!(evaluatedKey instanceof StringValue)) {
            throw new Error(`Object keys must be strings: got ${evaluatedKey.type}`);
          }
          mapping.set(evaluatedKey.value, this.evaluate(value, environment));
        }
        return new ObjectValue(mapping);
      }
      case "Identifier":
        return this.evaluateIdentifier(statement, environment);
      case "CallExpression":
        return this.evaluateCallExpression(statement, environment);
      case "MemberExpression":
        return this.evaluateMemberExpression(statement, environment);
      case "UnaryExpression":
        return this.evaluateUnaryExpression(statement, environment);
      case "BinaryExpression":
        return this.evaluateBinaryExpression(statement, environment);
      case "FilterExpression":
        return this.evaluateFilterExpression(statement, environment);
      case "TestExpression":
        return this.evaluateTestExpression(statement, environment);
      default:
        throw new SyntaxError(`Unknown node type: ${statement.type}`);
    }
  }
};
function convertToRuntimeValues(input) {
  switch (typeof input) {
    case "number":
      return new NumericValue(input);
    case "string":
      return new StringValue(input);
    case "boolean":
      return new BooleanValue(input);
    case "object":
      if (input === null) {
        return new NullValue();
      } else if (Array.isArray(input)) {
        return new ArrayValue(input.map(convertToRuntimeValues));
      } else {
        return new ObjectValue(
          new Map(Object.entries(input).map(([key, value]) => [key, convertToRuntimeValues(value)]))
        );
      }
    case "function":
      return new FunctionValue((args, _scope) => {
        var _a2;
        const result = (_a2 = input(...args.map((x) => x.value))) != null ? _a2 : null;
        return convertToRuntimeValues(result);
      });
    default:
      throw new Error(`Cannot convert to runtime value: ${input}`);
  }
}
var Template = class {
  /**
   * @param {string} template The template string
   */
  constructor(template) {
    __publicField(this, "parsed");
    const tokens = tokenize(template, {
      lstrip_blocks: true,
      trim_blocks: true
    });
    this.parsed = parse(tokens);
  }
  render(items) {
    const env2 = new Environment();
    env2.set("false", false);
    env2.set("true", true);
    env2.set("raise_exception", (args) => {
      throw new Error(args);
    });
    env2.set("range", range);
    for (const [key, value] of Object.entries(items)) {
      env2.set(key, value);
    }
    const interpreter = new Interpreter(env2);
    const result = interpreter.run(this.parsed);
    return result.value;
  }
};
function loadTokenizer(pretrained_model_name_or_path, options) {
  return __async(this, null, function* () {
    const info = yield Promise.all([
      getModelJSON(pretrained_model_name_or_path, "tokenizer.json", true, options),
      getModelJSON(pretrained_model_name_or_path, "tokenizer_config.json", true, options)
    ]);
    if (options.legacy !== null) {
      info[1].legacy = options.legacy;
    }
    return info;
  });
}
function regexSplit(text, regex) {
  const result = [];
  let prev = 0;
  for (const match of text.matchAll(regex)) {
    const fullMatch = match[0];
    if (prev < match.index) {
      result.push(text.slice(prev, match.index));
    }
    if (fullMatch.length > 0) {
      result.push(fullMatch);
    }
    prev = match.index + fullMatch.length;
  }
  if (prev < text.length) {
    result.push(text.slice(prev));
  }
  return result;
}
function createPattern(pattern, invert = true) {
  if (pattern.Regex !== void 0) {
    let regex = pattern.Regex.replace(/\\([#&~])/g, "$1");
    for (const [key, value] of PROBLEMATIC_REGEX_MAP) {
      regex = regex.replaceAll(key, value);
    }
    return new RegExp(regex, "gu");
  } else if (pattern.String !== void 0) {
    const escaped = escapeRegExp(pattern.String);
    return new RegExp(invert ? escaped : `(${escaped})`, "gu");
  } else {
    console.warn("Unknown pattern type:", pattern);
    return null;
  }
}
function objectToMap(obj) {
  return new Map(Object.entries(obj));
}
function prepareTensorForDecode(tensor) {
  const dims = tensor.dims;
  switch (dims.length) {
    case 1:
      return tensor.tolist();
    case 2:
      if (dims[0] !== 1) {
        throw new Error("Unable to decode tensor with `batch size !== 1`. Use `tokenizer.batch_decode(...)` for batched inputs.");
      }
      return tensor.tolist()[0];
    default:
      throw new Error(`Expected tensor to have 1-2 dimensions, got ${dims.length}.`);
  }
}
function clean_up_tokenization(text) {
  return text.replace(/ \./g, ".").replace(/ \?/g, "?").replace(/ \!/g, "!").replace(/ ,/g, ",").replace(/ \' /g, "'").replace(/ n\'t/g, "n't").replace(/ \'m/g, "'m").replace(/ \'s/g, "'s").replace(/ \'ve/g, "'ve").replace(/ \'re/g, "'re");
}
function remove_accents(text) {
  return text.replace(/[\u0300-\u036f]/g, "");
}
function lowercase_and_remove_accent(text) {
  return remove_accents(text.toLowerCase());
}
function fuse(arr, value, mapping) {
  var _a2, _b;
  const fused = [];
  let i = 0;
  while (i < arr.length) {
    fused.push(arr[i]);
    if (((_a2 = mapping.get(arr[i])) != null ? _a2 : value) !== value) {
      ++i;
      continue;
    }
    while (i < arr.length && ((_b = mapping.get(arr[i])) != null ? _b : value) === value) {
      ++i;
    }
  }
  return fused;
}
function whitespace_split(text) {
  return text.match(/\S+/g) || [];
}
const PUNCTUATION_REGEX = "\\p{P}\\u0021-\\u002F\\u003A-\\u0040\\u005B-\\u0060\\u007B-\\u007E";
const PROBLEMATIC_REGEX_MAP = /* @__PURE__ */ new Map([
  // This uses the case insensitive group modifier, which is not supported in JavaScript.
  // When parsing the regex, an "Invalid group" error is thrown.
  ["(?i:'s|'t|'re|'ve|'m|'ll|'d)", "(?:'([sS]|[tT]|[rR][eE]|[vV][eE]|[mM]|[lL][lL]|[dD]))"]
]);
class AddedToken {
  /**
   * Creates a new instance of AddedToken.
   * @param {Object} config Added token configuration object.
   * @param {string} config.content The content of the added token.
   * @param {number} config.id The id of the added token.
   * @param {boolean} [config.single_word=false] Whether this token must be a single word or can break words.
   * @param {boolean} [config.lstrip=false] Whether this token should strip whitespaces on its left.
   * @param {boolean} [config.rstrip=false] Whether this token should strip whitespaces on its right.
   * @param {boolean} [config.normalized=false] Whether this token should be normalized.
   * @param {boolean} [config.special=false] Whether this token is special.
   */
  constructor(config) {
    var _a2, _b, _c, _d, _e;
    this.content = config.content;
    this.id = config.id;
    this.single_word = (_a2 = config.single_word) != null ? _a2 : false;
    this.lstrip = (_b = config.lstrip) != null ? _b : false;
    this.rstrip = (_c = config.rstrip) != null ? _c : false;
    this.special = (_d = config.special) != null ? _d : false;
    this.normalized = (_e = config.normalized) != null ? _e : null;
  }
}
class TokenizerModel extends Callable {
  /**
   * Creates a new instance of TokenizerModel.
   * @param {Object} config The configuration object for the TokenizerModel.
   */
  constructor(config) {
    var _a2;
    super();
    this.config = config;
    this.vocab = [];
    this.tokens_to_ids = /* @__PURE__ */ new Map();
    this.unk_token_id = void 0;
    this.unk_token = void 0;
    this.end_of_word_suffix = void 0;
    this.fuse_unk = (_a2 = this.config.fuse_unk) != null ? _a2 : false;
  }
  /**
   * Instantiates a new TokenizerModel instance based on the configuration object provided.
   * @param {Object} config The configuration object for the TokenizerModel.
   * @param {...*} args Optional arguments to pass to the specific TokenizerModel constructor.
   * @returns {TokenizerModel} A new instance of a TokenizerModel.
   * @throws Will throw an error if the TokenizerModel type in the config is not recognized.
   */
  static fromConfig(config, ...args) {
    switch (config.type) {
      case "WordPiece":
        return new WordPieceTokenizer(config);
      case "Unigram":
        return new Unigram(config, ...args);
      case "BPE":
        return new BPE(config);
      default:
        if (config.vocab) {
          return new LegacyTokenizerModel(config, ...args);
        }
        throw new Error(`Unknown TokenizerModel type: ${config.type}`);
    }
  }
  /**
   * Internal function to call the TokenizerModel instance.
   * @param {string[]} tokens The tokens to encode.
   * @returns {string[]} The encoded token IDs.
   */
  _call(tokens) {
    let ids = this.encode(tokens);
    if (this.fuse_unk) {
      ids = fuse(ids, this.unk_token_id, this.tokens_to_ids);
    }
    return ids;
  }
  /**
   * Encodes a list of tokens into a list of token IDs.
   * @param {string[]} tokens The tokens to encode.
   * @returns {string[]} The encoded tokens.
   * @throws Will throw an error if not implemented in a subclass.
   */
  encode(tokens) {
    throw Error("encode should be implemented in subclass.");
  }
  /**
   * Converts a list of tokens into a list of token IDs.
   * @param {string[]} tokens The tokens to convert.
   * @returns {number[]} The converted token IDs.
   */
  convert_tokens_to_ids(tokens) {
    return tokens.map((t) => {
      var _a2;
      return (_a2 = this.tokens_to_ids.get(t)) != null ? _a2 : this.unk_token_id;
    });
  }
  /**
   * Converts a list of token IDs into a list of tokens.
   * @param {number[]} ids The token IDs to convert.
   * @returns {string[]} The converted tokens.
   */
  convert_ids_to_tokens(ids) {
    return ids.map((i) => {
      var _a2;
      return (_a2 = this.vocab[i]) != null ? _a2 : this.unk_token;
    });
  }
}
class WordPieceTokenizer extends TokenizerModel {
  /**
   * @param {Object} config The configuration object.
   * @param {Object} config.vocab A mapping of tokens to ids.
   * @param {string} config.unk_token The unknown token string.
   * @param {string} config.continuing_subword_prefix The prefix to use for continuing subwords.
   * @param {number} [config.max_input_chars_per_word=100] The maximum number of characters per word.
   */
  constructor(config) {
    var _a2;
    super(config);
    this.tokens_to_ids = objectToMap(config.vocab);
    this.unk_token_id = this.tokens_to_ids.get(config.unk_token);
    this.unk_token = config.unk_token;
    this.max_input_chars_per_word = (_a2 = config.max_input_chars_per_word) != null ? _a2 : 100;
    this.vocab = new Array(this.tokens_to_ids.size);
    for (const [key, value] of this.tokens_to_ids) {
      this.vocab[value] = key;
    }
  }
  /**
   * Encodes an array of tokens using WordPiece encoding.
   * @param {string[]} tokens The tokens to encode.
   * @returns {string[]} An array of encoded tokens.
   */
  encode(tokens) {
    const outputTokens = [];
    for (const token of tokens) {
      const chars = [...token];
      if (chars.length > this.max_input_chars_per_word) {
        outputTokens.push(this.unk_token);
        continue;
      }
      let isUnknown = false;
      let start = 0;
      const subTokens = [];
      while (start < chars.length) {
        let end = chars.length;
        let currentSubstring = null;
        while (start < end) {
          let substr = chars.slice(start, end).join("");
          if (start > 0) {
            substr = this.config.continuing_subword_prefix + substr;
          }
          if (this.tokens_to_ids.has(substr)) {
            currentSubstring = substr;
            break;
          }
          --end;
        }
        if (currentSubstring === null) {
          isUnknown = true;
          break;
        }
        subTokens.push(currentSubstring);
        start = end;
      }
      if (isUnknown) {
        outputTokens.push(this.unk_token);
      } else {
        outputTokens.push(...subTokens);
      }
    }
    return outputTokens;
  }
}
class Unigram extends TokenizerModel {
  /**
   * Create a new Unigram tokenizer model.
   * @param {Object} config The configuration object for the Unigram model.
   * @param {number} config.unk_id The ID of the unknown token
   * @param {any[][]} config.vocab A 2D array representing a mapping of tokens to scores.
   * @param {Object} moreConfig Additional configuration object for the Unigram model.
   */
  constructor(config, moreConfig) {
    super(config);
    const vocabSize = config.vocab.length;
    this.vocab = new Array(vocabSize);
    this.scores = new Array(vocabSize);
    for (let i = 0; i < vocabSize; ++i) {
      const piece = config.vocab[i];
      this.vocab[i] = piece[0];
      this.scores[i] = piece[1];
    }
    this.unk_token_id = config.unk_id;
    this.unk_token = this.vocab[config.unk_id];
    this.tokens_to_ids = new Map(this.vocab.map((x, i) => [x, i]));
    this.bosToken = " ";
    this.bosTokenId = this.tokens_to_ids.get(this.bosToken);
    this.eosToken = moreConfig.eos_token;
    this.eosTokenId = this.tokens_to_ids.get(this.eosToken);
    this.unkToken = this.vocab[this.unk_token_id];
    this.minScore = min(this.scores)[0];
    this.unkScore = this.minScore - 10;
    this.scores[this.unk_token_id] = this.unkScore;
    this.trie = new CharTrie();
    this.trie.extend(this.vocab);
    this.fuse_unk = true;
  }
  /**
   * Populates lattice nodes.
   * @param {TokenLattice} lattice The token lattice to populate with nodes.
   */
  populateNodes(lattice) {
    const sentence = lattice.sentence;
    const len = sentence.length;
    let beginPos = 0;
    while (beginPos < len) {
      const mblen = 1;
      let hasSingleNode = false;
      for (let token of this.trie.commonPrefixSearch(sentence.slice(beginPos))) {
        const tokenId = this.tokens_to_ids.get(token);
        const tokenScore = this.scores[tokenId];
        const n = token.length;
        lattice.insert(beginPos, n, tokenScore, tokenId);
        if (!hasSingleNode && n === mblen) {
          hasSingleNode = true;
        }
      }
      if (!hasSingleNode) {
        lattice.insert(beginPos, mblen, this.unkScore, this.unk_token_id);
      }
      beginPos += mblen;
    }
  }
  /**
   * Encodes an array of tokens into an array of subtokens using the unigram model.
   *
   * @param {string} normalized The normalized string.
   * @returns {string[]} An array of subtokens obtained by encoding the input tokens using the unigram model.
   */
  tokenize(normalized) {
    const lattice = new TokenLattice(normalized, this.bosTokenId, this.eosTokenId);
    this.populateNodes(lattice);
    return lattice.tokens();
  }
  /**
   * Encodes an array of tokens using Unigram encoding.
   * @param {string[]} tokens The tokens to encode.
   * @returns {string[]} An array of encoded tokens.
   */
  encode(tokens) {
    const toReturn = [];
    for (const token of tokens) {
      const tokenized = this.tokenize(token);
      toReturn.push(...tokenized);
    }
    return toReturn;
  }
}
const BYTES_TO_UNICODE = (() => {
  const bs = [
    ...Array.from({ length: "~".charCodeAt(0) - "!".charCodeAt(0) + 1 }, (_, i) => i + "!".charCodeAt(0)),
    ...Array.from({ length: "¬".charCodeAt(0) - "¡".charCodeAt(0) + 1 }, (_, i) => i + "¡".charCodeAt(0)),
    ...Array.from({ length: "ÿ".charCodeAt(0) - "®".charCodeAt(0) + 1 }, (_, i) => i + "®".charCodeAt(0))
  ];
  const cs = bs.slice();
  let n = 0;
  for (let b = 0; b < 256; ++b) {
    if (!bs.includes(b)) {
      bs.push(b);
      cs.push(256 + n);
      n += 1;
    }
  }
  const ccs = cs.map((n2) => String.fromCharCode(n2));
  return Object.fromEntries(bs.map((b, i) => [b, ccs[i]]));
})();
const UNICODE_TO_BYTES = reverseDictionary(BYTES_TO_UNICODE);
class BPE extends TokenizerModel {
  /**
   * Create a BPE instance.
   * @param {Object} config The configuration object for BPE.
   * @param {Object} config.vocab A mapping of tokens to ids.
   * @param {string[]} config.merges An array of BPE merges as strings.
   * @param {string} config.unk_token The unknown token used for out of vocabulary words.
   * @param {string} config.end_of_word_suffix The suffix to place at the end of each word.
   * @param {string} [config.continuing_subword_suffix] The suffix to insert between words.
   * @param {boolean} [config.byte_fallback=false] Whether to use spm byte-fallback trick (defaults to False)
   * @param {boolean} [config.ignore_merges=false] Whether or not to match tokens with the vocab before using merges.
   */
  constructor(config) {
    var _a2, _b, _c;
    super(config);
    this.BPE_SPLIT_TOKEN = " ";
    this.tokens_to_ids = objectToMap(config.vocab);
    this.unk_token_id = this.tokens_to_ids.get(config.unk_token);
    this.unk_token = config.unk_token;
    this.vocab = new Array(this.tokens_to_ids.size);
    for (const [key, value] of this.tokens_to_ids) {
      this.vocab[value] = key;
    }
    this.bpe_ranks = new Map(config.merges.map((x, i) => [x, i]));
    this.merges = config.merges.map((x) => x.split(this.BPE_SPLIT_TOKEN));
    this.end_of_word_suffix = config.end_of_word_suffix;
    this.continuing_subword_suffix = (_a2 = config.continuing_subword_suffix) != null ? _a2 : null;
    this.byte_fallback = (_b = this.config.byte_fallback) != null ? _b : false;
    if (this.byte_fallback) {
      this.text_encoder = new TextEncoder();
    }
    this.ignore_merges = (_c = this.config.ignore_merges) != null ? _c : false;
    this.cache = /* @__PURE__ */ new Map();
  }
  /**
   * Apply Byte-Pair-Encoding (BPE) to a given token. Efficient heap-based priority
   * queue implementation adapted from https://github.com/belladoreai/llama-tokenizer-js.
   * @param {string} token The token to encode.
   * @returns {string[]} The BPE encoded tokens.
   */
  bpe(token) {
    if (token.length === 0) {
      return [];
    }
    const cached = this.cache.get(token);
    if (cached !== void 0) {
      return cached;
    }
    const word = Array.from(token);
    if (this.end_of_word_suffix) {
      word[word.length - 1] += this.end_of_word_suffix;
    }
    let result = [];
    if (word.length > 1) {
      const queue = new PriorityQueue((a, b) => a.score < b.score);
      let startingNode = {
        token: word[0],
        bias: 0,
        prev: null,
        next: null
      };
      let previousNode = startingNode;
      for (let i = 1; i < word.length; ++i) {
        const currentNode = {
          bias: i / word.length,
          // Add fractional component to break ties
          token: word[i],
          prev: previousNode,
          next: null
        };
        previousNode.next = currentNode;
        this._add_node(queue, previousNode);
        previousNode = currentNode;
      }
      while (!queue.isEmpty()) {
        const node = queue.pop();
        if (node.deleted || !node.next || node.next.deleted) continue;
        node.deleted = true;
        node.next.deleted = true;
        if (node.prev) {
          const newPreviousNode = __spreadValues({}, node.prev);
          node.prev.deleted = true;
          node.prev = newPreviousNode;
          if (newPreviousNode.prev) {
            newPreviousNode.prev.next = newPreviousNode;
          } else {
            startingNode = newPreviousNode;
          }
        }
        const merged = {
          token: node.token + node.next.token,
          bias: node.bias,
          prev: node.prev,
          next: node.next.next
        };
        if (merged.prev) {
          merged.prev.next = merged;
          this._add_node(queue, merged.prev);
        } else {
          startingNode = merged;
        }
        if (merged.next) {
          merged.next.prev = merged;
          this._add_node(queue, merged);
        }
      }
      for (let currentNode = startingNode; currentNode !== null; currentNode = currentNode.next) {
        result.push(currentNode.token);
      }
    } else {
      result = word;
    }
    if (this.continuing_subword_suffix) {
      for (let i = 0; i < result.length - 1; ++i) {
        result[i] += this.continuing_subword_suffix;
      }
    }
    this.cache.set(token, result);
    return result;
  }
  /**
   * Helper function to add a node to the priority queue.
   * @param {PriorityQueue} queue 
   * @param {BPENode} node
   * @private
   */
  _add_node(queue, node) {
    const rank = this.bpe_ranks.get(node.token + this.BPE_SPLIT_TOKEN + node.next.token);
    if (rank !== void 0) {
      node.score = rank + node.bias;
      queue.push(node);
    }
  }
  /**
   * Encodes the input sequence of tokens using the BPE algorithm and returns the resulting subword tokens.
   * @param {string[]} tokens The input sequence of tokens to encode.
   * @returns {string[]} The resulting subword tokens after applying the BPE algorithm to the input sequence of tokens.
   */
  encode(tokens) {
    const outputTokens = [];
    for (const token of tokens) {
      if (this.ignore_merges && this.tokens_to_ids.has(token)) {
        outputTokens.push(token);
        continue;
      }
      const bpe_token_list = this.bpe(token);
      for (const t of bpe_token_list) {
        if (this.tokens_to_ids.has(t)) {
          outputTokens.push(t);
        } else {
          if (this.byte_fallback) {
            outputTokens.push(
              ...Array.from(this.text_encoder.encode(t)).map((x) => `<0x${x.toString(16).toUpperCase().padStart(2, "0")}>`)
            );
          } else {
            outputTokens.push(this.unk_token);
          }
        }
      }
    }
    return outputTokens;
  }
}
class LegacyTokenizerModel extends TokenizerModel {
  /**
   * Create a LegacyTokenizerModel instance.
   * @param {Object} config The configuration object for LegacyTokenizerModel.
   * @param {Object} config.vocab A (possibly nested) mapping of tokens to ids.
   * @param {Object} moreConfig Additional configuration object for the LegacyTokenizerModel model.
   */
  constructor(config, moreConfig) {
    super(config);
    this.tokens_to_ids = objectToMap(
      moreConfig.target_lang ? config.vocab[moreConfig.target_lang] : config.vocab
    );
    this.bos_token = moreConfig.bos_token;
    this.bos_token_id = this.tokens_to_ids.get(this.bos_token);
    this.eos_token = moreConfig.eos_token;
    this.eos_token_id = this.tokens_to_ids.get(this.eos_token);
    this.pad_token = moreConfig.pad_token;
    this.pad_token_id = this.tokens_to_ids.get(this.pad_token);
    this.unk_token = moreConfig.unk_token;
    this.unk_token_id = this.tokens_to_ids.get(this.unk_token);
    this.vocab = new Array(this.tokens_to_ids.size);
    for (const [key, value] of this.tokens_to_ids) {
      this.vocab[value] = key;
    }
  }
  encode(tokens) {
    return tokens;
  }
}
class Normalizer extends Callable {
  /**
   * @param {Object} config The configuration object for the normalizer.
   */
  constructor(config) {
    super();
    this.config = config;
  }
  /**
   * Factory method for creating normalizers from config objects.
   * @static
   * @param {Object} config The configuration object for the normalizer.
   * @returns {Normalizer} A Normalizer object.
   * @throws {Error} If an unknown Normalizer type is specified in the config.
   */
  static fromConfig(config) {
    if (config === null) return null;
    switch (config.type) {
      case "BertNormalizer":
        return new BertNormalizer(config);
      case "Precompiled":
        return new Precompiled(config);
      case "Sequence":
        return new NormalizerSequence(config);
      case "Replace":
        return new Replace(config);
      case "NFC":
        return new NFC(config);
      case "NFKC":
        return new NFKC(config);
      case "NFKD":
        return new NFKD(config);
      case "Strip":
        return new StripNormalizer(config);
      case "StripAccents":
        return new StripAccents(config);
      case "Lowercase":
        return new Lowercase(config);
      case "Prepend":
        return new Prepend(config);
      default:
        throw new Error(`Unknown Normalizer type: ${config.type}`);
    }
  }
  /**
   * Normalize the input text.
   * @abstract
   * @param {string} text The text to normalize.
   * @returns {string} The normalized text.
   * @throws {Error} If this method is not implemented in a subclass.
   */
  normalize(text) {
    throw Error("normalize should be implemented in subclass.");
  }
  /**
   * Alias for {@link Normalizer#normalize}.
   * @param {string} text The text to normalize.
   * @returns {string} The normalized text.
   */
  _call(text) {
    return this.normalize(text);
  }
}
class Replace extends Normalizer {
  /**
   * Normalize the input text by replacing the pattern with the content.
   * @param {string} text The input text to be normalized.
   * @returns {string} The normalized text after replacing the pattern with the content.
   */
  normalize(text) {
    const pattern = createPattern(this.config.pattern);
    return pattern === null ? text : text.replaceAll(pattern, this.config.content);
  }
}
class NFC extends Normalizer {
  /**
   * Normalize the input text by applying Unicode normalization form C (NFC).
   * @param {string} text The input text to be normalized.
   * @returns {string} The normalized text.
   */
  normalize(text) {
    text = text.normalize("NFC");
    return text;
  }
}
class NFKC extends Normalizer {
  /**
   * Normalize text using NFKC normalization.
   * @param {string} text The text to be normalized.
   * @returns {string} The normalized text.
   */
  normalize(text) {
    text = text.normalize("NFKC");
    return text;
  }
}
class NFKD extends Normalizer {
  /**
   * Normalize text using NFKD normalization.
   * @param {string} text The text to be normalized.
   * @returns {string} The normalized text.
   */
  normalize(text) {
    text = text.normalize("NFKD");
    return text;
  }
}
class StripNormalizer extends Normalizer {
  /**
   * Strip leading and/or trailing whitespace from the input text.
   * @param {string} text The input text.
   * @returns {string} The normalized text.
   */
  normalize(text) {
    if (this.config.strip_left && this.config.strip_right) {
      text = text.trim();
    } else {
      if (this.config.strip_left) {
        text = text.trimStart();
      }
      if (this.config.strip_right) {
        text = text.trimEnd();
      }
    }
    return text;
  }
}
class StripAccents extends Normalizer {
  /**
   * Remove all accents from the text.
   * @param {string} text The input text.
   * @returns {string} The normalized text without accents.
   */
  normalize(text) {
    text = remove_accents(text);
    return text;
  }
}
class Lowercase extends Normalizer {
  /**
   * Lowercases the input string.
   * @param {string} text The text to normalize.
   * @returns {string} The normalized text.
   */
  normalize(text) {
    text = text.toLowerCase();
    return text;
  }
}
class Prepend extends Normalizer {
  /**
   * Prepends the input string.
   * @param {string} text The text to normalize.
   * @returns {string} The normalized text.
   */
  normalize(text) {
    text = this.config.prepend + text;
    return text;
  }
}
class NormalizerSequence extends Normalizer {
  /**
  * Create a new instance of NormalizerSequence.
  * @param {Object} config The configuration object.
  * @param {Object[]} config.normalizers An array of Normalizer configuration objects.
  */
  constructor(config) {
    super(config);
    this.normalizers = config.normalizers.map((x) => Normalizer.fromConfig(x));
  }
  /**
  * Apply a sequence of Normalizers to the input text.
  * @param {string} text The text to normalize.
  * @returns {string} The normalized text.
  */
  normalize(text) {
    return this.normalizers.reduce((t, normalizer) => {
      return normalizer.normalize(t);
    }, text);
  }
}
class BertNormalizer extends Normalizer {
  /**
   * Adds whitespace around any CJK (Chinese, Japanese, or Korean) character in the input text.
   *
   * @param {string} text The input text to tokenize.
   * @returns {string} The tokenized text with whitespace added around CJK characters.
   */
  _tokenize_chinese_chars(text) {
    const output = [];
    for (let i = 0; i < text.length; ++i) {
      const char = text[i];
      const cp = char.charCodeAt(0);
      if (this._is_chinese_char(cp)) {
        output.push(" ");
        output.push(char);
        output.push(" ");
      } else {
        output.push(char);
      }
    }
    return output.join("");
  }
  /**
   * Checks whether the given Unicode codepoint represents a CJK (Chinese, Japanese, or Korean) character.
   *
   * A "chinese character" is defined as anything in the CJK Unicode block:
   * https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
   *
   * Note that the CJK Unicode block is NOT all Japanese and Korean characters, despite its name.
   * The modern Korean Hangul alphabet is a different block, as is Japanese Hiragana and Katakana.
   * Those alphabets are used to write space-separated words, so they are not treated specially
   * and are handled like all other languages.
   *
   * @param {number} cp The Unicode codepoint to check.
   * @returns {boolean} True if the codepoint represents a CJK character, false otherwise.
   */
  _is_chinese_char(cp) {
    return cp >= 19968 && cp <= 40959 || cp >= 13312 && cp <= 19903 || cp >= 131072 && cp <= 173791 || cp >= 173824 && cp <= 177983 || cp >= 177984 && cp <= 178207 || cp >= 178208 && cp <= 183983 || cp >= 63744 && cp <= 64255 || cp >= 194560 && cp <= 195103;
  }
  /**
   * Strips accents from the given text.
   * @param {string} text The text to strip accents from.
   * @returns {string} The text with accents removed.
   */
  stripAccents(text) {
    return text.normalize("NFD").replace(/[\u0300-\u036f]/g, "");
  }
  /**
   * Checks whether `char` is a control character.
   * @param {string} char The character to check.
   * @returns {boolean} Whether `char` is a control character.
   * @private
   */
  _is_control(char) {
    switch (char) {
      case "	":
      case "\n":
      case "\r":
        return false;
      default:
        return new RegExp("^\\p{Cc}|\\p{Cf}|\\p{Co}|\\p{Cs}$", "u").test(char);
    }
  }
  /**
   * Performs invalid character removal and whitespace cleanup on text.
   * @param {string} text The text to clean.
   * @returns {string} The cleaned text.
   * @private
   */
  _clean_text(text) {
    const output = [];
    for (const char of text) {
      const cp = char.charCodeAt(0);
      if (cp === 0 || cp === 65533 || this._is_control(char)) {
        continue;
      }
      if (/^\s$/.test(char)) {
        output.push(" ");
      } else {
        output.push(char);
      }
    }
    return output.join("");
  }
  /**
   * Normalizes the given text based on the configuration.
   * @param {string} text The text to normalize.
   * @returns {string} The normalized text.
   */
  normalize(text) {
    if (this.config.clean_text) {
      text = this._clean_text(text);
    }
    if (this.config.handle_chinese_chars) {
      text = this._tokenize_chinese_chars(text);
    }
    if (this.config.lowercase) {
      text = text.toLowerCase();
      if (this.config.strip_accents !== false) {
        text = this.stripAccents(text);
      }
    } else if (this.config.strip_accents) {
      text = this.stripAccents(text);
    }
    return text;
  }
}
class PreTokenizer extends Callable {
  /**
  * Factory method that returns an instance of a subclass of `PreTokenizer` based on the provided configuration.
  *
  * @static
  * @param {Object} config A configuration object for the pre-tokenizer.
  * @returns {PreTokenizer} An instance of a subclass of `PreTokenizer`.
  * @throws {Error} If the provided configuration object does not correspond to any known pre-tokenizer.
  */
  static fromConfig(config) {
    if (config === null) return null;
    switch (config.type) {
      case "BertPreTokenizer":
        return new BertPreTokenizer(config);
      case "Sequence":
        return new PreTokenizerSequence(config);
      case "Whitespace":
        return new WhitespacePreTokenizer(config);
      case "WhitespaceSplit":
        return new WhitespaceSplit(config);
      case "Metaspace":
        return new MetaspacePreTokenizer(config);
      case "ByteLevel":
        return new ByteLevelPreTokenizer(config);
      case "Split":
        return new SplitPreTokenizer(config);
      case "Punctuation":
        return new PunctuationPreTokenizer(config);
      case "Digits":
        return new DigitsPreTokenizer(config);
      case "Replace":
        return new ReplacePreTokenizer(config);
      default:
        throw new Error(`Unknown PreTokenizer type: ${config.type}`);
    }
  }
  /**
   * Method that should be implemented by subclasses to define the specific pre-tokenization logic.
   *
   * @abstract
   * @param {string} text The text to pre-tokenize.
   * @param {Object} [options] Additional options for the pre-tokenization logic.
   * @returns {string[]} The pre-tokenized text.
   * @throws {Error} If the method is not implemented in the subclass.
   */
  pre_tokenize_text(text, options) {
    throw Error("pre_tokenize_text should be implemented in subclass.");
  }
  /**
   * Tokenizes the given text into pre-tokens.
   * @param {string|string[]} text The text or array of texts to pre-tokenize.
   * @param {Object} [options] Additional options for the pre-tokenization logic.
   * @returns {string[]} An array of pre-tokens.
   */
  pre_tokenize(text, options) {
    return (Array.isArray(text) ? text.map((x) => this.pre_tokenize_text(x, options)) : this.pre_tokenize_text(text, options)).flat();
  }
  /**
   * Alias for {@link PreTokenizer#pre_tokenize}.
   * @param {string|string[]} text The text or array of texts to pre-tokenize.
   * @param {Object} [options] Additional options for the pre-tokenization logic.
   * @returns {string[]} An array of pre-tokens.
   */
  _call(text, options) {
    return this.pre_tokenize(text, options);
  }
}
class BertPreTokenizer extends PreTokenizer {
  /**
   * A PreTokenizer that splits text into wordpieces using a basic tokenization scheme
   * similar to that used in the original implementation of BERT.
   * 
   * @param {Object} config The configuration object.
   */
  constructor(config) {
    super();
    this.pattern = new RegExp(`[^\\s${PUNCTUATION_REGEX}]+|[${PUNCTUATION_REGEX}]`, "gu");
  }
  /**
   * Tokenizes a single text using the BERT pre-tokenization scheme.
   * 
   * @param {string} text The text to tokenize.
   * @param {Object} [options] Additional options for the pre-tokenization logic.
   * @returns {string[]} An array of tokens.
   */
  pre_tokenize_text(text, options) {
    return text.trim().match(this.pattern) || [];
  }
}
class ByteLevelPreTokenizer extends PreTokenizer {
  /**
   * Creates a new instance of the `ByteLevelPreTokenizer` class.
   * @param {Object} config The configuration object.
   */
  constructor(config) {
    var _a2;
    super();
    this.config = config;
    this.add_prefix_space = this.config.add_prefix_space;
    this.trim_offsets = this.config.trim_offsets;
    this.use_regex = (_a2 = this.config.use_regex) != null ? _a2 : true;
    this.pattern = new RegExp("'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)|\\s+", "gu");
    this.byte_encoder = BYTES_TO_UNICODE;
    this.text_encoder = new TextEncoder();
  }
  /**
   * Tokenizes a single piece of text using byte-level tokenization.
   * @param {string} text The text to tokenize.
   * @param {Object} [options] Additional options for the pre-tokenization logic.
   * @returns {string[]} An array of tokens.
   */
  pre_tokenize_text(text, options) {
    if (this.add_prefix_space && !text.startsWith(" ")) {
      text = " " + text;
    }
    const tokens = this.use_regex ? text.match(this.pattern) || [] : [text];
    return tokens.map(
      (token) => Array.from(this.text_encoder.encode(token), (byte) => this.byte_encoder[byte]).join("")
    );
  }
}
class SplitPreTokenizer extends PreTokenizer {
  /**
   * @param {Object} config The configuration options for the pre-tokenizer.
   * @param {Object} config.pattern The pattern used to split the text. Can be a string or a regex object.
   * @param {string|undefined} config.pattern.String The string to use for splitting. Only defined if the pattern is a string.
   * @param {string|undefined} config.pattern.Regex The regex to use for splitting. Only defined if the pattern is a regex.
   * @param {SplitDelimiterBehavior} config.behavior The behavior to use when splitting.
   * @param {boolean} config.invert Whether to split (invert=false) or match (invert=true) the pattern.
   */
  constructor(config) {
    super();
    this.config = config;
    this.pattern = createPattern(this.config.pattern, this.config.invert);
  }
  /**
   * Tokenizes text by splitting it using the given pattern.
   * @param {string} text The text to tokenize.
   * @param {Object} [options] Additional options for the pre-tokenization logic.
   * @returns {string[]} An array of tokens.
   */
  pre_tokenize_text(text, options) {
    if (this.pattern === null) {
      return [];
    }
    if (this.config.invert) {
      return text.match(this.pattern) || [];
    } else {
      return regexSplit(text, this.pattern);
    }
  }
}
class PunctuationPreTokenizer extends PreTokenizer {
  /**
   * @param {Object} config The configuration options for the pre-tokenizer.
   * @param {SplitDelimiterBehavior} config.behavior The behavior to use when splitting.
   */
  constructor(config) {
    super();
    this.config = config;
    this.pattern = new RegExp(`[^${PUNCTUATION_REGEX}]+|[${PUNCTUATION_REGEX}]+`, "gu");
  }
  /**
   * Tokenizes text by splitting it using the given pattern.
   * @param {string} text The text to tokenize.
   * @param {Object} [options] Additional options for the pre-tokenization logic.
   * @returns {string[]} An array of tokens.
   */
  pre_tokenize_text(text, options) {
    return text.match(this.pattern) || [];
  }
}
class DigitsPreTokenizer extends PreTokenizer {
  /**
   * @param {Object} config The configuration options for the pre-tokenizer.
   * @param {boolean} config.individual_digits Whether to split on individual digits.
   */
  constructor(config) {
    super();
    this.config = config;
    const digit_pattern = `[^\\d]+|\\d${this.config.individual_digits ? "" : "+"}`;
    this.pattern = new RegExp(digit_pattern, "gu");
  }
  /**
   * Tokenizes text by splitting it using the given pattern.
   * @param {string} text The text to tokenize.
   * @param {Object} [options] Additional options for the pre-tokenization logic.
   * @returns {string[]} An array of tokens.
   */
  pre_tokenize_text(text, options) {
    return text.match(this.pattern) || [];
  }
}
class PostProcessor extends Callable {
  /**
   * @param {Object} config The configuration for the post-processor.
   */
  constructor(config) {
    super();
    this.config = config;
  }
  /**
   * Factory method to create a PostProcessor object from a configuration object.
   *
   * @param {Object} config Configuration object representing a PostProcessor.
   * @returns {PostProcessor} A PostProcessor object created from the given configuration.
   * @throws {Error} If an unknown PostProcessor type is encountered.
   */
  static fromConfig(config) {
    if (config === null) return null;
    switch (config.type) {
      case "TemplateProcessing":
        return new TemplateProcessing(config);
      case "ByteLevel":
        return new ByteLevelPostProcessor(config);
      case "RobertaProcessing":
        return new RobertaProcessing(config);
      case "BertProcessing":
        return new BertProcessing(config);
      case "Sequence":
        return new PostProcessorSequence(config);
      default:
        throw new Error(`Unknown PostProcessor type: ${config.type}`);
    }
  }
  /**
   * Method to be implemented in subclass to apply post-processing on the given tokens.
   *
   * @param {Array} tokens The input tokens to be post-processed.
   * @param {...*} args Additional arguments required by the post-processing logic.
   * @returns {PostProcessedOutput} The post-processed tokens.
   * @throws {Error} If the method is not implemented in subclass.
   */
  post_process(tokens, ...args) {
    throw Error("post_process should be implemented in subclass.");
  }
  /**
   * Alias for {@link PostProcessor#post_process}.
   * @param {Array} tokens The text or array of texts to post-process.
   * @param {...*} args Additional arguments required by the post-processing logic.
   * @returns {PostProcessedOutput} The post-processed tokens.
   */
  _call(tokens, ...args) {
    return this.post_process(tokens, ...args);
  }
}
class BertProcessing extends PostProcessor {
  /**
   * @param {Object} config The configuration for the post-processor.
   * @param {string[]} config.cls The special tokens to add to the beginning of the input.
   * @param {string[]} config.sep The special tokens to add to the end of the input.
   */
  constructor(config) {
    super(config);
    this.cls = config.cls[0];
    this.sep = config.sep[0];
  }
  /**
   * Adds the special tokens to the beginning and end of the input.
   * @param {string[]} tokens The input tokens.
   * @param {string[]} [tokens_pair=null] An optional second set of input tokens.
   * @returns {PostProcessedOutput} The post-processed tokens with the special tokens added to the beginning and end.
   */
  post_process(tokens, tokens_pair = null, {
    add_special_tokens = true
  } = {}) {
    if (add_special_tokens) {
      tokens = mergeArrays([this.cls], tokens, [this.sep]);
    }
    let token_type_ids = new Array(tokens.length).fill(0);
    if (tokens_pair !== null) {
      const middle = add_special_tokens && this instanceof RobertaProcessing ? [this.sep] : [];
      const after = add_special_tokens ? [this.sep] : [];
      tokens = mergeArrays(tokens, middle, tokens_pair, after);
      token_type_ids = mergeArrays(token_type_ids, new Array(tokens_pair.length + middle.length + after.length).fill(1));
    }
    return { tokens, token_type_ids };
  }
}
class RobertaProcessing extends BertProcessing {
}
class TemplateProcessing extends PostProcessor {
  /**
   * Creates a new instance of `TemplateProcessing`.
   * @param {Object} config The configuration options for the post processor.
   * @param {Array} config.single The template for a single sequence of tokens.
   * @param {Array} config.pair The template for a pair of sequences of tokens.
   */
  constructor(config) {
    super(config);
    this.single = config.single;
    this.pair = config.pair;
  }
  /**
   * Replaces special tokens in the template with actual tokens.
   * @param {string[]} tokens The list of tokens for the first sequence.
   * @param {string[]} [tokens_pair=null] The list of tokens for the second sequence (optional).
   * @returns {PostProcessedOutput} An object containing the list of tokens with the special tokens replaced with actual tokens.
   */
  post_process(tokens, tokens_pair = null, {
    add_special_tokens = true
  } = {}) {
    const type = tokens_pair === null ? this.single : this.pair;
    let processedTokens = [];
    let types = [];
    for (const item of type) {
      if ("SpecialToken" in item) {
        if (add_special_tokens) {
          processedTokens.push(item.SpecialToken.id);
          types.push(item.SpecialToken.type_id);
        }
      } else if ("Sequence" in item) {
        if (item.Sequence.id === "A") {
          processedTokens = mergeArrays(processedTokens, tokens);
          types = mergeArrays(types, new Array(tokens.length).fill(item.Sequence.type_id));
        } else if (item.Sequence.id === "B") {
          processedTokens = mergeArrays(processedTokens, tokens_pair);
          types = mergeArrays(types, new Array(tokens_pair.length).fill(item.Sequence.type_id));
        }
      }
    }
    return { tokens: processedTokens, token_type_ids: types };
  }
}
class ByteLevelPostProcessor extends PostProcessor {
  /**
   * Post process the given tokens.
   * @param {string[]} tokens The list of tokens for the first sequence.
   * @param {string[]} [tokens_pair=null] The list of tokens for the second sequence (optional).
   * @returns {PostProcessedOutput} An object containing the post-processed tokens.
   */
  post_process(tokens, tokens_pair = null) {
    if (tokens_pair) {
      tokens = mergeArrays(tokens, tokens_pair);
    }
    return { tokens };
  }
}
class PostProcessorSequence extends PostProcessor {
  /**
   * Creates a new instance of PostProcessorSequence.
   * @param {Object} config The configuration object.
   * @param {Object[]} config.processors The list of post-processors to apply.
   */
  constructor(config) {
    super(config);
    this.processors = config.processors.map((x) => PostProcessor.fromConfig(x));
  }
  /**
   * Post process the given tokens.
   * @param {string[]} tokens The list of tokens for the first sequence.
   * @param {string[]} [tokens_pair=null] The list of tokens for the second sequence (optional).
   * @returns {PostProcessedOutput} An object containing the post-processed tokens.
   */
  post_process(tokens, tokens_pair = null, options = {}) {
    let token_type_ids;
    for (const processor of this.processors) {
      if (processor instanceof ByteLevelPostProcessor) {
        const output = processor.post_process(tokens);
        tokens = output.tokens;
        if (tokens_pair) {
          const pair_output = processor.post_process(tokens_pair);
          tokens_pair = pair_output.tokens;
        }
      } else {
        const output = processor.post_process(tokens, tokens_pair, options);
        tokens = output.tokens;
        token_type_ids = output.token_type_ids;
      }
    }
    return { tokens, token_type_ids };
  }
}
class Decoder extends Callable {
  /**
  * Creates an instance of `Decoder`.
  *
  * @param {Object} config The configuration object.
  */
  constructor(config) {
    super();
    this.config = config;
    this.added_tokens = [];
    this.end_of_word_suffix = null;
    this.trim_offsets = config.trim_offsets;
  }
  /**
  * Creates a decoder instance based on the provided configuration.
  *
  * @param {Object} config The configuration object.
  * @returns {Decoder} A decoder instance.
  * @throws {Error} If an unknown decoder type is provided.
