function _mergeNamespaces(b, n) {
for (var a = 0; a < n.length; a++) {
const u = n[a];
if (typeof u != "string" && !Array.isArray(u)) {
for (const c in u)
if (c !== "default" && !(c in b)) {
const f = Object.getOwnPropertyDescriptor(u, c);
f && Object.defineProperty(b, c, f.get ? f : {
enumerable: !0,
get: () => u[c]
})
}
}
}
return Object.freeze(Object.defineProperty(b, Symbol.toStringTag, {
value: "Module"
}))
}(function() {
const n = document.createElement("link").relList;
if (n && n.supports && n.supports("modulepreload")) return;
for (const c of document.querySelectorAll('link[rel="modulepreload"]')) u(c);
new MutationObserver(c => {
for (const f of c)
if (f.type === "childList")
for (const s of f.addedNodes) s.tagName === "LINK" && s.rel === "modulepreload" && u(s)
}).observe(document, {
childList: !0,
subtree: !0
});
function a(c) {
const f = {};
return c.integrity && (f.integrity = c.integrity), c.referrerPolicy && (f.referrerPolicy = c.referrerPolicy), c.crossOrigin === "use-credentials" ? f.credentials = "include" : c.crossOrigin === "anonymous" ? f.credentials = "omit" : f.credentials = "same-origin", f
}
function u(c) {
if (c.ep) return;
c.ep = !0;
const f = a(c);
fetch(c.href, f)
}
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function dispatchCallback(b, n) {
b && b(n)
}
function reverseDictionary(b) {
return Object.fromEntries(Object.entries(b).map(([n, a]) => [a, n]))
}
const Callable = class {
constructor() {
let b = function(...n) {
return b._call(...n)
};
return Object.setPrototypeOf(b, new.target.prototype)
}
_call(...b) {
throw Error("Must implement _call method in subclass")
}
};
function isTypedArray(b) {
return b?.prototype?.__proto__?.constructor?.name === "TypedArray"
}
function isIntegralNumber(b) {
return Number.isInteger(b) || typeof b == "bigint"
}
function exists(b) {
return b != null
}
function calculateDimensions(b) {
const n = [];
let a = b;
for (; Array.isArray(a);) n.push(a.length), a = a[0];
return n
}
function mergeArrays(...b) {
return Array.prototype.concat.apply([], b)
}
function calculateReflectOffset(b, n) {
return Math.abs((b + n) % (2 * n) - n)
}
const sharp = {},
ONNX_NODE = Object.freeze(Object.defineProperty({
__proto__: null,
default: sharp
}, Symbol.toStringTag, {
value: "Module"
}));
function getDefaultExportFromCjs(b) {
return b && b.__esModule && Object.prototype.hasOwnProperty.call(b, "default") ? b.default : b
}
function getAugmentedNamespace(b) {
if (b.__esModule) return b;
var n = b.default;
if (typeof n == "function") {
var a = function u() {
return this instanceof u ? Reflect.construct(n, arguments, this.constructor) : n.apply(this, arguments)
};
a.prototype = n.prototype
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Object.defineProperty(a, u, c.get ? c : {
enumerable: !0,
get: function() {
return b[u]
}
})
}), a
}
var ortWeb_min$1 = {
exports: {}
};
const backends = {},
backendsSortedByPriority = [],
registerBackend = (b, n, a) => {
if (n && typeof n.init == "function" && typeof n.createSessionHandler == "function") {
const u = backends[b];
if (u === void 0) backends[b] = {
backend: n,
priority: a
};
else {
if (u.priority > a) return;
if (u.priority === a && u.backend !== n) throw new Error(`cannot register backend "${b}" using priority ${a}`)
}
if (a >= 0) {
const c = backendsSortedByPriority.indexOf(b);
c !== -1 && backendsSortedByPriority.splice(c, 1);
for (let f = 0; f < backendsSortedByPriority.length; f++)
if (backends[backendsSortedByPriority[f]].priority <= a) {
backendsSortedByPriority.splice(f, 0, b);
return
} backendsSortedByPriority.push(b)
}
return
}
throw new TypeError("not a valid backend")
},
resolveBackend = async b => {
const n = b.length === 0 ? backendsSortedByPriority : b,
a = [];
for (const u of n) {
const c = backends[u];
if (c) {
if (c.initialized) return c.backend;
if (c.aborted) continue;
const f = !!c.initPromise;
try {
return f || (c.initPromise = c.backend.init()), await c.initPromise, c.initialized = !0, c.backend
} catch (s) {
f || a.push({
name: u,
err: s
}), c.aborted = !0
} finally {
delete c.initPromise
}
}
}
throw new Error(`no available backend found. ERR: ${a.map(u=>`[${u.name}] ${u.err}`).join(", ")}`)
};
class EnvImpl {
constructor() {
this.wasm = {}, this.webgl = {}, this.logLevelInternal = "warning"
}
set logLevel(n) {
if (n !== void 0) {
if (typeof n != "string" || ["verbose", "info", "warning", "error", "fatal"].indexOf(n) === -1) throw new Error(`Unsupported logging level: ${n}`);
this.logLevelInternal = n
}
}
get logLevel() {
return this.logLevelInternal
}
}
const env$2 = new EnvImpl,
isBigInt64ArrayAvailable = typeof BigInt64Array < "u" && typeof BigInt64Array.from == "function",
isBigUint64ArrayAvailable = typeof BigUint64Array < "u" && typeof BigUint64Array.from == "function",
NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP = new Map([
["float32", Float32Array],
["uint8", Uint8Array],
["int8", Int8Array],
["uint16", Uint16Array],
["int16", Int16Array],
["int32", Int32Array],
["bool", Uint8Array],
["float64", Float64Array],
["uint32", Uint32Array]
]),
NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP = new Map([
[Float32Array, "float32"],
[Uint8Array, "uint8"],
[Int8Array, "int8"],
[Uint16Array, "uint16"],
[Int16Array, "int16"],
[Int32Array, "int32"],
[Float64Array, "float64"],
[Uint32Array, "uint32"]
]);
isBigInt64ArrayAvailable && (NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set("int64", BigInt64Array), NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(BigInt64Array, "int64"));
isBigUint64ArrayAvailable && (NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.set("uint64", BigUint64Array), NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.set(BigUint64Array, "uint64"));
const calculateSize = b => {
let n = 1;
for (let a = 0; a < b.length; a++) {
const u = b[a];
if (typeof u != "number" || !Number.isSafeInteger(u)) throw new TypeError(`dims[${a}] must be an integer, got: ${u}`);
if (u < 0) throw new RangeError(`dims[${a}] must be a non-negative integer, got: ${u}`);
n *= u
}
return n
};
let Tensor$2 = class st {
constructor(n, a, u) {
let c, f, s;
if (typeof n == "string")
if (c = n, s = u, n === "string") {
if (!Array.isArray(a)) throw new TypeError("A string tensor's data must be a string array.");
f = a
} else {
const p = NUMERIC_TENSOR_TYPE_TO_TYPEDARRAY_MAP.get(n);
if (p === void 0) throw new TypeError(`Unsupported tensor type: ${n}.`);
if (Array.isArray(a)) f = p.from(a);
else if (a instanceof p) f = a;
else throw new TypeError(`A ${c} tensor's data must be type of ${p}`)
}
else if (s = a, Array.isArray(n)) {
if (n.length === 0) throw new TypeError("Tensor type cannot be inferred from an empty array.");
const p = typeof n[0];
if (p === "string") c = "string", f = n;
else if (p === "boolean") c = "bool", f = Uint8Array.from(n);
else throw new TypeError(`Invalid element type of data array: ${p}.`)
} else {
const p = NUMERIC_TENSOR_TYPEDARRAY_TO_TYPE_MAP.get(n.constructor);
if (p === void 0) throw new TypeError(`Unsupported type for tensor data: ${n.constructor}.`);
c = p, f = n
}
if (s === void 0) s = [f.length];
else if (!Array.isArray(s)) throw new TypeError("A tensor's dims must be a number array");
const h = calculateSize(s);
if (h !== f.length) throw new Error(`Tensor's size(${h}) does not match data length(${f.length}).`);
this.dims = s, this.type = c, this.data = f, this.size = h
}
static bufferToTensor(n, a) {
if (n === void 0) throw new Error("Image buffer must be defined");
if (a.height === void 0 || a.width === void 0) throw new Error("Image height and width must be defined");
const {
height: u,
width: c
} = a, f = a.norm;
let s, h;
f === void 0 || f.mean === void 0 ? s = 255 : s = f.mean, f === void 0 || f.bias === void 0 ? h = 0 : h = f.bias;
const p = a.bitmapFormat !== void 0 ? a.bitmapFormat : "RGBA",
l = a.tensorFormat !== void 0 && a.tensorFormat !== void 0 ? a.tensorFormat : "RGB",
o = u * c,
t = l === "RGBA" ? new Float32Array(o * 4) : new Float32Array(o * 3);
let e = 4,
r = 0,
i = 1,
d = 2,
g = 3,
m = 0,
_ = o,
y = o * 2,
T = -1;
p === "RGB" && (e = 3, r = 0, i = 1, d = 2, g = -1), l === "RGBA" ? T = o * 3 : l === "RBG" ? (m = 0, y = o, _ = o * 2) : l === "BGR" && (y = 0, _ = o, m = o * 2);
for (let S = 0; S < o; S++, r += e, d += e, i += e, g += e) t[m++] = (n[r] + h) / s, t[_++] = (n[i] + h) / s, t[y++] = (n[d] + h) / s, T !== -1 && g !== -1 && (t[T++] = (n[g] + h) / s);
return l === "RGBA" ? new st("float32", t, [1, 4, u, c]) : new st("float32", t, [1, 3, u, c])
}
static async fromImage(n, a) {
const u = typeof HTMLImageElement < "u" && n instanceof HTMLImageElement,
c = typeof ImageData < "u" && n instanceof ImageData,
f = typeof ImageBitmap < "u" && n instanceof ImageBitmap,
s = typeof String < "u" && (n instanceof String || typeof n == "string");
let h, p = {};
if (u) {
const l = document.createElement("canvas"),
o = l.getContext("2d");
if (o != null) {
let t = n.naturalHeight,
e = n.naturalWidth;
if (a !== void 0 && a.resizedHeight !== void 0 && a.resizedWidth !== void 0 && (t = a.resizedHeight, e = a.resizedWidth), a !== void 0) {
if (p = a, a.tensorFormat !== void 0) throw new Error("Image input config format must be RGBA for HTMLImageElement");
if (p.tensorFormat = "RGBA", a.height !== void 0 && a.height !== t) throw new Error("Image input config height doesn't match HTMLImageElement height");
if (p.height = t, a.width !== void 0 && a.width !== e) throw new Error("Image input config width doesn't match HTMLImageElement width");
p.width = e
} else p.tensorFormat = "RGBA", p.height = t, p.width = e;
l.width = e, l.height = t, o.drawImage(n, 0, 0, e, t), h = o.getImageData(0, 0, e, t).data
} else throw new Error("Can not access image data")
} else if (c) {
const l = "RGBA";
let o, t;
if (a !== void 0 && a.resizedWidth !== void 0 && a.resizedHeight !== void 0 ? (o = a.resizedHeight, t = a.resizedWidth) : (o = n.height, t = n.width), a !== void 0) {
if (p = a, a.bitmapFormat !== void 0 && a.bitmapFormat !== l) throw new Error("Image input config format must be RGBA for ImageData");
p.bitmapFormat = "RGBA"
} else p.bitmapFormat = "RGBA";
if (p.height = o, p.width = t, a !== void 0) {
const e = document.createElement("canvas");
e.width = t, e.height = o;
const r = e.getContext("2d");
if (r != null) r.putImageData(n, 0, 0), h = r.getImageData(0, 0, t, o).data;
else throw new Error("Can not access image data")
} else h = n.data
} else if (f) {
if (a === void 0) throw new Error("Please provide image config with format for Imagebitmap");
if (a.bitmapFormat !== void 0) throw new Error("Image input config format must be defined for ImageBitmap");
const l = document.createElement("canvas").getContext("2d");
if (l != null) {
const o = n.height,
t = n.width;
if (l.drawImage(n, 0, 0, t, o), h = l.getImageData(0, 0, t, o).data, a !== void 0) {
if (a.height !== void 0 && a.height !== o) throw new Error("Image input config height doesn't match ImageBitmap height");
if (p.height = o, a.width !== void 0 && a.width !== t) throw new Error("Image input config width doesn't match ImageBitmap width");
p.width = t
} else p.height = o, p.width = t;
return st.bufferToTensor(h, p)
} else throw new Error("Can not access image data")
} else {
if (s) return new Promise((l, o) => {
const t = document.createElement("canvas"),
e = t.getContext("2d");
if (!n || !e) return o();
const r = new Image;
r.crossOrigin = "Anonymous", r.src = n, r.onload = () => {
t.width = r.width, t.height = r.height, e.drawImage(r, 0, 0, t.width, t.height);
const i = e.getImageData(0, 0, t.width, t.height);
if (a !== void 0) {
if (a.height !== void 0 && a.height !== t.height) throw new Error("Image input config height doesn't match ImageBitmap height");
if (p.height = t.height, a.width !== void 0 && a.width !== t.width) throw new Error("Image input config width doesn't match ImageBitmap width");
p.width = t.width
} else p.height = t.height, p.width = t.width;
l(st.bufferToTensor(i.data, p))
}
});
throw new Error("Input data provided is not supported - aborted tensor creation")
}
if (h !== void 0) return st.bufferToTensor(h, p);
throw new Error("Input data provided is not supported - aborted tensor creation")
}
toImageData(n) {
var a, u;
const c = document.createElement("canvas").getContext("2d");
let f;
if (c != null) {
const s = this.dims[3],
h = this.dims[2],
p = this.dims[1],
l = n !== void 0 && n.format !== void 0 ? n.format : "RGB",
o = n !== void 0 && ((a = n.norm) === null || a === void 0 ? void 0 : a.mean) !== void 0 ? n.norm.mean : 255,
t = n !== void 0 && ((u = n.norm) === null || u === void 0 ? void 0 : u.bias) !== void 0 ? n.norm.bias : 0,
e = h * s;
if (n !== void 0) {
if (n.height !== void 0 && n.height !== h) throw new Error("Image output config height doesn't match tensor height");
if (n.width !== void 0 && n.width !== s) throw new Error("Image output config width doesn't match tensor width");
if (n.format !== void 0 && p === 4 && n.format !== "RGBA" || p === 3 && n.format !== "RGB" && n.format !== "BGR") throw new Error("Tensor format doesn't match input tensor dims")
}
const r = 4;
let i = 0,
d = 1,
g = 2,
m = 3,
_ = 0,
y = e,
T = e * 2,
w = -1;
l === "RGBA" ? (_ = 0, y = e, T = e * 2, w = e * 3) : l === "RGB" ? (_ = 0, y = e, T = e * 2) : l === "RBG" && (_ = 0, T = e, y = e * 2), f = c.createImageData(s, h);
for (let S = 0; S < h * s; i += r, d += r, g += r, m += r, S++) f.data[i] = (this.data[_++] - t) * o, f.data[d] = (this.data[y++] - t) * o, f.data[g] = (this.data[T++] - t) * o, f.data[m] = w === -1 ? 255 : (this.data[w++] - t) * o
} else throw new Error("Can not access image data");
return f
}
reshape(n) {
return new st(this.type, this.data, n)
}
};
const Tensor$1 = Tensor$2;
let InferenceSession$2 = class ln {
constructor(n) {
this.handler = n
}
async run(n, a, u) {
const c = {};
let f = {};
if (typeof n != "object" || n === null || n instanceof Tensor$1 || Array.isArray(n)) throw new TypeError("'feeds' must be an object that use input names as keys and OnnxValue as corresponding values.");
let s = !0;
if (typeof a == "object") {
if (a === null) throw new TypeError("Unexpected argument[1]: cannot be null.");
if (a instanceof Tensor$1) throw new TypeError("'fetches' cannot be a Tensor");
if (Array.isArray(a)) {
if (a.length === 0) throw new TypeError("'fetches' cannot be an empty array.");
s = !1;
for (const l of a) {
if (typeof l != "string") throw new TypeError("'fetches' must be a string array or an object.");
if (this.outputNames.indexOf(l) === -1) throw new RangeError(`'fetches' contains invalid output name: ${l}.`);
c[l] = null
}
if (typeof u == "object" && u !== null) f = u;
else if (typeof u < "u") throw new TypeError("'options' must be an object.")
} else {
let l = !1;
const o = Object.getOwnPropertyNames(a);
for (const t of this.outputNames)
if (o.indexOf(t) !== -1) {
const e = a[t];
(e === null || e instanceof Tensor$1) && (l = !0, s = !1, c[t] = e)
} if (l) {
if (typeof u == "object" && u !== null) f = u;
else if (typeof u < "u") throw new TypeError("'options' must be an object.")
} else f = a
}
} else if (typeof a < "u") throw new TypeError("Unexpected argument[1]: must be 'fetches' or 'options'.");
for (const l of this.inputNames)
if (typeof n[l] > "u") throw new Error(`input '${l}' is missing in 'feeds'.`);
if (s)
for (const l of this.outputNames) c[l] = null;
const h = await this.handler.run(n, c, f),
p = {};
for (const l in h) Object.hasOwnProperty.call(h, l) && (p[l] = new Tensor$1(h[l].type, h[l].data, h[l].dims));
return p
}
static async create(n, a, u, c) {
let f, s = {};
if (typeof n == "string") {
if (f = n, typeof a == "object" && a !== null) s = a;
else if (typeof a < "u") throw new TypeError("'options' must be an object.")
} else if (n instanceof Uint8Array) {
if (f = n, typeof a == "object" && a !== null) s = a;
else if (typeof a < "u") throw new TypeError("'options' must be an object.")
} else if (n instanceof ArrayBuffer || typeof SharedArrayBuffer < "u" && n instanceof SharedArrayBuffer) {
const t = n;
let e = 0,
r = n.byteLength;
if (typeof a == "object" && a !== null) s = a;
else if (typeof a == "number") {
if (e = a, !Number.isSafeInteger(e)) throw new RangeError("'byteOffset' must be an integer.");
if (e < 0 || e >= t.byteLength) throw new RangeError(`'byteOffset' is out of range [0, ${t.byteLength}).`);
if (r = n.byteLength - e, typeof u == "number") {
if (r = u, !Number.isSafeInteger(r)) throw new RangeError("'byteLength' must be an integer.");
if (r <= 0 || e + r > t.byteLength) throw new RangeError(`'byteLength' is out of range (0, ${t.byteLength-e}].`);
if (typeof c == "object" && c !== null) s = c;
else if (typeof c < "u") throw new TypeError("'options' must be an object.")
} else if (typeof u < "u") throw new TypeError("'byteLength' must be a number.")
} else if (typeof a < "u") throw new TypeError("'options' must be an object.");
f = new Uint8Array(t, e, r)
} else throw new TypeError("Unexpected argument[0]: must be 'path' or 'buffer'.");
const p = (s.executionProviders || []).map(t => typeof t == "string" ? t : t.name),
o = await (await resolveBackend(p)).createSessionHandler(f, s);
return new ln(o)
}
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,
lib = Object.freeze(Object.defineProperty({
__proto__: null,
InferenceSession: InferenceSession$1,
Tensor: Tensor$1,
env: env$2,
registerBackend
}, Symbol.toStringTag, {
value: "Module"
})),
require$$0 = getAugmentedNamespace(lib);
/*!
* ONNX Runtime Web v1.14.0
* Copyright (c) Microsoft Corporation. All rights reserved.
* Licensed under the MIT License.
*/
(function(module, exports) {
(function(b, n) {
module.exports = n(require$$0)
})(self, __WEBPACK_EXTERNAL_MODULE__1670__ => (() => {
var __webpack_modules__ = {
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O = typeof window == "object",
E = typeof importScripts == "function",
v = typeof process == "object" && typeof process.versions == "object" && typeof process.versions.node == "string",
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throw console.error('The "worker_threads" module is not supported in this node.js build - perhaps a newer version is needed?'), A
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k = console.warn.bind(console);
v && (y(), R = x => m.writeSync(1, x + `
`), k = x => m.writeSync(2, x + `
`));
var Y, C = t.print || R,
$ = t.printErr || k;
Object.assign(t, T), T = null, t.thisProgram && (w = t.thisProgram), t.quit && (S = t.quit), t.wasmBinary && (Y = t.wasmBinary);
var X = t.noExitRuntime || !1;
typeof WebAssembly != "object" && pe("no native wasm support detected");
var z, Z, J, ue, Se, Te, se, ye, be = !1,
Ie = typeof TextDecoder < "u" ? new TextDecoder("utf8") : void 0;
function Le(x, A, I) {
var F = (A >>>= 0) + I;
for (I = A; x[I] && !(I >= F);) ++I;
if (16 < I - A && x.buffer && Ie) return Ie.decode(x.buffer instanceof SharedArrayBuffer ? x.slice(A, I) : x.subarray(A, I));
for (F = ""; A < I;) {
var B = x[A++];
if (128 & B) {
var G = 63 & x[A++];
if ((224 & B) == 192) F += String.fromCharCode((31 & B) << 6 | G);
else {
var Q = 63 & x[A++];
65536 > (B = (240 & B) == 224 ? (15 & B) << 12 | G << 6 | Q : (7 & B) << 18 | G << 12 | Q << 6 | 63 & x[A++]) ? F += String.fromCharCode(B) : (B -= 65536, F += String.fromCharCode(55296 | B >> 10, 56320 | 1023 & B))
}
} else F += String.fromCharCode(B)
}
return F
}
function ve(x, A) {
return (x >>>= 0) ? Le(h(), x, A) : ""
}
function Ne(x, A, I, F) {
if (!(0 < F)) return 0;
var B = I >>>= 0;
F = I + F - 1;
for (var G = 0; G < x.length; ++G) {
var Q = x.charCodeAt(G);
if (55296 <= Q && 57343 >= Q && (Q = 65536 + ((1023 & Q) << 10) | 1023 & x.charCodeAt(++G)), 127 >= Q) {
if (I >= F) break;
A[I++ >>> 0] = Q
} else {
if (2047 >= Q) {
if (I + 1 >= F) break;
A[I++ >>> 0] = 192 | Q >> 6
} else {
if (65535 >= Q) {
if (I + 2 >= F) break;
A[I++ >>> 0] = 224 | Q >> 12
} else {
if (I + 3 >= F) break;
A[I++ >>> 0] = 240 | Q >> 18, A[I++ >>> 0] = 128 | Q >> 12 & 63
}
A[I++ >>> 0] = 128 | Q >> 6 & 63
}
A[I++ >>> 0] = 128 | 63 & Q
}
}
return A[I >>> 0] = 0, I - B
}
function Fe(x) {
for (var A = 0, I = 0; I < x.length; ++I) {
var F = x.charCodeAt(I);
127 >= F ? A++ : 2047 >= F ? A += 2 : 55296 <= F && 57343 >= F ? (A += 4, ++I) : A += 3
}
return A
}
function Me(x) {
J = x, t.HEAP8 = ue = new Int8Array(x), t.HEAP16 = new Int16Array(x), t.HEAP32 = Te = new Int32Array(x), t.HEAPU8 = Se = new Uint8Array(x), t.HEAPU16 = new Uint16Array(x), t.HEAPU32 = se = new Uint32Array(x), t.HEAPF32 = new Float32Array(x), t.HEAPF64 = ye = new Float64Array(x)
}
P && (J = t.buffer);
var Oe = t.INITIAL_MEMORY || 16777216;
if (P) z = t.wasmMemory, J = t.buffer;
else if (t.wasmMemory) z = t.wasmMemory;
else if (!((z = new WebAssembly.Memory({
initial: Oe / 65536,
maximum: 65536,
shared: !0
})).buffer instanceof SharedArrayBuffer)) throw $("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag"), v && console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)"), Error("bad memory");
z && (J = z.buffer), Oe = J.byteLength, Me(J);
var Be, Ue = [],
ze = [],
He = [],
Ke = [];
function Ge() {
return X || !1
}
function Ve() {
var x = t.preRun.shift();
Ue.unshift(x)
}
var Ae, Re = 0,
Ye = null;
function pe(x) {
throw P ? postMessage({
cmd: "onAbort",
arg: x
}) : t.onAbort && t.onAbort(x), $(x = "Aborted(" + x + ")"), be = !0, x = new WebAssembly.RuntimeError(x + ". Build with -sASSERTIONS for more info."), r(x), x
}
function dt() {
return Ae.startsWith("data:application/octet-stream;base64,")
}
function at() {
var x = Ae;
try {
if (x == Ae && Y) return new Uint8Array(Y);
if (g) return g(x);
throw "both async and sync fetching of the wasm failed"
} catch (A) {
pe(A)
}
}
Ae = "ort-wasm-threaded.wasm", dt() || (Ae = V(Ae));
var Et = {};
function Qe(x) {
this.name = "ExitStatus", this.message = "Program terminated with exit(" + x + ")", this.status = x
}
function ut(x) {
(x = re.Vb[x]) || pe(), re.mc(x)
}
function lt(x) {
var A = re.Cc();
if (!A) return 6;
re.ac.push(A), re.Vb[x.Ub] = A, A.Ub = x.Ub;
var I = {
cmd: "run",
start_routine: x.Ic,
arg: x.zc,
pthread_ptr: x.Ub
};
return A.$b = () => {
I.time = performance.now(), A.postMessage(I, x.Nc)
}, A.loaded && (A.$b(), delete A.$b), 0
}
function $e(x) {
if (P) return ee(1, 1, x);
Ge() || (re.oc(), t.onExit && t.onExit(x), be = !0), S(x, new Qe(x))
}
function nt(x, A) {
if (!A && P) throw Mt(x), "unwind";
Ge() || P || (Gt(), tt(He), zt(0), Ft[1].length && kt(1, 10), Ft[2].length && kt(2, 10), re.oc()), $e(x)
}
var re = {
Yb: [],
ac: [],
qc: [],
Vb: {},
fc: function() {
P && re.Ec()
},
Pc: function() {},
Ec: function() {
re.receiveObjectTransfer = re.Gc, re.threadInitTLS = re.pc, re.setExitStatus = re.nc, X = !1
},
nc: function() {},
oc: function() {
for (var x of Object.values(re.Vb)) re.mc(x);
for (x of re.Yb) x.terminate();
re.Yb = []
},
mc: function(x) {
var A = x.Ub;
delete re.Vb[A], re.Yb.push(x), re.ac.splice(re.ac.indexOf(x), 1), x.Ub = 0, Nt(A)
},
Gc: function() {},
pc: function() {
re.qc.forEach(x => x())
},
Fc: function(x, A) {
x.onmessage = I => {
var F = (I = I.data).cmd;
if (x.Ub && (re.Bc = x.Ub), I.targetThread && I.targetThread != It()) {
var B = re.Vb[I.Qc];
B ? B.postMessage(I, I.transferList) : $('Internal error! Worker sent a message "' + F + '" to target pthread ' + I.targetThread + ", but that thread no longer exists!")
} else F === "processProxyingQueue" ? N(I.queue) : F === "spawnThread" ? lt(I) : F === "cleanupThread" ? ut(I.thread) : F === "killThread" ? (I = I.thread, F = re.Vb[I], delete re.Vb[I], F.terminate(), Nt(I), re.ac.splice(re.ac.indexOf(F), 1), F.Ub = 0) : F === "cancelThread" ? re.Vb[I.thread].postMessage({
cmd: "cancel"
}) : F === "loaded" ? (x.loaded = !0, A && A(x), x.$b && (x.$b(), delete x.$b)) : F === "print" ? C("Thread " + I.threadId + ": " + I.text) : F === "printErr" ? $("Thread " + I.threadId + ": " + I.text) : F === "alert" ? alert("Thread " + I.threadId + ": " + I.text) : I.target === "setimmediate" ? x.postMessage(I) : F === "onAbort" ? t.onAbort && t.onAbort(I.arg) : F && $("worker sent an unknown command " + F);
re.Bc = void 0
}, x.onerror = I => {
throw $("worker sent an error! " + I.filename + ":" + I.lineno + ": " + I.message), I
}, v && (x.on("message", function(I) {
x.onmessage({
data: I
})
}), x.on("error", function(I) {
x.onerror(I)
}), x.on("detachedExit", function() {})), x.postMessage({
cmd: "load",
urlOrBlob: t.mainScriptUrlOrBlob || u,
wasmMemory: z,
wasmModule: Z
})
},
yc: function() {
var x = V("ort-wasm-threaded.worker.js");
re.Yb.push(new Worker(x))
},
Cc: function() {
return re.Yb.length == 0 && (re.yc(), re.Fc(re.Yb[0])), re.Yb.pop()
}
};
function tt(x) {
for (; 0 < x.length;) x.shift()(t)
}
function At(x) {
var A = de();
return x = x(), ce(A), x
}
function Mt(x) {
if (P) return ee(2, 0, x);
try {
nt(x)
} catch (A) {
A instanceof Qe || A == "unwind" || S(1, A)
}
}
t.PThread = re, t.establishStackSpace = function() {
var x = It(),
A = p()[x + 44 >> 2 >>> 0];
x = p()[x + 48 >> 2 >>> 0], Xt(A, A - x), ce(A)
};
var Je = [];
function we(x) {
var A = Je[x];
return A || (x >= Je.length && (Je.length = x + 1), Je[x] = A = Be.get(x)), A
}
t.invokeEntryPoint = function(x, A) {
x = we(x)(A), Ge() ? re.nc(x) : Yt(x)
};
var rt, ht, it = [],
ae = 0,
ie = 0;
function oe(x) {
this.Zb = x, this.Sb = x - 24, this.xc = function(A) {
l()[this.Sb + 4 >> 2 >>> 0] = A
}, this.bc = function() {
return l()[this.Sb + 4 >> 2 >>> 0]
}, this.wc = function(A) {
l()[this.Sb + 8 >> 2 >>> 0] = A
}, this.Dc = function() {
return l()[this.Sb + 8 >> 2 >>> 0]
}, this.rc = function() {
p()[this.Sb >> 2 >>> 0] = 0
}, this.hc = function(A) {
A = A ? 1 : 0, s()[this.Sb + 12 >> 0 >>> 0] = A
}, this.uc = function() {
return s()[this.Sb + 12 >> 0 >>> 0] != 0
}, this.ic = function(A) {
A = A ? 1 : 0, s()[this.Sb + 13 >> 0 >>> 0] = A
}, this.kc = function() {
return s()[this.Sb + 13 >> 0 >>> 0] != 0
}, this.fc = function(A, I) {
this.cc(0), this.xc(A), this.wc(I), this.rc(), this.hc(!1), this.ic(!1)
}, this.sc = function() {
Atomics.add(p(), this.Sb >> 2, 1)
}, this.Hc = function() {
return Atomics.sub(p(), this.Sb >> 2, 1) === 1
}, this.cc = function(A) {
l()[this.Sb + 16 >> 2 >>> 0] = A
}, this.tc = function() {
return l()[this.Sb + 16 >> 2 >>> 0]
}, this.vc = function() {
if (Kt(this.bc())) return l()[this.Zb >> 2 >>> 0];
var A = this.tc();
return A !== 0 ? A : this.Zb
}
}
function ft(x) {
return Ut(new oe(x).Sb)
}
function ot(x, A, I, F) {
return P ? ee(3, 1, x, A, I, F) : pt(x, A, I, F)
}
function pt(x, A, I, F) {
if (typeof SharedArrayBuffer > "u") return $("Current environment does not support SharedArrayBuffer, pthreads are not available!"), 6;
var B = [];
return P && B.length === 0 ? ot(x, A, I, F) : (x = {
Ic: I,
Ub: x,
zc: F,
Nc: B
}, P ? (x.Oc = "spawnThread", postMessage(x, B), 0) : lt(x))
}
function gt(x, A, I) {
return P ? ee(4, 1, x, A, I) : 0
}
function mt(x, A) {
if (P) return ee(5, 1, x, A)
}
function bt(x, A) {
if (P) return ee(6, 1, x, A)
}
function _t(x, A, I) {
if (P) return ee(7, 1, x, A, I)
}
function yt(x, A, I) {
return P ? ee(8, 1, x, A, I) : 0
}
function wt(x, A) {
if (P) return ee(9, 1, x, A)
}
function Tt(x, A, I) {
if (P) return ee(10, 1, x, A, I)
}
function vt(x, A, I, F) {
if (P) return ee(11, 1, x, A, I, F)
}
function xt(x, A, I, F) {
if (P) return ee(12, 1, x, A, I, F)
}
function St(x, A, I, F) {
if (P) return ee(13, 1, x, A, I, F)
}
function Ot(x) {
if (P) return ee(14, 1, x)
}
function M(x, A) {
if (P) return ee(15, 1, x, A)
}
function D(x, A, I) {
if (P) return ee(16, 1, x, A, I)
}
function N(x) {
Atomics.store(p(), x >> 2, 1), It() && Ht(x), Atomics.compareExchange(p(), x >> 2, 1, 0)
}
function j(x) {
return l()[x >>> 2] + 4294967296 * p()[x + 4 >>> 2]
}
function U(x, A, I, F, B, G) {
return P ? ee(17, 1, x, A, I, F, B, G) : -52
}
function H(x, A, I, F, B, G) {
if (P) return ee(18, 1, x, A, I, F, B, G)
}
function K(x) {
var A = Fe(x) + 1,
I = Lt(A);
return I && Ne(x, s(), I, A), I
}
function te(x, A, I) {
function F(ge) {
return (ge = ge.toTimeString().match(/\(([A-Za-z ]+)\)$/)) ? ge[1] : "GMT"
}
if (P) return ee(19, 1, x, A, I);
var B = new Date().getFullYear(),
G = new Date(B, 0, 1),
Q = new Date(B, 6, 1);
B = G.getTimezoneOffset();
var ne = Q.getTimezoneOffset(),
fe = Math.max(B, ne);
p()[x >> 2 >>> 0] = 60 * fe, p()[A >> 2 >>> 0] = +(B != ne), x = F(G), A = F(Q), x = K(x), A = K(A), ne < B ? (l()[I >> 2 >>> 0] = x, l()[I + 4 >> 2 >>> 0] = A) : (l()[I >> 2 >>> 0] = A, l()[I + 4 >> 2 >>> 0] = x)
}
function ee(x, A) {
var I = arguments.length - 2,
F = arguments;
return At(() => {
for (var B = $t(8 * I), G = B >> 3, Q = 0; Q < I; Q++) {
var ne = F[2 + Q];
o()[G + Q >>> 0] = ne
}
return Wt(x, I, B, A)
})
}
t.executeNotifiedProxyingQueue = N, ht = v ? () => {
var x = process.hrtime();
return 1e3 * x[0] + x[1] / 1e6
} : P ? () => performance.now() - t.__performance_now_clock_drift : () => performance.now();
var le, xe = [],
ke = {};
function Ce() {
if (!le) {
var x, A = {
USER: "web_user",
LOGNAME: "web_user",
PATH: "/",
PWD: "/",
HOME: "/home/web_user",
LANG: (typeof navigator == "object" && navigator.languages && navigator.languages[0] || "C").replace("-", "_") + ".UTF-8",
_: w || "./this.program"
};
for (x in ke) ke[x] === void 0 ? delete A[x] : A[x] = ke[x];
var I = [];
for (x in A) I.push(x + "=" + A[x]);
le = I
}
return le
}
function q(x, A) {
if (P) return ee(20, 1, x, A);
var I = 0;
return Ce().forEach(function(F, B) {
var G = A + I;
for (B = l()[x + 4 * B >> 2 >>> 0] = G, G = 0; G < F.length; ++G) s()[B++ >> 0 >>> 0] = F.charCodeAt(G);
s()[B >> 0 >>> 0] = 0, I += F.length + 1
}), 0
}
function me(x, A) {
if (P) return ee(21, 1, x, A);
var I = Ce();
l()[x >> 2 >>> 0] = I.length;
var F = 0;
return I.forEach(function(B) {
F += B.length + 1
}), l()[A >> 2 >>> 0] = F, 0
}
function Ee(x) {
return P ? ee(22, 1, x) : 52
}
function qe(x, A, I, F) {
return P ? ee(23, 1, x, A, I, F) : 52
}
function Ze(x, A, I, F, B) {
return P ? ee(24, 1, x, A, I, F, B) : 70
}
var Ft = [null, [],
[]
];
function kt(x, A) {
var I = Ft[x];
A === 0 || A === 10 ? ((x === 1 ? C : $)(Le(I, 0)), I.length = 0) : I.push(A)
}
function Rt(x, A, I, F) {
if (P) return ee(25, 1, x, A, I, F);
for (var B = 0, G = 0; G < I; G++) {
var Q = l()[A >> 2 >>> 0],
ne = l()[A + 4 >> 2 >>> 0];
A += 8;
for (var fe = 0; fe < ne; fe++) kt(x, h()[Q + fe >>> 0]);
B += ne
}
return l()[F >> 2 >>> 0] = B, 0
}
var je = 0;
function Pt(x) {
return x % 4 == 0 && (x % 100 != 0 || x % 400 == 0)
}
var jt = [31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],
Bt = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31];
function Vt(x, A, I, F) {
function B(W, _e, Pe) {
for (W = typeof W == "number" ? W.toString() : W || ""; W.length < _e;) W = Pe[0] + W;
return W
}
function G(W, _e) {
return B(W, _e, "0")
}
function Q(W, _e) {
function Pe(ct) {
return 0 > ct ? -1 : 0 < ct ? 1 : 0
}
var et;
return (et = Pe(W.getFullYear() - _e.getFullYear())) === 0 && (et = Pe(W.getMonth() - _e.getMonth())) === 0 && (et = Pe(W.getDate() - _e.getDate())), et
}
function ne(W) {
switch (W.getDay()) {
case 0:
return new Date(W.getFullYear() - 1, 11, 29);
case 1:
return W;
case 2:
return new Date(W.getFullYear(), 0, 3);
case 3:
return new Date(W.getFullYear(), 0, 2);
case 4:
return new Date(W.getFullYear(), 0, 1);
case 5:
return new Date(W.getFullYear() - 1, 11, 31);
case 6:
return new Date(W.getFullYear() - 1, 11, 30)
}
}
function fe(W) {
var _e = W.Wb;
for (W = new Date(new Date(W.Xb + 1900, 0, 1).getTime()); 0 < _e;) {
var Pe = W.getMonth(),
et = (Pt(W.getFullYear()) ? jt : Bt)[Pe];
if (!(_e > et - W.getDate())) {
W.setDate(W.getDate() + _e);
break
}
_e -= et - W.getDate() + 1, W.setDate(1), 11 > Pe ? W.setMonth(Pe + 1) : (W.setMonth(0), W.setFullYear(W.getFullYear() + 1))
}
return Pe = new Date(W.getFullYear() + 1, 0, 4), _e = ne(new Date(W.getFullYear(), 0, 4)), Pe = ne(Pe), 0 >= Q(_e, W) ? 0 >= Q(Pe, W) ? W.getFullYear() + 1 : W.getFullYear() : W.getFullYear() - 1
}
var ge = p()[F + 40 >> 2 >>> 0];
for (var De in F = {
Lc: p()[F >> 2 >>> 0],
Kc: p()[F + 4 >> 2 >>> 0],
dc: p()[F + 8 >> 2 >>> 0],
jc: p()[F + 12 >> 2 >>> 0],
ec: p()[F + 16 >> 2 >>> 0],
Xb: p()[F + 20 >> 2 >>> 0],
Tb: p()[F + 24 >> 2 >>> 0],
Wb: p()[F + 28 >> 2 >>> 0],
Rc: p()[F + 32 >> 2 >>> 0],
Jc: p()[F + 36 >> 2 >>> 0],
Mc: ge ? ve(ge) : ""
}, I = ve(I), ge = {
"%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"
}) I = I.replace(new RegExp(De, "g"), ge[De]);
var Xe = "Sunday Monday Tuesday Wednesday Thursday Friday Saturday".split(" "),
We = "January February March April May June July August September October November December".split(" ");
for (De in ge = {
"%a": function(W) {
return Xe[W.Tb].substring(0, 3)
},
"%A": function(W) {
return Xe[W.Tb]
},
"%b": function(W) {
return We[W.ec].substring(0, 3)
},
"%B": function(W) {
return We[W.ec]
},
"%C": function(W) {
return G((W.Xb + 1900) / 100 | 0, 2)
},
"%d": function(W) {
return G(W.jc, 2)
},
"%e": function(W) {
return B(W.jc, 2, " ")
},
"%g": function(W) {
return fe(W).toString().substring(2)
},
"%G": function(W) {
return fe(W)
},
"%H": function(W) {
return G(W.dc, 2)
},
"%I": function(W) {
return (W = W.dc) == 0 ? W = 12 : 12 < W && (W -= 12), G(W, 2)
},
"%j": function(W) {
for (var _e = 0, Pe = 0; Pe <= W.ec - 1; _e += (Pt(W.Xb + 1900) ? jt : Bt)[Pe++]);
return G(W.jc + _e, 3)
},
"%m": function(W) {
return G(W.ec + 1, 2)
},
"%M": function(W) {
return G(W.Kc, 2)
},
"%n": function() {
return `
`
},
"%p": function(W) {
return 0 <= W.dc && 12 > W.dc ? "AM" : "PM"
},
"%S": function(W) {
return G(W.Lc, 2)
},
"%t": function() {
return " "
},
"%u": function(W) {
return W.Tb || 7
},
"%U": function(W) {
return G(Math.floor((W.Wb + 7 - W.Tb) / 7), 2)
},
"%V": function(W) {
var _e = Math.floor((W.Wb + 7 - (W.Tb + 6) % 7) / 7);
if (2 >= (W.Tb + 371 - W.Wb - 2) % 7 && _e++, _e) _e == 53 && ((Pe = (W.Tb + 371 - W.Wb) % 7) == 4 || Pe == 3 && Pt(W.Xb) || (_e = 1));
else {
_e = 52;
var Pe = (W.Tb + 7 - W.Wb - 1) % 7;
(Pe == 4 || Pe == 5 && Pt(W.Xb % 400 - 1)) && _e++
}
return G(_e, 2)
},
"%w": function(W) {
return W.Tb
},
"%W": function(W) {
return G(Math.floor((W.Wb + 7 - (W.Tb + 6) % 7) / 7), 2)
},
"%y": function(W) {
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return "%"
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ma: function(x) {
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x: function() {
he(0);
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A && we(A)(x.Zb), ft(x.Zb)
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l: function() {
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t: ft,
M: function() {
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c: function(x, A, I) {
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pa: function() {
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Fa: function(x) {
qt(x, !E, 1, !O), re.pc()
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T: function(x) {
P ? postMessage({
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thread: x
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xa: pt,
j: function(x) {
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H: gt,
Ma: mt,
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oa: yt,
Ka: wt,
Ca: Tt,
Ja: vt,
V: xt,
va: St,
sa: Ot,
La: M,
ta: D,
Ta: function() {},
X: function() {
pe("To use dlopen, you need enable dynamic linking, see https://github.com/emscripten-core/emscripten/wiki/Linking")
},
Ua: function() {
pe("To use dlopen, you need enable dynamic linking, see https://github.com/emscripten-core/emscripten/wiki/Linking")
},
W: function() {
return Date.now()
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ya: function() {
return 2097152
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Oa: function() {
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za: function(x, A, I, F) {
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queue: F
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Ea: function() {
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Pa: function(x, A) {
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y: function() {
pe("")
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U: function() {
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ra: function() {
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B: ht,
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h().copyWithin(x >>> 0, A >>> 0, A + I >>> 0)
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Da: function(x, A, I) {
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try {
z.grow(B - J.byteLength + 65535 >>> 16), Me(z.buffer);
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Na: function() {
throw "unwind"
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Ga: q,
Ha: me,
J: nt,
I: Ee,
S: qe,
ga: Ze,
R: Rt,
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na: function x(A, I) {
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}
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var G = a(Object(function() {
var Q = new Error("Cannot find module 'crypto'");
throw Q.code = "MODULE_NOT_FOUND", Q
}()));
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} catch {}
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}());
