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var Xa;
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var ce;
var Yt = G(() => {
  "use strict";
  Fa();
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          if (!(r.data instanceof b)) throw new TypeError(`buffer should be of type ${b.name}`);
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          break;
        }
        case "texture": {
          if (i !== "float32") throw new TypeError(`unsupported type "${i}" to create tensor from texture`);
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          break;
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        case "gpu-buffer": {
          if (i !== "float32" && i !== "float16" && i !== "int32" && i !== "int64" && i !== "uint32" && i !== "uint8" && i !== "bool" && i !== "uint4" && i !== "int4") throw new TypeError(`unsupported type "${i}" to create tensor from gpu buffer`);
          this.gpuBufferData = r.gpuBuffer, this.downloader = r.download, this.disposer = r.dispose;
          break;
        }
        case "ml-tensor": {
          if (i !== "float32" && i !== "float16" && i !== "int32" && i !== "int64" && i !== "uint32" && i !== "uint64" && i !== "int8" && i !== "uint8" && i !== "bool" && i !== "uint4" && i !== "int4") throw new TypeError(`unsupported type "${i}" to create tensor from MLTensor`);
          this.mlTensorData = r.mlTensor, this.downloader = r.download, this.disposer = r.dispose;
          break;
        }
        default:
          throw new Error(`Tensor constructor: unsupported location '${this.dataLocation}'`);
      }
      else {
        let b, g;
        if (typeof r == "string") if (i = r, g = f, r === "string") {
          if (!Array.isArray(s)) throw new TypeError("A string tensor's data must be a string array.");
          b = s;
        } else {
          let w = nt.get(r);
          if (w === void 0) throw new TypeError(`Unsupported tensor type: ${r}.`);
          if (Array.isArray(s)) {
            if (r === "float16" && w === Uint16Array || r === "uint4" || r === "int4") throw new TypeError(`Creating a ${r} tensor from number array is not supported. Please use ${w.name} as data.`);
            r === "uint64" || r === "int64" ? b = w.from(s, BigInt) : b = w.from(s);
          } else if (s instanceof w) b = s;
          else if (s instanceof Uint8ClampedArray) if (r === "uint8") b = Uint8Array.from(s);
          else throw new TypeError("A Uint8ClampedArray tensor's data must be type of uint8");
          else if (r === "float16" && s instanceof Uint16Array && w !== Uint16Array) b = new globalThis.Float16Array(s.buffer, s.byteOffset, s.length);
          else throw new TypeError(`A ${i} tensor's data must be type of ${w}`);
        }
        else if (g = s, Array.isArray(r)) {
          if (r.length === 0) throw new TypeError("Tensor type cannot be inferred from an empty array.");
          let w = typeof r[0];
          if (w === "string") i = "string", b = r;
          else if (w === "boolean") i = "bool", b = Uint8Array.from(r);
          else throw new TypeError(`Invalid element type of data array: ${w}.`);
        } else if (r instanceof Uint8ClampedArray) i = "uint8", b = Uint8Array.from(r);
        else {
          let w = It.get(r.constructor);
          if (w === void 0) throw new TypeError(`Unsupported type for tensor data: ${r.constructor}.`);
          i = w, b = r;
        }
        if (g === void 0) g = [b.length];
        else if (!Array.isArray(g)) throw new TypeError("A tensor's dims must be a number array");
        p = g, this.cpuData = b, this.dataLocation = "cpu";
      }
      let l = Xa(p);
      if (this.cpuData && l !== this.cpuData.length && !((i === "uint4" || i === "int4") && Math.ceil(l / 2) === this.cpuData.length)) throw new Error(`Tensor's size(${l}) does not match data length(${this.cpuData.length}).`);
      this.type = i, this.dims = p, this.size = l;
    }
    static async fromImage(r, s) {
      return Ga(r, s);
    }
    static fromTexture(r, s) {
      return $a(r, s);
    }
    static fromGpuBuffer(r, s) {
      return za(r, s);
    }
    static fromMLTensor(r, s) {
      return Va(r, s);
    }
    static fromPinnedBuffer(r, s, f) {
      return ja(r, s, f);
    }
    toDataURL(r) {
      return Wa(this, r);
    }
    toImageData(r) {
      return ka(this, r);
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    get data() {
      if (this.ensureValid(), !this.cpuData) throw new Error("The data is not on CPU. Use `getData()` to download GPU data to CPU, or use `texture` or `gpuBuffer` property to access the GPU data directly.");
      return this.cpuData;
    }
    get location() {
      return this.dataLocation;
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      return this.gpuTextureData;
    }
    get gpuBuffer() {
      if (this.ensureValid(), !this.gpuBufferData) throw new Error("The data is not stored as a WebGPU buffer.");
      return this.gpuBufferData;
    }
    get mlTensor() {
      if (this.ensureValid(), !this.mlTensorData) throw new Error("The data is not stored as a WebNN MLTensor.");
      return this.mlTensorData;
    }
    async getData(r) {
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        case "cpu":
        case "cpu-pinned":
          return this.data;
        case "texture":
        case "gpu-buffer":
        case "ml-tensor": {
          if (!this.downloader) throw new Error("The current tensor is not created with a specified data downloader.");
          if (this.isDownloading) throw new Error("The current tensor is being downloaded.");
          try {
            this.isDownloading = true;
            let s = await this.downloader();
            return this.downloader = void 0, this.dataLocation = "cpu", this.cpuData = s, r && this.disposer && (this.disposer(), this.disposer = void 0), s;
          } finally {
            this.isDownloading = false;
          }
        }
        default:
          throw new Error(`cannot get data from location: ${this.dataLocation}`);
      }
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    return t >>>= 0, e = Te(e >>> 0), t = Te(t), Ae(e[t]);
  }
  function Ji(e) {
    9 < (e >>>= 0) && (Xe[e + 1] += 1);
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  function Jn(e, t, n, o, u) {
    return Dr[e >>> 0](t >>> 0, n >>> 0, o >>> 0, u >>> 0);
  }
  function Xi(e, t, n, o, u) {
    return Jn(e >>> 0, t >>> 0, n >>> 0, o >>> 0, u >>> 0);
  }
  function Zi() {
    return Ae([]);
  }
  function Ki(e) {
    e = Te(e >>> 0);
    for (var t = Array(e.length), n = 0; n < e.length; n++) t[n] = e[n];
    return Ae(t);
  }
  function Qi(e) {
    return Ae(Wt(e >>> 0));
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  function eu() {
    return Ae({});
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  function tu(e) {
    for (var t = Te(e >>>= 0); t.length; ) {
      var n = t.pop();
      t.pop()(n);
    }
    Ir(e);
  }
  function ru(e, t, n) {
    t >>>= 0, n >>>= 0, e = Te(e >>> 0), t = Te(t), n = Te(n), e[t] = n;
  }
  function nu(e, t) {
    e = ue(e), t >>>= 0, e = new Date(1e3 * e), (d(), I)[t >>> 2 >>> 0] = e.getUTCSeconds(), (d(), I)[t + 4 >>> 2 >>> 0] = e.getUTCMinutes(), (d(), I)[t + 8 >>> 2 >>> 0] = e.getUTCHours(), (d(), I)[t + 12 >>> 2 >>> 0] = e.getUTCDate(), (d(), I)[t + 16 >>> 2 >>> 0] = e.getUTCMonth(), (d(), I)[t + 20 >>> 2 >>> 0] = e.getUTCFullYear() - 1900, (d(), I)[t + 24 >>> 2 >>> 0] = e.getUTCDay(), e = (e.getTime() - Date.UTC(e.getUTCFullYear(), 0, 1, 0, 0, 0, 0)) / 864e5 | 0, (d(), I)[t + 28 >>> 2 >>> 0] = e;
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  var Xn = (e) => e % 4 == 0 && (e % 100 != 0 || e % 400 == 0), Zn = [0, 31, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335], Kn = [0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334];
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    e = ue(e), t >>>= 0, e = new Date(1e3 * e), (d(), I)[t >>> 2 >>> 0] = e.getSeconds(), (d(), I)[t + 4 >>> 2 >>> 0] = e.getMinutes(), (d(), I)[t + 8 >>> 2 >>> 0] = e.getHours(), (d(), I)[t + 12 >>> 2 >>> 0] = e.getDate(), (d(), I)[t + 16 >>> 2 >>> 0] = e.getMonth(), (d(), I)[t + 20 >>> 2 >>> 0] = e.getFullYear() - 1900, (d(), I)[t + 24 >>> 2 >>> 0] = e.getDay();
    var n = (Xn(e.getFullYear()) ? Zn : Kn)[e.getMonth()] + e.getDate() - 1 | 0;
    (d(), I)[t + 28 >>> 2 >>> 0] = n, (d(), I)[t + 36 >>> 2 >>> 0] = -60 * e.getTimezoneOffset(), n = new Date(e.getFullYear(), 6, 1).getTimezoneOffset();
    var o = new Date(e.getFullYear(), 0, 1).getTimezoneOffset();
    e = 0 | (n != o && e.getTimezoneOffset() == Math.min(o, n)), (d(), I)[t + 32 >>> 2 >>> 0] = e;
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  function au(e) {
    e >>>= 0;
    var t = new Date((d(), I)[e + 20 >>> 2 >>> 0] + 1900, (d(), I)[e + 16 >>> 2 >>> 0], (d(), I)[e + 12 >>> 2 >>> 0], (d(), I)[e + 8 >>> 2 >>> 0], (d(), I)[e + 4 >>> 2 >>> 0], (d(), I)[e >>> 2 >>> 0], 0), n = (d(), I)[e + 32 >>> 2 >>> 0], o = t.getTimezoneOffset(), u = new Date(t.getFullYear(), 6, 1).getTimezoneOffset(), c = new Date(t.getFullYear(), 0, 1).getTimezoneOffset(), h = Math.min(c, u);
    return 0 > n ? (d(), I)[e + 32 >>> 2 >>> 0] = +(u != c && h == o) : 0 < n != (h == o) && (u = Math.max(c, u), t.setTime(t.getTime() + 6e4 * ((0 < n ? h : u) - o))), (d(), I)[e + 24 >>> 2 >>> 0] = t.getDay(), n = (Xn(t.getFullYear()) ? Zn : Kn)[t.getMonth()] + t.getDate() - 1 | 0, (d(), I)[e + 28 >>> 2 >>> 0] = n, (d(), I)[e >>> 2 >>> 0] = t.getSeconds(), (d(), I)[e + 4 >>> 2 >>> 0] = t.getMinutes(), (d(), I)[e + 8 >>> 2 >>> 0] = t.getHours(), (d(), I)[e + 12 >>> 2 >>> 0] = t.getDate(), (d(), I)[e + 16 >>> 2 >>> 0] = t.getMonth(), (d(), I)[e + 20 >>> 2 >>> 0] = t.getYear(), e = t.getTime(), BigInt(isNaN(e) ? -1 : e / 1e3);
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  var Et = {}, su = () => performance.timeOrigin + performance.now();
  function to(e, t) {
    if (i) return H(18, 1, e, t);
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    var n = setTimeout(() => {
      delete Et[e], me(() => Wo(e, performance.timeOrigin + performance.now()));
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    return Et[e] = { id: n, Ve: t }, 0;
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    return Pr;
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    return e >>>= 0, t = ro(t >>> 0, n >>> 0), Yr[e](...t);