  */
  static fromConfig(config) {
    if (config === null) return null;
    switch (config.type) {
      case "WordPiece":
        return new WordPieceDecoder(config);
      case "Metaspace":
        return new MetaspaceDecoder(config);
      case "ByteLevel":
        return new ByteLevelDecoder(config);
      case "Replace":
        return new ReplaceDecoder(config);
      case "ByteFallback":
        return new ByteFallback(config);
      case "Fuse":
        return new FuseDecoder(config);
      case "Strip":
        return new StripDecoder(config);
      case "Sequence":
        return new DecoderSequence(config);
      case "CTC":
        return new CTCDecoder(config);
      case "BPEDecoder":
        return new BPEDecoder(config);
      default:
        throw new Error(`Unknown Decoder type: ${config.type}`);
    }
  }
  /**
  * Calls the `decode` method.
  *
  * @param {string[]} tokens The list of tokens.
  * @returns {string} The decoded string.
  */
  _call(tokens) {
    return this.decode(tokens);
  }
  /**
  * Decodes a list of tokens.
  * @param {string[]} tokens The list of tokens.
  * @returns {string} The decoded string.
  */
  decode(tokens) {
    return this.decode_chain(tokens).join("");
  }
  /**
   * Apply the decoder to a list of tokens.
   * 
   * @param {string[]} tokens The list of tokens.
   * @returns {string[]} The decoded list of tokens.
   * @throws {Error} If the `decode_chain` method is not implemented in the subclass.
   */
  decode_chain(tokens) {
    throw Error("`decode_chain` should be implemented in subclass.");
  }
}
class ReplaceDecoder extends Decoder {
  /** @type {Decoder['decode_chain']} */
  decode_chain(tokens) {
    const pattern = createPattern(this.config.pattern);
    return pattern === null ? tokens : tokens.map((token) => token.replaceAll(pattern, this.config.content));
  }
}
class ByteFallback extends Decoder {
  constructor(config) {
    super(config);
    this.text_decoder = new TextDecoder();
  }
  /** @type {Decoder['decode_chain']} */
  decode_chain(tokens) {
    const new_tokens = [];
    let previous_byte_tokens = [];
    for (const token of tokens) {
      let bytes = null;
      if (token.length === 6 && token.startsWith("<0x") && token.endsWith(">")) {
        const byte = parseInt(token.slice(3, 5), 16);
        if (!isNaN(byte)) {
          bytes = byte;
        }
      }
      if (bytes !== null) {
        previous_byte_tokens.push(bytes);
      } else {
        if (previous_byte_tokens.length > 0) {
          const string = this.text_decoder.decode(Uint8Array.from(previous_byte_tokens));
          new_tokens.push(string);
          previous_byte_tokens = [];
        }
        new_tokens.push(token);
      }
    }
    if (previous_byte_tokens.length > 0) {
      const string = this.text_decoder.decode(Uint8Array.from(previous_byte_tokens));
      new_tokens.push(string);
      previous_byte_tokens = [];
    }
    return new_tokens;
  }
}
class FuseDecoder extends Decoder {
  /** @type {Decoder['decode_chain']} */
  decode_chain(tokens) {
    return [tokens.join("")];
  }
}
class StripDecoder extends Decoder {
  constructor(config) {
    super(config);
    this.content = this.config.content;
    this.start = this.config.start;
    this.stop = this.config.stop;
  }
  /** @type {Decoder['decode_chain']} */
  decode_chain(tokens) {
    return tokens.map((token) => {
      let start_cut = 0;
      for (let i = 0; i < this.start; ++i) {
        if (token[i] === this.content) {
          start_cut = i + 1;
          continue;
        } else {
          break;
        }
      }
      let stop_cut = token.length;
      for (let i = 0; i < this.stop; ++i) {
        const index = token.length - i - 1;
        if (token[index] === this.content) {
          stop_cut = index;
          continue;
        } else {
          break;
        }
      }
      return token.slice(start_cut, stop_cut);
    });
  }
}
class WordPieceDecoder extends Decoder {
  /**
   * Creates a new instance of WordPieceDecoder.
   * @param {Object} config The configuration object.
   * @param {string} config.prefix The prefix used for WordPiece encoding.
   * @param {boolean} config.cleanup Whether to cleanup the decoded string.
   */
  constructor(config) {
    super(config);
    this.cleanup = config.cleanup;
  }
  /** @type {Decoder['decode_chain']} */
  decode_chain(tokens) {
    return tokens.map((token, i) => {
      if (i !== 0) {
        if (token.startsWith(this.config.prefix)) {
          token = token.replace(this.config.prefix, "");
        } else {
          token = " " + token;
        }
      }
      if (this.cleanup) {
        token = clean_up_tokenization(token);
      }
      return token;
    });
  }
}
class ByteLevelDecoder extends Decoder {
  /**
   * Create a `ByteLevelDecoder` object.
   * @param {Object} config Configuration object.
   */
  constructor(config) {
    super(config);
    this.byte_decoder = UNICODE_TO_BYTES;
    this.text_decoder = new TextDecoder("utf-8", {
      fatal: false,
      ignoreBOM: true
    });
    this.end_of_word_suffix = null;
  }
  /**
   * Convert an array of tokens to string by decoding each byte.
   * @param {string[]} tokens Array of tokens to be decoded.
   * @returns {string} The decoded string.
   */
  convert_tokens_to_string(tokens) {
    const text = tokens.join("");
    const byteArray = new Uint8Array([...text].map((c) => this.byte_decoder[c]));
    const decoded_text = this.text_decoder.decode(byteArray);
    return decoded_text;
  }
  /** @type {Decoder['decode_chain']} */
  decode_chain(tokens) {
    const sub_texts = [];
    let current_sub_text = [];
    for (const token of tokens) {
      if (this.added_tokens.find((x) => x.content === token) !== void 0) {
        if (current_sub_text.length > 0) {
          sub_texts.push(this.convert_tokens_to_string(current_sub_text));
          current_sub_text = [];
        }
        sub_texts.push(token);
      } else {
        current_sub_text.push(token);
      }
    }
    if (current_sub_text.length > 0) {
      sub_texts.push(this.convert_tokens_to_string(current_sub_text));
    }
    return sub_texts;
  }
}
class CTCDecoder extends Decoder {
  constructor(config) {
    super(config);
    this.pad_token = this.config.pad_token;
    this.word_delimiter_token = this.config.word_delimiter_token;
    this.cleanup = this.config.cleanup;
  }
  /**
   * Converts a connectionist-temporal-classification (CTC) output tokens into a single string.
   * @param {string[]} tokens Array of tokens to be decoded.
   * @returns {string} The decoded string.
   */
  convert_tokens_to_string(tokens) {
    if (tokens.length === 0) return "";
    const grouped_tokens = [tokens[0]];
    for (let i = 1; i < tokens.length; ++i) {
      if (tokens[i] !== grouped_tokens.at(-1)) {
        grouped_tokens.push(tokens[i]);
      }
    }
    const filtered_tokens = grouped_tokens.filter((token) => token !== this.pad_token);
    let text = filtered_tokens.join("");
    if (this.cleanup) {
      text = clean_up_tokenization(text).replaceAll(this.word_delimiter_token, " ").trim();
    }
    return text;
  }
  /** @type {Decoder['decode_chain']} */
  decode_chain(tokens) {
    return [this.convert_tokens_to_string(tokens)];
  }
}
class DecoderSequence extends Decoder {
  /**
   * Creates a new instance of DecoderSequence.
   * @param {Object} config The configuration object.
   * @param {Object[]} config.decoders The list of decoders to apply.
   */
  constructor(config) {
    super(config);
    this.decoders = config.decoders.map((x) => Decoder.fromConfig(x));
  }
  /** @type {Decoder['decode_chain']} */
  decode_chain(tokens) {
    return this.decoders.reduce((toks, decoder) => {
      return decoder.decode_chain(toks);
    }, tokens);
  }
}
class BPEDecoder extends Decoder {
  constructor(config) {
    super(config);
    this.suffix = this.config.suffix;
  }
  /** @type {Decoder['decode_chain']} */
  decode_chain(tokens) {
    return tokens.map((token, i) => {
      return token.replaceAll(this.suffix, i === tokens.length - 1 ? "" : " ");
    });
  }
}
class VitsDecoder extends Decoder {
  /** @type {Decoder['decode_chain']} */
  decode_chain(tokens) {
    let decoded = "";
    for (let i = 1; i < tokens.length; i += 2) {
      decoded += tokens[i];
    }
    return [decoded];
  }
}
class MetaspacePreTokenizer extends PreTokenizer {
  /**
   * @param {Object} config The configuration object for the MetaspacePreTokenizer.
   * @param {boolean} config.add_prefix_space Whether to add a prefix space to the first token.
   * @param {string} config.replacement The character to replace spaces with.
   * @param {string} [config.str_rep=config.replacement] An optional string representation of the replacement character.
   * @param {'first'|'never'|'always'} [config.prepend_scheme='always'] The metaspace prepending scheme.
   */
  constructor(config) {
    var _a2;
    super();
    this.addPrefixSpace = config.add_prefix_space;
    this.replacement = config.replacement;
    this.strRep = config.str_rep || this.replacement;
    this.prepend_scheme = (_a2 = config.prepend_scheme) != null ? _a2 : "always";
  }
  /**
   * This method takes a string, replaces spaces with the replacement character,
   * adds a prefix space if requested, and returns a new list of tokens.
   * @param {string} text The text to pre-tokenize.
   * @param {Object} [options] The options for the pre-tokenization.
   * @param {number} [options.section_index] The index of the section to pre-tokenize.
   * @returns {string[]} A new list of pre-tokenized tokens.
   */
  pre_tokenize_text(text, {
    section_index = void 0
  } = {}) {
    let normalized = text.replaceAll(" ", this.strRep);
    if (
      // We add a prefix space if:
      //  (1) The addPrefixSpace option is enabled and the normalized
      //      token does not already start with the replacement character.
      this.addPrefixSpace && !normalized.startsWith(this.replacement) && (this.prepend_scheme === "always" || this.prepend_scheme === "first" && section_index === 0)
    ) {
      normalized = this.strRep + normalized;
    }
    return [normalized];
  }
}
class MetaspaceDecoder extends Decoder {
  /**
   * Constructs a new MetaspaceDecoder object.
   * @param {Object} config The configuration object for the MetaspaceDecoder.
   * @param {boolean} config.add_prefix_space Whether to add a prefix space to the decoded string.
   * @param {string} config.replacement The string to replace spaces with.
   */
  constructor(config) {
    super(config);
    this.addPrefixSpace = config.add_prefix_space;
    this.replacement = config.replacement;
  }
  /** @type {Decoder['decode_chain']} */
  decode_chain(tokens) {
    const result = [];
    for (let i = 0; i < tokens.length; ++i) {
      let normalized = tokens[i].replaceAll(this.replacement, " ");
      if (this.addPrefixSpace && i == 0 && normalized.startsWith(" ")) {
        normalized = normalized.substring(1);
      }
      result.push(normalized);
    }
    return result;
  }
}
class Precompiled extends Normalizer {
  /**
   * Create a new instance of Precompiled normalizer.
   * @param {Object} config The configuration object.
   * @param {any} config.precompiled_charsmap Precompiled chars mapping.
   */
  constructor(config) {
    super(config);
    this.charsmap = config.precompiled_charsmap;
  }
  /**
   * Normalizes the given text by applying the precompiled charsmap.
   * @param {string} text The text to normalize.
   * @returns {string} The normalized text.
   */
  normalize(text) {
    text = text.replace(/[\u0001-\u0008\u000B\u000E-\u001F\u007F\u008F\u009F]/gm, "");
    text = text.replace(/[\u0009\u000A\u000C\u000D\u1680\u200B\u200C\u200E\u200F\u2028\u2029\u2581\uFEFF\uFFFD]/gm, " ");
    if (text.includes("～")) {
      const parts = text.split("～");
      text = parts.map((part) => part.normalize("NFKC")).join("～");
    } else {
      text = text.normalize("NFKC");
    }
    return text;
  }
}
class PreTokenizerSequence extends PreTokenizer {
  /**
   * Creates an instance of PreTokenizerSequence.
   * @param {Object} config The configuration object for the pre-tokenizer sequence.
   * @param {Object[]} config.pretokenizers An array of pre-tokenizer configurations.
   */
  constructor(config) {
    super();
    this.tokenizers = config.pretokenizers.map((x) => PreTokenizer.fromConfig(x));
  }
  /**
   * Applies each pre-tokenizer in the sequence to the input text in turn.
   * @param {string} text The text to pre-tokenize.
   * @param {Object} [options] Additional options for the pre-tokenization logic.
   * @returns {string[]} The pre-tokenized text.
   */
  pre_tokenize_text(text, options) {
    return this.tokenizers.reduce((preTokenizedText, tokenizer) => {
      return tokenizer.pre_tokenize(preTokenizedText, options);
    }, [text]);
  }
}
class WhitespacePreTokenizer extends PreTokenizer {
  /**
   * Creates an instance of WhitespacePreTokenizer.
   * @param {Object} config The configuration object for the pre-tokenizer.
   */
  constructor(config) {
    super();
  }
  /**
   * Pre-tokenizes the input text by splitting it on word boundaries.
   * @param {string} text The text to be pre-tokenized.
   * @param {Object} [options] Additional options for the pre-tokenization logic.
   * @returns {string[]} An array of tokens produced by splitting the input text on whitespace.
   */
  pre_tokenize_text(text, options) {
    return text.match(/\w+|[^\w\s]+/g) || [];
  }
}
class WhitespaceSplit extends PreTokenizer {
  /**
   * Creates an instance of WhitespaceSplit.
   * @param {Object} config The configuration object for the pre-tokenizer.
   */
  constructor(config) {
    super();
  }
  /**
   * Pre-tokenizes the input text by splitting it on whitespace characters.
   * @param {string} text The text to be pre-tokenized.
   * @param {Object} [options] Additional options for the pre-tokenization logic.
   * @returns {string[]} An array of tokens produced by splitting the input text on whitespace.
   */
  pre_tokenize_text(text, options) {
    return whitespace_split(text);
  }
}
class ReplacePreTokenizer extends PreTokenizer {
  /**
   * @param {Object} config The configuration options for the pre-tokenizer.
   * @param {Object} config.pattern The pattern used to split the text. Can be a string or a regex object.
   * @param {string} config.content What to replace the pattern with.
   */
  constructor(config) {
    super();
    this.config = config;
    this.pattern = createPattern(this.config.pattern);
    this.content = this.config.content;
  }
  /**
   * Pre-tokenizes the input text by replacing certain characters.
   * @param {string} text The text to be pre-tokenized.
   * @param {Object} [options] Additional options for the pre-tokenization logic.
   * @returns {string[]} An array of tokens produced by replacing certain characters.
   */
  pre_tokenize_text(text, options) {
    if (this.pattern === null) {
      return [text];
    }
    return [text.replaceAll(this.pattern, this.config.content)];
  }
}
const SPECIAL_TOKEN_ATTRIBUTES = [
  "bos_token",
  "eos_token",
  "unk_token",
  "sep_token",
  "pad_token",
  "cls_token",
  "mask_token"
  // additional_special_tokens (TODO)
];
function padHelper(item, length, value_fn, side) {
  for (const key of Object.keys(item)) {
    const diff = length - item[key].length;
    const value = value_fn(key);
    const padData = new Array(diff).fill(value);
    item[key] = side === "right" ? mergeArrays(item[key], padData) : mergeArrays(padData, item[key]);
  }
}
function truncateHelper(item, length) {
  for (const key of Object.keys(item)) {
    item[key].length = length;
  }
}
class PreTrainedTokenizer extends Callable {
  /**
   * Create a new PreTrainedTokenizer instance.
   * @param {Object} tokenizerJSON The JSON of the tokenizer.
   * @param {Object} tokenizerConfig The config of the tokenizer.
   */
  constructor(tokenizerJSON, tokenizerConfig) {
    var _a2, _b, _c, _d;
    super();
    __publicField(this, "return_token_type_ids", false);
    __publicField(this, "_default_chat_template", `{% for message in messages %}{{'<|im_start|>' + message['role'] + '
' + message['content'] + '<|im_end|>' + '
'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
' }}{% endif %}`);
    this._tokenizer_config = tokenizerConfig;
    this.normalizer = Normalizer.fromConfig(tokenizerJSON.normalizer);
    this.pre_tokenizer = PreTokenizer.fromConfig(tokenizerJSON.pre_tokenizer);
    this.model = TokenizerModel.fromConfig(tokenizerJSON.model, tokenizerConfig);
    this.post_processor = PostProcessor.fromConfig(tokenizerJSON.post_processor);
    this.decoder = Decoder.fromConfig(tokenizerJSON.decoder);
    this.special_tokens = [];
    this.all_special_ids = [];
    this.added_tokens = [];
    for (const addedToken of tokenizerJSON.added_tokens) {
      const token = new AddedToken(addedToken);
      this.added_tokens.push(token);
      this.model.tokens_to_ids.set(token.content, token.id);
      this.model.vocab[token.id] = token.content;
      if (token.special) {
        this.special_tokens.push(token.content);
        this.all_special_ids.push(token.id);
      }
    }
    this.additional_special_tokens = (_a2 = tokenizerConfig.additional_special_tokens) != null ? _a2 : [];
    this.special_tokens.push(...this.additional_special_tokens);
    this.special_tokens = [...new Set(this.special_tokens)];
    if (this.decoder) {
      this.decoder.added_tokens = this.added_tokens;
      this.decoder.end_of_word_suffix = this.model.end_of_word_suffix;
    }
    this.added_tokens_regex = this.added_tokens.length > 0 ? new RegExp(
      this.added_tokens.map((x) => `${x.lstrip ? "\\s*" : ""}(${escapeRegExp(x.content)})${x.rstrip ? "\\s*" : ""}`).join("|")
    ) : null;
    this.mask_token = this.getToken("mask_token");
    this.mask_token_id = this.model.tokens_to_ids.get(this.mask_token);
    this.pad_token = this.getToken("pad_token", "eos_token");
    this.pad_token_id = this.model.tokens_to_ids.get(this.pad_token);
    this.sep_token = this.getToken("sep_token");
    this.sep_token_id = this.model.tokens_to_ids.get(this.sep_token);
    this.unk_token = this.getToken("unk_token");
    this.unk_token_id = this.model.tokens_to_ids.get(this.unk_token);
    this.model_max_length = tokenizerConfig.model_max_length;
    this.remove_space = tokenizerConfig.remove_space;
    this.clean_up_tokenization_spaces = (_b = tokenizerConfig.clean_up_tokenization_spaces) != null ? _b : true;
    this.do_lowercase_and_remove_accent = (_c = tokenizerConfig.do_lowercase_and_remove_accent) != null ? _c : false;
    this.padding_side = "right";
    this.legacy = false;
    this.chat_template = (_d = tokenizerConfig.chat_template) != null ? _d : null;
    if (Array.isArray(this.chat_template)) {
      const chat_template = /* @__PURE__ */ Object.create(null);
      for (const { name: name2, template } of this.chat_template) {
        if (typeof name2 !== "string" || typeof template !== "string") {
          throw new Error('Chat template must be a list of objects with "name" and "template" properties');
        }
        chat_template[name2] = template;
      }
      this.chat_template = chat_template;
    }
    this._compiled_template_cache = /* @__PURE__ */ new Map();
  }
  /**
   * Returns the value of the first matching key in the tokenizer config object.
   * @param {...string} keys One or more keys to search for in the tokenizer config object.
   * @returns {string|null} The value associated with the first matching key, or null if no match is found.
   * @throws {Error} If an object is found for a matching key and its __type property is not "AddedToken".
   */
  getToken(...keys) {
    for (const key of keys) {
      const item = this._tokenizer_config[key];
      if (!item) continue;
      if (typeof item === "object") {
        if (item.__type === "AddedToken") {
          return item.content;
        } else {
          throw Error(`Unknown token: ${item}`);
        }
      } else {
        return item;
      }
    }
    return null;
  }
  /**
   * Loads a pre-trained tokenizer from the given `pretrained_model_name_or_path`. 
   * 
   * @param {string} pretrained_model_name_or_path The path to the pre-trained tokenizer.
   * @param {PretrainedTokenizerOptions} options Additional options for loading the tokenizer.
   * 
   * @throws {Error} Throws an error if the tokenizer.json or tokenizer_config.json files are not found in the `pretrained_model_name_or_path`.
   * @returns {Promise<PreTrainedTokenizer>} A new instance of the `PreTrainedTokenizer` class.
   */
  static from_pretrained(_0) {
    return __async(this, arguments, function* (pretrained_model_name_or_path, {
      progress_callback = null,
      config = null,
      cache_dir = null,
      local_files_only = false,
      revision = "main",
      legacy = null
    } = {}) {
      const info = yield loadTokenizer(pretrained_model_name_or_path, {
        progress_callback,
        cache_dir,
        local_files_only,
        revision,
        legacy
      });
      return new this(...info);
    });
  }
  /**
   * @typedef {number[]|number[][]|Tensor} BatchEncodingItem
   * 
   * @typedef {Object} BatchEncoding Holds the output of the tokenizer's call function.
   * @property {BatchEncodingItem} input_ids List of token ids to be fed to a model.
   * @property {BatchEncodingItem} attention_mask List of indices specifying which tokens should be attended to by the model.
   * @property {BatchEncodingItem} [token_type_ids] List of token type ids to be fed to a model.
   */
  /**
   * Encode/tokenize the given text(s).
   * @param {string|string[]} text The text to tokenize.
   * @param {Object} options An optional object containing the following properties:
   * @param {string|string[]} [options.text_pair=null] Optional second sequence to be encoded. If set, must be the same type as text.
   * @param {boolean|'max_length'} [options.padding=false] Whether to pad the input sequences.
   * @param {boolean} [options.add_special_tokens=true] Whether or not to add the special tokens associated with the corresponding model.
   * @param {boolean} [options.truncation=null] Whether to truncate the input sequences.
   * @param {number} [options.max_length=null] Maximum length of the returned list and optionally padding length.
   * @param {boolean} [options.return_tensor=true] Whether to return the results as Tensors or arrays.
   * @param {boolean} [options.return_token_type_ids=null] Whether to return the token type ids.
   * @returns {BatchEncoding} Object to be passed to the model.
   */
  _call(text, {
    text_pair = null,
    add_special_tokens = true,
    padding = false,
    truncation = null,
    max_length = null,
    return_tensor = true,
    // Different to HF
    return_token_type_ids = null
  } = {}) {
    const isBatched = Array.isArray(text);
    let encodedTokens;
    if (isBatched) {
      if (text.length === 0) {
        throw Error("text array must be non-empty");
      }
      if (text_pair !== null) {
        if (!Array.isArray(text_pair)) {
          throw Error("text_pair must also be an array");
        } else if (text.length !== text_pair.length) {
          throw Error("text and text_pair must have the same length");
        }
        encodedTokens = text.map(
          (t, i) => this._encode_plus(t, text_pair[i], { add_special_tokens, return_token_type_ids })
        );
      } else {
        encodedTokens = text.map((x) => this._encode_plus(x, null, { add_special_tokens, return_token_type_ids }));
      }
    } else {
      if (text === null || text === void 0) {
        throw Error("text may not be null or undefined");
      }
      if (Array.isArray(text_pair)) {
        throw Error("When specifying `text_pair`, since `text` is a string, `text_pair` must also be a string (i.e., not an array).");
      }
      encodedTokens = [this._encode_plus(text, text_pair, { add_special_tokens, return_token_type_ids })];
    }
    if (max_length === null) {
      if (padding === "max_length") {
        max_length = this.model_max_length;
      } else {
        max_length = max(encodedTokens.map((x) => x.input_ids.length))[0];
      }
    } else {
      if (!truncation) {
        console.warn(`Truncation was not explicitly activated but \`max_length\` is provided a specific value, please use \`truncation=true\` to explicitly truncate examples to max length.`);
      }
    }
    max_length = Math.min(max_length, this.model_max_length);
    if (padding || truncation) {
      for (let i = 0; i < encodedTokens.length; ++i) {
        if (encodedTokens[i].input_ids.length === max_length) {
          continue;
        } else if (encodedTokens[i].input_ids.length > max_length) {
          if (truncation) {
            truncateHelper(encodedTokens[i], max_length);
          }
        } else {
          if (padding) {
            padHelper(
              encodedTokens[i],
              max_length,
              (key) => key === "input_ids" ? this.pad_token_id : 0,
              this.padding_side
            );
          }
        }
      }
    }
    const result = {};
    if (return_tensor) {
      if (!(padding && truncation)) {
        if (encodedTokens.some((x) => {
          var _a2;
          for (const key of Object.keys(x)) {
            if (x[key].length !== ((_a2 = encodedTokens[0][key]) == null ? void 0 : _a2.length)) {
              return true;
            }
          }
          return false;
        })) {
          throw Error(
            "Unable to create tensor, you should probably activate truncation and/or padding with 'padding=true' and 'truncation=true' to have batched tensors with the same length."
          );
        }
      }
      const dims = [encodedTokens.length, encodedTokens[0].input_ids.length];
      for (const key of Object.keys(encodedTokens[0])) {
        result[key] = new Tensor(
          "int64",
          BigInt64Array.from(encodedTokens.flatMap((x) => x[key]).map(BigInt)),
          dims
        );
      }
    } else {
      for (const key of Object.keys(encodedTokens[0])) {
        result[key] = encodedTokens.map((x) => x[key]);
      }
      if (!isBatched) {
        for (const key of Object.keys(result)) {
          result[key] = result[key][0];
        }
      }
    }
    return (
      /** @type {BatchEncoding} */
      result
    );
  }
  /**
   * Encodes a single text using the preprocessor pipeline of the tokenizer.
   *
   * @param {string|null} text The text to encode.
   * @returns {string[]|null} The encoded tokens.
   */
  _encode_text(text) {
    if (text === null) return null;
    const sections = this.added_tokens_regex ? text.split(this.added_tokens_regex).filter((x) => x) : [text];
    const tokens = sections.map((x, section_index) => {
      const addedToken = this.added_tokens.find((t) => t.content === x);
      if (addedToken !== void 0) {
        return x;
      } else {
        if (this.remove_space === true) {
          x = x.trim().split(/\s+/).join(" ");
        }
        if (this.do_lowercase_and_remove_accent) {
          x = lowercase_and_remove_accent(x);
        }
        if (this.normalizer !== null) {
          x = this.normalizer(x);
        }
        if (x.length === 0) {
          return [];
        }
        const sectionTokens = this.pre_tokenizer !== null ? this.pre_tokenizer(x, {
          section_index
        }) : [x];
        const tokens2 = this.model(sectionTokens);
        return tokens2;
      }
    }).flat();
    return tokens;
  }
  /**
   * Encodes a single text or a pair of texts using the model's tokenizer.
   *
   * @param {string} text The text to encode.
   * @param {string|null} text_pair The optional second text to encode.
   * @param {Object} options An optional object containing the following properties:
   * @param {boolean} [options.add_special_tokens=true] Whether or not to add the special tokens associated with the corresponding model.
   * @param {boolean} [options.return_token_type_ids=null] Whether to return token_type_ids.
   * @returns {EncodingSingle} An object containing the encoded text.
   * @private
   */
  _encode_plus(text, text_pair = null, {
    add_special_tokens = true,
    return_token_type_ids = null
  } = {}) {
    const tokens = this._encode_text(text);
    const tokens2 = this._encode_text(text_pair);
    const combinedTokens = this.post_processor ? this.post_processor(tokens, tokens2, { add_special_tokens }) : { tokens: mergeArrays(tokens != null ? tokens : [], tokens2 != null ? tokens2 : []) };
    const input_ids = this.model.convert_tokens_to_ids(combinedTokens.tokens);
    const result = {
      input_ids,
      attention_mask: new Array(input_ids.length).fill(1)
    };
    if ((return_token_type_ids != null ? return_token_type_ids : this.return_token_type_ids) && combinedTokens.token_type_ids) {
      result.token_type_ids = combinedTokens.token_type_ids;
    }
    return result;
  }
  /**
   * Encodes a single text or a pair of texts using the model's tokenizer.
   *
   * @param {string} text The text to encode.
   * @param {string|null} text_pair The optional second text to encode.
   * @param {Object} options An optional object containing the following properties:
   * @param {boolean} [options.add_special_tokens=true] Whether or not to add the special tokens associated with the corresponding model.
   * @param {boolean} [options.return_token_type_ids=null] Whether to return token_type_ids.
   * @returns {number[]} An array of token IDs representing the encoded text(s).
   */
  encode(text, text_pair = null, {
    add_special_tokens = true,
    return_token_type_ids = null
  } = {}) {
    const { input_ids } = this._encode_plus(text, text_pair, {
      add_special_tokens,
      return_token_type_ids
    });
    return input_ids;
  }
  /**
   * Decode a batch of tokenized sequences.
   * @param {number[][]|Tensor} batch List/Tensor of tokenized input sequences.
   * @param {Object} decode_args (Optional) Object with decoding arguments.
   * @returns {string[]} List of decoded sequences.
   */
  batch_decode(batch, decode_args = {}) {
    if (batch instanceof Tensor) {
      batch = batch.tolist();
    }
    return batch.map((x) => this.decode(x, decode_args));
  }
  /**
   * Decodes a sequence of token IDs back to a string.
   *
   * @param {number[]|Tensor} token_ids List/Tensor of token IDs to decode.
   * @param {Object} [decode_args={}]
   * @param {boolean} [decode_args.skip_special_tokens=false] If true, special tokens are removed from the output string.
   * @param {boolean} [decode_args.clean_up_tokenization_spaces=true] If true, spaces before punctuations and abbreviated forms are removed.
   *
   * @returns {string} The decoded string.
   * @throws {Error} If `token_ids` is not a non-empty array of integers.
   */
  decode(token_ids, decode_args = {}) {
    if (token_ids instanceof Tensor) {
      token_ids = prepareTensorForDecode(token_ids);
    }
    if (!Array.isArray(token_ids) || token_ids.length === 0 || !isIntegralNumber(token_ids[0])) {
      throw Error("token_ids must be a non-empty array of integers.");
    }
    return this.decode_single(token_ids, decode_args);
  }
  /**
   * Decode a single list of token ids to a string.
   * @param {number[]} token_ids List of token ids to decode
   * @param {Object} decode_args Optional arguments for decoding
   * @param {boolean} [decode_args.skip_special_tokens=false] Whether to skip special tokens during decoding
   * @param {boolean} [decode_args.clean_up_tokenization_spaces=null] Whether to clean up tokenization spaces during decoding.
   * If null, the value is set to `this.decoder.cleanup` if it exists, falling back to `this.clean_up_tokenization_spaces` if it exists, falling back to `true`.
   * @returns {string} The decoded string
   */
  decode_single(token_ids, {
    skip_special_tokens = false,
    clean_up_tokenization_spaces = null
  }) {
    let tokens = this.model.convert_ids_to_tokens(token_ids);
    if (skip_special_tokens) {
      tokens = tokens.filter((x) => !this.special_tokens.includes(x));
    }
    let decoded = this.decoder ? this.decoder(tokens) : tokens.join(" ");
    if (this.decoder && this.decoder.end_of_word_suffix) {
      decoded = decoded.replaceAll(this.decoder.end_of_word_suffix, " ");
      if (skip_special_tokens) {
        decoded = decoded.trim();
      }
    }
    if (clean_up_tokenization_spaces != null ? clean_up_tokenization_spaces : this.clean_up_tokenization_spaces) {
      decoded = clean_up_tokenization(decoded);
    }
    return decoded;
  }
  get default_chat_template() {
    if (!this._warned_about_chat_template) {
      console.warn(
        "No chat template is defined for this tokenizer - using a default chat template that implements the ChatML format. If the default is not appropriate for your model, please set `tokenizer.chat_template` to an appropriate template. See https://huggingface.co/docs/transformers/main/chat_templating for more information."
      );
      this._warned_about_chat_template = true;
    }
    return this._default_chat_template;
  }
  /**
   * Converts a list of message objects with `"role"` and `"content"` keys to a list of token
   * ids. This method is intended for use with chat models, and will read the tokenizer's chat_template attribute to
   * determine the format and control tokens to use when converting. When chat_template is None, it will fall back
   * to the default_chat_template specified at the class level.
   * 
   * See [here](https://huggingface.co/docs/transformers/chat_templating) for more information.
   * 
   * **Example:** Applying a chat template to a conversation.
   * 
   * ```javascript
   * import { AutoTokenizer } from "@xenova/transformers";
   * 
   * const tokenizer = await AutoTokenizer.from_pretrained("Xenova/mistral-tokenizer-v1");
   * 
   * const chat = [
   *   { "role": "user", "content": "Hello, how are you?" },
   *   { "role": "assistant", "content": "I'm doing great. How can I help you today?" },
   *   { "role": "user", "content": "I'd like to show off how chat templating works!" },
   * ]
   * 
   * const text = tokenizer.apply_chat_template(chat, { tokenize: false });
   * // "<s>[INST] Hello, how are you? [/INST]I'm doing great. How can I help you today?</s> [INST] I'd like to show off how chat templating works! [/INST]"
   * 
   * const input_ids = tokenizer.apply_chat_template(chat, { tokenize: true, return_tensor: false });
   * // [1, 733, 16289, 28793, 22557, 28725, 910, 460, 368, 28804, 733, 28748, 16289, 28793, 28737, 28742, 28719, 2548, 1598, 28723, 1602, 541, 315, 1316, 368, 3154, 28804, 2, 28705, 733, 16289, 28793, 315, 28742, 28715, 737, 298, 1347, 805, 910, 10706, 5752, 1077, 3791, 28808, 733, 28748, 16289, 28793]
   * ```
   * 
   * @param {Message[]} conversation A list of message objects with `"role"` and `"content"` keys.
   * @param {Object} options An optional object containing the following properties:
   * @param {string} [options.chat_template=null] A Jinja template to use for this conversion. If
   * this is not passed, the model's default chat template will be used instead.
   * @param {boolean} [options.add_generation_prompt=false] Whether to end the prompt with the token(s) that indicate
   * the start of an assistant message. This is useful when you want to generate a response from the model.
   * Note that this argument will be passed to the chat template, and so it must be supported in the
   * template for this argument to have any effect.
   * @param {boolean} [options.tokenize=true] Whether to tokenize the output. If false, the output will be a string.
   * @param {boolean} [options.padding=false] Whether to pad sequences to the maximum length. Has no effect if tokenize is false.
   * @param {boolean} [options.truncation=false] Whether to truncate sequences to the maximum length. Has no effect if tokenize is false.
   * @param {number} [options.max_length=null] Maximum length (in tokens) to use for padding or truncation. Has no effect if tokenize is false.
   * If not specified, the tokenizer's `max_length` attribute will be used as a default.
   * @param {boolean} [options.return_tensor=true] Whether to return the output as a Tensor or an Array. Has no effect if tokenize is false.
   * @param {Object} [options.tokenizer_kwargs={}] Additional options to pass to the tokenizer.
   * @returns {string | Tensor | number[]| number[][]} The tokenized output.
   */
  apply_chat_template(conversation, _b = {}) {
    var _c = _b, {
      chat_template = null,
      add_generation_prompt = false,
      tokenize: tokenize2 = true,
      padding = false,
      truncation = false,
      max_length = null,
      return_tensor = true,
      tokenizer_kwargs = {}
    } = _c, kwargs = __objRest(_c, [
      "chat_template",
      "add_generation_prompt",
      "tokenize",
      "padding",
      "truncation",
      "max_length",
      "return_tensor",
      "tokenizer_kwargs"
    ]);
    var _a2, _b2;
    if (this.chat_template && typeof this.chat_template === "object" || this.chat_template === null && this.default_chat_template && typeof this.default_chat_template === "object") {
      const template_dict = (_a2 = this.chat_template) != null ? _a2 : this.default_chat_template;
      if (chat_template !== null && Object.hasOwn(template_dict, chat_template)) {
        chat_template = template_dict[chat_template];
      } else if (chat_template === null && "default" in template_dict) {
        chat_template = template_dict["default"];
      } else if (chat_template === null) {
        throw Error(
          `This model has multiple chat templates with no default specified! Please either pass a chat template or the name of the template you wish to use to the 'chat_template' argument. Available template names are ${Object.keys(template_dict).sort()}.`
        );
      }
    } else {
      chat_template != null ? chat_template : chat_template = (_b2 = this.chat_template) != null ? _b2 : this.default_chat_template;
    }
    if (typeof chat_template !== "string") {
      throw Error(`chat_template must be a string, but got ${typeof chat_template}`);
    }
    let compiledTemplate = this._compiled_template_cache.get(chat_template);
    if (compiledTemplate === void 0) {
      compiledTemplate = new Template(chat_template);
      this._compiled_template_cache.set(chat_template, compiledTemplate);
    }
    const special_tokens_map = /* @__PURE__ */ Object.create(null);
    for (const key of SPECIAL_TOKEN_ATTRIBUTES) {
      const value = this.getToken(key);
      if (value) {
        special_tokens_map[key] = value;
      }
    }
    const rendered = compiledTemplate.render(__spreadValues(__spreadValues({
      messages: conversation,
      add_generation_prompt
    }, special_tokens_map), kwargs));
    if (tokenize2) {
      return this._call(rendered, __spreadValues({
        add_special_tokens: false,
        padding,
        truncation,
        max_length,
        return_tensor
      }, tokenizer_kwargs)).input_ids;
    }
    return rendered;
  }
}
class BertTokenizer extends PreTrainedTokenizer {
  constructor() {
    super(...arguments);
    __publicField(this, "return_token_type_ids", true);
  }
}
class AlbertTokenizer extends PreTrainedTokenizer {
  constructor() {
    super(...arguments);
    __publicField(this, "return_token_type_ids", true);
  }
}
class MobileBertTokenizer extends PreTrainedTokenizer {
  constructor() {
    super(...arguments);
    __publicField(this, "return_token_type_ids", true);
  }
}
class SqueezeBertTokenizer extends PreTrainedTokenizer {
  constructor() {
    super(...arguments);
    __publicField(this, "return_token_type_ids", true);
  }
}
class DebertaTokenizer extends PreTrainedTokenizer {
  constructor() {
    super(...arguments);
    __publicField(this, "return_token_type_ids", true);
  }
}
class DebertaV2Tokenizer extends PreTrainedTokenizer {
  constructor() {
    super(...arguments);
    __publicField(this, "return_token_type_ids", true);
  }
}
class HerbertTokenizer extends PreTrainedTokenizer {
  constructor() {
    super(...arguments);
    __publicField(this, "return_token_type_ids", true);
  }
}
class ConvBertTokenizer extends PreTrainedTokenizer {
  constructor() {
    super(...arguments);
    __publicField(this, "return_token_type_ids", true);
  }
}
class RoFormerTokenizer extends PreTrainedTokenizer {
  constructor() {
    super(...arguments);
    __publicField(this, "return_token_type_ids", true);
  }
}
class DistilBertTokenizer extends PreTrainedTokenizer {
}
class CamembertTokenizer extends PreTrainedTokenizer {
}
class XLMTokenizer extends PreTrainedTokenizer {
  constructor(tokenizerJSON, tokenizerConfig) {
    super(tokenizerJSON, tokenizerConfig);
    __publicField(this, "return_token_type_ids", true);
    console.warn('WARNING: `XLMTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.');
  }
}
class ElectraTokenizer extends PreTrainedTokenizer {
  constructor() {
    super(...arguments);
    __publicField(this, "return_token_type_ids", true);
  }
}
class T5Tokenizer extends PreTrainedTokenizer {
}
class GPT2Tokenizer extends PreTrainedTokenizer {
  constructor() {
    super(...arguments);
    __publicField(this, "_default_chat_template", `{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}`);
  }
}
class BartTokenizer extends PreTrainedTokenizer {
}
class MBartTokenizer extends PreTrainedTokenizer {
  constructor(tokenizerJSON, tokenizerConfig) {
    super(tokenizerJSON, tokenizerConfig);
    this.languageRegex = /^[a-z]{2}_[A-Z]{2}$/;
    this.language_codes = this.special_tokens.filter((x) => this.languageRegex.test(x));
    this.lang_to_token = (x) => x;
  }
  /**
   * Helper function to build translation inputs for an `MBartTokenizer`.
   * @param {string|string[]} raw_inputs The text to tokenize.
   * @param {Object} tokenizer_options Options to be sent to the tokenizer
   * @param {Object} generate_kwargs Generation options.
   * @returns {Object} Object to be passed to the model.
   */
  _build_translation_inputs(raw_inputs, tokenizer_options, generate_kwargs) {
    return _build_translation_inputs(this, raw_inputs, tokenizer_options, generate_kwargs);
  }
}
class MBart50Tokenizer extends MBartTokenizer {
}
class RobertaTokenizer extends PreTrainedTokenizer {
}
class BloomTokenizer extends GPT2Tokenizer {
  // NOTE: `GPT2Tokenizer` to get the correct chat template
  constructor(tokenizerJSON, tokenizerConfig) {
    var _a2, _b;
    const splitChars = ".,!?…。，、।۔،";
    const patternObject = (_b = (_a2 = tokenizerJSON.pre_tokenizer) == null ? void 0 : _a2.pretokenizers[0]) == null ? void 0 : _b.pattern;
    if (patternObject && patternObject.Regex === ` ?[^(\\s|[${splitChars}])]+`) {
      patternObject.Regex = ` ?[^\\s${splitChars}]+`;
    }
    super(tokenizerJSON, tokenizerConfig);
  }
}
const SPIECE_UNDERLINE = "▁";
class LlamaTokenizer extends PreTrainedTokenizer {
  constructor(tokenizerJSON, tokenizerConfig) {
    var _a2, _b;
    super(tokenizerJSON, tokenizerConfig);
    __publicField(this, "_default_chat_template", `{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif USE_DEFAULT_PROMPT == true and not '<<SYS>>' in messages[0]['content'] %}{% set loop_messages = messages %}{% set system_message = 'DEFAULT_SYSTEM_MESSAGE' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>
' + system_message + '
<</SYS>>

' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'system' %}{{ '<<SYS>>
' + content.strip() + '
<</SYS>>

' }}{% elif message['role'] == 'assistant' %}{{ ' '  + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}`);
    __publicField(this, "DEFAULT_SYSTEM_PROMPT", "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.");
    this.use_default_system_prompt = (_a2 = tokenizerConfig.use_default_system_prompt) != null ? _a2 : false;
    this.legacy = (_b = tokenizerConfig.legacy) != null ? _b : true;
    if (!this.legacy) {
      this.normalizer = null;
      this.pre_tokenizer = new MetaspacePreTokenizer({
        replacement: SPIECE_UNDERLINE,
        add_prefix_space: true,
        prepend_scheme: "first"
      });
    }
  }
  /**
   * Helper function to handle legacy encoding of SPM tokenizers.
   * Adapted from https://github.com/huggingface/transformers/blob/e6dcf8abd6f65bb4b6dfc1831b20d9ba49ce00e2/src/transformers/models/t5/tokenization_t5.py#L374-L387
   * @param {string} text The text to encode.
   * @returns {string[]} The encoded tokens.
   */
  _encode_text(text) {
    if (text === null) return null;
    if (this.legacy || text.length === 0) {
      return super._encode_text(text);
    }
    let tokens = super._encode_text(SPIECE_UNDERLINE + text.replaceAll(SPIECE_UNDERLINE, " "));
    if (tokens.length > 1 && tokens[0] === SPIECE_UNDERLINE && this.special_tokens.includes(tokens[1])) {
      tokens = tokens.slice(1);
    }
    return tokens;
  }
  get default_chat_template() {
    return super.default_chat_template.replaceAll("USE_DEFAULT_PROMPT", this.use_default_system_prompt ? "true" : "false").replaceAll("DEFAULT_SYSTEM_MESSAGE", this.DEFAULT_SYSTEM_PROMPT.replaceAll("\n", "\\n").replaceAll("'", "\\'"));
  }
}
class CodeLlamaTokenizer extends LlamaTokenizer {
}
class XLMRobertaTokenizer extends PreTrainedTokenizer {
}
class MPNetTokenizer extends PreTrainedTokenizer {
}
class FalconTokenizer extends PreTrainedTokenizer {
}
class GPTNeoXTokenizer extends PreTrainedTokenizer {
}
class EsmTokenizer extends PreTrainedTokenizer {
}
class Qwen2Tokenizer extends PreTrainedTokenizer {
}
class GemmaTokenizer extends PreTrainedTokenizer {
  constructor() {
    super(...arguments);
    __publicField(this, "_default_chat_template", "{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}");
  }
}
class Grok1Tokenizer extends PreTrainedTokenizer {
}
function _build_translation_inputs(self2, raw_inputs, tokenizer_options, generate_kwargs) {
  if (!("language_codes" in self2) || !Array.isArray(self2.language_codes)) {
    throw new Error("Tokenizer must have `language_codes` attribute set and it should be an array of language ids.");
  }
  if (!("languageRegex" in self2) || !(self2.languageRegex instanceof RegExp)) {
    throw new Error("Tokenizer must have `languageRegex` attribute set and it should be a regular expression.");
  }
  if (!("lang_to_token" in self2) || typeof self2.lang_to_token !== "function") {
    throw new Error("Tokenizer must have `lang_to_token` attribute set and it should be a function.");
  }
  const src_lang_token = generate_kwargs.src_lang;
  const tgt_lang_token = generate_kwargs.tgt_lang;
  if (!self2.language_codes.includes(tgt_lang_token)) {
    throw new Error(`Target language code "${tgt_lang_token}" is not valid. Must be one of: {${self2.language_codes.join(", ")}}`);
  }
  if (src_lang_token !== void 0) {
    if (!self2.language_codes.includes(src_lang_token)) {
      throw new Error(`Source language code "${src_lang_token}" is not valid. Must be one of: {${self2.language_codes.join(", ")}}`);
    }
    for (const item of self2.post_processor.config.single) {
      if ("SpecialToken" in item && self2.languageRegex.test(item.SpecialToken.id)) {
        item.SpecialToken.id = self2.lang_to_token(src_lang_token);
        break;
      }
    }
  }
  generate_kwargs.forced_bos_token_id = self2.model.convert_tokens_to_ids([self2.lang_to_token(tgt_lang_token)])[0];
  return self2._call(raw_inputs, tokenizer_options);
}
class NllbTokenizer extends PreTrainedTokenizer {
  constructor(tokenizerJSON, tokenizerConfig) {
    super(tokenizerJSON, tokenizerConfig);
    this.languageRegex = /^[a-z]{3}_[A-Z][a-z]{3}$/;
    this.language_codes = this.special_tokens.filter((x) => this.languageRegex.test(x));
    this.lang_to_token = (x) => x;
  }
  /**
   * Helper function to build translation inputs for an `NllbTokenizer`.