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he(1, 0)
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he(1, 0)
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K: function(x) {
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f: function(x, A) {
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he(1, 0)
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P: function(x, A, I) {
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he(1, 0)
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Q: function(x, A, I) {
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he(1, 0)
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k: function(x, A, I) {
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if (ce(F), B !== B + 0) throw B;
he(1, 0)
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p: function(x, A, I, F) {
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he(1, 0)
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q: function(x, A, I, F, B) {
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he(1, 0)
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he(1, 0)
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he(1, 0)
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he(1, 0)
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aa: function(x, A, I, F, B, G, Q, ne) {
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he(1, 0)
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var ne = de();
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return Jt(x, A, I, F, B, G, Q)
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he(1, 0)
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Z: function(x, A, I, F, B) {
var G = de();
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he(1, 0)
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ca: function(x, A, I, F) {
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he(1, 0)
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return Qt(x)
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he(1, 0)
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ba: function(x, A) {
var I = de();
try {
return on(x, A)
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he(1, 0)
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Y: function(x, A, I) {
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he(1, 0)
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we(x)()
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he(1, 0)
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r: function(x, A) {
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we(x)(A)
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he(1, 0)
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he(1, 0)
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we(x)(A, I, F)
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var B = de();
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we(x)(A, I, F)
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if (ce(B), G !== G + 0) throw G;
he(1, 0)
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v: function(x, A, I, F, B) {
var G = de();
try {
we(x)(A, I, F, B)
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if (ce(G), Q !== Q + 0) throw Q;
he(1, 0)
}
},
u: function(x, A, I, F, B, G) {
var Q = de();
try {
we(x)(A, I, F, B, G)
} catch (ne) {
if (ce(Q), ne !== ne + 0) throw ne;
he(1, 0)
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},
O: function(x, A, I, F, B, G, Q) {
var ne = de();
try {
we(x)(A, I, F, B, G, Q)
} catch (fe) {
if (ce(ne), fe !== fe + 0) throw fe;
he(1, 0)
}
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A: function(x, A, I, F, B, G, Q, ne) {
var fe = de();
try {
we(x)(A, I, F, B, G, Q, ne)
} catch (ge) {
if (ce(fe), ge !== ge + 0) throw ge;
he(1, 0)
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ka: function(x, A, I, F, B, G, Q, ne, fe) {
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we(x)(A, I, F, B, G, Q, ne, fe)
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if (ce(ge), De !== De + 0) throw De;
he(1, 0)
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C: function(x, A, I, F, B, G, Q, ne, fe, ge, De) {
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try {
we(x)(A, I, F, B, G, Q, ne, fe, ge, De)
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we(x)(A, I, F, B, G, Q, ne, fe, ge, De, Xe, We, W, _e, Pe)
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if (ce(et), ct !== ct + 0) throw ct;
he(1, 0)
}
},
fa: function(x, A, I, F, B, G, Q, ne) {
var fe = de();
try {
en(x, A, I, F, B, G, Q, ne)
} catch (ge) {
if (ce(fe), ge !== ge + 0) throw ge;
he(1, 0)
}
},
da: function(x, A, I, F, B, G, Q, ne, fe, ge, De, Xe) {
var We = de();
try {
nn(x, A, I, F, B, G, Q, ne, fe, ge, De, Xe)
} catch (W) {
if (ce(We), W !== W + 0) throw W;
he(1, 0)
}
},
ea: function(x, A, I, F, B, G) {
var Q = de();
try {
tn(x, A, I, F, B, G)
} catch (ne) {
if (ce(Q), ne !== ne + 0) throw ne;
he(1, 0)
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},
o: function(x) {
return x
},
a: z || t.wasmMemory,
G: function(x) {
je = x
},
la: Vt,
z: function(x, A, I, F) {
return Vt(x, A, I, F)
}
};
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function x(B, G) {
t.asm = B.exports, re.qc.push(t.asm.sb), Be = t.asm.ub, ze.unshift(t.asm.Va), Z = G, P || (Re--, t.monitorRunDependencies && t.monitorRunDependencies(Re), Re == 0 && Ye && (B = Ye, Ye = null, B()))
}
function A(B) {
x(B.instance, B.module)
}
function I(B) {
return function() {
if (!Y && (O || E)) {
if (typeof fetch == "function" && !Ae.startsWith("file://")) return fetch(Ae, {
credentials: "same-origin"
}).then(function(G) {
if (!G.ok) throw "failed to load wasm binary file at '" + Ae + "'";
return G.arrayBuffer()
}).catch(function() {
return at()
});
if (d) return new Promise(function(G, Q) {
d(Ae, function(ne) {
G(new Uint8Array(ne))
}, Q)
})
}
return Promise.resolve().then(function() {
return at()
})
}().then(function(G) {
return WebAssembly.instantiate(G, F)
}).then(function(G) {
return G
}).then(B, function(G) {
$("failed to asynchronously prepare wasm: " + G), pe(G)
})
}
var F = {
a: dn
};
if (P || (Re++, t.monitorRunDependencies && t.monitorRunDependencies(Re)), t.instantiateWasm) try {
return t.instantiateWasm(F, x)
} catch (B) {
return $("Module.instantiateWasm callback failed with error: " + B), !1
}(Y || typeof WebAssembly.instantiateStreaming != "function" || dt() || Ae.startsWith("file://") || v || typeof fetch != "function" ? I(A) : fetch(Ae, {
credentials: "same-origin"
}).then(function(B) {
return WebAssembly.instantiateStreaming(B, F).then(A, function(G) {
return $("wasm streaming compile failed: " + G), $("falling back to ArrayBuffer instantiation"), I(A)
})
})).catch(r)
})(), t.___wasm_call_ctors = function() {
return (t.___wasm_call_ctors = t.asm.Va).apply(null, arguments)
}, t._OrtInit = function() {
return (t._OrtInit = t.asm.Wa).apply(null, arguments)
}, t._OrtCreateSessionOptions = function() {
return (t._OrtCreateSessionOptions = t.asm.Xa).apply(null, arguments)
}, t._OrtAppendExecutionProvider = function() {
return (t._OrtAppendExecutionProvider = t.asm.Ya).apply(null, arguments)
}, t._OrtAddSessionConfigEntry = function() {
return (t._OrtAddSessionConfigEntry = t.asm.Za).apply(null, arguments)
}, t._OrtReleaseSessionOptions = function() {
return (t._OrtReleaseSessionOptions = t.asm._a).apply(null, arguments)
}, t._OrtCreateSession = function() {
return (t._OrtCreateSession = t.asm.$a).apply(null, arguments)
}, t._OrtReleaseSession = function() {
return (t._OrtReleaseSession = t.asm.ab).apply(null, arguments)
}, t._OrtGetInputCount = function() {
return (t._OrtGetInputCount = t.asm.bb).apply(null, arguments)
}, t._OrtGetOutputCount = function() {
return (t._OrtGetOutputCount = t.asm.cb).apply(null, arguments)
}, t._OrtGetInputName = function() {
return (t._OrtGetInputName = t.asm.db).apply(null, arguments)
}, t._OrtGetOutputName = function() {
return (t._OrtGetOutputName = t.asm.eb).apply(null, arguments)
}, t._OrtFree = function() {
return (t._OrtFree = t.asm.fb).apply(null, arguments)
}, t._OrtCreateTensor = function() {
return (t._OrtCreateTensor = t.asm.gb).apply(null, arguments)
}, t._OrtGetTensorData = function() {
return (t._OrtGetTensorData = t.asm.hb).apply(null, arguments)
}, t._OrtReleaseTensor = function() {
return (t._OrtReleaseTensor = t.asm.ib).apply(null, arguments)
}, t._OrtCreateRunOptions = function() {
return (t._OrtCreateRunOptions = t.asm.jb).apply(null, arguments)
}, t._OrtAddRunConfigEntry = function() {
return (t._OrtAddRunConfigEntry = t.asm.kb).apply(null, arguments)
}, t._OrtReleaseRunOptions = function() {
return (t._OrtReleaseRunOptions = t.asm.lb).apply(null, arguments)
}, t._OrtRun = function() {
return (t._OrtRun = t.asm.mb).apply(null, arguments)
}, t._OrtEndProfiling = function() {
return (t._OrtEndProfiling = t.asm.nb).apply(null, arguments)
};
var It = t._pthread_self = function() {
return (It = t._pthread_self = t.asm.ob).apply(null, arguments)
},
Lt = t._malloc = function() {
return (Lt = t._malloc = t.asm.pb).apply(null, arguments)
},
Ut = t._free = function() {
return (Ut = t._free = t.asm.qb).apply(null, arguments)
},
zt = t._fflush = function() {
return (zt = t._fflush = t.asm.rb).apply(null, arguments)
};
t.__emscripten_tls_init = function() {
return (t.__emscripten_tls_init = t.asm.sb).apply(null, arguments)
};
var Gt = t.___funcs_on_exit = function() {
return (Gt = t.___funcs_on_exit = t.asm.tb).apply(null, arguments)
},
qt = t.__emscripten_thread_init = function() {
return (qt = t.__emscripten_thread_init = t.asm.vb).apply(null, arguments)
};
t.__emscripten_thread_crashed = function() {
return (t.__emscripten_thread_crashed = t.asm.wb).apply(null, arguments)
};
var Dt, Wt = t._emscripten_run_in_main_runtime_thread_js = function() {
return (Wt = t._emscripten_run_in_main_runtime_thread_js = t.asm.xb).apply(null, arguments)
},
Ht = t.__emscripten_proxy_execute_task_queue = function() {
return (Ht = t.__emscripten_proxy_execute_task_queue = t.asm.yb).apply(null, arguments)
},
Nt = t.__emscripten_thread_free_data = function() {
return (Nt = t.__emscripten_thread_free_data = t.asm.zb).apply(null, arguments)
},
Yt = t.__emscripten_thread_exit = function() {
return (Yt = t.__emscripten_thread_exit = t.asm.Ab).apply(null, arguments)
},
he = t._setThrew = function() {
return (he = t._setThrew = t.asm.Bb).apply(null, arguments)
},
Xt = t._emscripten_stack_set_limits = function() {
return (Xt = t._emscripten_stack_set_limits = t.asm.Cb).apply(null, arguments)
},
de = t.stackSave = function() {
return (de = t.stackSave = t.asm.Db).apply(null, arguments)
},
ce = t.stackRestore = function() {
return (ce = t.stackRestore = t.asm.Eb).apply(null, arguments)
},
$t = t.stackAlloc = function() {
return ($t = t.stackAlloc = t.asm.Fb).apply(null, arguments)
},
Ct = t.___cxa_can_catch = function() {
return (Ct = t.___cxa_can_catch = t.asm.Gb).apply(null, arguments)
},
Kt = t.___cxa_is_pointer_type = function() {
return (Kt = t.___cxa_is_pointer_type = t.asm.Hb).apply(null, arguments)
},
Qt = t.dynCall_j = function() {
return (Qt = t.dynCall_j = t.asm.Ib).apply(null, arguments)
},
Jt = t.dynCall_iiiiij = function() {
return (Jt = t.dynCall_iiiiij = t.asm.Jb).apply(null, arguments)
},
Zt = t.dynCall_jii = function() {
return (Zt = t.dynCall_jii = t.asm.Kb).apply(null, arguments)
},
en = t.dynCall_viiiiij = function() {
return (en = t.dynCall_viiiiij = t.asm.Lb).apply(null, arguments)
},
tn = t.dynCall_vjji = function() {
return (tn = t.dynCall_vjji = t.asm.Mb).apply(null, arguments)
},
nn = t.dynCall_viiijjjii = function() {
return (nn = t.dynCall_viiijjjii = t.asm.Nb).apply(null, arguments)
},
rn = t.dynCall_iij = function() {
return (rn = t.dynCall_iij = t.asm.Ob).apply(null, arguments)
},
on = t.dynCall_ji = function() {
return (on = t.dynCall_ji = t.asm.Pb).apply(null, arguments)
},
sn = t.dynCall_iiiiiij = function() {
return (sn = t.dynCall_iiiiiij = t.asm.Qb).apply(null, arguments)
},
an = t.dynCall_iiij = function() {
return (an = t.dynCall_iiij = t.asm.Rb).apply(null, arguments)
};
function un() {
function x() {
if (!Dt && (Dt = !0, t.calledRun = !0, !be) && (P || tt(ze), e(t), t.onRuntimeInitialized && t.onRuntimeInitialized(), !P)) {
if (t.postRun)
for (typeof t.postRun == "function" && (t.postRun = [t.postRun]); t.postRun.length;) {
var A = t.postRun.shift();
Ke.unshift(A)
}
tt(Ke)
}
}
if (!(0 < Re))
if (P) e(t), P || tt(ze), postMessage({
cmd: "loaded"
});
else {
if (t.preRun)
for (typeof t.preRun == "function" && (t.preRun = [t.preRun]); t.preRun.length;) Ve();
tt(Ue), 0 < Re || (t.setStatus ? (t.setStatus("Running..."), setTimeout(function() {
setTimeout(function() {
t.setStatus("")
}, 1), x()
}, 1)) : x())
}
}
if (t.UTF8ToString = ve, t.stringToUTF8 = function(x, A, I) {
return Ne(x, h(), A, I)
}, t.lengthBytesUTF8 = Fe, t.keepRuntimeAlive = Ge, t.wasmMemory = z, t.stackSave = de, t.stackRestore = ce, t.stackAlloc = $t, t.ExitStatus = Qe, t.PThread = re, Ye = function x() {
Dt || un(), Dt || (Ye = x)
}, t.preInit)
for (typeof t.preInit == "function" && (t.preInit = [t.preInit]); 0 < t.preInit.length;) t.preInit.pop()();
return un(), f.ready
});
b.exports = c
},
932: (b, n, a) => {
var u, c = (u = (u = typeof document < "u" && document.currentScript ? document.currentScript.src : void 0) || "/index.js", function(f) {
var s, h, p;
f = f || {}, s || (s = f !== void 0 ? f : {}), s.ready = new Promise(function(M, D) {
h = M, p = D
});
var l, o, t, e, r, i, d = Object.assign({}, s),
g = "./this.program",
m = (M, D) => {
throw D
},
_ = typeof window == "object",
y = typeof importScripts == "function",
T = typeof process == "object" && typeof process.versions == "object" && typeof process.versions.node == "string",
w = "";
T ? (w = y ? a(908).dirname(w) + "/" : "//", i = () => {
r || (e = a(1384), r = a(908))
}, l = function(M, D) {
return i(), M = r.normalize(M), e.readFileSync(M, D ? void 0 : "utf8")
}, t = M => ((M = l(M, !0)).buffer || (M = new Uint8Array(M)), M), o = (M, D, N) => {
i(), M = r.normalize(M), e.readFile(M, function(j, U) {
j ? N(j) : D(U.buffer)
})
}, 1 < process.argv.length && (g = process.argv[1].replace(/\\/g, "/")), process.argv.slice(2), process.on("uncaughtException", function(M) {
if (!(M instanceof ze)) throw M
}), process.on("unhandledRejection", function(M) {
throw M
}), m = (M, D) => {
if (v || 0 < Ie) throw process.exitCode = M, D;
D instanceof ze || E("exiting due to exception: " + D), process.exit(M)
}, s.inspect = function() {
return "[Emscripten Module object]"
}) : (_ || y) && (y ? w = self.location.href : typeof document < "u" && document.currentScript && (w = document.currentScript.src), u && (w = u), w = w.indexOf("blob:") !== 0 ? w.substr(0, w.replace(/[?#].*/, "").lastIndexOf("/") + 1) : "", l = M => {
var D = new XMLHttpRequest;
return D.open("GET", M, !1), D.send(null), D.responseText
}, y && (t = M => {
var D = new XMLHttpRequest;
return D.open("GET", M, !1), D.responseType = "arraybuffer", D.send(null), new Uint8Array(D.response)
}), o = (M, D, N) => {
var j = new XMLHttpRequest;
j.open("GET", M, !0), j.responseType = "arraybuffer", j.onload = () => {
j.status == 200 || j.status == 0 && j.response ? D(j.response) : N()
}, j.onerror = N, j.send(null)
});
var S, O = s.print || console.log.bind(console),
E = s.printErr || console.warn.bind(console);
Object.assign(s, d), d = null, s.thisProgram && (g = s.thisProgram), s.quit && (m = s.quit), s.wasmBinary && (S = s.wasmBinary);
var v = s.noExitRuntime || !1;
typeof WebAssembly != "object" && Me("no native wasm support detected");
var P, L, V, R, k, Y, C = !1,
$ = typeof TextDecoder < "u" ? new TextDecoder("utf8") : void 0;
function X(M, D, N) {
var j = (D >>>= 0) + N;
for (N = D; M[N] && !(N >= j);) ++N;
if (16 < N - D && M.buffer && $) return $.decode(M.subarray(D, N));
for (j = ""; D < N;) {
var U = M[D++];
if (128 & U) {
var H = 63 & M[D++];
if ((224 & U) == 192) j += String.fromCharCode((31 & U) << 6 | H);
else {
var K = 63 & M[D++];
65536 > (U = (240 & U) == 224 ? (15 & U) << 12 | H << 6 | K : (7 & U) << 18 | H << 12 | K << 6 | 63 & M[D++]) ? j += String.fromCharCode(U) : (U -= 65536, j += String.fromCharCode(55296 | U >> 10, 56320 | 1023 & U))
}
} else j += String.fromCharCode(U)
}
return j
}
function z(M, D) {
return (M >>>= 0) ? X(R, M, D) : ""
}
function Z(M, D, N, j) {
if (!(0 < j)) return 0;
var U = N >>>= 0;
j = N + j - 1;
for (var H = 0; H < M.length; ++H) {
var K = M.charCodeAt(H);
if (55296 <= K && 57343 >= K && (K = 65536 + ((1023 & K) << 10) | 1023 & M.charCodeAt(++H)), 127 >= K) {
if (N >= j) break;
D[N++ >>> 0] = K
} else {
if (2047 >= K) {
if (N + 1 >= j) break;
D[N++ >>> 0] = 192 | K >> 6
} else {
if (65535 >= K) {
if (N + 2 >= j) break;
D[N++ >>> 0] = 224 | K >> 12
} else {
if (N + 3 >= j) break;
D[N++ >>> 0] = 240 | K >> 18, D[N++ >>> 0] = 128 | K >> 12 & 63
}
D[N++ >>> 0] = 128 | K >> 6 & 63
}
D[N++ >>> 0] = 128 | 63 & K
}
}
return D[N >>> 0] = 0, N - U
}
function J(M) {
for (var D = 0, N = 0; N < M.length; ++N) {
var j = M.charCodeAt(N);
127 >= j ? D++ : 2047 >= j ? D += 2 : 55296 <= j && 57343 >= j ? (D += 4, ++N) : D += 3
}
return D
}
function ue() {
var M = P.buffer;
L = M, s.HEAP8 = V = new Int8Array(M), s.HEAP16 = new Int16Array(M), s.HEAP32 = k = new Int32Array(M), s.HEAPU8 = R = new Uint8Array(M), s.HEAPU16 = new Uint16Array(M), s.HEAPU32 = Y = new Uint32Array(M), s.HEAPF32 = new Float32Array(M), s.HEAPF64 = new Float64Array(M)
}
var Se, Te = [],
se = [],
ye = [],
be = [],
Ie = 0;
function Le() {
var M = s.preRun.shift();
Te.unshift(M)
}
var ve, Ne = 0,
Fe = null;
function Me(M) {
throw s.onAbort && s.onAbort(M), E(M = "Aborted(" + M + ")"), C = !0, M = new WebAssembly.RuntimeError(M + ". Build with -sASSERTIONS for more info."), p(M), M
}
function Oe() {
return ve.startsWith("data:application/octet-stream;base64,")
}
if (ve = "ort-wasm.wasm", !Oe()) {
var Be = ve;
ve = s.locateFile ? s.locateFile(Be, w) : w + Be
}
function Ue() {
var M = ve;
try {
if (M == ve && S) return new Uint8Array(S);
if (t) return t(M);
throw "both async and sync fetching of the wasm failed"
} catch (D) {
Me(D)
}
}
function ze(M) {
this.name = "ExitStatus", this.message = "Program terminated with exit(" + M + ")", this.status = M
}
function He(M) {
for (; 0 < M.length;) M.shift()(s)
}
var Ke = [],
Ge = 0,
Ve = 0;
function Ae(M) {
this.Db = M, this.zb = M - 24, this.Ub = function(D) {
Y[this.zb + 4 >> 2 >>> 0] = D
}, this.Eb = function() {
return Y[this.zb + 4 >> 2 >>> 0]
}, this.Sb = function(D) {
Y[this.zb + 8 >> 2 >>> 0] = D
}, this.Wb = function() {
return Y[this.zb + 8 >> 2 >>> 0]
}, this.Tb = function() {
k[this.zb >> 2 >>> 0] = 0
}, this.Ib = function(D) {
V[this.zb + 12 >> 0 >>> 0] = D ? 1 : 0
}, this.Pb = function() {
return V[this.zb + 12 >> 0 >>> 0] != 0
}, this.Jb = function(D) {
V[this.zb + 13 >> 0 >>> 0] = D ? 1 : 0
}, this.Lb = function() {
return V[this.zb + 13 >> 0 >>> 0] != 0
}, this.Rb = function(D, N) {
this.Fb(0), this.Ub(D), this.Sb(N), this.Tb(), this.Ib(!1), this.Jb(!1)
}, this.Nb = function() {
k[this.zb >> 2 >>> 0] += 1
}, this.Xb = function() {
var D = k[this.zb >> 2 >>> 0];
return k[this.zb >> 2 >>> 0] = D - 1, D === 1
}, this.Fb = function(D) {
Y[this.zb + 16 >> 2 >>> 0] = D
}, this.Ob = function() {
return Y[this.zb + 16 >> 2 >>> 0]
}, this.Qb = function() {
if (pt(this.Eb())) return Y[this.Db >> 2 >>> 0];
var D = this.Ob();
return D !== 0 ? D : this.Db
}
}
function Re(M) {
return rt(new Ae(M).zb)
}
var Ye = [];
function pe(M) {
var D = Ye[M];
return D || (M >= Ye.length && (Ye.length = M + 1), Ye[M] = D = Se.get(M)), D
}
function dt(M) {
var D = J(M) + 1,
N = we(D);
return N && Z(M, V, N, D), N
}
var at = {};
function Et() {
if (!Qe) {
var M, D = {
USER: "web_user",
LOGNAME: "web_user",
PATH: "/",
PWD: "/",
HOME: "/home/web_user",
LANG: (typeof navigator == "object" && navigator.languages && navigator.languages[0] || "C").replace("-", "_") + ".UTF-8",
_: g || "./this.program"
};
for (M in at) at[M] === void 0 ? delete D[M] : D[M] = at[M];
var N = [];
for (M in D) N.push(M + "=" + D[M]);
Qe = N
}
return Qe
}
var Qe, ut = [null, [],
[]
];
function lt(M, D) {
var N = ut[M];
D === 0 || D === 10 ? ((M === 1 ? O : E)(X(N, 0)), N.length = 0) : N.push(D)
}
var $e = 0;
function nt(M) {
return M % 4 == 0 && (M % 100 != 0 || M % 400 == 0)
}
var re = [31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],
tt = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31];
function At(M, D, N, j) {
function U(q, me, Ee) {
for (q = typeof q == "number" ? q.toString() : q || ""; q.length < me;) q = Ee[0] + q;
return q
}
function H(q, me) {
return U(q, me, "0")
}
function K(q, me) {
function Ee(Ze) {
return 0 > Ze ? -1 : 0 < Ze ? 1 : 0
}
var qe;
return (qe = Ee(q.getFullYear() - me.getFullYear())) === 0 && (qe = Ee(q.getMonth() - me.getMonth())) === 0 && (qe = Ee(q.getDate() - me.getDate())), qe
}
function te(q) {
switch (q.getDay()) {
case 0:
return new Date(q.getFullYear() - 1, 11, 29);
case 1:
return q;
case 2:
return new Date(q.getFullYear(), 0, 3);
case 3:
return new Date(q.getFullYear(), 0, 2);
case 4:
return new Date(q.getFullYear(), 0, 1);
case 5:
return new Date(q.getFullYear() - 1, 11, 31);
case 6:
return new Date(q.getFullYear() - 1, 11, 30)
}
}
function ee(q) {
var me = q.Bb;
for (q = new Date(new Date(q.Cb + 1900, 0, 1).getTime()); 0 < me;) {
var Ee = q.getMonth(),
qe = (nt(q.getFullYear()) ? re : tt)[Ee];
if (!(me > qe - q.getDate())) {
q.setDate(q.getDate() + me);
break
}
me -= qe - q.getDate() + 1, q.setDate(1), 11 > Ee ? q.setMonth(Ee + 1) : (q.setMonth(0), q.setFullYear(q.getFullYear() + 1))
}
return Ee = new Date(q.getFullYear() + 1, 0, 4), me = te(new Date(q.getFullYear(), 0, 4)), Ee = te(Ee), 0 >= K(me, q) ? 0 >= K(Ee, q) ? q.getFullYear() + 1 : q.getFullYear() : q.getFullYear() - 1
}
var le = k[j + 40 >> 2 >>> 0];
for (var xe in j = {
$b: k[j >> 2 >>> 0],
Zb: k[j + 4 >> 2 >>> 0],
Gb: k[j + 8 >> 2 >>> 0],
Kb: k[j + 12 >> 2 >>> 0],
Hb: k[j + 16 >> 2 >>> 0],
Cb: k[j + 20 >> 2 >>> 0],
Ab: k[j + 24 >> 2 >>> 0],
Bb: k[j + 28 >> 2 >>> 0],
bc: k[j + 32 >> 2 >>> 0],
Yb: k[j + 36 >> 2 >>> 0],
ac: le ? z(le) : ""
}, N = z(N), le = {
"%c": "%a %b %d %H:%M:%S %Y",
"%D": "%m/%d/%y",
"%F": "%Y-%m-%d",
"%h": "%b",
"%r": "%I:%M:%S %p",
"%R": "%H:%M",
"%T": "%H:%M:%S",
"%x": "%m/%d/%y",
"%X": "%H:%M:%S",
"%Ec": "%c",
"%EC": "%C",
"%Ex": "%m/%d/%y",
"%EX": "%H:%M:%S",
"%Ey": "%y",
"%EY": "%Y",
"%Od": "%d",
"%Oe": "%e",
"%OH": "%H",
"%OI": "%I",
"%Om": "%m",
"%OM": "%M",
"%OS": "%S",
"%Ou": "%u",
"%OU": "%U",
"%OV": "%V",
"%Ow": "%w",
"%OW": "%W",
"%Oy": "%y"
}) N = N.replace(new RegExp(xe, "g"), le[xe]);
var ke = "Sunday Monday Tuesday Wednesday Thursday Friday Saturday".split(" "),
Ce = "January February March April May June July August September October November December".split(" ");
for (xe in le = {
"%a": function(q) {
return ke[q.Ab].substring(0, 3)
},
"%A": function(q) {
return ke[q.Ab]
},
"%b": function(q) {
return Ce[q.Hb].substring(0, 3)
},
"%B": function(q) {
return Ce[q.Hb]
},
"%C": function(q) {
return H((q.Cb + 1900) / 100 | 0, 2)
},
"%d": function(q) {
return H(q.Kb, 2)
},
"%e": function(q) {
return U(q.Kb, 2, " ")
},
"%g": function(q) {
return ee(q).toString().substring(2)
},
"%G": function(q) {
return ee(q)
},
"%H": function(q) {
return H(q.Gb, 2)
},
"%I": function(q) {
return (q = q.Gb) == 0 ? q = 12 : 12 < q && (q -= 12), H(q, 2)
},
"%j": function(q) {
for (var me = 0, Ee = 0; Ee <= q.Hb - 1; me += (nt(q.Cb + 1900) ? re : tt)[Ee++]);
return H(q.Kb + me, 3)
},
"%m": function(q) {
return H(q.Hb + 1, 2)
},
"%M": function(q) {
return H(q.Zb, 2)
},
"%n": function() {
return `
`
},
"%p": function(q) {
return 0 <= q.Gb && 12 > q.Gb ? "AM" : "PM"
},
"%S": function(q) {
return H(q.$b, 2)
},
"%t": function() {
return " "
},
"%u": function(q) {
return q.Ab || 7
},
"%U": function(q) {
return H(Math.floor((q.Bb + 7 - q.Ab) / 7), 2)
},
"%V": function(q) {
var me = Math.floor((q.Bb + 7 - (q.Ab + 6) % 7) / 7);
if (2 >= (q.Ab + 371 - q.Bb - 2) % 7 && me++, me) me == 53 && ((Ee = (q.Ab + 371 - q.Bb) % 7) == 4 || Ee == 3 && nt(q.Cb) || (me = 1));
else {
me = 52;
var Ee = (q.Ab + 7 - q.Bb - 1) % 7;
(Ee == 4 || Ee == 5 && nt(q.Cb % 400 - 1)) && me++
}
return H(me, 2)
},
"%w": function(q) {
return q.Ab
},
"%W": function(q) {
return H(Math.floor((q.Bb + 7 - (q.Ab + 6) % 7) / 7), 2)
},
"%y": function(q) {
return (q.Cb + 1900).toString().substring(2)
},
"%Y": function(q) {
return q.Cb + 1900
},
"%z": function(q) {
var me = 0 <= (q = q.Yb);
return q = Math.abs(q) / 60, (me ? "+" : "-") + ("0000" + (q / 60 * 100 + q % 60)).slice(-4)
},
"%Z": function(q) {
return q.ac
},
"%%": function() {
return "%"
}
}, N = N.replace(/%%/g, "\0\0"), le) N.includes(xe) && (N = N.replace(new RegExp(xe, "g"), le[xe](j)));
return xe = function(q) {
var me = Array(J(q) + 1);
return Z(q, me, 0, me.length), me
}(N = N.replace(/\0\0/g, "%")), xe.length > D ? 0 : (V.set(xe, M >>> 0), xe.length - 1)
}
var Mt = {
a: function(M) {
return we(M + 24) + 24
},
m: function(M) {
return (M = new Ae(M)).Pb() || (M.Ib(!0), Ge--), M.Jb(!1), Ke.push(M), M.Nb(), M.Qb()
},
ia: function(M) {
throw E("Unexpected exception thrown, this is not properly supported - aborting"), C = !0, M
},
w: function() {
ae(0);
var M = Ke.pop();
if (M.Xb() && !M.Lb()) {
var D = M.Wb();
D && pe(D)(M.Db), Re(M.Db)
}
Ve = 0
},
d: function() {
var M = Ve;
if (!M) return $e = 0;
var D = new Ae(M);
D.Fb(M);
var N = D.Eb();
if (!N) return $e = 0, M;
for (var j = Array.prototype.slice.call(arguments), U = 0; U < j.length; U++) {
var H = j[U];
if (H === 0 || H === N) break;
if (ot(H, N, D.zb + 16)) return $e = H, M
}
return $e = N, M
},
k: function() {
var M = Ve;
if (!M) return $e = 0;
var D = new Ae(M);
D.Fb(M);
var N = D.Eb();
if (!N) return $e = 0, M;
for (var j = Array.prototype.slice.call(arguments), U = 0; U < j.length; U++) {
var H = j[U];
if (H === 0 || H === N) break;
if (ot(H, N, D.zb + 16)) return $e = H, M
}
return $e = N, M
},
g: function() {
var M = Ve;
if (!M) return $e = 0;
var D = new Ae(M);
D.Fb(M);
var N = D.Eb();
if (!N) return $e = 0, M;
for (var j = Array.prototype.slice.call(arguments), U = 0; U < j.length; U++) {
var H = j[U];
if (H === 0 || H === N) break;
if (ot(H, N, D.zb + 16)) return $e = H, M
}
return $e = N, M
},
s: Re,
L: function() {
var M = Ke.pop();
M || Me("no exception to throw");
var D = M.Db;
throw M.Lb() || (Ke.push(M), M.Jb(!0), M.Ib(!1), Ge++), Ve = D, D
},
b: function(M, D, N) {
throw new Ae(M).Rb(D, N), Ve = M, Ge++, M
},
la: function() {
return Ge
},
i: function(M) {
throw Ve || (Ve = M), M
},
H: function() {
return 0
},
Ba: function() {},
pa: function() {},
ra: function() {},
ka: function() {
return 0
},
za: function() {},
ua: function() {},
ya: function() {},
R: function() {},
qa: function() {},
na: function() {},
Aa: function() {},
oa: function() {},
Ha: function() {},
Ja: function() {
Me("To use dlopen, you need enable dynamic linking, see https://github.com/emscripten-core/emscripten/wiki/Linking")
},
Ia: function() {
Me("To use dlopen, you need enable dynamic linking, see https://github.com/emscripten-core/emscripten/wiki/Linking")
},
S: function() {
return Date.now()
},
Ca: function() {
return !0
},
Da: function(M, D) {
M = new Date(1e3 * (Y[M >>> 2] + 4294967296 * k[M + 4 >>> 2])), k[D >> 2 >>> 0] = M.getUTCSeconds(), k[D + 4 >> 2 >>> 0] = M.getUTCMinutes(), k[D + 8 >> 2 >>> 0] = M.getUTCHours(), k[D + 12 >> 2 >>> 0] = M.getUTCDate(), k[D + 16 >> 2 >>> 0] = M.getUTCMonth(), k[D + 20 >> 2 >>> 0] = M.getUTCFullYear() - 1900, k[D + 24 >> 2 >>> 0] = M.getUTCDay(), k[D + 28 >> 2 >>> 0] = (M.getTime() - Date.UTC(M.getUTCFullYear(), 0, 1, 0, 0, 0, 0)) / 864e5 | 0
},
Ea: function(M, D) {
M = new Date(1e3 * (Y[M >>> 2] + 4294967296 * k[M + 4 >>> 2])), k[D >> 2 >>> 0] = M.getSeconds(), k[D + 4 >> 2 >>> 0] = M.getMinutes(), k[D + 8 >> 2 >>> 0] = M.getHours(), k[D + 12 >> 2 >>> 0] = M.getDate(), k[D + 16 >> 2 >>> 0] = M.getMonth(), k[D + 20 >> 2 >>> 0] = M.getFullYear() - 1900, k[D + 24 >> 2 >>> 0] = M.getDay();
var N = new Date(M.getFullYear(), 0, 1);
k[D + 28 >> 2 >>> 0] = (M.getTime() - N.getTime()) / 864e5 | 0, k[D + 36 >> 2 >>> 0] = -60 * M.getTimezoneOffset();
var j = new Date(M.getFullYear(), 6, 1).getTimezoneOffset();
N = N.getTimezoneOffset(), k[D + 32 >> 2 >>> 0] = 0 | (j != N && M.getTimezoneOffset() == Math.min(N, j))
},
Fa: function(M) {
var D = new Date(k[M + 20 >> 2 >>> 0] + 1900, k[M + 16 >> 2 >>> 0], k[M + 12 >> 2 >>> 0], k[M + 8 >> 2 >>> 0], k[M + 4 >> 2 >>> 0], k[M >> 2 >>> 0], 0),
N = k[M + 32 >> 2 >>> 0],
j = D.getTimezoneOffset(),
U = new Date(D.getFullYear(), 0, 1),
H = new Date(D.getFullYear(), 6, 1).getTimezoneOffset(),
K = U.getTimezoneOffset(),
te = Math.min(K, H);
return 0 > N ? k[M + 32 >> 2 >>> 0] = +(H != K && te == j) : 0 < N != (te == j) && (H = Math.max(K, H), D.setTime(D.getTime() + 6e4 * ((0 < N ? te : H) - j))), k[M + 24 >> 2 >>> 0] = D.getDay(), k[M + 28 >> 2 >>> 0] = (D.getTime() - U.getTime()) / 864e5 | 0, k[M >> 2 >>> 0] = D.getSeconds(), k[M + 4 >> 2 >>> 0] = D.getMinutes(), k[M + 8 >> 2 >>> 0] = D.getHours(), k[M + 12 >> 2 >>> 0] = D.getDate(), k[M + 16 >> 2 >>> 0] = D.getMonth(), D.getTime() / 1e3 | 0
},
sa: function() {
return -52
},
ta: function() {},
Ga: function M(D, N, j) {
M.Vb || (M.Vb = !0, function(U, H, K) {
function te(Ce) {
return (Ce = Ce.toTimeString().match(/\(([A-Za-z ]+)\)$/)) ? Ce[1] : "GMT"
}
var ee = new Date().getFullYear(),
le = new Date(ee, 0, 1),
xe = new Date(ee, 6, 1);
ee = le.getTimezoneOffset();
var ke = xe.getTimezoneOffset();
k[U >> 2 >>> 0] = 60 * Math.max(ee, ke), k[H >> 2 >>> 0] = +(ee != ke), U = te(le), H = te(xe), U = dt(U), H = dt(H), ke < ee ? (Y[K >> 2 >>> 0] = U, Y[K + 4 >> 2 >>> 0] = H) : (Y[K >> 2 >>> 0] = H, Y[K + 4 >> 2 >>> 0] = U)
}(D, N, j))
},
B: function() {
Me("")
},
ma: function() {
return 4294901760
},
I: T ? () => {
var M = process.hrtime();
return 1e3 * M[0] + M[1] / 1e6
} : () => performance.now(),
xa: function(M, D, N) {
R.copyWithin(M >>> 0, D >>> 0, D + N >>> 0)
},
G: function(M) {
var D = R.length;
if (4294901760 < (M >>>= 0)) return !1;
for (var N = 1; 4 >= N; N *= 2) {
var j = D * (1 + .2 / N);
j = Math.min(j, M + 100663296);
var U = Math;
j = Math.max(M, j), U = U.min.call(U, 4294901760, j + (65536 - j % 65536) % 65536);
e: {
try {
P.grow(U - L.byteLength + 65535 >>> 16), ue();
var H = 1;
break e
} catch {}
H = void 0
}
if (H) return !0
}
return !1
},
va: function(M, D) {
var N = 0;
return Et().forEach(function(j, U) {
var H = D + N;
for (U = Y[M + 4 * U >> 2 >>> 0] = H, H = 0; H < j.length; ++H) V[U++ >> 0 >>> 0] = j.charCodeAt(H);
V[U >> 0 >>> 0] = 0, N += j.length + 1
}), 0
},
wa: function(M, D) {
var N = Et();
Y[M >> 2 >>> 0] = N.length;
var j = 0;
return N.forEach(function(U) {
j += U.length + 1
}), Y[D >> 2 >>> 0] = j, 0
},
ba: function(M) {
v || 0 < Ie || (it(), He(ye), ht(0), ut[1].length && lt(1, 10), ut[2].length && lt(2, 10)), v || 0 < Ie || (s.onExit && s.onExit(M), C = !0), m(M, new ze(M))
},
E: function() {
return 52
},
Q: function() {
return 52
},
ca: function() {
return 70
},
P: function(M, D, N, j) {
for (var U = 0, H = 0; H < N; H++) {
var K = Y[D >> 2 >>> 0],
te = Y[D + 4 >> 2 >>> 0];
D += 8;
for (var ee = 0; ee < te; ee++) lt(M, R[K + ee >>> 0]);
U += te
}
return Y[j >> 2 >>> 0] = U, 0
},
c: function() {
return $e
},
ja: function M(D, N) {
M.Mb || (M.Mb = function() {
if (typeof crypto == "object" && typeof crypto.getRandomValues == "function") {
var U = new Uint8Array(1);
return () => (crypto.getRandomValues(U), U[0])
}
if (T) try {
var H = a(Object(function() {
var K = new Error("Cannot find module 'crypto'");
throw K.code = "MODULE_NOT_FOUND", K
}()));
return () => H.randomBytes(1)[0]
} catch {}
return () => Me("randomDevice")
}());
for (var j = 0; j < N; j++) V[D + j >> 0 >>> 0] = M.Mb();
return 0
},
ea: function(M, D, N) {
var j = ie();
try {
return pe(M)(D, N)
} catch (U) {
if (oe(j), U !== U + 0) throw U;
ae(1, 0)
}
},
fa: function(M, D, N) {
var j = ie();
try {
return pe(M)(D, N)
} catch (U) {
if (oe(j), U !== U + 0) throw U;
ae(1, 0)
}
},
J: function(M) {
var D = ie();
try {
return pe(M)()
} catch (N) {
if (oe(D), N !== N + 0) throw N;
ae(1, 0)
}
},
e: function(M, D) {
var N = ie();
try {
return pe(M)(D)
} catch (j) {
if (oe(N), j !== j + 0) throw j;
ae(1, 0)
}
},
N: function(M, D, N) {
var j = ie();
try {
return pe(M)(D, N)
} catch (U) {
if (oe(j), U !== U + 0) throw U;
ae(1, 0)
}
},
O: function(M, D, N) {
var j = ie();
try {
return pe(M)(D, N)
} catch (U) {
if (oe(j), U !== U + 0) throw U;
ae(1, 0)
}
},
j: function(M, D, N) {
var j = ie();
try {
return pe(M)(D, N)
} catch (U) {
if (oe(j), U !== U + 0) throw U;
ae(1, 0)
}
},
o: function(M, D, N, j) {
var U = ie();
try {
return pe(M)(D, N, j)
} catch (H) {
if (oe(U), H !== H + 0) throw H;
ae(1, 0)
}
},
p: function(M, D, N, j, U) {
var H = ie();
try {
return pe(M)(D, N, j, U)
} catch (K) {
if (oe(H), K !== K + 0) throw K;
ae(1, 0)
}
},
M: function(M, D, N, j, U, H) {
var K = ie();
try {
return pe(M)(D, N, j, U, H)
} catch (te) {
if (oe(K), te !== te + 0) throw te;
ae(1, 0)
}
},
r: function(M, D, N, j, U, H) {
var K = ie();
try {
return pe(M)(D, N, j, U, H)
} catch (te) {
if (oe(K), te !== te + 0) throw te;
ae(1, 0)
}
},
v: function(M, D, N, j, U, H, K) {
var te = ie();
try {
return pe(M)(D, N, j, U, H, K)
} catch (ee) {
if (oe(te), ee !== ee + 0) throw ee;
ae(1, 0)
}
},
K: function(M, D, N, j, U, H, K, te) {
var ee = ie();
try {
return pe(M)(D, N, j, U, H, K, te)
} catch (le) {
if (oe(ee), le !== le + 0) throw le;
ae(1, 0)
}
},
D: function(M, D, N, j, U, H, K, te, ee, le, xe, ke) {
var Ce = ie();
try {
return pe(M)(D, N, j, U, H, K, te, ee, le, xe, ke)
} catch (q) {
if (oe(Ce), q !== q + 0) throw q;
ae(1, 0)
}
},
X: function(M, D, N, j, U, H, K, te) {
var ee = ie();
try {
return xt(M, D, N, j, U, H, K, te)
} catch (le) {
if (oe(ee), le !== le + 0) throw le;
ae(1, 0)
}
},
V: function(M, D, N, j, U, H, K) {
var te = ie();
try {
return mt(M, D, N, j, U, H, K)
} catch (ee) {
if (oe(te), ee !== ee + 0) throw ee;
ae(1, 0)
}
},
U: function(M, D, N, j, U) {
var H = ie();
try {
return St(M, D, N, j, U)
} catch (K) {
if (oe(H), K !== K + 0) throw K;
ae(1, 0)
}
},
Z: function(M, D, N, j) {
var U = ie();
try {
return Tt(M, D, N, j)
} catch (H) {
if (oe(U), H !== H + 0) throw H;
ae(1, 0)
}
},
W: function(M) {
var D = ie();
try {
return gt(M)
} catch (N) {
if (oe(D), N !== N + 0) throw N;
ae(1, 0)
}
},
Y: function(M, D) {
var N = ie();
try {
return vt(M, D)
} catch (j) {
if (oe(N), j !== j + 0) throw j;
ae(1, 0)
}
},
T: function(M, D, N) {
var j = ie();
try {
return bt(M, D, N)
} catch (U) {
if (oe(j), U !== U + 0) throw U;
ae(1, 0)
}
},
f: function(M) {
var D = ie();
try {
pe(M)()
} catch (N) {
if (oe(D), N !== N + 0) throw N;
ae(1, 0)
}
},
q: function(M, D) {
var N = ie();
try {
pe(M)(D)
} catch (j) {
if (oe(N), j !== j + 0) throw j;
ae(1, 0)
}
},
h: function(M, D, N) {
var j = ie();
try {
pe(M)(D, N)
} catch (U) {
if (oe(j), U !== U + 0) throw U;
ae(1, 0)
}
},
da: function(M, D, N, j) {
var U = ie();
try {
pe(M)(D, N, j)
} catch (H) {
if (oe(U), H !== H + 0) throw H;
ae(1, 0)
}
},
l: function(M, D, N, j) {
var U = ie();
try {
pe(M)(D, N, j)
} catch (H) {
if (oe(U), H !== H + 0) throw H;
ae(1, 0)
}
},
t: function(M, D, N, j, U) {
var H = ie();
try {
pe(M)(D, N, j, U)
} catch (K) {
if (oe(H), K !== K + 0) throw K;
ae(1, 0)
}
},
u: function(M, D, N, j, U, H) {
var K = ie();
try {
pe(M)(D, N, j, U, H)
} catch (te) {
if (oe(K), te !== te + 0) throw te;
ae(1, 0)
}
},
x: function(M, D, N, j, U, H, K) {
var te = ie();
try {
pe(M)(D, N, j, U, H, K)
} catch (ee) {
if (oe(te), ee !== ee + 0) throw ee;
ae(1, 0)
}
},
z: function(M, D, N, j, U, H, K, te) {
var ee = ie();
try {
pe(M)(D, N, j, U, H, K, te)
} catch (le) {
if (oe(ee), le !== le + 0) throw le;
ae(1, 0)
}
},
ga: function(M, D, N, j, U, H, K, te, ee) {
var le = ie();
try {
pe(M)(D, N, j, U, H, K, te, ee)
} catch (xe) {
if (oe(le), xe !== xe + 0) throw xe;
ae(1, 0)
}
},
A: function(M, D, N, j, U, H, K, te, ee, le, xe) {
var ke = ie();
try {
pe(M)(D, N, j, U, H, K, te, ee, le, xe)
} catch (Ce) {
if (oe(ke), Ce !== Ce + 0) throw Ce;
ae(1, 0)
}
},
C: function(M, D, N, j, U, H, K, te, ee, le, xe, ke, Ce, q, me, Ee) {
var qe = ie();
try {
pe(M)(D, N, j, U, H, K, te, ee, le, xe, ke, Ce, q, me, Ee)
} catch (Ze) {
if (oe(qe), Ze !== Ze + 0) throw Ze;
ae(1, 0)
}
},
aa: function(M, D, N, j, U, H, K, te) {
var ee = ie();
try {
_t(M, D, N, j, U, H, K, te)
} catch (le) {
if (oe(ee), le !== le + 0) throw le;
ae(1, 0)
}
},
_: function(M, D, N, j, U, H, K, te, ee, le, xe, ke) {
var Ce = ie();
try {
wt(M, D, N, j, U, H, K, te, ee, le, xe, ke)
} catch (q) {
if (oe(Ce), q !== q + 0) throw q;
ae(1, 0)
}
},
$: function(M, D, N, j, U, H) {
var K = ie();
try {
yt(M, D, N, j, U, H)
} catch (te) {
if (oe(K), te !== te + 0) throw te;
ae(1, 0)
}
},
n: function(M) {
return M
},
F: function(M) {
$e = M
},
ha: At,
y: function(M, D, N, j) {
return At(M, D, N, j)
}
};
(function() {
function M(U) {
s.asm = U.exports, P = s.asm.Ka, ue(), Se = s.asm.ib, se.unshift(s.asm.La), Ne--, s.monitorRunDependencies && s.monitorRunDependencies(Ne), Ne == 0 && Fe && (U = Fe, Fe = null, U())