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  var pu = () => {
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    return O(lt(e >>> 0, t >>> 0));
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  var hu = () => {
    throw z += 1, "unwind";
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    return 4294901760;
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    var t = _e(e) + 1, n = Ke(t);
    return n && Ue(e, n, t), n;
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    var t;
    return (t = /\bwasm-function\[\d+\]:(0x[0-9a-f]+)/.exec(e)) ? +t[1] : (t = /:(\d+):\d+(?:\)|$)/.exec(e)) ? 2147483648 | +t[1] : 0;
  }, oo = (e) => {
    for (var t of e) (e = kt(t)) && (Ze[e] = t);
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  function wu() {
    var e = Error().stack.toString().split(`
`);
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  function Ft(e) {
    if (!(e = Ze[e >>> 0])) return 0;
    var t;
    if (t = /^\s+at .*\.wasm\.(.*) \(.*\)$/.exec(e)) e = t[1];
    else if (t = /^\s+at (.*) \(.*\)$/.exec(e)) e = t[1];
    else {
      if (!(t = /^(.+?)@/.exec(e))) return 0;
      e = t[1];
    }
    return ve(Ft.ne ?? 0), Ft.ne = no(e), Ft.ne;
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  function Tu(e) {
    e >>>= 0;
    var t = (d(), X).length;
    if (e <= t || 4294901760 < e) return false;
    for (var n = 1; 4 >= n; n *= 2) {
      var o = t * (1 + 0.2 / n);
      o = Math.min(o, e + 100663296);
      e: {
        o = (Math.min(4294901760, 65536 * Math.ceil(Math.max(e, o) / 65536)) - ke.buffer.byteLength + 65535) / 65536 | 0;
        try {
          ke.grow(o), se();
          var u = 1;
          break e;
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        }
        u = void 0;
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      if (u) return true;
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    return false;
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  function vu(e, t, n) {
    if (e >>>= 0, t >>>= 0, Ze.ee == e) var o = Ze.xe;
    else (o = Error().stack.toString().split(`
`))[0] == "Error" && o.shift(), oo(o);
    for (var u = 3; o[u] && kt(o[u]) != e; ) ++u;
    for (e = 0; e < n && o[e + u]; ++e) (d(), I)[t + 4 * e >>> 2 >>> 0] = kt(o[e + u]);
    return e;
  }
  var Ce = (e) => {
    var t = _e(e) + 1, n = Vt(t);
    return Ue(e, n, t), n;
  }, mt = (e) => (d(), E)[e >>> 2 >>> 0] + 4294967296 * (d(), I)[e + 4 >>> 2 >>> 0], fe = [], ao = (e, t) => {
    fe[e >>> 0] = t;
  }, Re = [], Gt = [], ht = (e, t) => {
    Gt[e] = new Promise((n) => t.finally(() => n(e)));
  }, B = (e) => {
    if (e) return fe[e >>> 0];
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    for (e = (d(), E)[e >>> 2 >>> 0]; e; e = (d(), E)[e >>> 2 >>> 0]) t[(d(), I)[e + 4 >>> 2 >>> 0]](e);
  }, $t = (e, t, n) => {
    (d(), E)[e >>> 2 >>> 0] = t, (d(), E)[e + 4 >>> 2 >>> 0] = n;
  }, so = (e) => {
    var t = (d(), E)[e >>> 2 >>> 0];
    return e = (d(), E)[e + 4 >>> 2 >>> 0], lt(t, e);
  }, Ie = (e) => {
    var t = (d(), E)[e >>> 2 >>> 0];
    return e = (d(), E)[e + 4 >>> 2 >>> 0], t ? lt(t, e) : e === 0 ? "" : void 0;
  }, Eu = (e) => {
    var t = Ie(e + 4), n = (n = (d(), E)[e + 12 >>> 2 >>> 0]) ? B(n) : "auto";
    if (e += 16) {
      var o = B((d(), E)[e + 4 >>> 2 >>> 0]), u = (d(), E)[e + 16 >>> 2 >>> 0], c = (d(), E)[e + 20 >>> 2 >>> 0];
      if (u) {
        for (var h = {}, m = 0; m < u; ++m) {
          var y = c + 24 * m;
          h[so(y + 4)] = (d(), ae)[y + 16 >>> 3 >>> 0];
        }
        u = h;
      } else u = void 0;
      e = { module: o, constants: u, entryPoint: Ie(e + 8) };
    } else e = void 0;
    return { label: t, layout: n, compute: e };
  }, io = (e, t) => {
    function n(c, h, m, y = 0) {
      c = e[c] ?? y, (d(), E)[h + m >>> 2 >>> 0] = c;
    }
    function o(c, h, m, y = 0) {
      h += m, c = e[c] ?? y, (d(), E)[h >>> 2 >>> 0] = c, y = (d(), E)[h >>> 2 >>> 0], (d(), E)[h + 4 >>> 2 >>> 0] = (c - y) / 4294967296;
    }
    var u = (d(), E)[t >>> 2 >>> 0];
    n("maxTextureDimension1D", t, 4), n("maxTextureDimension2D", t, 8), n("maxTextureDimension3D", t, 12), n("maxTextureArrayLayers", t, 16), n("maxBindGroups", t, 20), n("maxBindGroupsPlusVertexBuffers", t, 24), n("maxBindingsPerBindGroup", t, 28), n("maxDynamicUniformBuffersPerPipelineLayout", t, 32), n("maxDynamicStorageBuffersPerPipelineLayout", t, 36), n("maxSampledTexturesPerShaderStage", t, 40), n("maxSamplersPerShaderStage", t, 44), n("maxStorageBuffersPerShaderStage", t, 48), n("maxStorageTexturesPerShaderStage", t, 52), n("maxUniformBuffersPerShaderStage", t, 56), n("minUniformBufferOffsetAlignment", t, 80), n("minStorageBufferOffsetAlignment", t, 84), o("maxUniformBufferBindingSize", t, 64), o("maxStorageBufferBindingSize", t, 72), n("maxVertexBuffers", t, 88), o("maxBufferSize", t, 96), n("maxVertexAttributes", t, 104), n("maxVertexBufferArrayStride", t, 108), n("maxInterStageShaderVariables", t, 112), n("maxColorAttachments", t, 116), n("maxColorAttachmentBytesPerSample", t, 120), n("maxComputeWorkgroupStorageSize", t, 124), n("maxComputeInvocationsPerWorkgroup", t, 128), n("maxComputeWorkgroupSizeX", t, 132), n("maxComputeWorkgroupSizeY", t, 136), n("maxComputeWorkgroupSizeZ", t, 140), n("maxComputeWorkgroupsPerDimension", t, 144), n("maxImmediateSize", t, 148), u !== 0 && (d(), n("maxStorageBuffersInVertexStage", u, 8, e.maxStorageBuffersPerShaderStage), n("maxStorageBuffersInFragmentStage", u, 16, e.maxStorageBuffersPerShaderStage), n("maxStorageTexturesInVertexStage", u, 12, e.maxStorageTexturesPerShaderStage), n("maxStorageTexturesInFragmentStage", u, 20, e.maxStorageTexturesPerShaderStage));
  }, Su = (e, t) => {
    (d(), E)[t + 52 >>> 2 >>> 0] = e.subgroupMinSize, (d(), E)[t + 56 >>> 2 >>> 0] = e.subgroupMaxSize;
    var n = no(e.vendor + e.architecture + e.device + e.description), o = _e(e.vendor);
    $t(t + 4, n, o), n += o, o = _e(e.architecture), $t(t + 12, n, o), n += o, o = _e(e.device), $t(t + 20, n, o), n += o, o = _e(e.description), $t(t + 28, n, o), n += o, (d(), I)[t + 36 >>> 2 >>> 0] = 2, n = e.isFallbackAdapter ? 3 : 4, (d(), I)[t + 40 >>> 2 >>> 0] = n, (d(), E)[t + 44 >>> 2 >>> 0] = 0, (d(), E)[t + 48 >>> 2 >>> 0] = 0, Ur(t, { 327739: (u) => {
      let c = 0, h = 0;
      if (e.subgroupMatrixConfigs) {
        c = e.subgroupMatrixConfigs.length, h = Ke(20 * c);
        for (let [m, y] of e.subgroupMatrixConfigs.entries()) {
          let x = h + 20 * m, N = fo.indexOf(y.componentType), F = fo.indexOf(y.resultComponentType);
          (d(), I)[x >>> 2 >>> 0] = N, (d(), I)[x + 4 >>> 2 >>> 0] = F, (d(), E)[x + 8 >>> 2 >>> 0] = y.M, (d(), E)[x + 12 >>> 2 >>> 0] = y.N, (d(), E)[x + 16 >>> 2 >>> 0] = y.K;
        }
      }
      (d(), E)[u + 12 >>> 2 >>> 0] = h, (d(), E)[u + 8 >>> 2 >>> 0] = c;
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  }, xu = [, , "uniform", "storage", "read-only-storage"], Au = [, "validation", "out-of-memory", "internal"], Iu = [, "compatibility", "core"], uo = { 1: "core-features-and-limits", 2: "depth-clip-control", 3: "depth32float-stencil8", 4: "texture-compression-bc", 5: "texture-compression-bc-sliced-3d", 6: "texture-compression-etc2", 7: "texture-compression-astc", 8: "texture-compression-astc-sliced-3d", 9: "timestamp-query", 10: "indirect-first-instance", 11: "shader-f16", 12: "rg11b10ufloat-renderable", 13: "bgra8unorm-storage", 14: "float32-filterable", 15: "float32-blendable", 16: "clip-distances", 17: "dual-source-blending", 18: "subgroups", 19: "texture-formats-tier1", 20: "texture-formats-tier2", 21: "primitive-index", 22: "texture-component-swizzle", 23: "subgroup-size-control", 327692: "chromium-experimental-unorm16-texture-formats", 327729: "chromium-experimental-multi-draw-indirect", 327732: "chromium-experimental-subgroup-matrix" }, Lu = [, "low-power", "high-performance"], Bu = [, "occlusion", "timestamp"], Ou = [, , "filtering", "non-filtering", "comparison"], Mu = [, , "write-only", "read-only", "read-write"], fo = [, "f32", "f16", "u32", "i32", "u8", "i8"], Cu = [, "r8unorm", "r8snorm", "r8uint", "r8sint", "r16unorm", "r16snorm", "r16uint", "r16sint", "r16float", "rg8unorm", "rg8snorm", "rg8uint", "rg8sint", "r32float", "r32uint", "r32sint", "rg16unorm", "rg16snorm", "rg16uint", "rg16sint", "rg16float", "rgba8unorm", "rgba8unorm-srgb", "rgba8snorm", "rgba8uint", "rgba8sint", "bgra8unorm", "bgra8unorm-srgb", "rgb10a2uint", "rgb10a2unorm", "rg11b10ufloat", "rgb9e5ufloat", "rg32float", "rg32uint", "rg32sint", "rgba16unorm", "rgba16snorm", "rgba16uint", "rgba16sint", "rgba16float", "rgba32float", "rgba32uint", "rgba32sint", "stencil8", "depth16unorm", "depth24plus", "depth24plus-stencil8", "depth32float", "depth32float-stencil8", "bc1-rgba-unorm", "bc1-rgba-unorm-srgb", "bc2-rgba-unorm", "bc2-rgba-unorm-srgb", "bc3-rgba-unorm", "bc3-rgba-unorm-srgb", "bc4-r-unorm", "bc4-r-snorm", "bc5-rg-unorm", "bc5-rg-snorm", "bc6h-rgb-ufloat", "bc6h-rgb-float", "bc7-rgba-unorm", "bc7-rgba-unorm-srgb", "etc2-rgb8unorm", "etc2-rgb8unorm-srgb", "etc2-rgb8a1unorm", "etc2-rgb8a1unorm-srgb", "etc2-rgba8unorm", "etc2-rgba8unorm-srgb", "eac-r11unorm", "eac-r11snorm", "eac-rg11unorm", "eac-rg11snorm", "astc-4x4-unorm", "astc-4x4-unorm-srgb", "astc-5x4-unorm", "astc-5x4-unorm-srgb", "astc-5x5-unorm", "astc-5x5-unorm-srgb", "astc-6x5-unorm", "astc-6x5-unorm-srgb", "astc-6x6-unorm", "astc-6x6-unorm-srgb", "astc-8x5-unorm", "astc-8x5-unorm-srgb", "astc-8x6-unorm", "astc-8x6-unorm-srgb", "astc-8x8-unorm", "astc-8x8-unorm-srgb", "astc-10x5-unorm", "astc-10x5-unorm-srgb", "astc-10x6-unorm", "astc-10x6-unorm-srgb", "astc-10x8-unorm", "astc-10x8-unorm-srgb", "astc-10x10-unorm", "astc-10x10-unorm-srgb", "astc-12x10-unorm", "astc-12x10-unorm-srgb", "astc-12x12-unorm", "astc-12x12-unorm-srgb"], Du = [, , "float", "unfilterable-float", "depth", "sint", "uint"], co = [, "1d", "2d", "2d-array", "cube", "cube-array", "3d"], Pu = { undefined: 1, unknown: 1, destroyed: 2 };
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    t = ue(t), n = ue(n), o >>>= 0, u >>>= 0, c >>>= 0;
    var h = B(e >>> 0);
    if (e = {}, c) {
      var m = (d(), E)[c + 12 >>> 2 >>> 0];
      if (m) {
        var y = (d(), E)[c + 16 >>> 2 >>> 0];
        e.requiredFeatures = Array.from((d(), E).subarray(y >>> 2 >>> 0, y + 4 * m >>> 2 >>> 0), (L) => uo[L]);