   * @param {string|string[]} raw_inputs The text to tokenize.
   * @param {Object} tokenizer_options Options to be sent to the tokenizer
   * @param {Object} generate_kwargs Generation options.
   * @returns {Object} Object to be passed to the model.
   */
  _build_translation_inputs(raw_inputs, tokenizer_options, generate_kwargs) {
    return _build_translation_inputs(this, raw_inputs, tokenizer_options, generate_kwargs);
  }
}
class M2M100Tokenizer extends PreTrainedTokenizer {
  constructor(tokenizerJSON, tokenizerConfig) {
    super(tokenizerJSON, tokenizerConfig);
    this.languageRegex = /^__[a-z]{2,3}__$/;
    this.language_codes = this.special_tokens.filter((x) => this.languageRegex.test(x)).map((x) => x.slice(2, -2));
    this.lang_to_token = (x) => `__${x}__`;
  }
  /**
   * Helper function to build translation inputs for an `M2M100Tokenizer`.
   * @param {string|string[]} raw_inputs The text to tokenize.
   * @param {Object} tokenizer_options Options to be sent to the tokenizer
   * @param {Object} generate_kwargs Generation options.
   * @returns {Object} Object to be passed to the model.
   */
  _build_translation_inputs(raw_inputs, tokenizer_options, generate_kwargs) {
    return _build_translation_inputs(this, raw_inputs, tokenizer_options, generate_kwargs);
  }
}
const WHISPER_LANGUAGES = [
  ["en", "english"],
  ["zh", "chinese"],
  ["de", "german"],
  ["es", "spanish"],
  ["ru", "russian"],
  ["ko", "korean"],
  ["fr", "french"],
  ["ja", "japanese"],
  ["pt", "portuguese"],
  ["tr", "turkish"],
  ["pl", "polish"],
  ["ca", "catalan"],
  ["nl", "dutch"],
  ["ar", "arabic"],
  ["sv", "swedish"],
  ["it", "italian"],
  ["id", "indonesian"],
  ["hi", "hindi"],
  ["fi", "finnish"],
  ["vi", "vietnamese"],
  ["he", "hebrew"],
  ["uk", "ukrainian"],
  ["el", "greek"],
  ["ms", "malay"],
  ["cs", "czech"],
  ["ro", "romanian"],
  ["da", "danish"],
  ["hu", "hungarian"],
  ["ta", "tamil"],
  ["no", "norwegian"],
  ["th", "thai"],
  ["ur", "urdu"],
  ["hr", "croatian"],
  ["bg", "bulgarian"],
  ["lt", "lithuanian"],
  ["la", "latin"],
  ["mi", "maori"],
  ["ml", "malayalam"],
  ["cy", "welsh"],
  ["sk", "slovak"],
  ["te", "telugu"],
  ["fa", "persian"],
  ["lv", "latvian"],
  ["bn", "bengali"],
  ["sr", "serbian"],
  ["az", "azerbaijani"],
  ["sl", "slovenian"],
  ["kn", "kannada"],
  ["et", "estonian"],
  ["mk", "macedonian"],
  ["br", "breton"],
  ["eu", "basque"],
  ["is", "icelandic"],
  ["hy", "armenian"],
  ["ne", "nepali"],
  ["mn", "mongolian"],
  ["bs", "bosnian"],
  ["kk", "kazakh"],
  ["sq", "albanian"],
  ["sw", "swahili"],
  ["gl", "galician"],
  ["mr", "marathi"],
  ["pa", "punjabi"],
  ["si", "sinhala"],
  ["km", "khmer"],
  ["sn", "shona"],
  ["yo", "yoruba"],
  ["so", "somali"],
  ["af", "afrikaans"],
  ["oc", "occitan"],
  ["ka", "georgian"],
  ["be", "belarusian"],
  ["tg", "tajik"],
  ["sd", "sindhi"],
  ["gu", "gujarati"],
  ["am", "amharic"],
  ["yi", "yiddish"],
  ["lo", "lao"],
  ["uz", "uzbek"],
  ["fo", "faroese"],
  ["ht", "haitian creole"],
  ["ps", "pashto"],
  ["tk", "turkmen"],
  ["nn", "nynorsk"],
  ["mt", "maltese"],
  ["sa", "sanskrit"],
  ["lb", "luxembourgish"],
  ["my", "myanmar"],
  ["bo", "tibetan"],
  ["tl", "tagalog"],
  ["mg", "malagasy"],
  ["as", "assamese"],
  ["tt", "tatar"],
  ["haw", "hawaiian"],
  ["ln", "lingala"],
  ["ha", "hausa"],
  ["ba", "bashkir"],
  ["jw", "javanese"],
  ["su", "sundanese"]
];
const WHISPER_LANGUAGE_MAPPING = new Map(WHISPER_LANGUAGES);
const WHISPER_TO_LANGUAGE_CODE_MAPPING = new Map([
  ...WHISPER_LANGUAGES.map(([k, v]) => [v, k]),
  ...[
    ["burmese", "my"],
    ["valencian", "ca"],
    ["flemish", "nl"],
    ["haitian", "ht"],
    ["letzeburgesch", "lb"],
    ["pushto", "ps"],
    ["panjabi", "pa"],
    ["moldavian", "ro"],
    ["moldovan", "ro"],
    ["sinhalese", "si"],
    ["castilian", "es"]
  ]
]);
class WhisperTokenizer extends PreTrainedTokenizer {
  constructor() {
    super(...arguments);
    __publicField(this, "_default_chat_template", `{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}`);
  }
  /**
   * Decodes automatic speech recognition (ASR) sequences.
   * @param {Array<{tokens: number[], token_timestamps?: number[], stride: number[]}>} sequences The sequences to decode.
   * @param {Object} options The options to use for decoding.
   * @returns {Array<string|{chunks?: undefined|Array<{language: string|null, timestamp: Array<number|null>, text: string}>}>} The decoded sequences.
   */
  _decode_asr(sequences, {
    return_timestamps = false,
    return_language = false,
    time_precision = null,
    force_full_sequences = true
  } = {}) {
    if (time_precision === null) {
      throw Error("Must specify time_precision");
    }
    let last_language = null;
    const returnWordTimestamps = return_timestamps === "word";
    function new_chunk() {
      return { "language": last_language, "timestamp": [null, null], "text": "" };
    }
    const chunks = [];
    let chunk = new_chunk();
    let time_offset = 0;
    const timestamp_begin = this.model.convert_tokens_to_ids(["<|notimestamps|>"])[0] + 1;
    let previous_tokens = [];
    let previous_token_timestamps = [];
    let skip = false;
    let right_stride_start = null;
    const all_special_ids = new Set(this.all_special_ids);
    for (const output of sequences) {
      const token_ids = output.tokens;
      const token_timestamps = returnWordTimestamps ? output.token_timestamps : null;
      let last_timestamp = null;
      let first_timestamp = timestamp_begin;
      if ("stride" in output) {
        const [chunk_len, stride_left, stride_right] = output.stride;
        time_offset -= stride_left;
        right_stride_start = chunk_len - stride_right;
        if (stride_left) {
          first_timestamp = stride_left / time_precision + timestamp_begin;
        }
        if (stride_right) {
          for (let i = token_ids.length - 1; i >= 0; --i) {
            const token = token_ids[i];
            if (token >= timestamp_begin) {
              if (last_timestamp !== null && (token - timestamp_begin) * time_precision < right_stride_start) {
                break;
              }
              last_timestamp = token;
            }
          }
        }
      }
      let current_tokens = [];
      let current_token_timestamps = [];
      for (let i = 0; i < token_ids.length; ++i) {
        const token = token_ids[i];
        if (all_special_ids.has(token)) {
          const text = this.decode([token]);
          const language = WHISPER_LANGUAGE_MAPPING.get(text.slice(2, -2));
          if (language !== void 0) {
            if (last_language !== null && language !== last_language && !return_timestamps) {
              previous_tokens.push(current_tokens);
              const resolved_tokens = this.findLongestCommonSequence(previous_tokens)[0];
              const resolved_text = this.decode(resolved_tokens);
              chunk.text = resolved_text;
              chunks.push(chunk);
              previous_tokens = [];
              current_tokens = [];
              chunk = new_chunk();
            }
            last_language = chunk.language = language;
          }
        } else if (token >= timestamp_begin) {
          const time = (token - timestamp_begin) * time_precision + time_offset;
          const rounded_time = round(time, 2);
          if (last_timestamp !== null && token >= last_timestamp) {
            skip = true;
          } else if (skip || previous_tokens.length > 0 && token < first_timestamp) {
            skip = false;
          } else if (chunk.timestamp[0] === null) {
            chunk.timestamp[0] = rounded_time;
          } else {
            if (rounded_time === chunk.timestamp[0]) ;
            else {
              chunk.timestamp[1] = rounded_time;
              previous_tokens.push(current_tokens);
              if (returnWordTimestamps) {
                previous_token_timestamps.push(current_token_timestamps);
              }
              const [resolved_tokens, resolved_token_timestamps] = this.findLongestCommonSequence(
                previous_tokens,
                previous_token_timestamps
              );
              const resolved_text = this.decode(resolved_tokens);
              chunk.text = resolved_text;
              if (returnWordTimestamps) {
                chunk.words = this.collateWordTimestamps(
                  resolved_tokens,
                  resolved_token_timestamps,
                  last_language
                );
              }
              chunks.push(chunk);
              previous_tokens = [];
              current_tokens = [];
              previous_token_timestamps = [];
              current_token_timestamps = [];
              chunk = new_chunk();
            }
          }
        } else {
          current_tokens.push(token);
          if (returnWordTimestamps) {
            let start_time = round(token_timestamps[i] + time_offset, 2);
            let end_time;
            if (i + 1 < token_timestamps.length) {
              end_time = round(token_timestamps[i + 1] + time_offset, 2);
            } else {
              end_time = null;
            }
            current_token_timestamps.push([start_time, end_time]);
          }
        }
      }
      if ("stride" in output) {
        const [chunk_len, stride_left, stride_right] = output.stride;
        time_offset += chunk_len - stride_right;
      }
      if (current_tokens.length > 0) {
        previous_tokens.push(current_tokens);
        if (returnWordTimestamps) {
          previous_token_timestamps.push(current_token_timestamps);
        }
      } else if (previous_tokens.every((p) => p.length === 0)) {
        chunk = new_chunk();
        previous_tokens = [];
        current_tokens = [];
        previous_token_timestamps = [];
        current_token_timestamps = [];
      }
    }
    if (previous_tokens.length > 0) {
      if (force_full_sequences && return_timestamps) {
        throw new Error(
          "Whisper did not predict an ending timestamp, which can happen if audio is cut off in the middle of a word. Also make sure WhisperTimeStampLogitsProcessor was used during generation."
        );
      }
      const [resolved_tokens, resolved_token_timestamps] = this.findLongestCommonSequence(previous_tokens, previous_token_timestamps);
      const resolved_text = this.decode(resolved_tokens);
      chunk.text = resolved_text;
      if (returnWordTimestamps) {
        chunk.words = this.collateWordTimestamps(
          resolved_tokens,
          resolved_token_timestamps,
          last_language
        );
      }
      chunks.push(chunk);
    }
    let optional = /* @__PURE__ */ Object.create(null);
    const full_text = chunks.map((chunk2) => chunk2.text).join("");
    if (return_timestamps || return_language) {
      for (let i = 0; i < chunks.length; ++i) {
        const chunk2 = chunks[i];
        if (!return_timestamps) {
          delete chunk2["timestamp"];
        }
        if (!return_language) {
          delete chunk2["language"];
        }
      }
      if (returnWordTimestamps) {
        const new_chunks = [];
        for (const chunk2 of chunks) {
          for (const word of chunk2.words) {
            new_chunks.push(word);
          }
        }
        optional = { "chunks": new_chunks };
      } else {
        optional = { "chunks": chunks };
      }
    }
    return [full_text, optional];
  }
  /**
   * Finds the longest common sequence among the provided sequences.
   * @param {number[][]} sequences An array of sequences of token ids to compare.
   * @returns {number[][]} The longest common sequence found.
   * @throws {Error} If there is a bug within the function.
   * @private
   */
  findLongestCommonSequence(sequences, token_timestamp_sequences = null) {
    let leftSequence = sequences[0];
    let leftLength = leftSequence.length;
    let totalSequence = [];
    const use_token_timestamp_sequences = Array.isArray(token_timestamp_sequences) && token_timestamp_sequences.length > 0;
    let total_token_timestamp_sequence = use_token_timestamp_sequences ? [] : null;
    let left_token_timestamp_sequence = use_token_timestamp_sequences ? token_timestamp_sequences[0] : null;
    for (let i = 1; i < sequences.length; ++i) {
      const rightSequence = sequences[i];
      let max2 = 0;
      let maxIndices = [leftLength, leftLength, 0, 0];
      const rightLength = rightSequence.length;
      for (let j = 1; j < leftLength + rightLength; ++j) {
        const eps = j / 1e4;
        const leftStart2 = Math.max(0, leftLength - j);
        const leftStop2 = Math.min(leftLength, leftLength + rightLength - j);
        const left = leftSequence.slice(leftStart2, leftStop2);
        const rightStart2 = Math.max(0, j - leftLength);
        const rightStop2 = Math.min(rightLength, j);
        const right = rightSequence.slice(rightStart2, rightStop2);
        if (left.length !== right.length) {
          throw new Error("There is a bug within whisper `decode_asr` function, please report it. Dropping to prevent bad inference.");
        }
        const matches = left.filter((elem, idx) => elem === right[idx]).length;
        const matching = matches / j + eps;
        if (matches > 1 && matching > max2) {
          max2 = matching;
          maxIndices = [leftStart2, leftStop2, rightStart2, rightStop2];
        }
      }
      const [leftStart, leftStop, rightStart, rightStop] = maxIndices;
      const leftMid = Math.floor((leftStop + leftStart) / 2);
      const rightMid = Math.floor((rightStop + rightStart) / 2);
      totalSequence.push(...leftSequence.slice(0, leftMid));
      leftSequence = rightSequence.slice(rightMid);
      leftLength = leftSequence.length;
      if (use_token_timestamp_sequences) {
        total_token_timestamp_sequence.push(...left_token_timestamp_sequence.slice(0, leftMid));
        left_token_timestamp_sequence = token_timestamp_sequences[i].slice(rightMid);
      }
    }
    totalSequence.push(...leftSequence);
    if (use_token_timestamp_sequences) {
      total_token_timestamp_sequence.push(...left_token_timestamp_sequence);
      return [totalSequence, total_token_timestamp_sequence];
    } else {
      return [totalSequence, []];
    }
  }
  /** @private */
  collateWordTimestamps(tokens, token_timestamps, language) {
    const [words, _, token_indices] = this.combineTokensIntoWords(tokens, language);
    const timings = [];
    for (let i = 0; i < words.length; ++i) {
      const indices = token_indices[i];
      timings.push({
        text: words[i],
        timestamp: [
          token_timestamps[indices.at(0)][0],
          token_timestamps[indices.at(-1)][1]
        ]
      });
    }
    return timings;
  }
  /**
   * Groups tokens by word. Returns a tuple containing a list of strings with the words,
   * and a list of `token_id` sequences with the tokens making up each word.
   * @param {number[]} tokens 
   * @param {string} [language] 
   * @param {string} prepend_punctionations 
   * @param {string} append_punctuations 
   * 
   * @private
   */
  combineTokensIntoWords(tokens, language, prepend_punctionations = `"'“¡¿([{-`, append_punctuations = `"'.。,，!！?？:：”)]}、`) {
    language = language != null ? language : "english";
    let words, word_tokens, token_indices;
    if (["chinese", "japanese", "thai", "lao", "myanmar"].includes(language)) {
      [words, word_tokens, token_indices] = this.splitTokensOnUnicode(tokens);
    } else {
      [words, word_tokens, token_indices] = this.splitTokensOnSpaces(tokens);
    }
    return this.mergePunctuations(words, word_tokens, token_indices, prepend_punctionations, append_punctuations);
  }
  /** @type {PreTrainedTokenizer['decode']} */
  decode(token_ids, decode_args) {
    let text;
    if (decode_args && decode_args.decode_with_timestamps) {
      if (token_ids instanceof Tensor) {
        token_ids = prepareTensorForDecode(token_ids);
      }
      text = this.decodeWithTimestamps(token_ids, decode_args);
    } else {
      text = super.decode(token_ids, decode_args);
    }
    return text;
  }
  /**
   * @param {number[]} token_ids List of token IDs to decode.
   * @param {Object} decode_args Optional arguments for decoding
   * @private
   */
  decodeWithTimestamps(token_ids, decode_args) {
    var _a2;
    const time_precision = (_a2 = decode_args == null ? void 0 : decode_args.time_precision) != null ? _a2 : 0.02;
    const timestamp_begin = Array.from(this.all_special_ids).at(-1) + 1;
    let outputs = [[]];
    for (const token of token_ids) {
      if (token >= timestamp_begin) {
        const timestamp = round((token - timestamp_begin) * time_precision, 2);
        outputs.push(`<|${timestamp}|>`);
        outputs.push([]);
      } else {
        outputs[outputs.length - 1].push(token);
      }
    }
    outputs = outputs.map(
      (s) => {
        if (typeof s === "string") {
          return s;
        } else {
          return super.decode(s, decode_args);
        }
      }
    );
    return outputs.join("");
  }
  /**
   * Combine tokens into words by splitting at any position where the tokens are decoded as valid unicode points.
   * @param {number[]} tokens 
   * @returns {*}
   * @private
   */
  splitTokensOnUnicode(tokens) {
    const decoded_full = this.decode(tokens, {
      // @ts-ignore
      decode_with_timestamps: true
    });
    const replacement_char = "�";
    const words = [];
    const word_tokens = [];
    const token_indices = [];
    let current_tokens = [];
    let current_indices = [];
    let unicode_offset = 0;
    for (let token_idx = 0; token_idx < tokens.length; ++token_idx) {
      const token = tokens[token_idx];
      current_tokens.push(token);
      current_indices.push(token_idx);
      const decoded = this.decode(current_tokens, {
        // @ts-ignore
        decode_with_timestamps: true
      });
      if (!decoded.includes(replacement_char) || decoded_full[unicode_offset + decoded.indexOf(replacement_char)] === replacement_char) {
        words.push(decoded);
        word_tokens.push(current_tokens);
        token_indices.push(current_indices);
        current_tokens = [];
        current_indices = [];
        unicode_offset += decoded.length;
      }
    }
    return [words, word_tokens, token_indices];
  }
  /**
   * Combine tokens into words by splitting at whitespace and punctuation tokens.
   * @param {number[]} tokens 
   * @private
   */
  splitTokensOnSpaces(tokens) {
    const [subwords, subword_tokens_list, subword_indices_list] = this.splitTokensOnUnicode(tokens);
    const words = [];
    const word_tokens = [];
    const token_indices = [];
    const punctuationRegex = new RegExp(`^[${PUNCTUATION_REGEX}]$`, "gu");
    for (let i = 0; i < subwords.length; ++i) {
      const subword = subwords[i];
      const subword_tokens = subword_tokens_list[i];
      const subword_indices = subword_indices_list[i];
      const special = subword_tokens[0] >= this.model.tokens_to_ids.get("<|endoftext|>");
      const with_space = subword.startsWith(" ");
      const trimmed = subword.trim();
      const punctuation = punctuationRegex.test(trimmed);
      if (special || with_space || punctuation || words.length === 0) {
        words.push(subword);
        word_tokens.push(subword_tokens);
        token_indices.push(subword_indices);
      } else {
        const ix = words.length - 1;
        words[ix] += subword;
        word_tokens[ix].push(...subword_tokens);
        token_indices[ix].push(...subword_indices);
      }
    }
    return [words, word_tokens, token_indices];
  }
  /**
   * Merges punctuation tokens with neighboring words.
   * @param {string[]} words 
   * @param {number[][]} tokens 
   * @param {number[][]} indices 
   * @param {string} prepended 
   * @param {string} appended 
   * @private
   */
  mergePunctuations(words, tokens, indices, prepended, appended) {
    const newWords = structuredClone(words);
    const newTokens = structuredClone(tokens);
    const newIndices = structuredClone(indices);
    let i = newWords.length - 2;
    let j = newWords.length - 1;
    while (i >= 0) {
      if (newWords[i].startsWith(" ") && prepended.includes(newWords[i].trim())) {
        newWords[j] = newWords[i] + newWords[j];
        newTokens[j] = mergeArrays(newTokens[i], newTokens[j]);
        newIndices[j] = mergeArrays(newIndices[i], newIndices[j]);
        newWords[i] = "";
        newTokens[i] = [];
        newIndices[i] = [];
      } else {
        j = i;
      }
      --i;
    }
    i = 0;
    j = 1;
    while (j < newWords.length) {
      if (!newWords[i].endsWith(" ") && appended.includes(newWords[j])) {
        newWords[i] += newWords[j];
        newTokens[i] = mergeArrays(newTokens[i], newTokens[j]);
        newIndices[i] = mergeArrays(newIndices[i], newIndices[j]);
        newWords[j] = "";
        newTokens[j] = [];
        newIndices[j] = [];
      } else {
        i = j;
      }
      ++j;
    }
    return [
      newWords.filter((x) => x),
      newTokens.filter((x) => x.length > 0),
      newIndices.filter((x) => x.length > 0)
    ];
  }
  /**
   * Helper function to build translation inputs for a `WhisperTokenizer`,
   * depending on the language, task, and whether to predict timestamp tokens.
   * 
   * Used to override the prefix tokens appended to the start of the label sequence.
   * 
   * **Example: Get ids for a language**
   * ```javascript
   * // instantiate the tokenizer and set the prefix token to Spanish
   * const tokenizer = await WhisperTokenizer.from_pretrained('Xenova/whisper-tiny');
   * const forced_decoder_ids = tokenizer.get_decoder_prompt_ids({ language: 'spanish' });
   * // [(1, 50262), (2, 50363)]
   * ```
   * 
   * @param {Object} options Options to generate the decoder prompt.
   * @param {string} [options.language] The language of the transcription text.
   * The corresponding language id token is appended to the start of the sequence for multilingual
   * speech recognition and speech translation tasks, e.g. for "Spanish" the token "<|es|>" is appended
   * to the start of sequence.
   * @param {string} [options.task] Task identifier to append at the start of sequence (if any).
   * This should be used for mulitlingual fine-tuning, with "transcribe" for speech recognition and
   * "translate" for speech translation.
   * @param {boolean} [options.no_timestamps] Whether to add the <|notimestamps|> token at the start of the sequence.
   * @returns {number[][]} The decoder prompt ids.
   */
  get_decoder_prompt_ids({
    language = null,
    task = null,
    no_timestamps = true
  } = {}) {
    const forced_decoder_ids = [];
    if (language) {
      language = language.toLowerCase();
      let language_code = WHISPER_TO_LANGUAGE_CODE_MAPPING.get(language);
      if (language_code === void 0) {
        if (WHISPER_LANGUAGE_MAPPING.has(language)) {
          language_code = language;
        } else {
          const is_language_code = language.length === 2;
          const langs = is_language_code ? WHISPER_LANGUAGE_MAPPING.keys() : WHISPER_LANGUAGE_MAPPING.values();
          throw new Error(`Language "${language}" is not supported. Must be one of: ${JSON.stringify(langs)}`);
        }
      }
      const language_token_id = this.model.tokens_to_ids.get(`<|${language_code}|>`);
      if (language_token_id === void 0) {
        throw new Error(`Unable to find language "${language_code}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);
      }
      forced_decoder_ids.push(language_token_id);
    } else {
      forced_decoder_ids.push(null);
    }
    if (task) {
      task = task.toLowerCase();
      if (task !== "transcribe" && task !== "translate") {
        throw new Error(`Task "${task}" is not supported. Must be one of: ["transcribe", "translate"]`);
      }
      const task_token_id = this.model.tokens_to_ids.get(`<|${task}|>`);
      if (task_token_id === void 0) {
        throw new Error(`Unable to find task "${task}" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.`);
      }
      forced_decoder_ids.push(task_token_id);
    } else {
      forced_decoder_ids.push(null);
    }
    if (no_timestamps) {
      const no_timestamps_id = this.model.tokens_to_ids.get(`<|notimestamps|>`);
      if (no_timestamps_id === void 0) {
        throw new Error('Unable to find "<|notimestamps|>" in model vocabulary. Please report this issue at https://github.com/xenova/transformers.js/issues/new/choose.');
      }
      forced_decoder_ids.push(no_timestamps_id);
    }
    return forced_decoder_ids.map((x, i) => [i + 1, x]).filter((x) => x[1] !== null);
  }
}
class CodeGenTokenizer extends PreTrainedTokenizer {
}
class CLIPTokenizer extends PreTrainedTokenizer {
}
class SiglipTokenizer extends PreTrainedTokenizer {
}
class MarianTokenizer extends PreTrainedTokenizer {
  /**
   * Create a new MarianTokenizer instance.
   * @param {Object} tokenizerJSON The JSON of the tokenizer.
   * @param {Object} tokenizerConfig The config of the tokenizer.
   */
  constructor(tokenizerJSON, tokenizerConfig) {
    super(tokenizerJSON, tokenizerConfig);
    this.languageRegex = /^(>>\w+<<)\s*/g;
    this.supported_language_codes = this.model.vocab.filter(
      (x) => this.languageRegex.test(x)
    );
    console.warn('WARNING: `MarianTokenizer` is not yet supported by Hugging Face\'s "fast" tokenizers library. Therefore, you may experience slightly inaccurate results.');
  }
  /**
   * Encodes a single text. Overriding this method is necessary since the language codes
   * must be removed before encoding with sentencepiece model.
   * @see https://github.com/huggingface/transformers/blob/12d51db243a00726a548a43cc333390ebae731e3/src/transformers/models/marian/tokenization_marian.py#L204-L213
   *
   * @param {string|null} text The text to encode.
   * @returns {Array} The encoded tokens.
   */
  _encode_text(text) {
    if (text === null) return null;
    const [matchInfo, ...remainder] = text.trim().split(this.languageRegex);
    if (remainder.length === 0) {
      return super._encode_text(matchInfo);
    } else if (remainder.length === 2) {
      const [language, text2] = remainder;
      if (!this.supported_language_codes.includes(language)) {
        console.warn(`Unsupported language code "${language}" detected, which may lead to unexpected behavior. Should be one of: ${JSON.stringify(this.supported_language_codes)}`);
      }
      return mergeArrays([language], super._encode_text(text2));
    }
  }
}
class Wav2Vec2CTCTokenizer extends PreTrainedTokenizer {
}
class BlenderbotTokenizer extends PreTrainedTokenizer {
  constructor() {
    super(...arguments);
    __publicField(this, "_default_chat_template", `{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ '  ' }}{% endif %}{% endfor %}{{ eos_token }}`);
  }
}
class BlenderbotSmallTokenizer extends BlenderbotTokenizer {
}
class SpeechT5Tokenizer extends PreTrainedTokenizer {
}
class NougatTokenizer extends PreTrainedTokenizer {
}
class VitsTokenizer extends PreTrainedTokenizer {
  constructor(tokenizerJSON, tokenizerConfig) {
    super(tokenizerJSON, tokenizerConfig);
    this.decoder = new VitsDecoder({});
  }
}
class CohereTokenizer extends PreTrainedTokenizer {
}
class AutoTokenizer {
  /**
   * Instantiate one of the tokenizer classes of the library from a pretrained model.
   * 
   * The tokenizer class to instantiate is selected based on the `tokenizer_class` property of the config object
   * (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible)
   * 
   * @param {string} pretrained_model_name_or_path The name or path of the pretrained model. Can be either:
   * - A string, the *model id* of a pretrained tokenizer hosted inside a model repo on huggingface.co.
   *   Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
   *   user or organization name, like `dbmdz/bert-base-german-cased`.
   * - A path to a *directory* containing tokenizer files, e.g., `./my_model_directory/`.
   * @param {PretrainedTokenizerOptions} options Additional options for loading the tokenizer.
   * 
   * @returns {Promise<PreTrainedTokenizer>} A new instance of the PreTrainedTokenizer class.
   */
  static from_pretrained(_0) {
    return __async(this, arguments, function* (pretrained_model_name_or_path, {
      quantized = true,
      progress_callback = null,
      config = null,
      cache_dir = null,
      local_files_only = false,
      revision = "main",
      legacy = null
    } = {}) {
      var _a2, _b;
      const [tokenizerJSON, tokenizerConfig] = yield loadTokenizer(pretrained_model_name_or_path, {
        progress_callback,
        cache_dir,
        local_files_only,
        revision,
        legacy
      });
      const tokenizerName = (_b = (_a2 = tokenizerConfig.tokenizer_class) == null ? void 0 : _a2.replace(/Fast$/, "")) != null ? _b : "PreTrainedTokenizer";
      let cls = this.TOKENIZER_CLASS_MAPPING[tokenizerName];
      if (!cls) {
        console.warn(`Unknown tokenizer class "${tokenizerName}", attempting to construct from base class.`);
        cls = PreTrainedTokenizer;
      }
      return new cls(tokenizerJSON, tokenizerConfig);
    });
  }
}
__publicField(AutoTokenizer, "TOKENIZER_CLASS_MAPPING", {
  T5Tokenizer,
  DistilBertTokenizer,
  CamembertTokenizer,
  DebertaTokenizer,
  DebertaV2Tokenizer,
  BertTokenizer,
  HerbertTokenizer,
  ConvBertTokenizer,
  RoFormerTokenizer,
  XLMTokenizer,
  ElectraTokenizer,
  MobileBertTokenizer,
  SqueezeBertTokenizer,
  AlbertTokenizer,
  GPT2Tokenizer,
  BartTokenizer,
  MBartTokenizer,
  MBart50Tokenizer,
  RobertaTokenizer,
  WhisperTokenizer,
  CodeGenTokenizer,
  CLIPTokenizer,
  SiglipTokenizer,
  MarianTokenizer,
  BloomTokenizer,
  NllbTokenizer,
  M2M100Tokenizer,
  LlamaTokenizer,
  CodeLlamaTokenizer,
  XLMRobertaTokenizer,
  MPNetTokenizer,
  FalconTokenizer,
  GPTNeoXTokenizer,
  EsmTokenizer,
  Wav2Vec2CTCTokenizer,
  BlenderbotTokenizer,
  BlenderbotSmallTokenizer,
  SpeechT5Tokenizer,
  NougatTokenizer,
  VitsTokenizer,
  Qwen2Tokenizer,
  GemmaTokenizer,
  Grok1Tokenizer,
  CohereTokenizer,
  // Base case:
  PreTrainedTokenizer
});
function loadConfig(pretrained_model_name_or_path, options) {
  return __async(this, null, function* () {
    let info = yield getModelJSON(pretrained_model_name_or_path, "config.json", true, options);
    return info;
  });
}
class PretrainedConfig {
  // NOTE: Typo in original
  /**
   * Create a new PreTrainedTokenizer instance.
   * @param {Object} configJSON The JSON of the config.
   */
  constructor(configJSON) {
    this.model_type = null;
    this.is_encoder_decoder = false;
    Object.assign(this, configJSON);
  }
  /**
   * Loads a pre-trained config from the given `pretrained_model_name_or_path`. 
   * 
   * @param {string} pretrained_model_name_or_path The path to the pre-trained config.
   * @param {PretrainedOptions} options Additional options for loading the config.
   * @throws {Error} Throws an error if the config.json is not found in the `pretrained_model_name_or_path`.
   * 
   * @returns {Promise<PretrainedConfig>} A new instance of the `PretrainedConfig` class.
   */
  static from_pretrained(_0) {
    return __async(this, arguments, function* (pretrained_model_name_or_path, {
      progress_callback = null,
      config = null,
      cache_dir = null,
      local_files_only = false,
      revision = "main"
    } = {}) {
      let data = config != null ? config : yield loadConfig(pretrained_model_name_or_path, {
        progress_callback,
        cache_dir,
        local_files_only,
        revision
      });
      return new this(data);
    });
  }
}
class AutoConfig {
  /** @type {PretrainedConfig.from_pretrained} */
  static from_pretrained(...args) {
    return __async(this, null, function* () {
      return PretrainedConfig.from_pretrained(...args);
    });
  }
}
class LogitsProcessorList extends Callable {
  /**
   * Constructs a new instance of `LogitsProcessorList`.
   */
  constructor() {
    super();
    this.processors = [];
  }
  /**
   * Adds a new logits processor to the list.
   *
   * @param {LogitsProcessor} item The logits processor function to add.
   */
  push(item) {
    this.processors.push(item);
  }
  /**
   * Adds multiple logits processors to the list.
   *
   * @param {LogitsProcessor[]} items The logits processor functions to add.
   */
  extend(items) {
    this.processors.push(...items);
  }
  /**
   * Applies all logits processors in the list to a batch of logits, modifying them in-place.
   *
   * @param {number[]} input_ids The input IDs for the language model.
   * @param {number[][]} batchedLogits A 2D array of logits, where each row corresponds to a single
   *                                                input sequence in the batch.
   */
  _call(input_ids, batchedLogits) {
    for (let logits of batchedLogits) {
      this.processors.forEach(
        (func) => func(input_ids, logits)
      );
    }
  }
  [Symbol.iterator]() {
    return this.processors.values();
  }
}
class LogitsProcessor extends Callable {
  /**
   * Apply the processor to the input logits.
   *
   * @abstract
   * @param {Array} input_ids The input ids.
   * @param {Tensor} logits The logits to process.
   * @throws {Error} Throws an error if `_call` is not implemented in the subclass.
   */
  _call(input_ids, logits) {
    throw Error("`_call` should be implemented in a subclass");
  }
}
class ForceTokensLogitsProcessor extends LogitsProcessor {
  /**
   * Constructs a new instance of `ForceTokensLogitsProcessor`.
   * 
   * @param {Array} forced_decoder_ids The ids of tokens that should be forced.
   */
  constructor(forced_decoder_ids) {
    super();
    this.force_token_map = Object.fromEntries(forced_decoder_ids != null ? forced_decoder_ids : []);
  }
  /**
   * Apply the processor to the input logits.
   *
   * @param {Array} input_ids The input ids.
   * @param {Tensor} logits The logits to process.
   * @returns {Tensor} The processed logits.
   */
  _call(input_ids, logits) {
    let map = this.force_token_map[input_ids.length];
    if (exists(map)) {
      logits.data.fill(-Infinity);
      logits.data[map] = 0;
    }
    return logits;
  }
}
class ForcedBOSTokenLogitsProcessor extends LogitsProcessor {
  /**
   * Create a ForcedBOSTokenLogitsProcessor.
   * @param {number} bos_token_id The ID of the beginning-of-sequence token to be forced.
   */
  constructor(bos_token_id) {
    super();
    this.bos_token_id = bos_token_id;
  }
  /**
   * Apply the BOS token forcing to the logits.
   * @param {Array} input_ids The input IDs.
   * @param {Object} logits The logits.
   * @returns {Object} The logits with BOS token forcing.
   */
  _call(input_ids, logits) {
    if (input_ids.length === 1) {
      logits.data.fill(-Infinity);
      logits.data[this.bos_token_id] = 0;
    }
    return logits;
  }
}
class ForcedEOSTokenLogitsProcessor extends LogitsProcessor {
  /**
   * Create a ForcedEOSTokenLogitsProcessor.
   * @param {number} max_length Max length of the sequence.
   * @param {number|number[]} forced_eos_token_id The ID of the end-of-sequence token to be forced.
   */
  constructor(max_length, forced_eos_token_id) {
    super();
    this.max_length = max_length;
    this.forced_eos_token_id = forced_eos_token_id;
  }
  /**
   * Apply the processor to input_ids and logits.
   * 
   * @param {number[]} input_ids The input ids.
   * @param {Tensor} logits The logits tensor.
   */
  _call(input_ids, logits) {
  }
}
class SuppressTokensAtBeginLogitsProcessor extends LogitsProcessor {
  /**
   * Create a SuppressTokensAtBeginLogitsProcessor.
   * @param {number[]} begin_suppress_tokens The IDs of the tokens to suppress.
   * @param {number} begin_index The number of tokens to generate before suppressing tokens.
   */
  constructor(begin_suppress_tokens, begin_index) {
    super();
    this.begin_suppress_tokens = begin_suppress_tokens;
    this.begin_index = begin_index;
  }
  /**
   * Apply the BOS token forcing to the logits.
   * @param {Array} input_ids The input IDs.
   * @param {Object} logits The logits.
   * @returns {Object} The logits with BOS token forcing.
   */
  _call(input_ids, logits) {
    if (input_ids.length === this.begin_index) {
      for (let token_id of this.begin_suppress_tokens) {
        logits.data[token_id] = -Infinity;
      }
    }
    return logits;
  }
}
class WhisperTimeStampLogitsProcessor extends LogitsProcessor {
  /**
   * Constructs a new WhisperTimeStampLogitsProcessor.
   * @param {Object} generate_config The config object passed to the `generate()` method of a transformer model.
   * @param {number} generate_config.eos_token_id The ID of the end-of-sequence token.
   * @param {number} generate_config.no_timestamps_token_id The ID of the token used to indicate that a token should not have a timestamp.
   * @param {number[][]} [generate_config.forced_decoder_ids] An array of two-element arrays representing decoder IDs that are forced to appear in the output. The second element of each array indicates whether the token is a timestamp.
   * @param {number} [generate_config.max_initial_timestamp_index] The maximum index at which an initial timestamp can appear.
   */
  constructor(generate_config) {
    super();
    this.eos_token_id = generate_config.eos_token_id;
    this.no_timestamps_token_id = generate_config.no_timestamps_token_id;
    this.timestamp_begin = this.no_timestamps_token_id + 1;
    this.begin_index = (generate_config.forced_decoder_ids || []).length + 2;
    if (generate_config.forced_decoder_ids.slice(-1)[0][1] === this.no_timestamps_token_id) {
      this.begin_index -= 1;
    }
    this.max_initial_timestamp_index = generate_config.max_initial_timestamp_index;
  }
  /**
   * Modify the logits to handle timestamp tokens.
   * @param {Array} input_ids The input sequence of tokens.
   * @param {Tensor} logits The logits output by the model.
   * @returns {Tensor} The modified logits.
   */
  _call(input_ids, logits) {
    const logitsData = (
      /** @type {Float32Array} */
      logits.data
    );
    logitsData[this.no_timestamps_token_id] = -Infinity;
    if (input_ids.length === this.begin_index - 1) {
      logitsData.fill(-Infinity);
      logitsData[this.timestamp_begin] = 0;
      return logits;
    }
    const seq = input_ids.slice(this.begin_index);
    const last_was_timestamp = seq.length >= 1 && seq[seq.length - 1] >= this.timestamp_begin;
    const penultimate_was_timestamp = seq.length < 2 || seq[seq.length - 2] >= this.timestamp_begin;
    if (last_was_timestamp) {
      if (penultimate_was_timestamp) {
        logitsData.subarray(this.timestamp_begin).fill(-Infinity);
      } else {
        logitsData.subarray(0, this.eos_token_id).fill(-Infinity);
      }
    }
    if (input_ids.length === this.begin_index && this.max_initial_timestamp_index !== null) {
      const last_allowed = this.timestamp_begin + this.max_initial_timestamp_index;
      logitsData.subarray(last_allowed + 1).fill(-Infinity);
    }
    const logprobs = log_softmax(logitsData);
    const timestamp_logprob = Math.log(logprobs.subarray(this.timestamp_begin).map(Math.exp).reduce((a, b) => a + b));
    const max_text_token_logprob = max(logprobs.subarray(0, this.timestamp_begin))[0];
    if (timestamp_logprob > max_text_token_logprob) {
      logitsData.subarray(0, this.timestamp_begin).fill(-Infinity);
    }
    return logits;
  }
}
class NoRepeatNGramLogitsProcessor extends LogitsProcessor {
  /**
   * Create a NoRepeatNGramLogitsProcessor.
   * @param {number} no_repeat_ngram_size The no-repeat-ngram size. All ngrams of this size can only occur once.
   */
  constructor(no_repeat_ngram_size) {
    super();
    this.no_repeat_ngram_size = no_repeat_ngram_size;
  }
  /**
   * Generate n-grams from a sequence of token ids.
   * @param {number[]} prevInputIds List of previous input ids
   * @returns {Map<string, number[]>} Map of generated n-grams
   */
  getNgrams(prevInputIds) {
    var _a2;
    const curLen = prevInputIds.length;
    const ngrams = [];
    for (let j = 0; j < curLen + 1 - this.no_repeat_ngram_size; ++j) {
      const ngram = [];
      for (let k = 0; k < this.no_repeat_ngram_size; ++k) {
        ngram.push(prevInputIds[j + k]);
      }
      ngrams.push(ngram);
    }
    const generatedNgram = /* @__PURE__ */ new Map();
    for (const ngram of ngrams) {
      const prevNgram = ngram.slice(0, ngram.length - 1);
      const prevNgramKey = JSON.stringify(prevNgram);
      const prevNgramValue = (_a2 = generatedNgram.get(prevNgramKey)) != null ? _a2 : [];
      prevNgramValue.push(ngram[ngram.length - 1]);
      generatedNgram.set(prevNgramKey, prevNgramValue);
    }
    return generatedNgram;
  }
  /**
   * Generate n-grams from a sequence of token ids.
   * @param {Map<string, number[]>} bannedNgrams Map of banned n-grams
   * @param {number[]} prevInputIds List of previous input ids
   * @returns {number[]} Map of generated n-grams
   */
  getGeneratedNgrams(bannedNgrams, prevInputIds) {
    var _a2;
    const ngramIdx = prevInputIds.slice(prevInputIds.length + 1 - this.no_repeat_ngram_size, prevInputIds.length);
    const banned = (_a2 = bannedNgrams.get(JSON.stringify(ngramIdx))) != null ? _a2 : [];
    return banned;
  }
  /**
   * Calculate banned n-gram tokens
   * @param {number[]} prevInputIds List of previous input ids
   * @returns {number[]} Map of generated n-grams
   */
  calcBannedNgramTokens(prevInputIds) {
    const bannedTokens = [];
    if (prevInputIds.length + 1 < this.no_repeat_ngram_size) {
      return bannedTokens;
    } else {
      const generatedNgrams = this.getNgrams(prevInputIds);
      const bannedTokens2 = this.getGeneratedNgrams(generatedNgrams, prevInputIds);
      return bannedTokens2;
    }
  }
  /**
   * Apply the no-repeat-ngram processor to the logits.
   * @param {Array} input_ids The input IDs.
   * @param {Object} logits The logits.
   * @returns {Object} The logits with no-repeat-ngram processing.
   */
  _call(input_ids, logits) {
    const bannedTokens = this.calcBannedNgramTokens(input_ids);
    for (const token of bannedTokens) {
      logits.data[token] = -Infinity;
    }
    return logits;
  }
}
class RepetitionPenaltyLogitsProcessor extends LogitsProcessor {
  /**
   * Create a RepetitionPenaltyLogitsProcessor.
   * @param {number} penalty The penalty to apply for repeated tokens.
   */
  constructor(penalty) {
    super();
    this.penalty = penalty;
  }
  /**
   * Apply the repetition penalty to the logits.
   * @param {Array} input_ids The input IDs.
   * @param {Object} logits The logits.
   * @returns {Object} The logits with repetition penalty processing.
   */
  _call(input_ids, logits) {
    for (const input_id of input_ids) {
      if (logits.data[input_id] < 0) {
        logits.data[input_id] *= this.penalty;
      } else {
        logits.data[input_id] /= this.penalty;
      }
    }
    return logits;
  }
}
class MinLengthLogitsProcessor extends LogitsProcessor {
  /**
   * Create a MinLengthLogitsProcessor.
   * @param {number} min_length The minimum length below which the score of `eos_token_id` is set to negative infinity.
   * @param {number|number[]} eos_token_id The ID/IDs of the end-of-sequence token.