}
function D(U) {
M(U.instance)
}
function N(U) {
return function() {
if (!S && (_ || y)) {
if (typeof fetch == "function" && !ve.startsWith("file://")) return fetch(ve, {
credentials: "same-origin"
}).then(function(H) {
if (!H.ok) throw "failed to load wasm binary file at '" + ve + "'";
return H.arrayBuffer()
}).catch(function() {
return Ue()
});
if (o) return new Promise(function(H, K) {
o(ve, function(te) {
H(new Uint8Array(te))
}, K)
})
}
return Promise.resolve().then(function() {
return Ue()
})
}().then(function(H) {
return WebAssembly.instantiate(H, j)
}).then(function(H) {
return H
}).then(U, function(H) {
E("failed to asynchronously prepare wasm: " + H), Me(H)
})
}
var j = {
a: Mt
};
if (Ne++, s.monitorRunDependencies && s.monitorRunDependencies(Ne), s.instantiateWasm) try {
return s.instantiateWasm(j, M)
} catch (U) {
return E("Module.instantiateWasm callback failed with error: " + U), !1
}(S || typeof WebAssembly.instantiateStreaming != "function" || Oe() || ve.startsWith("file://") || T || typeof fetch != "function" ? N(D) : fetch(ve, {
credentials: "same-origin"
}).then(function(U) {
return WebAssembly.instantiateStreaming(U, j).then(D, function(H) {
return E("wasm streaming compile failed: " + H), E("falling back to ArrayBuffer instantiation"), N(D)
})
})).catch(p)
})(), s.___wasm_call_ctors = function() {
return (s.___wasm_call_ctors = s.asm.La).apply(null, arguments)
}, s._OrtInit = function() {
return (s._OrtInit = s.asm.Ma).apply(null, arguments)
}, s._OrtCreateSessionOptions = function() {
return (s._OrtCreateSessionOptions = s.asm.Na).apply(null, arguments)
}, s._OrtAppendExecutionProvider = function() {
return (s._OrtAppendExecutionProvider = s.asm.Oa).apply(null, arguments)
}, s._OrtAddSessionConfigEntry = function() {
return (s._OrtAddSessionConfigEntry = s.asm.Pa).apply(null, arguments)
}, s._OrtReleaseSessionOptions = function() {
return (s._OrtReleaseSessionOptions = s.asm.Qa).apply(null, arguments)
}, s._OrtCreateSession = function() {
return (s._OrtCreateSession = s.asm.Ra).apply(null, arguments)
}, s._OrtReleaseSession = function() {
return (s._OrtReleaseSession = s.asm.Sa).apply(null, arguments)
}, s._OrtGetInputCount = function() {
return (s._OrtGetInputCount = s.asm.Ta).apply(null, arguments)
}, s._OrtGetOutputCount = function() {
return (s._OrtGetOutputCount = s.asm.Ua).apply(null, arguments)
}, s._OrtGetInputName = function() {
return (s._OrtGetInputName = s.asm.Va).apply(null, arguments)
}, s._OrtGetOutputName = function() {
return (s._OrtGetOutputName = s.asm.Wa).apply(null, arguments)
}, s._OrtFree = function() {
return (s._OrtFree = s.asm.Xa).apply(null, arguments)
}, s._OrtCreateTensor = function() {
return (s._OrtCreateTensor = s.asm.Ya).apply(null, arguments)
}, s._OrtGetTensorData = function() {
return (s._OrtGetTensorData = s.asm.Za).apply(null, arguments)
}, s._OrtReleaseTensor = function() {
return (s._OrtReleaseTensor = s.asm._a).apply(null, arguments)
}, s._OrtCreateRunOptions = function() {
return (s._OrtCreateRunOptions = s.asm.$a).apply(null, arguments)
}, s._OrtAddRunConfigEntry = function() {
return (s._OrtAddRunConfigEntry = s.asm.ab).apply(null, arguments)
}, s._OrtReleaseRunOptions = function() {
return (s._OrtReleaseRunOptions = s.asm.bb).apply(null, arguments)
}, s._OrtRun = function() {
return (s._OrtRun = s.asm.cb).apply(null, arguments)
}, s._OrtEndProfiling = function() {
return (s._OrtEndProfiling = s.asm.db).apply(null, arguments)
};
var Je, we = s._malloc = function() {
return (we = s._malloc = s.asm.eb).apply(null, arguments)
},
rt = s._free = function() {
return (rt = s._free = s.asm.fb).apply(null, arguments)
},
ht = s._fflush = function() {
return (ht = s._fflush = s.asm.gb).apply(null, arguments)
},
it = s.___funcs_on_exit = function() {
return (it = s.___funcs_on_exit = s.asm.hb).apply(null, arguments)
},
ae = s._setThrew = function() {
return (ae = s._setThrew = s.asm.jb).apply(null, arguments)
},
ie = s.stackSave = function() {
return (ie = s.stackSave = s.asm.kb).apply(null, arguments)
},
oe = s.stackRestore = function() {
return (oe = s.stackRestore = s.asm.lb).apply(null, arguments)
},
ft = s.stackAlloc = function() {
return (ft = s.stackAlloc = s.asm.mb).apply(null, arguments)
},
ot = s.___cxa_can_catch = function() {
return (ot = s.___cxa_can_catch = s.asm.nb).apply(null, arguments)
},
pt = s.___cxa_is_pointer_type = function() {
return (pt = s.___cxa_is_pointer_type = s.asm.ob).apply(null, arguments)
},
gt = s.dynCall_j = function() {
return (gt = s.dynCall_j = s.asm.pb).apply(null, arguments)
},
mt = s.dynCall_iiiiij = function() {
return (mt = s.dynCall_iiiiij = s.asm.qb).apply(null, arguments)
},
bt = s.dynCall_jii = function() {
return (bt = s.dynCall_jii = s.asm.rb).apply(null, arguments)
},
_t = s.dynCall_viiiiij = function() {
return (_t = s.dynCall_viiiiij = s.asm.sb).apply(null, arguments)
},
yt = s.dynCall_vjji = function() {
return (yt = s.dynCall_vjji = s.asm.tb).apply(null, arguments)
},
wt = s.dynCall_viiijjjii = function() {
return (wt = s.dynCall_viiijjjii = s.asm.ub).apply(null, arguments)
},
Tt = s.dynCall_iij = function() {
return (Tt = s.dynCall_iij = s.asm.vb).apply(null, arguments)
},
vt = s.dynCall_ji = function() {
return (vt = s.dynCall_ji = s.asm.wb).apply(null, arguments)
},
xt = s.dynCall_iiiiiij = function() {
return (xt = s.dynCall_iiiiiij = s.asm.xb).apply(null, arguments)
},
St = s.dynCall_iiij = function() {
return (St = s.dynCall_iiij = s.asm.yb).apply(null, arguments)
};
function Ot() {
function M() {
if (!Je && (Je = !0, s.calledRun = !0, !C)) {
if (He(se), h(s), s.onRuntimeInitialized && s.onRuntimeInitialized(), s.postRun)
for (typeof s.postRun == "function" && (s.postRun = [s.postRun]); s.postRun.length;) {
var D = s.postRun.shift();
be.unshift(D)
}
He(be)
}
}
if (!(0 < Ne)) {
if (s.preRun)
for (typeof s.preRun == "function" && (s.preRun = [s.preRun]); s.preRun.length;) Le();
He(Te), 0 < Ne || (s.setStatus ? (s.setStatus("Running..."), setTimeout(function() {
setTimeout(function() {
s.setStatus("")
}, 1), M()
}, 1)) : M())
}
}
if (s.UTF8ToString = z, s.stringToUTF8 = function(M, D, N) {
return Z(M, R, D, N)
}, s.lengthBytesUTF8 = J, s.stackSave = ie, s.stackRestore = oe, s.stackAlloc = ft, Fe = function M() {
Je || Ot(), Je || (Fe = M)
}, s.preInit)
for (typeof s.preInit == "function" && (s.preInit = [s.preInit]); 0 < s.preInit.length;) s.preInit.pop()();
return Ot(), f.ready
});
b.exports = c
},
4537: b => {
b.exports = function(n, a) {
for (var u = new Array(arguments.length - 1), c = 0, f = 2, s = !0; f < arguments.length;) u[c++] = arguments[f++];
return new Promise(function(h, p) {
u[c] = function(l) {
if (s)
if (s = !1, l) p(l);
else {
for (var o = new Array(arguments.length - 1), t = 0; t < o.length;) o[t++] = arguments[t];
h.apply(null, o)
}
};
try {
n.apply(a || null, u)
} catch (l) {
s && (s = !1, p(l))
}
})
}
},
7419: (b, n) => {
var a = n;
a.length = function(h) {
var p = h.length;
if (!p) return 0;
for (var l = 0; --p % 4 > 1 && h.charAt(p) === "=";) ++l;
return Math.ceil(3 * h.length) / 4 - l
};
for (var u = new Array(64), c = new Array(123), f = 0; f < 64;) c[u[f] = f < 26 ? f + 65 : f < 52 ? f + 71 : f < 62 ? f - 4 : f - 59 | 43] = f++;
a.encode = function(h, p, l) {
for (var o, t = null, e = [], r = 0, i = 0; p < l;) {
var d = h[p++];
switch (i) {
case 0:
e[r++] = u[d >> 2], o = (3 & d) << 4, i = 1;
break;
case 1:
e[r++] = u[o | d >> 4], o = (15 & d) << 2, i = 2;
break;
case 2:
e[r++] = u[o | d >> 6], e[r++] = u[63 & d], i = 0
}
r > 8191 && ((t || (t = [])).push(String.fromCharCode.apply(String, e)), r = 0)
}
return i && (e[r++] = u[o], e[r++] = 61, i === 1 && (e[r++] = 61)), t ? (r && t.push(String.fromCharCode.apply(String, e.slice(0, r))), t.join("")) : String.fromCharCode.apply(String, e.slice(0, r))
};
var s = "invalid encoding";
a.decode = function(h, p, l) {
for (var o, t = l, e = 0, r = 0; r < h.length;) {
var i = h.charCodeAt(r++);
if (i === 61 && e > 1) break;
if ((i = c[i]) === void 0) throw Error(s);
switch (e) {
case 0:
o = i, e = 1;
break;
case 1:
p[l++] = o << 2 | (48 & i) >> 4, o = i, e = 2;
break;
case 2:
p[l++] = (15 & o) << 4 | (60 & i) >> 2, o = i, e = 3;
break;
case 3:
p[l++] = (3 & o) << 6 | i, e = 0
}
}
if (e === 1) throw Error(s);
return l - t
}, a.test = function(h) {
return /^(?:[A-Za-z0-9+/]{4})*(?:[A-Za-z0-9+/]{2}==|[A-Za-z0-9+/]{3}=)?$/.test(h)
}
},
9211: b => {
function n() {
this._listeners = {}
}
b.exports = n, n.prototype.on = function(a, u, c) {
return (this._listeners[a] || (this._listeners[a] = [])).push({
fn: u,
ctx: c || this
}), this
}, n.prototype.off = function(a, u) {
if (a === void 0) this._listeners = {};
else if (u === void 0) this._listeners[a] = [];
else
for (var c = this._listeners[a], f = 0; f < c.length;) c[f].fn === u ? c.splice(f, 1) : ++f;
return this
}, n.prototype.emit = function(a) {
var u = this._listeners[a];
if (u) {
for (var c = [], f = 1; f < arguments.length;) c.push(arguments[f++]);
for (f = 0; f < u.length;) u[f].fn.apply(u[f++].ctx, c)
}
return this
}
},
945: b => {
function n(s) {
return typeof Float32Array < "u" ? function() {
var h = new Float32Array([-0]),
p = new Uint8Array(h.buffer),
l = p[3] === 128;
function o(i, d, g) {
h[0] = i, d[g] = p[0], d[g + 1] = p[1], d[g + 2] = p[2], d[g + 3] = p[3]
}
function t(i, d, g) {
h[0] = i, d[g] = p[3], d[g + 1] = p[2], d[g + 2] = p[1], d[g + 3] = p[0]
}
function e(i, d) {
return p[0] = i[d], p[1] = i[d + 1], p[2] = i[d + 2], p[3] = i[d + 3], h[0]
}
function r(i, d) {
return p[3] = i[d], p[2] = i[d + 1], p[1] = i[d + 2], p[0] = i[d + 3], h[0]
}
s.writeFloatLE = l ? o : t, s.writeFloatBE = l ? t : o, s.readFloatLE = l ? e : r, s.readFloatBE = l ? r : e
}() : function() {
function h(l, o, t, e) {
var r = o < 0 ? 1 : 0;
if (r && (o = -o), o === 0) l(1 / o > 0 ? 0 : 2147483648, t, e);
else if (isNaN(o)) l(2143289344, t, e);
else if (o > 34028234663852886e22) l((r << 31 | 2139095040) >>> 0, t, e);
else if (o < 11754943508222875e-54) l((r << 31 | Math.round(o / 1401298464324817e-60)) >>> 0, t, e);
else {
var i = Math.floor(Math.log(o) / Math.LN2);
l((r << 31 | i + 127 << 23 | 8388607 & Math.round(o * Math.pow(2, -i) * 8388608)) >>> 0, t, e)
}
}
function p(l, o, t) {
var e = l(o, t),
r = 2 * (e >> 31) + 1,
i = e >>> 23 & 255,
d = 8388607 & e;
return i === 255 ? d ? NaN : r * (1 / 0) : i === 0 ? 1401298464324817e-60 * r * d : r * Math.pow(2, i - 150) * (d + 8388608)
}
s.writeFloatLE = h.bind(null, a), s.writeFloatBE = h.bind(null, u), s.readFloatLE = p.bind(null, c), s.readFloatBE = p.bind(null, f)
}(), typeof Float64Array < "u" ? function() {
var h = new Float64Array([-0]),
p = new Uint8Array(h.buffer),
l = p[7] === 128;
function o(i, d, g) {
h[0] = i, d[g] = p[0], d[g + 1] = p[1], d[g + 2] = p[2], d[g + 3] = p[3], d[g + 4] = p[4], d[g + 5] = p[5], d[g + 6] = p[6], d[g + 7] = p[7]
}
function t(i, d, g) {
h[0] = i, d[g] = p[7], d[g + 1] = p[6], d[g + 2] = p[5], d[g + 3] = p[4], d[g + 4] = p[3], d[g + 5] = p[2], d[g + 6] = p[1], d[g + 7] = p[0]
}
function e(i, d) {
return p[0] = i[d], p[1] = i[d + 1], p[2] = i[d + 2], p[3] = i[d + 3], p[4] = i[d + 4], p[5] = i[d + 5], p[6] = i[d + 6], p[7] = i[d + 7], h[0]
}
function r(i, d) {
return p[7] = i[d], p[6] = i[d + 1], p[5] = i[d + 2], p[4] = i[d + 3], p[3] = i[d + 4], p[2] = i[d + 5], p[1] = i[d + 6], p[0] = i[d + 7], h[0]
}
s.writeDoubleLE = l ? o : t, s.writeDoubleBE = l ? t : o, s.readDoubleLE = l ? e : r, s.readDoubleBE = l ? r : e
}() : function() {
function h(l, o, t, e, r, i) {
var d = e < 0 ? 1 : 0;
if (d && (e = -e), e === 0) l(0, r, i + o), l(1 / e > 0 ? 0 : 2147483648, r, i + t);
else if (isNaN(e)) l(0, r, i + o), l(2146959360, r, i + t);
else if (e > 17976931348623157e292) l(0, r, i + o), l((d << 31 | 2146435072) >>> 0, r, i + t);
else {
var g;
if (e < 22250738585072014e-324) l((g = e / 5e-324) >>> 0, r, i + o), l((d << 31 | g / 4294967296) >>> 0, r, i + t);
else {
var m = Math.floor(Math.log(e) / Math.LN2);
m === 1024 && (m = 1023), l(4503599627370496 * (g = e * Math.pow(2, -m)) >>> 0, r, i + o), l((d << 31 | m + 1023 << 20 | 1048576 * g & 1048575) >>> 0, r, i + t)
}
}
}
function p(l, o, t, e, r) {
var i = l(e, r + o),
d = l(e, r + t),
g = 2 * (d >> 31) + 1,
m = d >>> 20 & 2047,
_ = 4294967296 * (1048575 & d) + i;
return m === 2047 ? _ ? NaN : g * (1 / 0) : m === 0 ? 5e-324 * g * _ : g * Math.pow(2, m - 1075) * (_ + 4503599627370496)
}
s.writeDoubleLE = h.bind(null, a, 0, 4), s.writeDoubleBE = h.bind(null, u, 4, 0), s.readDoubleLE = p.bind(null, c, 0, 4), s.readDoubleBE = p.bind(null, f, 4, 0)
}(), s
}
function a(s, h, p) {
h[p] = 255 & s, h[p + 1] = s >>> 8 & 255, h[p + 2] = s >>> 16 & 255, h[p + 3] = s >>> 24
}
function u(s, h, p) {
h[p] = s >>> 24, h[p + 1] = s >>> 16 & 255, h[p + 2] = s >>> 8 & 255, h[p + 3] = 255 & s
}
function c(s, h) {
return (s[h] | s[h + 1] << 8 | s[h + 2] << 16 | s[h + 3] << 24) >>> 0
}
function f(s, h) {
return (s[h] << 24 | s[h + 1] << 16 | s[h + 2] << 8 | s[h + 3]) >>> 0
}
b.exports = n(n)
},
7199: module => {
function inquire(moduleName) {
try {
var mod = eval("quire".replace(/^/, "re"))(moduleName);
if (mod && (mod.length || Object.keys(mod).length)) return mod
} catch (b) {}
return null
}
module.exports = inquire
},
6662: b => {
b.exports = function(n, a, u) {
var c = u || 8192,
f = c >>> 1,
s = null,
h = c;
return function(p) {
if (p < 1 || p > f) return n(p);
h + p > c && (s = n(c), h = 0);
var l = a.call(s, h, h += p);
return 7 & h && (h = 1 + (7 | h)), l
}
}
},
4997: (b, n) => {
var a = n;
a.length = function(u) {
for (var c = 0, f = 0, s = 0; s < u.length; ++s)(f = u.charCodeAt(s)) < 128 ? c += 1 : f < 2048 ? c += 2 : (64512 & f) == 55296 && (64512 & u.charCodeAt(s + 1)) == 56320 ? (++s, c += 4) : c += 3;
return c
}, a.read = function(u, c, f) {
if (f - c < 1) return "";
for (var s, h = null, p = [], l = 0; c < f;)(s = u[c++]) < 128 ? p[l++] = s : s > 191 && s < 224 ? p[l++] = (31 & s) << 6 | 63 & u[c++] : s > 239 && s < 365 ? (s = ((7 & s) << 18 | (63 & u[c++]) << 12 | (63 & u[c++]) << 6 | 63 & u[c++]) - 65536, p[l++] = 55296 + (s >> 10), p[l++] = 56320 + (1023 & s)) : p[l++] = (15 & s) << 12 | (63 & u[c++]) << 6 | 63 & u[c++], l > 8191 && ((h || (h = [])).push(String.fromCharCode.apply(String, p)), l = 0);
return h ? (l && h.push(String.fromCharCode.apply(String, p.slice(0, l))), h.join("")) : String.fromCharCode.apply(String, p.slice(0, l))
}, a.write = function(u, c, f) {
for (var s, h, p = f, l = 0; l < u.length; ++l)(s = u.charCodeAt(l)) < 128 ? c[f++] = s : s < 2048 ? (c[f++] = s >> 6 | 192, c[f++] = 63 & s | 128) : (64512 & s) == 55296 && (64512 & (h = u.charCodeAt(l + 1))) == 56320 ? (s = 65536 + ((1023 & s) << 10) + (1023 & h), ++l, c[f++] = s >> 18 | 240, c[f++] = s >> 12 & 63 | 128, c[f++] = s >> 6 & 63 | 128, c[f++] = 63 & s | 128) : (c[f++] = s >> 12 | 224, c[f++] = s >> 6 & 63 | 128, c[f++] = 63 & s | 128);
return f - p
}
},
3442: (b, n) => {
n.__esModule = !0;
var a = function() {
function u(c) {
if (!c) throw new TypeError("Invalid argument; `value` has no value.");
this.value = u.EMPTY, c && u.isGuid(c) && (this.value = c)
}
return u.isGuid = function(c) {
var f = c.toString();
return c && (c instanceof u || u.validator.test(f))
}, u.create = function() {
return new u([u.gen(2), u.gen(1), u.gen(1), u.gen(1), u.gen(3)].join("-"))
}, u.createEmpty = function() {
return new u("emptyguid")
}, u.parse = function(c) {
return new u(c)
}, u.raw = function() {
return [u.gen(2), u.gen(1), u.gen(1), u.gen(1), u.gen(3)].join("-")
}, u.gen = function(c) {
for (var f = "", s = 0; s < c; s++) f += (65536 * (1 + Math.random()) | 0).toString(16).substring(1);
return f
}, u.prototype.equals = function(c) {
return u.isGuid(c) && this.value === c.toString()
}, u.prototype.isEmpty = function() {
return this.value === u.EMPTY
}, u.prototype.toString = function() {
return this.value
}, u.prototype.toJSON = function() {
return {
value: this.value
}
}, u.validator = new RegExp("^[a-z0-9]{8}-[a-z0-9]{4}-[a-z0-9]{4}-[a-z0-9]{4}-[a-z0-9]{12}$", "i"), u.EMPTY = "00000000-0000-0000-0000-000000000000", u
}();
n.Guid = a
},
3720: b => {
b.exports = a;
var n = null;
try {
n = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([0, 97, 115, 109, 1, 0, 0, 0, 1, 13, 2, 96, 0, 1, 127, 96, 4, 127, 127, 127, 127, 1, 127, 3, 7, 6, 0, 1, 1, 1, 1, 1, 6, 6, 1, 127, 1, 65, 0, 11, 7, 50, 6, 3, 109, 117, 108, 0, 1, 5, 100, 105, 118, 95, 115, 0, 2, 5, 100, 105, 118, 95, 117, 0, 3, 5, 114, 101, 109, 95, 115, 0, 4, 5, 114, 101, 109, 95, 117, 0, 5, 8, 103, 101, 116, 95, 104, 105, 103, 104, 0, 0, 10, 191, 1, 6, 4, 0, 35, 0, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 126, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 127, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 128, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 129, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11, 36, 1, 1, 126, 32, 0, 173, 32, 1, 173, 66, 32, 134, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 130, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11])), {}).exports
} catch {}
function a(v, P, L) {
this.low = 0 | v, this.high = 0 | P, this.unsigned = !!L
}
function u(v) {
return (v && v.__isLong__) === !0
}
a.prototype.__isLong__, Object.defineProperty(a.prototype, "__isLong__", {
value: !0
}), a.isLong = u;
var c = {},
f = {};
function s(v, P) {
var L, V, R;
return P ? (R = 0 <= (v >>>= 0) && v < 256) && (V = f[v]) ? V : (L = p(v, (0 | v) < 0 ? -1 : 0, !0), R && (f[v] = L), L) : (R = -128 <= (v |= 0) && v < 128) && (V = c[v]) ? V : (L = p(v, v < 0 ? -1 : 0, !1), R && (c[v] = L), L)
}
function h(v, P) {
if (isNaN(v)) return P ? m : g;
if (P) {
if (v < 0) return m;
if (v >= r) return S
} else {
if (v <= -i) return O;
if (v + 1 >= i) return w
}
return v < 0 ? h(-v, P).neg() : p(v % e | 0, v / e | 0, P)
}
function p(v, P, L) {
return new a(v, P, L)
}
a.fromInt = s, a.fromNumber = h, a.fromBits = p;
var l = Math.pow;
function o(v, P, L) {
if (v.length === 0) throw Error("empty string");
if (v === "NaN" || v === "Infinity" || v === "+Infinity" || v === "-Infinity") return g;
if (typeof P == "number" ? (L = P, P = !1) : P = !!P, (L = L || 10) < 2 || 36 < L) throw RangeError("radix");
var V;
if ((V = v.indexOf("-")) > 0) throw Error("interior hyphen");
if (V === 0) return o(v.substring(1), P, L).neg();
for (var R = h(l(L, 8)), k = g, Y = 0; Y < v.length; Y += 8) {
var C = Math.min(8, v.length - Y),
$ = parseInt(v.substring(Y, Y + C), L);
if (C < 8) {
var X = h(l(L, C));
k = k.mul(X).add(h($))
} else k = (k = k.mul(R)).add(h($))
}
return k.unsigned = P, k
}
function t(v, P) {
return typeof v == "number" ? h(v, P) : typeof v == "string" ? o(v, P) : p(v.low, v.high, typeof P == "boolean" ? P : v.unsigned)
}
a.fromString = o, a.fromValue = t;
var e = 4294967296,
r = e * e,
i = r / 2,
d = s(1 << 24),
g = s(0);
a.ZERO = g;
var m = s(0, !0);
a.UZERO = m;
var _ = s(1);
a.ONE = _;
var y = s(1, !0);
a.UONE = y;
var T = s(-1);
a.NEG_ONE = T;
var w = p(-1, 2147483647, !1);
a.MAX_VALUE = w;
var S = p(-1, -1, !0);
a.MAX_UNSIGNED_VALUE = S;
var O = p(0, -2147483648, !1);
a.MIN_VALUE = O;
var E = a.prototype;
E.toInt = function() {
return this.unsigned ? this.low >>> 0 : this.low
}, E.toNumber = function() {
return this.unsigned ? (this.high >>> 0) * e + (this.low >>> 0) : this.high * e + (this.low >>> 0)
}, E.toString = function(v) {
if ((v = v || 10) < 2 || 36 < v) throw RangeError("radix");
if (this.isZero()) return "0";
if (this.isNegative()) {
if (this.eq(O)) {
var P = h(v),
L = this.div(P),
V = L.mul(P).sub(this);
return L.toString(v) + V.toInt().toString(v)
}
return "-" + this.neg().toString(v)
}
for (var R = h(l(v, 6), this.unsigned), k = this, Y = "";;) {
var C = k.div(R),
$ = (k.sub(C.mul(R)).toInt() >>> 0).toString(v);
if ((k = C).isZero()) return $ + Y;
for (; $.length < 6;) $ = "0" + $;
Y = "" + $ + Y
}
}, E.getHighBits = function() {
return this.high
}, E.getHighBitsUnsigned = function() {
return this.high >>> 0
}, E.getLowBits = function() {
return this.low
}, E.getLowBitsUnsigned = function() {
return this.low >>> 0
}, E.getNumBitsAbs = function() {
if (this.isNegative()) return this.eq(O) ? 64 : this.neg().getNumBitsAbs();
for (var v = this.high != 0 ? this.high : this.low, P = 31; P > 0 && !(v & 1 << P); P--);
return this.high != 0 ? P + 33 : P + 1
}, E.isZero = function() {
return this.high === 0 && this.low === 0
}, E.eqz = E.isZero, E.isNegative = function() {
return !this.unsigned && this.high < 0
}, E.isPositive = function() {
return this.unsigned || this.high >= 0
}, E.isOdd = function() {
return (1 & this.low) == 1
}, E.isEven = function() {
return (1 & this.low) == 0
}, E.equals = function(v) {
return u(v) || (v = t(v)), (this.unsigned === v.unsigned || this.high >>> 31 != 1 || v.high >>> 31 != 1) && this.high === v.high && this.low === v.low
}, E.eq = E.equals, E.notEquals = function(v) {
return !this.eq(v)
}, E.neq = E.notEquals, E.ne = E.notEquals, E.lessThan = function(v) {
return this.comp(v) < 0
}, E.lt = E.lessThan, E.lessThanOrEqual = function(v) {
return this.comp(v) <= 0
}, E.lte = E.lessThanOrEqual, E.le = E.lessThanOrEqual, E.greaterThan = function(v) {
return this.comp(v) > 0
}, E.gt = E.greaterThan, E.greaterThanOrEqual = function(v) {
return this.comp(v) >= 0
}, E.gte = E.greaterThanOrEqual, E.ge = E.greaterThanOrEqual, E.compare = function(v) {
if (u(v) || (v = t(v)), this.eq(v)) return 0;
var P = this.isNegative(),
L = v.isNegative();
return P && !L ? -1 : !P && L ? 1 : this.unsigned ? v.high >>> 0 > this.high >>> 0 || v.high === this.high && v.low >>> 0 > this.low >>> 0 ? -1 : 1 : this.sub(v).isNegative() ? -1 : 1
}, E.comp = E.compare, E.negate = function() {
return !this.unsigned && this.eq(O) ? O : this.not().add(_)
}, E.neg = E.negate, E.add = function(v) {
u(v) || (v = t(v));
var P = this.high >>> 16,
L = 65535 & this.high,
V = this.low >>> 16,
R = 65535 & this.low,
k = v.high >>> 16,
Y = 65535 & v.high,
C = v.low >>> 16,
$ = 0,
X = 0,
z = 0,
Z = 0;
return z += (Z += R + (65535 & v.low)) >>> 16, X += (z += V + C) >>> 16, $ += (X += L + Y) >>> 16, $ += P + k, p((z &= 65535) << 16 | (Z &= 65535), ($ &= 65535) << 16 | (X &= 65535), this.unsigned)
}, E.subtract = function(v) {
return u(v) || (v = t(v)), this.add(v.neg())
}, E.sub = E.subtract, E.multiply = function(v) {
if (this.isZero()) return g;
if (u(v) || (v = t(v)), n) return p(n.mul(this.low, this.high, v.low, v.high), n.get_high(), this.unsigned);
if (v.isZero()) return g;
if (this.eq(O)) return v.isOdd() ? O : g;
if (v.eq(O)) return this.isOdd() ? O : g;
if (this.isNegative()) return v.isNegative() ? this.neg().mul(v.neg()) : this.neg().mul(v).neg();
if (v.isNegative()) return this.mul(v.neg()).neg();
if (this.lt(d) && v.lt(d)) return h(this.toNumber() * v.toNumber(), this.unsigned);
var P = this.high >>> 16,
L = 65535 & this.high,
V = this.low >>> 16,
R = 65535 & this.low,
k = v.high >>> 16,
Y = 65535 & v.high,
C = v.low >>> 16,
$ = 65535 & v.low,
X = 0,
z = 0,
Z = 0,
J = 0;
return Z += (J += R * $) >>> 16, z += (Z += V * $) >>> 16, Z &= 65535, z += (Z += R * C) >>> 16, X += (z += L * $) >>> 16, z &= 65535, X += (z += V * C) >>> 16, z &= 65535, X += (z += R * Y) >>> 16, X += P * $ + L * C + V * Y + R * k, p((Z &= 65535) << 16 | (J &= 65535), (X &= 65535) << 16 | (z &= 65535), this.unsigned)
}, E.mul = E.multiply, E.divide = function(v) {
if (u(v) || (v = t(v)), v.isZero()) throw Error("division by zero");
var P, L, V;
if (n) return this.unsigned || this.high !== -2147483648 || v.low !== -1 || v.high !== -1 ? p((this.unsigned ? n.div_u : n.div_s)(this.low, this.high, v.low, v.high), n.get_high(), this.unsigned) : this;
if (this.isZero()) return this.unsigned ? m : g;
if (this.unsigned) {
if (v.unsigned || (v = v.toUnsigned()), v.gt(this)) return m;
if (v.gt(this.shru(1))) return y;
V = m
} else {
if (this.eq(O)) return v.eq(_) || v.eq(T) ? O : v.eq(O) ? _ : (P = this.shr(1).div(v).shl(1)).eq(g) ? v.isNegative() ? _ : T : (L = this.sub(v.mul(P)), V = P.add(L.div(v)));
if (v.eq(O)) return this.unsigned ? m : g;
if (this.isNegative()) return v.isNegative() ? this.neg().div(v.neg()) : this.neg().div(v).neg();
if (v.isNegative()) return this.div(v.neg()).neg();
V = g
}
for (L = this; L.gte(v);) {
P = Math.max(1, Math.floor(L.toNumber() / v.toNumber()));
for (var R = Math.ceil(Math.log(P) / Math.LN2), k = R <= 48 ? 1 : l(2, R - 48), Y = h(P), C = Y.mul(v); C.isNegative() || C.gt(L);) C = (Y = h(P -= k, this.unsigned)).mul(v);
Y.isZero() && (Y = _), V = V.add(Y), L = L.sub(C)
}
return V
}, E.div = E.divide, E.modulo = function(v) {
return u(v) || (v = t(v)), n ? p((this.unsigned ? n.rem_u : n.rem_s)(this.low, this.high, v.low, v.high), n.get_high(), this.unsigned) : this.sub(this.div(v).mul(v))
}, E.mod = E.modulo, E.rem = E.modulo, E.not = function() {
return p(~this.low, ~this.high, this.unsigned)
}, E.and = function(v) {
return u(v) || (v = t(v)), p(this.low & v.low, this.high & v.high, this.unsigned)
}, E.or = function(v) {
return u(v) || (v = t(v)), p(this.low | v.low, this.high | v.high, this.unsigned)
}, E.xor = function(v) {
return u(v) || (v = t(v)), p(this.low ^ v.low, this.high ^ v.high, this.unsigned)
}, E.shiftLeft = function(v) {
return u(v) && (v = v.toInt()), (v &= 63) == 0 ? this : v < 32 ? p(this.low << v, this.high << v | this.low >>> 32 - v, this.unsigned) : p(0, this.low << v - 32, this.unsigned)
}, E.shl = E.shiftLeft, E.shiftRight = function(v) {
return u(v) && (v = v.toInt()), (v &= 63) == 0 ? this : v < 32 ? p(this.low >>> v | this.high << 32 - v, this.high >> v, this.unsigned) : p(this.high >> v - 32, this.high >= 0 ? 0 : -1, this.unsigned)
}, E.shr = E.shiftRight, E.shiftRightUnsigned = function(v) {
if (u(v) && (v = v.toInt()), (v &= 63) == 0) return this;
var P = this.high;
return v < 32 ? p(this.low >>> v | P << 32 - v, P >>> v, this.unsigned) : p(v === 32 ? P : P >>> v - 32, 0, this.unsigned)
}, E.shru = E.shiftRightUnsigned, E.shr_u = E.shiftRightUnsigned, E.toSigned = function() {
return this.unsigned ? p(this.low, this.high, !1) : this
}, E.toUnsigned = function() {
return this.unsigned ? this : p(this.low, this.high, !0)
}, E.toBytes = function(v) {
return v ? this.toBytesLE() : this.toBytesBE()
}, E.toBytesLE = function() {
var v = this.high,
P = this.low;
return [255 & P, P >>> 8 & 255, P >>> 16 & 255, P >>> 24, 255 & v, v >>> 8 & 255, v >>> 16 & 255, v >>> 24]
}, E.toBytesBE = function() {
var v = this.high,
P = this.low;
return [v >>> 24, v >>> 16 & 255, v >>> 8 & 255, 255 & v, P >>> 24, P >>> 16 & 255, P >>> 8 & 255, 255 & P]
}, a.fromBytes = function(v, P, L) {
return L ? a.fromBytesLE(v, P) : a.fromBytesBE(v, P)
}, a.fromBytesLE = function(v, P) {
return new a(v[0] | v[1] << 8 | v[2] << 16 | v[3] << 24, v[4] | v[5] << 8 | v[6] << 16 | v[7] << 24, P)
}, a.fromBytesBE = function(v, P) {
return new a(v[4] << 24 | v[5] << 16 | v[6] << 8 | v[7], v[0] << 24 | v[1] << 16 | v[2] << 8 | v[3], P)
}
},
1446: (b, n, a) => {
var u, c, f, s = a(2100),
h = s.Reader,
p = s.Writer,
l = s.util,
o = s.roots.default || (s.roots.default = {});
o.onnx = ((f = {}).Version = (u = {}, (c = Object.create(u))[u[0] = "_START_VERSION"] = 0, c[u[1] = "IR_VERSION_2017_10_10"] = 1, c[u[2] = "IR_VERSION_2017_10_30"] = 2, c[u[3] = "IR_VERSION_2017_11_3"] = 3, c[u[4] = "IR_VERSION_2019_1_22"] = 4, c[u[5] = "IR_VERSION"] = 5, c), f.AttributeProto = function() {
function t(e) {
if (this.floats = [], this.ints = [], this.strings = [], this.tensors = [], this.graphs = [], e)
for (var r = Object.keys(e), i = 0; i < r.length; ++i) e[r[i]] != null && (this[r[i]] = e[r[i]])
}
return t.prototype.name = "", t.prototype.refAttrName = "", t.prototype.docString = "", t.prototype.type = 0, t.prototype.f = 0, t.prototype.i = l.Long ? l.Long.fromBits(0, 0, !1) : 0, t.prototype.s = l.newBuffer([]), t.prototype.t = null, t.prototype.g = null, t.prototype.floats = l.emptyArray, t.prototype.ints = l.emptyArray, t.prototype.strings = l.emptyArray, t.prototype.tensors = l.emptyArray, t.prototype.graphs = l.emptyArray, t.create = function(e) {
return new t(e)
}, t.encode = function(e, r) {
if (r || (r = p.create()), e.name != null && e.hasOwnProperty("name") && r.uint32(10).string(e.name), e.f != null && e.hasOwnProperty("f") && r.uint32(21).float(e.f), e.i != null && e.hasOwnProperty("i") && r.uint32(24).int64(e.i), e.s != null && e.hasOwnProperty("s") && r.uint32(34).bytes(e.s), e.t != null && e.hasOwnProperty("t") && o.onnx.TensorProto.encode(e.t, r.uint32(42).fork()).ldelim(), e.g != null && e.hasOwnProperty("g") && o.onnx.GraphProto.encode(e.g, r.uint32(50).fork()).ldelim(), e.floats != null && e.floats.length) {
r.uint32(58).fork();
for (var i = 0; i < e.floats.length; ++i) r.float(e.floats[i]);
r.ldelim()
}
if (e.ints != null && e.ints.length) {
for (r.uint32(66).fork(), i = 0; i < e.ints.length; ++i) r.int64(e.ints[i]);
r.ldelim()
}
if (e.strings != null && e.strings.length)
for (i = 0; i < e.strings.length; ++i) r.uint32(74).bytes(e.strings[i]);
if (e.tensors != null && e.tensors.length)
for (i = 0; i < e.tensors.length; ++i) o.onnx.TensorProto.encode(e.tensors[i], r.uint32(82).fork()).ldelim();
if (e.graphs != null && e.graphs.length)
for (i = 0; i < e.graphs.length; ++i) o.onnx.GraphProto.encode(e.graphs[i], r.uint32(90).fork()).ldelim();
return e.docString != null && e.hasOwnProperty("docString") && r.uint32(106).string(e.docString), e.type != null && e.hasOwnProperty("type") && r.uint32(160).int32(e.type), e.refAttrName != null && e.hasOwnProperty("refAttrName") && r.uint32(170).string(e.refAttrName), r
}, t.encodeDelimited = function(e, r) {
return this.encode(e, r).ldelim()
}, t.decode = function(e, r) {
e instanceof h || (e = h.create(e));
for (var i = r === void 0 ? e.len : e.pos + r, d = new o.onnx.AttributeProto; e.pos < i;) {
var g = e.uint32();
switch (g >>> 3) {
case 1:
d.name = e.string();
break;
case 21:
d.refAttrName = e.string();
break;
case 13:
d.docString = e.string();
break;
case 20:
d.type = e.int32();
break;
case 2:
d.f = e.float();
break;
case 3:
d.i = e.int64();
break;
case 4:
d.s = e.bytes();
break;
case 5:
d.t = o.onnx.TensorProto.decode(e, e.uint32());
break;
case 6:
d.g = o.onnx.GraphProto.decode(e, e.uint32());
break;
case 7:
if (d.floats && d.floats.length || (d.floats = []), (7 & g) == 2)
for (var m = e.uint32() + e.pos; e.pos < m;) d.floats.push(e.float());
else d.floats.push(e.float());
break;
case 8:
if (d.ints && d.ints.length || (d.ints = []), (7 & g) == 2)
for (m = e.uint32() + e.pos; e.pos < m;) d.ints.push(e.int64());
else d.ints.push(e.int64());
break;
case 9:
d.strings && d.strings.length || (d.strings = []), d.strings.push(e.bytes());
break;
case 10:
d.tensors && d.tensors.length || (d.tensors = []), d.tensors.push(o.onnx.TensorProto.decode(e, e.uint32()));
break;
case 11:
d.graphs && d.graphs.length || (d.graphs = []), d.graphs.push(o.onnx.GraphProto.decode(e, e.uint32()));
break;
default:
e.skipType(7 & g)
}
}
return d
}, t.decodeDelimited = function(e) {
return e instanceof h || (e = new h(e)), this.decode(e, e.uint32())
}, t.verify = function(e) {
if (typeof e != "object" || e === null) return "object expected";
if (e.name != null && e.hasOwnProperty("name") && !l.isString(e.name)) return "name: string expected";
if (e.refAttrName != null && e.hasOwnProperty("refAttrName") && !l.isString(e.refAttrName)) return "refAttrName: string expected";
if (e.docString != null && e.hasOwnProperty("docString") && !l.isString(e.docString)) return "docString: string expected";
if (e.type != null && e.hasOwnProperty("type")) switch (e.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 (e.f != null && e.hasOwnProperty("f") && typeof e.f != "number") return "f: number expected";
if (e.i != null && e.hasOwnProperty("i") && !(l.isInteger(e.i) || e.i && l.isInteger(e.i.low) && l.isInteger(e.i.high))) return "i: integer|Long expected";
if (e.s != null && e.hasOwnProperty("s") && !(e.s && typeof e.s.length == "number" || l.isString(e.s))) return "s: buffer expected";
if (e.t != null && e.hasOwnProperty("t") && (i = o.onnx.TensorProto.verify(e.t))) return "t." + i;
if (e.g != null && e.hasOwnProperty("g") && (i = o.onnx.GraphProto.verify(e.g))) return "g." + i;
if (e.floats != null && e.hasOwnProperty("floats")) {
if (!Array.isArray(e.floats)) return "floats: array expected";
for (var r = 0; r < e.floats.length; ++r)
if (typeof e.floats[r] != "number") return "floats: number[] expected"
}
if (e.ints != null && e.hasOwnProperty("ints")) {
if (!Array.isArray(e.ints)) return "ints: array expected";
for (r = 0; r < e.ints.length; ++r)
if (!(l.isInteger(e.ints[r]) || e.ints[r] && l.isInteger(e.ints[r].low) && l.isInteger(e.ints[r].high))) return "ints: integer|Long[] expected"
}
if (e.strings != null && e.hasOwnProperty("strings")) {
if (!Array.isArray(e.strings)) return "strings: array expected";
for (r = 0; r < e.strings.length; ++r)
if (!(e.strings[r] && typeof e.strings[r].length == "number" || l.isString(e.strings[r]))) return "strings: buffer[] expected"
}
if (e.tensors != null && e.hasOwnProperty("tensors")) {
if (!Array.isArray(e.tensors)) return "tensors: array expected";
for (r = 0; r < e.tensors.length; ++r)
if (i = o.onnx.TensorProto.verify(e.tensors[r])) return "tensors." + i
}
if (e.graphs != null && e.hasOwnProperty("graphs")) {
if (!Array.isArray(e.graphs)) return "graphs: array expected";
for (r = 0; r < e.graphs.length; ++r) {
var i;
if (i = o.onnx.GraphProto.verify(e.graphs[r])) return "graphs." + i
}
}
return null
}, t.fromObject = function(e) {
if (e instanceof o.onnx.AttributeProto) return e;
var r = new o.onnx.AttributeProto;
switch (e.name != null && (r.name = String(e.name)), e.refAttrName != null && (r.refAttrName = String(e.refAttrName)), e.docString != null && (r.docString = String(e.docString)), e.type) {
case "UNDEFINED":
case 0:
r.type = 0;
break;
case "FLOAT":
case 1:
r.type = 1;
break;
case "INT":
case 2:
r.type = 2;
break;
case "STRING":
case 3:
r.type = 3;
break;
case "TENSOR":
case 4:
r.type = 4;
break;
case "GRAPH":
case 5:
r.type = 5;
break;
case "FLOATS":
case 6:
r.type = 6;
break;
case "INTS":
case 7:
r.type = 7;
break;
case "STRINGS":
case 8:
r.type = 8;
break;
case "TENSORS":
case 9:
r.type = 9;
break;
case "GRAPHS":
case 10:
r.type = 10
}
if (e.f != null && (r.f = Number(e.f)), e.i != null && (l.Long ? (r.i = l.Long.fromValue(e.i)).unsigned = !1 : typeof e.i == "string" ? r.i = parseInt(e.i, 10) : typeof e.i == "number" ? r.i = e.i : typeof e.i == "object" && (r.i = new l.LongBits(e.i.low >>> 0, e.i.high >>> 0).toNumber())), e.s != null && (typeof e.s == "string" ? l.base64.decode(e.s, r.s = l.newBuffer(l.base64.length(e.s)), 0) : e.s.length && (r.s = e.s)), e.t != null) {
if (typeof e.t != "object") throw TypeError(".onnx.AttributeProto.t: object expected");
r.t = o.onnx.TensorProto.fromObject(e.t)
}
if (e.g != null) {
if (typeof e.g != "object") throw TypeError(".onnx.AttributeProto.g: object expected");
r.g = o.onnx.GraphProto.fromObject(e.g)
}
if (e.floats) {
if (!Array.isArray(e.floats)) throw TypeError(".onnx.AttributeProto.floats: array expected");
r.floats = [];
for (var i = 0; i < e.floats.length; ++i) r.floats[i] = Number(e.floats[i])
}
if (e.ints) {
if (!Array.isArray(e.ints)) throw TypeError(".onnx.AttributeProto.ints: array expected");
for (r.ints = [], i = 0; i < e.ints.length; ++i) l.Long ? (r.ints[i] = l.Long.fromValue(e.ints[i])).unsigned = !1 : typeof e.ints[i] == "string" ? r.ints[i] = parseInt(e.ints[i], 10) : typeof e.ints[i] == "number" ? r.ints[i] = e.ints[i] : typeof e.ints[i] == "object" && (r.ints[i] = new l.LongBits(e.ints[i].low >>> 0, e.ints[i].high >>> 0).toNumber())
}
if (e.strings) {
if (!Array.isArray(e.strings)) throw TypeError(".onnx.AttributeProto.strings: array expected");
for (r.strings = [], i = 0; i < e.strings.length; ++i) typeof e.strings[i] == "string" ? l.base64.decode(e.strings[i], r.strings[i] = l.newBuffer(l.base64.length(e.strings[i])), 0) : e.strings[i].length && (r.strings[i] = e.strings[i])
}
if (e.tensors) {
if (!Array.isArray(e.tensors)) throw TypeError(".onnx.AttributeProto.tensors: array expected");
for (r.tensors = [], i = 0; i < e.tensors.length; ++i) {
if (typeof e.tensors[i] != "object") throw TypeError(".onnx.AttributeProto.tensors: object expected");
r.tensors[i] = o.onnx.TensorProto.fromObject(e.tensors[i])
}
}
if (e.graphs) {
if (!Array.isArray(e.graphs)) throw TypeError(".onnx.AttributeProto.graphs: array expected");
for (r.graphs = [], i = 0; i < e.graphs.length; ++i) {
if (typeof e.graphs[i] != "object") throw TypeError(".onnx.AttributeProto.graphs: object expected");
r.graphs[i] = o.onnx.GraphProto.fromObject(e.graphs[i])
}
}
return r
}, t.toObject = function(e, r) {
r || (r = {});
var i = {};
if ((r.arrays || r.defaults) && (i.floats = [], i.ints = [], i.strings = [], i.tensors = [], i.graphs = []), r.defaults) {
if (i.name = "", i.f = 0, l.Long) {
var d = new l.Long(0, 0, !1);
i.i = r.longs === String ? d.toString() : r.longs === Number ? d.toNumber() : d
} else i.i = r.longs === String ? "0" : 0;