      }
      if (m = (d(), E)[c + 20 >>> 2 >>> 0]) {
        let L = function(he, de, Qe, et = false) {
          de += Qe, (de = (d(), E)[de >>> 2 >>> 0]) == 4294967295 || et && de == 0 || (x[he] = de);
        }, q = function(he, de, Qe) {
          de += Qe, Qe = (d(), E)[de >>> 2 >>> 0];
          var et = (d(), E)[de + 4 >>> 2 >>> 0];
          Qe == 4294967295 && et == 4294967295 || (x[he] = mt(de));
        };
        var N = L, F = q;
        y = (d(), E)[m >>> 2 >>> 0];
        var x = {};
        L("maxTextureDimension1D", m, 4), L("maxTextureDimension2D", m, 8), L("maxTextureDimension3D", m, 12), L("maxTextureArrayLayers", m, 16), L("maxBindGroups", m, 20), L("maxBindGroupsPlusVertexBuffers", m, 24), L("maxBindingsPerBindGroup", m, 28), L("maxDynamicUniformBuffersPerPipelineLayout", m, 32), L("maxDynamicStorageBuffersPerPipelineLayout", m, 36), L("maxSampledTexturesPerShaderStage", m, 40), L("maxSamplersPerShaderStage", m, 44), L("maxStorageBuffersPerShaderStage", m, 48), L("maxStorageTexturesPerShaderStage", m, 52), L("maxUniformBuffersPerShaderStage", m, 56), L("minUniformBufferOffsetAlignment", m, 80), L("minStorageBufferOffsetAlignment", m, 84), q("maxUniformBufferBindingSize", m, 64), q("maxStorageBufferBindingSize", m, 72), L("maxVertexBuffers", m, 88), q("maxBufferSize", m, 96), L("maxVertexAttributes", m, 104), L("maxVertexBufferArrayStride", m, 108), L("maxInterStageShaderVariables", m, 112), L("maxColorAttachments", m, 116), L("maxColorAttachmentBytesPerSample", m, 120), L("maxComputeWorkgroupStorageSize", m, 124), L("maxComputeInvocationsPerWorkgroup", m, 128), L("maxComputeWorkgroupSizeX", m, 132), L("maxComputeWorkgroupSizeY", m, 136), L("maxComputeWorkgroupSizeZ", m, 140), L("maxComputeWorkgroupsPerDimension", m, 144), L("maxImmediateSize", m, 148, true), y !== 0 && (d(), "maxStorageBuffersInVertexStage" in GPUSupportedLimits.prototype && (L("maxStorageBuffersInVertexStage", y, 8), L("maxStorageTexturesInVertexStage", y, 12), L("maxStorageBuffersInFragmentStage", y, 16), L("maxStorageTexturesInFragmentStage", y, 20))), e.requiredLimits = x;
      }
      (m = (d(), E)[c + 24 >>> 2 >>> 0]) && (m = { label: Ie(m + 4) }, e.defaultQueue = m), e.label = Ie(c + 4);
    }
    z += 1, ht(t, h.requestDevice(e).then((L) => {
      --z, me(() => {
        fe[u >>> 0] = L.queue, fe[o >>> 0] = L, z += 1, ht(n, L.lost.then((q) => {
          me(() => {
            L.onuncapturederror = () => {
            };
            var he = P(), de = Ce(q.message);
            kr(n, Pu[q.reason], de), D(he);
          }), --z;
        })), L.onuncapturederror = (q) => {
          var he = 5;
          q.error instanceof GPUValidationError ? he = 2 : q.error instanceof GPUOutOfMemoryError ? he = 3 : q.error instanceof GPUInternalError && (he = 4);
          var de = P();
          q = Ce(q.error.message), Uo(o, he, q), D(de);
        }, "adapterInfo" in L || (L.adapterInfo = h.info), $r(t, 1, o, 0);
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      --z, me(() => {
        var q = P(), he = Ce(L.message);
        $r(t, 3, o, he), n && kr(n, 4, he), D(q);
      });
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  function _u(e) {
    var t = B(e >>>= 0), n = Re[e];
    if (n) {
      for (var o = 0; o < n.length; ++o) n[o]();
      delete Re[e];
    }
    t.destroy();
  }
  function Ru(e, t, n) {
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                  if (k !== "disabled" && k !== "lazyRelease" && k !== "simple" && k !== "bucket") throw new Error(`${_} must be one of 'disabled', 'lazyRelease', 'simple' or 'bucket': ${k}`);
                  je(l, _, k, s);
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              }
            }
            let T = j().webgpuRegisterDevice(v);
            if (T) {
              let [A, C, _] = T;
              je(l, "deviceId", A.toString(), s), je(l, "webgpuInstance", C.toString(), s), je(l, "webgpuDevice", _.toString(), s);
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          break;
        case "wasm":
        case "cpu":
          continue;
        default:
          throw new Error(`not supported execution provider: ${p}`);
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      let b = be(p, s), g = l.length, w = 0, S = 0;
      if (g > 0) {
        w = j()._malloc(g * j().PTR_SIZE), s.push(w), S = j()._malloc(g * j().PTR_SIZE), s.push(S);
        for (let v = 0; v < g; v++) j().setValue(w + v * j().PTR_SIZE, l[v][0], "*"), j().setValue(S + v * j().PTR_SIZE, l[v][1], "*");
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      await j()._OrtAppendExecutionProvider(a, b, w, S, g) !== 0 && V(`Can't append execution provider: ${p}.`);
    }
  }, As = async (a) => {
    let r = j(), s = 0, f = [], i = a || {};
    Oc(i);
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      let p = Lc(i.graphOptimizationLevel ?? "all"), l = Bc(i.executionMode ?? "sequential"), b = typeof i.logId == "string" ? be(i.logId, f) : 0, g = i.logSeverityLevel ?? 2;
      if (!Number.isInteger(g) || g < 0 || g > 4) throw new Error(`log severity level is not valid: ${g}`);
      let w = i.logVerbosityLevel ?? 0;
      if (!Number.isInteger(w) || w < 0 || w > 4) throw new Error(`log verbosity level is not valid: ${w}`);
      let S = typeof i.optimizedModelFilePath == "string" ? be(i.optimizedModelFilePath, f) : 0;
      if (s = r._OrtCreateSessionOptions(p, !!i.enableCpuMemArena, !!i.enableMemPattern, l, !!i.enableProfiling, 0, b, g, w, S), s === 0 && V("Can't create session options."), i.executionProviders && await Mc(s, i, f), i.enableGraphCapture !== void 0) {
        if (typeof i.enableGraphCapture != "boolean") throw new Error(`enableGraphCapture must be a boolean value: ${i.enableGraphCapture}`);
        Ot(s, "enableGraphCapture", i.enableGraphCapture.toString(), f);
      }
      if (i.freeDimensionOverrides) for (let [v, T] of Object.entries(i.freeDimensionOverrides)) {
        if (typeof v != "string") throw new Error(`free dimension override name must be a string: ${v}`);
        if (typeof T != "number" || !Number.isInteger(T) || T < 0) throw new Error(`free dimension override value must be a non-negative integer: ${T}`);
        let A = be(v, f);
        r._OrtAddFreeDimensionOverride(s, A, T) !== 0 && V(`Can't set a free dimension override: ${v} - ${T}.`);
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      return i.extra !== void 0 && Bt(i.extra, "", /* @__PURE__ */ new WeakSet(), (v, T) => {
        Ot(s, v, T, f);
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});
var He;
var ur;
var bt;
var it;
var Mt;
var fr;
var cr;
var fn;
var ut = G(() => {
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      case "bool":
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      case "int16":
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        return 6;
      case "uint32":
        return 12;
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        return 1;
      case "float64":
        return 11;
      case "string":
        return 8;
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        return 7;
      case "uint64":
        return 13;
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        return 21;
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        throw new Error(`unsupported data type: ${a}`);
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    switch (a) {
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      case 2:
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      case 9:
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      case 5:
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      case 4:
        return "uint16";
      case 6:
        return "int32";
      case 12:
        return "uint32";
      case 10:
        return "float16";
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        return "float32";
      case 11:
        return "float64";
      case 8:
        return "string";
      case 7:
        return "int64";
      case 13:
        return "uint64";
      case 22:
        return "int4";
      case 21:
        return "uint4";
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        throw new Error(`unsupported data type: ${a}`);
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        return typeof Float16Array < "u" ? Float16Array : Uint16Array;
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        return Float32Array;
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        return Int8Array;
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        return Uint16Array;
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        return Uint8Array;
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        return Uint32Array;
      case "int64":
        return BigInt64Array;
      case "uint64":
        return BigUint64Array;
      default:
        throw new Error(`unsupported type: ${a}`);
    }
  }, Mt = (a) => {
    switch (a) {
      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: ${a}`);
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  }, fr = (a) => a === "float32" || a === "float16" || a === "int32" || a === "int64" || a === "uint32" || a === "uint8" || a === "bool" || a === "uint4" || a === "int4", cr = (a) => a === "float32" || a === "float16" || a === "int32" || a === "int64" || a === "uint32" || a === "uint64" || a === "int8" || a === "uint8" || a === "bool" || a === "uint4" || a === "int4", fn = (a) => {
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      case "cpu":
        return 1;
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        return 2;
      case "texture":
        return 3;
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        return 4;
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        return 5;
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        throw new Error(`unsupported data location: ${a}`);
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});
var Ct;
var cn = G(() => {
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  Xt();
  Ct = async (a) => {
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      let { readFile: r } = Xr("node:fs/promises");
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        let { createReadStream: s } = Xr("node:fs"), f = s(a), i = [];