   */
  constructor(min_length, eos_token_id) {
    super();
    this.min_length = min_length;
    this.eos_token_id = Array.isArray(eos_token_id) ? eos_token_id : [eos_token_id];
  }
  /**
   * Apply logit processor.
   * @param {Array} input_ids The input IDs.
   * @param {Object} logits The logits.
   * @returns {Object} The processed logits.
   */
  _call(input_ids, logits) {
    if (input_ids.length < this.min_length) {
      for (const eos_token of this.eos_token_id) {
        logits.data[eos_token] = -Infinity;
      }
    }
    return logits;
  }
}
class MinNewTokensLengthLogitsProcessor extends LogitsProcessor {
  /**
   * Create a MinNewTokensLengthLogitsProcessor.
   * @param {number} prompt_length_to_skip The input tokens length.
   * @param {number} min_new_tokens The minimum *new* tokens length below which the score of `eos_token_id` is set to negative infinity.
   * @param {number|number[]} eos_token_id The ID/IDs of the end-of-sequence token.
   */
  constructor(prompt_length_to_skip, min_new_tokens, eos_token_id) {
    super();
    this.prompt_length_to_skip = prompt_length_to_skip;
    this.min_new_tokens = min_new_tokens;
    this.eos_token_id = Array.isArray(eos_token_id) ? eos_token_id : [eos_token_id];
  }
  /**
   * Apply logit processor.
   * @param {Array} input_ids The input IDs.
   * @param {Object} logits The logits.
   * @returns {Object} The processed logits.
   */
  _call(input_ids, logits) {
    const new_tokens_length = input_ids.length - this.prompt_length_to_skip;
    if (new_tokens_length < this.min_new_tokens) {
      for (const eos_token of this.eos_token_id) {
        logits.data[eos_token] = -Infinity;
      }
    }
    return logits;
  }
}
class NoBadWordsLogitsProcessor extends LogitsProcessor {
  /**
   * Create a `NoBadWordsLogitsProcessor`.
   * @param {number[][]} bad_words_ids List of list of token ids that are not allowed to be generated.
   * @param {number|number[]} eos_token_id The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
   */
  constructor(bad_words_ids, eos_token_id) {
    super();
    this.bad_words_ids = bad_words_ids;
    this.eos_token_id = Array.isArray(eos_token_id) ? eos_token_id : [eos_token_id];
  }
  /**
   * Apply logit processor.
   * @param {Array} input_ids The input IDs.
   * @param {Object} logits The logits.
   * @returns {Object} The processed logits.
   */
  _call(input_ids, logits) {
    for (const bad_word_ids of this.bad_words_ids) {
      let mark = true;
      for (let i = 1; i <= bad_word_ids.length - 1 && bad_word_ids.length < input_ids.length; ++i) {
        if (bad_word_ids.at(-i - 1) !== input_ids.at(-i)) {
          mark = false;
          break;
        }
      }
      if (mark) {
        logits.data[bad_word_ids.at(-1)] = -Infinity;
      }
    }
    return logits;
  }
}
const GenerationConfig = (
  /** @type {any} */
  class {
    /**
     * Create a new GenerationConfig object.
     * @param {GenerationConfigType} kwargs 
     */
    constructor(kwargs = {}) {
      var _a2, _b, _c, _d, _e, _f, _g, _h, _i, _j, _k, _l, _m, _n, _o, _p, _q, _r, _s, _t, _u, _v, _w, _x, _y, _z, _A, _B, _C, _D, _E, _F, _G, _H, _I, _J, _K, _L, _M, _N, _O, _P, _Q, _R;
      this.max_length = (_a2 = kwargs.max_length) != null ? _a2 : 20;
      this.max_new_tokens = (_b = kwargs.max_new_tokens) != null ? _b : null;
      this.min_length = (_c = kwargs.min_length) != null ? _c : 0;
      this.min_new_tokens = (_d = kwargs.min_new_tokens) != null ? _d : null;
      this.early_stopping = (_e = kwargs.early_stopping) != null ? _e : false;
      this.max_time = (_f = kwargs.max_time) != null ? _f : null;
      this.do_sample = (_g = kwargs.do_sample) != null ? _g : false;
      this.num_beams = (_h = kwargs.num_beams) != null ? _h : 1;
      this.num_beam_groups = (_i = kwargs.num_beam_groups) != null ? _i : 1;
      this.penalty_alpha = (_j = kwargs.penalty_alpha) != null ? _j : null;
      this.use_cache = (_k = kwargs.use_cache) != null ? _k : true;
      this.temperature = (_l = kwargs.temperature) != null ? _l : 1;
      this.top_k = (_m = kwargs.top_k) != null ? _m : 50;
      this.top_p = (_n = kwargs.top_p) != null ? _n : 1;
      this.typical_p = (_o = kwargs.typical_p) != null ? _o : 1;
      this.epsilon_cutoff = (_p = kwargs.epsilon_cutoff) != null ? _p : 0;
      this.eta_cutoff = (_q = kwargs.eta_cutoff) != null ? _q : 0;
      this.diversity_penalty = (_r = kwargs.diversity_penalty) != null ? _r : 0;
      this.repetition_penalty = (_s = kwargs.repetition_penalty) != null ? _s : 1;
      this.encoder_repetition_penalty = (_t = kwargs.encoder_repetition_penalty) != null ? _t : 1;
      this.length_penalty = (_u = kwargs.length_penalty) != null ? _u : 1;
      this.no_repeat_ngram_size = (_v = kwargs.no_repeat_ngram_size) != null ? _v : 0;
      this.bad_words_ids = (_w = kwargs.bad_words_ids) != null ? _w : null;
      this.force_words_ids = (_x = kwargs.force_words_ids) != null ? _x : null;
      this.renormalize_logits = (_y = kwargs.renormalize_logits) != null ? _y : false;
      this.constraints = (_z = kwargs.constraints) != null ? _z : null;
      this.forced_bos_token_id = (_A = kwargs.forced_bos_token_id) != null ? _A : null;
      this.forced_eos_token_id = (_B = kwargs.forced_eos_token_id) != null ? _B : null;
      this.remove_invalid_values = (_C = kwargs.remove_invalid_values) != null ? _C : false;
      this.exponential_decay_length_penalty = (_D = kwargs.exponential_decay_length_penalty) != null ? _D : null;
      this.suppress_tokens = (_E = kwargs.suppress_tokens) != null ? _E : null;
      this.begin_suppress_tokens = (_F = kwargs.begin_suppress_tokens) != null ? _F : null;
      this.forced_decoder_ids = (_G = kwargs.forced_decoder_ids) != null ? _G : null;
      this.num_return_sequences = (_H = kwargs.num_return_sequences) != null ? _H : 1;
      this.output_attentions = (_I = kwargs.output_attentions) != null ? _I : false;
      this.output_hidden_states = (_J = kwargs.output_hidden_states) != null ? _J : false;
      this.output_scores = (_K = kwargs.output_scores) != null ? _K : false;
      this.return_dict_in_generate = (_L = kwargs.return_dict_in_generate) != null ? _L : false;
      this.pad_token_id = (_M = kwargs.pad_token_id) != null ? _M : null;
      this.bos_token_id = (_N = kwargs.bos_token_id) != null ? _N : null;
      this.eos_token_id = (_O = kwargs.eos_token_id) != null ? _O : null;
      this.encoder_no_repeat_ngram_size = (_P = kwargs.encoder_no_repeat_ngram_size) != null ? _P : 0;
      this.decoder_start_token_id = (_Q = kwargs.decoder_start_token_id) != null ? _Q : null;
      this.generation_kwargs = (_R = kwargs.generation_kwargs) != null ? _R : {};
    }
  }
);
class Sampler extends Callable {
  /**
   * Creates a new Sampler object with the specified generation config.
   * @param {GenerationConfigType} generation_config The generation config.
   */
  constructor(generation_config) {
    super();
    this.generation_config = generation_config;
  }
  /**
   * Executes the sampler, using the specified logits.
   * @param {Tensor} logits
   * @param {number} index
   * @returns {void}
   */
  _call(logits, index = -1) {
    return this.sample(logits, index);
  }
  /**
   * Abstract method for sampling the logits.
   * @param {Tensor} logits
   * @param {number} index
   * @throws {Error}
   */
  sample(logits, index) {
    throw Error("sample should be implemented in subclasses.");
  }
  /**
   * Returns the specified logits as an array, with temperature applied.
   * @param {Tensor} logits
   * @param {number} index
   * @returns {Float32Array}
   */
  getLogits(logits, index) {
    let vocabSize = logits.dims.at(-1);
    let logs = (
      /** @type {Float32Array} */
      logits.data
    );
    if (index === -1) {
      logs = logs.slice(-vocabSize);
    } else {
      let startIndex = index * vocabSize;
      logs = logs.slice(startIndex, startIndex + vocabSize);
    }
    if (this.generation_config.temperature > 0) {
      logs = logs.map((x) => x / this.generation_config.temperature);
    }
    return logs;
  }
  /**
   * Selects an item randomly based on the specified probabilities.
   * @param {Array} probabilities An array of probabilities to use for selection.
   * @returns {number} The index of the selected item.
   */
  randomSelect(probabilities) {
    let sumProbabilities = probabilities.reduce((acc, curr) => acc + curr, 0);
    let r = Math.random() * sumProbabilities;
    for (let i = 0; i < probabilities.length; ++i) {
      r -= probabilities[i];
      if (r <= 0) {
        return i;
      }
    }
    return 0;
  }
  /**
   * Returns a Sampler object based on the specified options.
   * @param {GenerationConfigType} generation_config An object containing options for the sampler.
   * @returns {Sampler} A Sampler object.
   */
  static getSampler(generation_config) {
    if (generation_config.do_sample) {
      return new MultinomialSampler(generation_config);
    } else if (generation_config.num_beams > 1) {
      return new BeamSearchSampler(generation_config);
    } else {
      if (generation_config.num_return_sequences > 1) {
        throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${generation_config.num_return_sequences}.`);
      }
      return new GreedySampler(generation_config);
    }
  }
}
class GreedySampler extends Sampler {
  /**
   * Sample the maximum probability of a given logits tensor.
   * @param {Tensor} logits
   * @param {number} [index=-1]
   * @returns {Array} An array with a single tuple, containing the index of the maximum value and a meaningless score (since this is a greedy search).
   */
  sample(logits, index = -1) {
    let logs = this.getLogits(logits, index);
    let argmax = max(logs)[1];
    return [
      [argmax, 0]
    ];
  }
}
class MultinomialSampler extends Sampler {
  /**
   * Sample from the logits.
   * @param {Tensor} logits
   * @param {number} index
   * @returns {Array}
   */
  sample(logits, index = -1) {
    let k = logits.dims.at(-1);
    if (this.generation_config.top_k > 0) {
      k = Math.min(this.generation_config.top_k, k);
    }
    const logs = this.getLogits(logits, index);
    const topLogits = getTopItems(logs, k);
    const probabilities = softmax(topLogits.map((x) => x[1]));
    return Array.from({ length: this.generation_config.num_beams }, () => {
      const sampledIndex = this.randomSelect(probabilities);
      return [
        topLogits[sampledIndex][0],
        // token id
        Math.log(probabilities[sampledIndex])
        // score
      ];
    });
  }
}
class BeamSearchSampler extends Sampler {
  /**
   * Sample from the logits.
   * @param {Tensor} logits
   * @param {number} index
   * @returns {Array}
   */
  sample(logits, index = -1) {
    let k = logits.dims.at(-1);
    if (this.generation_config.top_k > 0) {
      k = Math.min(this.generation_config.top_k, k);
    }
    const logs = this.getLogits(logits, index);
    const topLogits = getTopItems(logs, k);
    const probabilities = softmax(topLogits.map((x) => x[1]));
    return Array.from({ length: this.generation_config.num_beams }, (_, i) => {
      return [
        topLogits[i][0],
        // token id
        Math.log(probabilities[i])
        // score
      ];
    });
  }
}
const { InferenceSession, Tensor: ONNXTensor, env } = ONNX;
const MODEL_TYPES = {
  EncoderOnly: 0,
  EncoderDecoder: 1,
  Seq2Seq: 2,
  Vision2Seq: 3,
  DecoderOnly: 4,
  MaskGeneration: 5
};
const MODEL_TYPE_MAPPING = /* @__PURE__ */ new Map();
const MODEL_NAME_TO_CLASS_MAPPING = /* @__PURE__ */ new Map();
const MODEL_CLASS_TO_NAME_MAPPING = /* @__PURE__ */ new Map();
function constructSession(pretrained_model_name_or_path, fileName, options) {
  return __async(this, null, function* () {
    let modelFileName = `onnx/${fileName}${options.quantized ? "_quantized" : ""}.onnx`;
    let buffer = yield getModelFile(pretrained_model_name_or_path, modelFileName, true, options);
    try {
      return yield InferenceSession.create(buffer, {
        executionProviders
      });
    } catch (err) {
      if (executionProviders.length === 1 && executionProviders[0] === "wasm") {
        throw err;
      }
      console.warn(err);
      console.warn(
        "Something went wrong during model construction (most likely a missing operation). Using `wasm` as a fallback. "
      );
      return yield InferenceSession.create(buffer, {
        executionProviders: ["wasm"]
      });
    }
  });
}
function validateInputs(session, inputs) {
  const checkedInputs = /* @__PURE__ */ Object.create(null);
  const missingInputs = [];
  for (const inputName of session.inputNames) {
    const tensor = inputs[inputName];
    if (!(tensor instanceof Tensor)) {
      missingInputs.push(inputName);
      continue;
    }
    checkedInputs[inputName] = env.wasm.proxy ? tensor.clone() : tensor;
  }
  if (missingInputs.length > 0) {
    throw new Error(
      `An error occurred during model execution: "Missing the following inputs: ${missingInputs.join(", ")}.`
    );
  }
  const numInputsProvided = Object.keys(inputs).length;
  const numInputsNeeded = session.inputNames.length;
  if (numInputsProvided > numInputsNeeded) {
    let ignored = Object.keys(inputs).filter((inputName) => !session.inputNames.includes(inputName));
    console.warn(`WARNING: Too many inputs were provided (${numInputsProvided} > ${numInputsNeeded}). The following inputs will be ignored: "${ignored.join(", ")}".`);
  }
  return checkedInputs;
}
function sessionRun(session, inputs) {
  return __async(this, null, function* () {
    const checkedInputs = validateInputs(session, inputs);
    try {
      let output = yield session.run(checkedInputs);
      output = replaceTensors(output);
      return output;
    } catch (e) {
      console.error(`An error occurred during model execution: "${e}".`);
      console.error("Inputs given to model:", checkedInputs);
      throw e;
    }
  });
}
function replaceTensors(obj) {
  for (let prop in obj) {
    if (obj[prop] instanceof ONNXTensor) {
      obj[prop] = new Tensor(obj[prop]);
    } else if (typeof obj[prop] === "object") {
      replaceTensors(obj[prop]);
    }
  }
  return obj;
}
function toI64Tensor(items) {
  if (items instanceof Tensor) {
    return items;
  }
  if (items.length === 0) {
    throw Error("items must be non-empty");
  }
  if (Array.isArray(items[0])) {
    if (items.some((x) => x.length !== items[0].length)) {
      throw Error("Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' and/or 'truncation=True' to have batched tensors with the same length.");
    }
    return new Tensor(
      "int64",
      BigInt64Array.from(items.flat().map((x) => BigInt(x))),
      [items.length, items[0].length]
    );
  } else {
    return new Tensor(
      "int64",
      BigInt64Array.from(items.map((x) => BigInt(x))),
      [1, items.length]
    );
  }
}
function prepareAttentionMask(self2, tokens) {
  var _a2, _b;
  let pad_token_id = (_a2 = self2.config.pad_token_id) != null ? _a2 : null;
  let eos_token_id = (_b = self2.config.eos_token_id) != null ? _b : null;
  if (isIntegralNumber(eos_token_id)) {
    eos_token_id = [eos_token_id];
  }
  let is_pad_token_in_inputs = tokens.indexOf(pad_token_id) !== -1;
  let is_pad_token_not_equal_to_eos_token_id = eos_token_id === null || !eos_token_id.includes(pad_token_id);
  if (is_pad_token_in_inputs && is_pad_token_not_equal_to_eos_token_id) {
    let data = BigInt64Array.from(
      // Note: != so that int matches bigint
      // @ts-ignore
      tokens.data.map((x) => x != pad_token_id)
    );
    return new Tensor("int64", data, tokens.dims);
  } else {
    return ones_like(tokens);
  }
}
function preparePositionIds(session, feeds, use_cache_branch) {
  if (!session.inputNames.includes("position_ids")) return;
  const data = new BigInt64Array(feeds.attention_mask.data.length);
  for (let i = 0; i < feeds.attention_mask.dims[0]; ++i) {
    let start = i * feeds.attention_mask.dims[1];
    let sum = BigInt(0);
    for (let j = 0; j < feeds.attention_mask.dims[1]; ++j) {
      const index = start + j;
      if (feeds.attention_mask.data[index] === /* @__PURE__ */ BigInt("0")) {
        data[index] = BigInt(1);
      } else {
        data[index] = sum;
        sum += feeds.attention_mask.data[index];
      }
    }
  }
  feeds.position_ids = new Tensor("int64", data, feeds.attention_mask.dims);
  if (use_cache_branch) {
    feeds.position_ids = feeds.position_ids.slice(null, -1).unsqueeze_(-1);
  }
}
function boolTensor(value) {
  return new Tensor("bool", [value], [1]);
}
function seq2seqForward(self2, model_inputs) {
  return __async(this, null, function* () {
    let { encoder_outputs, past_key_values } = model_inputs;
    if (!encoder_outputs) {
      encoder_outputs = (yield encoderForward(self2, model_inputs)).last_hidden_state;
    }
    let decoderFeeds = {
      input_ids: model_inputs.decoder_input_ids,
      encoder_hidden_states: encoder_outputs
    };
    const use_cache_branch = !!past_key_values;
    if (self2.decoder_merged_session.inputNames.includes("use_cache_branch")) {
      decoderFeeds.use_cache_branch = boolTensor(use_cache_branch);
    }
    if (self2.decoder_merged_session.inputNames.includes("encoder_attention_mask")) {
      decoderFeeds.encoder_attention_mask = model_inputs.attention_mask;
    }
    preparePositionIds(self2.decoder_merged_session, decoderFeeds, use_cache_branch);
    self2.addPastKeyValues(decoderFeeds, past_key_values);
    const decoderResults = yield sessionRun(self2.decoder_merged_session, decoderFeeds);
    let logits = decoderResults.logits;
    past_key_values = self2.getPastKeyValues(decoderResults, past_key_values);
    const attns = self2.getAttentions(decoderResults);
    return new Seq2SeqLMOutput(__spreadValues({ logits, past_key_values, encoder_outputs }, attns));
  });
}
function seq2seqStartBeams(self2, inputTokenIds, generation_config, numOutputTokens) {
  var _a2, _b, _c, _d;
  let beams = [];
  let beamId = 0;
  const requires_attention_mask = (_a2 = self2.requires_attention_mask) != null ? _a2 : true;
  let decoder_input_ids = (_d = (_c = (_b = generation_config.decoder_input_ids) != null ? _b : generation_config.decoder_start_token_id) != null ? _c : generation_config.bos_token_id) != null ? _d : generation_config.eos_token_id;
  if (decoder_input_ids instanceof Tensor) {
    decoder_input_ids = decoder_input_ids.tolist().flat();
  } else if (!Array.isArray(decoder_input_ids)) {
    decoder_input_ids = [decoder_input_ids];
  }
  for (let tokens of inputTokenIds) {
    tokens.dims = [1, ...tokens.dims];
    let start = {
      inputs: tokens,
      encoder_outputs: null,
      prev_model_outputs: null,
      output_token_ids: decoder_input_ids,
      done: false,
      score: 0,
      id: beamId++
      // assign unique id to beams
    };
    if (requires_attention_mask) {
      start.attention_mask = prepareAttentionMask(self2, tokens);
    }
    beams.push(start);
  }
  return beams;
}
function seq2seqRunBeam(self2, beam) {
  return __async(this, null, function* () {
    var _a2;
    const input_name = self2.main_input_name;
    let decoder_input_ids = beam.output_token_ids;
    if (beam.prev_model_outputs) {
      decoder_input_ids = decoder_input_ids.slice(-1);
    }
    let model_inputs = {
      [input_name]: beam.inputs,
      decoder_input_ids: toI64Tensor(decoder_input_ids),
      encoder_outputs: beam.encoder_outputs,
      past_key_values: (_a2 = beam.prev_model_outputs) == null ? void 0 : _a2.past_key_values
    };
    if (beam.attention_mask) {
      model_inputs.attention_mask = beam.attention_mask;
    }
    let output = yield self2.forward(model_inputs);
    beam.prev_model_outputs = output;
    beam.encoder_outputs = output.encoder_outputs;
    return output;
  });
}
function seq2seqUpdatebeam(beam, newTokenId) {
  beam.output_token_ids = [...beam.output_token_ids, newTokenId];
}
function encoderForward(self2, model_inputs) {
  return __async(this, null, function* () {
    const encoderFeeds = /* @__PURE__ */ Object.create(null);
    for (const key of self2.session.inputNames) {
      encoderFeeds[key] = model_inputs[key];
    }
    if (self2.session.inputNames.includes("token_type_ids") && !encoderFeeds.token_type_ids) {
      encoderFeeds.token_type_ids = new Tensor(
        "int64",
        new BigInt64Array(encoderFeeds.input_ids.data.length),
        encoderFeeds.input_ids.dims
      );
    }
    return yield sessionRun(self2.session, encoderFeeds);
  });
}
function decoderForward(self2, model_inputs) {
  return __async(this, null, function* () {
    let { input_ids, past_key_values, attention_mask } = model_inputs;
    let decoderFeeds = {
      input_ids,
      attention_mask: attention_mask != null ? attention_mask : prepareAttentionMask(self2, input_ids)
    };
    const use_cache_branch = !!past_key_values;
    if (self2.session.inputNames.includes("use_cache_branch")) {
      decoderFeeds.use_cache_branch = boolTensor(use_cache_branch);
    }
    preparePositionIds(self2.session, decoderFeeds, use_cache_branch);
    self2.addPastKeyValues(decoderFeeds, past_key_values);
    let decoderResults = yield sessionRun(self2.session, decoderFeeds);
    let logits = decoderResults.logits;
    past_key_values = self2.getPastKeyValues(decoderResults, past_key_values);
    return { logits, past_key_values };
  });
}
function decoderStartBeams(self2, inputTokenIds, generation_config, numOutputTokens, inputs_attention_mask) {
  let beams = [];
  let beamId = 0;
  for (let tokens of inputTokenIds) {
    let output_token_ids = tokens.tolist().map(Number);
    tokens.dims = [1, ...tokens.dims];
    let attn_mask;
    if (inputs_attention_mask) {
      attn_mask = inputs_attention_mask[beamId];
      attn_mask.dims = [1, ...attn_mask.dims];
    } else {
      attn_mask = prepareAttentionMask(self2, tokens);
    }
    let start = {
      input: tokens,
      model_input_ids: tokens,
      attention_mask: attn_mask,
      prev_model_outputs: null,
      output_token_ids,
      num_output_tokens: numOutputTokens,
      done: false,
      score: 0,
      id: beamId++
      // assign unique id to beams
    };
    beams.push(start);
  }
  return beams;
}
function decoderRunBeam(self2, beam) {
  return __async(this, null, function* () {
    var _a2;
    let attnMaskData = new BigInt64Array(beam.output_token_ids.length).fill(/* @__PURE__ */ BigInt("1"));
    let model_inputs = {
      input_ids: beam.model_input_ids,
      attention_mask: new Tensor(
        "int64",
        attnMaskData,
        [1, attnMaskData.length]
      ),
      past_key_values: (_a2 = beam.prev_model_outputs) == null ? void 0 : _a2.past_key_values
    };
    let output = yield self2.forward(model_inputs);
    beam.prev_model_outputs = output;
    return output;
  });
}
function decoderUpdatebeam(beam, newTokenId) {
  beam.output_token_ids = [...beam.output_token_ids, newTokenId];
  beam.model_input_ids = new Tensor("int64", [BigInt(newTokenId)], [1, 1]);
}
class PreTrainedModel extends Callable {
  /**
   * Creates a new instance of the `PreTrainedModel` class.
   * @param {Object} config The model configuration.
   * @param {any} session session for the model.
   */
  constructor(config, session) {
    super();
    __publicField(this, "main_input_name", "input_ids");
    this.config = config;
    this.session = session;
    const modelName = MODEL_CLASS_TO_NAME_MAPPING.get(this.constructor);
    const modelType = MODEL_TYPE_MAPPING.get(modelName);
    this.can_generate = false;
    this._runBeam = null;
    this._getStartBeams = null;
    this._updateBeam = null;
    this._forward = null;
    if (modelType === MODEL_TYPES.DecoderOnly) {
      this.can_generate = true;
      this._runBeam = decoderRunBeam;
      this._getStartBeams = decoderStartBeams;
      this._updateBeam = decoderUpdatebeam;
      this._forward = decoderForward;
    } else if (modelType === MODEL_TYPES.Seq2Seq || modelType === MODEL_TYPES.Vision2Seq) {
      this.can_generate = true;
      this._runBeam = seq2seqRunBeam;
      this._getStartBeams = seq2seqStartBeams;
      this._updateBeam = seq2seqUpdatebeam;
      this._forward = seq2seqForward;
    } else if (modelType === MODEL_TYPES.EncoderDecoder) {
      this._forward = encoderForward;
    } else {
      this._forward = encoderForward;
    }
  }
  /**
  * Disposes of all the ONNX sessions that were created during inference.
  * @returns {Promise<unknown[]>} An array of promises, one for each ONNX session that is being disposed.
  * @todo Use https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/FinalizationRegistry
  */
  dispose() {
    return __async(this, null, function* () {
      const promises = [];
      for (let key of Object.keys(this)) {
        const item = this[key];
        if (item instanceof InferenceSession) {
          promises.push(item.handler.dispose());
        }
      }
      return yield Promise.all(promises);
    });
  }
  /**
   * Instantiate one of the model classes of the library from a pretrained model.
   * 
   * The model class to instantiate is selected based on the `model_type` property of the config object
   * (either passed as an argument or loaded from `pretrained_model_name_or_path` if possible)
   * 
   * @param {string} pretrained_model_name_or_path The name or path of the pretrained model. Can be either:
   * - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
   *   Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
   *   user or organization name, like `dbmdz/bert-base-german-cased`.
   * - A path to a *directory* containing model weights, e.g., `./my_model_directory/`.
   * @param {import('./utils/hub.js').PretrainedOptions} options Additional options for loading the model.
   * 
   * @returns {Promise<PreTrainedModel>} A new instance of the `PreTrainedModel` class.
   */
  static from_pretrained(_0) {
    return __async(this, arguments, function* (pretrained_model_name_or_path, {
      quantized = true,
      progress_callback = null,
      config = null,
      cache_dir = null,
      local_files_only = false,
      revision = "main",
      model_file_name = null
    } = {}) {
      var _a2, _b;
      let options = {
        quantized,
        progress_callback,
        config,
        cache_dir,
        local_files_only,
        revision,
        model_file_name
      };
      const modelName = MODEL_CLASS_TO_NAME_MAPPING.get(this);
      const modelType = MODEL_TYPE_MAPPING.get(modelName);
      let info;
      if (modelType === MODEL_TYPES.DecoderOnly) {
        info = yield Promise.all([
          AutoConfig.from_pretrained(pretrained_model_name_or_path, options),
          constructSession(pretrained_model_name_or_path, (_a2 = options.model_file_name) != null ? _a2 : "decoder_model_merged", options),
          getModelJSON(pretrained_model_name_or_path, "generation_config.json", false, options)
        ]);
      } else if (modelType === MODEL_TYPES.Seq2Seq || modelType === MODEL_TYPES.Vision2Seq) {
        info = yield Promise.all([
          AutoConfig.from_pretrained(pretrained_model_name_or_path, options),
          constructSession(pretrained_model_name_or_path, "encoder_model", options),
          constructSession(pretrained_model_name_or_path, "decoder_model_merged", options),
          getModelJSON(pretrained_model_name_or_path, "generation_config.json", false, options)
        ]);
      } else if (modelType === MODEL_TYPES.MaskGeneration) {
        info = yield Promise.all([
          AutoConfig.from_pretrained(pretrained_model_name_or_path, options),
          constructSession(pretrained_model_name_or_path, "vision_encoder", options),
          constructSession(pretrained_model_name_or_path, "prompt_encoder_mask_decoder", options)
        ]);
      } else if (modelType === MODEL_TYPES.EncoderDecoder) {
        info = yield Promise.all([
          AutoConfig.from_pretrained(pretrained_model_name_or_path, options),
          constructSession(pretrained_model_name_or_path, "encoder_model", options),
          constructSession(pretrained_model_name_or_path, "decoder_model_merged", options)
        ]);
      } else {
        if (modelType !== MODEL_TYPES.EncoderOnly) {
          console.warn(`Model type for '${modelName != null ? modelName : config == null ? void 0 : config.model_type}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`);
        }
        info = yield Promise.all([
          AutoConfig.from_pretrained(pretrained_model_name_or_path, options),
          constructSession(pretrained_model_name_or_path, (_b = options.model_file_name) != null ? _b : "model", options)
        ]);
      }
      return new this(...info);
    });
  }
  /**
   * Runs the model with the provided inputs
   * @param {Object} model_inputs Object containing input tensors
   * @returns {Promise<Object>} Object containing output tensors
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return yield this.forward(model_inputs);
    });
  }
  /**
   * Forward method for a pretrained model. If not overridden by a subclass, the correct forward method
   * will be chosen based on the model type.
   * @param {Object} model_inputs The input data to the model in the format specified in the ONNX model.
   * @returns {Promise<Object>} The output data from the model in the format specified in the ONNX model.
   * @throws {Error} This method must be implemented in subclasses.
   */
  forward(model_inputs) {
    return __async(this, null, function* () {
      return yield this._forward(this, model_inputs);
    });
  }
  /**
   * @param {import('./utils/generation.js').GenerationConfigType} generation_config 
   * @param {number} input_ids_seq_length The starting sequence length for the input ids.
   * @returns {LogitsProcessorList}
   * @private
   */
  _get_logits_processor(generation_config, input_ids_seq_length, logits_processor = null) {
    const processors = new LogitsProcessorList();
    if (generation_config.repetition_penalty !== null && generation_config.repetition_penalty !== 1) {
      processors.push(new RepetitionPenaltyLogitsProcessor(generation_config.repetition_penalty));
    }
    if (generation_config.no_repeat_ngram_size !== null && generation_config.no_repeat_ngram_size > 0) {
      processors.push(new NoRepeatNGramLogitsProcessor(generation_config.no_repeat_ngram_size));
    }
    if (generation_config.bad_words_ids !== null) {
      processors.push(new NoBadWordsLogitsProcessor(generation_config.bad_words_ids, generation_config.eos_token_id));
    }
    if (generation_config.min_length !== null && generation_config.eos_token_id !== null && generation_config.min_length > 0) {
      processors.push(new MinLengthLogitsProcessor(generation_config.min_length, generation_config.eos_token_id));
    }
    if (generation_config.min_new_tokens !== null && generation_config.eos_token_id !== null && generation_config.min_new_tokens > 0) {
      processors.push(new MinNewTokensLengthLogitsProcessor(
        input_ids_seq_length,
        generation_config.min_new_tokens,
        generation_config.eos_token_id
      ));
    }
    if (generation_config.forced_bos_token_id !== null) {
      processors.push(new ForcedBOSTokenLogitsProcessor(generation_config.forced_bos_token_id));
    }
    if (generation_config.forced_eos_token_id !== null) {
      processors.push(new ForcedEOSTokenLogitsProcessor(
        generation_config.max_length,
        generation_config.forced_eos_token_id
      ));
    }
    if (generation_config.begin_suppress_tokens !== null) {
      let begin_index = input_ids_seq_length > 1 || generation_config.forced_bos_token_id === null ? input_ids_seq_length : input_ids_seq_length + 1;
      if (generation_config.forced_decoder_ids !== null) {
        begin_index += generation_config.forced_decoder_ids[generation_config.forced_decoder_ids.length - 1][0];
      }
      processors.push(new SuppressTokensAtBeginLogitsProcessor(generation_config.begin_suppress_tokens, begin_index));
    }
    if (generation_config.forced_decoder_ids !== null) {
      processors.push(new ForceTokensLogitsProcessor(generation_config.forced_decoder_ids));
    }
    if (logits_processor !== null) {
      processors.extend(logits_processor);
    }
    return processors;
  }
  /**
   * This function merges multiple generation configs together to form a final generation config to be used by the model for text generation.
   * It first creates an empty `GenerationConfig` object, then it applies the model's own `generation_config` property to it. Finally, if a `generation_config` object was passed in the arguments, it overwrites the corresponding properties in the final config with those of the passed config object.
   * @param {import('./utils/generation.js').GenerationConfigType} generation_config A `GenerationConfig` object containing generation parameters.
   * @returns {import('./utils/generation.js').GenerationConfigType} The final generation config object to be used by the model for text generation.
   */
  _get_generation_config(generation_config) {
    let gen_config = new GenerationConfig(this.config);
    if ("generation_config" in this) {
      Object.assign(gen_config, this.generation_config);
    }
    if (generation_config !== null) {
      Object.assign(gen_config, generation_config);
    }
    return gen_config;
  }
  /**
   * @typedef {import('./utils/maths.js').TypedArray} TypedArray
   */
  /**
   * @typedef {{ sequences: Tensor, decoder_attentions: Tensor, cross_attentions: Tensor }} EncoderDecoderOutput
   * @typedef {Object} DecoderOutput
   * 
   * Generates text based on the given inputs and generation configuration using the model.
   * @param {Tensor|Array|TypedArray} inputs An array of input token IDs.
   * @param {Object|GenerationConfig|null} generation_config The generation configuration to use. If null, default configuration will be used.
   * @param {Object|null} logits_processor An optional logits processor to use. If null, a new LogitsProcessorList instance will be created.
   * @param {Object} options options
   * @param {Object} [options.inputs_attention_mask=null] An optional attention mask for the inputs.
   * @returns {Promise<number[][]|EncoderDecoderOutput|DecoderOutput>} An array of generated output sequences, where each sequence is an array of token IDs.
   * @throws {Error} Throws an error if the inputs array is empty.
   */
  generate(_0) {
    return __async(this, arguments, function* (inputs, generation_config = null, logits_processor = null, {
      inputs_attention_mask = null
    } = {}) {
      var _a2, _b, _c, _d, _e;
      if (!this.can_generate) {
        const modelName = MODEL_CLASS_TO_NAME_MAPPING.get(this.constructor);
        let errorMessage = `The current model class (${modelName}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;
        const modelType = this.config.model_type;
        const possibleInfo = (_c = (_b = (_a2 = MODEL_WITH_LM_HEAD_MAPPING_NAMES.get(modelType)) != null ? _a2 : MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES.get(modelType)) != null ? _b : MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES.get(modelType)) != null ? _c : MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES.get(modelType);
        if (possibleInfo) {
          errorMessage += ` Please use the following class instead: '${possibleInfo[0]}'`;
        }
        throw Error(errorMessage);
      }
      if (!(inputs instanceof Tensor) && !isTypedArray(inputs) && !Array.isArray(inputs)) {
        throw Error(`\`inputs\` must be a Tensor, TypedArray, or Array, but is "${inputs.constructor.name}".`);
      }
      let input_ids_seq_length;
      if (this.config.is_encoder_decoder) {
        input_ids_seq_length = 0;
      } else {
        input_ids_seq_length = inputs instanceof Tensor ? inputs.dims.at(-1) : inputs.length;
        if (input_ids_seq_length === 0) {
          throw Error("Must supply a non-empty array of input token ids.");
        }
      }
      generation_config = this._get_generation_config(generation_config);
      logits_processor = logits_processor != null ? logits_processor : new LogitsProcessorList();
      logits_processor = this._get_logits_processor(
        generation_config,
        input_ids_seq_length,
        logits_processor
      );
      let eos_token_ids = generation_config.eos_token_id;
      if (eos_token_ids !== null && !Array.isArray(eos_token_ids)) {
        eos_token_ids = [eos_token_ids];
      }
      let numOutputTokens = 1;
      const maxOutputTokens = numOutputTokens + ((_d = generation_config.max_new_tokens) != null ? _d : Infinity);
      const useMaxLength = Number.isInteger(generation_config.max_length) && ((_e = generation_config.max_new_tokens) != null ? _e : null) === null;
      let sampler = Sampler.getSampler(generation_config);
      let beams = this.getStartBeams(inputs, generation_config, numOutputTokens, inputs_attention_mask);
      while (beams.some((x) => !x.done) && numOutputTokens < maxOutputTokens) {
        let newest_beams = [];
        for (let beam of beams) {
          if (beam.done) {
            newest_beams.push(beam);
            continue;
          }
          if (useMaxLength && beam.output_token_ids.length >= generation_config.max_length) {
            beam.done = true;
            newest_beams.push(beam);
            continue;
          }
          let output = yield this.runBeam(beam);
          if (generation_config.output_attentions) {
            this.addAttentionsToBeam(beam, output);
          }
          if (generation_config.output_scores) ;
          let logits = output.logits.slice(null, -1, null);
          logits_processor(beam.output_token_ids, logits);
          let sampledTokens = sampler(logits);
          for (let [newTokenId, logProb] of sampledTokens) {
            let newBeam = __spreadValues({}, beam);
            this.updateBeam(newBeam, newTokenId);
            newBeam.score += logProb;
            if (eos_token_ids && eos_token_ids.includes(newTokenId)) {
              newBeam.done = true;
            }
            newest_beams.push(newBeam);
          }
        }
        ++numOutputTokens;
        newest_beams = this.groupBeams(newest_beams).map(
          (group) => group.sort((a, b) => b.score - a.score).slice(0, generation_config.num_beams)
          // remove outside beam width
        );
        beams = newest_beams.flat();
        if (generation_config.callback_function) {
          generation_config.callback_function(beams);
        }
      }
      const groupedBeams = this.groupBeams(beams);
      const getFlattened = (key) => groupedBeams.map(
        (batch) => {
          if (generation_config.num_return_sequences > 1) {
            return batch.slice(0, generation_config.num_return_sequences).map((x) => x[key]);
          } else {
            return [batch[0][key]];
          }
        }
      ).flat();
      const sequences = getFlattened("output_token_ids");
      if (generation_config.return_dict_in_generate) {
        const decoder_attentions = getFlattened("decoder_attentions");
        const cross_attentions = getFlattened("cross_attentions");
        return {
          sequences,
          decoder_attentions,
          cross_attentions
        };
      } else {
        return sequences;
      }
    });
  }
  /**
   * Helper function to add attentions to beam
   * @param {Object} beam 
   * @param {Object} output
   * @private 
   */
  addAttentionsToBeam(beam, output) {
    if (this.config.is_encoder_decoder) {
      if (!output.cross_attentions || output.cross_attentions.length === 0) {
        throw Error(
          "`output_attentions` is true, but the model did not produce cross-attentions. This is most likely because the model was not exported with `output_attentions=True`."
        );
      }
      if (!beam.cross_attentions) {
        beam.cross_attentions = [];
      }
      beam.cross_attentions.push(output.cross_attentions);
    }
    if (!output.decoder_attentions || output.decoder_attentions.length === 0) {
      throw Error(
        "`output_attentions` is true, but the model did not produce decoder-attentions. This is most likely because the model was not exported with `output_attentions=True`."
      );
    }
    if (!beam.decoder_attentions) {
      beam.decoder_attentions = [];
    }
    beam.decoder_attentions.push(output.decoder_attentions);
  }
  /**
   * Groups an array of beam objects by their ids.
   *
   * @param {Array} beams The array of beam objects to group.
   * @returns {Array} An array of arrays, where each inner array contains beam objects with the same id.
   */
  groupBeams(beams) {
    const groups = /* @__PURE__ */ Object.create(null);
    for (const obj of beams) {
      if (groups[obj.id] === void 0) {
        groups[obj.id] = [obj];
      } else {
        groups[obj.id].push(obj);
      }
    }
    return Object.values(groups);
  }
  /**
   * Returns an object containing past key values from the given decoder results object.
   *
   * @param {Object} decoderResults The decoder results object.
   * @param {Object} pastKeyValues The previous past key values.
   * @returns {Object} An object containing past key values.
   */
  getPastKeyValues(decoderResults, pastKeyValues) {
    const pkvs = /* @__PURE__ */ Object.create(null);
    for (const name2 in decoderResults) {
      if (name2.startsWith("present")) {
        let newName = name2.replace("present", "past_key_values");
        if (pastKeyValues && name2.includes("encoder")) {
          pkvs[newName] = pastKeyValues[newName];
        } else {
          pkvs[newName] = decoderResults[name2];
        }
      }
    }
    return pkvs;
  }
  /**
   * Returns an object containing attentions from the given decoder results object.
   *
   * @param {Object} decoderResults The decoder results object.
   * @returns {Object} An object containing attentions.
   */
  getAttentions(decoderResults) {
    const attns = /* @__PURE__ */ Object.create(null);
    for (const attnName of ["cross_attentions", "decoder_attentions"]) {
      const result = [];
      for (const name2 in decoderResults) {
        if (name2.startsWith(attnName)) {
          const index = name2.split(".").pop();
          result[index] = decoderResults[name2];
        }
      }
      attns[attnName] = result;
    }
    return attns;
  }
  /**
   * Adds past key values to the decoder feeds object. If pastKeyValues is null, creates new tensors for past key values.
   *
   * @param {Object} decoderFeeds The decoder feeds object to add past key values to.
   * @param {Object} pastKeyValues An object containing past key values.
   */
  addPastKeyValues(decoderFeeds, pastKeyValues) {
    var _a2;
    if (pastKeyValues) {
      Object.assign(decoderFeeds, pastKeyValues);
    } else {
      const batch_size = 1;
      if (this.config.is_encoder_decoder && ((_a2 = this.add_encoder_pkv) != null ? _a2 : true)) {
        let encoder_dims = [batch_size, this.num_encoder_heads, 0, this.encoder_dim_kv];
        let decoder_dims = [batch_size, this.num_decoder_heads, 0, this.decoder_dim_kv];
        for (let i = 0; i < this.num_decoder_layers; ++i) {
          decoderFeeds[`past_key_values.${i}.encoder.key`] = new Tensor("float32", [], encoder_dims);
          decoderFeeds[`past_key_values.${i}.encoder.value`] = new Tensor("float32", [], encoder_dims);
          decoderFeeds[`past_key_values.${i}.decoder.key`] = new Tensor("float32", [], decoder_dims);
          decoderFeeds[`past_key_values.${i}.decoder.value`] = new Tensor("float32", [], decoder_dims);
        }
      } else if (this.config.model_type === "falcon") {
        let dims = [batch_size * this.num_heads, 0, this.dim_kv];
        for (let i = 0; i < this.num_layers; ++i) {
          decoderFeeds[`past_key_values.${i}.key`] = new Tensor("float32", [], dims);
          decoderFeeds[`past_key_values.${i}.value`] = new Tensor("float32", [], dims);
        }
      } else if (this.config.multi_query) {
        let dims = [batch_size * this.num_heads, 0, 2 * this.dim_kv];
        for (let i = 0; i < this.num_layers; ++i) {
          decoderFeeds[`past_key_values.${i}.key_value`] = new Tensor("float32", [], dims);
        }
      } else if (this.config.model_type === "bloom") {
        let keyDims = [batch_size * this.num_heads, this.dim_kv, 0];
        let valueDims = [batch_size * this.num_heads, 0, this.dim_kv];
        for (let i = 0; i < this.num_layers; ++i) {
          decoderFeeds[`past_key_values.${i}.key`] = new Tensor("float32", [], keyDims);
          decoderFeeds[`past_key_values.${i}.value`] = new Tensor("float32", [], valueDims);
        }
      } else {
        let dims = [batch_size, this.num_heads, 0, this.dim_kv];
        for (let i = 0; i < this.num_layers; ++i) {
          decoderFeeds[`past_key_values.${i}.key`] = new Tensor("float32", [], dims);
          decoderFeeds[`past_key_values.${i}.value`] = new Tensor("float32", [], dims);
        }
      }
    }
  }
  /**
   * Initializes and returns the beam for text generation task
   * @param {Tensor} inputTokenIds The input token ids.
   * @param {Object} generation_config The generation config.
   * @param {number} numOutputTokens The number of tokens to be generated.
   * @param {Tensor} inputs_attention_mask Optional input attention mask.
   * @returns {any} A Beam object representing the initialized beam.
   * @private
   */
  getStartBeams(inputTokenIds, generation_config, numOutputTokens, inputs_attention_mask) {
    return this._getStartBeams(this, inputTokenIds, generation_config, numOutputTokens, inputs_attention_mask);
  }
  /**
   * Runs a single step of the beam search generation algorithm.
   * @param {any} beam The current beam being generated.
   * @returns {Promise<any>} The updated beam after a single generation step.