r.bytes === String ? i.s = "" : (i.s = [], r.bytes !== Array && (i.s = l.newBuffer(i.s))), i.t = null, i.g = null, i.docString = "", i.type = r.enums === String ? "UNDEFINED" : 0, i.refAttrName = ""
}
if (e.name != null && e.hasOwnProperty("name") && (i.name = e.name), e.f != null && e.hasOwnProperty("f") && (i.f = r.json && !isFinite(e.f) ? String(e.f) : e.f), e.i != null && e.hasOwnProperty("i") && (typeof e.i == "number" ? i.i = r.longs === String ? String(e.i) : e.i : i.i = r.longs === String ? l.Long.prototype.toString.call(e.i) : r.longs === Number ? new l.LongBits(e.i.low >>> 0, e.i.high >>> 0).toNumber() : e.i), e.s != null && e.hasOwnProperty("s") && (i.s = r.bytes === String ? l.base64.encode(e.s, 0, e.s.length) : r.bytes === Array ? Array.prototype.slice.call(e.s) : e.s), e.t != null && e.hasOwnProperty("t") && (i.t = o.onnx.TensorProto.toObject(e.t, r)), e.g != null && e.hasOwnProperty("g") && (i.g = o.onnx.GraphProto.toObject(e.g, r)), e.floats && e.floats.length) {
i.floats = [];
for (var g = 0; g < e.floats.length; ++g) i.floats[g] = r.json && !isFinite(e.floats[g]) ? String(e.floats[g]) : e.floats[g]
}
if (e.ints && e.ints.length)
for (i.ints = [], g = 0; g < e.ints.length; ++g) typeof e.ints[g] == "number" ? i.ints[g] = r.longs === String ? String(e.ints[g]) : e.ints[g] : i.ints[g] = r.longs === String ? l.Long.prototype.toString.call(e.ints[g]) : r.longs === Number ? new l.LongBits(e.ints[g].low >>> 0, e.ints[g].high >>> 0).toNumber() : e.ints[g];
if (e.strings && e.strings.length)
for (i.strings = [], g = 0; g < e.strings.length; ++g) i.strings[g] = r.bytes === String ? l.base64.encode(e.strings[g], 0, e.strings[g].length) : r.bytes === Array ? Array.prototype.slice.call(e.strings[g]) : e.strings[g];
if (e.tensors && e.tensors.length)
for (i.tensors = [], g = 0; g < e.tensors.length; ++g) i.tensors[g] = o.onnx.TensorProto.toObject(e.tensors[g], r);
if (e.graphs && e.graphs.length)
for (i.graphs = [], g = 0; g < e.graphs.length; ++g) i.graphs[g] = o.onnx.GraphProto.toObject(e.graphs[g], r);
return e.docString != null && e.hasOwnProperty("docString") && (i.docString = e.docString), e.type != null && e.hasOwnProperty("type") && (i.type = r.enums === String ? o.onnx.AttributeProto.AttributeType[e.type] : e.type), e.refAttrName != null && e.hasOwnProperty("refAttrName") && (i.refAttrName = e.refAttrName), i
}, t.prototype.toJSON = function() {
return this.constructor.toObject(this, s.util.toJSONOptions)
}, t.AttributeType = function() {
var e = {},
r = Object.create(e);
return r[e[0] = "UNDEFINED"] = 0, r[e[1] = "FLOAT"] = 1, r[e[2] = "INT"] = 2, r[e[3] = "STRING"] = 3, r[e[4] = "TENSOR"] = 4, r[e[5] = "GRAPH"] = 5, r[e[6] = "FLOATS"] = 6, r[e[7] = "INTS"] = 7, r[e[8] = "STRINGS"] = 8, r[e[9] = "TENSORS"] = 9, r[e[10] = "GRAPHS"] = 10, r
}(), t
}(), f.ValueInfoProto = function() {
function t(e) {
if (e)
for (var r = Object.keys(e), i = 0; i < r.length; ++i) e[r[i]] != null && (this[r[i]] = e[r[i]])
}
return t.prototype.name = "", t.prototype.type = null, t.prototype.docString = "", t.create = function(e) {
return new t(e)
}, t.encode = function(e, r) {
return r || (r = p.create()), e.name != null && e.hasOwnProperty("name") && r.uint32(10).string(e.name), e.type != null && e.hasOwnProperty("type") && o.onnx.TypeProto.encode(e.type, r.uint32(18).fork()).ldelim(), e.docString != null && e.hasOwnProperty("docString") && r.uint32(26).string(e.docString), r
}, t.encodeDelimited = function(e, r) {
return this.encode(e, r).ldelim()
}, t.decode = function(e, r) {
e instanceof h || (e = h.create(e));
for (var i = r === void 0 ? e.len : e.pos + r, d = new o.onnx.ValueInfoProto; e.pos < i;) {
var g = e.uint32();
switch (g >>> 3) {
case 1:
d.name = e.string();
break;
case 2:
d.type = o.onnx.TypeProto.decode(e, e.uint32());
break;
case 3:
d.docString = e.string();
break;
default:
e.skipType(7 & g)
}
}
return d
}, t.decodeDelimited = function(e) {
return e instanceof h || (e = new h(e)), this.decode(e, e.uint32())
}, t.verify = function(e) {
if (typeof e != "object" || e === null) return "object expected";
if (e.name != null && e.hasOwnProperty("name") && !l.isString(e.name)) return "name: string expected";
if (e.type != null && e.hasOwnProperty("type")) {
var r = o.onnx.TypeProto.verify(e.type);
if (r) return "type." + r
}
return e.docString != null && e.hasOwnProperty("docString") && !l.isString(e.docString) ? "docString: string expected" : null
}, t.fromObject = function(e) {
if (e instanceof o.onnx.ValueInfoProto) return e;
var r = new o.onnx.ValueInfoProto;
if (e.name != null && (r.name = String(e.name)), e.type != null) {
if (typeof e.type != "object") throw TypeError(".onnx.ValueInfoProto.type: object expected");
r.type = o.onnx.TypeProto.fromObject(e.type)
}
return e.docString != null && (r.docString = String(e.docString)), r
}, t.toObject = function(e, r) {
r || (r = {});
var i = {};
return r.defaults && (i.name = "", i.type = null, i.docString = ""), e.name != null && e.hasOwnProperty("name") && (i.name = e.name), e.type != null && e.hasOwnProperty("type") && (i.type = o.onnx.TypeProto.toObject(e.type, r)), e.docString != null && e.hasOwnProperty("docString") && (i.docString = e.docString), i
}, t.prototype.toJSON = function() {
return this.constructor.toObject(this, s.util.toJSONOptions)
}, t
}(), f.NodeProto = function() {
function t(e) {
if (this.input = [], this.output = [], this.attribute = [], e)
for (var r = Object.keys(e), i = 0; i < r.length; ++i) e[r[i]] != null && (this[r[i]] = e[r[i]])
}
return t.prototype.input = l.emptyArray, t.prototype.output = l.emptyArray, t.prototype.name = "", t.prototype.opType = "", t.prototype.domain = "", t.prototype.attribute = l.emptyArray, t.prototype.docString = "", t.create = function(e) {
return new t(e)
}, t.encode = function(e, r) {
if (r || (r = p.create()), e.input != null && e.input.length)
for (var i = 0; i < e.input.length; ++i) r.uint32(10).string(e.input[i]);
if (e.output != null && e.output.length)
for (i = 0; i < e.output.length; ++i) r.uint32(18).string(e.output[i]);
if (e.name != null && e.hasOwnProperty("name") && r.uint32(26).string(e.name), e.opType != null && e.hasOwnProperty("opType") && r.uint32(34).string(e.opType), e.attribute != null && e.attribute.length)
for (i = 0; i < e.attribute.length; ++i) o.onnx.AttributeProto.encode(e.attribute[i], r.uint32(42).fork()).ldelim();
return e.docString != null && e.hasOwnProperty("docString") && r.uint32(50).string(e.docString), e.domain != null && e.hasOwnProperty("domain") && r.uint32(58).string(e.domain), r
}, t.encodeDelimited = function(e, r) {
return this.encode(e, r).ldelim()
}, t.decode = function(e, r) {
e instanceof h || (e = h.create(e));
for (var i = r === void 0 ? e.len : e.pos + r, d = new o.onnx.NodeProto; e.pos < i;) {
var g = e.uint32();
switch (g >>> 3) {
case 1:
d.input && d.input.length || (d.input = []), d.input.push(e.string());
break;
case 2:
d.output && d.output.length || (d.output = []), d.output.push(e.string());
break;
case 3:
d.name = e.string();
break;
case 4:
d.opType = e.string();
break;
case 7:
d.domain = e.string();
break;
case 5:
d.attribute && d.attribute.length || (d.attribute = []), d.attribute.push(o.onnx.AttributeProto.decode(e, e.uint32()));
break;
case 6:
d.docString = e.string();
break;
default:
e.skipType(7 & g)
}
}
return d
}, t.decodeDelimited = function(e) {
return e instanceof h || (e = new h(e)), this.decode(e, e.uint32())
}, t.verify = function(e) {
if (typeof e != "object" || e === null) return "object expected";
if (e.input != null && e.hasOwnProperty("input")) {
if (!Array.isArray(e.input)) return "input: array expected";
for (var r = 0; r < e.input.length; ++r)
if (!l.isString(e.input[r])) return "input: string[] expected"
}
if (e.output != null && e.hasOwnProperty("output")) {
if (!Array.isArray(e.output)) return "output: array expected";
for (r = 0; r < e.output.length; ++r)
if (!l.isString(e.output[r])) return "output: string[] expected"
}
if (e.name != null && e.hasOwnProperty("name") && !l.isString(e.name)) return "name: string expected";
if (e.opType != null && e.hasOwnProperty("opType") && !l.isString(e.opType)) return "opType: string expected";
if (e.domain != null && e.hasOwnProperty("domain") && !l.isString(e.domain)) return "domain: string expected";
if (e.attribute != null && e.hasOwnProperty("attribute")) {
if (!Array.isArray(e.attribute)) return "attribute: array expected";
for (r = 0; r < e.attribute.length; ++r) {
var i = o.onnx.AttributeProto.verify(e.attribute[r]);
if (i) return "attribute." + i
}
}
return e.docString != null && e.hasOwnProperty("docString") && !l.isString(e.docString) ? "docString: string expected" : null
}, t.fromObject = function(e) {
if (e instanceof o.onnx.NodeProto) return e;
var r = new o.onnx.NodeProto;
if (e.input) {
if (!Array.isArray(e.input)) throw TypeError(".onnx.NodeProto.input: array expected");
r.input = [];
for (var i = 0; i < e.input.length; ++i) r.input[i] = String(e.input[i])
}
if (e.output) {
if (!Array.isArray(e.output)) throw TypeError(".onnx.NodeProto.output: array expected");
for (r.output = [], i = 0; i < e.output.length; ++i) r.output[i] = String(e.output[i])
}
if (e.name != null && (r.name = String(e.name)), e.opType != null && (r.opType = String(e.opType)), e.domain != null && (r.domain = String(e.domain)), e.attribute) {
if (!Array.isArray(e.attribute)) throw TypeError(".onnx.NodeProto.attribute: array expected");
for (r.attribute = [], i = 0; i < e.attribute.length; ++i) {
if (typeof e.attribute[i] != "object") throw TypeError(".onnx.NodeProto.attribute: object expected");
r.attribute[i] = o.onnx.AttributeProto.fromObject(e.attribute[i])
}
}
return e.docString != null && (r.docString = String(e.docString)), r
}, t.toObject = function(e, r) {
r || (r = {});
var i = {};
if ((r.arrays || r.defaults) && (i.input = [], i.output = [], i.attribute = []), r.defaults && (i.name = "", i.opType = "", i.docString = "", i.domain = ""), e.input && e.input.length) {
i.input = [];
for (var d = 0; d < e.input.length; ++d) i.input[d] = e.input[d]
}
if (e.output && e.output.length)
for (i.output = [], d = 0; d < e.output.length; ++d) i.output[d] = e.output[d];
if (e.name != null && e.hasOwnProperty("name") && (i.name = e.name), e.opType != null && e.hasOwnProperty("opType") && (i.opType = e.opType), e.attribute && e.attribute.length)
for (i.attribute = [], d = 0; d < e.attribute.length; ++d) i.attribute[d] = o.onnx.AttributeProto.toObject(e.attribute[d], r);
return e.docString != null && e.hasOwnProperty("docString") && (i.docString = e.docString), e.domain != null && e.hasOwnProperty("domain") && (i.domain = e.domain), i
}, t.prototype.toJSON = function() {
return this.constructor.toObject(this, s.util.toJSONOptions)
}, t
}(), f.ModelProto = function() {
function t(e) {
if (this.opsetImport = [], this.metadataProps = [], e)
for (var r = Object.keys(e), i = 0; i < r.length; ++i) e[r[i]] != null && (this[r[i]] = e[r[i]])
}
return t.prototype.irVersion = l.Long ? l.Long.fromBits(0, 0, !1) : 0, t.prototype.opsetImport = l.emptyArray, t.prototype.producerName = "", t.prototype.producerVersion = "", t.prototype.domain = "", t.prototype.modelVersion = l.Long ? l.Long.fromBits(0, 0, !1) : 0, t.prototype.docString = "", t.prototype.graph = null, t.prototype.metadataProps = l.emptyArray, t.create = function(e) {
return new t(e)
}, t.encode = function(e, r) {
if (r || (r = p.create()), e.irVersion != null && e.hasOwnProperty("irVersion") && r.uint32(8).int64(e.irVersion), e.producerName != null && e.hasOwnProperty("producerName") && r.uint32(18).string(e.producerName), e.producerVersion != null && e.hasOwnProperty("producerVersion") && r.uint32(26).string(e.producerVersion), e.domain != null && e.hasOwnProperty("domain") && r.uint32(34).string(e.domain), e.modelVersion != null && e.hasOwnProperty("modelVersion") && r.uint32(40).int64(e.modelVersion), e.docString != null && e.hasOwnProperty("docString") && r.uint32(50).string(e.docString), e.graph != null && e.hasOwnProperty("graph") && o.onnx.GraphProto.encode(e.graph, r.uint32(58).fork()).ldelim(), e.opsetImport != null && e.opsetImport.length)
for (var i = 0; i < e.opsetImport.length; ++i) o.onnx.OperatorSetIdProto.encode(e.opsetImport[i], r.uint32(66).fork()).ldelim();
if (e.metadataProps != null && e.metadataProps.length)
for (i = 0; i < e.metadataProps.length; ++i) o.onnx.StringStringEntryProto.encode(e.metadataProps[i], r.uint32(114).fork()).ldelim();
return r
}, t.encodeDelimited = function(e, r) {
return this.encode(e, r).ldelim()
}, t.decode = function(e, r) {
e instanceof h || (e = h.create(e));
for (var i = r === void 0 ? e.len : e.pos + r, d = new o.onnx.ModelProto; e.pos < i;) {
var g = e.uint32();
switch (g >>> 3) {
case 1:
d.irVersion = e.int64();
break;
case 8:
d.opsetImport && d.opsetImport.length || (d.opsetImport = []), d.opsetImport.push(o.onnx.OperatorSetIdProto.decode(e, e.uint32()));
break;
case 2:
d.producerName = e.string();
break;
case 3:
d.producerVersion = e.string();
break;
case 4:
d.domain = e.string();
break;
case 5:
d.modelVersion = e.int64();
break;
case 6:
d.docString = e.string();
break;
case 7:
d.graph = o.onnx.GraphProto.decode(e, e.uint32());
break;
case 14:
d.metadataProps && d.metadataProps.length || (d.metadataProps = []), d.metadataProps.push(o.onnx.StringStringEntryProto.decode(e, e.uint32()));
break;
default:
e.skipType(7 & g)
}
}
return d
}, t.decodeDelimited = function(e) {
return e instanceof h || (e = new h(e)), this.decode(e, e.uint32())
}, t.verify = function(e) {
if (typeof e != "object" || e === null) return "object expected";
if (e.irVersion != null && e.hasOwnProperty("irVersion") && !(l.isInteger(e.irVersion) || e.irVersion && l.isInteger(e.irVersion.low) && l.isInteger(e.irVersion.high))) return "irVersion: integer|Long expected";
if (e.opsetImport != null && e.hasOwnProperty("opsetImport")) {
if (!Array.isArray(e.opsetImport)) return "opsetImport: array expected";
for (var r = 0; r < e.opsetImport.length; ++r)
if (i = o.onnx.OperatorSetIdProto.verify(e.opsetImport[r])) return "opsetImport." + i
}
if (e.producerName != null && e.hasOwnProperty("producerName") && !l.isString(e.producerName)) return "producerName: string expected";
if (e.producerVersion != null && e.hasOwnProperty("producerVersion") && !l.isString(e.producerVersion)) return "producerVersion: string expected";
if (e.domain != null && e.hasOwnProperty("domain") && !l.isString(e.domain)) return "domain: string expected";
if (e.modelVersion != null && e.hasOwnProperty("modelVersion") && !(l.isInteger(e.modelVersion) || e.modelVersion && l.isInteger(e.modelVersion.low) && l.isInteger(e.modelVersion.high))) return "modelVersion: integer|Long expected";
if (e.docString != null && e.hasOwnProperty("docString") && !l.isString(e.docString)) return "docString: string expected";
if (e.graph != null && e.hasOwnProperty("graph") && (i = o.onnx.GraphProto.verify(e.graph))) return "graph." + i;
if (e.metadataProps != null && e.hasOwnProperty("metadataProps")) {
if (!Array.isArray(e.metadataProps)) return "metadataProps: array expected";
for (r = 0; r < e.metadataProps.length; ++r) {
var i;
if (i = o.onnx.StringStringEntryProto.verify(e.metadataProps[r])) return "metadataProps." + i
}
}
return null
}, t.fromObject = function(e) {
if (e instanceof o.onnx.ModelProto) return e;
var r = new o.onnx.ModelProto;
if (e.irVersion != null && (l.Long ? (r.irVersion = l.Long.fromValue(e.irVersion)).unsigned = !1 : typeof e.irVersion == "string" ? r.irVersion = parseInt(e.irVersion, 10) : typeof e.irVersion == "number" ? r.irVersion = e.irVersion : typeof e.irVersion == "object" && (r.irVersion = new l.LongBits(e.irVersion.low >>> 0, e.irVersion.high >>> 0).toNumber())), e.opsetImport) {
if (!Array.isArray(e.opsetImport)) throw TypeError(".onnx.ModelProto.opsetImport: array expected");
r.opsetImport = [];
for (var i = 0; i < e.opsetImport.length; ++i) {
if (typeof e.opsetImport[i] != "object") throw TypeError(".onnx.ModelProto.opsetImport: object expected");
r.opsetImport[i] = o.onnx.OperatorSetIdProto.fromObject(e.opsetImport[i])
}
}
if (e.producerName != null && (r.producerName = String(e.producerName)), e.producerVersion != null && (r.producerVersion = String(e.producerVersion)), e.domain != null && (r.domain = String(e.domain)), e.modelVersion != null && (l.Long ? (r.modelVersion = l.Long.fromValue(e.modelVersion)).unsigned = !1 : typeof e.modelVersion == "string" ? r.modelVersion = parseInt(e.modelVersion, 10) : typeof e.modelVersion == "number" ? r.modelVersion = e.modelVersion : typeof e.modelVersion == "object" && (r.modelVersion = new l.LongBits(e.modelVersion.low >>> 0, e.modelVersion.high >>> 0).toNumber())), e.docString != null && (r.docString = String(e.docString)), e.graph != null) {
if (typeof e.graph != "object") throw TypeError(".onnx.ModelProto.graph: object expected");
r.graph = o.onnx.GraphProto.fromObject(e.graph)
}
if (e.metadataProps) {
if (!Array.isArray(e.metadataProps)) throw TypeError(".onnx.ModelProto.metadataProps: array expected");
for (r.metadataProps = [], i = 0; i < e.metadataProps.length; ++i) {
if (typeof e.metadataProps[i] != "object") throw TypeError(".onnx.ModelProto.metadataProps: object expected");
r.metadataProps[i] = o.onnx.StringStringEntryProto.fromObject(e.metadataProps[i])
}
}
return r
}, t.toObject = function(e, r) {
r || (r = {});
var i = {};
if ((r.arrays || r.defaults) && (i.opsetImport = [], i.metadataProps = []), r.defaults) {
if (l.Long) {
var d = new l.Long(0, 0, !1);
i.irVersion = r.longs === String ? d.toString() : r.longs === Number ? d.toNumber() : d
} else i.irVersion = r.longs === String ? "0" : 0;
i.producerName = "", i.producerVersion = "", i.domain = "", l.Long ? (d = new l.Long(0, 0, !1), i.modelVersion = r.longs === String ? d.toString() : r.longs === Number ? d.toNumber() : d) : i.modelVersion = r.longs === String ? "0" : 0, i.docString = "", i.graph = null
}
if (e.irVersion != null && e.hasOwnProperty("irVersion") && (typeof e.irVersion == "number" ? i.irVersion = r.longs === String ? String(e.irVersion) : e.irVersion : i.irVersion = r.longs === String ? l.Long.prototype.toString.call(e.irVersion) : r.longs === Number ? new l.LongBits(e.irVersion.low >>> 0, e.irVersion.high >>> 0).toNumber() : e.irVersion), e.producerName != null && e.hasOwnProperty("producerName") && (i.producerName = e.producerName), e.producerVersion != null && e.hasOwnProperty("producerVersion") && (i.producerVersion = e.producerVersion), e.domain != null && e.hasOwnProperty("domain") && (i.domain = e.domain), e.modelVersion != null && e.hasOwnProperty("modelVersion") && (typeof e.modelVersion == "number" ? i.modelVersion = r.longs === String ? String(e.modelVersion) : e.modelVersion : i.modelVersion = r.longs === String ? l.Long.prototype.toString.call(e.modelVersion) : r.longs === Number ? new l.LongBits(e.modelVersion.low >>> 0, e.modelVersion.high >>> 0).toNumber() : e.modelVersion), e.docString != null && e.hasOwnProperty("docString") && (i.docString = e.docString), e.graph != null && e.hasOwnProperty("graph") && (i.graph = o.onnx.GraphProto.toObject(e.graph, r)), e.opsetImport && e.opsetImport.length) {
i.opsetImport = [];
for (var g = 0; g < e.opsetImport.length; ++g) i.opsetImport[g] = o.onnx.OperatorSetIdProto.toObject(e.opsetImport[g], r)
}
if (e.metadataProps && e.metadataProps.length)
for (i.metadataProps = [], g = 0; g < e.metadataProps.length; ++g) i.metadataProps[g] = o.onnx.StringStringEntryProto.toObject(e.metadataProps[g], r);
return i
}, t.prototype.toJSON = function() {
return this.constructor.toObject(this, s.util.toJSONOptions)
}, t
}(), f.StringStringEntryProto = function() {
function t(e) {
if (e)
for (var r = Object.keys(e), i = 0; i < r.length; ++i) e[r[i]] != null && (this[r[i]] = e[r[i]])
}
return t.prototype.key = "", t.prototype.value = "", t.create = function(e) {
return new t(e)
}, t.encode = function(e, r) {
return r || (r = p.create()), e.key != null && e.hasOwnProperty("key") && r.uint32(10).string(e.key), e.value != null && e.hasOwnProperty("value") && r.uint32(18).string(e.value), r
}, t.encodeDelimited = function(e, r) {
return this.encode(e, r).ldelim()
}, t.decode = function(e, r) {
e instanceof h || (e = h.create(e));
for (var i = r === void 0 ? e.len : e.pos + r, d = new o.onnx.StringStringEntryProto; e.pos < i;) {
var g = e.uint32();
switch (g >>> 3) {
case 1:
d.key = e.string();
break;
case 2:
d.value = e.string();
break;
default:
e.skipType(7 & g)
}
}
return d
}, t.decodeDelimited = function(e) {
return e instanceof h || (e = new h(e)), this.decode(e, e.uint32())
}, t.verify = function(e) {
return typeof e != "object" || e === null ? "object expected" : e.key != null && e.hasOwnProperty("key") && !l.isString(e.key) ? "key: string expected" : e.value != null && e.hasOwnProperty("value") && !l.isString(e.value) ? "value: string expected" : null
}, t.fromObject = function(e) {
if (e instanceof o.onnx.StringStringEntryProto) return e;
var r = new o.onnx.StringStringEntryProto;
return e.key != null && (r.key = String(e.key)), e.value != null && (r.value = String(e.value)), r
}, t.toObject = function(e, r) {
r || (r = {});
var i = {};
return r.defaults && (i.key = "", i.value = ""), e.key != null && e.hasOwnProperty("key") && (i.key = e.key), e.value != null && e.hasOwnProperty("value") && (i.value = e.value), i
}, t.prototype.toJSON = function() {
return this.constructor.toObject(this, s.util.toJSONOptions)
}, t
}(), f.TensorAnnotation = function() {
function t(e) {
if (this.quantParameterTensorNames = [], e)
for (var r = Object.keys(e), i = 0; i < r.length; ++i) e[r[i]] != null && (this[r[i]] = e[r[i]])
}
return t.prototype.tensorName = "", t.prototype.quantParameterTensorNames = l.emptyArray, t.create = function(e) {
return new t(e)
}, t.encode = function(e, r) {
if (r || (r = p.create()), e.tensorName != null && e.hasOwnProperty("tensorName") && r.uint32(10).string(e.tensorName), e.quantParameterTensorNames != null && e.quantParameterTensorNames.length)
for (var i = 0; i < e.quantParameterTensorNames.length; ++i) o.onnx.StringStringEntryProto.encode(e.quantParameterTensorNames[i], r.uint32(18).fork()).ldelim();
return r
}, t.encodeDelimited = function(e, r) {
return this.encode(e, r).ldelim()
}, t.decode = function(e, r) {
e instanceof h || (e = h.create(e));
for (var i = r === void 0 ? e.len : e.pos + r, d = new o.onnx.TensorAnnotation; e.pos < i;) {
var g = e.uint32();
switch (g >>> 3) {
case 1:
d.tensorName = e.string();
break;
case 2:
d.quantParameterTensorNames && d.quantParameterTensorNames.length || (d.quantParameterTensorNames = []), d.quantParameterTensorNames.push(o.onnx.StringStringEntryProto.decode(e, e.uint32()));
break;
default:
e.skipType(7 & g)
}
}
return d
}, t.decodeDelimited = function(e) {
return e instanceof h || (e = new h(e)), this.decode(e, e.uint32())
}, t.verify = function(e) {
if (typeof e != "object" || e === null) return "object expected";
if (e.tensorName != null && e.hasOwnProperty("tensorName") && !l.isString(e.tensorName)) return "tensorName: string expected";
if (e.quantParameterTensorNames != null && e.hasOwnProperty("quantParameterTensorNames")) {
if (!Array.isArray(e.quantParameterTensorNames)) return "quantParameterTensorNames: array expected";
for (var r = 0; r < e.quantParameterTensorNames.length; ++r) {
var i = o.onnx.StringStringEntryProto.verify(e.quantParameterTensorNames[r]);
if (i) return "quantParameterTensorNames." + i
}
}
return null
}, t.fromObject = function(e) {
if (e instanceof o.onnx.TensorAnnotation) return e;
var r = new o.onnx.TensorAnnotation;
if (e.tensorName != null && (r.tensorName = String(e.tensorName)), e.quantParameterTensorNames) {
if (!Array.isArray(e.quantParameterTensorNames)) throw TypeError(".onnx.TensorAnnotation.quantParameterTensorNames: array expected");
r.quantParameterTensorNames = [];
for (var i = 0; i < e.quantParameterTensorNames.length; ++i) {
if (typeof e.quantParameterTensorNames[i] != "object") throw TypeError(".onnx.TensorAnnotation.quantParameterTensorNames: object expected");
r.quantParameterTensorNames[i] = o.onnx.StringStringEntryProto.fromObject(e.quantParameterTensorNames[i])
}
}
return r
}, t.toObject = function(e, r) {
r || (r = {});
var i = {};
if ((r.arrays || r.defaults) && (i.quantParameterTensorNames = []), r.defaults && (i.tensorName = ""), e.tensorName != null && e.hasOwnProperty("tensorName") && (i.tensorName = e.tensorName), e.quantParameterTensorNames && e.quantParameterTensorNames.length) {
i.quantParameterTensorNames = [];
for (var d = 0; d < e.quantParameterTensorNames.length; ++d) i.quantParameterTensorNames[d] = o.onnx.StringStringEntryProto.toObject(e.quantParameterTensorNames[d], r)
}
return i
}, t.prototype.toJSON = function() {
return this.constructor.toObject(this, s.util.toJSONOptions)
}, t
}(), f.GraphProto = function() {
function t(e) {
if (this.node = [], this.initializer = [], this.input = [], this.output = [], this.valueInfo = [], this.quantizationAnnotation = [], e)
for (var r = Object.keys(e), i = 0; i < r.length; ++i) e[r[i]] != null && (this[r[i]] = e[r[i]])
}
return t.prototype.node = l.emptyArray, t.prototype.name = "", t.prototype.initializer = l.emptyArray, t.prototype.docString = "", t.prototype.input = l.emptyArray, t.prototype.output = l.emptyArray, t.prototype.valueInfo = l.emptyArray, t.prototype.quantizationAnnotation = l.emptyArray, t.create = function(e) {
return new t(e)
}, t.encode = function(e, r) {
if (r || (r = p.create()), e.node != null && e.node.length)
for (var i = 0; i < e.node.length; ++i) o.onnx.NodeProto.encode(e.node[i], r.uint32(10).fork()).ldelim();
if (e.name != null && e.hasOwnProperty("name") && r.uint32(18).string(e.name), e.initializer != null && e.initializer.length)
for (i = 0; i < e.initializer.length; ++i) o.onnx.TensorProto.encode(e.initializer[i], r.uint32(42).fork()).ldelim();
if (e.docString != null && e.hasOwnProperty("docString") && r.uint32(82).string(e.docString), e.input != null && e.input.length)
for (i = 0; i < e.input.length; ++i) o.onnx.ValueInfoProto.encode(e.input[i], r.uint32(90).fork()).ldelim();
if (e.output != null && e.output.length)
for (i = 0; i < e.output.length; ++i) o.onnx.ValueInfoProto.encode(e.output[i], r.uint32(98).fork()).ldelim();
if (e.valueInfo != null && e.valueInfo.length)
for (i = 0; i < e.valueInfo.length; ++i) o.onnx.ValueInfoProto.encode(e.valueInfo[i], r.uint32(106).fork()).ldelim();
if (e.quantizationAnnotation != null && e.quantizationAnnotation.length)
for (i = 0; i < e.quantizationAnnotation.length; ++i) o.onnx.TensorAnnotation.encode(e.quantizationAnnotation[i], r.uint32(114).fork()).ldelim();
return r
}, t.encodeDelimited = function(e, r) {
return this.encode(e, r).ldelim()
}, t.decode = function(e, r) {
e instanceof h || (e = h.create(e));
for (var i = r === void 0 ? e.len : e.pos + r, d = new o.onnx.GraphProto; e.pos < i;) {
var g = e.uint32();
switch (g >>> 3) {
case 1:
d.node && d.node.length || (d.node = []), d.node.push(o.onnx.NodeProto.decode(e, e.uint32()));
break;
case 2:
d.name = e.string();
break;
case 5:
d.initializer && d.initializer.length || (d.initializer = []), d.initializer.push(o.onnx.TensorProto.decode(e, e.uint32()));
break;
case 10:
d.docString = e.string();
break;
case 11:
d.input && d.input.length || (d.input = []), d.input.push(o.onnx.ValueInfoProto.decode(e, e.uint32()));
break;
case 12:
d.output && d.output.length || (d.output = []), d.output.push(o.onnx.ValueInfoProto.decode(e, e.uint32()));
break;
case 13:
d.valueInfo && d.valueInfo.length || (d.valueInfo = []), d.valueInfo.push(o.onnx.ValueInfoProto.decode(e, e.uint32()));
break;
case 14:
d.quantizationAnnotation && d.quantizationAnnotation.length || (d.quantizationAnnotation = []), d.quantizationAnnotation.push(o.onnx.TensorAnnotation.decode(e, e.uint32()));
break;
default:
e.skipType(7 & g)
}
}
return d
}, t.decodeDelimited = function(e) {
return e instanceof h || (e = new h(e)), this.decode(e, e.uint32())
}, t.verify = function(e) {
if (typeof e != "object" || e === null) return "object expected";
if (e.node != null && e.hasOwnProperty("node")) {
if (!Array.isArray(e.node)) return "node: array expected";
for (var r = 0; r < e.node.length; ++r)
if (i = o.onnx.NodeProto.verify(e.node[r])) return "node." + i
}
if (e.name != null && e.hasOwnProperty("name") && !l.isString(e.name)) return "name: string expected";
if (e.initializer != null && e.hasOwnProperty("initializer")) {
if (!Array.isArray(e.initializer)) return "initializer: array expected";
for (r = 0; r < e.initializer.length; ++r)
if (i = o.onnx.TensorProto.verify(e.initializer[r])) return "initializer." + i
}
if (e.docString != null && e.hasOwnProperty("docString") && !l.isString(e.docString)) return "docString: string expected";
if (e.input != null && e.hasOwnProperty("input")) {
if (!Array.isArray(e.input)) return "input: array expected";
for (r = 0; r < e.input.length; ++r)
if (i = o.onnx.ValueInfoProto.verify(e.input[r])) return "input." + i
}
if (e.output != null && e.hasOwnProperty("output")) {
if (!Array.isArray(e.output)) return "output: array expected";
for (r = 0; r < e.output.length; ++r)
if (i = o.onnx.ValueInfoProto.verify(e.output[r])) return "output." + i
}
if (e.valueInfo != null && e.hasOwnProperty("valueInfo")) {
if (!Array.isArray(e.valueInfo)) return "valueInfo: array expected";
for (r = 0; r < e.valueInfo.length; ++r)
if (i = o.onnx.ValueInfoProto.verify(e.valueInfo[r])) return "valueInfo." + i
}
if (e.quantizationAnnotation != null && e.hasOwnProperty("quantizationAnnotation")) {
if (!Array.isArray(e.quantizationAnnotation)) return "quantizationAnnotation: array expected";
for (r = 0; r < e.quantizationAnnotation.length; ++r) {
var i;
if (i = o.onnx.TensorAnnotation.verify(e.quantizationAnnotation[r])) return "quantizationAnnotation." + i
}
}
return null
}, t.fromObject = function(e) {
if (e instanceof o.onnx.GraphProto) return e;
var r = new o.onnx.GraphProto;
if (e.node) {
if (!Array.isArray(e.node)) throw TypeError(".onnx.GraphProto.node: array expected");
r.node = [];
for (var i = 0; i < e.node.length; ++i) {
if (typeof e.node[i] != "object") throw TypeError(".onnx.GraphProto.node: object expected");
r.node[i] = o.onnx.NodeProto.fromObject(e.node[i])
}
}
if (e.name != null && (r.name = String(e.name)), e.initializer) {
if (!Array.isArray(e.initializer)) throw TypeError(".onnx.GraphProto.initializer: array expected");
for (r.initializer = [], i = 0; i < e.initializer.length; ++i) {
if (typeof e.initializer[i] != "object") throw TypeError(".onnx.GraphProto.initializer: object expected");
r.initializer[i] = o.onnx.TensorProto.fromObject(e.initializer[i])
}
}
if (e.docString != null && (r.docString = String(e.docString)), e.input) {
if (!Array.isArray(e.input)) throw TypeError(".onnx.GraphProto.input: array expected");
for (r.input = [], i = 0; i < e.input.length; ++i) {
if (typeof e.input[i] != "object") throw TypeError(".onnx.GraphProto.input: object expected");
r.input[i] = o.onnx.ValueInfoProto.fromObject(e.input[i])
}
}
if (e.output) {
if (!Array.isArray(e.output)) throw TypeError(".onnx.GraphProto.output: array expected");
for (r.output = [], i = 0; i < e.output.length; ++i) {
if (typeof e.output[i] != "object") throw TypeError(".onnx.GraphProto.output: object expected");
r.output[i] = o.onnx.ValueInfoProto.fromObject(e.output[i])
}
}
if (e.valueInfo) {
if (!Array.isArray(e.valueInfo)) throw TypeError(".onnx.GraphProto.valueInfo: array expected");
for (r.valueInfo = [], i = 0; i < e.valueInfo.length; ++i) {
if (typeof e.valueInfo[i] != "object") throw TypeError(".onnx.GraphProto.valueInfo: object expected");
r.valueInfo[i] = o.onnx.ValueInfoProto.fromObject(e.valueInfo[i])
}
}
if (e.quantizationAnnotation) {
if (!Array.isArray(e.quantizationAnnotation)) throw TypeError(".onnx.GraphProto.quantizationAnnotation: array expected");
for (r.quantizationAnnotation = [], i = 0; i < e.quantizationAnnotation.length; ++i) {
if (typeof e.quantizationAnnotation[i] != "object") throw TypeError(".onnx.GraphProto.quantizationAnnotation: object expected");
r.quantizationAnnotation[i] = o.onnx.TensorAnnotation.fromObject(e.quantizationAnnotation[i])
}
}
return r
}, t.toObject = function(e, r) {
r || (r = {});
var i = {};
if ((r.arrays || r.defaults) && (i.node = [], i.initializer = [], i.input = [], i.output = [], i.valueInfo = [], i.quantizationAnnotation = []), r.defaults && (i.name = "", i.docString = ""), e.node && e.node.length) {
i.node = [];
for (var d = 0; d < e.node.length; ++d) i.node[d] = o.onnx.NodeProto.toObject(e.node[d], r)
}
if (e.name != null && e.hasOwnProperty("name") && (i.name = e.name), e.initializer && e.initializer.length)
for (i.initializer = [], d = 0; d < e.initializer.length; ++d) i.initializer[d] = o.onnx.TensorProto.toObject(e.initializer[d], r);
if (e.docString != null && e.hasOwnProperty("docString") && (i.docString = e.docString), e.input && e.input.length)
for (i.input = [], d = 0; d < e.input.length; ++d) i.input[d] = o.onnx.ValueInfoProto.toObject(e.input[d], r);
if (e.output && e.output.length)
for (i.output = [], d = 0; d < e.output.length; ++d) i.output[d] = o.onnx.ValueInfoProto.toObject(e.output[d], r);
if (e.valueInfo && e.valueInfo.length)
for (i.valueInfo = [], d = 0; d < e.valueInfo.length; ++d) i.valueInfo[d] = o.onnx.ValueInfoProto.toObject(e.valueInfo[d], r);
if (e.quantizationAnnotation && e.quantizationAnnotation.length)
for (i.quantizationAnnotation = [], d = 0; d < e.quantizationAnnotation.length; ++d) i.quantizationAnnotation[d] = o.onnx.TensorAnnotation.toObject(e.quantizationAnnotation[d], r);
return i
}, t.prototype.toJSON = function() {
return this.constructor.toObject(this, s.util.toJSONOptions)
}, t
}(), f.TensorProto = function() {
function t(e) {
if (this.dims = [], this.floatData = [], this.int32Data = [], this.stringData = [], this.int64Data = [], this.externalData = [], this.doubleData = [], this.uint64Data = [], e)
for (var r = Object.keys(e), i = 0; i < r.length; ++i) e[r[i]] != null && (this[r[i]] = e[r[i]])
}
return t.prototype.dims = l.emptyArray, t.prototype.dataType = 0, t.prototype.segment = null, t.prototype.floatData = l.emptyArray, t.prototype.int32Data = l.emptyArray, t.prototype.stringData = l.emptyArray, t.prototype.int64Data = l.emptyArray, t.prototype.name = "", t.prototype.docString = "", t.prototype.rawData = l.newBuffer([]), t.prototype.externalData = l.emptyArray, t.prototype.dataLocation = 0, t.prototype.doubleData = l.emptyArray, t.prototype.uint64Data = l.emptyArray, t.create = function(e) {
return new t(e)
}, t.encode = function(e, r) {
if (r || (r = p.create()), e.dims != null && e.dims.length) {
r.uint32(10).fork();
for (var i = 0; i < e.dims.length; ++i) r.int64(e.dims[i]);
r.ldelim()
}
if (e.dataType != null && e.hasOwnProperty("dataType") && r.uint32(16).int32(e.dataType), e.segment != null && e.hasOwnProperty("segment") && o.onnx.TensorProto.Segment.encode(e.segment, r.uint32(26).fork()).ldelim(), e.floatData != null && e.floatData.length) {
for (r.uint32(34).fork(), i = 0; i < e.floatData.length; ++i) r.float(e.floatData[i]);
r.ldelim()
}
if (e.int32Data != null && e.int32Data.length) {
for (r.uint32(42).fork(), i = 0; i < e.int32Data.length; ++i) r.int32(e.int32Data[i]);
r.ldelim()
}
if (e.stringData != null && e.stringData.length)
for (i = 0; i < e.stringData.length; ++i) r.uint32(50).bytes(e.stringData[i]);
if (e.int64Data != null && e.int64Data.length) {
for (r.uint32(58).fork(), i = 0; i < e.int64Data.length; ++i) r.int64(e.int64Data[i]);
r.ldelim()
}
if (e.name != null && e.hasOwnProperty("name") && r.uint32(66).string(e.name), e.rawData != null && e.hasOwnProperty("rawData") && r.uint32(74).bytes(e.rawData), e.doubleData != null && e.doubleData.length) {
for (r.uint32(82).fork(), i = 0; i < e.doubleData.length; ++i) r.double(e.doubleData[i]);
r.ldelim()
}
if (e.uint64Data != null && e.uint64Data.length) {
for (r.uint32(90).fork(), i = 0; i < e.uint64Data.length; ++i) r.uint64(e.uint64Data[i]);
r.ldelim()
}
if (e.docString != null && e.hasOwnProperty("docString") && r.uint32(98).string(e.docString), e.externalData != null && e.externalData.length)
for (i = 0; i < e.externalData.length; ++i) o.onnx.StringStringEntryProto.encode(e.externalData[i], r.uint32(106).fork()).ldelim();
return e.dataLocation != null && e.hasOwnProperty("dataLocation") && r.uint32(112).int32(e.dataLocation), r
}, t.encodeDelimited = function(e, r) {
return this.encode(e, r).ldelim()
}, t.decode = function(e, r) {
e instanceof h || (e = h.create(e));
for (var i = r === void 0 ? e.len : e.pos + r, d = new o.onnx.TensorProto; e.pos < i;) {
var g = e.uint32();
switch (g >>> 3) {
case 1:
if (d.dims && d.dims.length || (d.dims = []), (7 & g) == 2)
for (var m = e.uint32() + e.pos; e.pos < m;) d.dims.push(e.int64());
else d.dims.push(e.int64());
break;
case 2:
d.dataType = e.int32();
break;
case 3:
d.segment = o.onnx.TensorProto.Segment.decode(e, e.uint32());
break;
case 4:
if (d.floatData && d.floatData.length || (d.floatData = []), (7 & g) == 2)
for (m = e.uint32() + e.pos; e.pos < m;) d.floatData.push(e.float());
else d.floatData.push(e.float());
break;
case 5:
if (d.int32Data && d.int32Data.length || (d.int32Data = []), (7 & g) == 2)