        for await (let p of f) i.push(p);
        return new Uint8Array(Buffer.concat(i));
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      throw r;
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    else {
      let r = await fetch(a);
      if (!r.ok) throw new Error(`failed to load external data file: ${a}`);
      let s = r.headers.get("Content-Length"), f = s ? parseInt(s, 10) : 0;
      if (f < 1073741824) return new Uint8Array(await r.arrayBuffer());
      {
        if (!r.body) throw new Error(`failed to load external data file: ${a}, no response body.`);
        let i = r.body.getReader(), p;
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          p = new ArrayBuffer(f);
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          if (b instanceof RangeError) {
            let g = Math.ceil(f / 65536);
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        let l = 0;
        for (; ; ) {
          let { done: b, value: g } = await i.read();
          if (b) break;
          let w = g.byteLength;
          new Uint8Array(p, l, w).set(g), l += w;
        }
        return new Uint8Array(p, 0, f);
      }
    }
    else return a instanceof Blob ? new Uint8Array(await a.arrayBuffer()) : a instanceof Uint8Array ? a : new Uint8Array(a);
  };
});
var Ls;
var Bs = G(() => {
  "use strict";
  ut();
  Ls = (a, r) => new (it(r))(a);
});
var Cc;
var Dc;
var Os;
var Ms;
var Cs;
var Pc;
var le;
var dn = G(() => {
  "use strict";
  ut();
  Cc = ["V", "I", "W", "E", "F"], Dc = (a, r) => {
    console.log(`[${Cc[a]},${(/* @__PURE__ */ new Date()).toISOString()}]${r}`);
  }, Cs = (a, r) => {
    Os = a, Ms = r;
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    let s = Mt(a), f = Mt(Os);
    s >= f && Dc(s, typeof r == "function" ? r() : r);
  }, le = (...a) => {
    Ms && Pc(...a);
  };
});
var Ps;
var Uc;
var Us;
var _c;
var Ds;
var Rc;
var _s;
var dr;
var lr;
var ln;
var Rs;
var Ns = G(() => {
  "use strict";
  ut();
  dn();
  Ps = /* @__PURE__ */ new Map([["float32", 32], ["float16", 16], ["int32", 32], ["uint32", 32], ["int64", 64], ["uint64", 64], ["int8", 8], ["uint8", 8], ["int4", 4], ["uint4", 4]]), Uc = (a, r) => {
    if (r === "int32") return a;
    let s = Ps.get(r);
    if (!s) throw new Error(`WebNN backend does not support data type: ${r}`);
    let f = s / 8;
    if (a.byteLength % f !== 0) throw new Error(`Invalid Uint8Array length - must be a multiple of ${f}.`);
    let i = a.byteLength / f, p = new (it(r))(a.buffer, a.byteOffset, i);
    switch (r) {
      case "int64":
      case "uint64": {
        let l = new Int32Array(i);
        for (let b = 0; b < i; b++) {
          let g = p[b];
          if (g > 2147483647n || g < -2147483648n) throw new Error("Can not convert int64 data to int32 - value out of range.");
          l[b] = Number(g);
        }
        return new Uint8Array(l.buffer);
      }
      case "int8":
      case "uint8":
      case "uint32": {
        if (r === "uint32" && p.some((b) => b > 2147483647)) throw new Error("Can not convert uint32 data to int32 - value out of range.");
        let l = Int32Array.from(p, Number);
        return new Uint8Array(l.buffer);
      }
      default:
        throw new Error(`Unsupported data conversion from ${r} to 'int32'`);
    }
  }, Us = (a, r) => {
    if (r === "int32") return a;
    if (a.byteLength % 4 !== 0) throw new Error("Invalid Uint8Array length - must be a multiple of 4 (int32).");
    let s = a.byteLength / 4, f = new Int32Array(a.buffer, a.byteOffset, s);
    switch (r) {
      case "int64": {
        let i = BigInt64Array.from(f, BigInt);
        return new Uint8Array(i.buffer);
      }
      case "uint64": {
        if (f.some((p) => p < 0)) throw new Error("Can not convert int32 data to uin64 - negative value found.");
        let i = BigUint64Array.from(f, BigInt);
        return new Uint8Array(i.buffer);
      }
      case "int8": {
        if (f.some((p) => p < -128 || p > 127)) throw new Error("Can not convert int32 data to int8 - value out of range.");
        let i = Int8Array.from(f, Number);
        return new Uint8Array(i.buffer);
      }
      case "uint8": {
        if (f.some((i) => i < 0 || i > 255)) throw new Error("Can not convert int32 data to uint8 - value out of range.");
        return Uint8Array.from(f, Number);
      }
      case "uint32": {
        if (f.some((p) => p < 0)) throw new Error("Can not convert int32 data to uint32 - negative value found.");
        let i = Uint32Array.from(f, Number);
        return new Uint8Array(i.buffer);
      }
      default:
        throw new Error(`Unsupported data conversion from 'int32' to ${r}`);
    }
  }, _c = 1, Ds = () => _c++, Rc = /* @__PURE__ */ new Map([["int8", "int32"], ["uint8", "int32"], ["uint32", "int32"], ["int64", "int32"]]), _s = (a, r) => {
    let s = Ps.get(a);
    if (!s) throw new Error(`WebNN backend does not support data type: ${a}`);
    return r.length > 0 ? Math.ceil(r.reduce((f, i) => f * i) * s / 8) : 0;
  }, dr = class {
    constructor(r) {
      this.isDataConverted = false;
      let { sessionId: s, context: f, tensor: i, dataType: p, shape: l, fallbackDataType: b } = r;
      this.sessionId = s, this.mlContext = f, this.mlTensor = i, this.dataType = p, this.tensorShape = l, this.fallbackDataType = b;
    }
    get tensor() {
      return this.mlTensor;
    }
    get type() {
      return this.dataType;
    }
    get fallbackType() {
      return this.fallbackDataType;
    }
    get shape() {
      return this.tensorShape;
    }
    get byteLength() {
      return _s(this.dataType, this.tensorShape);
    }
    destroy() {
      le("verbose", () => "[WebNN] TensorWrapper.destroy"), this.mlTensor.destroy();
    }
    write(r) {
      this.mlContext.writeTensor(this.mlTensor, r);
    }
    async read(r) {
      if (this.fallbackDataType) {
        let s = await this.mlContext.readTensor(this.mlTensor), f = Us(new Uint8Array(s), this.dataType);
        if (r) {
          (r instanceof ArrayBuffer ? new Uint8Array(r) : new Uint8Array(r.buffer, r.byteOffset, r.byteLength)).set(f);
          return;
        } else return new Uint8Array(f).buffer;
      } else return r ? this.mlContext.readTensor(this.mlTensor, r) : this.mlContext.readTensor(this.mlTensor);
    }
    canReuseTensor(r, s, f) {
      return this.mlContext === r && this.dataType === s && this.tensorShape.length === f.length && this.tensorShape.every((i, p) => i === f[p]);
    }
    setIsDataConverted(r) {
      this.isDataConverted = r;
    }
  }, lr = class {
    constructor(r, s) {
      this.tensorManager = r;
      this.wrapper = s;
    }
    get tensorWrapper() {
      return this.wrapper;
    }
    releaseTensor() {
      this.tensorWrapper && (this.tensorManager.releaseTensor(this.tensorWrapper), this.wrapper = void 0);
    }
    async ensureTensor(r, s, f, i) {
      let p = this.tensorManager.getMLContext(r), l = this.tensorManager.getMLOpSupportLimits(r), b;
      if (!l?.input.dataTypes.includes(s)) {
        if (b = Rc.get(s), !b || !l?.input.dataTypes.includes(b)) throw new Error(`WebNN backend does not support data type: ${s}`);
        le("verbose", () => `[WebNN] TensorIdTracker.ensureTensor: fallback dataType from ${s} to ${b}`);
      }
      if (this.wrapper) {
        if (this.wrapper.canReuseTensor(p, s, f)) return this.wrapper.tensor;
        if (i) {
          if (this.wrapper.byteLength !== _s(s, f)) throw new Error("Unable to copy data to tensor with different size.");
          this.activeUpload = new Uint8Array(await this.wrapper.read());
        }
        this.tensorManager.releaseTensor(this.wrapper);
      }
      let g = typeof MLTensorUsage > "u" ? void 0 : MLTensorUsage.READ | MLTensorUsage.WRITE;
      return this.wrapper = await this.tensorManager.getCachedTensor(r, s, f, g, true, true, b), i && this.activeUpload && (this.wrapper.write(this.activeUpload), this.activeUpload = void 0), this.wrapper.tensor;
    }
    upload(r) {
      let s = r;
      if (this.wrapper) {
        if (this.wrapper.fallbackType) if (this.wrapper.fallbackType === "int32") s = Uc(r, this.wrapper.type), this.wrapper.setIsDataConverted(true);
        else throw new Error(`Unsupported fallback data type: ${this.wrapper.fallbackType}`);
        if (r.byteLength === this.wrapper.byteLength) {
          this.wrapper.write(s);
          return;
        } else le("verbose", () => "Data size does not match tensor size. Releasing tensor."), this.releaseTensor();
      }
      this.activeUpload ? this.activeUpload.set(s) : this.activeUpload = new Uint8Array(s);
    }
    async download(r) {
      if (this.activeUpload) {
        let s = this.wrapper?.isDataConverted ? Us(this.activeUpload, this.wrapper?.type) : this.activeUpload;
        if (r) {
          r instanceof ArrayBuffer ? new Uint8Array(r).set(s) : new Uint8Array(r.buffer, r.byteOffset, r.byteLength).set(s);
          return;
        } else return s.buffer;
      }
      if (!this.wrapper) throw new Error("Tensor has not been created.");
      return r ? this.wrapper.read(r) : this.wrapper.read();
    }
  }, ln = class {
    constructor(r) {
      this.backend = r;
      this.tensorTrackersById = /* @__PURE__ */ new Map();
      this.freeTensors = [];
      this.externalTensors = /* @__PURE__ */ new Set();
    }
    getMLContext(r) {
      let s = this.backend.getMLContext(r);
      if (!s) throw new Error("MLContext not found for session.");
      return s;
    }
    getMLOpSupportLimits(r) {
      return this.backend.getMLOpSupportLimits(r);
    }
    reserveTensorId() {
      let r = Ds();
      return this.tensorTrackersById.set(r, new lr(this)), r;
    }
    releaseTensorId(r) {
      let s = this.tensorTrackersById.get(r);
      s && (this.tensorTrackersById.delete(r), s.tensorWrapper && this.releaseTensor(s.tensorWrapper));
    }
    async ensureTensor(r, s, f, i, p) {
      le("verbose", () => `[WebNN] TensorManager.ensureTensor {tensorId: ${s}, dataType: ${f}, shape: ${i}, copyOld: ${p}}`);
      let l = this.tensorTrackersById.get(s);
      if (!l) throw new Error("Tensor not found.");
      return l.ensureTensor(r, f, i, p);
    }
    upload(r, s) {
      let f = this.tensorTrackersById.get(r);
      if (!f) throw new Error("Tensor not found.");
      f.upload(s);
    }
    async download(r, s) {