   * @private
   */
  runBeam(beam) {
    return __async(this, null, function* () {
      return yield this._runBeam(this, beam);
    });
  }
  /**
   * Update a beam with a new token ID.
   * @param {Object} beam The beam to update.
   * @param {number} newTokenId The new token ID to add to the beam's output.
   * @private
   */
  updateBeam(beam, newTokenId) {
    return this._updateBeam(beam, newTokenId);
  }
}
class ModelOutput {
}
class BertPreTrainedModel extends PreTrainedModel {
}
class BertModel extends BertPreTrainedModel {
}
class BertForMaskedLM extends BertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<MaskedLMOutput>} An object containing the model's output logits for masked language modeling.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new MaskedLMOutput(yield __superGet(BertForMaskedLM.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class BertForSequenceClassification extends BertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} An object containing the model's output logits for sequence classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(BertForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class BertForTokenClassification extends BertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for token classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new TokenClassifierOutput(yield __superGet(BertForTokenClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class BertForQuestionAnswering extends BertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<QuestionAnsweringModelOutput>} An object containing the model's output logits for question answering.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new QuestionAnsweringModelOutput(yield __superGet(BertForQuestionAnswering.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class NomicBertPreTrainedModel extends PreTrainedModel {
}
class NomicBertModel extends NomicBertPreTrainedModel {
}
class RoFormerPreTrainedModel extends PreTrainedModel {
}
class RoFormerModel extends RoFormerPreTrainedModel {
}
class RoFormerForMaskedLM extends RoFormerPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<MaskedLMOutput>} An object containing the model's output logits for masked language modeling.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new MaskedLMOutput(yield __superGet(RoFormerForMaskedLM.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class RoFormerForSequenceClassification extends RoFormerPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} An object containing the model's output logits for sequence classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(RoFormerForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class RoFormerForTokenClassification extends RoFormerPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for token classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new TokenClassifierOutput(yield __superGet(RoFormerForTokenClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class RoFormerForQuestionAnswering extends RoFormerPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<QuestionAnsweringModelOutput>} An object containing the model's output logits for question answering.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new QuestionAnsweringModelOutput(yield __superGet(RoFormerForQuestionAnswering.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class ConvBertPreTrainedModel extends PreTrainedModel {
}
class ConvBertModel extends ConvBertPreTrainedModel {
}
class ConvBertForMaskedLM extends ConvBertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<MaskedLMOutput>} An object containing the model's output logits for masked language modeling.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new MaskedLMOutput(yield __superGet(ConvBertForMaskedLM.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class ConvBertForSequenceClassification extends ConvBertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} An object containing the model's output logits for sequence classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(ConvBertForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class ConvBertForTokenClassification extends ConvBertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for token classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new TokenClassifierOutput(yield __superGet(ConvBertForTokenClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class ConvBertForQuestionAnswering extends ConvBertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<QuestionAnsweringModelOutput>} An object containing the model's output logits for question answering.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new QuestionAnsweringModelOutput(yield __superGet(ConvBertForQuestionAnswering.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class ElectraPreTrainedModel extends PreTrainedModel {
}
class ElectraModel extends ElectraPreTrainedModel {
}
class ElectraForMaskedLM extends ElectraPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<MaskedLMOutput>} An object containing the model's output logits for masked language modeling.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new MaskedLMOutput(yield __superGet(ElectraForMaskedLM.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class ElectraForSequenceClassification extends ElectraPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} An object containing the model's output logits for sequence classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(ElectraForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class ElectraForTokenClassification extends ElectraPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for token classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new TokenClassifierOutput(yield __superGet(ElectraForTokenClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class ElectraForQuestionAnswering extends ElectraPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<QuestionAnsweringModelOutput>} An object containing the model's output logits for question answering.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new QuestionAnsweringModelOutput(yield __superGet(ElectraForQuestionAnswering.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class CamembertPreTrainedModel extends PreTrainedModel {
}
class CamembertModel extends CamembertPreTrainedModel {
}
class CamembertForMaskedLM extends CamembertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<MaskedLMOutput>} An object containing the model's output logits for masked language modeling.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new MaskedLMOutput(yield __superGet(CamembertForMaskedLM.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class CamembertForSequenceClassification extends CamembertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} An object containing the model's output logits for sequence classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(CamembertForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class CamembertForTokenClassification extends CamembertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for token classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new TokenClassifierOutput(yield __superGet(CamembertForTokenClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class CamembertForQuestionAnswering extends CamembertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<QuestionAnsweringModelOutput>} An object containing the model's output logits for question answering.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new QuestionAnsweringModelOutput(yield __superGet(CamembertForQuestionAnswering.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class DebertaPreTrainedModel extends PreTrainedModel {
}
class DebertaModel extends DebertaPreTrainedModel {
}
class DebertaForMaskedLM extends DebertaPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<MaskedLMOutput>} An object containing the model's output logits for masked language modeling.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new MaskedLMOutput(yield __superGet(DebertaForMaskedLM.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class DebertaForSequenceClassification extends DebertaPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} An object containing the model's output logits for sequence classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(DebertaForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class DebertaForTokenClassification extends DebertaPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for token classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new TokenClassifierOutput(yield __superGet(DebertaForTokenClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class DebertaForQuestionAnswering extends DebertaPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<QuestionAnsweringModelOutput>} An object containing the model's output logits for question answering.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new QuestionAnsweringModelOutput(yield __superGet(DebertaForQuestionAnswering.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class DebertaV2PreTrainedModel extends PreTrainedModel {
}
class DebertaV2Model extends DebertaV2PreTrainedModel {
}
class DebertaV2ForMaskedLM extends DebertaV2PreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<MaskedLMOutput>} An object containing the model's output logits for masked language modeling.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new MaskedLMOutput(yield __superGet(DebertaV2ForMaskedLM.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class DebertaV2ForSequenceClassification extends DebertaV2PreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} An object containing the model's output logits for sequence classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(DebertaV2ForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class DebertaV2ForTokenClassification extends DebertaV2PreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for token classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new TokenClassifierOutput(yield __superGet(DebertaV2ForTokenClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class DebertaV2ForQuestionAnswering extends DebertaV2PreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<QuestionAnsweringModelOutput>} An object containing the model's output logits for question answering.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new QuestionAnsweringModelOutput(yield __superGet(DebertaV2ForQuestionAnswering.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class DistilBertPreTrainedModel extends PreTrainedModel {
}
class DistilBertModel extends DistilBertPreTrainedModel {
}
class DistilBertForSequenceClassification extends DistilBertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} An object containing the model's output logits for sequence classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(DistilBertForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class DistilBertForTokenClassification extends DistilBertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for token classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new TokenClassifierOutput(yield __superGet(DistilBertForTokenClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class DistilBertForQuestionAnswering extends DistilBertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<QuestionAnsweringModelOutput>} An object containing the model's output logits for question answering.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new QuestionAnsweringModelOutput(yield __superGet(DistilBertForQuestionAnswering.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class DistilBertForMaskedLM extends DistilBertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<MaskedLMOutput>} returned object
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new MaskedLMOutput(yield __superGet(DistilBertForMaskedLM.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class EsmPreTrainedModel extends PreTrainedModel {
}
class EsmModel extends EsmPreTrainedModel {
}
class EsmForMaskedLM extends EsmPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<MaskedLMOutput>} An object containing the model's output logits for masked language modeling.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new MaskedLMOutput(yield __superGet(EsmForMaskedLM.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class EsmForSequenceClassification extends EsmPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} An object containing the model's output logits for sequence classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(EsmForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class EsmForTokenClassification extends EsmPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for token classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new TokenClassifierOutput(yield __superGet(EsmForTokenClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class MobileBertPreTrainedModel extends PreTrainedModel {
}
class MobileBertModel extends MobileBertPreTrainedModel {
}
class MobileBertForMaskedLM extends MobileBertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<MaskedLMOutput>} returned object
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new MaskedLMOutput(yield __superGet(MobileBertForMaskedLM.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class MobileBertForSequenceClassification extends MobileBertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} returned object
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(MobileBertForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class MobileBertForQuestionAnswering extends MobileBertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<QuestionAnsweringModelOutput>} returned object
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new QuestionAnsweringModelOutput(yield __superGet(MobileBertForQuestionAnswering.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class MPNetPreTrainedModel extends PreTrainedModel {
}
class MPNetModel extends MPNetPreTrainedModel {
}
class MPNetForMaskedLM extends MPNetPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<MaskedLMOutput>} An object containing the model's output logits for masked language modeling.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new MaskedLMOutput(yield __superGet(MPNetForMaskedLM.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class MPNetForSequenceClassification extends MPNetPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} An object containing the model's output logits for sequence classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(MPNetForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class MPNetForTokenClassification extends MPNetPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for token classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new TokenClassifierOutput(yield __superGet(MPNetForTokenClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class MPNetForQuestionAnswering extends MPNetPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<QuestionAnsweringModelOutput>} An object containing the model's output logits for question answering.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new QuestionAnsweringModelOutput(yield __superGet(MPNetForQuestionAnswering.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class SqueezeBertPreTrainedModel extends PreTrainedModel {
}
class SqueezeBertModel extends SqueezeBertPreTrainedModel {
}
class SqueezeBertForMaskedLM extends SqueezeBertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<MaskedLMOutput>} returned object
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new MaskedLMOutput(yield __superGet(SqueezeBertForMaskedLM.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class SqueezeBertForSequenceClassification extends SqueezeBertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} returned object
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(SqueezeBertForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class SqueezeBertForQuestionAnswering extends SqueezeBertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<QuestionAnsweringModelOutput>} returned object
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new QuestionAnsweringModelOutput(yield __superGet(SqueezeBertForQuestionAnswering.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class AlbertPreTrainedModel extends PreTrainedModel {
}
class AlbertModel extends AlbertPreTrainedModel {
}
class AlbertForSequenceClassification extends AlbertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} returned object
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(AlbertForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class AlbertForQuestionAnswering extends AlbertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<QuestionAnsweringModelOutput>} returned object
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new QuestionAnsweringModelOutput(yield __superGet(AlbertForQuestionAnswering.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class AlbertForMaskedLM extends AlbertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<MaskedLMOutput>} returned object
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new MaskedLMOutput(yield __superGet(AlbertForMaskedLM.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class T5PreTrainedModel extends PreTrainedModel {
}
class T5Model extends T5PreTrainedModel {
}
class T5ForConditionalGeneration extends T5PreTrainedModel {
  /**
   * Creates a new instance of the `T5ForConditionalGeneration` class.
   * @param {Object} config The model configuration.
   * @param {any} session session for the model.
   * @param {any} decoder_merged_session session for the decoder.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, decoder_merged_session, generation_config) {
    super(config, session);
    this.decoder_merged_session = decoder_merged_session;
    this.generation_config = generation_config;
    this.num_decoder_layers = this.config.num_decoder_layers;
    this.num_decoder_heads = this.config.num_heads;
    this.decoder_dim_kv = this.config.d_kv;
    this.num_encoder_layers = this.config.num_layers;
    this.num_encoder_heads = this.config.num_heads;
    this.encoder_dim_kv = this.config.d_kv;
  }
}
class LongT5PreTrainedModel extends PreTrainedModel {
}
class LongT5Model extends LongT5PreTrainedModel {
}
class LongT5ForConditionalGeneration extends LongT5PreTrainedModel {
  /**
   * Creates a new instance of the `LongT5ForConditionalGeneration` class.
   * @param {Object} config The model configuration.
   * @param {any} session session for the model.
   * @param {any} decoder_merged_session session for the decoder.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, decoder_merged_session, generation_config) {
    super(config, session);
    this.decoder_merged_session = decoder_merged_session;
    this.generation_config = generation_config;
    this.num_decoder_layers = this.config.num_decoder_layers;
    this.num_decoder_heads = this.config.num_heads;
    this.decoder_dim_kv = this.config.d_kv;
    this.num_encoder_layers = this.config.num_layers;
    this.num_encoder_heads = this.config.num_heads;
    this.encoder_dim_kv = this.config.d_kv;
  }
}
class MT5PreTrainedModel extends PreTrainedModel {
}
class MT5Model extends MT5PreTrainedModel {
}
class MT5ForConditionalGeneration extends MT5PreTrainedModel {
  /**
   * Creates a new instance of the `MT5ForConditionalGeneration` class.
   * @param {any} config The model configuration.
   * @param {any} session The ONNX session containing the encoder weights.
   * @param {any} decoder_merged_session The ONNX session containing the merged decoder weights.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, decoder_merged_session, generation_config) {
    super(config, session);
    this.decoder_merged_session = decoder_merged_session;
    this.generation_config = generation_config;
    this.num_decoder_layers = this.config.num_decoder_layers;
    this.num_decoder_heads = this.config.num_heads;
    this.decoder_dim_kv = this.config.d_kv;
    this.num_encoder_layers = this.config.num_layers;
    this.num_encoder_heads = this.config.num_heads;
    this.encoder_dim_kv = this.config.d_kv;
  }
}
class BartPretrainedModel extends PreTrainedModel {
}
class BartModel extends BartPretrainedModel {
}
class BartForConditionalGeneration extends BartPretrainedModel {
  /**
   * Creates a new instance of the `BartForConditionalGeneration` class.
   * @param {Object} config The configuration object for the Bart model.
   * @param {Object} session The ONNX session used to execute the model.
   * @param {Object} decoder_merged_session The ONNX session used to execute the decoder.
   * @param {Object} generation_config The generation configuration object.
   */
  constructor(config, session, decoder_merged_session, generation_config) {
    super(config, session);
    this.decoder_merged_session = decoder_merged_session;
    this.generation_config = generation_config;
    this.num_decoder_layers = this.config.decoder_layers;
    this.num_decoder_heads = this.config.decoder_attention_heads;
    this.decoder_dim_kv = this.config.d_model / this.num_decoder_heads;
    this.num_encoder_layers = this.config.encoder_layers;
    this.num_encoder_heads = this.config.encoder_attention_heads;
    this.encoder_dim_kv = this.config.d_model / this.num_encoder_heads;
  }
}
class BartForSequenceClassification extends BartPretrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} An object containing the model's output logits for sequence classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(BartForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class MBartPreTrainedModel extends PreTrainedModel {
}
class MBartModel extends MBartPreTrainedModel {
}
class MBartForConditionalGeneration extends MBartPreTrainedModel {
  /**
   * Creates a new instance of the `MBartForConditionalGeneration` class.
   * @param {Object} config The configuration object for the Bart model.
   * @param {Object} session The ONNX session used to execute the model.
   * @param {Object} decoder_merged_session The ONNX session used to execute the decoder.
   * @param {Object} generation_config The generation configuration object.
   */
  constructor(config, session, decoder_merged_session, generation_config) {
    super(config, session);
    this.decoder_merged_session = decoder_merged_session;
    this.generation_config = generation_config;
    this.num_decoder_layers = this.config.decoder_layers;
    this.num_decoder_heads = this.config.decoder_attention_heads;
    this.decoder_dim_kv = this.config.d_model / this.num_decoder_heads;
    this.num_encoder_layers = this.config.encoder_layers;
    this.num_encoder_heads = this.config.encoder_attention_heads;
    this.encoder_dim_kv = this.config.d_model / this.num_encoder_heads;
  }
}
class MBartForSequenceClassification extends MBartPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} An object containing the model's output logits for sequence classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(MBartForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class MBartForCausalLM extends MBartPreTrainedModel {
  /**
   * Creates a new instance of the `MBartForCausalLM` class.
   * @param {Object} config Configuration object for the model.
   * @param {Object} decoder_merged_session ONNX Session object for the decoder.
   * @param {Object} generation_config Configuration object for the generation process.
   */
  constructor(config, decoder_merged_session, generation_config) {
    super(config, decoder_merged_session);
    this.generation_config = generation_config;
    this.num_decoder_layers = this.config.decoder_layers;
    this.num_decoder_heads = this.config.decoder_attention_heads;
    this.decoder_dim_kv = this.config.d_model / this.num_decoder_heads;
    this.num_encoder_layers = this.config.encoder_layers;
    this.num_encoder_heads = this.config.encoder_attention_heads;
    this.encoder_dim_kv = this.config.d_model / this.num_encoder_heads;
  }
}
class BlenderbotPreTrainedModel extends PreTrainedModel {
}
class BlenderbotModel extends BlenderbotPreTrainedModel {
}
class BlenderbotForConditionalGeneration extends BlenderbotPreTrainedModel {
  /**
   * Creates a new instance of the `BlenderbotForConditionalGeneration` class.
   * @param {any} config The model configuration.
   * @param {any} session The ONNX session containing the encoder weights.
   * @param {any} decoder_merged_session The ONNX session containing the merged decoder weights.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, decoder_merged_session, generation_config) {
    super(config, session);
    this.decoder_merged_session = decoder_merged_session;
    this.generation_config = generation_config;
    this.num_decoder_layers = this.config.decoder_layers;
    this.num_decoder_heads = this.config.decoder_attention_heads;
    this.decoder_dim_kv = this.config.d_model / this.num_decoder_heads;
    this.num_encoder_layers = this.config.encoder_layers;
    this.num_encoder_heads = this.config.encoder_attention_heads;
    this.encoder_dim_kv = this.config.d_model / this.num_encoder_heads;
  }
}
class BlenderbotSmallPreTrainedModel extends PreTrainedModel {
}
class BlenderbotSmallModel extends BlenderbotSmallPreTrainedModel {
}
class BlenderbotSmallForConditionalGeneration extends BlenderbotSmallPreTrainedModel {
  /**
   * Creates a new instance of the `BlenderbotForConditionalGeneration` class.
   * @param {any} config The model configuration.
   * @param {any} session The ONNX session containing the encoder weights.
   * @param {any} decoder_merged_session The ONNX session containing the merged decoder weights.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, decoder_merged_session, generation_config) {
    super(config, session);
    this.decoder_merged_session = decoder_merged_session;
    this.generation_config = generation_config;
    this.num_decoder_layers = this.config.decoder_layers;
    this.num_decoder_heads = this.config.decoder_attention_heads;
    this.decoder_dim_kv = this.config.d_model / this.num_decoder_heads;
    this.num_encoder_layers = this.config.encoder_layers;
    this.num_encoder_heads = this.config.encoder_attention_heads;
    this.encoder_dim_kv = this.config.d_model / this.num_encoder_heads;
  }
}
class RobertaPreTrainedModel extends PreTrainedModel {
}
class RobertaModel extends RobertaPreTrainedModel {
}
class RobertaForMaskedLM extends RobertaPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<MaskedLMOutput>} returned object
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new MaskedLMOutput(yield __superGet(RobertaForMaskedLM.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class RobertaForSequenceClassification extends RobertaPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} returned object
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(RobertaForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class RobertaForTokenClassification extends RobertaPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for token classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new TokenClassifierOutput(yield __superGet(RobertaForTokenClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class RobertaForQuestionAnswering extends RobertaPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<QuestionAnsweringModelOutput>} returned object
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new QuestionAnsweringModelOutput(yield __superGet(RobertaForQuestionAnswering.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class XLMPreTrainedModel extends PreTrainedModel {
}
class XLMModel extends XLMPreTrainedModel {
}
class XLMWithLMHeadModel extends XLMPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<MaskedLMOutput>} returned object
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new MaskedLMOutput(yield __superGet(XLMWithLMHeadModel.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class XLMForSequenceClassification extends XLMPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} returned object
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(XLMForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class XLMForTokenClassification extends XLMPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for token classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new TokenClassifierOutput(yield __superGet(XLMForTokenClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class XLMForQuestionAnswering extends XLMPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<QuestionAnsweringModelOutput>} returned object
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new QuestionAnsweringModelOutput(yield __superGet(XLMForQuestionAnswering.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class XLMRobertaPreTrainedModel extends PreTrainedModel {
}
class XLMRobertaModel extends XLMRobertaPreTrainedModel {
}
class XLMRobertaForMaskedLM extends XLMRobertaPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<MaskedLMOutput>} returned object
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new MaskedLMOutput(yield __superGet(XLMRobertaForMaskedLM.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class XLMRobertaForSequenceClassification extends XLMRobertaPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} returned object
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(XLMRobertaForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class XLMRobertaForTokenClassification extends XLMRobertaPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for token classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new TokenClassifierOutput(yield __superGet(XLMRobertaForTokenClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class XLMRobertaForQuestionAnswering extends XLMRobertaPreTrainedModel {
  /**
   * Calls the model on new inputs.
   *
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<QuestionAnsweringModelOutput>} returned object
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new QuestionAnsweringModelOutput(yield __superGet(XLMRobertaForQuestionAnswering.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class ASTPreTrainedModel extends PreTrainedModel {
}
class ASTModel extends ASTPreTrainedModel {
}
class ASTForAudioClassification extends ASTPreTrainedModel {
}
class WhisperPreTrainedModel extends PreTrainedModel {
}
class WhisperModel extends WhisperPreTrainedModel {
}
class WhisperForConditionalGeneration extends WhisperPreTrainedModel {
  /**
   * Creates a new instance of the `WhisperForConditionalGeneration` class.
   * @param {Object} config Configuration object for the model.
   * @param {Object} session ONNX Session object for the model.
   * @param {Object} decoder_merged_session ONNX Session object for the decoder.
   * @param {Object} generation_config Configuration object for the generation process.
   */
  constructor(config, session, decoder_merged_session, generation_config) {
    super(config, session);
    __publicField(this, "requires_attention_mask", false);
    __publicField(this, "main_input_name", "input_features");
    this.decoder_merged_session = decoder_merged_session;
    this.generation_config = generation_config;
    this.num_decoder_layers = this.config.decoder_layers;
    this.num_decoder_heads = this.config.decoder_attention_heads;
    this.decoder_dim_kv = this.config.d_model / this.num_decoder_heads;
    this.num_encoder_layers = this.config.encoder_layers;
    this.num_encoder_heads = this.config.encoder_attention_heads;
    this.encoder_dim_kv = this.config.d_model / this.num_encoder_heads;
  }
  /**
   * @typedef {Object} WhisperGenerationConfig
   * @extends GenerationConfig
   * @property {boolean} [return_timestamps=null] Whether to return the timestamps with the text. This enables the `WhisperTimestampsLogitsProcessor`.
   * @property {boolean} [return_token_timestamps=null] Whether to return token-level timestamps
   * with the text. This can be used with or without the `return_timestamps` option. To get word-level
   * timestamps, use the tokenizer to group the tokens into words.
   * @property {number} [num_frames=null]  The number of audio frames available in this chunk. This is only used generating word-level timestamps.
   */
  /**
   * Generates outputs based on input and generation configuration.
   * @param {Object} inputs Input data for the model.
   * @param {WhisperGenerationConfig} generation_config Configuration object for the generation process.
   * @param {Object} logits_processor Optional logits processor object.
   * @returns {Promise<Object>} Promise object represents the generated outputs.
   */
  generate(inputs, generation_config = null, logits_processor = null) {
    return __async(this, null, function* () {
      var _a2;
      generation_config = this._get_generation_config(generation_config);
      (_a2 = generation_config.return_timestamps) != null ? _a2 : generation_config.return_timestamps = false;
      if (generation_config.return_timestamps) {
        logits_processor = [new WhisperTimeStampLogitsProcessor(generation_config)];
      }
      if (generation_config.return_token_timestamps) {
        generation_config.output_attentions = true;
        generation_config.return_dict_in_generate = true;
        if (generation_config.task === "translate") {
          console.warn("Token-level timestamps may not be reliable for task 'translate'.");
        }
        if (!generation_config.alignment_heads) {
          throw new Error(
            "Model generation config has no `alignment_heads`, token-level timestamps not available. See https://gist.github.com/hollance/42e32852f24243b748ae6bc1f985b13a on how to add this property to the generation config."
          );
        }
      }
      const outputs = yield __superGet(WhisperForConditionalGeneration.prototype, this, "generate").call(this, inputs, generation_config, logits_processor);
      if (generation_config.return_token_timestamps && generation_config.alignment_heads) {
        outputs["token_timestamps"] = this._extract_token_timestamps(
          outputs,
          generation_config.alignment_heads,
          generation_config.num_frames
        );
      }
      return outputs;
    });
  }
  /**
   * Calculates token-level timestamps using the encoder-decoder cross-attentions and
   * dynamic time-warping (DTW) to map each output token to a position in the input audio.
   * @param {Object} generate_outputs Outputs generated by the model
   * @param {Tensor[][][]} generate_outputs.cross_attentions The cross attentions output by the model
   * @param {Tensor[][][]} generate_outputs.decoder_attentions The decoder attentions output by the model
   * @param {number[][]} generate_outputs.sequences The sequences output by the model
   * @param {number[][]} alignment_heads Alignment heads of the model
   * @param {number} [num_frames=null] Number of frames in the input audio.
   * @param {number} [time_precision=0.02] Precision of the timestamps in seconds
   * @returns {Tensor} tensor containing the timestamps in seconds for each predicted token
   */
  _extract_token_timestamps(generate_outputs, alignment_heads, num_frames = null, time_precision = 0.02) {
    if (!generate_outputs.cross_attentions) {
      throw new Error(
        "Model outputs must contain cross attentions to extract timestamps. This is most likely because the model was not exported with `output_attentions=True`."
      );
    }
    let median_filter_width = this.config.median_filter_width;
    if (median_filter_width === void 0) {
      console.warn("Model config has no `median_filter_width`, using default value of 7.");
      median_filter_width = 7;
    }
    const batchedMatrices = generate_outputs.cross_attentions.map((batch) => {
      let cross_attentions = Array.from(
        { length: this.config.decoder_layers },
        (_, i) => cat(batch.map((x) => x[i]), 2)
      );
      let weights = stack(alignment_heads.map(([l, h]) => {
        return num_frames ? cross_attentions[l].slice(null, h, null, [0, num_frames]) : cross_attentions[l].slice(null, h);
      }));
      weights = weights.transpose(1, 0, 2, 3);
      let [std, calculatedMean] = std_mean(weights, -2, 0, true);
      let smoothedWeights = weights.clone();
      for (let a = 0; a < smoothedWeights.dims[0]; ++a) {
        let aTensor = smoothedWeights[a];
        for (let b = 0; b < aTensor.dims[0]; ++b) {
          let bTensor = aTensor[b];
          const stdTensor = std[a][b][0];
          const meanTensor = calculatedMean[a][b][0];
          for (let c = 0; c < bTensor.dims[0]; ++c) {
            let cTensor = bTensor[c];
            for (let d = 0; d < cTensor.data.length; ++d) {
              cTensor.data[d] = (cTensor.data[d] - meanTensor.data[d]) / stdTensor.data[d];
            }
            cTensor.data.set(medianFilter(cTensor.data, median_filter_width));
          }
        }
      }
      const matrix = mean(smoothedWeights, 1);
      return matrix;
    });
    const timestampsShape = [generate_outputs.sequences.length, generate_outputs.sequences[0].length];
    const timestamps = new Tensor(
      "float32",
      new Float32Array(timestampsShape[0] * timestampsShape[1]),
      timestampsShape
    );
    for (let batch_idx = 0; batch_idx < timestampsShape[0]; ++batch_idx) {
      const matrix = batchedMatrices[batch_idx].neg().squeeze_(0);
      let [text_indices, time_indices] = dynamicTimeWarping(matrix);
      let diffs = Array.from({ length: text_indices.length - 1 }, (v, i) => text_indices[i + 1] - text_indices[i]);
      let jumps = mergeArrays([1], diffs).map((x) => !!x);
      let jump_times = [];
      for (let i = 0; i < jumps.length; ++i) {
        if (jumps[i]) {
          jump_times.push(time_indices[i] * time_precision);
        }
      }
      timestamps[batch_idx].data.set(jump_times, 1);
    }
    return timestamps;
  }
}
class VisionEncoderDecoderModel extends PreTrainedModel {
  /**
   * Creates a new instance of the `VisionEncoderDecoderModel` class.
   * @param {Object} config The configuration object specifying the hyperparameters and other model settings.
   * @param {Object} session The ONNX session containing the encoder model.
   * @param {any} decoder_merged_session The ONNX session containing the merged decoder model.
   * @param {Object} generation_config Configuration object for the generation process.
   */
  constructor(config, session, decoder_merged_session, generation_config) {
    var _a2;
    super(config, session);
    __publicField(this, "main_input_name", "pixel_values");
    this.decoder_merged_session = decoder_merged_session;
    this.generation_config = generation_config;
    const encoderConfig = this.config.encoder;
    const decoderConfig = this.config.decoder;
    const encoderModelType = encoderConfig.model_type;
    const encoderModel = (_a2 = MODEL_MAPPING_NAMES_ENCODER_ONLY.get(encoderModelType)) != null ? _a2 : MODEL_MAPPING_NAMES_ENCODER_DECODER.get(encoderModelType);
    if (!encoderModel) {
      console.warn(`Model type for encoder '${encoderModelType}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`);
    }
    const decoderModel = MODEL_WITH_LM_HEAD_MAPPING_NAMES.get(decoderConfig.model_type);
    if (!decoderModel) {
      throw new Error(`Unable to construct \`VisionEncoderDecoder\` due to unsupported decoder: "${this.config.decoder.model_type}"`);
    }
    const decoderModelClass = decoderModel[1];
    const decoder = new decoderModelClass(decoderConfig, decoder_merged_session, generation_config);
    this.add_encoder_pkv = "num_decoder_layers" in decoder;
    if (this.add_encoder_pkv) {
      this.num_decoder_layers = decoder.num_decoder_layers;
      this.num_decoder_heads = decoder.num_decoder_heads;
      this.decoder_dim_kv = decoder.decoder_dim_kv;
      this.num_encoder_layers = decoder.num_encoder_layers;
      this.num_encoder_heads = decoder.num_encoder_heads;
      this.encoder_dim_kv = decoder.encoder_dim_kv;
    } else {
      this.num_layers = decoder.num_layers;
      this.num_heads = decoder.num_heads;
      this.dim_kv = decoder.dim_kv;
    }
  }
}
class CLIPPreTrainedModel extends PreTrainedModel {
}
class CLIPModel extends CLIPPreTrainedModel {
}
class CLIPTextModelWithProjection extends CLIPPreTrainedModel {
  /** @type {PreTrainedModel.from_pretrained} */
  static from_pretrained(_0) {
    return __async(this, arguments, function* (pretrained_model_name_or_path, options = {}) {
      var _a2;
      (_a2 = options.model_file_name) != null ? _a2 : options.model_file_name = "text_model";
      return __superGet(CLIPTextModelWithProjection, this, "from_pretrained").call(this, pretrained_model_name_or_path, options);
    });
  }
}
class CLIPVisionModelWithProjection extends CLIPPreTrainedModel {
  /** @type {PreTrainedModel.from_pretrained} */
  static from_pretrained(_0) {
    return __async(this, arguments, function* (pretrained_model_name_or_path, options = {}) {
      var _a2;
      (_a2 = options.model_file_name) != null ? _a2 : options.model_file_name = "vision_model";
      return __superGet(CLIPVisionModelWithProjection, this, "from_pretrained").call(this, pretrained_model_name_or_path, options);
    });
  }
}
class SiglipPreTrainedModel extends PreTrainedModel {
}
class SiglipModel extends SiglipPreTrainedModel {
}
class SiglipTextModel extends SiglipPreTrainedModel {
  /** @type {PreTrainedModel.from_pretrained} */
  static from_pretrained(_0) {
    return __async(this, arguments, function* (pretrained_model_name_or_path, options = {}) {
      var _a2;
      (_a2 = options.model_file_name) != null ? _a2 : options.model_file_name = "text_model";
      return __superGet(SiglipTextModel, this, "from_pretrained").call(this, pretrained_model_name_or_path, options);
    });
  }
}
class SiglipVisionModel extends CLIPPreTrainedModel {
  /** @type {PreTrainedModel.from_pretrained} */
  static from_pretrained(_0) {
    return __async(this, arguments, function* (pretrained_model_name_or_path, options = {}) {
      var _a2;
      (_a2 = options.model_file_name) != null ? _a2 : options.model_file_name = "vision_model";
      return __superGet(SiglipVisionModel, this, "from_pretrained").call(this, pretrained_model_name_or_path, options);
    });
  }
}
class ChineseCLIPPreTrainedModel extends PreTrainedModel {
}
class ChineseCLIPModel extends ChineseCLIPPreTrainedModel {
}
class CLIPSegPreTrainedModel extends PreTrainedModel {
}
class CLIPSegModel extends CLIPSegPreTrainedModel {
}
class CLIPSegForImageSegmentation extends CLIPSegPreTrainedModel {
}
class GPT2PreTrainedModel extends PreTrainedModel {
  /**
   * Creates a new instance of the `GPT2PreTrainedModel` class.
   * @param {Object} config The configuration of the model.
   * @param {any} session The ONNX session containing the model weights.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, generation_config) {
    super(config, session);
    this.generation_config = generation_config;
    this.config.pad_token_id = this.config.eos_token_id;
    this.num_heads = this.config.n_head;
    this.num_layers = this.config.n_layer;
    this.dim_kv = this.config.n_embd / this.num_heads;
  }
}
class GPT2Model extends GPT2PreTrainedModel {
}
class GPT2LMHeadModel extends GPT2PreTrainedModel {
}
class GPTNeoPreTrainedModel extends PreTrainedModel {
  /**
   * Creates a new instance of the `GPTNeoPreTrainedModel` class.
   * @param {Object} config The configuration of the model.
   * @param {any} session The ONNX session containing the model weights.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, generation_config) {
    super(config, session);
    this.generation_config = generation_config;
    this.config.pad_token_id = this.config.eos_token_id;
    this.num_heads = this.config.num_heads;
    this.num_layers = this.config.num_layers;
    this.dim_kv = this.config.hidden_size / this.num_heads;
  }
}
class GPTNeoModel extends GPTNeoPreTrainedModel {
}
class GPTNeoForCausalLM extends GPTNeoPreTrainedModel {
}
class GPTNeoXPreTrainedModel extends PreTrainedModel {
  /**
   * Creates a new instance of the `GPTNeoXPreTrainedModel` class.
   * @param {Object} config The configuration of the model.
   * @param {any} session The ONNX session containing the model weights.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, generation_config) {
    super(config, session);
    this.generation_config = generation_config;
    this.config.pad_token_id = this.config.eos_token_id;
    this.num_heads = this.config.num_attention_heads;
    this.num_layers = this.config.num_hidden_layers;
    this.dim_kv = this.config.hidden_size / this.num_heads;
  }
}
class GPTNeoXModel extends GPTNeoXPreTrainedModel {
}
class GPTNeoXForCausalLM extends GPTNeoXPreTrainedModel {
}
class GPTJPreTrainedModel extends PreTrainedModel {
  /**
   * Creates a new instance of the `GPTJPreTrainedModel` class.
   * @param {Object} config The configuration of the model.
   * @param {any} session The ONNX session containing the model weights.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, generation_config) {
    super(config, session);
    this.generation_config = generation_config;
    this.config.pad_token_id = this.config.eos_token_id;
    this.num_heads = this.config.n_head;
    this.num_layers = this.config.n_layer;
    this.dim_kv = this.config.n_embd / this.num_heads;
  }
}
class GPTJModel extends GPTJPreTrainedModel {
}
class GPTJForCausalLM extends GPTJPreTrainedModel {
}
class GPTBigCodePreTrainedModel extends PreTrainedModel {
  /**
   * Creates a new instance of the `GPTBigCodePreTrainedModel` class.
   * @param {Object} config The configuration of the model.
   * @param {any} session The ONNX session containing the model weights.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, generation_config) {
    super(config, session);
    this.generation_config = generation_config;
    this.config.pad_token_id = this.config.eos_token_id;
    this.num_heads = this.config.n_head;
    this.num_layers = this.config.n_layer;
    this.dim_kv = this.config.n_embd / this.num_heads;
  }
}
class GPTBigCodeModel extends GPTBigCodePreTrainedModel {
}
class GPTBigCodeForCausalLM extends GPTBigCodePreTrainedModel {
}
class CodeGenPreTrainedModel extends PreTrainedModel {
  /**
   * Creates a new instance of the `CodeGenPreTrainedModel` class.
   * @param {Object} config The model configuration object.
   * @param {Object} session The ONNX session object.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, generation_config) {
    super(config, session);
    this.generation_config = generation_config;
    this.config.pad_token_id = this.config.eos_token_id;
    this.num_heads = this.config.n_head;
    this.num_layers = this.config.n_layer;
    this.dim_kv = this.config.n_embd / this.num_heads;
  }
}
class CodeGenModel extends CodeGenPreTrainedModel {
}
class CodeGenForCausalLM extends CodeGenPreTrainedModel {
}
class LlamaPreTrainedModel extends PreTrainedModel {
  /**
   * Creates a new instance of the `LlamaPreTrainedModel` class.
   * @param {Object} config The model configuration object.
   * @param {Object} session The ONNX session object.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, generation_config) {
    var _a2;
    super(config, session);
    this.generation_config = generation_config;
    this.config.pad_token_id = this.config.eos_token_id;
    this.num_heads = (_a2 = this.config.num_key_value_heads) != null ? _a2 : this.config.num_attention_heads;
    this.num_layers = this.config.num_hidden_layers;
    this.dim_kv = this.config.hidden_size / this.config.num_attention_heads;
  }
}
class LlamaModel extends LlamaPreTrainedModel {
}
class LlamaForCausalLM extends LlamaPreTrainedModel {
}
class Qwen2PreTrainedModel extends PreTrainedModel {
  /**
   * Creates a new instance of the `Qwen2PreTrainedModel` class.
   * @param {Object} config The model configuration object.
   * @param {Object} session The ONNX session object.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, generation_config) {
    var _a2;
    super(config, session);
    this.generation_config = generation_config;
    this.config.pad_token_id = this.config.eos_token_id;
    this.num_heads = (_a2 = this.config.num_key_value_heads) != null ? _a2 : this.config.num_attention_heads;
    this.num_layers = this.config.num_hidden_layers;
    this.dim_kv = this.config.hidden_size / this.config.num_attention_heads;
  }
}
class Qwen2Model extends Qwen2PreTrainedModel {
}
class Qwen2ForCausalLM extends Qwen2PreTrainedModel {
}
class PhiPreTrainedModel extends PreTrainedModel {
  /**
   * Creates a new instance of the `PhiPreTrainedModel` class.
   * @param {Object} config The model configuration object.
   * @param {Object} session The ONNX session object.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, generation_config) {
    super(config, session);
    this.generation_config = generation_config;
    this.config.pad_token_id = this.config.eos_token_id;
    this.num_heads = this.config.num_attention_heads;
    this.num_layers = this.config.num_hidden_layers;
    this.dim_kv = this.config.hidden_size / this.num_heads;
  }
}
class PhiModel extends PhiPreTrainedModel {
}
class PhiForCausalLM extends PhiPreTrainedModel {
}
class BloomPreTrainedModel extends PreTrainedModel {
  /**
   * Creates a new instance of the `BloomPreTrainedModel` class.
   * @param {Object} config The configuration of the model.
   * @param {any} session The ONNX session containing the model weights.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, generation_config) {
    super(config, session);
    this.generation_config = generation_config;
    this.config.pad_token_id = this.config.eos_token_id;
    this.num_heads = this.config.n_head;
    this.num_layers = this.config.n_layer;
    this.dim_kv = this.config.hidden_size / this.num_heads;
  }
}
class BloomModel extends BloomPreTrainedModel {
}
class BloomForCausalLM extends BloomPreTrainedModel {
}
class MptPreTrainedModel extends PreTrainedModel {
  /**
   * Creates a new instance of the `MptPreTrainedModel` class.
   * @param {Object} config The model configuration object.
   * @param {Object} session The ONNX session object.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, generation_config) {
    super(config, session);
    this.generation_config = generation_config;
    this.config.pad_token_id = this.config.eos_token_id;
    this.num_heads = this.config.n_heads;
    this.num_layers = this.config.n_layers;
    this.dim_kv = this.config.d_model / this.num_heads;
  }
}
class MptModel extends MptPreTrainedModel {
}
class MptForCausalLM extends MptPreTrainedModel {
}
class OPTPreTrainedModel extends PreTrainedModel {
  /**
   * Creates a new instance of the `OPTPreTrainedModel` class.
   * @param {Object} config The model configuration object.
   * @param {Object} session The ONNX session object.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, generation_config) {
    super(config, session);
    this.generation_config = generation_config;
    this.config.pad_token_id = this.config.eos_token_id;
    this.num_heads = this.config.num_attention_heads;
    this.num_layers = this.config.num_hidden_layers;
    this.dim_kv = this.config.hidden_size / this.num_heads;
  }
}
class OPTModel extends OPTPreTrainedModel {
}
class OPTForCausalLM extends OPTPreTrainedModel {
}
class ViTPreTrainedModel extends PreTrainedModel {
}
class ViTModel extends ViTPreTrainedModel {
}
class ViTForImageClassification extends ViTPreTrainedModel {
  /**
   * @param {any} model_inputs
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(ViTForImageClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class FastViTPreTrainedModel extends PreTrainedModel {
}
class FastViTModel extends FastViTPreTrainedModel {
}
class FastViTForImageClassification extends FastViTPreTrainedModel {
  /**
   * @param {any} model_inputs
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(FastViTForImageClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class VitMattePreTrainedModel extends PreTrainedModel {
}
class VitMatteForImageMatting extends VitMattePreTrainedModel {
  /**
   * @param {any} model_inputs
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new ImageMattingOutput(yield __superGet(VitMatteForImageMatting.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class MobileViTPreTrainedModel extends PreTrainedModel {
}
class MobileViTModel extends MobileViTPreTrainedModel {
}
class MobileViTForImageClassification extends MobileViTPreTrainedModel {
  /**
   * @param {any} model_inputs
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(MobileViTForImageClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class MobileViTV2PreTrainedModel extends PreTrainedModel {
}
class MobileViTV2Model extends MobileViTV2PreTrainedModel {
}
class MobileViTV2ForImageClassification extends MobileViTV2PreTrainedModel {
  /**
   * @param {any} model_inputs
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(MobileViTV2ForImageClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class OwlViTPreTrainedModel extends PreTrainedModel {
}
class OwlViTModel extends OwlViTPreTrainedModel {
}
class OwlViTForObjectDetection extends OwlViTPreTrainedModel {
}
class Owlv2PreTrainedModel extends PreTrainedModel {
}
class Owlv2Model extends Owlv2PreTrainedModel {
}
class Owlv2ForObjectDetection extends Owlv2PreTrainedModel {
}
class BeitPreTrainedModel extends PreTrainedModel {
}
class BeitModel extends BeitPreTrainedModel {
}
class BeitForImageClassification extends BeitPreTrainedModel {
  /**
   * @param {any} model_inputs
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(BeitForImageClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class DetrPreTrainedModel extends PreTrainedModel {
}
class DetrModel extends DetrPreTrainedModel {
}
class DetrForObjectDetection extends DetrPreTrainedModel {
  /**
   * @param {any} model_inputs
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new DetrObjectDetectionOutput(yield __superGet(DetrForObjectDetection.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class DetrForSegmentation extends DetrPreTrainedModel {
  /**
   * Runs the model with the provided inputs
   * @param {Object} model_inputs Model inputs
   * @returns {Promise<DetrSegmentationOutput>} Object containing segmentation outputs
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new DetrSegmentationOutput(yield __superGet(DetrForSegmentation.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class DetrObjectDetectionOutput extends ModelOutput {
  /**
   * @param {Object} output The output of the model.
   * @param {Tensor} output.logits Classification logits (including no-object) for all queries.
   * @param {Tensor} output.pred_boxes Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height).
   * These values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding possible padding).
   */
  constructor({ logits, pred_boxes }) {
    super();
    this.logits = logits;
    this.pred_boxes = pred_boxes;
  }
}
class DetrSegmentationOutput extends ModelOutput {
  /**
   * @param {Object} output The output of the model.
   * @param {Tensor} output.logits The output logits of the model.
   * @param {Tensor} output.pred_boxes Predicted boxes.
   * @param {Tensor} output.pred_masks Predicted masks.