for (m = e.uint32() + e.pos; e.pos < m;) d.int32Data.push(e.int32());
else d.int32Data.push(e.int32());
break;
case 6:
d.stringData && d.stringData.length || (d.stringData = []), d.stringData.push(e.bytes());
break;
case 7:
if (d.int64Data && d.int64Data.length || (d.int64Data = []), (7 & g) == 2)
for (m = e.uint32() + e.pos; e.pos < m;) d.int64Data.push(e.int64());
else d.int64Data.push(e.int64());
break;
case 8:
d.name = e.string();
break;
case 12:
d.docString = e.string();
break;
case 9:
d.rawData = e.bytes();
break;
case 13:
d.externalData && d.externalData.length || (d.externalData = []), d.externalData.push(o.onnx.StringStringEntryProto.decode(e, e.uint32()));
break;
case 14:
d.dataLocation = e.int32();
break;
case 10:
if (d.doubleData && d.doubleData.length || (d.doubleData = []), (7 & g) == 2)
for (m = e.uint32() + e.pos; e.pos < m;) d.doubleData.push(e.double());
else d.doubleData.push(e.double());
break;
case 11:
if (d.uint64Data && d.uint64Data.length || (d.uint64Data = []), (7 & g) == 2)
for (m = e.uint32() + e.pos; e.pos < m;) d.uint64Data.push(e.uint64());
else d.uint64Data.push(e.uint64());
break;
default:
e.skipType(7 & g)
}
}
return d
}, t.decodeDelimited = function(e) {
return e instanceof h || (e = new h(e)), this.decode(e, e.uint32())
}, t.verify = function(e) {
if (typeof e != "object" || e === null) return "object expected";
if (e.dims != null && e.hasOwnProperty("dims")) {
if (!Array.isArray(e.dims)) return "dims: array expected";
for (var r = 0; r < e.dims.length; ++r)
if (!(l.isInteger(e.dims[r]) || e.dims[r] && l.isInteger(e.dims[r].low) && l.isInteger(e.dims[r].high))) return "dims: integer|Long[] expected"
}
if (e.dataType != null && e.hasOwnProperty("dataType") && !l.isInteger(e.dataType)) return "dataType: integer expected";
if (e.segment != null && e.hasOwnProperty("segment") && (i = o.onnx.TensorProto.Segment.verify(e.segment))) return "segment." + i;
if (e.floatData != null && e.hasOwnProperty("floatData")) {
if (!Array.isArray(e.floatData)) return "floatData: array expected";
for (r = 0; r < e.floatData.length; ++r)
if (typeof e.floatData[r] != "number") return "floatData: number[] expected"
}
if (e.int32Data != null && e.hasOwnProperty("int32Data")) {
if (!Array.isArray(e.int32Data)) return "int32Data: array expected";
for (r = 0; r < e.int32Data.length; ++r)
if (!l.isInteger(e.int32Data[r])) return "int32Data: integer[] expected"
}
if (e.stringData != null && e.hasOwnProperty("stringData")) {
if (!Array.isArray(e.stringData)) return "stringData: array expected";
for (r = 0; r < e.stringData.length; ++r)
if (!(e.stringData[r] && typeof e.stringData[r].length == "number" || l.isString(e.stringData[r]))) return "stringData: buffer[] expected"
}
if (e.int64Data != null && e.hasOwnProperty("int64Data")) {
if (!Array.isArray(e.int64Data)) return "int64Data: array expected";
for (r = 0; r < e.int64Data.length; ++r)
if (!(l.isInteger(e.int64Data[r]) || e.int64Data[r] && l.isInteger(e.int64Data[r].low) && l.isInteger(e.int64Data[r].high))) return "int64Data: integer|Long[] expected"
}
if (e.name != null && e.hasOwnProperty("name") && !l.isString(e.name)) return "name: string expected";
if (e.docString != null && e.hasOwnProperty("docString") && !l.isString(e.docString)) return "docString: string expected";
if (e.rawData != null && e.hasOwnProperty("rawData") && !(e.rawData && typeof e.rawData.length == "number" || l.isString(e.rawData))) return "rawData: buffer expected";
if (e.externalData != null && e.hasOwnProperty("externalData")) {
if (!Array.isArray(e.externalData)) return "externalData: array expected";
for (r = 0; r < e.externalData.length; ++r) {
var i;
if (i = o.onnx.StringStringEntryProto.verify(e.externalData[r])) return "externalData." + i
}
}
if (e.dataLocation != null && e.hasOwnProperty("dataLocation")) switch (e.dataLocation) {
default:
return "dataLocation: enum value expected";
case 0:
case 1:
}
if (e.doubleData != null && e.hasOwnProperty("doubleData")) {
if (!Array.isArray(e.doubleData)) return "doubleData: array expected";
for (r = 0; r < e.doubleData.length; ++r)
if (typeof e.doubleData[r] != "number") return "doubleData: number[] expected"
}
if (e.uint64Data != null && e.hasOwnProperty("uint64Data")) {
if (!Array.isArray(e.uint64Data)) return "uint64Data: array expected";
for (r = 0; r < e.uint64Data.length; ++r)
if (!(l.isInteger(e.uint64Data[r]) || e.uint64Data[r] && l.isInteger(e.uint64Data[r].low) && l.isInteger(e.uint64Data[r].high))) return "uint64Data: integer|Long[] expected"
}
return null
}, t.fromObject = function(e) {
if (e instanceof o.onnx.TensorProto) return e;
var r = new o.onnx.TensorProto;
if (e.dims) {
if (!Array.isArray(e.dims)) throw TypeError(".onnx.TensorProto.dims: array expected");
r.dims = [];
for (var i = 0; i < e.dims.length; ++i) l.Long ? (r.dims[i] = l.Long.fromValue(e.dims[i])).unsigned = !1 : typeof e.dims[i] == "string" ? r.dims[i] = parseInt(e.dims[i], 10) : typeof e.dims[i] == "number" ? r.dims[i] = e.dims[i] : typeof e.dims[i] == "object" && (r.dims[i] = new l.LongBits(e.dims[i].low >>> 0, e.dims[i].high >>> 0).toNumber())
}
if (e.dataType != null && (r.dataType = 0 | e.dataType), e.segment != null) {
if (typeof e.segment != "object") throw TypeError(".onnx.TensorProto.segment: object expected");
r.segment = o.onnx.TensorProto.Segment.fromObject(e.segment)
}
if (e.floatData) {
if (!Array.isArray(e.floatData)) throw TypeError(".onnx.TensorProto.floatData: array expected");
for (r.floatData = [], i = 0; i < e.floatData.length; ++i) r.floatData[i] = Number(e.floatData[i])
}
if (e.int32Data) {
if (!Array.isArray(e.int32Data)) throw TypeError(".onnx.TensorProto.int32Data: array expected");
for (r.int32Data = [], i = 0; i < e.int32Data.length; ++i) r.int32Data[i] = 0 | e.int32Data[i]
}
if (e.stringData) {
if (!Array.isArray(e.stringData)) throw TypeError(".onnx.TensorProto.stringData: array expected");
for (r.stringData = [], i = 0; i < e.stringData.length; ++i) typeof e.stringData[i] == "string" ? l.base64.decode(e.stringData[i], r.stringData[i] = l.newBuffer(l.base64.length(e.stringData[i])), 0) : e.stringData[i].length && (r.stringData[i] = e.stringData[i])
}
if (e.int64Data) {
if (!Array.isArray(e.int64Data)) throw TypeError(".onnx.TensorProto.int64Data: array expected");
for (r.int64Data = [], i = 0; i < e.int64Data.length; ++i) l.Long ? (r.int64Data[i] = l.Long.fromValue(e.int64Data[i])).unsigned = !1 : typeof e.int64Data[i] == "string" ? r.int64Data[i] = parseInt(e.int64Data[i], 10) : typeof e.int64Data[i] == "number" ? r.int64Data[i] = e.int64Data[i] : typeof e.int64Data[i] == "object" && (r.int64Data[i] = new l.LongBits(e.int64Data[i].low >>> 0, e.int64Data[i].high >>> 0).toNumber())
}
if (e.name != null && (r.name = String(e.name)), e.docString != null && (r.docString = String(e.docString)), e.rawData != null && (typeof e.rawData == "string" ? l.base64.decode(e.rawData, r.rawData = l.newBuffer(l.base64.length(e.rawData)), 0) : e.rawData.length && (r.rawData = e.rawData)), e.externalData) {
if (!Array.isArray(e.externalData)) throw TypeError(".onnx.TensorProto.externalData: array expected");
for (r.externalData = [], i = 0; i < e.externalData.length; ++i) {
if (typeof e.externalData[i] != "object") throw TypeError(".onnx.TensorProto.externalData: object expected");
r.externalData[i] = o.onnx.StringStringEntryProto.fromObject(e.externalData[i])
}
}
switch (e.dataLocation) {
case "DEFAULT":
case 0:
r.dataLocation = 0;
break;
case "EXTERNAL":
case 1:
r.dataLocation = 1
}
if (e.doubleData) {
if (!Array.isArray(e.doubleData)) throw TypeError(".onnx.TensorProto.doubleData: array expected");
for (r.doubleData = [], i = 0; i < e.doubleData.length; ++i) r.doubleData[i] = Number(e.doubleData[i])
}
if (e.uint64Data) {
if (!Array.isArray(e.uint64Data)) throw TypeError(".onnx.TensorProto.uint64Data: array expected");
for (r.uint64Data = [], i = 0; i < e.uint64Data.length; ++i) l.Long ? (r.uint64Data[i] = l.Long.fromValue(e.uint64Data[i])).unsigned = !0 : typeof e.uint64Data[i] == "string" ? r.uint64Data[i] = parseInt(e.uint64Data[i], 10) : typeof e.uint64Data[i] == "number" ? r.uint64Data[i] = e.uint64Data[i] : typeof e.uint64Data[i] == "object" && (r.uint64Data[i] = new l.LongBits(e.uint64Data[i].low >>> 0, e.uint64Data[i].high >>> 0).toNumber(!0))
}
return r
}, t.toObject = function(e, r) {
r || (r = {});
var i = {};
if ((r.arrays || r.defaults) && (i.dims = [], i.floatData = [], i.int32Data = [], i.stringData = [], i.int64Data = [], i.doubleData = [], i.uint64Data = [], i.externalData = []), r.defaults && (i.dataType = 0, i.segment = null, i.name = "", r.bytes === String ? i.rawData = "" : (i.rawData = [], r.bytes !== Array && (i.rawData = l.newBuffer(i.rawData))), i.docString = "", i.dataLocation = r.enums === String ? "DEFAULT" : 0), e.dims && e.dims.length) {
i.dims = [];
for (var d = 0; d < e.dims.length; ++d) typeof e.dims[d] == "number" ? i.dims[d] = r.longs === String ? String(e.dims[d]) : e.dims[d] : i.dims[d] = r.longs === String ? l.Long.prototype.toString.call(e.dims[d]) : r.longs === Number ? new l.LongBits(e.dims[d].low >>> 0, e.dims[d].high >>> 0).toNumber() : e.dims[d]
}
if (e.dataType != null && e.hasOwnProperty("dataType") && (i.dataType = e.dataType), e.segment != null && e.hasOwnProperty("segment") && (i.segment = o.onnx.TensorProto.Segment.toObject(e.segment, r)), e.floatData && e.floatData.length)
for (i.floatData = [], d = 0; d < e.floatData.length; ++d) i.floatData[d] = r.json && !isFinite(e.floatData[d]) ? String(e.floatData[d]) : e.floatData[d];
if (e.int32Data && e.int32Data.length)
for (i.int32Data = [], d = 0; d < e.int32Data.length; ++d) i.int32Data[d] = e.int32Data[d];
if (e.stringData && e.stringData.length)
for (i.stringData = [], d = 0; d < e.stringData.length; ++d) i.stringData[d] = r.bytes === String ? l.base64.encode(e.stringData[d], 0, e.stringData[d].length) : r.bytes === Array ? Array.prototype.slice.call(e.stringData[d]) : e.stringData[d];
if (e.int64Data && e.int64Data.length)
for (i.int64Data = [], d = 0; d < e.int64Data.length; ++d) typeof e.int64Data[d] == "number" ? i.int64Data[d] = r.longs === String ? String(e.int64Data[d]) : e.int64Data[d] : i.int64Data[d] = r.longs === String ? l.Long.prototype.toString.call(e.int64Data[d]) : r.longs === Number ? new l.LongBits(e.int64Data[d].low >>> 0, e.int64Data[d].high >>> 0).toNumber() : e.int64Data[d];
if (e.name != null && e.hasOwnProperty("name") && (i.name = e.name), e.rawData != null && e.hasOwnProperty("rawData") && (i.rawData = r.bytes === String ? l.base64.encode(e.rawData, 0, e.rawData.length) : r.bytes === Array ? Array.prototype.slice.call(e.rawData) : e.rawData), e.doubleData && e.doubleData.length)
for (i.doubleData = [], d = 0; d < e.doubleData.length; ++d) i.doubleData[d] = r.json && !isFinite(e.doubleData[d]) ? String(e.doubleData[d]) : e.doubleData[d];
if (e.uint64Data && e.uint64Data.length)
for (i.uint64Data = [], d = 0; d < e.uint64Data.length; ++d) typeof e.uint64Data[d] == "number" ? i.uint64Data[d] = r.longs === String ? String(e.uint64Data[d]) : e.uint64Data[d] : i.uint64Data[d] = r.longs === String ? l.Long.prototype.toString.call(e.uint64Data[d]) : r.longs === Number ? new l.LongBits(e.uint64Data[d].low >>> 0, e.uint64Data[d].high >>> 0).toNumber(!0) : e.uint64Data[d];
if (e.docString != null && e.hasOwnProperty("docString") && (i.docString = e.docString), e.externalData && e.externalData.length)
for (i.externalData = [], d = 0; d < e.externalData.length; ++d) i.externalData[d] = o.onnx.StringStringEntryProto.toObject(e.externalData[d], r);
return e.dataLocation != null && e.hasOwnProperty("dataLocation") && (i.dataLocation = r.enums === String ? o.onnx.TensorProto.DataLocation[e.dataLocation] : e.dataLocation), i
}, t.prototype.toJSON = function() {
return this.constructor.toObject(this, s.util.toJSONOptions)
}, t.DataType = function() {
var e = {},
r = Object.create(e);
return r[e[0] = "UNDEFINED"] = 0, r[e[1] = "FLOAT"] = 1, r[e[2] = "UINT8"] = 2, r[e[3] = "INT8"] = 3, r[e[4] = "UINT16"] = 4, r[e[5] = "INT16"] = 5, r[e[6] = "INT32"] = 6, r[e[7] = "INT64"] = 7, r[e[8] = "STRING"] = 8, r[e[9] = "BOOL"] = 9, r[e[10] = "FLOAT16"] = 10, r[e[11] = "DOUBLE"] = 11, r[e[12] = "UINT32"] = 12, r[e[13] = "UINT64"] = 13, r[e[14] = "COMPLEX64"] = 14, r[e[15] = "COMPLEX128"] = 15, r[e[16] = "BFLOAT16"] = 16, r
}(), t.Segment = function() {
function e(r) {
if (r)
for (var i = Object.keys(r), d = 0; d < i.length; ++d) r[i[d]] != null && (this[i[d]] = r[i[d]])
}
return e.prototype.begin = l.Long ? l.Long.fromBits(0, 0, !1) : 0, e.prototype.end = l.Long ? l.Long.fromBits(0, 0, !1) : 0, e.create = function(r) {
return new e(r)
}, e.encode = function(r, i) {
return i || (i = p.create()), r.begin != null && r.hasOwnProperty("begin") && i.uint32(8).int64(r.begin), r.end != null && r.hasOwnProperty("end") && i.uint32(16).int64(r.end), i
}, e.encodeDelimited = function(r, i) {
return this.encode(r, i).ldelim()
}, e.decode = function(r, i) {
r instanceof h || (r = h.create(r));
for (var d = i === void 0 ? r.len : r.pos + i, g = new o.onnx.TensorProto.Segment; r.pos < d;) {
var m = r.uint32();
switch (m >>> 3) {
case 1:
g.begin = r.int64();
break;
case 2:
g.end = r.int64();
break;
default:
r.skipType(7 & m)
}
}
return g
}, e.decodeDelimited = function(r) {
return r instanceof h || (r = new h(r)), this.decode(r, r.uint32())
}, e.verify = function(r) {
return typeof r != "object" || r === null ? "object expected" : r.begin != null && r.hasOwnProperty("begin") && !(l.isInteger(r.begin) || r.begin && l.isInteger(r.begin.low) && l.isInteger(r.begin.high)) ? "begin: integer|Long expected" : r.end != null && r.hasOwnProperty("end") && !(l.isInteger(r.end) || r.end && l.isInteger(r.end.low) && l.isInteger(r.end.high)) ? "end: integer|Long expected" : null
}, e.fromObject = function(r) {
if (r instanceof o.onnx.TensorProto.Segment) return r;
var i = new o.onnx.TensorProto.Segment;
return r.begin != null && (l.Long ? (i.begin = l.Long.fromValue(r.begin)).unsigned = !1 : typeof r.begin == "string" ? i.begin = parseInt(r.begin, 10) : typeof r.begin == "number" ? i.begin = r.begin : typeof r.begin == "object" && (i.begin = new l.LongBits(r.begin.low >>> 0, r.begin.high >>> 0).toNumber())), r.end != null && (l.Long ? (i.end = l.Long.fromValue(r.end)).unsigned = !1 : typeof r.end == "string" ? i.end = parseInt(r.end, 10) : typeof r.end == "number" ? i.end = r.end : typeof r.end == "object" && (i.end = new l.LongBits(r.end.low >>> 0, r.end.high >>> 0).toNumber())), i
}, e.toObject = function(r, i) {
i || (i = {});
var d = {};
if (i.defaults) {
if (l.Long) {
var g = new l.Long(0, 0, !1);
d.begin = i.longs === String ? g.toString() : i.longs === Number ? g.toNumber() : g
} else d.begin = i.longs === String ? "0" : 0;
l.Long ? (g = new l.Long(0, 0, !1), d.end = i.longs === String ? g.toString() : i.longs === Number ? g.toNumber() : g) : d.end = i.longs === String ? "0" : 0
}
return r.begin != null && r.hasOwnProperty("begin") && (typeof r.begin == "number" ? d.begin = i.longs === String ? String(r.begin) : r.begin : d.begin = i.longs === String ? l.Long.prototype.toString.call(r.begin) : i.longs === Number ? new l.LongBits(r.begin.low >>> 0, r.begin.high >>> 0).toNumber() : r.begin), r.end != null && r.hasOwnProperty("end") && (typeof r.end == "number" ? d.end = i.longs === String ? String(r.end) : r.end : d.end = i.longs === String ? l.Long.prototype.toString.call(r.end) : i.longs === Number ? new l.LongBits(r.end.low >>> 0, r.end.high >>> 0).toNumber() : r.end), d
}, e.prototype.toJSON = function() {
return this.constructor.toObject(this, s.util.toJSONOptions)
}, e
}(), t.DataLocation = function() {
var e = {},
r = Object.create(e);
return r[e[0] = "DEFAULT"] = 0, r[e[1] = "EXTERNAL"] = 1, r
}(), t
}(), f.TensorShapeProto = function() {
function t(e) {
if (this.dim = [], e)
for (var r = Object.keys(e), i = 0; i < r.length; ++i) e[r[i]] != null && (this[r[i]] = e[r[i]])
}
return t.prototype.dim = l.emptyArray, t.create = function(e) {
return new t(e)
}, t.encode = function(e, r) {
if (r || (r = p.create()), e.dim != null && e.dim.length)
for (var i = 0; i < e.dim.length; ++i) o.onnx.TensorShapeProto.Dimension.encode(e.dim[i], r.uint32(10).fork()).ldelim();
return r
}, t.encodeDelimited = function(e, r) {
return this.encode(e, r).ldelim()
}, t.decode = function(e, r) {
e instanceof h || (e = h.create(e));
for (var i = r === void 0 ? e.len : e.pos + r, d = new o.onnx.TensorShapeProto; e.pos < i;) {
var g = e.uint32();
g >>> 3 == 1 ? (d.dim && d.dim.length || (d.dim = []), d.dim.push(o.onnx.TensorShapeProto.Dimension.decode(e, e.uint32()))) : e.skipType(7 & g)
}
return d
}, t.decodeDelimited = function(e) {
return e instanceof h || (e = new h(e)), this.decode(e, e.uint32())
}, t.verify = function(e) {
if (typeof e != "object" || e === null) return "object expected";
if (e.dim != null && e.hasOwnProperty("dim")) {
if (!Array.isArray(e.dim)) return "dim: array expected";
for (var r = 0; r < e.dim.length; ++r) {
var i = o.onnx.TensorShapeProto.Dimension.verify(e.dim[r]);
if (i) return "dim." + i
}
}
return null
}, t.fromObject = function(e) {
if (e instanceof o.onnx.TensorShapeProto) return e;
var r = new o.onnx.TensorShapeProto;
if (e.dim) {
if (!Array.isArray(e.dim)) throw TypeError(".onnx.TensorShapeProto.dim: array expected");
r.dim = [];
for (var i = 0; i < e.dim.length; ++i) {
if (typeof e.dim[i] != "object") throw TypeError(".onnx.TensorShapeProto.dim: object expected");
r.dim[i] = o.onnx.TensorShapeProto.Dimension.fromObject(e.dim[i])
}
}
return r
}, t.toObject = function(e, r) {
r || (r = {});
var i = {};
if ((r.arrays || r.defaults) && (i.dim = []), e.dim && e.dim.length) {
i.dim = [];
for (var d = 0; d < e.dim.length; ++d) i.dim[d] = o.onnx.TensorShapeProto.Dimension.toObject(e.dim[d], r)
}
return i
}, t.prototype.toJSON = function() {
return this.constructor.toObject(this, s.util.toJSONOptions)
}, t.Dimension = function() {
function e(i) {
if (i)
for (var d = Object.keys(i), g = 0; g < d.length; ++g) i[d[g]] != null && (this[d[g]] = i[d[g]])
}
var r;
return e.prototype.dimValue = l.Long ? l.Long.fromBits(0, 0, !1) : 0, e.prototype.dimParam = "", e.prototype.denotation = "", Object.defineProperty(e.prototype, "value", {
get: l.oneOfGetter(r = ["dimValue", "dimParam"]),
set: l.oneOfSetter(r)
}), e.create = function(i) {
return new e(i)
}, e.encode = function(i, d) {
return d || (d = p.create()), i.dimValue != null && i.hasOwnProperty("dimValue") && d.uint32(8).int64(i.dimValue), i.dimParam != null && i.hasOwnProperty("dimParam") && d.uint32(18).string(i.dimParam), i.denotation != null && i.hasOwnProperty("denotation") && d.uint32(26).string(i.denotation), d
}, e.encodeDelimited = function(i, d) {
return this.encode(i, d).ldelim()
}, e.decode = function(i, d) {
i instanceof h || (i = h.create(i));
for (var g = d === void 0 ? i.len : i.pos + d, m = new o.onnx.TensorShapeProto.Dimension; i.pos < g;) {
var _ = i.uint32();
switch (_ >>> 3) {
case 1:
m.dimValue = i.int64();
break;
case 2:
m.dimParam = i.string();
break;
case 3:
m.denotation = i.string();
break;
default:
i.skipType(7 & _)
}
}
return m
}, e.decodeDelimited = function(i) {
return i instanceof h || (i = new h(i)), this.decode(i, i.uint32())
}, e.verify = function(i) {
if (typeof i != "object" || i === null) return "object expected";
var d = {};
if (i.dimValue != null && i.hasOwnProperty("dimValue") && (d.value = 1, !(l.isInteger(i.dimValue) || i.dimValue && l.isInteger(i.dimValue.low) && l.isInteger(i.dimValue.high)))) return "dimValue: integer|Long expected";
if (i.dimParam != null && i.hasOwnProperty("dimParam")) {
if (d.value === 1) return "value: multiple values";
if (d.value = 1, !l.isString(i.dimParam)) return "dimParam: string expected"
}
return i.denotation != null && i.hasOwnProperty("denotation") && !l.isString(i.denotation) ? "denotation: string expected" : null
}, e.fromObject = function(i) {
if (i instanceof o.onnx.TensorShapeProto.Dimension) return i;
var d = new o.onnx.TensorShapeProto.Dimension;
return i.dimValue != null && (l.Long ? (d.dimValue = l.Long.fromValue(i.dimValue)).unsigned = !1 : typeof i.dimValue == "string" ? d.dimValue = parseInt(i.dimValue, 10) : typeof i.dimValue == "number" ? d.dimValue = i.dimValue : typeof i.dimValue == "object" && (d.dimValue = new l.LongBits(i.dimValue.low >>> 0, i.dimValue.high >>> 0).toNumber())), i.dimParam != null && (d.dimParam = String(i.dimParam)), i.denotation != null && (d.denotation = String(i.denotation)), d
}, e.toObject = function(i, d) {
d || (d = {});
var g = {};
return d.defaults && (g.denotation = ""), i.dimValue != null && i.hasOwnProperty("dimValue") && (typeof i.dimValue == "number" ? g.dimValue = d.longs === String ? String(i.dimValue) : i.dimValue : g.dimValue = d.longs === String ? l.Long.prototype.toString.call(i.dimValue) : d.longs === Number ? new l.LongBits(i.dimValue.low >>> 0, i.dimValue.high >>> 0).toNumber() : i.dimValue, d.oneofs && (g.value = "dimValue")), i.dimParam != null && i.hasOwnProperty("dimParam") && (g.dimParam = i.dimParam, d.oneofs && (g.value = "dimParam")), i.denotation != null && i.hasOwnProperty("denotation") && (g.denotation = i.denotation), g
}, e.prototype.toJSON = function() {
return this.constructor.toObject(this, s.util.toJSONOptions)
}, e
}(), t
}(), f.TypeProto = function() {
function t(r) {
if (r)
for (var i = Object.keys(r), d = 0; d < i.length; ++d) r[i[d]] != null && (this[i[d]] = r[i[d]])
}
var e;
return t.prototype.tensorType = null, t.prototype.denotation = "", Object.defineProperty(t.prototype, "value", {
get: l.oneOfGetter(e = ["tensorType"]),
set: l.oneOfSetter(e)
}), t.create = function(r) {
return new t(r)
}, t.encode = function(r, i) {
return i || (i = p.create()), r.tensorType != null && r.hasOwnProperty("tensorType") && o.onnx.TypeProto.Tensor.encode(r.tensorType, i.uint32(10).fork()).ldelim(), r.denotation != null && r.hasOwnProperty("denotation") && i.uint32(50).string(r.denotation), i
}, t.encodeDelimited = function(r, i) {
return this.encode(r, i).ldelim()
}, t.decode = function(r, i) {
r instanceof h || (r = h.create(r));
for (var d = i === void 0 ? r.len : r.pos + i, g = new o.onnx.TypeProto; r.pos < d;) {
var m = r.uint32();
switch (m >>> 3) {
case 1:
g.tensorType = o.onnx.TypeProto.Tensor.decode(r, r.uint32());
break;
case 6:
g.denotation = r.string();
break;
default:
r.skipType(7 & m)
}
}
return g
}, t.decodeDelimited = function(r) {
return r instanceof h || (r = new h(r)), this.decode(r, r.uint32())
}, t.verify = function(r) {
if (typeof r != "object" || r === null) return "object expected";
if (r.tensorType != null && r.hasOwnProperty("tensorType")) {
var i = o.onnx.TypeProto.Tensor.verify(r.tensorType);
if (i) return "tensorType." + i
}
return r.denotation != null && r.hasOwnProperty("denotation") && !l.isString(r.denotation) ? "denotation: string expected" : null
}, t.fromObject = function(r) {
if (r instanceof o.onnx.TypeProto) return r;
var i = new o.onnx.TypeProto;
if (r.tensorType != null) {
if (typeof r.tensorType != "object") throw TypeError(".onnx.TypeProto.tensorType: object expected");
i.tensorType = o.onnx.TypeProto.Tensor.fromObject(r.tensorType)
}
return r.denotation != null && (i.denotation = String(r.denotation)), i
}, t.toObject = function(r, i) {
i || (i = {});
var d = {};
return i.defaults && (d.denotation = ""), r.tensorType != null && r.hasOwnProperty("tensorType") && (d.tensorType = o.onnx.TypeProto.Tensor.toObject(r.tensorType, i), i.oneofs && (d.value = "tensorType")), r.denotation != null && r.hasOwnProperty("denotation") && (d.denotation = r.denotation), d
}, t.prototype.toJSON = function() {
return this.constructor.toObject(this, s.util.toJSONOptions)
}, t.Tensor = function() {
function r(i) {
if (i)
for (var d = Object.keys(i), g = 0; g < d.length; ++g) i[d[g]] != null && (this[d[g]] = i[d[g]])
}
return r.prototype.elemType = 0, r.prototype.shape = null, r.create = function(i) {
return new r(i)
}, r.encode = function(i, d) {
return d || (d = p.create()), i.elemType != null && i.hasOwnProperty("elemType") && d.uint32(8).int32(i.elemType), i.shape != null && i.hasOwnProperty("shape") && o.onnx.TensorShapeProto.encode(i.shape, d.uint32(18).fork()).ldelim(), d
}, r.encodeDelimited = function(i, d) {
return this.encode(i, d).ldelim()
}, r.decode = function(i, d) {
i instanceof h || (i = h.create(i));
for (var g = d === void 0 ? i.len : i.pos + d, m = new o.onnx.TypeProto.Tensor; i.pos < g;) {
var _ = i.uint32();
switch (_ >>> 3) {
case 1:
m.elemType = i.int32();
break;
case 2:
m.shape = o.onnx.TensorShapeProto.decode(i, i.uint32());
break;
default:
i.skipType(7 & _)
}
}
return m
}, r.decodeDelimited = function(i) {
return i instanceof h || (i = new h(i)), this.decode(i, i.uint32())
}, r.verify = function(i) {
if (typeof i != "object" || i === null) return "object expected";
if (i.elemType != null && i.hasOwnProperty("elemType") && !l.isInteger(i.elemType)) return "elemType: integer expected";
if (i.shape != null && i.hasOwnProperty("shape")) {
var d = o.onnx.TensorShapeProto.verify(i.shape);
if (d) return "shape." + d
}
return null
}, r.fromObject = function(i) {
if (i instanceof o.onnx.TypeProto.Tensor) return i;
var d = new o.onnx.TypeProto.Tensor;
if (i.elemType != null && (d.elemType = 0 | i.elemType), i.shape != null) {
if (typeof i.shape != "object") throw TypeError(".onnx.TypeProto.Tensor.shape: object expected");
d.shape = o.onnx.TensorShapeProto.fromObject(i.shape)
}
return d
}, r.toObject = function(i, d) {
d || (d = {});
var g = {};
return d.defaults && (g.elemType = 0, g.shape = null), i.elemType != null && i.hasOwnProperty("elemType") && (g.elemType = i.elemType), i.shape != null && i.hasOwnProperty("shape") && (g.shape = o.onnx.TensorShapeProto.toObject(i.shape, d)), g
}, r.prototype.toJSON = function() {
return this.constructor.toObject(this, s.util.toJSONOptions)
}, r
}(), t
}(), f.OperatorSetIdProto = function() {
function t(e) {
if (e)
for (var r = Object.keys(e), i = 0; i < r.length; ++i) e[r[i]] != null && (this[r[i]] = e[r[i]])
}
return t.prototype.domain = "", t.prototype.version = l.Long ? l.Long.fromBits(0, 0, !1) : 0, t.create = function(e) {
return new t(e)
}, t.encode = function(e, r) {
return r || (r = p.create()), e.domain != null && e.hasOwnProperty("domain") && r.uint32(10).string(e.domain), e.version != null && e.hasOwnProperty("version") && r.uint32(16).int64(e.version), r
}, t.encodeDelimited = function(e, r) {
return this.encode(e, r).ldelim()
}, t.decode = function(e, r) {
e instanceof h || (e = h.create(e));
for (var i = r === void 0 ? e.len : e.pos + r, d = new o.onnx.OperatorSetIdProto; e.pos < i;) {
var g = e.uint32();
switch (g >>> 3) {
case 1:
d.domain = e.string();
break;
case 2:
d.version = e.int64();
break;
default:
e.skipType(7 & g)
}
}
return d
}, t.decodeDelimited = function(e) {
return e instanceof h || (e = new h(e)), this.decode(e, e.uint32())
}, t.verify = function(e) {
return typeof e != "object" || e === null ? "object expected" : e.domain != null && e.hasOwnProperty("domain") && !l.isString(e.domain) ? "domain: string expected" : e.version != null && e.hasOwnProperty("version") && !(l.isInteger(e.version) || e.version && l.isInteger(e.version.low) && l.isInteger(e.version.high)) ? "version: integer|Long expected" : null
}, t.fromObject = function(e) {
if (e instanceof o.onnx.OperatorSetIdProto) return e;
var r = new o.onnx.OperatorSetIdProto;
return e.domain != null && (r.domain = String(e.domain)), e.version != null && (l.Long ? (r.version = l.Long.fromValue(e.version)).unsigned = !1 : typeof e.version == "string" ? r.version = parseInt(e.version, 10) : typeof e.version == "number" ? r.version = e.version : typeof e.version == "object" && (r.version = new l.LongBits(e.version.low >>> 0, e.version.high >>> 0).toNumber())), r
}, t.toObject = function(e, r) {
r || (r = {});
var i = {};
if (r.defaults)
if (i.domain = "", l.Long) {
var d = new l.Long(0, 0, !1);
i.version = r.longs === String ? d.toString() : r.longs === Number ? d.toNumber() : d
} else i.version = r.longs === String ? "0" : 0;
return e.domain != null && e.hasOwnProperty("domain") && (i.domain = e.domain), e.version != null && e.hasOwnProperty("version") && (typeof e.version == "number" ? i.version = r.longs === String ? String(e.version) : e.version : i.version = r.longs === String ? l.Long.prototype.toString.call(e.version) : r.longs === Number ? new l.LongBits(e.version.low >>> 0, e.version.high >>> 0).toNumber() : e.version), i
}, t.prototype.toJSON = function() {
return this.constructor.toObject(this, s.util.toJSONOptions)
}, t
}(), f), b.exports = o
},
2100: (b, n, a) => {
b.exports = a(9482)
},
9482: (b, n, a) => {
var u = n;
function c() {
u.util._configure(), u.Writer._configure(u.BufferWriter), u.Reader._configure(u.BufferReader)
}
u.build = "minimal", u.Writer = a(1173), u.BufferWriter = a(3155), u.Reader = a(1408), u.BufferReader = a(593), u.util = a(9693), u.rpc = a(5994), u.roots = a(5054), u.configure = c, c()
},
1408: (b, n, a) => {
b.exports = p;
var u, c = a(9693),
f = c.LongBits,
s = c.utf8;
function h(d, g) {
return RangeError("index out of range: " + d.pos + " + " + (g || 1) + " > " + d.len)
}
function p(d) {
this.buf = d, this.pos = 0, this.len = d.length
}
var l, o = typeof Uint8Array < "u" ? function(d) {
if (d instanceof Uint8Array || Array.isArray(d)) return new p(d);
throw Error("illegal buffer")
} : function(d) {
if (Array.isArray(d)) return new p(d);
throw Error("illegal buffer")
},
t = function() {
return c.Buffer ? function(d) {
return (p.create = function(g) {
return c.Buffer.isBuffer(g) ? new u(g) : o(g)
})(d)
} : o
};
function e() {
var d = new f(0, 0),
g = 0;
if (!(this.len - this.pos > 4)) {
for (; g < 3; ++g) {
if (this.pos >= this.len) throw h(this);
if (d.lo = (d.lo | (127 & this.buf[this.pos]) << 7 * g) >>> 0, this.buf[this.pos++] < 128) return d
}
return d.lo = (d.lo | (127 & this.buf[this.pos++]) << 7 * g) >>> 0, d
}
for (; g < 4; ++g)
if (d.lo = (d.lo | (127 & this.buf[this.pos]) << 7 * g) >>> 0, this.buf[this.pos++] < 128) return d;
if (d.lo = (d.lo | (127 & this.buf[this.pos]) << 28) >>> 0, d.hi = (d.hi | (127 & this.buf[this.pos]) >> 4) >>> 0, this.buf[this.pos++] < 128) return d;
if (g = 0, this.len - this.pos > 4) {
for (; g < 5; ++g)
if (d.hi = (d.hi | (127 & this.buf[this.pos]) << 7 * g + 3) >>> 0, this.buf[this.pos++] < 128) return d
} else
for (; g < 5; ++g) {
if (this.pos >= this.len) throw h(this);
if (d.hi = (d.hi | (127 & this.buf[this.pos]) << 7 * g + 3) >>> 0, this.buf[this.pos++] < 128) return d
}
throw Error("invalid varint encoding")
}
function r(d, g) {
return (d[g - 4] | d[g - 3] << 8 | d[g - 2] << 16 | d[g - 1] << 24) >>> 0
}
function i() {
if (this.pos + 8 > this.len) throw h(this, 8);
return new f(r(this.buf, this.pos += 4), r(this.buf, this.pos += 4))
}
p.create = t(), p.prototype._slice = c.Array.prototype.subarray || c.Array.prototype.slice, p.prototype.uint32 = (l = 4294967295, function() {
if (l = (127 & this.buf[this.pos]) >>> 0, this.buf[this.pos++] < 128 || (l = (l | (127 & this.buf[this.pos]) << 7) >>> 0, this.buf[this.pos++] < 128) || (l = (l | (127 & this.buf[this.pos]) << 14) >>> 0, this.buf[this.pos++] < 128) || (l = (l | (127 & this.buf[this.pos]) << 21) >>> 0, this.buf[this.pos++] < 128) || (l = (l | (15 & this.buf[this.pos]) << 28) >>> 0, this.buf[this.pos++] < 128)) return l;
if ((this.pos += 5) > this.len) throw this.pos = this.len, h(this, 10);
return l
}), p.prototype.int32 = function() {
return 0 | this.uint32()
}, p.prototype.sint32 = function() {
var d = this.uint32();
return d >>> 1 ^ -(1 & d) | 0
}, p.prototype.bool = function() {
return this.uint32() !== 0
}, p.prototype.fixed32 = function() {
if (this.pos + 4 > this.len) throw h(this, 4);
return r(this.buf, this.pos += 4)
}, p.prototype.sfixed32 = function() {
if (this.pos + 4 > this.len) throw h(this, 4);
return 0 | r(this.buf, this.pos += 4)
}, p.prototype.float = function() {
if (this.pos + 4 > this.len) throw h(this, 4);
var d = c.float.readFloatLE(this.buf, this.pos);
return this.pos += 4, d
}, p.prototype.double = function() {
if (this.pos + 8 > this.len) throw h(this, 4);
var d = c.float.readDoubleLE(this.buf, this.pos);
return this.pos += 8, d
}, p.prototype.bytes = function() {
var d = this.uint32(),
g = this.pos,
m = this.pos + d;
if (m > this.len) throw h(this, d);
return this.pos += d, Array.isArray(this.buf) ? this.buf.slice(g, m) : g === m ? new this.buf.constructor(0) : this._slice.call(this.buf, g, m)
}, p.prototype.string = function() {
var d = this.bytes();
return s.read(d, 0, d.length)
}, p.prototype.skip = function(d) {
if (typeof d == "number") {
if (this.pos + d > this.len) throw h(this, d);
this.pos += d
} else
do
if (this.pos >= this.len) throw h(this); while (128 & this.buf[this.pos++]);
return this
}, p.prototype.skipType = function(d) {
switch (d) {
case 0:
this.skip();
break;
case 1:
this.skip(8);
break;
case 2:
this.skip(this.uint32());
break;
case 3:
for (;
(d = 7 & this.uint32()) != 4;) this.skipType(d);
break;
case 5:
this.skip(4);
break;
default:
throw Error("invalid wire type " + d + " at offset " + this.pos)
}
return this
}, p._configure = function(d) {
u = d, p.create = t(), u._configure();
var g = c.Long ? "toLong" : "toNumber";
c.merge(p.prototype, {
int64: function() {
return e.call(this)[g](!1)
},
uint64: function() {
return e.call(this)[g](!0)
},
sint64: function() {
return e.call(this).zzDecode()[g](!1)
},
fixed64: function() {
return i.call(this)[g](!0)
},
sfixed64: function() {
return i.call(this)[g](!1)
}
})
}
},
593: (b, n, a) => {
b.exports = f;
var u = a(1408);
(f.prototype = Object.create(u.prototype)).constructor = f;
var c = a(9693);
function f(s) {
u.call(this, s)
}
f._configure = function() {
c.Buffer && (f.prototype._slice = c.Buffer.prototype.slice)
}, f.prototype.string = function() {
var s = this.uint32();
return this.buf.utf8Slice ? this.buf.utf8Slice(this.pos, this.pos = Math.min(this.pos + s, this.len)) : this.buf.toString("utf-8", this.pos, this.pos = Math.min(this.pos + s, this.len))
}, f._configure()
},
5054: b => {
b.exports = {}
},
5994: (b, n, a) => {
n.Service = a(7948)
},
7948: (b, n, a) => {
b.exports = c;
var u = a(9693);
function c(f, s, h) {
if (typeof f != "function") throw TypeError("rpcImpl must be a function");
u.EventEmitter.call(this), this.rpcImpl = f, this.requestDelimited = !!s, this.responseDelimited = !!h
}(c.prototype = Object.create(u.EventEmitter.prototype)).constructor = c, c.prototype.rpcCall = function f(s, h, p, l, o) {
if (!l) throw TypeError("request must be specified");
var t = this;
if (!o) return u.asPromise(f, t, s, h, p, l);
if (t.rpcImpl) try {
return t.rpcImpl(s, h[t.requestDelimited ? "encodeDelimited" : "encode"](l).finish(), function(e, r) {
if (e) return t.emit("error", e, s), o(e);
if (r !== null) {
if (!(r instanceof p)) try {
r = p[t.responseDelimited ? "decodeDelimited" : "decode"](r)
} catch (i) {
return t.emit("error", i, s), o(i)
}
return t.emit("data", r, s), o(null, r)
}
t.end(!0)
})
} catch (e) {
return t.emit("error", e, s), void setTimeout(function() {
o(e)
}, 0)
} else setTimeout(function() {
o(Error("already ended"))
}, 0)
}, c.prototype.end = function(f) {
return this.rpcImpl && (f || this.rpcImpl(null, null, null), this.rpcImpl = null, this.emit("end").off()), this
}
},
1945: (b, n, a) => {
b.exports = c;
var u = a(9693);
function c(p, l) {
this.lo = p >>> 0, this.hi = l >>> 0
}
var f = c.zero = new c(0, 0);
f.toNumber = function() {
return 0
}, f.zzEncode = f.zzDecode = function() {
return this
}, f.length = function() {
return 1
};
var s = c.zeroHash = "\0\0\0\0\0\0\0\0";
c.fromNumber = function(p) {
if (p === 0) return f;
var l = p < 0;
l && (p = -p);
var o = p >>> 0,
t = (p - o) / 4294967296 >>> 0;
return l && (t = ~t >>> 0, o = ~o >>> 0, ++o > 4294967295 && (o = 0, ++t > 4294967295 && (t = 0))), new c(o, t)
}, c.from = function(p) {
if (typeof p == "number") return c.fromNumber(p);
if (u.isString(p)) {
if (!u.Long) return c.fromNumber(parseInt(p, 10));
p = u.Long.fromString(p)
}
return p.low || p.high ? new c(p.low >>> 0, p.high >>> 0) : f
}, c.prototype.toNumber = function(p) {
if (!p && this.hi >>> 31) {
var l = 1 + ~this.lo >>> 0,
o = ~this.hi >>> 0;
return l || (o = o + 1 >>> 0), -(l + 4294967296 * o)
}
return this.lo + 4294967296 * this.hi
}, c.prototype.toLong = function(p) {
return u.Long ? new u.Long(0 | this.lo, 0 | this.hi, !!p) : {
low: 0 | this.lo,
high: 0 | this.hi,
unsigned: !!p
}
};
var h = String.prototype.charCodeAt;
c.fromHash = function(p) {
return p === s ? f : new c((h.call(p, 0) | h.call(p, 1) << 8 | h.call(p, 2) << 16 | h.call(p, 3) << 24) >>> 0, (h.call(p, 4) | h.call(p, 5) << 8 | h.call(p, 6) << 16 | h.call(p, 7) << 24) >>> 0)
}, c.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)
}, c.prototype.zzEncode = function() {
var p = this.hi >> 31;
return this.hi = ((this.hi << 1 | this.lo >>> 31) ^ p) >>> 0, this.lo = (this.lo << 1 ^ p) >>> 0, this
}, c.prototype.zzDecode = function() {
var p = -(1 & this.lo);
return this.lo = ((this.lo >>> 1 | this.hi << 31) ^ p) >>> 0, this.hi = (this.hi >>> 1 ^ p) >>> 0, this