      le("verbose", () => `[WebNN] TensorManager.download {tensorId: ${r}, dstBuffer: ${s?.byteLength}}`);
      let f = this.tensorTrackersById.get(r);
      if (!f) throw new Error("Tensor not found.");
      return f.download(s);
    }
    releaseTensorsForSession(r) {
      for (let s of this.freeTensors) s.sessionId === r && s.destroy();
      this.freeTensors = this.freeTensors.filter((s) => s.sessionId !== r);
    }
    registerTensor(r, s, f, i) {
      let p = this.getMLContext(r), l = Ds(), b = new dr({ sessionId: r, context: p, tensor: s, dataType: f, shape: i });
      return this.tensorTrackersById.set(l, new lr(this, b)), this.externalTensors.add(b), l;
    }
    async getCachedTensor(r, s, f, i, p, l, b) {
      let g = this.getMLContext(r);
      for (let [S, v] of this.freeTensors.entries()) if (v.canReuseTensor(g, s, f)) {
        le("verbose", () => `[WebNN] Reusing tensor {dataType: ${s}, ${b ? `fallbackDataType: ${b},` : ""} shape: ${f}`);
        let T = this.freeTensors.splice(S, 1)[0];
        return T.sessionId = r, T;
      }
      le("verbose", () => `[WebNN] MLContext.createTensor {dataType: ${s}, ${b ? `fallbackDataType: ${b},` : ""} shape: ${f}}`);
      let w = await g.createTensor({ dataType: b ?? s, shape: f, dimensions: f, usage: i, writable: p, readable: l });
      return new dr({ sessionId: r, context: g, tensor: w, dataType: s, shape: f, fallbackDataType: b });
    }
    releaseTensor(r) {
      this.externalTensors.has(r) && this.externalTensors.delete(r), this.freeTensors.push(r);
    }
  }, Rs = (...a) => new ln(...a);
});
var Ws = {};
At(Ws, { WebNNBackend: () => pn });
var pr;
var Nc;
var pn;
var ks = G(() => {
  "use strict";
  ut();
  Ve();
  Bs();
  Ns();
  dn();
  pr = /* @__PURE__ */ new Map([[1, "float32"], [10, "float16"], [6, "int32"], [12, "uint32"], [7, "int64"], [13, "uint64"], [22, "int4"], [21, "uint4"], [3, "int8"], [2, "uint8"], [9, "uint8"]]), Nc = (a, r) => {
    if (a === r) return true;
    if (a === void 0 || r === void 0) return false;
    let s = Object.keys(a).sort(), f = Object.keys(r).sort();
    return s.length === f.length && s.every((i, p) => i === f[p] && a[i] === r[i]);
  }, pn = class {
    constructor(r) {
      this.tensorManager = Rs(this);
      this.mlContextBySessionId = /* @__PURE__ */ new Map();
      this.sessionIdsByMLContext = /* @__PURE__ */ new Map();
      this.mlContextCache = [];
      this.sessionGraphInputs = /* @__PURE__ */ new Map();
      this.sessionGraphOutputs = /* @__PURE__ */ new Map();
      this.temporaryGraphInputs = [];
      this.temporaryGraphOutputs = [];
      this.temporarySessionTensorIds = /* @__PURE__ */ new Map();
      this.mlOpSupportLimitsBySessionId = /* @__PURE__ */ new Map();
      Cs(r.logLevel, !!r.debug);
    }
    get currentSessionId() {
      if (this.activeSessionId === void 0) throw new Error("No active session");
      return this.activeSessionId;
    }
    onRunStart(r) {
      le("verbose", () => `[WebNN] onRunStart {sessionId: ${r}}`), this.activeSessionId = r;
    }
    onRunEnd(r) {
      le("verbose", () => `[WebNN] onRunEnd {sessionId: ${r}}`);
      let s = this.temporarySessionTensorIds.get(r);
      if (s) {
        for (let f of s) le("verbose", () => `[WebNN] releasing temporary tensor {tensorId: ${f}}`), this.tensorManager.releaseTensorId(f);
        this.temporarySessionTensorIds.delete(r), this.activeSessionId = void 0;
      }
    }
    async createMLContext(r) {
      if (r instanceof GPUDevice) {
        let f = this.mlContextCache.findIndex((i) => i.gpuDevice === r);
        if (f !== -1) return this.mlContextCache[f].mlContext;
        {
          let i = await navigator.ml.createContext(r);
          return this.mlContextCache.push({ gpuDevice: r, mlContext: i }), i;
        }
      } else if (r === void 0) {
        let f = this.mlContextCache.findIndex((i) => i.options === void 0 && i.gpuDevice === void 0);
        if (f !== -1) return this.mlContextCache[f].mlContext;
        {
          let i = await navigator.ml.createContext();
          return this.mlContextCache.push({ mlContext: i }), i;
        }
      }
      let s = this.mlContextCache.findIndex((f) => Nc(f.options, r));
      if (s !== -1) return this.mlContextCache[s].mlContext;
      {
        let f = await navigator.ml.createContext(r);
        return this.mlContextCache.push({ options: r, mlContext: f }), f;
      }
    }
    registerMLContext(r, s) {
      this.mlContextBySessionId.set(r, s);
      let f = this.sessionIdsByMLContext.get(s);
      f || (f = /* @__PURE__ */ new Set(), this.sessionIdsByMLContext.set(s, f)), f.add(r), this.mlOpSupportLimitsBySessionId.has(r) || this.mlOpSupportLimitsBySessionId.set(r, s.opSupportLimits()), this.temporaryGraphInputs.length > 0 && (this.sessionGraphInputs.set(r, this.temporaryGraphInputs), this.temporaryGraphInputs = []), this.temporaryGraphOutputs.length > 0 && (this.sessionGraphOutputs.set(r, this.temporaryGraphOutputs), this.temporaryGraphOutputs = []);
    }
    onReleaseSession(r) {
      this.sessionGraphInputs.delete(r), this.sessionGraphOutputs.delete(r);
      let s = this.mlContextBySessionId.get(r);
      if (!s) return;
      this.tensorManager.releaseTensorsForSession(r), this.mlContextBySessionId.delete(r), this.mlOpSupportLimitsBySessionId.delete(r);
      let f = this.sessionIdsByMLContext.get(s);
      if (f.delete(r), f.size === 0) {
        this.sessionIdsByMLContext.delete(s);
        let i = this.mlContextCache.findIndex((p) => p.mlContext === s);
        i !== -1 && this.mlContextCache.splice(i, 1);
      }
    }
    getMLContext(r) {
      return this.mlContextBySessionId.get(r);
    }
    getMLOpSupportLimits(r) {
      return this.mlOpSupportLimitsBySessionId.get(r);
    }
    reserveTensorId() {
      return this.tensorManager.reserveTensorId();
    }
    releaseTensorId(r) {
      le("verbose", () => `[WebNN] releaseTensorId {tensorId: ${r}}`), this.tensorManager.releaseTensorId(r);
    }
    async ensureTensor(r, s, f, i, p) {
      let l = pr.get(f);
      if (!l) throw new Error(`Unsupported ONNX data type: ${f}`);
      return this.tensorManager.ensureTensor(r ?? this.currentSessionId, s, l, i, p);
    }
    async createTemporaryTensor(r, s, f) {
      le("verbose", () => `[WebNN] createTemporaryTensor {onnxDataType: ${s}, shape: ${f}}`);
      let i = pr.get(s);
      if (!i) throw new Error(`Unsupported ONNX data type: ${s}`);
      let p = this.tensorManager.reserveTensorId();
      await this.tensorManager.ensureTensor(r, p, i, f, false);
      let l = this.temporarySessionTensorIds.get(r);
      return l ? l.push(p) : this.temporarySessionTensorIds.set(r, [p]), p;
    }
    uploadTensor(r, s) {
      if (!j().shouldTransferToMLTensor) throw new Error("Trying to upload to a MLTensor while shouldTransferToMLTensor is false");
      le("verbose", () => `[WebNN] uploadTensor {tensorId: ${r}, data: ${s.byteLength}}`), this.tensorManager.upload(r, s);
    }
    async downloadTensor(r, s) {
      return this.tensorManager.download(r, s);
    }
    createMLTensorDownloader(r, s) {
      return async () => {
        let f = await this.tensorManager.download(r);
        return Ls(f, s);
      };
    }
    registerMLTensor(r, s, f, i) {
      let p = pr.get(f);
      if (!p) throw new Error(`Unsupported ONNX data type: ${f}`);
      let l = this.tensorManager.registerTensor(r, s, p, i);
      return le("verbose", () => `[WebNN] registerMLTensor {tensor: ${s}, dataType: ${p}, dimensions: ${i}} -> {tensorId: ${l}}`), l;
    }
    registerGraphInput(r) {
      this.temporaryGraphInputs.push(r);
    }
    registerGraphOutput(r) {
      this.temporaryGraphOutputs.push(r);
    }
    isGraphInput(r, s) {
      let f = this.sessionGraphInputs.get(r);
      return f ? f.includes(s) : false;
    }
    isGraphOutput(r, s) {
      let f = this.sessionGraphOutputs.get(r);
      return f ? f.includes(s) : false;
    }
    isGraphInputOutputTypeSupported(r, s, f = true) {
      let i = pr.get(He(s)), p = this.mlOpSupportLimitsBySessionId.get(r);
      return typeof i > "u" ? false : f ? !!p?.input.dataTypes.includes(i) : !!p?.output.dataTypes.includes(i);
    }
    flush() {
    }
  };
});
var Wc;
var Qt;
var er;
var ft;
var kc;
var Fs;
var Lt;
var tr;
var rr;
var Gs;
var nr;
var or;
var ar;
var nn = G(() => {
  "use strict";
  ze();
  xs();
  Is();
  ut();
  Ve();
  ir();
  cn();
  Wc = (a, r) => {
    j()._OrtInit(a, r) !== 0 && V("Can't initialize onnxruntime.");
  }, Qt = async (a) => {
    Wc(a.wasm.numThreads, Mt(a.logLevel));
  }, er = async (a, r) => {
    j().asyncInit?.();
    let s = a.webgpu.adapter;
    if (r === "webgpu") {
      if (typeof navigator > "u" || !navigator.gpu) throw new Error("WebGPU is not supported in current environment");
      if (s) {
        if (typeof s.limits != "object" || typeof s.features != "object" || typeof s.requestDevice != "function") throw new Error("Invalid GPU adapter set in `env.webgpu.adapter`. It must be a GPUAdapter object.");
      } else {
        let f = a.webgpu.powerPreference;
        if (f !== void 0 && f !== "low-power" && f !== "high-performance") throw new Error(`Invalid powerPreference setting: "${f}"`);
        let i = a.webgpu.forceFallbackAdapter;
        if (i !== void 0 && typeof i != "boolean") throw new Error(`Invalid forceFallbackAdapter setting: "${i}"`);
        if (s = await navigator.gpu.requestAdapter({ powerPreference: f, forceFallbackAdapter: i }), !s) throw new Error('Failed to get GPU adapter. You may need to enable flag "--enable-unsafe-webgpu" if you are using Chrome.');
      }
    }
    if (r === "webnn" && (typeof navigator > "u" || !navigator.ml)) throw new Error("WebNN is not supported in current environment");
    if (r === "webgpu" && j().webgpuInit((f) => {
      a.webgpu.device = f;
    }), r === "webnn") {
      let f = new (ks(), Ht(Ws)).WebNNBackend(a);
      j().webnnInit([f, () => f.reserveTensorId(), (i) => f.releaseTensorId(i), async (i, p, l, b, g) => f.ensureTensor(i, p, l, b, g), (i, p) => {
        f.uploadTensor(i, p);
      }, async (i, p) => f.downloadTensor(i, p), (i, p) => f.registerMLContext(i, p), !!a.trace]);
    }
  }, ft = /* @__PURE__ */ new Map(), kc = (a) => {
    let r = j(), s = r.stackSave();
    try {
      let f = r.PTR_SIZE, i = r.stackAlloc(2 * f);