   */
  constructor({ logits, pred_boxes, pred_masks }) {
    super();
    this.logits = logits;
    this.pred_boxes = pred_boxes;
    this.pred_masks = pred_masks;
  }
}
class TableTransformerPreTrainedModel extends PreTrainedModel {
}
class TableTransformerModel extends TableTransformerPreTrainedModel {
}
class TableTransformerForObjectDetection extends TableTransformerPreTrainedModel {
  /**
   * @param {any} model_inputs
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new TableTransformerObjectDetectionOutput(yield __superGet(TableTransformerForObjectDetection.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class TableTransformerObjectDetectionOutput extends DetrObjectDetectionOutput {
}
class DeiTPreTrainedModel extends PreTrainedModel {
}
class DeiTModel extends DeiTPreTrainedModel {
}
class DeiTForImageClassification extends DeiTPreTrainedModel {
  /**
   * @param {any} model_inputs
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(DeiTForImageClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class ResNetPreTrainedModel extends PreTrainedModel {
}
class ResNetModel extends ResNetPreTrainedModel {
}
class ResNetForImageClassification extends ResNetPreTrainedModel {
  /**
   * @param {any} model_inputs
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(ResNetForImageClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class SwinPreTrainedModel extends PreTrainedModel {
}
class SwinModel extends SwinPreTrainedModel {
}
class SwinForImageClassification extends SwinPreTrainedModel {
  /**
   * @param {any} model_inputs
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(SwinForImageClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class Swin2SRPreTrainedModel extends PreTrainedModel {
}
class Swin2SRModel extends Swin2SRPreTrainedModel {
}
class Swin2SRForImageSuperResolution extends Swin2SRPreTrainedModel {
}
class DPTPreTrainedModel extends PreTrainedModel {
}
class DPTModel extends DPTPreTrainedModel {
}
class DPTForDepthEstimation extends DPTPreTrainedModel {
}
class DepthAnythingPreTrainedModel extends PreTrainedModel {
}
class DepthAnythingForDepthEstimation extends DepthAnythingPreTrainedModel {
}
class GLPNPreTrainedModel extends PreTrainedModel {
}
class GLPNModel extends GLPNPreTrainedModel {
}
class GLPNForDepthEstimation extends GLPNPreTrainedModel {
}
class DonutSwinPreTrainedModel extends PreTrainedModel {
}
class DonutSwinModel extends DonutSwinPreTrainedModel {
}
class ConvNextPreTrainedModel extends PreTrainedModel {
}
class ConvNextModel extends ConvNextPreTrainedModel {
}
class ConvNextForImageClassification extends ConvNextPreTrainedModel {
  /**
   * @param {any} model_inputs
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(ConvNextForImageClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class ConvNextV2PreTrainedModel extends PreTrainedModel {
}
class ConvNextV2Model extends ConvNextV2PreTrainedModel {
}
class ConvNextV2ForImageClassification extends ConvNextV2PreTrainedModel {
  /**
   * @param {any} model_inputs
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(ConvNextV2ForImageClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class Dinov2PreTrainedModel extends PreTrainedModel {
}
class Dinov2Model extends Dinov2PreTrainedModel {
}
class Dinov2ForImageClassification extends Dinov2PreTrainedModel {
  /**
   * @param {any} model_inputs
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(Dinov2ForImageClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class YolosPreTrainedModel extends PreTrainedModel {
}
class YolosModel extends YolosPreTrainedModel {
}
class YolosForObjectDetection extends YolosPreTrainedModel {
  /**
   * @param {any} model_inputs
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new YolosObjectDetectionOutput(yield __superGet(YolosForObjectDetection.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class YolosObjectDetectionOutput extends ModelOutput {
  /**
   * @param {Object} output The output of the model.
   * @param {Tensor} output.logits Classification logits (including no-object) for all queries.
   * @param {Tensor} output.pred_boxes Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height).
   * These values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding possible padding).
   */
  constructor({ logits, pred_boxes }) {
    super();
    this.logits = logits;
    this.pred_boxes = pred_boxes;
  }
}
class SamPreTrainedModel extends PreTrainedModel {
}
class SamModel extends SamPreTrainedModel {
  /**
   * Creates a new instance of the `SamModel` class.
   * @param {Object} config The configuration object specifying the hyperparameters and other model settings.
   * @param {Object} vision_encoder The ONNX session containing the vision encoder model.
   * @param {any} prompt_encoder_mask_decoder The ONNX session containing the prompt encoder and mask decoder model.
   */
  constructor(config, vision_encoder, prompt_encoder_mask_decoder) {
    super(config, vision_encoder);
    this.prompt_encoder_mask_decoder = prompt_encoder_mask_decoder;
  }
  /**
   * Compute image embeddings and positional image embeddings, given the pixel values of an image.
   * @param {Object} model_inputs Object containing the model inputs.
   * @param {Tensor} model_inputs.pixel_values Pixel values obtained using a `SamProcessor`.
   * @returns {Promise<{ image_embeddings: Tensor, image_positional_embeddings: Tensor }>} The image embeddings and positional image embeddings.
   */
  get_image_embeddings(_0) {
    return __async(this, arguments, function* ({ pixel_values }) {
      return yield encoderForward(this, { pixel_values });
    });
  }
  /**
   * @typedef {Object} SamModelInputs Object containing the model inputs.
   * @property {Tensor} pixel_values Pixel values as a Tensor with shape `(batch_size, num_channels, height, width)`.
   * These can be obtained using a `SamProcessor`.
   * @property {Tensor} input_points Input 2D spatial points with shape `(batch_size, num_points, 2)`.
   * This is used by the prompt encoder to encode the prompt.
   * @property {Tensor} [input_labels] Input labels for the points, as a Tensor of shape `(batch_size, point_batch_size, num_points)`.
   * This is used by the prompt encoder to encode the prompt. There are 4 types of labels:
   *  - `1`: the point is a point that contains the object of interest
   *  - `0`: the point is a point that does not contain the object of interest
   *  - `-1`: the point corresponds to the background
   *  - `-10`: the point is a padding point, thus should be ignored by the prompt encoder
   * @property {Tensor} [image_embeddings] Image embeddings used by the mask decoder.
   * @property {Tensor} [image_positional_embeddings] Image positional embeddings used by the mask decoder.
   */
  /**
   * @param {SamModelInputs} model_inputs Object containing the model inputs.
   * @returns {Promise<Object>} The output of the model.
   */
  forward(model_inputs) {
    return __async(this, null, function* () {
      if (!model_inputs.image_embeddings || !model_inputs.image_positional_embeddings) {
        model_inputs = __spreadValues(__spreadValues({}, model_inputs), yield this.get_image_embeddings(model_inputs));
      }
      if (!model_inputs.input_labels) {
        const shape = model_inputs.input_points.dims.slice(0, -1);
        const numElements = shape.reduce((a, b) => a * b, 1);
        model_inputs.input_labels = new Tensor(
          "int64",
          new BigInt64Array(numElements).fill(/* @__PURE__ */ BigInt("1")),
          shape
        );
      }
      return yield sessionRun(this.prompt_encoder_mask_decoder, {
        input_points: model_inputs.input_points,
        input_labels: model_inputs.input_labels,
        image_embeddings: model_inputs.image_embeddings,
        image_positional_embeddings: model_inputs.image_positional_embeddings
      });
    });
  }
  /**
   * Runs the model with the provided inputs
   * @param {Object} model_inputs Model inputs
   * @returns {Promise<SamImageSegmentationOutput>} Object containing segmentation outputs
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SamImageSegmentationOutput(yield __superGet(SamModel.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class SamImageSegmentationOutput extends ModelOutput {
  /**
   * @param {Object} output The output of the model.
   * @param {Tensor} output.iou_scores The output logits of the model.
   * @param {Tensor} output.pred_masks Predicted boxes.
   */
  constructor({ iou_scores, pred_masks }) {
    super();
    this.iou_scores = iou_scores;
    this.pred_masks = pred_masks;
  }
}
class MarianPreTrainedModel extends PreTrainedModel {
}
class MarianModel extends MarianPreTrainedModel {
}
class MarianMTModel extends MarianPreTrainedModel {
  /**
   * Creates a new instance of the `MarianMTModel` class.
  * @param {Object} config The model configuration object.
  * @param {Object} session The ONNX session object.
  * @param {any} decoder_merged_session 
  * @param {any} generation_config 
  */
  constructor(config, session, decoder_merged_session, generation_config) {
    super(config, session);
    this.decoder_merged_session = decoder_merged_session;
    this.generation_config = generation_config;
    this.num_decoder_layers = this.config.decoder_layers;
    this.num_decoder_heads = this.config.decoder_attention_heads;
    this.decoder_dim_kv = this.config.d_model / this.num_decoder_heads;
    this.num_encoder_layers = this.config.encoder_layers;
    this.num_encoder_heads = this.config.encoder_attention_heads;
    this.encoder_dim_kv = this.config.d_model / this.num_encoder_heads;
  }
}
class M2M100PreTrainedModel extends PreTrainedModel {
}
class M2M100Model extends M2M100PreTrainedModel {
}
class M2M100ForConditionalGeneration extends M2M100PreTrainedModel {
  /**
   * Creates a new instance of the `M2M100ForConditionalGeneration` class.
  * @param {Object} config The model configuration object.
  * @param {Object} session The ONNX session object.
  * @param {any} decoder_merged_session 
  * @param {any} generation_config 
  */
  constructor(config, session, decoder_merged_session, generation_config) {
    super(config, session);
    this.decoder_merged_session = decoder_merged_session;
    this.generation_config = generation_config;
    this.num_decoder_layers = this.config.decoder_layers;
    this.num_decoder_heads = this.config.decoder_attention_heads;
    this.decoder_dim_kv = this.config.d_model / this.num_decoder_heads;
    this.num_encoder_layers = this.config.encoder_layers;
    this.num_encoder_heads = this.config.encoder_attention_heads;
    this.encoder_dim_kv = this.config.d_model / this.num_encoder_heads;
  }
}
class Wav2Vec2PreTrainedModel extends PreTrainedModel {
}
class Wav2Vec2Model extends Wav2Vec2PreTrainedModel {
}
class Wav2Vec2ForCTC extends Wav2Vec2PreTrainedModel {
  /**
   * @param {Object} model_inputs
   * @param {Tensor} model_inputs.input_values Float values of input raw speech waveform.
   * @param {Tensor} model_inputs.attention_mask Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1]
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new CausalLMOutput(yield __superGet(Wav2Vec2ForCTC.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class Wav2Vec2ForSequenceClassification extends Wav2Vec2PreTrainedModel {
  /**
   * Calls the model on new inputs.
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} An object containing the model's output logits for sequence classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(Wav2Vec2ForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class Wav2Vec2ForAudioFrameClassification extends Wav2Vec2PreTrainedModel {
  /**
   * Calls the model on new inputs.
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for sequence classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new TokenClassifierOutput(yield __superGet(Wav2Vec2ForAudioFrameClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class UniSpeechPreTrainedModel extends PreTrainedModel {
}
class UniSpeechModel extends UniSpeechPreTrainedModel {
}
class UniSpeechForCTC extends UniSpeechPreTrainedModel {
  /**
   * @param {Object} model_inputs
   * @param {Tensor} model_inputs.input_values Float values of input raw speech waveform.
   * @param {Tensor} model_inputs.attention_mask Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1]
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new CausalLMOutput(yield __superGet(UniSpeechForCTC.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class UniSpeechForSequenceClassification extends UniSpeechPreTrainedModel {
  /**
   * Calls the model on new inputs.
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} An object containing the model's output logits for sequence classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(UniSpeechForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class UniSpeechSatPreTrainedModel extends PreTrainedModel {
}
class UniSpeechSatModel extends UniSpeechSatPreTrainedModel {
}
class UniSpeechSatForCTC extends UniSpeechSatPreTrainedModel {
  /**
   * @param {Object} model_inputs
   * @param {Tensor} model_inputs.input_values Float values of input raw speech waveform.
   * @param {Tensor} model_inputs.attention_mask Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1]
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new CausalLMOutput(yield __superGet(UniSpeechSatForCTC.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class UniSpeechSatForSequenceClassification extends UniSpeechSatPreTrainedModel {
  /**
   * Calls the model on new inputs.
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} An object containing the model's output logits for sequence classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(UniSpeechSatForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class UniSpeechSatForAudioFrameClassification extends UniSpeechSatPreTrainedModel {
  /**
   * Calls the model on new inputs.
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for sequence classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new TokenClassifierOutput(yield __superGet(UniSpeechSatForAudioFrameClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class Wav2Vec2BertPreTrainedModel extends PreTrainedModel {
}
class Wav2Vec2BertModel extends Wav2Vec2BertPreTrainedModel {
}
class Wav2Vec2BertForCTC extends Wav2Vec2BertPreTrainedModel {
  /**
   * @param {Object} model_inputs
   * @param {Tensor} model_inputs.input_features Float values of input mel-spectrogram.
   * @param {Tensor} model_inputs.attention_mask Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1]
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new CausalLMOutput(yield __superGet(Wav2Vec2BertForCTC.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class Wav2Vec2BertForSequenceClassification extends Wav2Vec2BertPreTrainedModel {
  /**
   * Calls the model on new inputs.
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} An object containing the model's output logits for sequence classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(Wav2Vec2BertForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class HubertModel extends Wav2Vec2PreTrainedModel {
}
class HubertForCTC extends Wav2Vec2PreTrainedModel {
  /**
   * @param {Object} model_inputs
   * @param {Tensor} model_inputs.input_values Float values of input raw speech waveform.
   * @param {Tensor} model_inputs.attention_mask Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1]
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new CausalLMOutput(yield __superGet(HubertForCTC.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class HubertForSequenceClassification extends Wav2Vec2PreTrainedModel {
  /**
   * Calls the model on new inputs.
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} An object containing the model's output logits for sequence classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(HubertForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class WavLMPreTrainedModel extends PreTrainedModel {
}
class WavLMModel extends WavLMPreTrainedModel {
}
class WavLMForCTC extends WavLMPreTrainedModel {
  /**
   * @param {Object} model_inputs
   * @param {Tensor} model_inputs.input_values Float values of input raw speech waveform.
   * @param {Tensor} model_inputs.attention_mask Mask to avoid performing convolution and attention on padding token indices. Mask values selected in [0, 1]
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new CausalLMOutput(yield __superGet(WavLMForCTC.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class WavLMForSequenceClassification extends WavLMPreTrainedModel {
  /**
   * Calls the model on new inputs.
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<SequenceClassifierOutput>} An object containing the model's output logits for sequence classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(WavLMForSequenceClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class WavLMForXVector extends WavLMPreTrainedModel {
  /**
   * Calls the model on new inputs.
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<XVectorOutput>} An object containing the model's output logits and speaker embeddings.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new XVectorOutput(yield __superGet(WavLMForXVector.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class WavLMForAudioFrameClassification extends WavLMPreTrainedModel {
  /**
   * Calls the model on new inputs.
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<TokenClassifierOutput>} An object containing the model's output logits for sequence classification.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new TokenClassifierOutput(yield __superGet(WavLMForAudioFrameClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class SpeechT5PreTrainedModel extends PreTrainedModel {
}
class SpeechT5ForSpeechToText extends SpeechT5PreTrainedModel {
}
class SpeechT5ForTextToSpeech extends SpeechT5PreTrainedModel {
  /**
   * Creates a new instance of the `SpeechT5ForTextToSpeech` class.
   * @param {Object} config The model configuration.
   * @param {any} session session for the model.
   * @param {any} decoder_merged_session session for the decoder.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, decoder_merged_session, generation_config) {
    super(config, session);
    this.decoder_merged_session = decoder_merged_session;
    this.generation_config = generation_config;
    this.num_decoder_layers = this.config.decoder_layers;
    this.num_decoder_heads = this.config.decoder_attention_heads;
    this.decoder_dim_kv = this.config.hidden_size / this.num_decoder_heads;
    this.num_encoder_layers = this.config.encoder_layers;
    this.num_encoder_heads = this.config.encoder_attention_heads;
    this.encoder_dim_kv = this.config.hidden_size / this.num_encoder_heads;
  }
  /**
   * @typedef {Object} SpeechOutput
   * @property {Tensor} [spectrogram] The predicted log-mel spectrogram of shape
   * `(output_sequence_length, config.num_mel_bins)`. Returned when no `vocoder` is provided
   * @property {Tensor} [waveform] The predicted waveform of shape `(num_frames,)`. Returned when a `vocoder` is provided.
   * @property {Tensor} [cross_attentions] The outputs of the decoder's cross-attention layers of shape
   * `(config.decoder_layers, config.decoder_attention_heads, output_sequence_length, input_sequence_length)`. returned when `output_cross_attentions` is `true`.
   */
  /**
   * Converts a sequence of input tokens into a sequence of mel spectrograms, which are subsequently turned into a speech waveform using a vocoder.
   * @param {Tensor} input_values Indices of input sequence tokens in the vocabulary.
   * @param {Tensor} speaker_embeddings Tensor containing the speaker embeddings.
   * @param {Object} options Optional parameters for generating speech.
   * @param {number} [options.threshold=0.5] The generated sequence ends when the predicted stop token probability exceeds this value.
   * @param {number} [options.minlenratio=0.0] Used to calculate the minimum required length for the output sequence.
   * @param {number} [options.maxlenratio=20.0] Used to calculate the maximum allowed length for the output sequence.
   * @param {Object} [options.vocoder=null] The vocoder that converts the mel spectrogram into a speech waveform. If `null`, the output is the mel spectrogram.
   * @param {boolean} [options.output_cross_attentions=false] Whether or not to return the attentions tensors of the decoder's cross-attention layers.
   * @returns {Promise<SpeechOutput>} A promise which resolves to an object containing the spectrogram, waveform, and cross-attention tensors.
   */
  generate_speech(_0, _1) {
    return __async(this, arguments, function* (input_values, speaker_embeddings, {
      threshold = 0.5,
      minlenratio = 0,
      maxlenratio = 20,
      vocoder = null
      // output_cross_attentions = false, // TODO add
    } = {}) {
      const model_inputs = {
        input_ids: input_values
      };
      const { encoder_outputs, encoder_attention_mask } = yield encoderForward(this, model_inputs);
      const r = encoder_outputs.dims[1] / this.config.reduction_factor;
      const maxlen = Math.floor(r * maxlenratio);
      const minlen = Math.floor(r * minlenratio);
      const num_mel_bins = this.config.num_mel_bins;
      let spectrogramParts = [];
      let past_key_values = null;
      let decoder_outputs = null;
      let idx = 0;
      while (true) {
        ++idx;
        const use_cache_branch = boolTensor(!!decoder_outputs);
        let output_sequence;
        if (decoder_outputs) {
          output_sequence = decoder_outputs.output_sequence_out;
        } else {
          output_sequence = new Tensor(
            "float32",
            new Float32Array(num_mel_bins),
            [1, 1, num_mel_bins]
          );
        }
        let decoderFeeds = {
          use_cache_branch,
          output_sequence,
          encoder_attention_mask,
          speaker_embeddings,
          encoder_hidden_states: encoder_outputs
        };
        this.addPastKeyValues(decoderFeeds, past_key_values);
        decoder_outputs = yield sessionRun(this.decoder_merged_session, decoderFeeds);
        past_key_values = this.getPastKeyValues(decoder_outputs, past_key_values);
        const { prob, spectrum } = decoder_outputs;
        spectrogramParts.push(spectrum);
        if (idx >= minlen && // Finished when stop token or maximum length is reached.
        (Array.from(prob.data).filter((p) => p >= threshold).length > 0 || idx >= maxlen)) {
          break;
        }
      }
      const spectrogram = cat(spectrogramParts);
      const { waveform } = yield sessionRun(vocoder.session, { spectrogram });
      return {
        spectrogram,
        waveform
        // cross_attentions: null, // TODO add
      };
    });
  }
}
class SpeechT5HifiGan extends PreTrainedModel {
  constructor() {
    super(...arguments);
    __publicField(this, "main_input_name", "spectrogram");
  }
}
class TrOCRPreTrainedModel extends PreTrainedModel {
  /**
   * Creates a new instance of the `TrOCRPreTrainedModel` class.
   * @param {Object} config The configuration of the model.
   * @param {any} session The ONNX session containing the model weights.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, generation_config) {
    super(config, session);
    this.generation_config = generation_config;
    this.config.pad_token_id = this.config.eos_token_id;
    this.num_encoder_layers = this.num_decoder_layers = this.config.decoder_layers;
    this.num_encoder_heads = this.num_decoder_heads = this.config.decoder_attention_heads;
    this.encoder_dim_kv = this.decoder_dim_kv = this.config.d_model / this.num_decoder_heads;
  }
}
class TrOCRForCausalLM extends TrOCRPreTrainedModel {
}
class MistralPreTrainedModel extends PreTrainedModel {
  /**
   * Creates a new instance of the `MistralPreTrainedModel` class.
   * @param {Object} config The configuration of the model.
   * @param {any} session The ONNX session containing the model weights.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, generation_config) {
    super(config, session);
    this.generation_config = generation_config;
    this.config.pad_token_id = this.config.eos_token_id;
    this.num_heads = this.config.num_key_value_heads;
    this.num_layers = this.config.num_hidden_layers;
    this.dim_kv = this.config.hidden_size / this.config.num_attention_heads;
  }
}
class MistralModel extends MistralPreTrainedModel {
}
class MistralForCausalLM extends MistralPreTrainedModel {
}
class Starcoder2PreTrainedModel extends PreTrainedModel {
  /**
   * Creates a new instance of the `Starcoder2PreTrainedModel` class.
   * @param {Object} config The configuration of the model.
   * @param {any} session The ONNX session containing the model weights.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, generation_config) {
    super(config, session);
    this.generation_config = generation_config;
    this.config.pad_token_id = this.config.eos_token_id;
    this.num_heads = this.config.num_key_value_heads;
    this.num_layers = this.config.num_hidden_layers;
    this.dim_kv = this.config.hidden_size / this.config.num_attention_heads;
  }
}
class Starcoder2Model extends Starcoder2PreTrainedModel {
}
class Starcoder2ForCausalLM extends Starcoder2PreTrainedModel {
}
class FalconPreTrainedModel extends PreTrainedModel {
  /**
   * Creates a new instance of the `FalconPreTrainedModel` class.
   * @param {Object} config The configuration of the model.
   * @param {any} session The ONNX session containing the model weights.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, generation_config) {
    super(config, session);
    this.generation_config = generation_config;
    this.config.pad_token_id = this.config.eos_token_id;
    this.num_heads = this.config.num_attention_heads;
    this.num_layers = this.config.num_hidden_layers;
    this.dim_kv = this.config.hidden_size / this.config.num_attention_heads;
  }
}
class FalconModel extends FalconPreTrainedModel {
}
class FalconForCausalLM extends FalconPreTrainedModel {
}
class ClapPreTrainedModel extends PreTrainedModel {
}
class ClapModel extends ClapPreTrainedModel {
}
class ClapTextModelWithProjection extends ClapPreTrainedModel {
  /** @type {PreTrainedModel.from_pretrained} */
  static from_pretrained(_0) {
    return __async(this, arguments, function* (pretrained_model_name_or_path, options = {}) {
      var _a2;
      (_a2 = options.model_file_name) != null ? _a2 : options.model_file_name = "text_model";
      return __superGet(ClapTextModelWithProjection, this, "from_pretrained").call(this, pretrained_model_name_or_path, options);
    });
  }
}
class ClapAudioModelWithProjection extends ClapPreTrainedModel {
  /** @type {PreTrainedModel.from_pretrained} */
  static from_pretrained(_0) {
    return __async(this, arguments, function* (pretrained_model_name_or_path, options = {}) {
      var _a2;
      (_a2 = options.model_file_name) != null ? _a2 : options.model_file_name = "audio_model";
      return __superGet(ClapAudioModelWithProjection, this, "from_pretrained").call(this, pretrained_model_name_or_path, options);
    });
  }
}
class VitsPreTrainedModel extends PreTrainedModel {
}
class VitsModel extends VitsPreTrainedModel {
  /**
   * Calls the model on new inputs.
   * @param {Object} model_inputs The inputs to the model.
   * @returns {Promise<VitsModelOutput>} The outputs for the VITS model.
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new VitsModelOutput(yield __superGet(VitsModel.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class SegformerPreTrainedModel extends PreTrainedModel {
}
class SegformerForImageClassification extends SegformerPreTrainedModel {
}
class SegformerForSemanticSegmentation extends SegformerPreTrainedModel {
}
class StableLmPreTrainedModel extends PreTrainedModel {
  /**
   * Creates a new instance of the `StableLmPreTrainedModel` class.
   * @param {Object} config The configuration of the model.
   * @param {any} session The ONNX session containing the model weights.
   * @param {GenerationConfig} generation_config The generation configuration.
   */
  constructor(config, session, generation_config) {
    super(config, session);
    this.generation_config = generation_config;
    this.config.pad_token_id = this.config.eos_token_id;
    this.num_heads = this.config.num_attention_heads;
    this.num_layers = this.config.num_hidden_layers;
    this.dim_kv = this.config.hidden_size / this.num_heads;
  }
}
class StableLmForCausalLM extends StableLmPreTrainedModel {
}
class EfficientNetPreTrainedModel extends PreTrainedModel {
}
class EfficientNetModel extends EfficientNetPreTrainedModel {
}
class EfficientNetForImageClassification extends EfficientNetPreTrainedModel {
  /**
   * @param {any} model_inputs
   */
  _call(model_inputs) {
    return __async(this, null, function* () {
      return new SequenceClassifierOutput(yield __superGet(EfficientNetForImageClassification.prototype, this, "_call").call(this, model_inputs));
    });
  }
}
class PretrainedMixin {
  /** @type {PreTrainedModel.from_pretrained} */
  static from_pretrained(_0) {
    return __async(this, arguments, function* (pretrained_model_name_or_path, {
      quantized = true,
      progress_callback = null,
      config = null,
      cache_dir = null,
      local_files_only = false,
      revision = "main",
      model_file_name = null
    } = {}) {
      let options = {
        quantized,
        progress_callback,
        config,
        cache_dir,
        local_files_only,
        revision,
        model_file_name
      };
      config = yield AutoConfig.from_pretrained(pretrained_model_name_or_path, options);
      if (!options.config) {
        options.config = config;
      }
      if (!this.MODEL_CLASS_MAPPINGS) {
        throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: " + this.name);
      }
      for (let MODEL_CLASS_MAPPING of this.MODEL_CLASS_MAPPINGS) {
        const modelInfo = MODEL_CLASS_MAPPING.get(config.model_type);
        if (!modelInfo) {
          continue;
        }
        return yield modelInfo[1].from_pretrained(pretrained_model_name_or_path, options);
      }
      if (this.BASE_IF_FAIL) {
        console.warn(`Unknown model class "${config.model_type}", attempting to construct from base class.`);
        return yield PreTrainedModel.from_pretrained(pretrained_model_name_or_path, options);
      } else {
        throw Error(`Unsupported model type: ${config.model_type}`);
      }
    });
  }
}
/**
 * Mapping from model type to model class.
 * @type {Map<string, Object>[]}
 */
__publicField(PretrainedMixin, "MODEL_CLASS_MAPPINGS", null);
/**
 * Whether to attempt to instantiate the base class (`PretrainedModel`) if 
 * the model type is not found in the mapping.
 */
__publicField(PretrainedMixin, "BASE_IF_FAIL", false);
const MODEL_MAPPING_NAMES_ENCODER_ONLY = /* @__PURE__ */ new Map([
  ["bert", ["BertModel", BertModel]],
  ["nomic_bert", ["NomicBertModel", NomicBertModel]],
  ["roformer", ["RoFormerModel", RoFormerModel]],
  ["electra", ["ElectraModel", ElectraModel]],
  ["esm", ["EsmModel", EsmModel]],
  ["convbert", ["ConvBertModel", ConvBertModel]],
  ["camembert", ["CamembertModel", CamembertModel]],
  ["deberta", ["DebertaModel", DebertaModel]],
  ["deberta-v2", ["DebertaV2Model", DebertaV2Model]],
  ["mpnet", ["MPNetModel", MPNetModel]],
  ["albert", ["AlbertModel", AlbertModel]],
  ["distilbert", ["DistilBertModel", DistilBertModel]],
  ["roberta", ["RobertaModel", RobertaModel]],
  ["xlm", ["XLMModel", XLMModel]],
  ["xlm-roberta", ["XLMRobertaModel", XLMRobertaModel]],
  ["clap", ["ClapModel", ClapModel]],
  ["clip", ["CLIPModel", CLIPModel]],
  ["clipseg", ["CLIPSegModel", CLIPSegModel]],
  ["chinese_clip", ["ChineseCLIPModel", ChineseCLIPModel]],
  ["siglip", ["SiglipModel", SiglipModel]],
  ["mobilebert", ["MobileBertModel", MobileBertModel]],
  ["squeezebert", ["SqueezeBertModel", SqueezeBertModel]],
  ["wav2vec2", ["Wav2Vec2Model", Wav2Vec2Model]],
  ["wav2vec2-bert", ["Wav2Vec2BertModel", Wav2Vec2BertModel]],
  ["unispeech", ["UniSpeechModel", UniSpeechModel]],
  ["unispeech-sat", ["UniSpeechSatModel", UniSpeechSatModel]],
  ["hubert", ["HubertModel", HubertModel]],
  ["wavlm", ["WavLMModel", WavLMModel]],
  ["audio-spectrogram-transformer", ["ASTModel", ASTModel]],
  ["vits", ["VitsModel", VitsModel]],
  ["detr", ["DetrModel", DetrModel]],
  ["table-transformer", ["TableTransformerModel", TableTransformerModel]],
  ["vit", ["ViTModel", ViTModel]],
  ["fastvit", ["FastViTModel", FastViTModel]],
  ["mobilevit", ["MobileViTModel", MobileViTModel]],
  ["mobilevitv2", ["MobileViTV2Model", MobileViTV2Model]],
  ["owlvit", ["OwlViTModel", OwlViTModel]],
  ["owlv2", ["Owlv2Model", Owlv2Model]],
  ["beit", ["BeitModel", BeitModel]],
  ["deit", ["DeiTModel", DeiTModel]],
  ["convnext", ["ConvNextModel", ConvNextModel]],
  ["convnextv2", ["ConvNextV2Model", ConvNextV2Model]],
  ["dinov2", ["Dinov2Model", Dinov2Model]],
  ["resnet", ["ResNetModel", ResNetModel]],
  ["swin", ["SwinModel", SwinModel]],
  ["swin2sr", ["Swin2SRModel", Swin2SRModel]],
  ["donut-swin", ["DonutSwinModel", DonutSwinModel]],
  ["yolos", ["YolosModel", YolosModel]],
  ["dpt", ["DPTModel", DPTModel]],
  ["glpn", ["GLPNModel", GLPNModel]],
  ["hifigan", ["SpeechT5HifiGan", SpeechT5HifiGan]],
  ["efficientnet", ["EfficientNetModel", EfficientNetModel]]
]);
const MODEL_MAPPING_NAMES_ENCODER_DECODER = /* @__PURE__ */ new Map([
  ["t5", ["T5Model", T5Model]],
  ["longt5", ["LongT5Model", LongT5Model]],
  ["mt5", ["MT5Model", MT5Model]],
  ["bart", ["BartModel", BartModel]],
  ["mbart", ["MBartModel", MBartModel]],
  ["marian", ["MarianModel", MarianModel]],
  ["whisper", ["WhisperModel", WhisperModel]],
  ["m2m_100", ["M2M100Model", M2M100Model]],
  ["blenderbot", ["BlenderbotModel", BlenderbotModel]],
  ["blenderbot-small", ["BlenderbotSmallModel", BlenderbotSmallModel]]
]);
const MODEL_MAPPING_NAMES_DECODER_ONLY = /* @__PURE__ */ new Map([
  ["bloom", ["BloomModel", BloomModel]],
  ["gpt2", ["GPT2Model", GPT2Model]],
  ["gptj", ["GPTJModel", GPTJModel]],
  ["gpt_bigcode", ["GPTBigCodeModel", GPTBigCodeModel]],
  ["gpt_neo", ["GPTNeoModel", GPTNeoModel]],
  ["gpt_neox", ["GPTNeoXModel", GPTNeoXModel]],
  ["codegen", ["CodeGenModel", CodeGenModel]],
  ["llama", ["LlamaModel", LlamaModel]],
  ["qwen2", ["Qwen2Model", Qwen2Model]],
  ["phi", ["PhiModel", PhiModel]],
  ["mpt", ["MptModel", MptModel]],
  ["opt", ["OPTModel", OPTModel]],
  ["mistral", ["MistralModel", MistralModel]],
  ["starcoder2", ["Starcoder2Model", Starcoder2Model]],
  ["falcon", ["FalconModel", FalconModel]]
]);
const MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["speecht5", ["SpeechT5ForSpeechToText", SpeechT5ForSpeechToText]],
  ["whisper", ["WhisperForConditionalGeneration", WhisperForConditionalGeneration]]
]);
const MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["speecht5", ["SpeechT5ForTextToSpeech", SpeechT5ForTextToSpeech]]
]);
const MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["vits", ["VitsModel", VitsModel]]
]);
const MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["bert", ["BertForSequenceClassification", BertForSequenceClassification]],
  ["roformer", ["RoFormerForSequenceClassification", RoFormerForSequenceClassification]],
  ["electra", ["ElectraForSequenceClassification", ElectraForSequenceClassification]],
  ["esm", ["EsmForSequenceClassification", EsmForSequenceClassification]],
  ["convbert", ["ConvBertForSequenceClassification", ConvBertForSequenceClassification]],
  ["camembert", ["CamembertForSequenceClassification", CamembertForSequenceClassification]],
  ["deberta", ["DebertaForSequenceClassification", DebertaForSequenceClassification]],
  ["deberta-v2", ["DebertaV2ForSequenceClassification", DebertaV2ForSequenceClassification]],
  ["mpnet", ["MPNetForSequenceClassification", MPNetForSequenceClassification]],
  ["albert", ["AlbertForSequenceClassification", AlbertForSequenceClassification]],
  ["distilbert", ["DistilBertForSequenceClassification", DistilBertForSequenceClassification]],
  ["roberta", ["RobertaForSequenceClassification", RobertaForSequenceClassification]],
  ["xlm", ["XLMForSequenceClassification", XLMForSequenceClassification]],
  ["xlm-roberta", ["XLMRobertaForSequenceClassification", XLMRobertaForSequenceClassification]],
  ["bart", ["BartForSequenceClassification", BartForSequenceClassification]],
  ["mbart", ["MBartForSequenceClassification", MBartForSequenceClassification]],
  ["mobilebert", ["MobileBertForSequenceClassification", MobileBertForSequenceClassification]],
  ["squeezebert", ["SqueezeBertForSequenceClassification", SqueezeBertForSequenceClassification]]
]);
const MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["bert", ["BertForTokenClassification", BertForTokenClassification]],
  ["roformer", ["RoFormerForTokenClassification", RoFormerForTokenClassification]],
  ["electra", ["ElectraForTokenClassification", ElectraForTokenClassification]],
  ["esm", ["EsmForTokenClassification", EsmForTokenClassification]],
  ["convbert", ["ConvBertForTokenClassification", ConvBertForTokenClassification]],
  ["camembert", ["CamembertForTokenClassification", CamembertForTokenClassification]],
  ["deberta", ["DebertaForTokenClassification", DebertaForTokenClassification]],
  ["deberta-v2", ["DebertaV2ForTokenClassification", DebertaV2ForTokenClassification]],
  ["mpnet", ["MPNetForTokenClassification", MPNetForTokenClassification]],
  ["distilbert", ["DistilBertForTokenClassification", DistilBertForTokenClassification]],
  ["roberta", ["RobertaForTokenClassification", RobertaForTokenClassification]],
  ["xlm", ["XLMForTokenClassification", XLMForTokenClassification]],
  ["xlm-roberta", ["XLMRobertaForTokenClassification", XLMRobertaForTokenClassification]]
]);
const MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["t5", ["T5ForConditionalGeneration", T5ForConditionalGeneration]],
  ["longt5", ["LongT5ForConditionalGeneration", LongT5ForConditionalGeneration]],
  ["mt5", ["MT5ForConditionalGeneration", MT5ForConditionalGeneration]],
  ["bart", ["BartForConditionalGeneration", BartForConditionalGeneration]],
  ["mbart", ["MBartForConditionalGeneration", MBartForConditionalGeneration]],
  ["marian", ["MarianMTModel", MarianMTModel]],
  ["m2m_100", ["M2M100ForConditionalGeneration", M2M100ForConditionalGeneration]],
  ["blenderbot", ["BlenderbotForConditionalGeneration", BlenderbotForConditionalGeneration]],
  ["blenderbot-small", ["BlenderbotSmallForConditionalGeneration", BlenderbotSmallForConditionalGeneration]]
]);
const MODEL_WITH_LM_HEAD_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["bloom", ["BloomForCausalLM", BloomForCausalLM]],
  ["gpt2", ["GPT2LMHeadModel", GPT2LMHeadModel]],
  ["gptj", ["GPTJForCausalLM", GPTJForCausalLM]],
  ["gpt_bigcode", ["GPTBigCodeForCausalLM", GPTBigCodeForCausalLM]],
  ["gpt_neo", ["GPTNeoForCausalLM", GPTNeoForCausalLM]],
  ["gpt_neox", ["GPTNeoXForCausalLM", GPTNeoXForCausalLM]],
  ["codegen", ["CodeGenForCausalLM", CodeGenForCausalLM]],
  ["llama", ["LlamaForCausalLM", LlamaForCausalLM]],
  ["qwen2", ["Qwen2ForCausalLM", Qwen2ForCausalLM]],
  ["phi", ["PhiForCausalLM", PhiForCausalLM]],
  ["mpt", ["MptForCausalLM", MptForCausalLM]],
  ["opt", ["OPTForCausalLM", OPTForCausalLM]],
  ["mbart", ["MBartForCausalLM", MBartForCausalLM]],
  ["mistral", ["MistralForCausalLM", MistralForCausalLM]],
  ["starcoder2", ["Starcoder2ForCausalLM", Starcoder2ForCausalLM]],
  ["falcon", ["FalconForCausalLM", FalconForCausalLM]],
  ["trocr", ["TrOCRForCausalLM", TrOCRForCausalLM]],
  ["stablelm", ["StableLmForCausalLM", StableLmForCausalLM]]
]);
const MODEL_FOR_MASKED_LM_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["bert", ["BertForMaskedLM", BertForMaskedLM]],
  ["roformer", ["RoFormerForMaskedLM", RoFormerForMaskedLM]],
  ["electra", ["ElectraForMaskedLM", ElectraForMaskedLM]],
  ["esm", ["EsmForMaskedLM", EsmForMaskedLM]],
  ["convbert", ["ConvBertForMaskedLM", ConvBertForMaskedLM]],
  ["camembert", ["CamembertForMaskedLM", CamembertForMaskedLM]],
  ["deberta", ["DebertaForMaskedLM", DebertaForMaskedLM]],
  ["deberta-v2", ["DebertaV2ForMaskedLM", DebertaV2ForMaskedLM]],
  ["mpnet", ["MPNetForMaskedLM", MPNetForMaskedLM]],
  ["albert", ["AlbertForMaskedLM", AlbertForMaskedLM]],
  ["distilbert", ["DistilBertForMaskedLM", DistilBertForMaskedLM]],
  ["roberta", ["RobertaForMaskedLM", RobertaForMaskedLM]],
  ["xlm", ["XLMWithLMHeadModel", XLMWithLMHeadModel]],
  ["xlm-roberta", ["XLMRobertaForMaskedLM", XLMRobertaForMaskedLM]],
  ["mobilebert", ["MobileBertForMaskedLM", MobileBertForMaskedLM]],
  ["squeezebert", ["SqueezeBertForMaskedLM", SqueezeBertForMaskedLM]]
]);
const MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["bert", ["BertForQuestionAnswering", BertForQuestionAnswering]],
  ["roformer", ["RoFormerForQuestionAnswering", RoFormerForQuestionAnswering]],
  ["electra", ["ElectraForQuestionAnswering", ElectraForQuestionAnswering]],
  ["convbert", ["ConvBertForQuestionAnswering", ConvBertForQuestionAnswering]],
  ["camembert", ["CamembertForQuestionAnswering", CamembertForQuestionAnswering]],
  ["deberta", ["DebertaForQuestionAnswering", DebertaForQuestionAnswering]],
  ["deberta-v2", ["DebertaV2ForQuestionAnswering", DebertaV2ForQuestionAnswering]],
  ["mpnet", ["MPNetForQuestionAnswering", MPNetForQuestionAnswering]],
  ["albert", ["AlbertForQuestionAnswering", AlbertForQuestionAnswering]],
  ["distilbert", ["DistilBertForQuestionAnswering", DistilBertForQuestionAnswering]],
  ["roberta", ["RobertaForQuestionAnswering", RobertaForQuestionAnswering]],
  ["xlm", ["XLMForQuestionAnswering", XLMForQuestionAnswering]],
  ["xlm-roberta", ["XLMRobertaForQuestionAnswering", XLMRobertaForQuestionAnswering]],
  ["mobilebert", ["MobileBertForQuestionAnswering", MobileBertForQuestionAnswering]],
  ["squeezebert", ["SqueezeBertForQuestionAnswering", SqueezeBertForQuestionAnswering]]
]);
const MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["vision-encoder-decoder", ["VisionEncoderDecoderModel", VisionEncoderDecoderModel]]
]);
const MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["vit", ["ViTForImageClassification", ViTForImageClassification]],
  ["fastvit", ["FastViTForImageClassification", FastViTForImageClassification]],
  ["mobilevit", ["MobileViTForImageClassification", MobileViTForImageClassification]],
  ["mobilevitv2", ["MobileViTV2ForImageClassification", MobileViTV2ForImageClassification]],
  ["beit", ["BeitForImageClassification", BeitForImageClassification]],
  ["deit", ["DeiTForImageClassification", DeiTForImageClassification]],
  ["convnext", ["ConvNextForImageClassification", ConvNextForImageClassification]],
  ["convnextv2", ["ConvNextV2ForImageClassification", ConvNextV2ForImageClassification]],
  ["dinov2", ["Dinov2ForImageClassification", Dinov2ForImageClassification]],
  ["resnet", ["ResNetForImageClassification", ResNetForImageClassification]],
  ["swin", ["SwinForImageClassification", SwinForImageClassification]],
  ["segformer", ["SegformerForImageClassification", SegformerForImageClassification]],
  ["efficientnet", ["EfficientNetForImageClassification", EfficientNetForImageClassification]]
]);
const MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["detr", ["DetrForObjectDetection", DetrForObjectDetection]],
  ["table-transformer", ["TableTransformerForObjectDetection", TableTransformerForObjectDetection]],
  ["yolos", ["YolosForObjectDetection", YolosForObjectDetection]]
]);
const MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["owlvit", ["OwlViTForObjectDetection", OwlViTForObjectDetection]],
  ["owlv2", ["Owlv2ForObjectDetection", Owlv2ForObjectDetection]]
]);
const MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["detr", ["DetrForSegmentation", DetrForSegmentation]],
  ["clipseg", ["CLIPSegForImageSegmentation", CLIPSegForImageSegmentation]]
]);
const MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["segformer", ["SegformerForSemanticSegmentation", SegformerForSemanticSegmentation]]
]);
const MODEL_FOR_MASK_GENERATION_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["sam", ["SamModel", SamModel]]
]);
const MODEL_FOR_CTC_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["wav2vec2", ["Wav2Vec2ForCTC", Wav2Vec2ForCTC]],
  ["wav2vec2-bert", ["Wav2Vec2BertForCTC", Wav2Vec2BertForCTC]],
  ["unispeech", ["UniSpeechForCTC", UniSpeechForCTC]],
  ["unispeech-sat", ["UniSpeechSatForCTC", UniSpeechSatForCTC]],
  ["wavlm", ["WavLMForCTC", WavLMForCTC]],
  ["hubert", ["HubertForCTC", HubertForCTC]]
]);
const MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["wav2vec2", ["Wav2Vec2ForSequenceClassification", Wav2Vec2ForSequenceClassification]],
  ["wav2vec2-bert", ["Wav2Vec2BertForSequenceClassification", Wav2Vec2BertForSequenceClassification]],
  ["unispeech", ["UniSpeechForSequenceClassification", UniSpeechForSequenceClassification]],
  ["unispeech-sat", ["UniSpeechSatForSequenceClassification", UniSpeechSatForSequenceClassification]],
  ["wavlm", ["WavLMForSequenceClassification", WavLMForSequenceClassification]],
  ["hubert", ["HubertForSequenceClassification", HubertForSequenceClassification]],
  ["audio-spectrogram-transformer", ["ASTForAudioClassification", ASTForAudioClassification]]
]);
const MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["wavlm", ["WavLMForXVector", WavLMForXVector]]
]);
const MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["unispeech-sat", ["UniSpeechSatForAudioFrameClassification", UniSpeechSatForAudioFrameClassification]],
  ["wavlm", ["WavLMForAudioFrameClassification", WavLMForAudioFrameClassification]],
  ["wav2vec2", ["Wav2Vec2ForAudioFrameClassification", Wav2Vec2ForAudioFrameClassification]]
]);
const MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["vitmatte", ["VitMatteForImageMatting", VitMatteForImageMatting]]
]);
const MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["swin2sr", ["Swin2SRForImageSuperResolution", Swin2SRForImageSuperResolution]]
]);
const MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["dpt", ["DPTForDepthEstimation", DPTForDepthEstimation]],
  ["depth_anything", ["DepthAnythingForDepthEstimation", DepthAnythingForDepthEstimation]],
  ["glpn", ["GLPNForDepthEstimation", GLPNForDepthEstimation]]
]);
const MODEL_FOR_IMAGE_FEATURE_EXTRACTION_MAPPING_NAMES = /* @__PURE__ */ new Map([
  ["clip", ["CLIPVisionModelWithProjection", CLIPVisionModelWithProjection]],
  ["siglip", ["SiglipVisionModel", SiglipVisionModel]]
]);
const MODEL_CLASS_TYPE_MAPPING = [
  [MODEL_MAPPING_NAMES_ENCODER_ONLY, MODEL_TYPES.EncoderOnly],
  [MODEL_MAPPING_NAMES_ENCODER_DECODER, MODEL_TYPES.EncoderDecoder],
  [MODEL_MAPPING_NAMES_DECODER_ONLY, MODEL_TYPES.DecoderOnly],
  [MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
  [MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
  [MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES, MODEL_TYPES.Seq2Seq],
  [MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES, MODEL_TYPES.Seq2Seq],
  [MODEL_WITH_LM_HEAD_MAPPING_NAMES, MODEL_TYPES.DecoderOnly],
  [MODEL_FOR_MASKED_LM_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
  [MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
  [MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES, MODEL_TYPES.Vision2Seq],
  [MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
  [MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
  [MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
  [MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
  [MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
  [MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
  [MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
  [MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
  [MODEL_FOR_MASK_GENERATION_MAPPING_NAMES, MODEL_TYPES.MaskGeneration],
  [MODEL_FOR_CTC_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
  [MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
  [MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES, MODEL_TYPES.Seq2Seq],
  [MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
  [MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
  [MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly],
  // Custom:
  [MODEL_FOR_IMAGE_FEATURE_EXTRACTION_MAPPING_NAMES, MODEL_TYPES.EncoderOnly]
];
for (const [mappings, type] of MODEL_CLASS_TYPE_MAPPING) {
  for (const [name2, model] of mappings.values()) {
    MODEL_TYPE_MAPPING.set(name2, type);
    MODEL_CLASS_TO_NAME_MAPPING.set(model, name2);
    MODEL_NAME_TO_CLASS_MAPPING.set(name2, model);
  }
}
const CUSTOM_MAPPING = [
  ["CLIPTextModelWithProjection", CLIPTextModelWithProjection, MODEL_TYPES.EncoderOnly],
  ["SiglipTextModel", SiglipTextModel, MODEL_TYPES.EncoderOnly],
  ["ClapTextModelWithProjection", ClapTextModelWithProjection, MODEL_TYPES.EncoderOnly],
  ["ClapAudioModelWithProjection", ClapAudioModelWithProjection, MODEL_TYPES.EncoderOnly]
];
for (const [name2, model, type] of CUSTOM_MAPPING) {
  MODEL_TYPE_MAPPING.set(name2, type);
  MODEL_CLASS_TO_NAME_MAPPING.set(model, name2);
  MODEL_NAME_TO_CLASS_MAPPING.set(name2, model);
}
class AutoModel extends PretrainedMixin {
}
/** @type {Map<string, Object>[]} */
// @ts-ignore
__publicField(AutoModel, "MODEL_CLASS_MAPPINGS", MODEL_CLASS_TYPE_MAPPING.map((x) => x[0]));
__publicField(AutoModel, "BASE_IF_FAIL", true);
class Seq2SeqLMOutput extends ModelOutput {
  /**
   * @param {Object} output The output of the model.