}, c.prototype.length = function() {
var p = this.lo,
l = (this.lo >>> 28 | this.hi << 4) >>> 0,
o = this.hi >>> 24;
return o === 0 ? l === 0 ? p < 16384 ? p < 128 ? 1 : 2 : p < 2097152 ? 3 : 4 : l < 16384 ? l < 128 ? 5 : 6 : l < 2097152 ? 7 : 8 : o < 128 ? 9 : 10
}
},
9693: function(b, n, a) {
var u = n;
function c(s, h, p) {
for (var l = Object.keys(h), o = 0; o < l.length; ++o) s[l[o]] !== void 0 && p || (s[l[o]] = h[l[o]]);
return s
}
function f(s) {
function h(p, l) {
if (!(this instanceof h)) return new h(p, l);
Object.defineProperty(this, "message", {
get: function() {
return p
}
}), Error.captureStackTrace ? Error.captureStackTrace(this, h) : Object.defineProperty(this, "stack", {
value: new Error().stack || ""
}), l && c(this, l)
}
return (h.prototype = Object.create(Error.prototype)).constructor = h, Object.defineProperty(h.prototype, "name", {
get: function() {
return s
}
}), h.prototype.toString = function() {
return this.name + ": " + this.message
}, h
}
u.asPromise = a(4537), u.base64 = a(7419), u.EventEmitter = a(9211), u.float = a(945), u.inquire = a(7199), u.utf8 = a(4997), u.pool = a(6662), u.LongBits = a(1945), u.isNode = !!(a.g !== void 0 && a.g && a.g.process && a.g.process.versions && a.g.process.versions.node), u.global = u.isNode && a.g || typeof window < "u" && window || typeof self < "u" && self || this, u.emptyArray = Object.freeze ? Object.freeze([]) : [], u.emptyObject = Object.freeze ? Object.freeze({}) : {}, u.isInteger = Number.isInteger || function(s) {
return typeof s == "number" && isFinite(s) && Math.floor(s) === s
}, u.isString = function(s) {
return typeof s == "string" || s instanceof String
}, u.isObject = function(s) {
return s && typeof s == "object"
}, u.isset = u.isSet = function(s, h) {
var p = s[h];
return !(p == null || !s.hasOwnProperty(h)) && (typeof p != "object" || (Array.isArray(p) ? p.length : Object.keys(p).length) > 0)
}, u.Buffer = function() {
try {
var s = u.inquire("buffer").Buffer;
return s.prototype.utf8Write ? s : null
} catch {
return null
}
}(), u._Buffer_from = null, u._Buffer_allocUnsafe = null, u.newBuffer = function(s) {
return typeof s == "number" ? u.Buffer ? u._Buffer_allocUnsafe(s) : new u.Array(s) : u.Buffer ? u._Buffer_from(s) : typeof Uint8Array > "u" ? s : new Uint8Array(s)
}, u.Array = typeof Uint8Array < "u" ? Uint8Array : Array, u.Long = u.global.dcodeIO && u.global.dcodeIO.Long || u.global.Long || u.inquire("long"), u.key2Re = /^true|false|0|1$/, u.key32Re = /^-?(?:0|[1-9][0-9]*)$/, u.key64Re = /^(?:[\\x00-\\xff]{8}|-?(?:0|[1-9][0-9]*))$/, u.longToHash = function(s) {
return s ? u.LongBits.from(s).toHash() : u.LongBits.zeroHash
}, u.longFromHash = function(s, h) {
var p = u.LongBits.fromHash(s);
return u.Long ? u.Long.fromBits(p.lo, p.hi, h) : p.toNumber(!!h)
}, u.merge = c, u.lcFirst = function(s) {
return s.charAt(0).toLowerCase() + s.substring(1)
}, u.newError = f, u.ProtocolError = f("ProtocolError"), u.oneOfGetter = function(s) {
for (var h = {}, p = 0; p < s.length; ++p) h[s[p]] = 1;
return function() {
for (var l = Object.keys(this), o = l.length - 1; o > -1; --o)
if (h[l[o]] === 1 && this[l[o]] !== void 0 && this[l[o]] !== null) return l[o]
}
}, u.oneOfSetter = function(s) {
return function(h) {
for (var p = 0; p < s.length; ++p) s[p] !== h && delete this[s[p]]
}
}, u.toJSONOptions = {
longs: String,
enums: String,
bytes: String,
json: !0
}, u._configure = function() {
var s = u.Buffer;
s ? (u._Buffer_from = s.from !== Uint8Array.from && s.from || function(h, p) {
return new s(h, p)
}, u._Buffer_allocUnsafe = s.allocUnsafe || function(h) {
return new s(h)
}) : u._Buffer_from = u._Buffer_allocUnsafe = null
}
},
1173: (b, n, a) => {
b.exports = t;
var u, c = a(9693),
f = c.LongBits,
s = c.base64,
h = c.utf8;
function p(_, y, T) {
this.fn = _, this.len = y, this.next = void 0, this.val = T
}
function l() {}
function o(_) {
this.head = _.head, this.tail = _.tail, this.len = _.len, this.next = _.states
}
function t() {
this.len = 0, this.head = new p(l, 0, 0), this.tail = this.head, this.states = null
}
var e = function() {
return c.Buffer ? function() {
return (t.create = function() {
return new u
})()
} : function() {
return new t
}
};
function r(_, y, T) {
y[T] = 255 & _
}
function i(_, y) {
this.len = _, this.next = void 0, this.val = y
}
function d(_, y, T) {
for (; _.hi;) y[T++] = 127 & _.lo | 128, _.lo = (_.lo >>> 7 | _.hi << 25) >>> 0, _.hi >>>= 7;
for (; _.lo > 127;) y[T++] = 127 & _.lo | 128, _.lo = _.lo >>> 7;
y[T++] = _.lo
}
function g(_, y, T) {
y[T] = 255 & _, y[T + 1] = _ >>> 8 & 255, y[T + 2] = _ >>> 16 & 255, y[T + 3] = _ >>> 24
}
t.create = e(), t.alloc = function(_) {
return new c.Array(_)
}, c.Array !== Array && (t.alloc = c.pool(t.alloc, c.Array.prototype.subarray)), t.prototype._push = function(_, y, T) {
return this.tail = this.tail.next = new p(_, y, T), this.len += y, this
}, i.prototype = Object.create(p.prototype), i.prototype.fn = function(_, y, T) {
for (; _ > 127;) y[T++] = 127 & _ | 128, _ >>>= 7;
y[T] = _
}, t.prototype.uint32 = function(_) {
return this.len += (this.tail = this.tail.next = new i((_ >>>= 0) < 128 ? 1 : _ < 16384 ? 2 : _ < 2097152 ? 3 : _ < 268435456 ? 4 : 5, _)).len, this
}, t.prototype.int32 = function(_) {
return _ < 0 ? this._push(d, 10, f.fromNumber(_)) : this.uint32(_)
}, t.prototype.sint32 = function(_) {
return this.uint32((_ << 1 ^ _ >> 31) >>> 0)
}, t.prototype.uint64 = function(_) {
var y = f.from(_);
return this._push(d, y.length(), y)
}, t.prototype.int64 = t.prototype.uint64, t.prototype.sint64 = function(_) {
var y = f.from(_).zzEncode();
return this._push(d, y.length(), y)
}, t.prototype.bool = function(_) {
return this._push(r, 1, _ ? 1 : 0)
}, t.prototype.fixed32 = function(_) {
return this._push(g, 4, _ >>> 0)
}, t.prototype.sfixed32 = t.prototype.fixed32, t.prototype.fixed64 = function(_) {
var y = f.from(_);
return this._push(g, 4, y.lo)._push(g, 4, y.hi)
}, t.prototype.sfixed64 = t.prototype.fixed64, t.prototype.float = function(_) {
return this._push(c.float.writeFloatLE, 4, _)
}, t.prototype.double = function(_) {
return this._push(c.float.writeDoubleLE, 8, _)
};
var m = c.Array.prototype.set ? function(_, y, T) {
y.set(_, T)
} : function(_, y, T) {
for (var w = 0; w < _.length; ++w) y[T + w] = _[w]
};
t.prototype.bytes = function(_) {
var y = _.length >>> 0;
if (!y) return this._push(r, 1, 0);
if (c.isString(_)) {
var T = t.alloc(y = s.length(_));
s.decode(_, T, 0), _ = T
}
return this.uint32(y)._push(m, y, _)
}, t.prototype.string = function(_) {
var y = h.length(_);
return y ? this.uint32(y)._push(h.write, y, _) : this._push(r, 1, 0)
}, t.prototype.fork = function() {
return this.states = new o(this), this.head = this.tail = new p(l, 0, 0), this.len = 0, this
}, t.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 p(l, 0, 0), this.len = 0), this
}, t.prototype.ldelim = function() {
var _ = this.head,
y = this.tail,
T = this.len;
return this.reset().uint32(T), T && (this.tail.next = _.next, this.tail = y, this.len += T), this
}, t.prototype.finish = function() {
for (var _ = this.head.next, y = this.constructor.alloc(this.len), T = 0; _;) _.fn(_.val, y, T), T += _.len, _ = _.next;
return y
}, t._configure = function(_) {
u = _, t.create = e(), u._configure()
}
},
3155: (b, n, a) => {
b.exports = f;
var u = a(1173);
(f.prototype = Object.create(u.prototype)).constructor = f;
var c = a(9693);
function f() {
u.call(this)
}
function s(h, p, l) {
h.length < 40 ? c.utf8.write(h, p, l) : p.utf8Write ? p.utf8Write(h, l) : p.write(h, l)
}
f._configure = function() {
f.alloc = c._Buffer_allocUnsafe, f.writeBytesBuffer = c.Buffer && c.Buffer.prototype instanceof Uint8Array && c.Buffer.prototype.set.name === "set" ? function(h, p, l) {
p.set(h, l)
} : function(h, p, l) {
if (h.copy) h.copy(p, l, 0, h.length);
else
for (var o = 0; o < h.length;) p[l++] = h[o++]
}
}, f.prototype.bytes = function(h) {
c.isString(h) && (h = c._Buffer_from(h, "base64"));
var p = h.length >>> 0;
return this.uint32(p), p && this._push(f.writeBytesBuffer, p, h), this
}, f.prototype.string = function(h) {
var p = c.Buffer.byteLength(h);
return this.uint32(p), p && this._push(s, p, h), this
}, f._configure()
},
7714: (b, n, a) => {
n.R = void 0;
const u = a(6919),
c = a(7448);
n.R = new class {
async init() {}
async createSessionHandler(f, s) {
const h = new u.Session(s);
return await h.loadModel(f), new c.OnnxjsSessionHandler(h)
}
}
},
4200: (b, n, a) => {
n.c8 = n.rX = void 0;
const u = a(1670),
c = a(5381),
f = a(2157),
s = a(2306);
n.rX = () => {
if ((typeof u.env.wasm.initTimeout != "number" || u.env.wasm.initTimeout < 0) && (u.env.wasm.initTimeout = 0), typeof u.env.wasm.simd != "boolean" && (u.env.wasm.simd = !0), typeof u.env.wasm.proxy != "boolean" && (u.env.wasm.proxy = !1), typeof u.env.wasm.numThreads != "number" || !Number.isInteger(u.env.wasm.numThreads) || u.env.wasm.numThreads <= 0) {
const h = typeof navigator > "u" ? (0, c.cpus)().length : navigator.hardwareConcurrency;
u.env.wasm.numThreads = Math.min(4, Math.ceil((h || 1) / 2))
}
}, n.c8 = new class {
async init() {
(0, n.rX)(), await (0, f.initWasm)()
}
async createSessionHandler(h, p) {
const l = new s.OnnxruntimeWebAssemblySessionHandler;
return await l.loadModel(h, p), Promise.resolve(l)
}
}
},
6018: function(b, n, a) {
var u = this && this.__createBinding || (Object.create ? function(s, h, p, l) {
l === void 0 && (l = p);
var o = Object.getOwnPropertyDescriptor(h, p);
o && !("get" in o ? !h.__esModule : o.writable || o.configurable) || (o = {
enumerable: !0,
get: function() {
return h[p]
}
}), Object.defineProperty(s, l, o)
} : function(s, h, p, l) {
l === void 0 && (l = p), s[l] = h[p]
}),
c = this && this.__exportStar || function(s, h) {
for (var p in s) p === "default" || Object.prototype.hasOwnProperty.call(h, p) || u(h, s, p)
};
Object.defineProperty(n, "__esModule", {
value: !0
}), c(a(1670), n);
const f = a(1670);
{
const s = a(7714).R;
(0, f.registerBackend)("webgl", s, -10)
} {
const s = a(4200).c8;
(0, f.registerBackend)("cpu", s, 10), (0, f.registerBackend)("wasm", s, 10), (0, f.registerBackend)("xnnpack", s, 9)
}
},
246: (b, n) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.createAttributeWithCacheKey = void 0;
class a {
constructor(c) {
Object.assign(this, c)
}
get cacheKey() {
return this._cacheKey || (this._cacheKey = Object.getOwnPropertyNames(this).sort().map(c => `${this[c]}`).join(";")), this._cacheKey
}
}
n.createAttributeWithCacheKey = u => new a(u)
},
7778: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.Attribute = void 0;
const u = a(1446),
c = a(9395),
f = a(9162),
s = a(2517);
var h = c.onnxruntime.experimental.fbs;
class p {
constructor(o) {
if (this._attributes = new Map, o != null) {
for (const t of o) t instanceof u.onnx.AttributeProto ? this._attributes.set(t.name, [p.getValue(t), p.getType(t)]) : t instanceof h.Attribute && this._attributes.set(t.name(), [p.getValue(t), p.getType(t)]);
if (this._attributes.size < o.length) throw new Error("duplicated attribute names")
}
}
set(o, t, e) {
this._attributes.set(o, [e, t])
}
delete(o) {
this._attributes.delete(o)
}
getFloat(o, t) {
return this.get(o, "float", t)
}
getInt(o, t) {
return this.get(o, "int", t)
}
getString(o, t) {
return this.get(o, "string", t)
}
getTensor(o, t) {
return this.get(o, "tensor", t)
}
getFloats(o, t) {
return this.get(o, "floats", t)
}
getInts(o, t) {
return this.get(o, "ints", t)
}
getStrings(o, t) {
return this.get(o, "strings", t)
}
getTensors(o, t) {
return this.get(o, "tensors", t)
}
get(o, t, e) {
const r = this._attributes.get(o);
if (r === void 0) {
if (e !== void 0) return e;
throw new Error(`required attribute not found: ${o}`)
}
if (r[1] !== t) throw new Error(`type mismatch: expected ${t} but got ${r[1]}`);
return r[0]
}
static getType(o) {
const t = o instanceof u.onnx.AttributeProto ? o.type : o.type();
switch (t) {
case u.onnx.AttributeProto.AttributeType.FLOAT:
return "float";
case u.onnx.AttributeProto.AttributeType.INT:
return "int";
case u.onnx.AttributeProto.AttributeType.STRING:
return "string";
case u.onnx.AttributeProto.AttributeType.TENSOR:
return "tensor";
case u.onnx.AttributeProto.AttributeType.FLOATS:
return "floats";
case u.onnx.AttributeProto.AttributeType.INTS:
return "ints";
case u.onnx.AttributeProto.AttributeType.STRINGS:
return "strings";
case u.onnx.AttributeProto.AttributeType.TENSORS:
return "tensors";
default:
throw new Error(`attribute type is not supported yet: ${u.onnx.AttributeProto.AttributeType[t]}`)
}
}
static getValue(o) {
const t = o instanceof u.onnx.AttributeProto ? o.type : o.type();
if (t === u.onnx.AttributeProto.AttributeType.GRAPH || t === u.onnx.AttributeProto.AttributeType.GRAPHS) throw new Error("graph attribute is not supported yet");
const e = this.getValueNoCheck(o);
if (t === u.onnx.AttributeProto.AttributeType.INT && s.LongUtil.isLong(e)) return s.LongUtil.longToNumber(e);
if (t === u.onnx.AttributeProto.AttributeType.INTS) {
const r = e,
i = new Array(r.length);
for (let d = 0; d < r.length; d++) {
const g = r[d];
i[d] = s.LongUtil.longToNumber(g)
}
return i
}
if (t === u.onnx.AttributeProto.AttributeType.TENSOR) return o instanceof u.onnx.AttributeProto ? f.Tensor.fromProto(e) : f.Tensor.fromOrtTensor(e);
if (t === u.onnx.AttributeProto.AttributeType.TENSORS) {
if (o instanceof u.onnx.AttributeProto) return e.map(r => f.Tensor.fromProto(r));
if (o instanceof h.Attribute) return e.map(r => f.Tensor.fromOrtTensor(r))
}
if (t === u.onnx.AttributeProto.AttributeType.STRING && o instanceof u.onnx.AttributeProto) {
const r = e;
return (0, s.decodeUtf8String)(r)
}
return t === u.onnx.AttributeProto.AttributeType.STRINGS && o instanceof u.onnx.AttributeProto ? e.map(s.decodeUtf8String) : e
}
static getValueNoCheck(o) {
return o instanceof u.onnx.AttributeProto ? this.getValueNoCheckFromOnnxFormat(o) : this.getValueNoCheckFromOrtFormat(o)
}
static getValueNoCheckFromOnnxFormat(o) {
switch (o.type) {
case u.onnx.AttributeProto.AttributeType.FLOAT:
return o.f;
case u.onnx.AttributeProto.AttributeType.INT:
return o.i;
case u.onnx.AttributeProto.AttributeType.STRING:
return o.s;
case u.onnx.AttributeProto.AttributeType.TENSOR:
return o.t;
case u.onnx.AttributeProto.AttributeType.GRAPH:
return o.g;
case u.onnx.AttributeProto.AttributeType.FLOATS:
return o.floats;
case u.onnx.AttributeProto.AttributeType.INTS:
return o.ints;
case u.onnx.AttributeProto.AttributeType.STRINGS:
return o.strings;
case u.onnx.AttributeProto.AttributeType.TENSORS:
return o.tensors;
case u.onnx.AttributeProto.AttributeType.GRAPHS:
return o.graphs;
default:
throw new Error(`unsupported attribute type: ${u.onnx.AttributeProto.AttributeType[o.type]}`)
}
}
static getValueNoCheckFromOrtFormat(o) {
switch (o.type()) {
case h.AttributeType.FLOAT:
return o.f();
case h.AttributeType.INT:
return o.i();
case h.AttributeType.STRING:
return o.s();
case h.AttributeType.TENSOR:
return o.t();
case h.AttributeType.GRAPH:
return o.g();
case h.AttributeType.FLOATS:
return o.floatsArray();
case h.AttributeType.INTS: {
const t = [];
for (let e = 0; e < o.intsLength(); e++) t.push(o.ints(e));
return t
}
case h.AttributeType.STRINGS: {
const t = [];
for (let e = 0; e < o.stringsLength(); e++) t.push(o.strings(e));
return t
}
case h.AttributeType.TENSORS: {
const t = [];
for (let e = 0; e < o.tensorsLength(); e++) t.push(o.tensors(e));
return t
}
default:
throw new Error(`unsupported attribute type: ${h.AttributeType[o.type()]}`)
}
}
}
n.Attribute = p
},
7091: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.resolveBackend = n.backend = void 0;
const u = a(5038),
c = new Map;
async function f(s) {
const h = n.backend;
if (h[s] !== void 0 && function(p) {
const l = p;
return "initialize" in l && typeof l.initialize == "function" && "createSessionHandler" in l && typeof l.createSessionHandler == "function" && "dispose" in l && typeof l.dispose == "function"
}(h[s])) {
const p = h[s];
let l = p.initialize();
if (typeof l == "object" && "then" in l && (l = await l), l) return c.set(s, p), p
}
}
n.backend = {
webgl: new u.WebGLBackend
}, n.resolveBackend = async function s(h) {
if (!h) return s(["webgl"]);
{
const p = typeof h == "string" ? [h] : h;
for (const l of p) {
const o = c.get(l);
if (o) return o;
const t = await f(l);
if (t) return t
}
}
throw new Error("no available backend to use")
}
},
5038: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.WebGLBackend = void 0;
const u = a(1670),
c = a(6231),
f = a(6416),
s = a(7305);
n.WebGLBackend = class {
get contextId() {
return u.env.webgl.contextId
}
set contextId(h) {
u.env.webgl.contextId = h
}
get matmulMaxBatchSize() {
return u.env.webgl.matmulMaxBatchSize
}
set matmulMaxBatchSize(h) {
u.env.webgl.matmulMaxBatchSize = h
}
get textureCacheMode() {
return u.env.webgl.textureCacheMode
}
set textureCacheMode(h) {
u.env.webgl.textureCacheMode = h
}
get pack() {
return u.env.webgl.pack
}
set pack(h) {
u.env.webgl.pack = h
}
get async() {
return u.env.webgl.async
}
set async(h) {
u.env.webgl.async = h
}
initialize() {
try {
return this.glContext = (0, s.createWebGLContext)(this.contextId), typeof this.matmulMaxBatchSize != "number" && (this.matmulMaxBatchSize = 16), typeof this.textureCacheMode != "string" && (this.textureCacheMode = "full"), typeof this.pack != "boolean" && (this.pack = !1), typeof this.async != "boolean" && (this.async = !1), c.Logger.setWithEnv(u.env), c.Logger.verbose("WebGLBackend", `Created WebGLContext: ${typeof this.glContext} with matmulMaxBatchSize: ${this.matmulMaxBatchSize}; textureCacheMode: ${this.textureCacheMode}; pack: ${this.pack}; async: ${this.async}.`), !0
} catch (h) {
return c.Logger.warning("WebGLBackend", `Unable to initialize WebGLBackend. ${h}`), !1
}
}
createSessionHandler(h) {
return new f.WebGLSessionHandler(this, h)
}
dispose() {
this.glContext.dispose()
}
}
},
5107: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.CoordsGlslLib = void 0;
const u = a(2517),
c = a(8520),
f = a(5060),
s = a(7859),
h = a(9390);
class p extends c.GlslLib {
constructor(o) {
super(o)
}
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 c.GlslLibRoutine(`
vec2 offsetToCoords(int offset, int width, int height) {
int t = offset / width;
int s = offset - t*width;
vec2 coords = (vec2(s,t) + vec2(0.5,0.5)) / vec2(width, height);
return coords;
}
`)
}
}
coordsToOffset() {
return {
coordsToOffset: new c.GlslLibRoutine(`
int coordsToOffset(vec2 coords, int width, int height) {
float s = coords.s * float(width);
float t = coords.t * float(height);
int offset = int(t) * width + int(s);
return offset;
}
`)
}
}
getOutputSamplingSnippet() {
const o = this.context.outputTextureLayout;
return o.isPacked ? this.getPackedOutputSamplingSnippet(o) : this.getUnpackedOutputSamplingSnippet(o)
}
getPackedOutputSamplingSnippet(o) {
const t = o.unpackedShape,
e = [o.width, o.height],
r = {},
i = "getOutputCoords";
switch (t.length) {
case 0:
r[i] = this.getOutputScalarCoords();
break;
case 1:
r[i] = this.getOutputPacked1DCoords(t, e);
break;
case 2:
r[i] = this.getOutputPacked2DCoords(t, e);
break;
case 3:
r[i] = this.getOutputPacked3DCoords(t, e);
break;
default:
r[i] = this.getOutputPackedNDCoords(t, e)
}
const d = `
void setOutput(vec4 val) {
${(0,f.getGlsl)(this.context.glContext.version).output} = val;
}
`;
return r.floatTextureSetRGBA = new c.GlslLibRoutine(d), r
}
getUnpackedOutputSamplingSnippet(o) {
const t = o.unpackedShape,
e = [o.width, o.height],
r = {},
i = "getOutputCoords";
switch (t.length) {
case 0:
r[i] = this.getOutputScalarCoords();
break;
case 1:
r[i] = this.getOutputUnpacked1DCoords(t, e);
break;
case 2:
r[i] = this.getOutputUnpacked2DCoords(t, e);
break;
case 3:
r[i] = this.getOutputUnpacked3DCoords(t, e);
break;
case 4:
r[i] = this.getOutputUnpacked4DCoords(t, e);
break;
case 5:
r[i] = this.getOutputUnpacked5DCoords(t, e);
break;
case 6:
r[i] = this.getOutputUnpacked6DCoords(t, e);
break;
default:
throw new Error(`Unsupported output dimensionality: ${t.length}`)
}
const d = `
void setOutput(float val) {
${(0,f.getGlsl)(this.context.glContext.version).output} = vec4(val, 0, 0, 0);
}
`;
return r.floatTextureSetR = new c.GlslLibRoutine(d), r
}
getOutputScalarCoords() {
return new c.GlslLibRoutine(`
int getOutputCoords() {
return 0;
}
`)
}
getOutputPacked1DCoords(o, t) {
const e = t;
let r = "";
return e[0] === 1 ? (r = `
int getOutputCoords() {
return 2 * int(TexCoords.y * ${e[1]}.0);
}
`, new c.GlslLibRoutine(r)) : e[1] === 1 ? (r = `
int getOutputCoords() {
return 2 * int(TexCoords.x * ${e[0]}.0);
}
`, new c.GlslLibRoutine(r)) : (r = `
int getOutputCoords() {
ivec2 resTexRC = ivec2(TexCoords.xy *
vec2(${e[0]}, ${e[1]}));
return 2 * (resTexRC.y * ${e[0]} + resTexRC.x);
}
`, new c.GlslLibRoutine(r))
}
getOutputPacked2DCoords(o, t) {
let e = "";
if (u.ArrayUtil.arraysEqual(o, t)) return e = `
ivec2 getOutputCoords() {
return 2 * ivec2(TexCoords.xy * vec2(${t[0]}, ${t[1]}));
}
`, new c.GlslLibRoutine(e);
const r = t,
i = Math.ceil(o[1] / 2);
return e = `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(TexCoords.xy *
vec2(${r[0]}, ${r[1]}));
int index = resTexRC.y * ${r[0]} + resTexRC.x;
// reverse r and c order for packed texture
int r = imod(index, ${i}) * 2;
int c = 2 * (index / ${i});
return ivec2(r, c);
}
`, new c.GlslLibRoutine(e)
}
getOutputPacked3DCoords(o, t) {
const e = [t[0], t[1]],
r = Math.ceil(o[2] / 2),
i = r * Math.ceil(o[1] / 2),
d = `
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(TexCoords.xy *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.y * ${e[0]} + resTexRC.x;
int b = index / ${i};
index -= b * ${i};
// reverse r and c order for packed texture
int r = imod(index, ${r}) * 2;
int c = 2 * (index / ${r});
return ivec3(b, r, c);
}
`;
return new c.GlslLibRoutine(d)
}
getOutputPackedNDCoords(o, t) {
const e = [t[0], t[1]],
r = Math.ceil(o[o.length - 1] / 2),
i = r * Math.ceil(o[o.length - 2] / 2);
let d = i,
g = "",
m = "b, r, c";
for (let y = 2; y < o.length - 1; y++) d *= o[o.length - y - 1], g = `
int b${y} = index / ${d};
index -= b${y} * ${d};
` + g, m = `b${y}, ` + m;
const _ = `
ivec${o.length} getOutputCoords() {
ivec2 resTexRC = ivec2(TexCoords.xy *
vec2(${e[0]}, ${e[1]}));
int index = resTexRC.y * ${e[0]} + resTexRC.x;
${g}
int b = index / ${i};
index -= b * ${i};
// reverse r and c order for packed texture
int r = imod(index, ${r}) * 2;
int c = 2 * (index / ${r});
return ivec${o.length}(${m});
}
`;
return new c.GlslLibRoutine(_)
}
getOutputUnpacked1DCoords(o, t) {
const e = `
int getOutputCoords() {
ivec2 resTexRC = ivec2(TexCoords.xy *
vec2(${t[0]}, ${t[1]}));
return resTexRC.y * ${t[0]} + resTexRC.x;
}
`;
return new c.GlslLibRoutine(e)
}
getOutputUnpacked2DCoords(o, t) {
const e = `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(TexCoords.xy *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.y * ${t[0]} + resTexRC.x;
int r = index / ${o[1]};
int c = index - r * ${o[1]};
return ivec2(r, c);
}
`;
return new c.GlslLibRoutine(e)
}
getOutputUnpacked3DCoords(o, t) {
let e = "";
const r = o.length;
let i = null;
r < 2 && (i = []), i = new Array(r - 1), i[r - 2] = o[r - 1];
for (let m = r - 3; m >= 0; --m) i[m] = i[m + 1] * o[m + 1];
const d = ["r", "c", "d"],
g = i.map((m, _) => `int ${d[_]} = index / ${m}; ${_===i.length-1?`int ${d[_+1]} = index - ${d[_]} * ${m}`:`index -= ${d[_]} * ${m}`};`).join("");
return e = `
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(TexCoords.xy *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.y * ${t[0]} + resTexRC.x;
${g}
return ivec3(r, c, d);
}
`, new c.GlslLibRoutine(e)
}
getOutputUnpacked4DCoords(o, t) {
let e = "";
const r = o.length;
let i = null;
r < 2 && (i = []), i = new Array(r - 1), i[r - 2] = o[r - 1];
for (let m = r - 3; m >= 0; --m) i[m] = i[m + 1] * o[m + 1];
const d = ["r", "c", "d", "d2"],
g = i.map((m, _) => `int ${d[_]} = index / ${m}; ${_===i.length-1?`int ${d[_+1]} = index - ${d[_]} * ${m}`:`index -= ${d[_]} * ${m}`};`).join("");
return e = `
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(TexCoords.xy *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.y * ${t[0]} + resTexRC.x;
${g}
return ivec4(r, c, d, d2);
}
`, new c.GlslLibRoutine(e)
}
getOutputUnpacked5DCoords(o, t) {
let e = "";
const r = o.length;
let i = null;
r < 2 && (i = []), i = new Array(r - 1), i[r - 2] = o[r - 1];
for (let m = r - 3; m >= 0; --m) i[m] = i[m + 1] * o[m + 1];
const d = ["r", "c", "d", "d2", "d3"],
g = i.map((m, _) => `int ${d[_]} = index / ${m}; ${_===i.length-1?`int ${d[_+1]} = index - ${d[_]} * ${m}`:`index -= ${d[_]} * ${m}`};`).join("");
return e = `
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(TexCoords.xy *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.y * ${t[0]} + resTexRC.x;
${g}
return ivec5(r, c, d, d2, d3);
}
`, new c.GlslLibRoutine(e)
}
getOutputUnpacked6DCoords(o, t) {
let e = "";
const r = o.length;
let i = null;
r < 2 && (i = []), i = new Array(r - 1), i[r - 2] = o[r - 1];
for (let m = r - 3; m >= 0; --m) i[m] = i[m + 1] * o[m + 1];
const d = ["r", "c", "d", "d2", "d3", "d4"],
g = i.map((m, _) => `int ${d[_]} = index / ${m}; ${_===i.length-1?`int ${d[_+1]} = index - ${d[_]} * ${m}`:`index -= ${d[_]} * ${m}`};`).join("");
return e = `
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(TexCoords.xy *
vec2(${t[0]}, ${t[1]}));
int index = resTexRC.y * ${t[0]} + resTexRC.x;
${g}
return ivec6(r, c, d, d2, d3, d4);
}
`, new c.GlslLibRoutine(e)
}
getCommonUtilFuncs() {
const o = {};
let t = "uvFromFlat";
o[t] = new c.GlslLibRoutine(`
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
int texC = index / texNumR;
int texR = index - texC * texNumR;
// TODO: swap texR, texC order in following function so row is corresponding to u and column is corresponding to
// v.
return (vec2(texR, texC) + halfCR) / vec2(texNumR, texNumC);
}
`), t = "packedUVfrom1D", o[t] = new c.GlslLibRoutine(`
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
int texelIndex = index / 2;
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`), t = "packedUVfrom2D", o[t] = new c.GlslLibRoutine(`
vec2 packedUVfrom2D(int texNumR, int texNumC, int texelsInLogicalRow, int row, int col) {
int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`), t = "packedUVfrom3D", o[t] = new c.GlslLibRoutine(`
vec2 packedUVfrom3D(int texNumR, int texNumC,
int texelsInBatch, int texelsInLogicalRow, int b,
int row, int col) {
int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`), t = "sampleTexture";
const e = (0, f.getGlsl)(this.context.glContext.version);
return o[t] = new c.GlslLibRoutine(`
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${e.texture2D}(textureSampler, uv).r;
}`), o
}
getInputsSamplingSnippets() {
const o = {},
t = this.context.outputTextureLayout;
return this.context.programInfo.inputNames.forEach((e, r) => {
const i = this.context.inputTextureLayouts[r],
d = (0, h.generateShaderFuncNameFromInputSamplerName)(e);
i.isPacked ? o[d] = this.getPackedSamplerFromInput(d, e, i) : o[d] = this.getUnpackedSamplerFromInput(d, e, i);
const g = (0, h.generateShaderFuncNameFromInputSamplerNameAtOutCoords)(e);
i.unpackedShape.length <= t.unpackedShape.length && (i.isPacked ? o[g] = this.getPackedSamplerAtOutputCoords(g, i, t, e) : o[g] = this.getUnpackedSamplerAtOutputCoords(g, i, t, e))
}), o
}
getPackedSamplerAtOutputCoords(o, t, e, r) {
const i = t.unpackedShape,
d = e.unpackedShape,
g = r,
m = (0, h.generateShaderFuncNameFromInputSamplerName)(g),
_ = i.length,
y = d.length,
T = u.BroadcastUtil.getBroadcastDims(i, d),
w = (0, h.getCoordsDataType)(y),
S = y - _;
let O;
const E = (0, h.getGlChannels)();
O = _ === 0 ? "" : y < 2 && T.length >= 1 ? "coords = 0;" : T.map(k => `coords.${E[k+S]} = 0;`).join(`
`);
let v = "";
v = y < 2 && _ > 0 ? "coords" : i.map((k, Y) => `coords.${E[Y+S]}`).join(", ");
let P = "return outputValue;";
const L = u.ShapeUtil.size(i) === 1,
V = u.ShapeUtil.size(d) === 1;
if (_ !== 1 || L || V) {
if (L && !V) P = y === 1 ? `
return vec4(outputValue.x, outputValue.x, 0., 0.);
` : `
return vec4(outputValue.x);
`;
else if (T.length) {
const k = _ - 2,
Y = _ - 1;
T.indexOf(k) > -1 && T.indexOf(Y) > -1 ? P = "return vec4(outputValue.x);" : T.indexOf(k) > -1 ? P = "return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);" : T.indexOf(Y) > -1 && (P = "return vec4(outputValue.xx, outputValue.zz);")
}
} else P = `
return vec4(outputValue.xy, outputValue.xy);
`;
const R = `
vec4 ${o}() {
${w} coords = getOutputCoords();
int lastDim = coords.${E[y-1]};
coords.${E[y-1]} = coords.${E[y-2]};
coords.${E[y-2]} = lastDim;
${O}
vec4 outputValue = ${m}(${v});
${P}
}
`;
return new c.GlslLibRoutine(R, ["coordinates.getOutputCoords"])
}
getUnpackedSamplerAtOutputCoords(o, t, e, r) {
const i = [e.width, e.height],
d = [t.width, t.height],
g = t.unpackedShape.length,
m = e.unpackedShape.length,
_ = t.unpackedShape,
y = e.unpackedShape,
T = (0, h.generateShaderFuncNameFromInputSamplerName)(r);
if (g === m && u.ArrayUtil.arraysEqual(d, i)) {
const V = `
float ${o}() {
return sampleTexture(${r}, TexCoords);
}
`;
return new c.GlslLibRoutine(V, ["coordinates.sampleTexture"])
}
const w = (0, h.getCoordsDataType)(m),
S = u.BroadcastUtil.getBroadcastDims(_, y),
O = m - g;
let E;
const v = (0, h.getGlChannels)();
E = g === 0 ? "" : m < 2 && S.length >= 1 ? "coords = 0;" : S.map(V => `coords.${v[V+O]} = 0;`).join(`
`);
let P = "";
P = m < 2 && g > 0 ? "coords" : t.unpackedShape.map((V, R) => `coords.${v[R+O]}`).join(", ");
const L = `
float ${o}() {
${w} coords = getOutputCoords();
${E}
return ${T}(${P});
}
`;
return new c.GlslLibRoutine(L, ["coordinates.getOutputCoords"])
}
getPackedSamplerFromInput(o, t, e) {
switch (e.unpackedShape.length) {
case 0:
return this.getPackedSamplerScalar(o, t);
case 1:
return this.getPackedSampler1D(o, t, e);
case 2:
return this.getPackedSampler2D(o, t, e);
case 3:
return this.getPackedSampler3D(o, t, e);
default:
return this.getPackedSamplerND(o, t, e)
}
}
getUnpackedSamplerFromInput(o, t, e) {
const r = e.unpackedShape;
switch (r.length) {
case 0:
return this.getUnpackedSamplerScalar(o, t, e);
case 1:
return this.getUnpackedSampler1D(o, t, e);
case 2:
return this.getUnpackedSampler2D(o, t, e);
case 3:
return this.getUnpackedSampler3D(o, t, e);
case 4:
return this.getUnpackedSampler4D(o, t, e);
case 5:
return this.getUnpackedSampler5D(o, t, e);
case 6:
return this.getUnpackedSampler6D(o, t, e);
default:
throw new Error(`Unsupported dimension ${r.length}-D`)
}
}
getPackedSamplerScalar(o, t) {
const e = `
vec4 ${o}() {
return ${(0,f.getGlsl)(this.context.glContext.version).texture2D}(${t}, halfCR);
}
`;
return new c.GlslLibRoutine(e)
}
getPackedSampler1D(o, t, e) {
const r = [e.width, e.height],
i = [r[1], r[0]],
d = (0, f.getGlsl)(this.context.glContext.version),
g = `vec4 ${o}(int index) {
vec2 uv = packedUVfrom1D(
${i[0]}, ${i[1]}, index);
return ${d.texture2D}(${t}, uv);
}`;
return new c.GlslLibRoutine(g, ["coordinates.packedUVfrom1D"])
}
getPackedSampler2D(o, t, e) {
const r = e.unpackedShape,
i = [e.width, e.height],
d = (0, f.getGlsl)(this.context.glContext.version),
g = i[0],
m = i[1];
if (i != null && u.ArrayUtil.arraysEqual(r, i)) {
const w = `vec4 ${o}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${m}.0, ${g}.0);
return ${d.texture2D}(${t}, uv);
}`;
return new c.GlslLibRoutine(w)
}
const _ = i,
y = Math.ceil(r[1] / 2),
T = `vec4 ${o}(int row, int col) {
vec2 uv = packedUVfrom2D(${_[1]}, ${_[0]}, ${y}, row, col);
return ${d.texture2D}(${t}, uv);
}`;
return new c.GlslLibRoutine(T, ["coordinates.packedUVfrom2D"])
}
getPackedSampler3D(o, t, e) {
const r = e.unpackedShape,
i = [e.width, e.height],
d = [i[0], i[1]],
g = (0, f.getGlsl)(this.context.glContext.version);
if (r[0] === 1) {
const w = r.slice(1),
S = [1, 2],
O = (0, h.squeezeInputShape)(r, w),
E = ["b", "row", "col"],
v = JSON.parse(JSON.stringify(e));
v.unpackedShape = O;
const P = this.getPackedSamplerFromInput(o, t, v),
L = `${P.routineBody}
vec4 ${o}(int b, int row, int col) {
return ${o}(${(0,h.getSqueezedParams)(E,S)});
} `;
return new c.GlslLibRoutine(L, P.dependencies)
}
const m = d[0],
_ = d[1],
y = Math.ceil(r[2] / 2),
T = `vec4 ${o}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${_}, ${m}, ${y*Math.ceil(r[1]/2)}, ${y}, b, row, col);
return ${g.texture2D}(${t}, uv);}`;
return new c.GlslLibRoutine(T, ["coordinates.packedUVfrom3D"])
}
getPackedSamplerND(o, t, e) {
const r = e.unpackedShape,
i = r.length,
d = [e.width, e.height],
g = (0, f.getGlsl)(this.context.glContext.version),
m = [d[0], d[1]],
_ = m[1],
y = m[0],
T = Math.ceil(r[i - 1] / 2);
let w = T * Math.ceil(r[i - 2] / 2),
S = "int b, int row, int col",
O = `b * ${w} + (row / 2) * ${T} + (col / 2)`;
for (let v = 2; v < i - 1; v++) S = `int b${v}, ` + S, w *= r[i - v - 1], O = `b${v} * ${w} + ` + O;
const E = `vec4 ${o}(${S}) {
int index = ${O};
int texR = index / ${y};
int texC = index - texR * ${y};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${y}, ${_});
return ${g.texture2D}(${t}, uv);
}`;
return new c.GlslLibRoutine(E)
}
getUnpackedSamplerScalar(o, t, e) {
const [r, i] = [e.width, e.height];
if (r === 1 && i === 1) {
const g = `
float ${o}() {
return sampleTexture(${t}, halfCR);
}
`;
return new c.GlslLibRoutine(g, ["coordinates.sampleTexture"])
}
const d = `
float ${o}() {
int offset_${t} = coordsToOffset(TexCoords, ${r}, ${i});
vec2 uv = uvFromFlat(${r}, ${i}, offset_${t});
return sampleTexture(${t}, uv);
}
`;
return new c.GlslLibRoutine(d, ["coordinates.uvFromFlat", "coordinates.sampleTexture", "coordinates.coordsToOffset"])
}
getUnpackedSampler1D(o, t, e) {
const r = e.width,
i = e.height;
if (i === 1 && r === 1) {
const g = `
float ${o}(int index) {
return sampleTexture(${t}, halfCR);
}
`;
return new c.GlslLibRoutine(g, ["coordinates.sampleTexture"])
}
if (i === 1) {
const g = `
float ${o}(int index) {
vec2 uv = vec2((float(index) + 0.5) / ${r}.0, 0.5);
return sampleTexture(${t}, uv);
}
`;
return new c.GlslLibRoutine(g, ["coordinates.sampleTexture"])
}
if (r === 1) {
const g = `
float ${o}(int index) {
vec2 uv = vec2(0.5, (float(index) + 0.5) / ${i}.0);
return sampleTexture(${t}, uv);
}
`;
return new c.GlslLibRoutine(g, ["coordinates.sampleTexture"])
}
const d = `
float ${o}(int index) {
vec2 uv = uvFromFlat(${r}, ${i}, index);
return sampleTexture(${t}, uv);
}
`;
return new c.GlslLibRoutine(d, ["coordinates.uvFromFlat", "coordinates.sampleTexture"])
}
getUnpackedSampler2D(o, t, e) {
const r = e.unpackedShape,
i = [e.height, e.width];
if (i != null && u.ArrayUtil.arraysEqual(r, i)) {
const w = `
float ${o}(int row, int col) {
vec2 uv = (vec2(row, col) + halfCR) / vec2(${i[1]}.0, ${i[0]}.0);
return sampleTexture(${t}, uv);
}
`;
return new c.GlslLibRoutine(w, ["coordinates.sampleTexture"])
}
const {
newShape: d,
keptDims: g
} = (0, s.squeezeShape)(r), m = d;
if (m.length < r.length) {
const w = (0, h.squeezeInputShape)(r, m),
S = JSON.parse(JSON.stringify(e));
S.unpackedShape = w;
const O = ["col", "row"],
E = `
${this.getUnpackedSamplerFromInput(o,t,S).routineBody}
float ${o}(int row, int col) {
return ${o}(${(0,h.getSqueezedParams)(O,g)});
}
`;
return new c.GlslLibRoutine(E, ["coordinates.sampleTexture"])
}
const _ = i[1],
y = i[0];
if (y === 1) {
const w = `
float ${o}(int row, int col) {
int offset_${t} = coordsToOffset(TexCoords, ${_}, ${y});
float index = dot(vec3(row, col, offset_${t}), vec3(${r[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${_}.0);
return sampleTexture(${t}, uv);
}
`;
return new c.GlslLibRoutine(w, ["coordinates.sampleTexture", "coordinates.coordsToOffset"])
}
if (_ === 1) {
const w = `
float ${o}(int row, int col) {
int offset_${t} = coordsToOffset(TexCoords, ${_}, ${y});
float index = dot(vec3(row, col, offset_${t}), vec3(${r[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${y}.0, 0.5);
return sampleTexture(${t}, uv);
}
`;
return new c.GlslLibRoutine(w, ["coordinates.sampleTexture", "coordinates.coordsToOffset"])
}
const T = `
float ${o}(int row, int col) {
int index = col * ${r[1]} + row;
vec2 uv = uvFromFlat(${_}, ${y}, index);
return sampleTexture(${t}, uv);
}
`;
return new c.GlslLibRoutine(T, ["coordinates.uvFromFlat", "coordinates.sampleTexture", "coordinates.coordsToOffset"])
}
getUnpackedSampler3D(o, t, e) {
const r = e.unpackedShape,
i = r[1] * r[2],
d = r[2],
{
newShape: g,
keptDims: m
} = (0, s.squeezeShape)(r),
_ = g;
if (_.length < r.length) {
const T = (0, h.squeezeInputShape)(r, _),
w = ["batch", "col", "row"],
S = JSON.parse(JSON.stringify(e));
S.unpackedShape = T;
const O = this.getUnpackedSamplerFromInput(o, t, S),
E = m.reverse(),
v = `
${O.routineBody}
float ${o}(int batch, int row, int col) {
return ${o}(${(0,h.getSqueezedParams)(w,E)});
}
`;
return new c.GlslLibRoutine(v, O.dependencies)
}
const y = `
float ${o}(int depth, int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = depth * ${i} + col * ${d} + row;
vec2 uv = uvFromFlat(${e.width}, ${e.height}, index);
return sampleTexture(${t}, uv);
}
`;
return new c.GlslLibRoutine(y, ["coordinates.uvFromFlat", "coordinates.sampleTexture", "coordinates.coordsToOffset"])
}
getUnpackedSampler4D(o, t, e) {
const r = e.unpackedShape,
i = r[3],
d = r[2] * i,
g = `