      r._OrtGetInputOutputCount(a, i, i + f) !== 0 && V("Can't get session input/output count.");
      let l = f === 4 ? "i32" : "i64";
      return [Number(r.getValue(i, l)), Number(r.getValue(i + f, l))];
    } finally {
      r.stackRestore(s);
    }
  }, Fs = (a, r) => {
    let s = j(), f = s.stackSave(), i = 0;
    try {
      let p = s.PTR_SIZE, l = s.stackAlloc(2 * p);
      s._OrtGetInputOutputMetadata(a, r, l, l + p) !== 0 && V("Can't get session input/output metadata.");
      let g = Number(s.getValue(l, "*"));
      i = Number(s.getValue(l + p, "*"));
      let w = s.HEAP32[i / 4];
      if (w === 0) return [g, 0];
      let S = s.HEAPU32[i / 4 + 1], v = [];
      for (let T = 0; T < S; T++) {
        let A = Number(s.getValue(i + 8 + T * p, "*"));
        v.push(A !== 0 ? s.UTF8ToString(A) : Number(s.getValue(i + 8 + (T + S) * p, "*")));
      }
      return [g, w, v];
    } finally {
      s.stackRestore(f), i !== 0 && s._OrtFree(i);
    }
  }, Lt = (a) => {
    let r = j(), s = r._malloc(a.byteLength);
    if (s === 0) throw new Error(`Can't create a session. failed to allocate a buffer of size ${a.byteLength}.`);
    return r.HEAPU8.set(a, s), [s, a.byteLength];
  }, tr = async (a, r) => {
    let s, f, i = j();
    Array.isArray(a) ? [s, f] = a : a.buffer === i.HEAPU8.buffer ? [s, f] = [a.byteOffset, a.byteLength] : [s, f] = Lt(a);
    let p = 0, l = 0, b = 0, g = [], w = [], S = [];
    try {
      if ([l, g] = await As(r), r?.externalData && i.mountExternalData) {
        let O = [];
        for (let $ of r.externalData) {
          let oe = typeof $ == "string" ? $ : $.path, d = typeof $ == "string" ? $ : $.data;
          O.push(Ct(d).then((te) => {
            i.mountExternalData(oe, te);
          }));
        }
        await Promise.all(O);
      }
      for (let O of r?.executionProviders ?? []) if ((typeof O == "string" ? O : O.name) === "webnn") {
        if (i.shouldTransferToMLTensor = false, typeof O != "string") {
          let oe = O, d = oe?.context, te = oe?.gpuDevice, Z = oe?.deviceType, X = oe?.powerPreference;
          d ? i.currentContext = d : te ? i.currentContext = await i.webnnCreateMLContext(te) : i.currentContext = await i.webnnCreateMLContext({ deviceType: Z, powerPreference: X });
        } else i.currentContext = await i.webnnCreateMLContext();
        break;
      }
      p = await i._OrtCreateSession(s, f, l), i.webgpuOnCreateSession?.(p), p === 0 && V("Can't create a session."), i.jsepOnCreateSession?.(), i.currentContext && (i.webnnRegisterMLContext(p, i.currentContext), i.currentContext = void 0, i.shouldTransferToMLTensor = true);
      let [v, T] = kc(p), A = !!r?.enableGraphCapture, C = [], _ = [], k = [], U = [], M = [];
      for (let O = 0; O < v; O++) {
        let [$, oe, d] = Fs(p, O);
        $ === 0 && V("Can't get an input name."), w.push($);
        let te = i.UTF8ToString($);
        C.push(te), k.push(oe === 0 ? { name: te, isTensor: false } : { name: te, isTensor: true, type: ur(oe), shape: d });
      }
      for (let O = 0; O < T; O++) {
        let [$, oe, d] = Fs(p, O + v);
        $ === 0 && V("Can't get an output name."), S.push($);
        let te = i.UTF8ToString($);
        _.push(te), U.push(oe === 0 ? { name: te, isTensor: false } : { name: te, isTensor: true, type: ur(oe), shape: d });
        {
          if (A && r?.preferredOutputLocation === void 0) {
            M.push("gpu-buffer");
            continue;
          }
          let Z = typeof r?.preferredOutputLocation == "string" ? r.preferredOutputLocation : r?.preferredOutputLocation?.[te] ?? "cpu", X = i.webnnIsGraphOutput;
          if (Z === "cpu" && X && X(p, te)) {
            M.push("ml-tensor-cpu-output");
            continue;
          }
          if (Z !== "cpu" && Z !== "cpu-pinned" && Z !== "gpu-buffer" && Z !== "ml-tensor") throw new Error(`Not supported preferred output location: ${Z}.`);
          if (A && Z !== "gpu-buffer") throw new Error(`Not supported preferred output location: ${Z}. Only 'gpu-buffer' location is supported when enableGraphCapture is true.`);
          M.push(Z);
        }
      }
      let Y = null;
      return M.some((O) => O === "gpu-buffer" || O === "ml-tensor" || O === "ml-tensor-cpu-output") && (b = i._OrtCreateBinding(p), b === 0 && V("Can't create IO binding."), Y = { handle: b, outputPreferredLocations: M, outputPreferredLocationsEncoded: M.map((O) => O === "ml-tensor-cpu-output" ? "ml-tensor" : O).map((O) => fn(O)) }), ft.set(p, [p, w, S, Y, A, false]), [p, C, _, k, U];
    } catch (v) {
      throw w.forEach((T) => i._OrtFree(T)), S.forEach((T) => i._OrtFree(T)), b !== 0 && i._OrtReleaseBinding(b) !== 0 && V("Can't release IO binding."), p !== 0 && i._OrtReleaseSession(p) !== 0 && V("Can't release session."), v;
    } finally {
      i._free(s), l !== 0 && i._OrtReleaseSessionOptions(l) !== 0 && V("Can't release session options."), g.forEach((v) => i._free(v)), i.unmountExternalData?.();
    }
  }, rr = (a) => {
    let r = j(), s = ft.get(a);
    if (!s) throw new Error(`cannot release session. invalid session id: ${a}`);
    let [f, i, p, l, b] = s;
    l && (b && r._OrtClearBoundOutputs(l.handle) !== 0 && V("Can't clear bound outputs."), r._OrtReleaseBinding(l.handle) !== 0 && V("Can't release IO binding.")), r.jsepOnReleaseSession?.(a), r.webnnOnReleaseSession?.(a), r.webgpuOnReleaseSession?.(a), i.forEach((g) => r._OrtFree(g)), p.forEach((g) => r._OrtFree(g)), r._OrtReleaseSession(f) !== 0 && V("Can't release session."), ft.delete(a);
  }, Gs = async (a, r, s, f, i, p, l = false) => {
    if (!a) {
      r.push(0);
      return;
    }
    let b = j(), g = b.PTR_SIZE, w = a[0], S = a[1], v = a[3], T = v, A, C;
    if (w === "string" && (v === "gpu-buffer" || v === "ml-tensor")) throw new Error("String tensor is not supported on GPU.");
    if (l && v !== "gpu-buffer") throw new Error(`External buffer must be provided for input/output index ${p} when enableGraphCapture is true.`);
    if (v === "gpu-buffer") {
      let U = a[2].gpuBuffer;
      C = bt(He(w), S);
      {
        let M = b.webgpuRegisterBuffer;
        if (!M) throw new Error('Tensor location "gpu-buffer" is not supported without using WebGPU.');
        A = M(U, f);
      }
    } else if (v === "ml-tensor") {
      let U = a[2].mlTensor;
      C = bt(He(w), S);
      let M = b.webnnRegisterMLTensor;
      if (!M) throw new Error('Tensor location "ml-tensor" is not supported without using WebNN.');
      A = M(f, U, He(w), S);
    } else {
      let U = a[2];
      if (Array.isArray(U)) {
        C = g * U.length, A = b._malloc(C), s.push(A);
        for (let M = 0; M < U.length; M++) {
          if (typeof U[M] != "string") throw new TypeError(`tensor data at index ${M} is not a string`);
          b.setValue(A + M * g, be(U[M], s), "*");
        }
      } else {
        let M = b.webnnIsGraphInput, Y = b.webnnIsGraphOutput;
        if (w !== "string" && M && Y) {
          let O = b.UTF8ToString(i);
          if (M(f, O) || Y(f, O)) {
            let $ = He(w);
            C = bt($, S), T = "ml-tensor";
            let oe = b.webnnCreateTemporaryTensor, d = b.webnnUploadTensor;
            if (!oe || !d) throw new Error('Tensor location "ml-tensor" is not supported without using WebNN.');
            let te = await oe(f, $, S);
            d(te, new Uint8Array(U.buffer, U.byteOffset, U.byteLength)), A = te;
          } else C = U.byteLength, A = b._malloc(C), s.push(A), b.HEAPU8.set(new Uint8Array(U.buffer, U.byteOffset, C), A);
        } else C = U.byteLength, A = b._malloc(C), s.push(A), b.HEAPU8.set(new Uint8Array(U.buffer, U.byteOffset, C), A);
      }
    }
    let _ = b.stackSave(), k = b.stackAlloc(4 * S.length);
    try {
      S.forEach((M, Y) => b.setValue(k + Y * g, M, g === 4 ? "i32" : "i64"));
      let U = b._OrtCreateTensor(He(w), A, C, k, S.length, fn(T));
      U === 0 && V(`Can't create tensor for input/output. session=${f}, index=${p}.`), r.push(U);
    } finally {
      b.stackRestore(_);
    }
  }, nr = async (a, r, s, f, i, p) => {
    let l = j(), b = l.PTR_SIZE, g = ft.get(a);
    if (!g) throw new Error(`cannot run inference. invalid session id: ${a}`);
    let w = g[0], S = g[1], v = g[2], T = g[3], A = g[4], C = g[5], _ = r.length, k = f.length, U = 0, M = [], Y = [], O = [], $ = [], oe = [], d = l.stackSave(), te = l.stackAlloc(_ * b), Z = l.stackAlloc(_ * b), X = l.stackAlloc(k * b), De = l.stackAlloc(k * b);
    try {
      [U, M] = Ss(p), Ge("wasm prepareInputOutputTensor");
      for (let R = 0; R < _; R++) await Gs(s[R], Y, $, a, S[r[R]], r[R], A);
      for (let R = 0; R < k; R++) await Gs(i[R], O, $, a, v[f[R]], _ + f[R], A);
      $e("wasm prepareInputOutputTensor");
      for (let R = 0; R < _; R++) l.setValue(te + R * b, Y[R], "*"), l.setValue(Z + R * b, S[r[R]], "*");
      for (let R = 0; R < k; R++) l.setValue(X + R * b, O[R], "*"), l.setValue(De + R * b, v[f[R]], "*");
      if (T && !C) {
        let { handle: R, outputPreferredLocations: ae, outputPreferredLocationsEncoded: pe } = T;
        if (S.length !== _) throw new Error(`input count from feeds (${_}) is expected to be always equal to model's input count (${S.length}).`);
        Ge("wasm bindInputsOutputs");
        for (let J = 0; J < _; J++) {
          let ge = r[J];
          await l._OrtBindInput(R, S[ge], Y[J]) !== 0 && V(`Can't bind input[${J}] for session=${a}.`);
        }
        for (let J = 0; J < k; J++) {
          let ge = f[J];
          i[J]?.[3] ? (oe.push(O[J]), l._OrtBindOutput(R, v[ge], O[J], 0) !== 0 && V(`Can't bind pre-allocated output[${J}] for session=${a}.`)) : l._OrtBindOutput(R, v[ge], 0, pe[ge]) !== 0 && V(`Can't bind output[${J}] to ${ae[J]} for session=${a}.`);
        }
        $e("wasm bindInputsOutputs"), ft.set(a, [w, S, v, T, A, true]);
      }
      l.jsepOnRunStart?.(w), l.webnnOnRunStart?.(w);
      let K;
      T ? K = await l._OrtRunWithBinding(w, T.handle, k, X, U) : K = await l._OrtRun(w, Z, te, _, De, k, X, U), K !== 0 && V("failed to call OrtRun().");
      let I = [], E = [];
      Ge("wasm ProcessOutputTensor");
      for (let R = 0; R < k; R++) {
        let ae = Number(l.getValue(X + R * b, "*"));
        if (ae === O[R] || oe.includes(O[R])) {
          I.push(i[R]), ae !== O[R] && l._OrtReleaseTensor(ae) !== 0 && V("Can't release tensor.");
          continue;
        }