   * @param {Tensor} output.logits The output logits of the model.
   * @param {Tensor} output.past_key_values An tensor of key/value pairs that represent the previous state of the model.
   * @param {Tensor} output.encoder_outputs The output of the encoder in a sequence-to-sequence model.
   * @param {Tensor} [output.decoder_attentions] Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
   * @param {Tensor} [output.cross_attentions] Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
   */
  constructor({ logits, past_key_values, encoder_outputs, decoder_attentions = null, cross_attentions = null }) {
    super();
    this.logits = logits;
    this.past_key_values = past_key_values;
    this.encoder_outputs = encoder_outputs;
    this.decoder_attentions = decoder_attentions;
    this.cross_attentions = cross_attentions;
  }
}
class SequenceClassifierOutput extends ModelOutput {
  /**
   * @param {Object} output The output of the model.
   * @param {Tensor} output.logits classification (or regression if config.num_labels==1) scores (before SoftMax).
   */
  constructor({ logits }) {
    super();
    this.logits = logits;
  }
}
class XVectorOutput extends ModelOutput {
  /**
   * @param {Object} output The output of the model.
   * @param {Tensor} output.logits Classification hidden states before AMSoftmax, of shape `(batch_size, config.xvector_output_dim)`.
   * @param {Tensor} output.embeddings Utterance embeddings used for vector similarity-based retrieval, of shape `(batch_size, config.xvector_output_dim)`.
   */
  constructor({ logits, embeddings }) {
    super();
    this.logits = logits;
    this.embeddings = embeddings;
  }
}
class TokenClassifierOutput extends ModelOutput {
  /**
   * @param {Object} output The output of the model.
   * @param {Tensor} output.logits Classification scores (before SoftMax).
   */
  constructor({ logits }) {
    super();
    this.logits = logits;
  }
}
class MaskedLMOutput extends ModelOutput {
  /**
   * @param {Object} output The output of the model.
   * @param {Tensor} output.logits Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
   */
  constructor({ logits }) {
    super();
    this.logits = logits;
  }
}
class QuestionAnsweringModelOutput extends ModelOutput {
  /**
   * @param {Object} output The output of the model.
   * @param {Tensor} output.start_logits Span-start scores (before SoftMax).
   * @param {Tensor} output.end_logits Span-end scores (before SoftMax).
   */
  constructor({ start_logits, end_logits }) {
    super();
    this.start_logits = start_logits;
    this.end_logits = end_logits;
  }
}
class CausalLMOutput extends ModelOutput {
  /**
   * @param {Object} output The output of the model.
   * @param {Tensor} output.logits Prediction scores of the language modeling head (scores for each vocabulary token before softmax).
   */
  constructor({ logits }) {
    super();
    this.logits = logits;
  }
}
class ImageMattingOutput extends ModelOutput {
  /**
   * @param {Object} output The output of the model.
   * @param {Tensor} output.alphas Estimated alpha values, of shape `(batch_size, num_channels, height, width)`.
   */
  constructor({ alphas }) {
    super();
    this.alphas = alphas;
  }
}
class VitsModelOutput extends ModelOutput {
  /**
   * @param {Object} output The output of the model.
   * @param {Tensor} output.waveform The final audio waveform predicted by the model, of shape `(batch_size, sequence_length)`.
   * @param {Tensor} output.spectrogram The log-mel spectrogram predicted at the output of the flow model.
   * This spectrogram is passed to the Hi-Fi GAN decoder model to obtain the final audio waveform.
   */
  constructor({ waveform, spectrogram }) {
    super();
    this.waveform = waveform;
    this.spectrogram = spectrogram;
  }
}
const BROWSER_ENV = typeof self !== "undefined";
if (BROWSER_ENV) ;
else if (sharp) ;
else {
  throw new Error("Unable to load image processing library.");
}
class LRUNode {
  constructor(key, value, prev = null, next = null, frequency = 1, lastAccessed = Date.now()) {
    this.key = key;
    this.value = value;
    this.prev = prev;
    this.next = next;
    this.frequency = frequency;
    this.lastAccessed = lastAccessed;
  }
}
class LRUCache {
  constructor(capacity) {
    __publicField(this, "capacity");
    __publicField(this, "cache");
    __publicField(this, "head");
    __publicField(this, "tail");
    this.capacity = capacity > 0 ? capacity : 100;
    this.cache = /* @__PURE__ */ new Map();
    this.head = new LRUNode(null, null);
    this.tail = new LRUNode(null, null);
    this.head.next = this.tail;
    this.tail.prev = this.head;
  }
  addToHead(node) {
    node.prev = this.head;
    node.next = this.head.next;
    this.head.next.prev = node;
    this.head.next = node;
  }
  removeNode(node) {
    node.prev.next = node.next;
    node.next.prev = node.prev;
  }
  moveToHead(node) {
    this.removeNode(node);
    this.addToHead(node);
  }
  findVictimNode() {
    let victim = this.tail.prev;
    let minScore = this.calculateEvictionScore(victim);
    let current = this.tail.prev;
    let count = 0;
    const maxCheck = Math.min(5, this.cache.size);
    while (current && current !== this.head && count < maxCheck) {
      const score = this.calculateEvictionScore(current);
      if (score < minScore) {
        minScore = score;
        victim = current;
      }
      current = current.prev;
      count++;
    }
    return victim;
  }
  calculateEvictionScore(node) {
    const now = Date.now();
    const timeSinceAccess = now - node.lastAccessed;
    const timeWeight = 1 / (1 + timeSinceAccess / (1e3 * 60));
    const frequencyWeight = Math.log(node.frequency + 1);
    return frequencyWeight * timeWeight;
  }
  get(key) {
    const node = this.cache.get(key);
    if (node) {
      node.frequency++;
      node.lastAccessed = Date.now();
      this.moveToHead(node);
      return node.value;
    }
    return null;
  }
  set(key, value) {
    const existingNode = this.cache.get(key);
    if (existingNode) {
      existingNode.value = value;
      this.moveToHead(existingNode);
    } else {
      const newNode = new LRUNode(key, value);
      if (this.cache.size >= this.capacity) {
        const victimNode = this.findVictimNode();
        this.removeNode(victimNode);
        this.cache.delete(victimNode.key);
      }
      this.cache.set(key, newNode);
      this.addToHead(newNode);
    }
  }
  has(key) {
    return this.cache.has(key);
  }
  clear() {
    this.cache.clear();
    this.head.next = this.tail;
    this.tail.prev = this.head;
  }
  get size() {
    return this.cache.size;
  }
  /**
   * Get cache statistics
   */
  getStats() {
    return {
      size: this.cache.size,
      capacity: this.capacity,
      usage: this.cache.size / this.capacity
    };
  }
}
class SIMDMathEngine {
  constructor() {
    __publicField(this, "wasmModule", null);
    __publicField(this, "simdMath", null);
    __publicField(this, "isInitialized", false);
    __publicField(this, "isInitializing", false);
    __publicField(this, "initPromise", null);
    __publicField(this, "alignedBufferPool", /* @__PURE__ */ new Map());
    __publicField(this, "maxPoolSize", 5);
  }
  initialize() {
    return __async(this, null, function* () {
      if (this.isInitialized) return;
      if (this.isInitializing && this.initPromise) return this.initPromise;
      this.isInitializing = true;
      this.initPromise = this._doInitialize().finally(() => {
        this.isInitializing = false;
      });
      return this.initPromise;
    });
  }
  _doInitialize() {
    return __async(this, null, function* () {
      try {
        console.log("SIMDMathEngine: Initializing WebAssembly module...");
        const wasmUrl = chrome.runtime.getURL("workers/simd_math.js");
        const wasmModule = yield import(wasmUrl);
        const wasmInstance = yield wasmModule.default();
        this.wasmModule = {
          SIMDMath: wasmModule.SIMDMath,
          memory: wasmInstance.memory,
          default: wasmModule.default
        };
        this.simdMath = new this.wasmModule.SIMDMath();
        this.isInitialized = true;
        console.log("SIMDMathEngine: WebAssembly module initialized successfully");
      } catch (error) {
        console.error("SIMDMathEngine: Failed to initialize WebAssembly module:", error);
        this.isInitialized = false;
        throw error;
      }
    });
  }
  /**
   * Get aligned buffer (16-byte aligned, suitable for SIMD)
   */
  getAlignedBuffer(size) {
    if (!this.alignedBufferPool.has(size)) {
      this.alignedBufferPool.set(size, []);
    }
    const pool = this.alignedBufferPool.get(size);
    if (pool.length > 0) {
      return pool.pop();
    }
    const buffer = new ArrayBuffer(size * 4 + 15);
    const alignedOffset = (16 - buffer.byteLength % 16) % 16;
    return new Float32Array(buffer, alignedOffset, size);
  }
  /**
   * Release aligned buffer back to pool
   */
  releaseAlignedBuffer(buffer) {
    const size = buffer.length;
    const pool = this.alignedBufferPool.get(size);
    if (pool && pool.length < this.maxPoolSize) {
      buffer.fill(0);
      pool.push(buffer);
    }
  }
  /**
   * Check if vector is already aligned
   */
  isAligned(array) {
    return array.byteOffset % 16 === 0;
  }
  /**
   * Ensure vector alignment, create aligned copy if not aligned
   */
  ensureAligned(array) {
    if (this.isAligned(array)) {
      return { aligned: array, needsRelease: false };
    }
    const aligned = this.getAlignedBuffer(array.length);
    aligned.set(array);
    return { aligned, needsRelease: true };
  }
  /**
   * SIMD-optimized cosine similarity calculation
   */
  cosineSimilarity(vecA, vecB) {
    return __async(this, null, function* () {
      if (!this.isInitialized) {
        yield this.initialize();
      }
      if (!this.simdMath) {
        throw new Error("SIMD math engine not initialized");
      }
      const { aligned: alignedA, needsRelease: releaseA } = this.ensureAligned(vecA);
      const { aligned: alignedB, needsRelease: releaseB } = this.ensureAligned(vecB);
      try {
        const result = this.simdMath.cosine_similarity(alignedA, alignedB);
        return result;
      } finally {
        if (releaseA) this.releaseAlignedBuffer(alignedA);
        if (releaseB) this.releaseAlignedBuffer(alignedB);
      }
    });
  }
  /**
   * Batch similarity calculation
   */
  batchSimilarity(vectors, query) {
    return __async(this, null, function* () {
      if (!this.isInitialized) {
        yield this.initialize();
      }
      if (!this.simdMath) {
        throw new Error("SIMD math engine not initialized");
      }
      const vectorDim = query.length;
      const numVectors = vectors.length;
      const packedVectors = this.getAlignedBuffer(numVectors * vectorDim);
      const { aligned: alignedQuery, needsRelease: releaseQuery } = this.ensureAligned(query);
      try {
        let offset = 0;
        for (const vector of vectors) {
          packedVectors.set(vector, offset);
          offset += vectorDim;
        }
        const results = this.simdMath.batch_similarity(packedVectors, alignedQuery, vectorDim);
        return Array.from(results);
      } finally {
        this.releaseAlignedBuffer(packedVectors);
        if (releaseQuery) this.releaseAlignedBuffer(alignedQuery);
      }
    });
  }
  /**
   * Similarity matrix calculation
   */
  similarityMatrix(vectorsA, vectorsB) {
    return __async(this, null, function* () {
      if (!this.isInitialized) {
        yield this.initialize();
      }
      if (!this.simdMath || vectorsA.length === 0 || vectorsB.length === 0) {
        return [];
      }
      const vectorDim = vectorsA[0].length;
      const numA = vectorsA.length;
      const numB = vectorsB.length;
      const packedA = this.getAlignedBuffer(numA * vectorDim);
      const packedB = this.getAlignedBuffer(numB * vectorDim);
      try {
        let offsetA = 0;
        for (const vector of vectorsA) {
          packedA.set(vector, offsetA);
          offsetA += vectorDim;
        }
        let offsetB = 0;
        for (const vector of vectorsB) {
          packedB.set(vector, offsetB);
          offsetB += vectorDim;
        }
        const flatResults = this.simdMath.similarity_matrix(packedA, packedB, vectorDim);
        const matrix = [];
        for (let i = 0; i < numA; i++) {
          const row = [];
          for (let j = 0; j < numB; j++) {
            row.push(flatResults[i * numB + j]);
          }
          matrix.push(row);
        }
        return matrix;
      } finally {
        this.releaseAlignedBuffer(packedA);
        this.releaseAlignedBuffer(packedB);
      }
    });
  }
  /**
   * Check SIMD support
   */
  static checkSIMDSupport() {
    return __async(this, null, function* () {
      try {
        console.log("SIMDMathEngine: Checking SIMD support...");
        const userAgent = navigator.userAgent;
        const browserInfo = SIMDMathEngine.getBrowserInfo();
        console.log("Browser info:", browserInfo);
        console.log("User Agent:", userAgent);
        if (typeof WebAssembly !== "object") {
          console.log("WebAssembly not supported");
          return false;
        }
        console.log("✅ WebAssembly basic support: OK");
        if (typeof WebAssembly.validate !== "function") {
          console.log("❌ WebAssembly.validate not available");
          return false;
        }
        console.log("✅ WebAssembly.validate: OK");
        const basicWasm = new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0]);
        const basicValid = WebAssembly.validate(basicWasm);
        console.log("✅ Basic WASM validation:", basicValid);
        console.log("Testing SIMD WASM module...");
        let wasmSIMDSupported = false;
        try {
          const simdWasm = new Uint8Array([
            0,
            97,
            115,
            109,
            // WASM magic
            1,
            0,
            0,
            0,
            // version
            1,
            5,
            1,
            // type section
            96,
            0,
            1,
            123,
            // function type: () -> v128
            3,
            2,
            1,
            0,
            // function section
            10,
            10,
            1,
            // code section
            8,
            0,
            // function body
            253,
            12,
            // v128.const
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            0,
            11
            // end
          ]);
          wasmSIMDSupported = WebAssembly.validate(simdWasm);
          console.log("Method 1 - Standard SIMD test result:", wasmSIMDSupported);
        } catch (error) {
          console.log("Method 1 failed:", error);
        }
        if (!wasmSIMDSupported) {
          try {
            const simpleSimdWasm = new Uint8Array([
              0,
              97,
              115,
              109,
              // WASM magic
              1,
              0,
              0,
              0,
              // version
              1,
              6,
              1,
              // type section
              96,
              1,
              127,
              1,
              123,
              // function type: (i32) -> v128
              3,
              2,
              1,
              0,
              // function section
              10,
              7,
              1,
              // code section
              5,
              0,
              // function body
              32,
              0,
              // local.get 0
              253,
              13,
              // i32x4.splat
              11
              // end
            ]);
            wasmSIMDSupported = WebAssembly.validate(simpleSimdWasm);
            console.log("Method 2 - Simple SIMD test result:", wasmSIMDSupported);
          } catch (error) {
            console.log("Method 2 failed:", error);
          }
        }
        if (!wasmSIMDSupported) {
          try {
            const featureTest = WebAssembly.validate(
              new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0])
            );
            if (featureTest) {
              const chromeMatch = userAgent.match(/Chrome\/(\d+)/);
              if (chromeMatch && parseInt(chromeMatch[1]) >= 91) {
                console.log("Method 3 - Chrome version check: SIMD should be available");
                wasmSIMDSupported = true;
              }
            }
          } catch (error) {
            console.log("Method 3 failed:", error);
          }
        }
        if (!wasmSIMDSupported) {
          console.log("❌ SIMD not supported. Browser requirements:");
          console.log("- Chrome 91+, Firefox 89+, Safari 16.4+, Edge 91+");
          console.log("Your browser should support SIMD. Possible issues:");
          console.log("1. Extension context limitations");
          console.log("2. Security policies");
          console.log("3. Feature flags disabled");
        } else {
          console.log("✅ SIMD supported!");
        }
        return wasmSIMDSupported;
      } catch (error) {
        console.error("SIMD support check failed:", error);
        if (error instanceof Error) {
          console.error("Error details:", {
            name: error.name,
            message: error.message,
            stack: error.stack
          });
        }
        return false;
      }
    });
  }
  /**
   * Get browser information
   */
  static getBrowserInfo() {
    const userAgent = navigator.userAgent;
    let browserName = "Unknown";
    let version = "Unknown";
    let supported = false;
    if (userAgent.includes("Chrome/")) {
      browserName = "Chrome";
      const match = userAgent.match(/Chrome\/(\d+)/);
      if (match) {
        version = match[1];
        supported = parseInt(version) >= 91;
      }
    } else if (userAgent.includes("Firefox/")) {
      browserName = "Firefox";
      const match = userAgent.match(/Firefox\/(\d+)/);
      if (match) {
        version = match[1];
        supported = parseInt(version) >= 89;
      }
    } else if (userAgent.includes("Safari/") && !userAgent.includes("Chrome/")) {
      browserName = "Safari";
      const match = userAgent.match(/Version\/(\d+\.\d+)/);
      if (match) {
        version = match[1];
        const versionNum = parseFloat(version);
        supported = versionNum >= 16.4;
      }
    } else if (userAgent.includes("Edg/")) {
      browserName = "Edge";
      const match = userAgent.match(/Edg\/(\d+)/);
      if (match) {
        version = match[1];
        supported = parseInt(version) >= 91;
      }
    }
    return { name: browserName, version, supported };
  }
  getStats() {
    return {
      isInitialized: this.isInitialized,
      isInitializing: this.isInitializing,
      bufferPoolStats: Array.from(this.alignedBufferPool.entries()).map(([size, buffers]) => ({
        size,
        pooled: buffers.length,
        maxPoolSize: this.maxPoolSize
      }))
    };
  }
  dispose() {
    if (this.simdMath) {
      try {
        this.simdMath.free();
      } catch (error) {
        console.warn("Failed to free SIMD math instance:", error);
      }
      this.simdMath = null;
    }
    this.alignedBufferPool.clear();
    this.wasmModule = null;
    this.isInitialized = false;
    this.isInitializing = false;
    this.initPromise = null;
  }
}
var MessageTarget = /* @__PURE__ */ ((MessageTarget2) => {
  MessageTarget2["Offscreen"] = "offscreen";
  MessageTarget2["ContentScript"] = "content_script";
  MessageTarget2["Background"] = "background";
  return MessageTarget2;
})(MessageTarget || {});
const BACKGROUND_MESSAGE_TYPES = {
  SWITCH_SEMANTIC_MODEL: "switch_semantic_model",
  GET_MODEL_STATUS: "get_model_status",
  UPDATE_MODEL_STATUS: "update_model_status",
  GET_STORAGE_STATS: "get_storage_stats",
  CLEAR_ALL_DATA: "clear_all_data",
  GET_SERVER_STATUS: "get_server_status",
  REFRESH_SERVER_STATUS: "refresh_server_status",
  SERVER_STATUS_CHANGED: "server_status_changed",
  INITIALIZE_SEMANTIC_ENGINE: "initialize_semantic_engine"
};
const OFFSCREEN_MESSAGE_TYPES = {
  SIMILARITY_ENGINE_INIT: "similarityEngineInit",
  SIMILARITY_ENGINE_COMPUTE: "similarityEngineCompute",
  SIMILARITY_ENGINE_BATCH_COMPUTE: "similarityEngineBatchCompute",
  SIMILARITY_ENGINE_STATUS: "similarityEngineStatus"
};
var SendMessageType = /* @__PURE__ */ ((SendMessageType2) => {
  SendMessageType2["ScreenshotPreparePageForCapture"] = "preparePageForCapture";
  SendMessageType2["ScreenshotGetPageDetails"] = "getPageDetails";
  SendMessageType2["ScreenshotGetElementDetails"] = "getElementDetails";
  SendMessageType2["ScreenshotScrollPage"] = "scrollPage";
  SendMessageType2["ScreenshotResetPageAfterCapture"] = "resetPageAfterCapture";
  SendMessageType2["WebFetcherGetHtmlContent"] = "getHtmlContent";
  SendMessageType2["WebFetcherGetTextContent"] = "getTextContent";
  SendMessageType2["ClickElement"] = "clickElement";
  SendMessageType2["FillElement"] = "fillElement";
  SendMessageType2["GetInteractiveElements"] = "getInteractiveElements";
  SendMessageType2["NetworkSendRequest"] = "sendPureNetworkRequest";
  SendMessageType2["SimulateKeyboard"] = "simulateKeyboard";
  SendMessageType2["SimilarityEngineInit"] = "similarityEngineInit";
  SendMessageType2["SimilarityEngineComputeBatch"] = "similarityEngineComputeBatch";
  return SendMessageType2;
})(SendMessageType || {});
const CACHE_NAME = "onnx-model-cache-v1";
const CACHE_EXPIRY_DAYS = 30;
const MAX_CACHE_SIZE_MB = 500;
const _ModelCacheManager = class _ModelCacheManager {
  static getInstance() {
    if (!_ModelCacheManager.instance) {
      _ModelCacheManager.instance = new _ModelCacheManager();
    }
    return _ModelCacheManager.instance;
  }
  constructor() {
  }
  getCacheMetadataKey(modelUrl) {
    const encodedUrl = encodeURIComponent(modelUrl);
    return `https://cache-metadata.local/${encodedUrl}`;
  }
  isCacheExpired(metadata) {
    const now = Date.now();
    const expiryTime = metadata.timestamp + CACHE_EXPIRY_DAYS * 24 * 60 * 60 * 1e3;
    return now > expiryTime;
  }
  isMetadataUrl(url) {
    return url.startsWith("https://cache-metadata.local/");
  }
  collectCacheEntries() {
    return __async(this, null, function* () {
      const cache = yield caches.open(CACHE_NAME);
      const keys = yield cache.keys();
      const entries = [];
      let totalSize = 0;
      let entryCount = 0;
      for (const request of keys) {
        if (this.isMetadataUrl(request.url)) continue;
        const response = yield cache.match(request);
        if (response) {
          const blob = yield response.blob();
          const size = blob.size;
          totalSize += size;
          entryCount++;
          const metadataResponse = yield cache.match(this.getCacheMetadataKey(request.url));
          let timestamp = 0;
          if (metadataResponse) {
            try {
              const metadata = yield metadataResponse.json();
              timestamp = metadata.timestamp;
            } catch (error) {
              console.warn("Failed to parse cache metadata:", error);
            }
          }
          entries.push({
            url: request.url,
            timestamp,
            size
          });
        }
      }
      return { entries, totalSize, entryCount };
    });
  }
  cleanupCacheOnDemand(newDataSize = 0) {
    return __async(this, null, function* () {
      const cache = yield caches.open(CACHE_NAME);
      const { entries, totalSize } = yield this.collectCacheEntries();
      const maxSizeBytes = MAX_CACHE_SIZE_MB * 1024 * 1024;
      const projectedSize = totalSize + newDataSize;
      if (projectedSize <= maxSizeBytes) {
        return;
      }
      console.log(
        `Cache size (${(totalSize / 1024 / 1024).toFixed(2)}MB) + new data (${(newDataSize / 1024 / 1024).toFixed(2)}MB) exceeds limit (${MAX_CACHE_SIZE_MB}MB), cleaning up...`
      );
      const expiredEntries = [];
      const validEntries = [];
      for (const entry of entries) {
        const metadataResponse = yield cache.match(this.getCacheMetadataKey(entry.url));
        let isExpired = false;
        if (metadataResponse) {
          try {
            const metadata = yield metadataResponse.json();
            isExpired = this.isCacheExpired(metadata);
          } catch (error) {
            isExpired = true;
          }
        } else {
          isExpired = true;
        }
        if (isExpired) {
          expiredEntries.push(entry);
        } else {
          validEntries.push(entry);
        }
      }
      let currentSize = totalSize;
      for (const entry of expiredEntries) {
        yield cache.delete(entry.url);
        yield cache.delete(this.getCacheMetadataKey(entry.url));
        currentSize -= entry.size;
        console.log(
          `Cleaned up expired cache entry: ${entry.url} (${(entry.size / 1024 / 1024).toFixed(2)}MB)`
        );
      }
      if (currentSize + newDataSize > maxSizeBytes) {
        validEntries.sort((a, b) => a.timestamp - b.timestamp);
        for (const entry of validEntries) {
          if (currentSize + newDataSize <= maxSizeBytes) break;
          yield cache.delete(entry.url);
          yield cache.delete(this.getCacheMetadataKey(entry.url));
          currentSize -= entry.size;
          console.log(
            `Cleaned up old cache entry: ${entry.url} (${(entry.size / 1024 / 1024).toFixed(2)}MB)`
          );
        }
      }
      console.log(`Cache cleanup complete. New size: ${(currentSize / 1024 / 1024).toFixed(2)}MB`);
    });
  }
  storeCacheMetadata(modelUrl, size) {
    return __async(this, null, function* () {
      const cache = yield caches.open(CACHE_NAME);
      const metadata = {
        timestamp: Date.now(),
        modelUrl,
        size,
        version: CACHE_NAME
      };
      const metadataResponse = new Response(JSON.stringify(metadata), {
        headers: { "Content-Type": "application/json" }
      });
      yield cache.put(this.getCacheMetadataKey(modelUrl), metadataResponse);
    });
  }
  getCachedModelData(modelUrl) {
    return __async(this, null, function* () {
      const cache = yield caches.open(CACHE_NAME);
      const cachedResponse = yield cache.match(modelUrl);
      if (!cachedResponse) {
        return null;
      }
      const metadataResponse = yield cache.match(this.getCacheMetadataKey(modelUrl));
      if (metadataResponse) {
        try {
          const metadata = yield metadataResponse.json();
          if (!this.isCacheExpired(metadata)) {
            console.log("Model found in cache and not expired. Loading from cache.");
            return cachedResponse.arrayBuffer();
          } else {
            console.log("Cached model is expired, removing...");
            yield this.deleteCacheEntry(modelUrl);
            return null;
          }
        } catch (error) {
          console.warn("Failed to parse cache metadata, treating as expired:", error);
          yield this.deleteCacheEntry(modelUrl);
          return null;
        }
      } else {
        console.log("Cached model has no metadata, treating as expired...");
        yield this.deleteCacheEntry(modelUrl);
        return null;
      }
    });
  }
  storeModelData(modelUrl, data) {
    return __async(this, null, function* () {
      yield this.cleanupCacheOnDemand(data.byteLength);
      const cache = yield caches.open(CACHE_NAME);
      const response = new Response(data);
      yield cache.put(modelUrl, response);
      yield this.storeCacheMetadata(modelUrl, data.byteLength);
      console.log(
        `Model cached successfully (${(data.byteLength / 1024 / 1024).toFixed(2)}MB): ${modelUrl}`
      );
    });
  }
  deleteCacheEntry(modelUrl) {
    return __async(this, null, function* () {
      const cache = yield caches.open(CACHE_NAME);
      yield cache.delete(modelUrl);
      yield cache.delete(this.getCacheMetadataKey(modelUrl));
    });
  }
  clearAllCache() {
    return __async(this, null, function* () {
      const cache = yield caches.open(CACHE_NAME);
      const keys = yield cache.keys();
      for (const request of keys) {
        yield cache.delete(request);
      }
      console.log("All model cache entries cleared");
    });
  }
  getCacheStats() {
    return __async(this, null, function* () {
      const { entries, totalSize, entryCount } = yield this.collectCacheEntries();
      const cache = yield caches.open(CACHE_NAME);
      const cacheEntries = [];
      for (const entry of entries) {
        const metadataResponse = yield cache.match(this.getCacheMetadataKey(entry.url));
        let expired = false;
        if (metadataResponse) {
          try {
            const metadata = yield metadataResponse.json();
            expired = this.isCacheExpired(metadata);
          } catch (error) {
            expired = true;
          }
        } else {
          expired = true;
        }
        const age = entry.timestamp > 0 ? `${Math.round((Date.now() - entry.timestamp) / (1e3 * 60 * 60 * 24))} days` : "unknown";
        cacheEntries.push({
          url: entry.url,
          size: entry.size,
          sizeMB: Number((entry.size / 1024 / 1024).toFixed(2)),
          timestamp: entry.timestamp,
          age,
          expired
        });
      }
      return {
        totalSize,
        totalSizeMB: Number((totalSize / 1024 / 1024).toFixed(2)),
        entryCount,
        entries: cacheEntries.sort((a, b) => b.timestamp - a.timestamp)
      };
    });
  }
  manualCleanup() {
    return __async(this, null, function* () {
      yield this.cleanupCacheOnDemand(0);
      console.log("Manual cache cleanup completed");
    });
  }
  /**
   * Check if a specific model is cached and not expired
   * @param modelUrl The model URL to check
   * @returns Promise<boolean> True if model is cached and valid
   */
  isModelCached(modelUrl) {
    return __async(this, null, function* () {
      try {
        const cache = yield caches.open(CACHE_NAME);
        const cachedResponse = yield cache.match(modelUrl);
        if (!cachedResponse) {
          return false;
        }
        const metadataResponse = yield cache.match(this.getCacheMetadataKey(modelUrl));
        if (metadataResponse) {
          try {
            const metadata = yield metadataResponse.json();
            return !this.isCacheExpired(metadata);
          } catch (error) {
            console.warn("Failed to parse cache metadata for cache check:", error);
            return false;
          }
        } else {
          return false;
        }
      } catch (error) {
        console.error("Error checking model cache:", error);
        return false;
      }
    });
  }
  /**
   * Check if any valid (non-expired) model cache exists
   * @returns Promise<boolean> True if at least one valid model cache exists
   */
  hasAnyValidCache() {
    return __async(this, null, function* () {
      try {
        const cache = yield caches.open(CACHE_NAME);
        const keys = yield cache.keys();
        for (const request of keys) {
          if (this.isMetadataUrl(request.url)) continue;
          const metadataResponse = yield cache.match(this.getCacheMetadataKey(request.url));
          if (metadataResponse) {
            try {
              const metadata = yield metadataResponse.json();
              if (!this.isCacheExpired(metadata)) {
                return true;
              }
            } catch (error) {
              continue;
            }
          }
        }
        return false;
      } catch (error) {
        console.error("Error checking for valid cache:", error);
        return false;
      }
    });
  }
};
__publicField(_ModelCacheManager, "instance", null);
let ModelCacheManager = _ModelCacheManager;
function getCachedModelData(modelUrl) {
  return __async(this, null, function* () {
    const cacheManager = ModelCacheManager.getInstance();
    const cachedData = yield cacheManager.getCachedModelData(modelUrl);
    if (cachedData) {
      return cachedData;
    }
    console.log("Model not found in cache or expired. Fetching from network...");
    try {
      const response = yield fetch(modelUrl);
      if (!response.ok) {
        throw new Error(`Failed to fetch model: ${response.status} ${response.statusText}`);
      }
      const arrayBuffer = yield response.arrayBuffer();
      yield cacheManager.storeModelData(modelUrl, arrayBuffer);
      console.log(
        `Model fetched from network and successfully cached (${(arrayBuffer.byteLength / 1024 / 1024).toFixed(2)}MB).`
      );
      return arrayBuffer;
    } catch (error) {
      console.error(`Error fetching or caching model:`, error);
      yield cacheManager.deleteCacheEntry(modelUrl);
      throw error;
    }
  });
}
function clearModelCache() {
  return __async(this, null, function* () {
    try {
      const cacheManager = ModelCacheManager.getInstance();
      yield cacheManager.clearAllCache();
    } catch (error) {
      console.error("Failed to clear model cache:", error);
      throw error;
    }
  });
}
function getCacheStats() {
  return __async(this, null, function* () {
    try {
      const cacheManager = ModelCacheManager.getInstance();
      return yield cacheManager.getCacheStats();
    } catch (error) {
      console.error("Failed to get cache stats:", error);
      throw error;
    }
  });
}
function cleanupModelCache() {
  return __async(this, null, function* () {
    try {
      const cacheManager = ModelCacheManager.getInstance();
      yield cacheManager.manualCleanup();
    } catch (error) {
      console.error("Failed to cleanup cache:", error);
      throw error;
    }
  });
}
const PREDEFINED_MODELS = {
  // Multilingual model - default recommendation
  "multilingual-e5-small": {
    modelIdentifier: "Xenova/multilingual-e5-small",
    dimension: 384,
    description: "Multilingual E5 Small - Lightweight multilingual model supporting 100+ languages",
    language: "multilingual",
    performance: "excellent",
    size: "116MB",
    // Quantized version
    latency: "20ms",
    multilingualFeatures: {
      languageSupport: "100+",
      crossLanguageRetrieval: "good",
      chineseEnglishMixed: "good"
    },
    modelSpecificConfig: {
      requiresTokenTypeIds: false
      // E5 model doesn't require token_type_ids
    }
  },
  "multilingual-e5-base": {
    modelIdentifier: "Xenova/multilingual-e5-base",
    dimension: 768,
    description: "Multilingual E5 base - Medium-scale multilingual model supporting 100+ languages",
    language: "multilingual",
    performance: "excellent",
    size: "279MB",
    // Quantized version
    latency: "30ms",
    multilingualFeatures: {
      languageSupport: "100+",
      crossLanguageRetrieval: "excellent",
      chineseEnglishMixed: "excellent"
    },
    modelSpecificConfig: {
      requiresTokenTypeIds: false
      // E5 model doesn't require token_type_ids
    }
  }
};
function getModelInfo(preset) {
  return PREDEFINED_MODELS[preset];
}
function getOnnxFileNameForVersion(_version = "quantized") {
  return "model_quantized.onnx";
}
class EmbeddingMemoryPool {
  constructor() {
    __publicField(this, "pools", /* @__PURE__ */ new Map());
    __publicField(this, "maxPoolSize", 10);
    __publicField(this, "stats", { allocated: 0, reused: 0, released: 0 });
  }
  getEmbedding(size) {
    const pool = this.pools.get(size);
    if (pool && pool.length > 0) {
      this.stats.reused++;
      return pool.pop();
    }
    this.stats.allocated++;
    return new Float32Array(size);
  }
  releaseEmbedding(embedding) {
    const size = embedding.length;
    if (!this.pools.has(size)) {
      this.pools.set(size, []);
    }
    const pool = this.pools.get(size);
    if (pool.length < this.maxPoolSize) {
      embedding.fill(0);
      pool.push(embedding);
      this.stats.released++;
    }
  }
  getStats() {
    return __spreadValues({}, this.stats);
  }
  clear() {
    this.pools.clear();
    this.stats = { allocated: 0, reused: 0, released: 0 };
  }
}
class SemanticSimilarityEngine {
  constructor(options = {}) {
    __publicField(this, "worker", null);
    __publicField(this, "tokenizer", null);
    __publicField(this, "isInitialized", false);
    __publicField(this, "isInitializing", false);
    __publicField(this, "initPromise", null);
    __publicField(this, "nextTokenId", 0);
    __publicField(this, "pendingMessages", /* @__PURE__ */ new Map());
    __publicField(this, "useOffscreen", false);
    // Whether to use offscreen mode
    __publicField(this, "config");
    __publicField(this, "embeddingCache");
    // Added: tokenization cache
    __publicField(this, "tokenizationCache");
    // Added: memory pool manager
    __publicField(this, "memoryPool");
    // Added: SIMD math engine
    __publicField(this, "simdMath", null);
    __publicField(this, "useSIMD", false);
    __publicField(this, "cacheStats", {
      embedding: { hits: 0, misses: 0, size: 0 },
      tokenization: { hits: 0, misses: 0, size: 0 }
    });
    __publicField(this, "performanceStats", {
      totalEmbeddingComputations: 0,
      totalEmbeddingTime: 0,
      averageEmbeddingTime: 0,
      totalTokenizationTime: 0,
      averageTokenizationTime: 0,
      totalSimilarityComputations: 0,
      totalSimilarityTime: 0,
      averageSimilarityTime: 0,
      workerStats: null
    });
    __publicField(this, "runningWorkerTasks", 0);
    __publicField(this, "workerTaskQueue", []);
    console.log("SemanticSimilarityEngine: Constructor called with options:", {
      useLocalFiles: options.useLocalFiles,
      modelIdentifier: options.modelIdentifier,
      forceOffscreen: options.forceOffscreen,
      modelPreset: options.modelPreset,
      modelVersion: options.modelVersion
    });
    let modelConfig = __spreadValues({}, options);
    if (options.modelPreset && PREDEFINED_MODELS[options.modelPreset]) {
      const preset = PREDEFINED_MODELS[options.modelPreset];
      const modelVersion = options.modelVersion || "quantized";
      const baseModelIdentifier = preset.modelIdentifier;
      const onnxFileName = getOnnxFileNameForVersion(modelVersion);
      const modelSpecificConfig = preset.modelSpecificConfig || {};
      modelConfig = __spreadProps(__spreadValues({}, options), {
        modelIdentifier: baseModelIdentifier,
        // Use base identifier
        onnxModelFile: onnxFileName,
        // Set corresponding version ONNX filename
        dimension: preset.dimension,
        modelVersion,
        requiresTokenTypeIds: modelSpecificConfig.requiresTokenTypeIds !== false
        // Default to true unless explicitly set to false
      });
      console.log(
        `SemanticSimilarityEngine: Using model preset "${options.modelPreset}" with version "${modelVersion}":`,
        preset
      );
      console.log(`SemanticSimilarityEngine: Base model identifier: ${baseModelIdentifier}`);
      console.log(`SemanticSimilarityEngine: ONNX file for version: ${onnxFileName}`);
      console.log(
        `SemanticSimilarityEngine: Requires token_type_ids: ${modelConfig.requiresTokenTypeIds}`
      );
    }
    this.config = __spreadProps(__spreadValues({}, modelConfig), {
      modelIdentifier: modelConfig.modelIdentifier || "Xenova/bge-small-en-v1.5",
      localModelPathPrefix: modelConfig.localModelPathPrefix || "models/",
      onnxModelFile: modelConfig.onnxModelFile || "model.onnx",
      maxLength: modelConfig.maxLength || 256,
      cacheSize: modelConfig.cacheSize || 500,
      numThreads: modelConfig.numThreads || (typeof navigator !== "undefined" && navigator.hardwareConcurrency ? Math.max(1, Math.floor(navigator.hardwareConcurrency / 2)) : 2),
      executionProviders: modelConfig.executionProviders || (typeof WebAssembly === "object" && WebAssembly.validate(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0])) ? ["wasm"] : ["webgl"]),
      useLocalFiles: (() => {
        console.log(
          "SemanticSimilarityEngine: DEBUG - modelConfig.useLocalFiles:",
          modelConfig.useLocalFiles
        );
        console.log(
          "SemanticSimilarityEngine: DEBUG - modelConfig.useLocalFiles !== undefined:",
          modelConfig.useLocalFiles !== void 0
        );
        const result = modelConfig.useLocalFiles !== void 0 ? modelConfig.useLocalFiles : true;
        console.log("SemanticSimilarityEngine: DEBUG - final useLocalFiles value:", result);
        return result;
      })(),
      workerPath: modelConfig.workerPath || "js/similarity.worker.js",
      // Will be overridden by WXT's `new URL`
      concurrentLimit: modelConfig.concurrentLimit || Math.max(
        1,
        modelConfig.numThreads || (typeof navigator !== "undefined" && navigator.hardwareConcurrency ? Math.max(1, Math.floor(navigator.hardwareConcurrency / 2)) : 2)
      ),
      forceOffscreen: modelConfig.forceOffscreen || false,
      modelPreset: modelConfig.modelPreset || "bge-small-en-v1.5",
      dimension: modelConfig.dimension || 384,
      modelVersion: modelConfig.modelVersion || "quantized",
      requiresTokenTypeIds: modelConfig.requiresTokenTypeIds !== false
      // Default to true
    });
    console.log("SemanticSimilarityEngine: Final config:", {
      useLocalFiles: this.config.useLocalFiles,
      modelIdentifier: this.config.modelIdentifier,
      forceOffscreen: this.config.forceOffscreen
    });
    this.embeddingCache = new LRUCache(this.config.cacheSize);
    this.tokenizationCache = new LRUCache(
      Math.min(this.config.cacheSize, 200)
    );
    this.memoryPool = new EmbeddingMemoryPool();
    this.simdMath = new SIMDMathEngine();
  }
  /**
   * Detect if current runtime environment supports Worker
   */
  isWorkerSupported() {
    try {
      if (typeof importScripts === "function") {
        return false;
      }
      return typeof Worker !== "undefined";
    } catch (e) {
      return false;
    }
  }
  /**
   * Detect if in offscreen document environment
   */
  isInOffscreenDocument() {
    try {
      return typeof window !== "undefined" && window.location && window.location.pathname.includes("offscreen");
    } catch (e) {
      return false;
    }
  }
  /**
   * Ensure offscreen document exists
   */
  ensureOffscreenDocument() {
    return __async(this, null, function* () {
      return OffscreenManager.getInstance().ensureOffscreenDocument();
    });
  }
  // Helper function to safely convert tensor data to number array
  convertTensorDataToNumbers(data) {
    if (data instanceof BigInt64Array) {
      return Array.from(data, (val) => Number(val));
    } else if (data instanceof Int32Array) {
      return Array.from(data);
    } else {
      return Array.from(data);
    }
  }
  _sendMessageToWorker(type, payload, transferList) {
    return new Promise((resolve, reject) => {
      if (!this.worker) {
        reject(new Error("Worker is not initialized."));
        return;
      }
      const id = this.nextTokenId++;
      this.pendingMessages.set(id, { resolve, reject, type });
      if (transferList && transferList.length > 0) {
        this.worker.postMessage({ id, type, payload }, transferList);
      } else {
        this.worker.postMessage({ id, type, payload });
      }
    });
  }
  _setupWorker() {
    console.log("SemanticSimilarityEngine: Setting up worker...");
    try {
      const workerUrl = chrome.runtime.getURL("workers/similarity.worker.js");
      console.log(`SemanticSimilarityEngine: Trying chrome.runtime.getURL ${workerUrl}`);
      this.worker = new Worker(workerUrl);
      console.log(`SemanticSimilarityEngine: Method 1 successful with path`);
    } catch (error) {
      console.warn("Method (chrome.runtime.getURL) failed:", error);
    }
    if (!this.worker) {
      throw new Error("Worker creation failed");
    }
    this.worker.onmessage = (event) => {
      const { id, status, payload, stats } = event.data;
      const promiseCallbacks = this.pendingMessages.get(id);
      if (!promiseCallbacks) return;
      this.pendingMessages.delete(id);
      if (stats) {
        this.performanceStats.workerStats = stats;
      }
      if (status === "success") {
        promiseCallbacks.resolve(payload);
      } else {
        const error = new Error(
          (payload == null ? void 0 : payload.message) || `Worker error for task ${promiseCallbacks.type}`
        );
        error.name = (payload == null ? void 0 : payload.name) || "WorkerError";
        error.stack = (payload == null ? void 0 : payload.stack) || void 0;
        console.error(
          `Error from worker (task ${id}, type ${promiseCallbacks.type}):`,
          error,
          event.data
        );
        promiseCallbacks.reject(error);
      }
    };
    this.worker.onerror = (error) => {
      console.error("==== Unhandled error in SemanticSimilarityEngine Worker ====");
      console.error("Event Message:", error.message);
      console.error("Event Filename:", error.filename);
      console.error("Event Lineno:", error.lineno);
      console.error("Event Colno:", error.colno);
      if (error.error) {
        console.error("Actual Error Name:", error.error.name);
        console.error("Actual Error Message:", error.error.message);
        console.error("Actual Error Stack:", error.error.stack);
      } else {
        console.error("Actual Error object (event.error) is not available. Error details:", {
          message: error.message,
          filename: error.filename,
          lineno: error.lineno,
          colno: error.colno
        });
      }
      console.error("==========================================================");
      this.pendingMessages.forEach((callbacks) => {
        callbacks.reject(new Error(`Worker terminated or unhandled error: ${error.message}`));
      });
      this.pendingMessages.clear();
      this.isInitialized = false;
      this.isInitializing = false;
    };
  }
  initialize() {
    return __async(this, null, function* () {