float ${o}(int row, int col, int depth, int depth2) {
int index = row * ${r[1]*d} + col * ${d} +
depth2 * ${i} + depth;
vec2 uv = uvFromFlat(${e.width}, ${e.height}, index);
return sampleTexture(${t}, uv);
}
`;
return new c.GlslLibRoutine(g, ["coordinates.uvFromFlat", "coordinates.sampleTexture"])
}
getUnpackedSampler5D(o, t, e) {
const r = e.unpackedShape,
i = r[4],
d = r[3] * i,
g = r[2] * d,
m = r[1] * g,
{
newShape: _,
keptDims: y
} = (0, s.squeezeShape)(r);
if (_.length < r.length) {
const w = (0, h.squeezeInputShape)(r, _),
S = ["row", "col", "depth", "depth2", "depth3"],
O = JSON.parse(JSON.stringify(e));
O.unpackedShape = w;
const E = `
${this.getUnpackedSamplerFromInput(o,t,O).routineBody}
float ${o}(int row, int col, int depth, int depth2, int depth3) {
return ${o}(${(0,h.getSqueezedParams)(S,y)});
}
`;
return new c.GlslLibRoutine(E, ["coordinates.sampleTexture", "coordinates.uvFromFlat"])
}
const T = `
float ${o}(int row, int col, int depth, int depth2, int depth3) {
int index = row * ${m} + col * ${g} + depth * ${d} +
depth3 * ${i} + depth2;
vec2 uv = uvFromFlat(${e.width}, ${e.height}, index);
return sampleTexture(${t}, uv);
}
`;
return new c.GlslLibRoutine(T, ["coordinates.sampleTexture", "coordinates.uvFromFlat"])
}
getUnpackedSampler6D(o, t, e) {
const r = e.unpackedShape,
i = r[5],
d = r[4] * i,
g = r[3] * d,
m = r[2] * g,
_ = r[1] * m,
{
newShape: y,
keptDims: T
} = (0, s.squeezeShape)(r);
if (y.length < r.length) {
const S = (0, h.squeezeInputShape)(r, y),
O = ["row", "col", "depth", "depth2", "depth3", "depth4"],
E = JSON.parse(JSON.stringify(e));
E.unpackedShape = S;
const v = `
${this.getUnpackedSamplerFromInput(o,t,E).routineBody}
float ${o}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${o}(${(0,h.getSqueezedParams)(O,T)});
}
`;
return new c.GlslLibRoutine(v, ["coordinates.sampleTexture", "coordinates.uvFromFlat"])
}
const w = `
float ${o}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = row * ${_} + col * ${m} + depth * ${g} +
depth2 * ${d} + depth3 * ${i} + depth4;
vec2 uv = uvFromFlat(${e.width}, ${e.height}, index);
return sampleTexture(${t}, uv);
}
`;
return new c.GlslLibRoutine(w, ["coordinates.uvFromFlat", "coordinates.sampleTexture", "coordinates.coordsToOffset"])
}
toVec() {
const o = this.context.outputTextureLayout,
t = o.shape.length,
e = o.strides,
r = o.width,
i = o.height,
d = [];
for (let m = 0; m < t - 1; ++m) d.push(`
c[${m}] = offset / ${e[m]};`), d.push(`
offset -= c[${m}] * ${e[m]};`);
d.push(`
c[${t-1}] = offset;`);
const g = `
void toVec(vec2 texCoords, out int c[${t}]) {
int offset = coordsToOffset(texCoords, ${r}, ${i});
${d.join("")}
}
void toVec(int offset, out int c[${t}]) {
${d.join("")}
}
`;
return {
toVec: new c.GlslLibRoutine(g, ["coordinates.coordsToOffset"])
}
}
valueFrom() {
const o = {};
return this.context.programInfo.inputNames.forEach((t, e) => {
const r = this.context.inputTextureLayouts[e],
i = (r.unpackedShape.length > 0 ? r.unpackedShape : r.shape).length;
let d = `_${t}`;
o[d] = new c.GlslLibRoutine(this.getValueFromSingle(t, i, r.width, r.height, !1), [`shapeUtils.indicesToOffset${d}`, "coordinates.offsetToCoords", "fragcolor.getColorAsFloat"]), d += "_T", o[d] = new c.GlslLibRoutine(this.getValueFromSingle(t, i, r.width, r.height, !0), [`shapeUtils.indicesToOffset${d}`, "coordinates.offsetToCoords", "fragcolor.getColorAsFloat"])
}), o
}
getValueFromSingle(o, t, e, r, i) {
let d = `_${o}`;
return i && (d += "_T"), `
float ${d}(int m[${t}]) {
int offset = indicesToOffset${d}(m);
vec2 coords = offsetToCoords(offset, ${e}, ${r});
float value = getColorAsFloat(${(0,f.getGlsl)(this.context.glContext.version).texture2D}(${o}, coords));
return value;
}
`
}
getPackedValueFrom(o, t, e, r, i) {
let d = `_${o}_Pack`;
return i && (d += "_T"), `
vec4 ${d}(int m[${t}]) {
int offset = indicesToOffset_${o}(m);
vec2 coords = offsetToCoords(offset, ${e}, ${r});
return ${(0,f.getGlsl)(this.context.glContext.version).texture2D}(${o}, coords);
}
`
}
}
n.CoordsGlslLib = p
},
8520: (b, n) => {
var a;
Object.defineProperty(n, "__esModule", {
value: !0
}), n.TopologicalSortGlslRoutines = n.GlslLibRoutineNode = n.GlslLibRoutine = n.GlslLib = n.GlslContext = n.FunctionType = void 0, (a = n.FunctionType || (n.FunctionType = {}))[a.ValueBased = 0] = "ValueBased", a[a.Positional = 1] = "Positional", n.GlslContext = class {
constructor(u, c, f, s) {
this.glContext = u, this.programInfo = c, this.inputTextureLayouts = f, this.outputTextureLayout = s
}
}, n.GlslLib = class {
constructor(u) {
this.context = u
}
}, n.GlslLibRoutine = class {
constructor(u, c) {
this.routineBody = u, this.dependencies = c
}
}, n.GlslLibRoutineNode = class {
constructor(u, c, f) {
this.name = u, this.dependencies = f || [], c && (this.routineBody = c)
}
addDependency(u) {
u && this.dependencies.push(u)
}
}, n.TopologicalSortGlslRoutines = class {
static returnOrderedNodes(u) {
if (!u || u.length === 0) return [];
if (u.length === 1) return u;
const c = new Set,
f = new Set,
s = new Array;
return this.createOrderedNodes(u, c, f, s), s
}
static createOrderedNodes(u, c, f, s) {
for (let h = 0; h < u.length; ++h) this.dfsTraverse(u[h], c, f, s)
}
static dfsTraverse(u, c, f, s) {
if (!u || f.has(u.name)) return;
if (c.has(u.name)) throw new Error("Cyclic dependency detected. Can't topologically sort routines needed for shader.");
c.add(u.name);
const h = u.dependencies;
if (h && h.length > 0)
for (let p = 0; p < h.length; ++p) this.dfsTraverse(h[p], c, f, s);
s.push(u), f.add(u.name), c.delete(u.name)
}
}
},
7341: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.EncodingGlslLib = void 0;
const u = a(8520);
class c extends u.GlslLib {
constructor(s) {
super(s)
}
getFunctions() {
return Object.assign(Object.assign({}, this.encodeFloat32()), this.decodeFloat32())
}
getCustomTypes() {
return {}
}
encodeFloat32() {
return {
encode: new u.GlslLibRoutine(`highp vec4 encode(highp float f) {
return vec4(f, 0.0, 0.0, 0.0);
}
`)
}
}
decodeFloat32() {
return {
decode: new u.GlslLibRoutine(`highp float decode(highp vec4 rgba) {
return rgba.r;
}
`)
}
}
encodeUint8() {
const s = c.isLittleEndian() ? "rgba.rgba=rgba.abgr;" : "";
return {
encode: new u.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)));
${s}
rgba = rgba / 255.0; // values need to be normalized to [0,1]
return rgba;
}
`)
}
}
decodeUint8() {
const s = c.isLittleEndian() ? "rgba.rgba=rgba.abgr;" : "";
return {
decode: new u.GlslLibRoutine(`
highp float decode(highp vec4 rgba) {
rgba = rgba * 255.0; // values need to be de-normalized from [0,1] to [0,255]
${s}
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 s = new ArrayBuffer(4),
h = new Uint32Array(s),
p = new Uint8Array(s);
if (h[0] = 3735928559, p[0] === 239) return !0;
if (p[0] === 222) return !1;
throw new Error("unknown endianness")
}
}
n.EncodingGlslLib = c
},
9894: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.FragColorGlslLib = void 0;
const u = a(8520),
c = a(5060);
class f extends u.GlslLib {
constructor(h) {
super(h)
}
getFunctions() {
return Object.assign(Object.assign({}, this.setFragColor()), this.getColorAsFloat())
}
getCustomTypes() {
return {}
}
setFragColor() {
const h = (0, c.getGlsl)(this.context.glContext.version);
return {
setFragColor: new u.GlslLibRoutine(`
void setFragColor(float value) {
${h.output} = encode(value);
}
`, ["encoding.encode"])
}
}
getColorAsFloat() {
return {
getColorAsFloat: new u.GlslLibRoutine(`
float getColorAsFloat(vec4 color) {
return decode(color);
}
`, ["encoding.decode"])
}
}
}
n.FragColorGlslLib = f
},
2848: (b, n) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.replaceInlines = void 0;
const a = /@inline[\s\n\r]+(\w+)[\s\n\r]+([0-9a-zA-Z_]+)\s*\(([^)]*)\)\s*{(([^}]|[\n\r])*)}/gm;
n.replaceInlines = function(u) {
const c = {};
let f;
for (;
(f = a.exec(u)) !== null;) {
const s = f[3].split(",").map(h => {
const p = h.trim().split(" ");
return p && p.length === 2 ? {
type: p[0],
name: p[1]
} : null
}).filter(h => h !== null);
c[f[2]] = {
params: s,
body: f[4]
}
}
for (const s in c) {
const h = "(\\w+)?\\s+([_0-9a-zA-Z]+)\\s+=\\s+__FUNC__\\((.*)\\)\\s*;".replace("__FUNC__", s),
p = new RegExp(h, "gm");
for (;
(f = p.exec(u)) !== null;) {
const l = f[1],
o = f[2],
t = f[3].split(","),
e = l ? `${l} ${o};` : "";
let r = c[s].body,
i = "";
c[s].params.forEach((g, m) => {
g && (i += `${g.type} ${g.name} = ${t[m]};
`)
}), r = `${i}
${r}`, r = r.replace("return", `${o} = `);
const d = `
${e}
{
${r}
}
`;
u = u.replace(f[0], d)
}
}
return u.replace(a, "")
}
},
8879: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.GlslPreprocessor = void 0;
const u = a(8520),
c = a(2848),
f = a(5483),
s = a(5060);
n.GlslPreprocessor = class {
constructor(h, p, l, o) {
this.libs = {}, this.glslLibRoutineDependencyGraph = {}, this.context = new u.GlslContext(h, p, l, o), Object.keys(f.glslRegistry).forEach(e => {
const r = new f.glslRegistry[e](this.context);
this.libs[e] = r
});
const t = this.glslLibRoutineDependencyGraph;
for (const e in this.libs) {
const r = this.libs[e].getFunctions();
for (const i in r) {
const d = e + "." + i;
let g;
t[d] ? (g = t[d], g.routineBody = r[i].routineBody) : (g = new u.GlslLibRoutineNode(d, r[i].routineBody), t[d] = g);
const m = r[i].dependencies;
if (m)
for (let _ = 0; _ < m.length; ++_)
if (t[m[_]]) g.addDependency(t[m[_]]);
else {
const y = new u.GlslLibRoutineNode(m[_]);
t[m[_]] = y, g.addDependency(y)
}
}
}
}
preprocess() {
const h = this.context.programInfo;
let p = h.shaderSource;
return this.context.programInfo.hasMain || (p = `${p}
${(0,s.getDefaultFragShaderMain)(this.context.glContext.version,this.context.outputTextureLayout.shape.length)}`), p = (0, c.replaceInlines)(p), `${(0,s.getFragShaderPreamble)(this.context.glContext.version)}
${this.getUniforms(h.inputNames,h.variables)}
${this.getImports(p)}
${p}`
}
getImports(h) {
const p = this.selectGlslLibRoutinesToBeIncluded(h);
if (p.length === 0) return "";
let l = "";
for (let o = 0; o < p.length; ++o) {
if (!p[o].routineBody) throw new Error(`Missing body for the Glsl Library routine: ${p[o].name}`);
l += p[o].routineBody + `
`
}
return l
}
selectGlslLibRoutinesToBeIncluded(h) {
const p = [];
return Object.keys(this.glslLibRoutineDependencyGraph).forEach(l => {
const o = l.split(".")[1];
h.indexOf(o) !== -1 && p.push(this.glslLibRoutineDependencyGraph[l])
}), u.TopologicalSortGlslRoutines.returnOrderedNodes(p)
}
getUniforms(h, p) {
const l = [];
if (h)
for (const o of h) l.push(`uniform sampler2D ${o};`);
if (p)
for (const o of p) l.push(`uniform ${o.type} ${o.name}${o.arrayLength?`[${o.arrayLength}]`:""};`);
return l.join(`
`)
}
}
},
5483: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.glslRegistry = void 0;
const u = a(5107),
c = a(7341),
f = a(9894),
s = a(2655),
h = a(3891);
n.glslRegistry = {
encoding: c.EncodingGlslLib,
fragcolor: f.FragColorGlslLib,
vec: h.VecGlslLib,
shapeUtils: s.ShapeUtilsGlslLib,
coordinates: u.CoordsGlslLib
}
},
2655: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.ShapeUtilsGlslLib = void 0;
const u = a(8520);
class c extends u.GlslLib {
constructor(s) {
super(s)
}
getFunctions() {
return Object.assign(Object.assign(Object.assign(Object.assign(Object.assign({}, this.bcastIndex()), this.bcastMatmulIndex()), this.offsetToIndices()), this.indicesToOffset()), this.incrementIndices())
}
getCustomTypes() {
return {}
}
bcastIndex() {
const s = this.context.outputTextureLayout.shape.length,
h = {};
return this.context.programInfo.inputNames.forEach((p, l) => {
const o = this.context.inputTextureLayouts[l].unpackedShape;
if (o.length <= s) {
const t = o.length,
e = s - t,
r = `bcastIndices_${p}`;
let i = "";
for (let g = 0; g < t; ++g) i += `
realIndices[${g}] = int( mod(float(bcastedIndices[${e+g}]), ${o[g]}.0) );
`;
const d = `
void ${r} (int bcastedIndices[${s}], out int realIndices[${t}]) {
${i}
}
`;
h[r] = new u.GlslLibRoutine(d)
}
}), h
}
bcastMatmulIndex() {
const s = this.context.outputTextureLayout.shape.length,
h = {};
return this.context.programInfo.inputNames.forEach((p, l) => {
const o = this.context.inputTextureLayouts[l].shape;
if (!(o.length < 2 || o.length > s)) {
const t = o.length,
e = s - t,
r = `bcastMatmulIndices_${p}`;
let i = "";
for (let g = 0; g < t - 2; ++g) i += `
realIndices[${g}] = int( mod(float(bcastedIndices[${e+g}]), ${o[g]}.0) );
`;
const d = `
void ${r}(int bcastedIndices[${s}], out int realIndices[${t}]) {
${i}
realIndices[${t-1}] = bcastedIndices[${s-1}];
realIndices[${t-2}] = bcastedIndices[${s-2}];
}
`;
h[r] = new u.GlslLibRoutine(d)
}
}), h
}
indicesToOffset() {
const s = {};
return this.context.programInfo.inputNames.forEach((h, p) => {
const l = this.context.inputTextureLayouts[p].shape,
o = this.context.inputTextureLayouts[p].strides,
t = l.length;
let e = `indicesToOffset_${h}`;
s[e] = new u.GlslLibRoutine(c.indexToOffsetSingle(e, t, o)), e = `indicesToOffset_${h}_T`, s[e] = new u.GlslLibRoutine(c.indexToOffsetSingle(e, t, o.slice().reverse()))
}), s
}
static indexToOffsetSingle(s, h, p) {
let l = "";
for (let o = h - 1; o >= 0; --o) l += `
offset += indices[${o}] * ${p[o]};
`;
return `
int ${s}(int indices[${h}]) {
int offset = 0;
${l}
return offset;
}
`
}
offsetToIndices() {
const s = {};
return this.context.programInfo.inputNames.forEach((h, p) => {
const l = this.context.inputTextureLayouts[p].shape,
o = this.context.inputTextureLayouts[p].strides,
t = l.length;
let e = `offsetToIndices_${h}`;
s[e] = new u.GlslLibRoutine(c.offsetToIndicesSingle(e, t, o)), e = `offsetToIndices_${h}_T`, s[e] = new u.GlslLibRoutine(c.offsetToIndicesSingle(e, t, o.slice().reverse()))
}), s
}
static offsetToIndicesSingle(s, h, p) {
const l = [];
for (let o = 0; o < h - 1; ++o) l.push(`
indices[${o}] = offset / ${p[o]};`), l.push(`
offset -= indices[${o}] * ${p[o]};`);
return l.push(`
indices[${h-1}] = offset;`), `
void ${s}(int offset, out int indices[${h}]) {
${l.join("")}
}
`
}
incrementIndices() {
const s = {};
return this.context.programInfo.inputNames.forEach((h, p) => {
const l = this.context.inputTextureLayouts[p].shape,
o = l.length,
t = `incrementIndices_${h}`;
let e = "";
for (let i = 0; i < o; ++i) e += `
shape[${i}] = ${l[i]};`;
const r = `
void ${t}(int axis, out int indices[${o}]) {
int shape[${o}];
${e};
for(int i = ${o} -1 ; i >= 0; --i) {
if(i > axis) continue;
indices[i] += 1;
if(indices[i] < shape[i]) {
break;
}
indices[i] = 0;
}
}
`;
s[t] = new u.GlslLibRoutine(r)
}), s
}
}
n.ShapeUtilsGlslLib = c
},
5060: (b, n) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.getDefaultFragShaderMain = n.getFragShaderPreamble = n.getVertexShaderSource = n.getGlsl = void 0;
const a = {
version: "",
attribute: "attribute",
varyingVertex: "varying",
varyingFrag: "varying",
texture2D: "texture2D",
output: "gl_FragColor",
outputDeclaration: ""
},
u = {
version: "#version 300 es",
attribute: "in",
varyingVertex: "out",
varyingFrag: "in",
texture2D: "texture",
output: "outputColor",
outputDeclaration: "out vec4 outputColor;"
};
function c(f) {
return f === 1 ? a : u
}
n.getGlsl = c, n.getVertexShaderSource = function(f) {
const s = c(f);
return `${s.version}
precision highp float;
${s.attribute} vec3 position;
${s.attribute} vec2 textureCoord;
${s.varyingVertex} vec2 TexCoords;
void main()
{
gl_Position = vec4(position, 1.0);
TexCoords = textureCoord;
}`
}, n.getFragShaderPreamble = function(f) {
const s = c(f);
return `${s.version}
precision highp float;
precision highp int;
precision highp sampler2D;
${s.varyingFrag} vec2 TexCoords;
${s.outputDeclaration}
const vec2 halfCR = vec2(0.5, 0.5);
// Custom vector types to handle higher dimenalities.
struct ivec5
{
int x;
int y;
int z;
int w;
int u;
};
struct ivec6
{
int x;
int y;
int z;
int w;
int u;
int v;
};
int imod(int x, int y) {
return x - y * (x / y);
}
`
}, n.getDefaultFragShaderMain = function(f, s) {
return `
void main() {
int indices[${s}];
toVec(TexCoords, indices);
vec4 result = vec4(process(indices));
${c(f).output} = result;
}
`
}
},
3891: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.VecGlslLib = void 0;
const u = a(8520);
class c extends u.GlslLib {
constructor(s) {
super(s)
}
getCustomTypes() {
return {}
}
getFunctions() {
return Object.assign(Object.assign(Object.assign(Object.assign({}, this.binaryVecFunctions()), this.copyVec()), this.setVecItem()), this.getVecItem())
}
binaryVecFunctions() {
const s = this.context.outputTextureLayout.shape.length,
h = {
add: "+=",
sub: "-=",
mul: "*=",
div: "/="
},
p = {};
for (const l in h) {
const o = `${l}Vec`;
let t = "";
for (let r = 0; r < s; ++r) t += `
dest[${r}] ${h[l]} src[${r}];
`;
const e = `
void ${o}(int src[${s}], out int dest[${s}]) {
${t}
}
`;
p[o] = new u.GlslLibRoutine(e)
}
return p
}
copyVec() {
const s = this.context.outputTextureLayout.shape.length;
let h = "";
for (let l = 0; l < s; ++l) h += `
dest[${l}] = src[${l}];
`;
const p = `
void copyVec(int src[${s}], out int dest[${s}]) {
${h}
}
`;
return {
copyVec: new u.GlslLibRoutine(p)
}
}
setVecItem() {
const s = this.context.outputTextureLayout.shape.length;
let h = `
if(index < 0)
index =${s} + index;
if (index == 0)
m[0] = value;
`;
for (let l = 1; l < s - 1; ++l) h += `
else if (index == ${l})
m[${l}] = value;
`;
h += `
else
m[${s-1}] = value;
`;
const p = `
void setVecItem(out int m[${s}], int index, int value) {
${h}
}
`;
return {
setVecItem: new u.GlslLibRoutine(p)
}
}
getVecItem() {
const s = this.context.outputTextureLayout.shape.length;
let h = `
if(index < 0)
index = ${s} + index;
if (index == 0)
return m[0];
`;
for (let l = 1; l < s - 1; ++l) h += `
else if (index == ${l})
return m[${l}];
`;
h += `
else
return m[${s-1}];
`;
const p = `
int getVecItem(int m[${s}], int index) {
${h}
}
`;
return {
getVecItem: new u.GlslLibRoutine(p)
}
}
}
n.VecGlslLib = c
},
8316: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.WebGLInferenceHandler = void 0;
const u = a(6231),
c = a(9162),
f = a(2517),
s = a(2403),
h = a(7019),
p = a(8710),
l = a(5611),
o = a(4057),
t = a(2039);
n.WebGLInferenceHandler = class {
constructor(e) {
this.session = e, this.packedTextureDataCache = new Map, this.unpackedTextureDataCache = new Map
}
calculateTextureWidthAndHeight(e, r) {
return (0, o.calculateTextureWidthAndHeight)(this.session.layoutStrategy, e, r)
}
executeProgram(e, r) {
if (r.length < e.inputNames.length) throw new Error(`Input size mustn't be less than ${e.inputNames.length}.`);
if (e.inputNames.length !== e.inputTypes.length) throw new Error("input names size does not match input types");
const i = [];
for (let T = 0; T < e.inputNames.length; ++T) i[T] = this.getOrCreateTextureData(r[T], e.inputTypes[T]);
const d = ((T, w) => {
const S = w.map(E => `${E.unpackedShape.join(",")};${E.width}x${E.height}`).join("_");
let O = T.name;
return T.cacheHint && (O += "[" + T.cacheHint + "]"), O += ":" + S, O
})(e, i);
let g = this.session.programManager.getArtifact(d);
const m = g ? g.programInfo : typeof e.get == "function" ? e.get() : e,
_ = (0, o.createTextureLayoutFromTextureType)(this.session.layoutStrategy, m.output.dims, m.output.textureType),
y = this.createTextureData(_, m.output.type);
return g || (g = this.session.programManager.build(m, i, y), this.session.programManager.setArtifact(d, g)), this.runProgram(g, i, y), y
}
run(e, r) {
return this.executeProgram(e, r).tensor
}
runProgram(e, r, i) {
for (let d = 0; d < r.length; ++d)
if (!!r[d].isPacked != (e.programInfo.inputTypes[d] === t.TextureType.packed)) throw new Error(`input[${d}] property packed inconsistent`);
if (!!i.isPacked != (e.programInfo.output.textureType === t.TextureType.packed)) throw new Error("output property packed inconsistent");
this.session.programManager.run(e, r, i)
}
getOrCreateTextureData(e, r) {
let i = this.getTextureData(e.dataId, r === t.TextureType.packed);
if (!i && (i = this.getTextureData(e.dataId, r !== t.TextureType.packed), i)) return r === t.TextureType.packed ? this.pack(i) : this.unpack(i);
if (!i) {
const d = (0, o.createTextureLayoutFromTextureType)(this.session.layoutStrategy, e.dims, r);
if (r === t.TextureType.packedLastDimension) {
const _ = e.dims;
if (_.length === 4) {
const y = [_[0], Math.ceil(_[1] * _[2] * _[3] / 4)],
T = (0, o.createTextureLayoutFromTextureType)(this.session.layoutStrategy, y, r);
let w = e.numberData;
if (_[1] * _[2] * _[3] % 4 != 0) {
const S = _[0],
O = _[1] * _[2] * _[3],
E = Math.ceil(O * 1 / 4) * 4;
w = new Float32Array(S * E);
for (let v = 0; v < S; ++v) {
const P = v * O,
L = v * E + v % 1 * O;
w.set(e.numberData.subarray(P, P + O), L)
}
}
return this.createTextureData(T, e.type, w, e, 1)
}
}
if (r === t.TextureType.packed) {
const g = (0, o.createTextureLayoutFromShape)(this.session.layoutStrategy, e.dims, 1, [], {
reverseWH: !0
}),
m = this.createTextureData(g, e.type, e.numberData, e, 1);
i = this.pack(m)
} else i = this.createTextureData(d, e.type, e.numberData, e, 1)
}
return i
}
createTextureDataFromLayoutBindTensor(e, r, i, d) {
return this.createTextureData(e, r, i, d, 1)
}
createTextureData(e, r, i, d, g) {
u.Logger.verbose("InferenceHandler", `Creating TextureData: layout:[${JSON.stringify(e)}]`);
const m = this.session.textureManager.createTextureFromLayout(r, e, i, g);
return this.createTextureDataFromTexture(e, r, m, d)
}
reshapeUnpacked(e, r) {
const i = this.getOrCreateTextureData(e, t.TextureType.unpacked),
d = {
channels: i.channels,
height: i.height,
width: i.width,
shape: r.length !== 0 ? r : [1],
strides: f.ShapeUtil.computeStrides(r),
unpackedShape: r
};
return this.createTextureDataFromTexture(d, e.type, i.texture).tensor
}
reshapePacked(e, r) {
const i = this.getOrCreateTextureData(e, t.TextureType.packed);
if ((0, h.isReshapeCheap)(e.dims, r)) {
const y = {
channels: i.channels,
height: i.height,
width: i.width,
shape: r.length !== 0 ? r : [1],
strides: f.ShapeUtil.computeStrides(r),
unpackedShape: r,
isPacked: !0
};
return this.createTextureDataFromTexture(y, e.type, i.texture).tensor
}
const d = (0, h.processDims3D)(e.dims),
g = (0, h.processDims3D)(r),
m = this.reshapePacked(e, d),
_ = this.run((0, h.createPackedReshape3DProgramInfoLoader)(this, m, g), [m]);
return this.reshapePacked(_, r)
}
cast(e, r) {
const i = this.getOrCreateTextureData(e, t.TextureType.unpacked);
return this.createTextureDataFromTexture(i, r, i.texture).tensor
}
createTextureDataFromTexture(e, r, i, d, g) {
const m = Object.assign(Object.assign({}, e), {
tensor: d || new c.Tensor(e.unpackedShape, r, _ => this.readTexture(m), async _ => this.readTextureAsync(m), void 0, g),
texture: i
});
return this.setTextureData(m.tensor.dataId, m, e.isPacked), m
}
getTextureData(e, r = !1) {
return this.session.isInitializer(e) ? this.session.getTextureData(e, r) : r ? this.packedTextureDataCache.get(e) : this.unpackedTextureDataCache.get(e)
}
setTextureData(e, r, i = !1) {
this.session.isInitializer(e) ? this.session.setTextureData(e, r, i) : (i ? this.packedTextureDataCache : this.unpackedTextureDataCache).set(e, r)
}
isTextureLayoutCached(e, r = !1) {
return !!this.getTextureData(e.dataId, r)
}
dispose() {
this.session.textureManager.clearActiveTextures(), this.packedTextureDataCache.forEach(e => this.session.textureManager.releaseTexture(e)), this.packedTextureDataCache = new Map, this.unpackedTextureDataCache.forEach(e => this.session.textureManager.releaseTexture(e)), this.unpackedTextureDataCache = new Map
}
readTexture(e) {
return e.isPacked ? this.readTexture(this.unpack(e)) : this.session.backend.glContext.isFloat32DownloadSupported ? this.session.textureManager.readTexture(e, e.tensor.type, e.channels) : this.session.textureManager.readUint8TextureAsFloat((0, p.encodeAsUint8)(this, e))
}
async readTextureAsync(e) {
return e.isPacked ? this.readTextureAsync(this.unpack(e)) : this.session.backend.glContext.isFloat32DownloadSupported ? this.session.textureManager.readTextureAsync(e, e.tensor.type, e.channels) : this.session.textureManager.readUint8TextureAsFloat((0, p.encodeAsUint8)(this, e))
}
pack(e) {
return this.executeProgram((0, s.createPackProgramInfoLoader)(this, e.tensor), [e.tensor])
}
unpack(e) {
return this.executeProgram((0, l.createUnpackProgramInfoLoader)(this, e.tensor), [e.tensor])
}
}
},
1640: function(b, n, a) {
var u = this && this.__createBinding || (Object.create ? function(z, Z, J, ue) {
ue === void 0 && (ue = J);
var Se = Object.getOwnPropertyDescriptor(Z, J);
Se && !("get" in Se ? !Z.__esModule : Se.writable || Se.configurable) || (Se = {
enumerable: !0,
get: function() {
return Z[J]
}
}), Object.defineProperty(z, ue, Se)
} : function(z, Z, J, ue) {
ue === void 0 && (ue = J), z[ue] = Z[J]
}),
c = this && this.__setModuleDefault || (Object.create ? function(z, Z) {
Object.defineProperty(z, "default", {
enumerable: !0,
value: Z
})
} : function(z, Z) {
z.default = Z
}),
f = this && this.__importStar || function(z) {
if (z && z.__esModule) return z;
var Z = {};
if (z != null)
for (var J in z) J !== "default" && Object.prototype.hasOwnProperty.call(z, J) && u(Z, z, J);
return c(Z, z), Z
};
Object.defineProperty(n, "__esModule", {
value: !0
}), n.WEBGL_OP_RESOLVE_RULES = void 0;
const s = a(2898),
h = f(a(7839)),
p = a(4196),
l = a(2069),
o = a(8138),
t = a(9663),
e = a(5193),
r = a(7992),
i = a(1253),
d = a(4776),
g = a(6572),
m = a(3346),
_ = a(5623),
y = a(2870),
T = a(2143),
w = a(4939),
S = a(718),
O = a(2268),
E = a(8117),
v = a(2278),
P = a(5524),
L = a(5975),
V = a(3933),
R = a(6558),
k = a(5723),
Y = a(3738),
C = f(a(4909)),
$ = a(8428),
X = a(9793);
n.WEBGL_OP_RESOLVE_RULES = [
["Abs", "", "6+", C.abs],
["Acos", "", "7+", C.acos],
["Add", "", "7+", h.add],
["And", "", "7+", h.and],
["Asin", "", "7+", C.asin],
["Atan", "", "7+", C.atan],
["AveragePool", "", "7+", T.averagePool, T.parseAveragePoolAttributes],
["BatchNormalization", "", "7+", s.batchNormalization, s.parseBatchNormalizationAttributes],
["Cast", "", "6+", p.cast, p.parseCastAttributes],
["Ceil", "", "6+", C.ceil],
["Clip", "", "6-10", C.clip, C.parseClipAttributes],
["Clip", "", "11+", C.clipV11],
["Concat", "", "4+", l.concat, l.parseConcatAttributes],
["Conv", "", "1+", o.conv, o.parseConvAttributes],
["ConvTranspose", "", "1+", t.convTranspose, t.parseConvTransposeAttributes],
["Cos", "", "7+", C.cos],
["Div", "", "7+", h.div],
["Dropout", "", "7+", C.identity],
["DepthToSpace", "", "1+", e.depthToSpace, e.parseDepthToSpaceAttributes],
["Equal", "", "7+", h.equal],
["Elu", "", "6+", C.elu, C.parseEluAttributes],
["Exp", "", "6+", C.exp],
["Flatten", "", "1+", r.flatten, r.parseFlattenAttributes],
["Floor", "", "6+", C.floor],
["FusedConv", "com.microsoft", "1+", o.conv, o.parseConvAttributes],
["Gather", "", "1+", i.gather, i.parseGatherAttributes],
["Gemm", "", "7-10", d.gemm, d.parseGemmAttributesV7],
["Gemm", "", "11+", d.gemm, d.parseGemmAttributesV11],
["GlobalAveragePool", "", "1+", T.globalAveragePool, T.parseGlobalAveragePoolAttributes],
["GlobalMaxPool", "", "1+", T.globalMaxPool],
["Greater", "", "7+", h.greater],
["Identity", "", "1+", C.identity],
["ImageScaler", "", "1+", g.imageScaler, g.parseImageScalerAttributes],
["InstanceNormalization", "", "6+", m.instanceNormalization, m.parseInstanceNormalizationAttributes],
["LeakyRelu", "", "6+", C.leakyRelu, C.parseLeakyReluAttributes],
["Less", "", "7+", h.less],
["Log", "", "6+", C.log],
["MatMul", "", "1+", _.matMul, _.parseMatMulAttributes],
["MaxPool", "", "1+", T.maxPool, T.parseMaxPoolAttributes],
["Mul", "", "7+", h.mul],
["Neg", "", "6+", C.neg],
["Not", "", "1+", C.not],
["Or", "", "7+", h.or],
["Pad", "", "2-10", y.padV2, y.parsePadAttributesV2],
["Pad", "", "11+", y.padV11, y.parsePadAttributesV11],
["Pow", "", "7+", h.pow],
["PRelu", "", "7+", h.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+", C.relu],
["Reshape", "", "5+", S.reshape],
["Resize", "", "10", O.resize, O.parseResizeAttributesV10],
["Resize", "", "11+", O.resize, O.parseResizeAttributesV11],
["Shape", "", "1+", E.shape],
["Sigmoid", "", "6+", C.sigmoid],
["Sin", "", "7+", C.sin],
["Slice", "", "10+", v.sliceV10],
["Slice", "", "1-9", v.slice, v.parseSliceAttributes],
["Softmax", "", "1-12", P.softmax, P.parseSoftmaxAttributes],
["Softmax", "", "13+", P.softmaxV13, P.parseSoftmaxAttributesV13],
["Split", "", "2-12", L.split, L.parseSplitAttributes],
["Sqrt", "", "6+", C.sqrt],
["Squeeze", "", "1-12", V.squeeze, V.parseSqueezeAttributes],
["Squeeze", "", "13+", V.squeezeV13],
["Sub", "", "7+", h.sub],
["Sum", "", "6+", R.sum],
["Tan", "", "7+", C.tan],
["Tanh", "", "6+", C.tanh],
["Tile", "", "6+", k.tile],
["Transpose", "", "1+", Y.transpose, Y.parseTransposeAttributes],
["Upsample", "", "7-8", X.upsample, X.parseUpsampleAttributesV7],
["Upsample", "", "9", X.upsample, X.parseUpsampleAttributesV9],
["Unsqueeze", "", "1-12", $.unsqueeze, $.parseUnsqueezeAttributes],
["Unsqueeze", "", "13+", $.unsqueezeV13],
["Xor", "", "7+", h.xor]
]
},
2898: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.parseBatchNormalizationAttributes = n.batchNormalization = void 0;
const u = a(246),
c = a(5060),
f = a(2039),
s = {
name: "BatchNormalization",
inputNames: ["A", "Scale", "B", "Mean", "Variance"],
inputTypes: [f.TextureType.unpacked, f.TextureType.unpacked, f.TextureType.unpacked, f.TextureType.unpacked, f.TextureType.unpacked]
};
n.batchNormalization = (l, o, t) => (p(o), [l.run(Object.assign(Object.assign({}, s), {
cacheHint: t.cacheKey,
get: () => h(l, o, t)
}), o)]), n.parseBatchNormalizationAttributes = l => {
const o = l.attributes.getFloat("epsilon", 1e-5),
t = l.attributes.getFloat("momentum", .9),
e = l.attributes.getInt("spatial", 1);
return (0, u.createAttributeWithCacheKey)({
epsilon: o,
momentum: t,
spatial: e
})
};
const h = (l, o, t) => {
const e = (0, c.getGlsl)(l.session.backend.glContext.version),
r = o[0].dims.length,
[i, d] = l.calculateTextureWidthAndHeight(o[1].dims, f.TextureType.unpacked),
g = `
float process(int[${r}] indices) {
vec2 position = offsetToCoords(indices[1], ${i}, ${d});
float scale = getColorAsFloat(${e.texture2D}(Scale, position));
float mean = getColorAsFloat(${e.texture2D}(Mean, position));
float variance = getColorAsFloat(${e.texture2D}(Variance, position));
float b = getColorAsFloat(${e.texture2D}(B, position));
return scale * ( (_A(indices) - mean) / sqrt(variance + float(${t.epsilon})) ) + b;
}`;
return Object.assign(Object.assign({}, s), {
output: {
dims: o[0].dims,
type: o[0].type,
textureType: f.TextureType.unpacked
},
shaderSource: g
})
},
p = l => {
if (!l || l.length !== 5) throw new Error("BatchNormalization requires 5 inputs.");
const o = l[0],
t = l[1],
e = l[2],
r = l[3],
i = l[4];
if (o.dims.length < 3 || t.dims.length !== 1 || e.dims.length !== 1 || r.dims.length !== 1 || i.dims.length !== 1) throw new Error("invalid input shape.");
if (t.dims[0] !== o.dims[1] || e.dims[0] !== o.dims[1] || r.dims[0] !== o.dims[1] || i.dims[0] !== o.dims[1]) throw new Error("invalid input shape.");
if (o.type !== "float32" && o.type !== "float64" || t.type !== "float32" && t.type !== "float64" || e.type !== "float32" && e.type !== "float64" || r.type !== "float32" && r.type !== "float64" || i.type !== "float32" && i.type !== "float64") throw new Error("invalid input tensor types.")
}
},
7839: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.xor = n.sub = n.pRelu = n.pow = n.or = n.mul = n.less = n.greater = n.equal = n.div = n.and = n.add = n.glslPRelu = n.glslPow = n.glslXor = n.glslOr = n.glslAnd = n.glslLess = n.glslGreater = n.glslEqual = n.glslSub = n.glslMul = n.glslDiv = n.glslAdd = void 0;
const u = a(2517),
c = a(8520),
f = a(5060),
s = a(2039);
function h() {
const w = "add_";
return {
body: `
float ${w}(float a, float b) {
return a + b;
}
vec4 ${w}(vec4 v1, vec4 v2) {
return v1 + v2;
}
`,
name: w,
type: c.FunctionType.ValueBased
}
}
function p() {
const w = "div_";
return {
body: `
float ${w}(float a, float b) {
return a / b;
}
vec4 ${w}(vec4 v1, vec4 v2) {
return v1 / v2;
}
`,
name: w,
type: c.FunctionType.ValueBased
}
}
function l() {
const w = "mul_";
return {
body: `
float ${w}(float a, float b) {
return a * b;
}
vec4 ${w}(vec4 v1, vec4 v2) {
return v1 * v2;
}
`,
name: w,
type: c.FunctionType.ValueBased
}
}
function o() {
const w = "sub_";
return {
body: `
float ${w}(float a, float b) {
return a - b;
}
vec4 ${w}(vec4 v1, vec4 v2) {
return v1 - v2;
}
`,
name: w,
type: c.FunctionType.ValueBased
}
}
function t() {
const w = "equal_";
return {
body: `
float ${w}(float a, float b) {
return float(a == b);
}
vec4 ${w}(vec4 v1, vec4 v2) {
return vec4(equal(v1, v2));
}
`,
name: w,
type: c.FunctionType.ValueBased
}
}
function e() {
const w = "greater_";
return {
body: `
float ${w}(float a, float b) {
return float(a > b);
}
vec4 ${w}(vec4 v1, vec4 v2) {
return vec4( v1.r > v2.r ,
v1.g > v2.g,
v1.b > v2.b,
v1.a > v2.a );
}
`,
name: w,
type: c.FunctionType.ValueBased
}
}
function r() {
const w = "less_";
return {
body: `
float ${w}(float a, float b) {
return float(a < b);
}
vec4 ${w}(vec4 v1, vec4 v2) {
return vec4( v1.r < v2.r ,
v1.g < v2.g,
v1.b < v2.b,
v1.a < v2.a );
}
`,
name: w,
type: c.FunctionType.ValueBased
}
}
function i() {
const w = "and_";
return {
body: `
float ${w}(float a, float b) {
return float( bool(a) && bool(b) );
}
vec4 ${w}(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: w,
type: c.FunctionType.ValueBased
}
}
function d() {
const w = "or_";
return {
body: `
float ${w}(float a, float b) {
return float( bool(a) || bool(b) );
}
vec4 ${w}(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: w,
type: c.FunctionType.ValueBased
}
}
function g() {
const w = "xor_";
return {
body: `
float ${w}(float a, float b) {
return float( bool(a) ^^ bool(b) );
}
vec4 ${w}(vec4 v1, vec4 v2) {
bvec4 b1 = bvec4(v1);
bvec4 b2 = bvec4(v2);
return vec4( b1.r ^^ b2.r ,
b1.g ^^ b2.g,
b1.b ^^ b2.b,
b1.a ^^ b2.a );
}
`,
name: w,
type: c.FunctionType.ValueBased
}
}
function m() {
return function(w) {
const S = `${w}_`;
return {
body: `
float ${S}(float a, float b) {
return ${w}(a, b);
}
vec4 ${S}(vec4 v1, vec4 v2) {
return ${w}(v1, v2);
}
`,
name: S,
type: c.FunctionType.ValueBased
}
}("pow")
}
function _() {
const w = "prelu_";
return {
body: `
float ${w}(float a, float b) {
return a < 0.0 ? a * b: a;
}
vec4 ${w}(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: w,
type: c.FunctionType.ValueBased
}
}
n.glslAdd = h, n.glslDiv = p, n.glslMul = l, n.glslSub = o, n.glslEqual = t, n.glslGreater = e, n.glslLess = r, n.glslAnd = i, n.glslOr = d, n.glslXor = g, n.glslPow = m, n.glslPRelu = _;
const y = (w, S, O, E = S[0].type, v) => {
const P = w.session.pack ? s.TextureType.packed : s.TextureType.unpacked;
return {
name: O.name,
inputNames: ["A", "B"],
inputTypes: [P, P],
cacheHint: v,
get: () => T(w, S, O, E)
}
},
T = (w, S, O, E = S[0].type) => {
const v = w.session.pack ? s.TextureType.packed : s.TextureType.unpacked,
P = !u.ShapeUtil.areEqual(S[0].dims, S[1].dims);
let L = S[0].dims;
const V = w.session.pack;
if (P) {
const Y = u.BroadcastUtil.calcShape(S[0].dims, S[1].dims, !1);
if (!Y) throw new Error("Can't perform binary op on the given tensors");
L = Y;
const C = L.length,
$ = S[0].dims.length !== 0 ? S[0].dims.length : 1,
X = S[1].dims.length !== 0 ? S[1].dims.length : 1,
z = S[0].dims.length !== 0 ? "bcastIndices_A(indices, aindices);" : "aindices[0] = 0;",
Z = S[1].dims.length !== 0 ? "bcastIndices_B(indices, bindices);" : "bindices[0] = 0;",
J = (0, f.getGlsl)(w.session.backend.glContext.version),
ue = V ? `
${O.body}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = ${O.name}(a, b);
${J.output} = result;
}` : `
${O.body}
float process(int indices[${C}]) {
int aindices[${$}];
int bindices[${X}];
${z}
${Z}
return ${O.name}(_A(aindices), _B(bindices));
}`;
return {
name: O.name,
inputNames: ["A", "B"],
inputTypes: [v, v],
output: {
dims: L,
type: E,
textureType: v
},
shaderSource: ue,
hasMain: V
}
}
const R = (0, f.getGlsl)(w.session.backend.glContext.version),
k = `
${O.body}
void main() {
vec4 v1 = ${R.texture2D}(A, TexCoords);
vec4 v2 = ${R.texture2D}(B, TexCoords);
vec4 result = ${O.name}(v1, v2);
${R.output} = result;
}
`;
return {
name: O.name,
inputNames: ["A", "B"],
inputTypes: [v, v],
output: {
dims: S[0].dims,
type: E,
textureType: v
},
shaderSource: k,
hasMain: !0
}
};
n.add = (w, S) => [w.run(y(w, S, h()), S)], n.and = (w, S) => [w.run(y(w, S, i(), "bool"), S)], n.div = (w, S) => [w.run(y(w, S, p()), S)], n.equal = (w, S) => [w.run(y(w, S, t(), "bool"), S)], n.greater = (w, S) => [w.run(y(w, S, e(), "bool"), S)], n.less = (w, S) => [w.run(y(w, S, r(), "bool"), S)], n.mul = (w, S) => [w.run(y(w, S, l()), S)], n.or = (w, S) => [w.run(y(w, S, d(), "bool"), S)], n.pow = (w, S) => [w.run(y(w, S, m()), S)], n.pRelu = (w, S) => [w.run(y(w, S, _()), S)], n.sub = (w, S) => [w.run(y(w, S, o()), S)], n.xor = (w, S) => [w.run(y(w, S, g(), "bool"), S)]
},
4196: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.parseCastAttributes = n.cast = void 0;
const u = a(2517);
n.cast = (f, s, h) => (c(s), [f.cast(s[0], h)]), n.parseCastAttributes = f => u.ProtoUtil.tensorDataTypeFromProto(f.attributes.getInt("to"));
const c = f => {
if (!f || f.length !== 1) throw new Error("Cast requires 1 input.");
if (f[0].type === "string") throw new Error("Invalid input type.")