        let pe = l.stackSave(), J = l.stackAlloc(4 * b), ge = false, ne, se = 0;
        try {
          l._OrtGetTensorData(ae, J, J + b, J + 2 * b, J + 3 * b) !== 0 && V(`Can't access output tensor data on index ${R}.`);
          let we = b === 4 ? "i32" : "i64", qe = Number(l.getValue(J, we));
          se = l.getValue(J + b, "*");
          let wt = l.getValue(J + b * 2, "*"), Tt = Number(l.getValue(J + b * 3, we)), Se = [];
          for (let re = 0; re < Tt; re++) Se.push(Number(l.getValue(wt + re * b, we)));
          l._OrtFree(wt) !== 0 && V("Can't free memory for tensor dims.");
          let xe = Se.reduce((re, Q) => re * Q, 1);
          ne = ur(qe);
          let Be = T?.outputPreferredLocations[f[R]];
          if (ne === "string") {
            if (Be === "gpu-buffer" || Be === "ml-tensor") throw new Error("String tensor is not supported on GPU.");
            let re = [];
            for (let Q = 0; Q < xe; Q++) {
              let z = l.getValue(se + Q * b, "*"), H = l.getValue(se + (Q + 1) * b, "*"), Ye = Q === xe - 1 ? void 0 : H - z;
              re.push(l.UTF8ToString(z, Ye));
            }
            I.push([ne, Se, re, "cpu"]);
          } else if (Be === "gpu-buffer" && xe > 0) {
            let re = l.webgpuGetBuffer;
            if (!re) throw new Error('preferredLocation "gpu-buffer" is not supported without using WebGPU.');
            let Q = re(se), z = bt(qe, xe);
            if (z === void 0 || !fr(ne)) throw new Error(`Unsupported data type: ${ne}`);
            ge = true;
            {
              l.webgpuRegisterBuffer(Q, a, se);
              let H = l.webgpuCreateDownloader(Q, z, a);
              I.push([ne, Se, { gpuBuffer: Q, download: async () => {
                let Ye = await H();
                return new (it(ne))(Ye);
              }, dispose: () => {
                l._OrtReleaseTensor(ae) !== 0 && V("Can't release tensor.");
              } }, "gpu-buffer"]);
            }
          } else if (Be === "ml-tensor" && xe > 0) {
            let re = l.webnnEnsureTensor, Q = l.webnnIsGraphInputOutputTypeSupported;
            if (!re || !Q) throw new Error('preferredLocation "ml-tensor" is not supported without using WebNN.');
            if (bt(qe, xe) === void 0 || !cr(ne)) throw new Error(`Unsupported data type: ${ne}`);
            if (!Q(a, ne, false)) throw new Error(`preferredLocation "ml-tensor" for ${ne} output is not supported by current WebNN Context.`);
            let H = await re(a, se, qe, Se, false);
            ge = true, I.push([ne, Se, { mlTensor: H, download: l.webnnCreateMLTensorDownloader(se, ne), dispose: () => {
              l.webnnReleaseTensorId(se), l._OrtReleaseTensor(ae);
            } }, "ml-tensor"]);
          } else if (Be === "ml-tensor-cpu-output" && xe > 0) {
            let re = l.webnnCreateMLTensorDownloader(se, ne)(), Q = I.length;
            ge = true, E.push((async () => {
              let z = [Q, await re];
              return l.webnnReleaseTensorId(se), l._OrtReleaseTensor(ae), z;
            })()), I.push([ne, Se, [], "cpu"]);
          } else {
            let re = it(ne), Q = new re(xe);
            new Uint8Array(Q.buffer, Q.byteOffset, Q.byteLength).set(l.HEAPU8.subarray(se, se + Q.byteLength)), I.push([ne, Se, Q, "cpu"]);
          }
        } finally {
          l.stackRestore(pe), ne === "string" && se && l._free(se), ge || l._OrtReleaseTensor(ae);
        }
      }
      T && !A && (l._OrtClearBoundOutputs(T.handle) !== 0 && V("Can't clear bound outputs."), ft.set(a, [w, S, v, T, A, false]));
      for (let [R, ae] of await Promise.all(E)) I[R][2] = ae;
      return $e("wasm ProcessOutputTensor"), I;
    } finally {
      l.webnnOnRunEnd?.(w), l.stackRestore(d), s.forEach((K) => {
        K && K[3] === "gpu-buffer" && l.webgpuUnregisterBuffer(K[2].gpuBuffer);
      }), i.forEach((K) => {
        K && K[3] === "gpu-buffer" && l.webgpuUnregisterBuffer(K[2].gpuBuffer);
      }), Y.forEach((K) => l._OrtReleaseTensor(K)), O.forEach((K) => l._OrtReleaseTensor(K)), $.forEach((K) => l._free(K)), U !== 0 && l._OrtReleaseRunOptions(U), M.forEach((K) => l._free(K));
    }
  }, or = (a) => {
    let r = j(), s = ft.get(a);
    if (!s) throw new Error("invalid session id");
    let f = s[0], i = r._OrtEndProfiling(f);
    i === 0 && V("Can't get an profile file name."), r._OrtFree(i);
  }, ar = (a) => {
    let r = [];
    for (let s of a) {
      let f = s[2];
      !Array.isArray(f) && "buffer" in f && r.push(f.buffer);
    }
    return r;
  };
});
var ct;
var Ee;
var Dt;
var hr;
var br;
var mr;
var mn;
var hn;
var gt;
var yt;
var Gc;
var $s;
var zs;
var Vs;
var js;
var Hs;
var qs;
var Ys;
var bn = G(() => {
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  ze();
  nn();
  Ve();
  Zt();
  ct = () => !!ee.wasm.proxy && typeof document < "u", Dt = false, hr = false, br = false, hn = /* @__PURE__ */ new Map(), gt = (a, r) => {
    let s = hn.get(a);
    s ? s.push(r) : hn.set(a, [r]);
  }, yt = () => {
    if (Dt || !hr || br || !Ee) throw new Error("worker not ready");
  }, Gc = (a) => {
    switch (a.data.type) {
      case "init-wasm":
        Dt = false, a.data.err ? (br = true, mn[1](a.data.err)) : (hr = true, mn[0]()), mr && (URL.revokeObjectURL(mr), mr = void 0);
        break;
      case "init-ep":
      case "copy-from":
      case "create":
      case "release":
      case "run":
      case "end-profiling": {
        let r = hn.get(a.data.type);
        a.data.err ? r.shift()[1](a.data.err) : r.shift()[0](a.data.out);
        break;
      }
      default:
    }
  }, $s = async () => {
    if (!hr) {
      if (Dt) throw new Error("multiple calls to 'initWasm()' detected.");
      if (br) throw new Error("previous call to 'initWasm()' failed.");
      if (Dt = true, ct()) return new Promise((a, r) => {
        Ee?.terminate(), Ts().then(([s, f]) => {
          try {
            Ee = f, Ee.onerror = (p) => r(p), Ee.onmessage = Gc, mn = [a, r];
            let i = { type: "init-wasm", in: ee };
            !i.in.wasm.wasmPaths && (s || an) && (i.in.wasm.wasmPaths = { wasm: new URL("ort-wasm-simd-threaded.asyncify.wasm", import.meta.url).href }), Ee.postMessage(i), mr = s;
          } catch (i) {
            r(i);
          }
        }, r);
      });
      try {
        await Kt(ee.wasm), await Qt(ee), hr = true;
      } catch (a) {
        throw br = true, a;
      } finally {
        Dt = false;
      }
    }
  }, zs = async (a) => {
    if (ct()) return yt(), new Promise((r, s) => {
      gt("init-ep", [r, s]);
      let f = { type: "init-ep", in: { epName: a, env: ee } };
      Ee.postMessage(f);
    });
    await er(ee, a);
  }, Vs = async (a) => ct() ? (yt(), new Promise((r, s) => {
    gt("copy-from", [r, s]);
    let f = { type: "copy-from", in: { buffer: a } };
    Ee.postMessage(f, [a.buffer]);
  })) : Lt(a), js = async (a, r) => {
    if (ct()) {
      if (r?.preferredOutputLocation) throw new Error('session option "preferredOutputLocation" is not supported for proxy.');
      return yt(), new Promise((s, f) => {
        gt("create", [s, f]);
        let i = { type: "create", in: { model: a, options: { ...r } } }, p = [];
        a instanceof Uint8Array && p.push(a.buffer), Ee.postMessage(i, p);
      });
    } else return tr(a, r);
  }, Hs = async (a) => {
    if (ct()) return yt(), new Promise((r, s) => {
      gt("release", [r, s]);
      let f = { type: "release", in: a };
      Ee.postMessage(f);
    });
    rr(a);
  }, qs = async (a, r, s, f, i, p) => {
    if (ct()) {
      if (s.some((l) => l[3] !== "cpu")) throw new Error("input tensor on GPU is not supported for proxy.");
      if (i.some((l) => l)) throw new Error("pre-allocated output tensor is not supported for proxy.");
      return yt(), new Promise((l, b) => {
        gt("run", [l, b]);
        let g = s, w = { type: "run", in: { sessionId: a, inputIndices: r, inputs: g, outputIndices: f, options: p } };
        Ee.postMessage(w, ar(g));
      });
    } else return nr(a, r, s, f, i, p);
  }, Ys = async (a) => {
    if (ct()) return yt(), new Promise((r, s) => {
      gt("end-profiling", [r, s]);
      let f = { type: "end-profiling", in: a };
      Ee.postMessage(f);
    });
    or(a);
  };
});
var Js;
var $c;
var gr;
var Xs = G(() => {
  "use strict";
  ze();
  bn();
  ut();
  Xt();
  cn();
  Js = (a, r) => {
    switch (a.location) {
      case "cpu":
        return [a.type, a.dims, a.data, "cpu"];
      case "gpu-buffer":
        return [a.type, a.dims, { gpuBuffer: a.gpuBuffer }, "gpu-buffer"];
      case "ml-tensor":
        return [a.type, a.dims, { mlTensor: a.mlTensor }, "ml-tensor"];
      default:
        throw new Error(`invalid data location: ${a.location} for ${r()}`);
    }
  }, $c = (a) => {
    switch (a[3]) {
      case "cpu":
        return new Le(a[0], a[2], a[1]);
      case "gpu-buffer": {
        let r = a[0];
        if (!fr(r)) throw new Error(`not supported data type: ${r} for deserializing GPU tensor`);
        let { gpuBuffer: s, download: f, dispose: i } = a[2];
        return Le.fromGpuBuffer(s, { dataType: r, dims: a[1], download: f, dispose: i });
      }
      case "ml-tensor": {
        let r = a[0];
        if (!cr(r)) throw new Error(`not supported data type: ${r} for deserializing MLTensor tensor`);
        let { mlTensor: s, download: f, dispose: i } = a[2];
        return Le.fromMLTensor(s, { dataType: r, dims: a[1], download: f, dispose: i });
      }
      default:
        throw new Error(`invalid data location: ${a[3]}`);
    }
  }, gr = class {
    async fetchModelAndCopyToWasmMemory(r) {
      return Vs(await Ct(r));
    }
    async loadModel(r, s) {
      ot();
      let f;
      typeof r == "string" ? f = await this.fetchModelAndCopyToWasmMemory(r) : f = r, [this.sessionId, this.inputNames, this.outputNames, this.inputMetadata, this.outputMetadata] = await js(f, s), at();
    }
    async dispose() {
      return Hs(this.sessionId);
    }
    async run(r, s, f) {
      ot();
      let i = [], p = [];
      Object.entries(r).forEach((T) => {
        let A = T[0], C = T[1], _ = this.inputNames.indexOf(A);
        if (_ === -1) throw new Error(`invalid input '${A}'`);
        i.push(C), p.push(_);
      });
      let l = [], b = [];
      Object.entries(s).forEach((T) => {
        let A = T[0], C = T[1], _ = this.outputNames.indexOf(A);
        if (_ === -1) throw new Error(`invalid output '${A}'`);