      if (this.isInitialized) return Promise.resolve();
      if (this.isInitializing && this.initPromise) return this.initPromise;
      this.isInitializing = true;
      this.initPromise = this._doInitialize().finally(() => {
        this.isInitializing = false;
      });
      return this.initPromise;
    });
  }
  /**
   * 带进度回调的初始化方法
   */
  initializeWithProgress(onProgress) {
    return __async(this, null, function* () {
      if (this.isInitialized) return Promise.resolve();
      if (this.isInitializing && this.initPromise) return this.initPromise;
      this.isInitializing = true;
      this.initPromise = this._doInitializeWithProgress(onProgress).finally(() => {
        this.isInitializing = false;
      });
      return this.initPromise;
    });
  }
  /**
   * 带进度回调的内部初始化方法
   */
  _doInitializeWithProgress(onProgress) {
    return __async(this, null, function* () {
      console.log("SemanticSimilarityEngine: Initializing with progress tracking...");
      const startTime = performance.now();
      const reportProgress = (status, progress, message) => {
        if (onProgress) {
          onProgress({ status, progress, message });
        }
      };
      try {
        reportProgress("initializing", 5, "Starting initialization...");
        const workerSupported = this.isWorkerSupported();
        const inOffscreenDocument = this.isInOffscreenDocument();
        if (inOffscreenDocument) {
          this.useOffscreen = false;
          console.log(
            "SemanticSimilarityEngine: Running in offscreen document, using direct Worker mode to prevent recursion"
          );
        } else {
          this.useOffscreen = this.config.forceOffscreen || !workerSupported;
        }
        console.log(
          `SemanticSimilarityEngine: Worker supported: ${workerSupported}, In offscreen: ${inOffscreenDocument}, Using offscreen: ${this.useOffscreen}`
        );
        reportProgress("initializing", 10, "Environment detection complete");
        if (this.useOffscreen) {
          reportProgress("initializing", 15, "Setting up offscreen document...");
          yield this.ensureOffscreenDocument();
          console.log("SemanticSimilarityEngine: Sending config to offscreen:", {
            useLocalFiles: this.config.useLocalFiles,
            modelIdentifier: this.config.modelIdentifier,
            localModelPathPrefix: this.config.localModelPathPrefix
          });
          const configToSend = {
            modelIdentifier: this.config.modelIdentifier,
            localModelPathPrefix: this.config.localModelPathPrefix,
            onnxModelFile: this.config.onnxModelFile,
            maxLength: this.config.maxLength,
            cacheSize: this.config.cacheSize,
            numThreads: this.config.numThreads,
            executionProviders: this.config.executionProviders,
            useLocalFiles: Boolean(this.config.useLocalFiles),
            // 强制转换为布尔值
            workerPath: this.config.workerPath,
            concurrentLimit: this.config.concurrentLimit,
            forceOffscreen: this.config.forceOffscreen,
            modelPreset: this.config.modelPreset,
            modelVersion: this.config.modelVersion,
            dimension: this.config.dimension
          };
          const serializedConfig = JSON.parse(JSON.stringify(configToSend));
          reportProgress("initializing", 20, "Delegating to offscreen document...");
          const response = yield chrome.runtime.sendMessage({
            target: "offscreen",
            type: OFFSCREEN_MESSAGE_TYPES.SIMILARITY_ENGINE_INIT,
            config: serializedConfig
          });
          if (!response || !response.success) {
            throw new Error((response == null ? void 0 : response.error) || "Failed to initialize engine in offscreen document");
          }
          reportProgress("ready", 100, "Initialized via offscreen document");
          console.log("SemanticSimilarityEngine: Initialized via offscreen document");
        } else {
          yield this._initializeDirectWorkerWithProgress(reportProgress);
        }
        this.isInitialized = true;
        console.log(
          `SemanticSimilarityEngine: Initialization complete in ${(performance.now() - startTime).toFixed(2)}ms`
        );
      } catch (error) {
        console.error("SemanticSimilarityEngine: Initialization failed.", error);
        const errorMessage = error instanceof Error ? error.message : "Unknown error";
        reportProgress("error", 0, `Initialization failed: ${errorMessage}`);
        if (this.worker) this.worker.terminate();
        this.worker = null;
        this.isInitialized = false;
        this.isInitializing = false;
        this.initPromise = null;
        const enhancedError = new Error(errorMessage);
        enhancedError.name = "ModelInitializationError";
        throw enhancedError;
      }
    });
  }
  _doInitialize() {
    return __async(this, null, function* () {
      var _a2, _b;
      console.log("SemanticSimilarityEngine: Initializing...");
      const startTime = performance.now();
      try {
        const workerSupported = this.isWorkerSupported();
        const inOffscreenDocument = this.isInOffscreenDocument();
        if (inOffscreenDocument) {
          this.useOffscreen = false;
          console.log(
            "SemanticSimilarityEngine: Running in offscreen document, using direct Worker mode to prevent recursion"
          );
        } else {
          this.useOffscreen = this.config.forceOffscreen || !workerSupported;
        }
        console.log(
          `SemanticSimilarityEngine: Worker supported: ${workerSupported}, In offscreen: ${inOffscreenDocument}, Using offscreen: ${this.useOffscreen}`
        );
        if (this.useOffscreen) {
          yield this.ensureOffscreenDocument();
          console.log("SemanticSimilarityEngine: Sending config to offscreen:", {
            useLocalFiles: this.config.useLocalFiles,
            modelIdentifier: this.config.modelIdentifier,
            localModelPathPrefix: this.config.localModelPathPrefix
          });
          const configToSend = {
            modelIdentifier: this.config.modelIdentifier,
            localModelPathPrefix: this.config.localModelPathPrefix,
            onnxModelFile: this.config.onnxModelFile,
            maxLength: this.config.maxLength,
            cacheSize: this.config.cacheSize,
            numThreads: this.config.numThreads,
            executionProviders: this.config.executionProviders,
            useLocalFiles: Boolean(this.config.useLocalFiles),
            // 强制转换为布尔值
            workerPath: this.config.workerPath,
            concurrentLimit: this.config.concurrentLimit,
            forceOffscreen: this.config.forceOffscreen,
            modelPreset: this.config.modelPreset,
            modelVersion: this.config.modelVersion,
            dimension: this.config.dimension
          };
          console.log(
            "SemanticSimilarityEngine: DEBUG - configToSend.useLocalFiles:",
            configToSend.useLocalFiles
          );
          console.log(
            "SemanticSimilarityEngine: DEBUG - typeof configToSend.useLocalFiles:",
            typeof configToSend.useLocalFiles
          );
          console.log("SemanticSimilarityEngine: Explicit config to send:", configToSend);
          console.log(
            "SemanticSimilarityEngine: DEBUG - this.config.useLocalFiles value:",
            this.config.useLocalFiles
          );
          console.log(
            "SemanticSimilarityEngine: DEBUG - typeof this.config.useLocalFiles:",
            typeof this.config.useLocalFiles
          );
          const serializedConfig = JSON.parse(JSON.stringify(configToSend));
          console.log(
            "SemanticSimilarityEngine: DEBUG - serializedConfig.useLocalFiles:",
            serializedConfig.useLocalFiles
          );
          const response = yield chrome.runtime.sendMessage({
            target: "offscreen",
            type: OFFSCREEN_MESSAGE_TYPES.SIMILARITY_ENGINE_INIT,
            config: serializedConfig
            // 使用原始配置，不强制修改 useLocalFiles
          });
          if (!response || !response.success) {
            throw new Error((response == null ? void 0 : response.error) || "Failed to initialize engine in offscreen document");
          }
          console.log("SemanticSimilarityEngine: Initialized via offscreen document");
        } else {
          this._setupWorker();
          env$1.allowRemoteModels = !this.config.useLocalFiles;
          env$1.allowLocalModels = this.config.useLocalFiles;
          console.log(`SemanticSimilarityEngine: TransformersEnv config:`, {
            allowRemoteModels: env$1.allowRemoteModels,
            allowLocalModels: env$1.allowLocalModels,
            useLocalFiles: this.config.useLocalFiles
          });
          if ((_b = (_a2 = env$1.backends) == null ? void 0 : _a2.onnx) == null ? void 0 : _b.wasm) {
            env$1.backends.onnx.wasm.numThreads = this.config.numThreads;
          }
          let tokenizerIdentifier = this.config.modelIdentifier;
          if (this.config.useLocalFiles) {
            tokenizerIdentifier = this.config.modelIdentifier;
          }
          console.log(
            `SemanticSimilarityEngine: Loading tokenizer from ${tokenizerIdentifier} (local_files_only: ${this.config.useLocalFiles})`
          );
          const tokenizerConfig = {
            quantized: false,
            local_files_only: this.config.useLocalFiles
          };
          if (!this.config.requiresTokenTypeIds) {
            tokenizerConfig.return_token_type_ids = false;
          }
          console.log(`SemanticSimilarityEngine: Full tokenizer config:`, {
            tokenizerIdentifier,
            localModelPathPrefix: this.config.localModelPathPrefix,
            modelIdentifier: this.config.modelIdentifier,
            useLocalFiles: this.config.useLocalFiles,
            local_files_only: this.config.useLocalFiles,
            requiresTokenTypeIds: this.config.requiresTokenTypeIds,
            tokenizerConfig
          });
          this.tokenizer = yield AutoTokenizer.from_pretrained(tokenizerIdentifier, tokenizerConfig);
          console.log("SemanticSimilarityEngine: Tokenizer loaded.");
          if (this.config.useLocalFiles) {
            const onnxModelPathForWorker = chrome.runtime.getURL(
              `models/${this.config.modelIdentifier}/${this.config.onnxModelFile}`
            );
            console.log(
              `SemanticSimilarityEngine: Instructing worker to load local ONNX model from ${onnxModelPathForWorker}`
            );
            yield this._sendMessageToWorker("init", {
              modelPath: onnxModelPathForWorker,
              numThreads: this.config.numThreads,
              executionProviders: this.config.executionProviders
            });
          } else {
            const modelIdParts = this.config.modelIdentifier.split("/");
            const modelNameForUrl = modelIdParts.length > 1 ? this.config.modelIdentifier : `Xenova/${this.config.modelIdentifier}`;
            const onnxModelUrl = `https://huggingface.co/${modelNameForUrl}/resolve/main/onnx/${this.config.onnxModelFile}`;
            if (!this.config.modelIdentifier.includes("/")) {
              console.warn(
                `Warning: modelIdentifier "${this.config.modelIdentifier}" might not be a full HuggingFace path. Assuming Xenova prefix for remote URL.`
              );
            }
            console.log(`SemanticSimilarityEngine: Getting cached model data from ${onnxModelUrl}`);
            const modelData = yield getCachedModelData(onnxModelUrl);
            console.log(
              `SemanticSimilarityEngine: Sending cached model data to worker (${modelData.byteLength} bytes)`
            );
            yield this._sendMessageToWorker(
              "init",
              {
                modelData,
                numThreads: this.config.numThreads,
                executionProviders: this.config.executionProviders
              },
              [modelData]
            );
          }
          console.log("SemanticSimilarityEngine: Worker reported model initialized.");
          try {
            console.log("SemanticSimilarityEngine: Checking SIMD support...");
            const simdSupported = yield SIMDMathEngine.checkSIMDSupport();
            if (simdSupported) {
              console.log("SemanticSimilarityEngine: SIMD supported, initializing...");
              yield this.simdMath.initialize();
              this.useSIMD = true;
              console.log("SemanticSimilarityEngine: ✅ SIMD acceleration enabled");
            } else {
              console.log(
                "SemanticSimilarityEngine: ❌ SIMD not supported, using JavaScript fallback"
              );
              console.log("SemanticSimilarityEngine: To enable SIMD, please use:");
              console.log("  - Chrome 91+ (May 2021)");
              console.log("  - Firefox 89+ (June 2021)");
              console.log("  - Safari 16.4+ (March 2023)");
              console.log("  - Edge 91+ (May 2021)");
              this.useSIMD = false;
            }
          } catch (simdError) {
            console.warn(
              "SemanticSimilarityEngine: SIMD initialization failed, using JavaScript fallback:",
              simdError
            );
            this.useSIMD = false;
          }
        }
        this.isInitialized = true;
        console.log(
          `SemanticSimilarityEngine: Initialization complete in ${(performance.now() - startTime).toFixed(2)}ms`
        );
      } catch (error) {
        console.error("SemanticSimilarityEngine: Initialization failed.", error);
        if (this.worker) this.worker.terminate();
        this.worker = null;
        this.isInitialized = false;
        this.isInitializing = false;
        this.initPromise = null;
        const errorMessage = error instanceof Error ? error.message : "Unknown error";
        const enhancedError = new Error(errorMessage);
        enhancedError.name = "ModelInitializationError";
        throw enhancedError;
      }
    });
  }
  /**
   * 直接Worker模式的初始化，支持进度回调
   */
  _initializeDirectWorkerWithProgress(reportProgress) {
    return __async(this, null, function* () {
      var _a2, _b;
      reportProgress("initializing", 25, "Setting up worker...");
      this._setupWorker();
      env$1.allowRemoteModels = !this.config.useLocalFiles;
      env$1.allowLocalModels = this.config.useLocalFiles;
      console.log(`SemanticSimilarityEngine: TransformersEnv config:`, {
        allowRemoteModels: env$1.allowRemoteModels,
        allowLocalModels: env$1.allowLocalModels,
        useLocalFiles: this.config.useLocalFiles
      });
      if ((_b = (_a2 = env$1.backends) == null ? void 0 : _a2.onnx) == null ? void 0 : _b.wasm) {
        env$1.backends.onnx.wasm.numThreads = this.config.numThreads;
      }
      let tokenizerIdentifier = this.config.modelIdentifier;
      if (this.config.useLocalFiles) {
        tokenizerIdentifier = this.config.modelIdentifier;
      }
      reportProgress("downloading", 40, "Loading tokenizer...");
      console.log(
        `SemanticSimilarityEngine: Loading tokenizer from ${tokenizerIdentifier} (local_files_only: ${this.config.useLocalFiles})`
      );
      const tokenizerProgressCallback = (progress) => {
        if (progress.status === "downloading") {
          const progressPercent = Math.min(40 + (progress.progress || 0) * 0.3, 70);
          reportProgress(
            "downloading",
            progressPercent,
            `Downloading tokenizer: ${progress.file || ""}`
          );
        }
      };
      const tokenizerConfig = {
        quantized: false,
        local_files_only: this.config.useLocalFiles
      };
      if (!this.config.requiresTokenTypeIds) {
        tokenizerConfig.return_token_type_ids = false;
      }
      try {
        if (!this.config.useLocalFiles) {
          tokenizerConfig.progress_callback = tokenizerProgressCallback;
        }
        this.tokenizer = yield AutoTokenizer.from_pretrained(tokenizerIdentifier, tokenizerConfig);
      } catch (error) {
        console.log(
          "SemanticSimilarityEngine: Progress callback not supported, using standard loading"
        );
        delete tokenizerConfig.progress_callback;
        this.tokenizer = yield AutoTokenizer.from_pretrained(tokenizerIdentifier, tokenizerConfig);
      }
      reportProgress("downloading", 70, "Tokenizer loaded, setting up ONNX model...");
      console.log("SemanticSimilarityEngine: Tokenizer loaded.");
      if (this.config.useLocalFiles) {
        const onnxModelPathForWorker = chrome.runtime.getURL(
          `models/${this.config.modelIdentifier}/${this.config.onnxModelFile}`
        );
        reportProgress("downloading", 80, "Loading local ONNX model...");
        console.log(
          `SemanticSimilarityEngine: Instructing worker to load local ONNX model from ${onnxModelPathForWorker}`
        );
        yield this._sendMessageToWorker("init", {
          modelPath: onnxModelPathForWorker,
          numThreads: this.config.numThreads,
          executionProviders: this.config.executionProviders
        });
      } else {
        const modelIdParts = this.config.modelIdentifier.split("/");
        const modelNameForUrl = modelIdParts.length > 1 ? this.config.modelIdentifier : `Xenova/${this.config.modelIdentifier}`;
        const onnxModelUrl = `https://huggingface.co/${modelNameForUrl}/resolve/main/onnx/${this.config.onnxModelFile}`;
        if (!this.config.modelIdentifier.includes("/")) {
          console.warn(
            `Warning: modelIdentifier "${this.config.modelIdentifier}" might not be a full HuggingFace path. Assuming Xenova prefix for remote URL.`
          );
        }
        reportProgress("downloading", 80, "Loading cached ONNX model...");
        console.log(`SemanticSimilarityEngine: Getting cached model data from ${onnxModelUrl}`);
        const modelData = yield getCachedModelData(onnxModelUrl);
        console.log(
          `SemanticSimilarityEngine: Sending cached model data to worker (${modelData.byteLength} bytes)`
        );
        yield this._sendMessageToWorker(
          "init",
          {
            modelData,
            numThreads: this.config.numThreads,
            executionProviders: this.config.executionProviders
          },
          [modelData]
        );
      }
      console.log("SemanticSimilarityEngine: Worker reported model initialized.");
      reportProgress("initializing", 90, "Setting up SIMD acceleration...");
      try {
        console.log("SemanticSimilarityEngine: Checking SIMD support...");
        const simdSupported = yield SIMDMathEngine.checkSIMDSupport();
        if (simdSupported) {
          console.log("SemanticSimilarityEngine: SIMD supported, initializing...");
          yield this.simdMath.initialize();
          this.useSIMD = true;
          console.log("SemanticSimilarityEngine: ✅ SIMD acceleration enabled");
        } else {
          console.log("SemanticSimilarityEngine: ❌ SIMD not supported, using JavaScript fallback");
          this.useSIMD = false;
        }
      } catch (simdError) {
        console.warn(
          "SemanticSimilarityEngine: SIMD initialization failed, using JavaScript fallback:",
          simdError
        );
        this.useSIMD = false;
      }
      reportProgress("ready", 100, "Initialization complete");
    });
  }
  warmupModel() {
    return __async(this, null, function* () {
      if (!this.isInitialized && !this.isInitializing) {
        yield this.initialize();
      } else if (this.isInitializing && this.initPromise) {
        yield this.initPromise;
      }
      if (!this.isInitialized) throw new Error("Engine not initialized after warmup attempt.");
      console.log("SemanticSimilarityEngine: Warming up model...");
      const warmupTexts = [
        // 短文本
        "Hello",
        "你好",
        "Test",
        // 中等长度文本
        "Hello world, this is a test.",
        "你好世界，这是一个测试。",
        "The quick brown fox jumps over the lazy dog.",
        // 长文本
        "This is a longer text that contains multiple sentences. It helps warm up the model for various text lengths.",
        "这是一个包含多个句子的较长文本。它有助于为各种文本长度预热模型。"
      ];
      try {
        console.log("SemanticSimilarityEngine: Phase 1 - Individual warmup...");
        for (const text of warmupTexts.slice(0, 4)) {
          yield this.getEmbedding(text);
        }
        console.log("SemanticSimilarityEngine: Phase 2 - Batch warmup...");
        yield this.getEmbeddingsBatch(warmupTexts.slice(4));
        console.log("SemanticSimilarityEngine: Model warmup complete. Cache preserved.");
        console.log(`Embedding cache: ${this.cacheStats.embedding.size} items`);
        console.log(`Tokenization cache: ${this.cacheStats.tokenization.size} items`);
      } catch (error) {
        console.warn("SemanticSimilarityEngine: Warmup failed. This might not be critical.", error);
      }
    });
  }
  _tokenizeText(text) {
    return __async(this, null, function* () {
      if (!this.tokenizer) throw new Error("Tokenizer not initialized.");
      if (typeof text === "string") {
        const cacheKey = `tokenize:${text}`;
        const cached = this.tokenizationCache.get(cacheKey);
        if (cached) {
          this.cacheStats.tokenization.hits++;
          this.cacheStats.tokenization.size = this.tokenizationCache.size;
          return cached;
        }
        this.cacheStats.tokenization.misses++;
        const startTime2 = performance.now();
        const tokenizerOptions2 = {
          padding: true,
          truncation: true,
          max_length: this.config.maxLength,
          return_tensors: "np"
        };
        if (!this.config.requiresTokenTypeIds) {
          tokenizerOptions2.return_token_type_ids = false;
        }
        const result2 = yield this.tokenizer(text, tokenizerOptions2);
        this.performanceStats.totalTokenizationTime += performance.now() - startTime2;
        this.performanceStats.averageTokenizationTime = this.performanceStats.totalTokenizationTime / (this.cacheStats.tokenization.hits + this.cacheStats.tokenization.misses);
        this.tokenizationCache.set(cacheKey, result2);
        this.cacheStats.tokenization.size = this.tokenizationCache.size;
        return result2;
      }
      const startTime = performance.now();
      const tokenizerOptions = {
        padding: true,
        truncation: true,
        max_length: this.config.maxLength,
        return_tensors: "np"
      };
      if (!this.config.requiresTokenTypeIds) {
        tokenizerOptions.return_token_type_ids = false;
      }
      const result = yield this.tokenizer(text, tokenizerOptions);
      this.performanceStats.totalTokenizationTime += performance.now() - startTime;
      return result;
    });
  }
  _extractEmbeddingFromWorkerOutput(workerOutput, attentionMaskArray) {
    if (!workerOutput.data || !workerOutput.dims)
      throw new Error("Invalid worker output for embedding extraction.");
    const lastHiddenStateData = workerOutput.data instanceof Float32Array ? workerOutput.data : new Float32Array(workerOutput.data);
    const dims = workerOutput.dims;
    const seqLength = dims[1];
    const hiddenSize = dims[2];
    const embedding = this.memoryPool.getEmbedding(hiddenSize);
    let validTokens = 0;
    for (let i = 0; i < seqLength; i++) {
      if (attentionMaskArray[i] === 1) {
        const offset = i * hiddenSize;
        for (let j = 0; j < hiddenSize; j++) {
          embedding[j] += lastHiddenStateData[offset + j];
        }
        validTokens++;
      }
    }
    if (validTokens > 0) {
      for (let i = 0; i < hiddenSize; i++) {
        embedding[i] /= validTokens;
      }
    }
    return this.normalizeVector(embedding);
  }
  _extractBatchEmbeddingsFromWorkerOutput(workerOutput, attentionMasksBatch) {
    if (!workerOutput.data || !workerOutput.dims)
      throw new Error("Invalid worker output for batch embedding extraction.");
    const lastHiddenStateData = workerOutput.data instanceof Float32Array ? workerOutput.data : new Float32Array(workerOutput.data);
    const dims = workerOutput.dims;
    const batchSize = dims[0];
    const seqLength = dims[1];
    const hiddenSize = dims[2];
    const embeddings = [];
    for (let b = 0; b < batchSize; b++) {
      const embedding = this.memoryPool.getEmbedding(hiddenSize);
      let validTokens = 0;
      const currentAttentionMask = attentionMasksBatch[b];
      for (let i = 0; i < seqLength; i++) {
        if (currentAttentionMask[i] === 1) {
          const offset = (b * seqLength + i) * hiddenSize;
          for (let j = 0; j < hiddenSize; j++) {
            embedding[j] += lastHiddenStateData[offset + j];
          }
          validTokens++;
        }
      }
      if (validTokens > 0) {
        for (let i = 0; i < hiddenSize; i++) {
          embedding[i] /= validTokens;
        }
      }
      embeddings.push(this.normalizeVector(embedding));
    }
    return embeddings;
  }
  getEmbedding(_0) {
    return __async(this, arguments, function* (text, options = {}) {
      var _a2;
      if (!this.isInitialized) yield this.initialize();
      const cacheKey = this.getCacheKey(text, options);
      const cached = this.embeddingCache.get(cacheKey);
      if (cached) {
        this.cacheStats.embedding.hits++;
        this.cacheStats.embedding.size = this.embeddingCache.size;
        return cached;
      }
      this.cacheStats.embedding.misses++;
      if (this.useOffscreen) {
        const response = yield chrome.runtime.sendMessage({
          target: "offscreen",
          type: OFFSCREEN_MESSAGE_TYPES.SIMILARITY_ENGINE_COMPUTE,
          text,
          options
        });
        if (!response || !response.success) {
          throw new Error((response == null ? void 0 : response.error) || "Failed to get embedding from offscreen document");
        }
        if (!response.embedding || !Array.isArray(response.embedding)) {
          throw new Error("Invalid embedding data received from offscreen document");
        }
        console.log("SemanticSimilarityEngine: Received embedding from offscreen:", {
          length: response.embedding.length,
          type: typeof response.embedding,
          isArray: Array.isArray(response.embedding),
          firstFewValues: response.embedding.slice(0, 5)
        });
        const embedding = new Float32Array(response.embedding);
        console.log("SemanticSimilarityEngine: Converted embedding:", {
          length: embedding.length,
          type: typeof embedding,
          constructor: embedding.constructor.name,
          isFloat32Array: embedding instanceof Float32Array,
          firstFewValues: Array.from(embedding.slice(0, 5))
        });
        this.embeddingCache.set(cacheKey, embedding);
        this.cacheStats.embedding.size = this.embeddingCache.size;
        this.performanceStats.totalEmbeddingComputations++;
        return embedding;
      }
      if (this.runningWorkerTasks >= this.config.concurrentLimit) {
        yield this.waitForWorkerSlot();
      }
      this.runningWorkerTasks++;
      const startTime = performance.now();
      try {
        const tokenized = yield this._tokenizeText(text);
        const inputIdsData = this.convertTensorDataToNumbers(tokenized.input_ids.data);
        const attentionMaskData = this.convertTensorDataToNumbers(tokenized.attention_mask.data);
        const tokenTypeIdsData = tokenized.token_type_ids ? this.convertTensorDataToNumbers(tokenized.token_type_ids.data) : void 0;
        const workerPayload = {
          input_ids: inputIdsData,
          attention_mask: attentionMaskData,
          token_type_ids: tokenTypeIdsData,
          dims: {
            input_ids: tokenized.input_ids.dims,
            attention_mask: tokenized.attention_mask.dims,
            token_type_ids: (_a2 = tokenized.token_type_ids) == null ? void 0 : _a2.dims
          }
        };
        const workerOutput = yield this._sendMessageToWorker("infer", workerPayload);
        const embedding = this._extractEmbeddingFromWorkerOutput(workerOutput, attentionMaskData);
        this.embeddingCache.set(cacheKey, embedding);
        this.cacheStats.embedding.size = this.embeddingCache.size;
        this.performanceStats.totalEmbeddingComputations++;
        this.performanceStats.totalEmbeddingTime += performance.now() - startTime;
        this.performanceStats.averageEmbeddingTime = this.performanceStats.totalEmbeddingTime / this.performanceStats.totalEmbeddingComputations;
        return embedding;
      } finally {
        this.runningWorkerTasks--;
        this.processWorkerQueue();
      }
    });
  }
  getEmbeddingsBatch(_0) {
    return __async(this, arguments, function* (texts, options = {}) {
      var _a2;
      if (!this.isInitialized) yield this.initialize();
      if (!texts || texts.length === 0) return [];
      if (this.useOffscreen) {
        const results2 = new Array(texts.length).fill(void 0);
        const uncachedTexts = [];
        const uncachedIndices = [];
        texts.forEach((text, index) => {
          const cacheKey = this.getCacheKey(text, options);
          const cached = this.embeddingCache.get(cacheKey);
          if (cached) {
            results2[index] = cached;
            this.cacheStats.embedding.hits++;
          } else {
            uncachedTexts.push(text);
            uncachedIndices.push(index);
            this.cacheStats.embedding.misses++;
          }
        });
        if (uncachedTexts.length === 0) {
          return results2;
        }
        const response = yield chrome.runtime.sendMessage({
          target: "offscreen",
          type: OFFSCREEN_MESSAGE_TYPES.SIMILARITY_ENGINE_BATCH_COMPUTE,
          texts: uncachedTexts,
          options
        });
        if (!response || !response.success) {
          throw new Error(
            (response == null ? void 0 : response.error) || "Failed to get embeddings batch from offscreen document"
          );
        }
        response.embeddings.forEach((embeddingArray, batchIndex) => {
          const embedding = new Float32Array(embeddingArray);
          const originalIndex = uncachedIndices[batchIndex];
          const originalText = uncachedTexts[batchIndex];
          results2[originalIndex] = embedding;
          const cacheKey = this.getCacheKey(originalText, options);
          this.embeddingCache.set(cacheKey, embedding);
        });
        this.cacheStats.embedding.size = this.embeddingCache.size;
        this.performanceStats.totalEmbeddingComputations += uncachedTexts.length;
        return results2;
      }
      const results = new Array(texts.length).fill(void 0);
      const uncachedTextsMap = /* @__PURE__ */ new Map();
      const textsToTokenize = [];
      texts.forEach((text, index) => {
        const cacheKey = this.getCacheKey(text, options);
        const cached = this.embeddingCache.get(cacheKey);
        if (cached) {
          results[index] = cached;
          this.cacheStats.embedding.hits++;
        } else {
          if (!uncachedTextsMap.has(text)) {
            uncachedTextsMap.set(text, []);
            textsToTokenize.push(text);
          }
          uncachedTextsMap.get(text).push(index);
          this.cacheStats.embedding.misses++;
        }
      });
      this.cacheStats.embedding.size = this.embeddingCache.size;
      if (textsToTokenize.length === 0) return results;
      if (this.runningWorkerTasks >= this.config.concurrentLimit) {
        yield this.waitForWorkerSlot();
      }
      this.runningWorkerTasks++;
      const startTime = performance.now();
      try {
        const tokenizedBatch = yield this._tokenizeText(textsToTokenize);
        const workerPayload = {
          input_ids: this.convertTensorDataToNumbers(tokenizedBatch.input_ids.data),
          attention_mask: this.convertTensorDataToNumbers(tokenizedBatch.attention_mask.data),
          token_type_ids: tokenizedBatch.token_type_ids ? this.convertTensorDataToNumbers(tokenizedBatch.token_type_ids.data) : void 0,
          dims: {
            input_ids: tokenizedBatch.input_ids.dims,
            attention_mask: tokenizedBatch.attention_mask.dims,
            token_type_ids: (_a2 = tokenizedBatch.token_type_ids) == null ? void 0 : _a2.dims
          }
        };
        const workerOutput = yield this._sendMessageToWorker("batchInfer", workerPayload);
        const attentionMasksForBatch = [];
        const batchSize = tokenizedBatch.input_ids.dims[0];
        const seqLength = tokenizedBatch.input_ids.dims[1];
        const rawAttentionMaskData = this.convertTensorDataToNumbers(
          tokenizedBatch.attention_mask.data
        );
        for (let i = 0; i < batchSize; ++i) {
          attentionMasksForBatch.push(rawAttentionMaskData.slice(i * seqLength, (i + 1) * seqLength));
        }
        const batchEmbeddings = this._extractBatchEmbeddingsFromWorkerOutput(
          workerOutput,
          attentionMasksForBatch
        );
        batchEmbeddings.forEach((embedding, batchIdx) => {
          const originalText = textsToTokenize[batchIdx];
          const cacheKey = this.getCacheKey(originalText, options);
          this.embeddingCache.set(cacheKey, embedding);
          const originalResultIndices = uncachedTextsMap.get(originalText);
          originalResultIndices.forEach((idx) => {
            results[idx] = embedding;
          });
        });
        this.cacheStats.embedding.size = this.embeddingCache.size;
        this.performanceStats.totalEmbeddingComputations += textsToTokenize.length;
        this.performanceStats.totalEmbeddingTime += performance.now() - startTime;
        this.performanceStats.averageEmbeddingTime = this.performanceStats.totalEmbeddingTime / this.performanceStats.totalEmbeddingComputations;
        return results;
      } finally {
        this.runningWorkerTasks--;
        this.processWorkerQueue();
      }
    });
  }
  computeSimilarity(_0, _1) {
    return __async(this, arguments, function* (text1, text2, options = {}) {
      if (!this.isInitialized) yield this.initialize();
      this.validateInput(text1, text2);
      const simStartTime = performance.now();
      const [embedding1, embedding2] = yield Promise.all([
        this.getEmbedding(text1, options),
        this.getEmbedding(text2, options)
      ]);
      const similarity = this.cosineSimilarity(embedding1, embedding2);
      console.log("computeSimilarity:", similarity);
      this.performanceStats.totalSimilarityComputations++;
      this.performanceStats.totalSimilarityTime += performance.now() - simStartTime;
      this.performanceStats.averageSimilarityTime = this.performanceStats.totalSimilarityTime / this.performanceStats.totalSimilarityComputations;
      return similarity;
    });
  }
  computeSimilarityBatch(_0) {
    return __async(this, arguments, function* (pairs, options = {}) {
      if (!this.isInitialized) yield this.initialize();
      if (!pairs || pairs.length === 0) return [];
      if (this.useOffscreen) {
        const response = yield chrome.runtime.sendMessage({
          target: "offscreen",
          type: OFFSCREEN_MESSAGE_TYPES.SIMILARITY_ENGINE_BATCH_COMPUTE,
          pairs,
          options
        });
        if (!response || !response.success) {
          throw new Error((response == null ? void 0 : response.error) || "Failed to compute similarities in offscreen document");
        }
        return response.similarities;
      }
      const simStartTime = performance.now();
      const uniqueTextsSet = /* @__PURE__ */ new Set();
      pairs.forEach((pair) => {
        this.validateInput(pair.text1, pair.text2);
        uniqueTextsSet.add(pair.text1);
        uniqueTextsSet.add(pair.text2);
      });
      const uniqueTextsArray = Array.from(uniqueTextsSet);
      const embeddingsArray = yield this.getEmbeddingsBatch(uniqueTextsArray, options);
      const embeddingMap = /* @__PURE__ */ new Map();
      uniqueTextsArray.forEach((text, index) => {
        embeddingMap.set(text, embeddingsArray[index]);
      });
      const similarities = pairs.map((pair) => {
        const emb1 = embeddingMap.get(pair.text1);
        const emb2 = embeddingMap.get(pair.text2);
        if (!emb1 || !emb2) {
          console.warn("Embeddings not found for pair:", pair);
          return 0;
        }
        return this.cosineSimilarity(emb1, emb2);
      });
      this.performanceStats.totalSimilarityComputations += pairs.length;
      this.performanceStats.totalSimilarityTime += performance.now() - simStartTime;
      this.performanceStats.averageSimilarityTime = this.performanceStats.totalSimilarityTime / this.performanceStats.totalSimilarityComputations;
      return similarities;
    });
  }
  computeSimilarityMatrix(_0, _1) {
    return __async(this, arguments, function* (texts1, texts2, options = {}) {
      if (!this.isInitialized) yield this.initialize();
      if (!texts1 || !texts2 || texts1.length === 0 || texts2.length === 0) return [];
      const simStartTime = performance.now();
      const allTextsSet = /* @__PURE__ */ new Set([...texts1, ...texts2]);
      texts1.forEach((t) => this.validateInput(t, "valid_dummy"));
      texts2.forEach((t) => this.validateInput(t, "valid_dummy"));
      const allTextsArray = Array.from(allTextsSet);
      const embeddingsArray = yield this.getEmbeddingsBatch(allTextsArray, options);
      const embeddingMap = /* @__PURE__ */ new Map();
      allTextsArray.forEach((text, index) => {
        embeddingMap.set(text, embeddingsArray[index]);
      });
      if (this.useSIMD && this.simdMath) {
        try {
          const embeddings1 = texts1.map((text) => embeddingMap.get(text)).filter(Boolean);
          const embeddings2 = texts2.map((text) => embeddingMap.get(text)).filter(Boolean);
          if (embeddings1.length === texts1.length && embeddings2.length === texts2.length) {
            const matrix2 = yield this.simdMath.similarityMatrix(embeddings1, embeddings2);
            this.performanceStats.totalSimilarityComputations += texts1.length * texts2.length;
            this.performanceStats.totalSimilarityTime += performance.now() - simStartTime;
            this.performanceStats.averageSimilarityTime = this.performanceStats.totalSimilarityTime / this.performanceStats.totalSimilarityComputations;
            return matrix2;
          }
        } catch (error) {
          console.warn("SIMD matrix computation failed, falling back to JavaScript:", error);
        }
      }
      const matrix = [];
      for (const textA of texts1) {
        const row = [];
        const embA = embeddingMap.get(textA);
        if (!embA) {
          console.warn(`Embedding not found for text1: "${textA}"`);
          texts2.forEach(() => row.push(0));
          matrix.push(row);
          continue;
        }
        for (const textB of texts2) {
          const embB = embeddingMap.get(textB);
          if (!embB) {
            console.warn(`Embedding not found for text2: "${textB}"`);
            row.push(0);
            continue;
          }
          row.push(this.cosineSimilarity(embA, embB));
        }
        matrix.push(row);
      }
      this.performanceStats.totalSimilarityComputations += texts1.length * texts2.length;
      this.performanceStats.totalSimilarityTime += performance.now() - simStartTime;
      this.performanceStats.averageSimilarityTime = this.performanceStats.totalSimilarityTime / this.performanceStats.totalSimilarityComputations;
      return matrix;
    });
  }
  cosineSimilarity(vecA, vecB) {
    if (!vecA || !vecB || vecA.length !== vecB.length) {
      console.warn("Cosine similarity: Invalid vectors provided.", vecA, vecB);
      return 0;
    }
    if (this.useSIMD && this.simdMath) {
      try {
        return this.cosineSimilarityJS(vecA, vecB);
      } catch (error) {
        console.warn("SIMD cosine similarity failed, falling back to JavaScript:", error);
        return this.cosineSimilarityJS(vecA, vecB);
      }
    }
    return this.cosineSimilarityJS(vecA, vecB);
  }
  cosineSimilarityJS(vecA, vecB) {
    let dotProduct = 0;
    let normA = 0;
    let normB = 0;
    for (let i = 0; i < vecA.length; i++) {
      dotProduct += vecA[i] * vecB[i];
      normA += vecA[i] * vecA[i];
      normB += vecB[i] * vecB[i];
    }
    const magnitude = Math.sqrt(normA) * Math.sqrt(normB);
    return magnitude === 0 ? 0 : dotProduct / magnitude;
  }
  // 新增：异步 SIMD 优化的余弦相似度
  cosineSimilaritySIMD(vecA, vecB) {
    return __async(this, null, function* () {
      if (!vecA || !vecB || vecA.length !== vecB.length) {
        console.warn("Cosine similarity: Invalid vectors provided.", vecA, vecB);
        return 0;
      }
      if (this.useSIMD && this.simdMath) {
        try {
          return yield this.simdMath.cosineSimilarity(vecA, vecB);
        } catch (error) {
          console.warn("SIMD cosine similarity failed, falling back to JavaScript:", error);
        }
      }
      return this.cosineSimilarityJS(vecA, vecB);
    });
  }
  normalizeVector(vector) {
    let norm = 0;
    for (let i = 0; i < vector.length; i++) norm += vector[i] * vector[i];
    norm = Math.sqrt(norm);
    if (norm === 0) return vector;
    const normalized = new Float32Array(vector.length);
    for (let i = 0; i < vector.length; i++) normalized[i] = vector[i] / norm;
    return normalized;
  }
  validateInput(text1, text2) {
    if (typeof text1 !== "string" || text2 !== "valid_dummy" && typeof text2 !== "string") {
      throw new Error("输入必须是字符串");
    }
    if (text1.trim().length === 0 || text2 !== "valid_dummy" && text2.trim().length === 0) {
      throw new Error("输入文本不能为空");
    }
    const roughCharLimit = this.config.maxLength * 5;
    if (text1.length > roughCharLimit || text2 !== "valid_dummy" && text2.length > roughCharLimit) {
      console.warn("输入文本可能过长，将由分词器截断。");
    }
  }
  getCacheKey(text, _options = {}) {
    return text;
  }
  getPerformanceStats() {
    return __spreadProps(__spreadValues({}, this.performanceStats), {
      cacheStats: __spreadProps(__spreadValues({}, this.cacheStats), {
        embedding: __spreadProps(__spreadValues({}, this.cacheStats.embedding), {
          hitRate: this.cacheStats.embedding.hits + this.cacheStats.embedding.misses > 0 ? this.cacheStats.embedding.hits / (this.cacheStats.embedding.hits + this.cacheStats.embedding.misses) : 0
        }),
        tokenization: __spreadProps(__spreadValues({}, this.cacheStats.tokenization), {
          hitRate: this.cacheStats.tokenization.hits + this.cacheStats.tokenization.misses > 0 ? this.cacheStats.tokenization.hits / (this.cacheStats.tokenization.hits + this.cacheStats.tokenization.misses) : 0
        })
      }),
      memoryPool: this.memoryPool.getStats(),
      memoryUsage: this.getMemoryUsage(),
      isInitialized: this.isInitialized,
      isInitializing: this.isInitializing,
      config: this.config,
      pendingWorkerTasks: this.workerTaskQueue.length,
      runningWorkerTasks: this.runningWorkerTasks
    });
  }
  waitForWorkerSlot() {
    return __async(this, null, function* () {
      return new Promise((resolve) => {
        this.workerTaskQueue.push(resolve);
      });
    });
  }
  processWorkerQueue() {
    if (this.workerTaskQueue.length > 0 && this.runningWorkerTasks < this.config.concurrentLimit) {
      const resolve = this.workerTaskQueue.shift();
      if (resolve) resolve();
    }
  }
  // 新增：获取 Worker 统计信息
  getWorkerStats() {
    return __async(this, null, function* () {
      if (!this.worker || !this.isInitialized) return null;
      try {
        const response = yield this._sendMessageToWorker("getStats");
        return response;
      } catch (error) {
        console.warn("Failed to get worker stats:", error);
        return null;
      }
    });
  }
  // 新增：清理 Worker 缓冲区
  clearWorkerBuffers() {
    return __async(this, null, function* () {
      if (!this.worker || !this.isInitialized) return;
      try {
        yield this._sendMessageToWorker("clearBuffers");
        console.log("SemanticSimilarityEngine: Worker buffers cleared.");
      } catch (error) {
        console.warn("Failed to clear worker buffers:", error);
      }
    });
  }
  // 新增：清理所有缓存
  clearAllCaches() {
    this.embeddingCache.clear();
    this.tokenizationCache.clear();
    this.cacheStats = {
      embedding: { hits: 0, misses: 0, size: 0 },
      tokenization: { hits: 0, misses: 0, size: 0 }
    };
    console.log("SemanticSimilarityEngine: All caches cleared.");
  }
  // 新增：获取内存使用情况
  getMemoryUsage() {
    const embeddingStats = this.embeddingCache.getStats();
    const tokenizationStats = this.tokenizationCache.getStats();
    return {
      embeddingCacheUsage: embeddingStats.usage,
      tokenizationCacheUsage: tokenizationStats.usage,
      totalCacheUsage: (embeddingStats.usage + tokenizationStats.usage) / 2
    };
  }
  dispose() {
    return __async(this, null, function* () {
      console.log("SemanticSimilarityEngine: Disposing...");
      yield this.clearWorkerBuffers();
      if (this.worker) {
        this.worker.terminate();
        this.worker = null;
      }
      if (this.simdMath) {
        this.simdMath.dispose();
        this.simdMath = null;
      }
      this.tokenizer = null;
      this.embeddingCache.clear();
      this.tokenizationCache.clear();
      this.memoryPool.clear();
      this.pendingMessages.clear();
      this.workerTaskQueue = [];
      this.isInitialized = false;
      this.isInitializing = false;
      this.initPromise = null;
      this.useSIMD = false;
      console.log("SemanticSimilarityEngine: Disposed.");
    });
  }
}
export {
  BACKGROUND_MESSAGE_TYPES as B,
  MessageTarget as M,
  OFFSCREEN_MESSAGE_TYPES as O,
  PREDEFINED_MODELS as P,
  SendMessageType as S,
  SemanticSimilarityEngine as a,
  clearModelCache as b,
  cleanupModelCache as c,
  getModelInfo as d,
  getCacheStats as g
};