        l.push(C), b.push(_);
      });
      let g = i.map((T, A) => Js(T, () => `input "${this.inputNames[p[A]]}"`)), w = l.map((T, A) => T ? Js(T, () => `output "${this.outputNames[b[A]]}"`) : null), S = await qs(this.sessionId, p, g, b, w, f), v = {};
      for (let T = 0; T < S.length; T++) v[this.outputNames[b[T]]] = l[T] ?? $c(S[T]);
      return at(), v;
    }
    startProfiling() {
    }
    endProfiling() {
      Ys(this.sessionId);
    }
  };
});
var Ks = {};
At(Ks, { OnnxruntimeWebAssemblyBackend: () => yr, initializeFlags: () => Zs, wasmBackend: () => zc });
var Zs;
var yr;
var zc;
var Qs = G(() => {
  "use strict";
  ze();
  bn();
  Xs();
  Zs = () => {
    (typeof ee.wasm.initTimeout != "number" || ee.wasm.initTimeout < 0) && (ee.wasm.initTimeout = 0);
    let a = ee.wasm.simd;
    if (typeof a != "boolean" && a !== void 0 && a !== "fixed" && a !== "relaxed" && (console.warn(`Property "env.wasm.simd" is set to unknown value "${a}". Reset it to \`false\` and ignore SIMD feature checking.`), ee.wasm.simd = false), typeof ee.wasm.proxy != "boolean" && (ee.wasm.proxy = false), typeof ee.wasm.trace != "boolean" && (ee.wasm.trace = false), typeof ee.wasm.numThreads != "number" || !Number.isInteger(ee.wasm.numThreads) || ee.wasm.numThreads <= 0) if (typeof self < "u" && !self.crossOriginIsolated) ee.wasm.numThreads = 1;
    else {
      let r = typeof navigator > "u" ? Xr("node:os").cpus().length : navigator.hardwareConcurrency;
      ee.wasm.numThreads = Math.min(4, Math.ceil((r || 1) / 2));
    }
  }, yr = class {
    async init(r) {
      Zs(), await $s(), await zs(r);
    }
    async createInferenceSessionHandler(r, s) {
      let f = new gr();
      return await f.loadModel(r, s), f;
    }
  }, zc = new yr();
});
ze();
ze();
ze();
var is = "1.30.0";
{
  let a = (Qs(), Ht(Ks)).wasmBackend;
  rt("webgpu", a, 5), rt("webnn", a, 5), rt("cpu", a, 10), rt("wasm", a, 10);
}
Object.defineProperty(ee.versions, "web", { value: is, enumerable: true });

// src/calibration.json
var calibration_default = {
  randomWalk: 0.8068229166666667,
  measured: {
    "Timestral-5M": {
      randomWalk: {
        mean: 0.8068229166666667,
        min: 0.7520833333333333,
        max: 0.8708333333333333,
        n: 20
      },
      sine: {
        mean: 0.930885416666667,
        min: 0.7916666666666666,
        max: 0.9895833333333334,
        n: 20
      },
      flatThenJump: {
        mean: 0.8483333333333333,
        min: 0.6854166666666667,
        max: 0.9854166666666667,
        n: 20
      },
      noise: {
        mean: 0.7763020833333334,
        min: 0.7572916666666667,
        max: 0.7927083333333333,
        n: 20
      }
    },
    "Timestral-28M": {
      randomWalk: {
        mean: 0.8169270833333334,
        min: 0.746875,
        max: 0.8802083333333334,
        n: 20
      },
      sine: {
        mean: 0.9510416666666668,
        min: 0.884375,
        max: 0.9979166666666667,
        n: 20
      },
      flatThenJump: {
        mean: 0.84734375,
        min: 0.6552083333333333,
        max: 0.9947916666666666,
        n: 20
      },
      noise: {
        mean: 0.80234375,
        min: 0.7854166666666667,
        max: 0.8260416666666667,
        n: 20
      }
    }
  }
};

// src/config.ts
var SAMPLE_HZ = 32;
var PATCH = 32;
var CONTEXT = 512;
var HORIZON = 32;
var NQ = 21;
var WARMUP = 2 * SAMPLE_HZ;
var PLAY = 30 * SAMPLE_HZ;
var REFERENCE = calibration_default.randomWalk;

// src/model/prep.ts
var MIN_SCALE = 1e-6;
var NP = CONTEXT / PATCH;
var TOKENS = NP + 1;
var EPS32 = 11920928955078125e-23;
var CAP_K = 1e4;
var f32 = Math.fround;
function padded(context) {
  const out = new Float64Array(CONTEXT).fill(NaN);
  const n = Math.min(context.length, CONTEXT);
  for (let i = 0; i < n; i++) out[CONTEXT - n + i] = context[context.length - n + i];
  return out;
}
function scaleRow(x, obs, out, offset) {
  const locs = new Float64Array(CONTEXT);
  const scales = new Float64Array(CONTEXT);
  let cumW = 0, cumX = 0, m2 = 0, prev = 0;
  for (let t = 0; t < CONTEXT; t++) {
    const w = obs[t] ? 1 : 0;
    const v = obs[t] ? x[t] : 0;
    cumW += w;
    cumX += w * v;
    const den = Math.max(cumW, 1);
    const mean = cumX / den;
    m2 += (v - prev) * (v - mean) * w;
    const variance = m2 / Math.max(den - 1, 1);
    locs[t] = f32(mean);
    scales[t] = f32(Math.max(Math.sqrt(variance), MIN_SCALE));
    prev = mean;
  }
  for (let p = 0; p < NP; p++) {
    const end = p * PATCH + PATCH - 1;
    const loc = locs[end], sc = Math.max(scales[end], EPS32);
    for (let k = 0; k < PATCH; k++) {
      const t = p * PATCH + k;
      out[offset + t] = obs[t] ? f32(Math.asinh(f32(f32(f32(x[t]) - loc) / sc))) : 0;
    }
  }
  return { loc: locs[CONTEXT - 1], scale: scales[CONTEXT - 1] };
}
function prepareRows(context) {
  const x = padded(context);
  const obs = Array.from(x, (v) => Number.isFinite(v));
  const scaled = new Float32Array(2 * CONTEXT);
  const missing = new Float32Array(2 * CONTEXT);
  const loc = [0, 0], scale = [0, 0];
  const ctxMin = [0, 0], ctxMax = [0, 0];
  let any = false, nonneg = true;
  for (let r = 0; r < 2; r++) {
    const row = Float64Array.from(x, (v) => Number.isFinite(v) ? r === 0 ? f32(v) : -f32(v) : 0);
    const s = scaleRow(row, obs, scaled, r * CONTEXT);
    loc[r] = s.loc;
    scale[r] = s.scale;
    let lo = Infinity, hi = -Infinity;
    for (let t = 0; t < CONTEXT; t++) {
      missing[r * CONTEXT + t] = obs[t] ? 0 : 1;
      if (!obs[t]) continue;
      lo = Math.min(lo, row[t]);
      hi = Math.max(hi, row[t]);
      if (r === 0) {
        any = true;
        if (row[t] < 0) nonneg = false;
      }
    }
    ctxMin[r] = Number.isFinite(lo) ? lo : 0;
    ctxMax[r] = Number.isFinite(hi) ? hi : 0;
  }
  const obsPatch = Array.from({ length: NP }, (_, p) => obs.slice(p * PATCH, (p + 1) * PATCH).some(Boolean));
  let started = false;
  const keyValid = obsPatch.map((o) => started = started || o);
  const firstObs = Math.max(0, obsPatch.indexOf(true));
  const timeMask = new Uint8Array(2 * TOKENS * TOKENS);
  const positions = new BigInt64Array(2 * TOKENS);
  for (let r = 0; r < 2; r++) {
    for (let q = 0; q < TOKENS; q++) {
      positions[r * TOKENS + q] = BigInt(q - firstObs);
      for (let k = 0; k < TOKENS; k++) {
        let v;
        if (q < NP && k < NP) v = k <= q && keyValid[k] || q === k;
        else if (q < NP) v = false;
        else if (k < NP) v = keyValid[k];
        else v = true;
        timeMask[(r * TOKENS + q) * TOKENS + k] = v ? 1 : 0;
      }
    }
  }
  const variateMask = new Uint8Array(TOKENS * 4);
  for (let s = 0; s < TOKENS; s++) variateMask.set([1, 0, 0, 1], s * 4);
  return { scaled, missing, timeMask, variateMask, positions, loc, scale, ctxMin, ctxMax, nonnegative: any && nonneg };
}
function clampNonfinite(v, offset) {
  let lo = Infinity, hi = -Infinity;
  for (let j2 = 0; j2 < NQ; j2++) {
    const x = v[offset + j2];
    if (Number.isFinite(x)) {
      lo = Math.min(lo, x);
      hi = Math.max(hi, x);
    }
  }
  for (let j2 = 0; j2 < NQ; j2++) {
    const x = v[offset + j2];
    if (x === Infinity) v[offset + j2] = Number.isFinite(hi) ? hi : 0;
    else if (x === -Infinity) v[offset + j2] = Number.isFinite(lo) ? lo : 0;
  }
}
function finishForecast(preds, p) {
  const rows = [0, 1].map((r) => {
    const out = new Float64Array(HORIZON * NQ);
    const cap = p.scale[r] * CAP_K;
    for (let h = 0; h < HORIZON; h++) {
      for (let j2 = 0; j2 < NQ; j2++) {
        const z = preds[(r * HORIZON + h) * NQ + j2];
        if (Number.isNaN(z)) throw new Error("forecast produced NaN values; the input is degenerate");
        out[h * NQ + j2] = f32(f32(Math.sinh(z)) * p.scale[r] + p.loc[r]);
      }
      clampNonfinite(out, h * NQ);
      for (let j2 = 0; j2 < NQ; j2++) {
        const v = out[h * NQ + j2];
        out[h * NQ + j2] = Math.min(Math.max(v, p.ctxMin[r] - cap), p.ctxMax[r] + cap);
      }
    }
    return out;
  });
  const q = new Float64Array(HORIZON * NQ);
  for (let h = 0; h < HORIZON; h++) {
    for (let j2 = 0; j2 < NQ; j2++) {
      let v = 0.5 * (rows[0][h * NQ + j2] + -rows[1][h * NQ + (NQ - 1 - j2)]);
      if (p.nonnegative) v = Math.max(v, 0);
      q[h * NQ + j2] = v;
    }
  }
  return q;
}

// src/model/session.ts
async function createWithFallback(create, backend) {
  try {
    return { session: await create(backend), backend };
  } catch (err) {
    if (backend === "wasm") throw err;
    return { session: await create("wasm"), backend: "wasm" };
  }
}

// src/model/worker.ts
ee.wasm.wasmPaths = new URL("./ort/", self.location.href).href;
ee.wasm.numThreads = 1;
var session = null;
var post = (m, transfer = []) => self.postMessage(m, transfer);
self.onmessage = async (e) => {
  const msg = e.data;
  try {
    if (msg.type === "load") {
      const bytes = msg.bytes ?? await (await fetch(msg.url)).arrayBuffer();
      const bin = new Uint8Array(bytes);
      const made = await createWithFallback((b) => bc.create(bin, { executionProviders: [b] }), msg.backend);
      session = made.session;
      post({ type: "loaded", backend: made.backend });
      return;
    }
    if (!session) throw new Error("model not loaded");
    const t0 = performance.now();
    const p = prepareRows(msg.context);
    const feeds = {
      scaled: new Le("float32", p.scaled, [2, CONTEXT]),
      missing: new Le("float32", p.missing, [2, CONTEXT]),
      time_mask: new Le("bool", p.timeMask, [2, 1, TOKENS, TOKENS]),
      variate_mask: new Le("bool", p.variateMask, [TOKENS, 1, 2, 2]),
      positions: new Le("int64", p.positions, [2, TOKENS])
    };
    const out = await session.run(feeds);
    const preds = out.preds.data;
    if (preds.length !== 2 * HORIZON * NQ) throw new Error(`unexpected preds length ${preds.length}`);
    const quantiles = finishForecast(preds, p);
    post({ type: "result", id: msg.id, quantiles, ms: performance.now() - t0 }, [quantiles.buffer]);
  } catch (err) {
    post({ type: "error", id: msg.type === "forecast" ? msg.id : void 0, message: err instanceof Error ? err.message : String(err) });
  }
};
/*! Bundled license information:

onnxruntime-web/dist/ort.webgpu.bundle.min.mjs:
  (*!
   * ONNX Runtime Web v1.30.0
   * Copyright (c) Microsoft Corporation. All rights reserved.
   * Licensed under the MIT License.
   *)
*/
//# sourceMappingURL=worker.js.map