}
},
1163: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.createPackedConcatProgramInfoLoader = void 0;
const u = a(5060),
c = a(2039),
f = a(9390),
s = a(2827);
n.createPackedConcatProgramInfoLoader = (p, l, o) => {
const t = (e = l.length, r = o.cacheKey, {
name: "Concat (packed)",
inputNames: Array.from({
length: e
}, (i, d) => `X${d}`),
inputTypes: Array(e).fill(c.TextureType.packed),
cacheHint: r
});
var e, r;
return Object.assign(Object.assign({}, t), {
get: () => ((i, d, g, m) => {
const _ = g[0].dims.slice();
if (m >= _.length || m < -1 * _.length) throw new Error("axis specified for concat doesn't match input dimensionality");
m < 0 && (m = _.length + m);
const y = _.slice(0);
for (let z = 1; z < g.length; z++) {
const Z = g[z].dims.slice();
for (let J = 0; J < _.length; J++)
if (J === m) y[m] += Z[J];
else if (_[J] !== Z[J]) throw new Error("non concat dimensions must match")
}
const T = y.length,
w = (0, s.getChannels)("coords", T),
S = (0, f.getCoordsDataType)(T),
O = (0, s.unpackFromChannel)(),
E = g.map(z => z.dims),
v = (0, f.getGlChannels)(T),
P = new Array(E.length - 1);
P[0] = E[0][m];
for (let z = 1; z < P.length; z++) P[z] = P[z - 1] + E[z][m];
const L = v[m],
V = v.slice(-2),
R = v.join();
let k = `if (${L} < ${P[0]}) {
return getChannel(
getX0(${R}), vec2(${V.join()}));
}`;
for (let z = 1; z < P.length; z++) {
const Z = P[z - 1];
k += `
if (${L} < ${P[z]} && ${L} >= ${P[z-1]}) {
return getChannel(
getX${z}(${h(v,L,Z)}),
vec2(${h(V,L,Z)}));
}`
}
const Y = P.length,
C = P[P.length - 1];
k += `
return getChannel(
getX${Y}(${h(v,L,C)}),
vec2(${h(V,L,C)}));`;
const $ = (0, u.getGlsl)(i.session.backend.glContext.version),
X = `
${O}
float getValue(${v.map(z=>"int "+z)}) {
${k}
}
void main() {
${S} coords = getOutputCoords();
int lastDim = coords.${v[T-1]};
coords.${v[T-1]} = coords.${v[T-2]};
coords.${v[T-2]} = lastDim;
vec4 result = vec4(getValue(${w}), 0., 0., 0.);
${w[T-1]} = ${w[T-1]} + 1;
if (${w[T-1]} < ${y[T-1]}) {
result.g = getValue(${w});
}
${w[T-2]} = ${w[T-2]} + 1;
if (${w[T-2]} < ${y[T-2]}) {
result.a = getValue(${w});
}
${w[T-1]} = ${w[T-1]} - 1;
if (${w[T-2]} < ${y[T-2]} &&
${w[T-1]} < ${y[T-1]}) {
result.b = getValue(${w});
}
${$.output} = result;
}
`;
return Object.assign(Object.assign({}, d), {
output: {
dims: y,
type: g[0].type,
textureType: c.TextureType.packed
},
shaderSource: X,
hasMain: !0
})
})(p, t, l, o.axis)
})
};
const h = (p, l, o) => {
const t = p.indexOf(l);
return p.map((e, r) => r === t ? `${e} - ${o}` : e).join()
}
},
2069: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.parseConcatAttributes = n.concat = void 0;
const u = a(246),
c = a(2039),
f = a(1163);
n.concat = (e, r, i) => (t(r), e.session.pack && r[0].dims.length > 1 ? [e.run((0, f.createPackedConcatProgramInfoLoader)(e, r, i), r)] : [e.run(s(e, r, i), r)]);
const s = (e, r, i) => {
const d = (g = r.length, m = i.cacheKey, {
name: "Concat",
inputNames: Array.from({
length: g
}, (_, y) => `X${y}`),
inputTypes: Array(g).fill(c.TextureType.unpacked),
cacheHint: m
});
var g, m;
return Object.assign(Object.assign({}, d), {
get: () => ((_, y, T, w) => {
const S = T[0].dims.slice();
if (w >= S.length || w < -1 * S.length) throw new Error("axis specified for concat doesn't match input dimensionality");
w < 0 && (w = S.length + w);
const O = S.slice(0);
for (let R = 1; R < T.length; R++) {
const k = T[R].dims.slice();
for (let Y = 0; Y < S.length; Y++)
if (Y === w) O[w] += k[Y];
else if (S[Y] !== k[Y]) throw new Error("non concat dimensions must match")
}
const E = O.length,
v = new Array(T.length);
let P = 0;
for (let R = 0; R < v.length; ++R) P += T[R].dims[w], v[R] = P;
let L = "";
L = T.length < 5 ? h(v) : p(v);
const V = `
${l(T.length,E)}
${o(v)}
${L}
float process(int indices[${E}]) {
int textureIndex = getTextureWhereDataResides (indices[${w}]);
if(textureIndex != 0) {
indices[${w}] = indices[${w}] - int(getSizeInConcatAxisValueFromIndex(textureIndex-int(1)));
}
return fetchDataFromCorrectTexture(textureIndex, indices);
}`;
return Object.assign(Object.assign({}, y), {
output: {
dims: O,
type: T[0].type,
textureType: c.TextureType.unpacked
},
shaderSource: V
})
})(0, d, r, i.axis)
})
},
h = e => `int getTextureWhereDataResides(int index) {
${e.map((r,i)=>`if(index<${r}) {return ${i};}
`).join("")}
}`,
p = e => h(e),
l = (e, r) => {
const i = [`float fetchDataFromCorrectTexture(int textureIndex, int indices[${r}]) {`];
for (let d = 0; d < e; ++d) d === 0 ? i.push(` if (textureIndex == ${d}) { return _X${d}(indices); }`) : d === e - 1 ? i.push(` else { return _X${d}(indices); }`) : i.push(` else if (textureIndex == ${d}) { return _X${d}(indices); }`);
return i.push(" }"), i.join(`
`)
},
o = e => {
const r = ["int getSizeInConcatAxisValueFromIndex(int index) {"];
for (let i = 0; i < e.length; ++i) i === 0 ? r.push(` if (index == ${i}) { return ${e[i]}; }`) : i === e.length - 1 ? r.push(` else { return ${e[i]}; }`) : r.push(` else if (index == ${i}) { return ${e[i]}; }`);
return r.push(" }"), r.join(`
`)
};
n.parseConcatAttributes = e => (0, u.createAttributeWithCacheKey)({
axis: e.attributes.getInt("axis")
});
const t = e => {
if (!e || e.length < 1) throw new Error("too few inputs");
const r = e[0].type,
i = e[0].dims.length;
if (r === "string") throw new Error("string tensor is not supported yet");
for (const d of e) {
if (d.type !== r) throw new Error("input tensors should be one type");
if (d.dims.length !== i) throw new Error("input tensors should have the same shape")
}
}
},
4770: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.createUnpackedGroupedConvProgramInfoLoader = void 0;
const u = a(6231),
c = a(5060),
f = a(2039),
s = a(8138),
h = a(2823);
n.createUnpackedGroupedConvProgramInfoLoader = (p, l, o) => {
const t = (e = l.length > 2, r = o.cacheKey, {
name: "GroupedConv",
inputNames: e ? ["X", "W", "Bias"] : ["X", "W"],
inputTypes: e ? [f.TextureType.unpacked, f.TextureType.unpacked, f.TextureType.unpacked] : [f.TextureType.unpacked, f.TextureType.unpacked],
cacheHint: r
});
var e, r;
return Object.assign(Object.assign({}, t), {
get: () => ((i, d, g, m) => {
const _ = d.length > 2 ? "value += getBias(output_channel);" : "",
y = d[0].dims.slice(),
T = d[1].dims.slice(),
w = T[0] / m.group;
u.Logger.verbose("GroupedConv", `autpPad:${m.autoPad}, dilations:${m.dilations}, group:${m.group}, kernelShape:${m.kernelShape}, pads:${m.pads}, strides:${m.strides}`);
const S = (0, s.calculateOutputShape)(y, T, m.dilations, m.pads, m.strides),
O = (0, c.getGlsl)(i.session.backend.glContext.version),
{
activationFunction: E,
applyActivation: v
} = (0, h.getActivationSnippet)(m),
P = `
const ivec2 strides = ivec2(${m.strides[0]}, ${m.strides[1]});
const ivec2 pads = ivec2(${m.pads[0]}, ${m.pads[1]});
${E}
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 / ${w};
float value = 0.0;
for (int wInChannel = 0; wInChannel < ${T[1]}; wInChannel++) {
int input_channel = group_id * ${T[1]} + wInChannel;
for (int wHeight = 0; wHeight < ${T[2]}; wHeight++) {
int xHeight = xRCCorner.x + wHeight * ${m.dilations[0]};
if (xHeight < 0 || xHeight >= ${y[2]}) {
continue;
}
for (int wWidth = 0; wWidth < ${T[3]}; wWidth++) {
int xWidth = xRCCorner.y + wWidth * ${m.dilations[1]};
if (xWidth < 0 || xWidth >= ${y[3]}) {
continue;
}
float xVal = getX(batch, input_channel, xWidth, xHeight);
float wVal = getW(output_channel, wInChannel, wWidth, wHeight);
value += xVal*wVal;
}
}
}
${_}
${v}
${O.output} = vec4(value, .0, .0, .0);
}
`;
return Object.assign(Object.assign({}, g), {
output: {
dims: S,
type: d[0].type,
textureType: f.TextureType.unpacked
},
shaderSource: P,
hasMain: !0
})
})(p, l, t, o)
})
}
},
1386: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.conv2DPacked = n.conv2DPackedPointwise = void 0;
const u = a(8138),
c = a(8555),
f = a(708);
n.conv2DPackedPointwise = (s, h, p) => {
const l = h[0].dims,
o = h[1].dims,
t = (0, u.calculateOutputShape)(l, o, p.dilations, p.pads, p.strides),
e = s.reshapePacked(h[0], [l[1], l[2] * l[3]]),
r = s.reshapePacked(h[1], [o[0], o[1]]),
i = h.length > 2 ? [r, e, h[2]] : [r, e],
d = s.run((0, f.createPackedMatmulProgramInfoLoader)(s, i, p), i);
return s.reshapePacked(d, t)
}, n.conv2DPacked = (s, h, p) => {
const l = h[0].dims,
o = h[1].dims,
t = (0, u.calculateOutputShape)(l, o, p.dilations, p.pads, p.strides),
e = s.run((0, c.createPackedIm2ColProgramInfoLoader)(s, h[0], h[1], t, p), [h[0]]),
r = s.reshapePacked(h[1], [o[0], o[1] * o[2] * o[3]]),
i = h.length === 3 ? [r, e, h[2]] : [r, e],
d = s.run((0, f.createPackedMatmulProgramInfoLoader)(s, i, p), i);
return s.reshapePacked(d, t)
}
},
9663: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.parseConvTransposeAttributes = n.convTranspose = void 0;
const u = a(246),
c = a(5060),
f = a(2039),
s = a(2823),
h = (r, i, d, g, m, _) => (r - 1) * i + d + (g - 1) * m + 1 - _,
p = (r, i, d, g, m) => {
const _ = Math.floor(r / 2);
i === "SAME_UPPER" ? (d[g] = _, d[m] = r - _) : i === "SAME_LOWER" && (d[g] = r - _, d[m] = _)
};
n.convTranspose = (r, i, d) => (e(i, d), l(r, i, d));
const l = (r, i, d) => {
const g = t(d, i);
return [o(r, i, g)]
},
o = (r, i, d) => r.run(((g, m, _) => {
const y = (T = m.length > 2, w = _.cacheKey, {
name: "ConvTranspose",
inputNames: T ? ["X", "W", "B"] : ["X", "W"],
inputTypes: T ? [f.TextureType.unpacked, f.TextureType.unpacked, f.TextureType.unpacked] : [f.TextureType.unpacked, f.TextureType.unpacked],
cacheHint: w
});
var T, w;
return Object.assign(Object.assign({}, y), {
get: () => ((S, O, E, v) => {
const P = O.length > 2 ? "getB(output_channel)" : "0.0",
L = O[0].dims,
V = O[1].dims,
R = V[1],
k = V[0] / v.group,
Y = [O[0].dims[0], O[1].dims[1] * v.group, ...v.outputShape],
C = (0, c.getGlsl)(S.session.backend.glContext.version),
{
activationFunction: $,
applyActivation: X
} = (0, s.getActivationSnippet)(v),
z = `
const ivec2 strides = ivec2(${v.strides[0]}, ${v.strides[1]});
const ivec2 pads = ivec2(${v.pads[0]}, ${v.pads[1]});
${$}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int output_channel = coords.y;
ivec2 loc = coords.zw + pads;
int group_id = output_channel / ${R};
int wOutChannel = output_channel - group_id * ${R};
float value = ${P};
for (int inChannelOffset = 0; inChannelOffset < ${k}; inChannelOffset++) {
int input_channel = group_id * ${k} + inChannelOffset;
for (int wWOff = 0; wWOff < ${V[2]}; wWOff++) {
for (int wHOff = 0; wHOff < ${V[3]}; wHOff++) {
ivec2 wOff = ivec2(wWOff * ${v.dilations[0]}, wHOff * ${v.dilations[1]});
ivec2 wLoc = loc - wOff;
ivec2 wLocIn = wLoc / strides;
if (
wLocIn * strides == wLoc &&
wLocIn.x >= 0 && wLocIn.x < ${L[2]} &&
wLocIn.y >= 0 && wLocIn.y < ${L[3]}
) {
float xVal = getX(batch, input_channel, wLocIn.y, wLocIn.x);
float wVal = getW(input_channel, wOutChannel, wHOff, wWOff);
value += xVal * wVal;
}
}
}
}
${X}
${C.output} = vec4(value, .0, .0, .0);
}
`;
return Object.assign(Object.assign({}, E), {
output: {
dims: Y,
type: O[0].type,
textureType: f.TextureType.unpacked
},
shaderSource: z,
hasMain: !0
})
})(g, m, y, _)
})
})(r, i, d), i),
t = (r, i) => {
const d = r.kernelShape.slice();
if (r.kernelShape.length === 0)
for (let y = 2; y < i[1].dims.length; ++y) d.push(i[1].dims[y]);
const g = r.pads.slice(),
m = r.outputShape.slice();
((y, T, w, S, O, E, v, P) => {
const L = y.length - 2,
V = P.length === 0;
for (let R = 0; R < L; ++R) {
const k = V ? y[R + 2] * E[R] : P[R],
Y = h(y[R + 2], E[R], O[R], T[R], w[R], k);
p(Y, S, O, R, R + L), V && P.push(E[R] * (y[R + 2] - 1) + v[R] + (T[R] - 1) * w[R] + 1 - O[R] - O[R + L])
}
})(i[0].dims, d, r.dilations, r.autoPad, g, r.strides, r.outputPadding, m);
const _ = Object.assign({}, r);
return Object.assign(_, {
kernelShape: d,
pads: g,
outputShape: m,
cacheKey: r.cacheKey
}), _
};
n.parseConvTransposeAttributes = r => {
const i = r.attributes,
d = (0, s.parseInternalActivationAttributes)(i),
g = i.getString("auto_pad", "NOTSET"),
m = i.getInts("dilations", [1, 1]),
_ = i.getInt("group", 1),
y = i.getInts("kernel_shape", []),
T = i.getInts("output_padding", [0, 0]),
w = i.getInts("output_shape", []),
S = i.getInts("pads", [0, 0, 0, 0]),
O = i.getInts("strides", [1, 1]);
return (0, u.createAttributeWithCacheKey)(Object.assign({
autoPad: g,
dilations: m,
group: _,
kernelShape: y,
outputPadding: T,
outputShape: w,
pads: S,
strides: O
}, d))
};
const e = (r, i) => {
if (!r || r.length !== 2 && r.length !== 3) throw new Error("Conv requires 2 or 3 inputs");
if (r[0].dims.length !== 4 || r[1].dims.length !== 4) throw new Error("currently only support 2-dimensional conv");
if (r[0].dims[1] !== r[1].dims[0]) throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");
const d = r[1].dims[1] * i.group;
if (r.length === 3 && (r[2].dims.length !== 1 || r[2].dims[0] !== d)) throw new Error("invalid bias");
const g = r[0].dims.length - 2;
if (i.dilations.length !== g) throw new Error(`dilations should be ${g}D`);
if (i.strides.length !== g) throw new Error(`strides should be ${g}D`);
if (i.pads.length !== 2 * g) throw new Error(`pads should be ${2*g}D`);
if (i.outputPadding.length !== g) throw new Error(`output_padding should be ${g}D`);
if (i.kernelShape.length !== 0 && i.kernelShape.length !== r[1].dims.length - 2) throw new Error("invalid kernel shape");
if (i.outputShape.length !== 0 && i.outputShape.length !== r[0].dims.length - 2) throw new Error("invalid output shape");
if (r[0].type !== "float32" || r[1].type !== "float32") throw new Error("ConvTranspose input(X,W) should be float tensor");
if (r.length === 3 && r[2].type !== "float32") throw new Error("ConvTranspose input(bias) should be float tensor")
}
},
8138: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.parseConvAttributes = n.conv = n.calculateOutputShape = void 0;
const u = a(246),
c = a(2517),
f = a(4770),
s = a(1386),
h = a(9828),
p = a(2823),
l = a(3248),
o = a(5623);
n.calculateOutputShape = (g, m, _, y, T) => {
const w = g[0],
S = g.slice(2),
O = S.length,
E = m[0],
v = m.slice(2).map((L, V) => L + (L - 1) * (_[V] - 1)),
P = S.map((L, V) => L + y[V] + y[V + O]).map((L, V) => Math.floor((L - v[V] + T[V]) / T[V]));
return [w, E].concat(...P)
}, n.conv = (g, m, _) => (d(m, _), t(g, m, _));
const t = (g, m, _) => {
const y = i(_, m),
T = g.session.pack,
w = y.kernelShape[0] === 1 && y.kernelShape[1] === 1;
return y.group > 1 ? [g.run((0, f.createUnpackedGroupedConvProgramInfoLoader)(g, m, y), m)] : w && T ? [e(g, m, y)] : T && m[0].dims.length === 4 && m[0].dims[0] === 1 && !w ? [(0, s.conv2DPacked)(g, m, y)] : [r(g, m, y)]
},
e = (g, m, _) => {
const y = m[0].dims,
T = m[1].dims,
w = (0, n.calculateOutputShape)(y, T, _.dilations, _.pads, _.strides),
S = g.reshapeUnpacked(m[0], [y[1], y[2] * y[3]]),
O = g.reshapeUnpacked(m[1], [T[0], T[1]]),
E = m.length > 2 ? [O, S, m[2]] : [O, S],
v = g.run((0, o.createMatmulProgramInfoLoader)(E, _), E);
return g.reshapeUnpacked(v, w)
},
r = (g, m, _) => {
const y = m[0].dims,
T = m[1].dims,
w = (0, n.calculateOutputShape)(y, T, _.dilations, _.pads, _.strides),
S = g.run((0, l.createIm2ColProgramInfoLoader)(g, m[0], m[1], w, _), [m[0]]),
O = m.length === 3 ? [S, m[1], m[2]] : [S, m[1]];
return g.run((0, h.createDotProductProgramInfoLoader)(g, m, w, _), O)
},
i = (g, m) => {
const _ = g.kernelShape.slice();
if (g.kernelShape.length === 0)
for (let w = 2; w < m[1].dims.length; ++w) _.push(m[1].dims[w]);
const y = g.pads.slice();
c.PoolConvUtil.adjustPadsBasedOnAutoPad(m[0].dims, g.strides, g.dilations, _, y, g.autoPad);
const T = Object.assign({}, g);
return Object.assign(T, {
kernelShape: _,
pads: y,
cacheKey: g.cacheKey
}), T
};
n.parseConvAttributes = g => {
const m = g.attributes,
_ = (0, p.parseInternalActivationAttributes)(m),
y = m.getString("auto_pad", "NOTSET"),
T = m.getInts("dilations", [1, 1]),
w = m.getInt("group", 1),
S = m.getInts("kernel_shape", []),
O = m.getInts("pads", [0, 0, 0, 0]),
E = m.getInts("strides", [1, 1]);
return (0, u.createAttributeWithCacheKey)(Object.assign({
autoPad: y,
dilations: T,
group: w,
kernelShape: S,
pads: O,
strides: E
}, _))
};
const d = (g, m) => {
if (!g || g.length !== 2 && g.length !== 3) throw new Error("Conv requires 2 or 3 inputs");
if (g[0].dims.length !== 4 || g[1].dims.length !== 4) throw new Error("currently only support 2-dimensional conv");
if (g[0].dims[1] !== g[1].dims[1] * m.group) throw new Error("FILTER_IN_CHANNEL should be equal to DATA_CHANNEL");
if (g.length === 3 && (g[2].dims.length !== 1 || g[1].dims[0] !== g[2].dims[0])) throw new Error("invalid bias");
const _ = g[0].dims.length - 2;
if (m.dilations.length !== _) throw new Error(`dilations should be ${_}D`);
if (m.strides.length !== _) throw new Error(`strides should be ${_}D`);
if (m.pads.length !== 2 * _) throw new Error(`pads should be ${2*_}D`);
if (m.kernelShape.length !== 0 && m.kernelShape.length !== g[1].dims.length - 2) throw new Error("invalid kernel shape");
if (g[0].type !== "float32" || g[1].type !== "float32") throw new Error("Conv input(X,W) should be float tensor");
if (g.length === 3 && g[2].type !== "float32") throw new Error("Conv input(bias) should be float tensor")
}
},
5193: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.parseDepthToSpaceAttributes = n.depthToSpace = void 0;
const u = a(3738);
n.depthToSpace = (f, s, h) => {
c(s);
const p = h.blocksize,
l = p * p,
o = h.mode === "DCR" ? [0, 3, 4, 1, 5, 2] : [0, 1, 4, 2, 5, 3],
t = h.mode === "DCR" ? [s[0].dims[0], p, p, s[0].dims[1] / l, s[0].dims[2], s[0].dims[3]] : [s[0].dims[0], s[0].dims[1] / l, p, p, s[0].dims[2], s[0].dims[3]],
e = f.reshapeUnpacked(s[0], t),
r = {
perm: o,
cacheKey: `${o}`
},
[i] = (0, u.transpose)(f, [e], r),
d = [s[0].dims[0], s[0].dims[1] / l, s[0].dims[2] * p, s[0].dims[3] * p];
return [f.reshapeUnpacked(i, d)]
}, n.parseDepthToSpaceAttributes = f => {
const s = f.attributes.getInt("blocksize");
if (s < 1) throw new Error(`blocksize must be >= 1, but got : ${s} for DepthToSpace`);
const h = f.attributes.getString("mode", "DCR");
if (h !== "DCR" && h !== "CRD") throw new Error(`unrecognized mode: ${h} for DepthToSpace`);
return {
mode: h,
blocksize: s
}
};
const c = f => {
if (f.length !== 1) throw new Error(`DepthToSpace expect 1 inputs, but got ${f.length}`);
if (f[0].type === "string" || f[0].dims.length !== 4) throw new TypeError("DepthToSpace input should be a 4-D numeric tensor")
}
},
9828: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.createDotProductProgramInfoLoader = void 0;
const u = a(2517),
c = a(5060),
f = a(2039),
s = a(2823),
h = a(3248);
n.createDotProductProgramInfoLoader = (p, l, o, t) => {
const e = ((r, i) => ({
name: "ConvDotProduct",
inputNames: r ? ["Im2Col", "K", "B"] : ["Im2Col", "K"],
inputTypes: r ? [f.TextureType.unpacked, f.TextureType.packedLastDimension, f.TextureType.unpacked] : [f.TextureType.unpacked, f.TextureType.packedLastDimension],
cacheKey: i.activationCacheKey
}))(l.length > 2, t);
return Object.assign(Object.assign({}, e), {
get: () => ((r, i, d, g, m) => {
const _ = d[0].dims,
y = d[1].dims,
T = [y[0], Math.ceil(_[1] * y[2] * y[3] / 4)],
w = (0, h.calculateIm2ColDims)(_, y, g),
[S, O] = r.calculateTextureWidthAndHeight(T, f.TextureType.packedLastDimension),
E = u.ShapeUtil.computeStrides(w),
[v, P] = r.calculateTextureWidthAndHeight(w, f.TextureType.packedLastDimension),
L = g.length,
V = d.length < 3 ? "0.0" : "_B(b)",
R = Math.ceil(_[1] * y[2] * y[3] / 4),
{
activationFunction: k,
applyActivation: Y
} = (0, s.getActivationSnippet)(m),
C = (0, c.getGlsl)(r.session.backend.glContext.version),
$ = `
${k}
float process(int indices[${L}]) {
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] * ${E[0]} + im2col[1] * ${E[1]} + im2col[2] * ${E[2]};
int kernelOffset = indices[1] * ${T[1]};
float value = ${V};
for (int i = 0; i < ${R}; ++i) {
vec2 im2colCoords = offsetToCoords(im2colOffset, ${v}, ${P});
vec2 kernelCoords = offsetToCoords(kernelOffset, ${S}, ${O});
value += dot(${C.texture2D}(Im2Col, im2colCoords), ${C.texture2D}(K, kernelCoords));
++im2colOffset;
++kernelOffset;
}
${Y}
return value;
}`;
return Object.assign(Object.assign({}, i), {
output: {
dims: g,
type: d[0].type,
textureType: f.TextureType.unpacked
},
shaderSource: $
})
})(p, e, l, o, t)
})
}
},
7992: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.parseFlattenAttributes = n.flatten = void 0;
const u = a(2517);
n.flatten = (f, s, h) => {
c(s, h);
const p = u.ShapeUtil.flattenShape(s[0].dims, h);
return [f.reshapeUnpacked(s[0], p)]
}, n.parseFlattenAttributes = f => f.attributes.getInt("axis", 1);
const c = (f, s) => {
if (!f || f.length !== 1) throw new Error("Flatten requires 1 input.");
const h = f[0].dims.length;
if (h === 0) throw new Error("scalar tensor is not supported.");
if (s < -h || s > h) throw new Error("Invalid axis");
if (f[0].type === "string") throw new Error("string tensor is not supported.")
}
},
2823: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.parseInternalActivationAttributes = n.getActivationSnippet = void 0;
const u = a(2517),
c = a(4909);
n.getActivationSnippet = function(f) {
let s;
switch (f.activation) {
case "Relu":
s = (0, c.glslRelu)();
break;
case "Sigmoid":
s = (0, c.glslSigmoid)();
break;
case "Clip":
s = (0, c.glslClip)(f.clipMin, f.clipMax);
break;
default:
return {
activationFunction: "", applyActivation: ""
}
}
const h = s.name;
return {
activationFunction: s.body,
applyActivation: `value = ${h}_(value);`
}
}, n.parseInternalActivationAttributes = f => {
const s = f.getString("activation", "");
if (s === "Clip") {
const [h, p] = f.getFloats("activation_params", [u.MIN_CLIP, u.MAX_CLIP]);
return {
activation: s,
clipMax: p,
clipMin: h,
activationCacheKey: `${s}:${h},${p}`
}
}
return {
activation: s,
activationCacheKey: s
}
}
},
1253: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.parseGatherAttributes = n.gather = void 0;
const u = a(246),
c = a(782),
f = a(2517),
s = a(2039);
n.gather = (o, t, e) => (l(t, e.axis), [o.run(p(o, t, e), t)]), n.parseGatherAttributes = o => (0, u.createAttributeWithCacheKey)({
axis: o.attributes.getInt("axis", 0)
});
const h = {
name: "Gather",
inputNames: ["A", "B"],
inputTypes: [s.TextureType.unpacked, s.TextureType.unpacked]
},
p = (o, t, e) => {
const r = Object.assign(Object.assign({}, h), {
cacheHint: e.cacheKey
});
return Object.assign(Object.assign({}, r), {
get: () => ((i, d, g, m) => {
const _ = g[0].dims.slice(),
y = g[1].dims.slice(),
T = new Array(_.length + y.length - 1);
m = f.ShapeUtil.normalizeAxis(m, _.length);
const w = [];
for (let O = 0; O < T.length; O++) O < m ? (T[O] = _[O], w.push(`inputIdx[${O}] = outputIdx[${O}];`)) : O < m + y.length ? (T[O] = y[O - m], w.push(`indexDataIdx[${O-m}] = outputIdx[${O}];`)) : (T[O] = _[O - y.length + 1], w.push(`inputIdx[${O-y.length+1}] = outputIdx[${O}];`));
const S = `
float process(int outputIdx[${T.length||1}]) {
int inputIdx[${_.length}];
int indexDataIdx[${y.length||1}];
indexDataIdx[0] = 0;
${w.join(`
`)}
int idx = int(_B(indexDataIdx));
inputIdx[${m}] = idx < 0 ? idx + ${_[m]} : idx;
return _A(inputIdx);
}`;
return Object.assign(Object.assign({}, d), {
output: {
dims: T,
type: g[0].type,
textureType: s.TextureType.unpacked
},
shaderSource: S
})
})(0, r, t, e.axis)
})
},
l = (o, t) => {
if (!o || o.length !== 2) throw new Error("Gather requires 2 inputs.");
const e = o[0].dims.length;
if (e < 1) throw new Error("Invalid input shape.");
if (t < -e || t > e - 1) throw new Error("Invalid axis.");
if (c.NUMBER_TYPES.indexOf(o[0].type) === -1) throw new Error("Invaid input type.");
if (o[1].type !== "int32" && o[1].type !== "int16") throw new Error("Invaid input type.")
}
},
4776: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.parseGemmAttributesV11 = n.parseGemmAttributesV7 = n.gemm = void 0;
const u = a(246),
c = a(2517),
f = a(2039);
n.gemm = (o, t, e) => (l(t, e), [o.run(h(t, e), t)]);
const s = (o, t) => {
const e = o.attributes.getInt("transA", 0) !== 0,
r = o.attributes.getInt("transB", 0) !== 0,
i = o.attributes.getFloat("alpha", 1),
d = o.attributes.getFloat("beta", 1);
return (0, u.createAttributeWithCacheKey)({
transA: e,
transB: r,
alpha: i,
beta: d,
isOptionalC: t
})
};
n.parseGemmAttributesV7 = o => s(o, !1), n.parseGemmAttributesV11 = o => s(o, !0);
const h = (o, t) => {
const e = {
name: "Gemm",
inputNames: o.length === 3 ? ["A", "B", "C"] : ["A", "B"],
inputTypes: o.length === 3 ? [f.TextureType.unpacked, f.TextureType.unpacked, f.TextureType.unpacked] : [f.TextureType.unpacked, f.TextureType.unpacked],
key: t.cacheKey
};
return Object.assign(Object.assign({}, e), {
get: () => p(e, o, t)
})
},
p = (o, t, e) => {
const r = t[0].dims.slice(),
i = t[1].dims.slice(),
[d, g] = c.GemmUtil.getShapeOfGemmResult(r, e.transA, i, e.transB, t.length === 3 ? t[2].dims : void 0),
m = [d, g];
if (!m) throw new Error("Can't use gemm on the given tensors");
let _ = r[r.length - 1],
y = "";
e.transA && (_ = r[0]), e.transA && e.transB ? y = "value += _A_T(a) * _B_T(b);" : e.transA && !e.transB ? y = "value += _A_T(a) * _B(b);" : !e.transA && e.transB ? y = "value += _A(a) * _B_T(b);" : e.transA || e.transB || (y = "value += _A(a) * _B(b);");
const T = m.length,
w = `
float process(int indices[${T}]) {
int a[${T}];
int b[${T}];
${t.length===3?`int c[${t[2].dims.length}];`:""}
copyVec(indices, a);
copyVec(indices, b);
${t.length===3?"bcastIndices_C(indices, c);":""}
float value = 0.0;
for (int k=0; k<${_}; ++k) {
a[${T-1}] = k;
b[${T-2}] = k;
${y}
}
value = value * alpha;
${t.length===3?"value += beta * _C(c);":""}
return value;
}`;
return Object.assign(Object.assign({}, o), {
output: {
dims: m,
type: t[0].type,
textureType: f.TextureType.unpacked
},
variables: [{
name: "alpha",
type: "float",
data: e.alpha
}, {
name: "beta",
type: "float",
data: e.beta
}],
shaderSource: w
})
},
l = (o, t) => {
if (!o) throw new Error("Input is missing");
if (t.isOptionalC && (o.length < 2 || o.length > 3)) throw new Error("Invaid input shape.");
if (!t.isOptionalC && o.length !== 3) throw new Error("Gemm requires 3 inputs");
if (o.length === 3 && o[2].dims.length !== 1 && o[2].dims.length !== 2) throw new Error("Invalid input shape of C");
if (o[0].type !== "float32" && o[0].type !== "float64" || o[1].type !== "float32" && o[1].type !== "float64" || o.length === 3 && o[2].type !== "float32" && o[2].type !== "float64") throw new Error("Invalid input type.");
if (o[0].type !== o[1].type || o.length === 3 && o[0].type !== o[2].type) throw new Error("Input types are mismatched")
}
},
8555: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.createPackedIm2ColProgramInfoLoader = void 0;
const u = a(5060),
c = a(2039),
f = a(2827);
n.createPackedIm2ColProgramInfoLoader = (s, h, p, l, o) => {
const t = (e = o.cacheKey, {
name: "Im2Col (packed)",
inputNames: ["A"],
inputTypes: [c.TextureType.packed],
cacheHint: e
});
var e;
return Object.assign(Object.assign({}, t), {
get: () => ((r, i, d, g, m, _) => {
const y = d.dims,
T = g.dims,
w = m.length,
S = [T[1] * T[2] * T[3], m[2] * m[3]],
O = T[2] * T[3],
E = (0, f.unpackFromChannel)(),
v = (0, u.getGlsl)(r.session.backend.glContext.version);
let P = "";
for (let V = 0; V <= 1; V++)
for (let R = 0; R <= 1; R++) P += `
blockIndex = rc.x + ${R};
pos = rc.y + ${V};
if(blockIndex < ${S[1]} && pos < ${S[0]}) {
offsetY = int(blockIndex / (${m[w-1]})) * ${_.strides[0]} -
${_.pads[0]};
d0 = offsetY + ${_.dilations[0]} * (imod(pos, ${O}) / ${T[2]});
if(d0 < ${y[2]} && d0 >= 0) {
offsetX = imod(blockIndex, ${m[w-1]}) * ${_.strides[1]} -
${_.pads[1]};
d1 = offsetX + ${_.dilations[1]} * imod(imod(pos, ${O}), ${T[2]});
if(d1 < ${y[3]} && d1 >= 0) {
ch = int(float(pos)/ ${O}.);
innerDims = vec2(d0, d1);
result[${2*V+R}] = getChannel(
getA(0, ch, int(innerDims.x),
int(innerDims.y)), innerDims);
}
}
}
`;
const L = `
${E}
void main() {
ivec2 rc = getOutputCoords();
vec4 result = vec4(0.0);
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
vec2 innerDims;
${P}
${v.output} = result;
}
`;
return Object.assign(Object.assign({}, i), {
output: {
dims: S,
type: d.type,
textureType: c.TextureType.packed
},
shaderSource: L,
hasMain: !0
})
})(s, t, h, p, l, o)
})
}
},
3248: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.calculateIm2ColDims = n.createIm2ColProgramInfoLoader = void 0;
const u = a(2039);
n.createIm2ColProgramInfoLoader = (c, f, s, h, p) => {
const l = (o = p.cacheKey, {
name: "Im2Col",
inputNames: ["X"],
inputTypes: [u.TextureType.unpacked],
cacheHint: o
});
var o;
return Object.assign(Object.assign({}, l), {
get: () => ((t, e, r, i, d, g) => {
const m = r.dims,
_ = i.dims,
y = d.length,
T = (0, n.calculateIm2ColDims)(m, _, d, 4),
w = `
const int XC = ${m[1]};
const int XH = ${m[2]};
const int XW = ${m[3]};
const int KH = ${g.kernelShape[0]};
const int KW = ${g.kernelShape[1]};
const int dilationH = ${g.dilations[0]};
const int dilationW = ${g.dilations[1]};
const int strideH = ${g.strides[0]};
const int strideW = ${g.strides[1]};
const int padH = ${g.pads[0]};
const int padW = ${g.pads[1]};
const int KHKW = KH*KW;
const int XCKHKW = XC * KHKW;
const int outputChannels = 4;
vec4 process(int indices[${y}]) {
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[${m.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({}, e), {
output: {
dims: T,
type: r.type,
textureType: u.TextureType.packedLastDimension
},
shaderSource: w
})
})(0, l, f, s, h, p)
})
}, n.calculateIm2ColDims = (c, f, s, h = 4) => [s[0], s[2], s[3], Math.ceil(c[1] * f[2] * f[3] / h)]
},
6572: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.parseImageScalerAttributes = n.imageScaler = void 0;
const u = a(246),
c = a(2039);
n.imageScaler = (l, o, t) => (p(o), [l.run(s(l, o, t), o)]), n.parseImageScalerAttributes = l => {
const o = l.attributes.getFloat("scale"),
t = l.attributes.getFloats("bias");
return (0, u.createAttributeWithCacheKey)({
scale: o,
bias: t
})
};
const f = {
name: "ImageScaler",
inputNames: ["X"],
inputTypes: [c.TextureType.unpacked]
},
s = (l, o, t) => {
const e = Object.assign(Object.assign({}, f), {
cacheHint: t.cacheKey
});
return Object.assign(Object.assign({}, e), {
get: () => ((r, i, d, g) => {
const m = d[0].dims.slice(),
_ = m.length,
y = `
${h(g.bias.length)}
float process(int indices[${_}]) {
return _X(indices) * scale + getBias(bias, indices[1]);
}`;
return Object.assign(Object.assign({}, i), {
output: {
dims: m,
type: d[0].type,
textureType: c.TextureType.unpacked
},
variables: [{
name: "bias",
type: "float",
arrayLength: g.bias.length,
data: g.bias
}, {
name: "scale",
type: "float",
data: g.scale
}],
shaderSource: y
})
})(0, e, o, t)
})
},
h = l => {
const o = [`float getBias(float bias[${l}], int channel) {`];
for (let t = 0; t < l; ++t) t === 0 ? o.push(` if (channel == ${t}) { return bias[${t}]; }`) : t === l - 1 ? o.push(` else { return bias[${t}]; }`) : o.push(` else if (channel == ${t}) { return bias[${t}]; }`);
return o.push(" }"), o.join(`
`)
},
p = l => {
if (!l || l.length !== 1) throw new Error("ImageScaler requires 1 input.");
if (l[0].dims.length !== 4) throw new Error("Invalid input shape.");
if (l[0].type !== "float32" && l[0].type !== "float64") throw new Error("Invalid input type.")
}
},
3346: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.parseInstanceNormalizationAttributes = n.instanceNormalization = void 0;
const u = a(5060),
c = a(2039);
n.instanceNormalization = (o, t, e) => {
l(t);
const r = o.run(s(t[0]), t);
return [o.run(p(o, t[0], e, r.dims), [t[0], r, t[1], t[2]])]
}, n.parseInstanceNormalizationAttributes = o => o.attributes.getFloat("epsilon", 1e-5);
const f = {
name: "InstanceNormalization_MeanAndVariance",
inputNames: ["X"],
inputTypes: [c.TextureType.unpacked]
},
s = o => Object.assign(Object.assign({}, f), {
get: () => ((t, e) => {
const r = e.dims.slice(),
i = r[1],
d = r[2] * r[3],
g = [r[0], i],
m = `
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<${r[2]}; a2++) {
a[2] = a2;
for(int a3=0; a3<${r[3]}; a3++) {
a[3] = a3;
float x = _X(a);
temp += x;
}
}
float mean = temp / float(${d});
temp = 0.0;
for(int a2=0; a2<${r[2]}; a2++) {
a[2] = a2;
for(int a3=0; a3<${r[3]}; a3++) {
a[3] = a3;
float x = _X(a);
temp += (x - mean) * (x - mean);
}
}
v.r = mean;
v.g = temp / float(${d});
return v;
}`;
return Object.assign(Object.assign({}, t), {
output: {
dims: g,
type: e.type,
textureType: c.TextureType.packedLastDimension
},
shaderSource: m
})
})(f, o)
}),
h = {
name: "InstanceNormalization_ComputeOutput",
inputNames: ["X", "MeanAndVariance", "Scale", "B"],
inputTypes: [c.TextureType.unpacked, c.TextureType.packedLastDimension, c.TextureType.unpacked, c.TextureType.unpacked]
},
p = (o, t, e, r) => {
const i = Object.assign(Object.assign({}, h), {
cacheHint: `${e}`
});
return Object.assign(Object.assign({}, i), {
get: () => ((d, g, m, _, y) => {
const T = (0, u.getGlsl)(d.session.backend.glContext.version),
[w, S] = d.calculateTextureWidthAndHeight(y, c.TextureType.packedLastDimension),
[O, E] = [w / 4, S],
v = `
vec4 get_MeanAndVariance(int[2] mv) {
int offset = indicesToOffset_MeanAndVariance(mv);
vec2 coords = offsetToCoords(offset, ${O}, ${E});
return ${T.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({}, g), {
output: {
dims: m.dims,
type: m.type,
textureType: c.TextureType.unpacked
},
variables: [{
name: "epsilon",
type: "float",
data: _
}],
shaderSource: v
})
})(o, i, t, e, r)
})
},
l = o => {
if (!o || o.length !== 3) throw new Error("InstanceNormalization requires 3 inputs.");
const t = o[0],
e = o[1],
r = o[2];
if (t.dims.length < 3 || e.dims.length !== 1 || r.dims.length !== 1) throw new Error("Invalid input shape.");
if (e.dims[0] !== t.dims[1] || r.dims[0] !== t.dims[1]) throw new Error("Input shapes are mismatched.");
if (t.type !== "float32" && t.type !== "float64" || e.type !== "float32" && e.type !== "float64" || r.type !== "float32" && r.type !== "float64") throw new Error("Invalid input type.");
if (o[0].dims.length !== 4) throw new Error("Only support 4-D input shape.")
}
},
708: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.createPackedMatmulProgramInfoLoader = void 0;
const u = a(2517),
c = a(5060),
f = a(2039),
s = a(9390),
h = a(2823),
p = a(5623);
n.createPackedMatmulProgramInfoLoader = (l, o, t) => {
const e = (r = o.length > 2, i = t.activationCacheKey, {
name: "MatMul (packed)",
inputNames: r ? ["A", "B", "Bias"] : ["A", "B"],
inputTypes: r ? [f.TextureType.packed, f.TextureType.packed, f.TextureType.packed] : [f.TextureType.packed, f.TextureType.packed],
cacheHint: i
});
var r, i;
return Object.assign(Object.assign({}, e), {
get: () => ((d, g, m, _) => {
const y = m.length > 2,
T = y ? "value += getBiasForMatmul();" : "",
w = m[0].dims,
S = m[1].dims,
O = u.BroadcastUtil.calcShape(w, S, !0),
E = !u.ShapeUtil.areEqual(m[0].dims, m[1].dims);
if (!O) throw new Error("Can't use matmul on the given tensors");
const v = w[w.length - 1],
P = Math.ceil(v / 2),
L = w.length,
V = S.length,
R = (0, c.getGlsl)(d.session.backend.glContext.version),
k = (0, s.getCoordsDataType)(O.length),
Y = O.length,
C = (0, s.getGlChannels)(),
{
activationFunction: $,
applyActivation: X
} = (0, h.getActivationSnippet)(_),
z = y ? `${(0,p.getBiasForMatmul)(k,C,m[2].dims,O,!0)}` : "",
Z = E ? `${function(Te,se,ye,be){let Ie=[],Le=[];const ve=ye[0].dims,Ne=ye[1].dims,Fe=ve.length,Me=Ne.length,Oe=be.length,Be=Oe-Fe,Ue=Oe-Me;Ie=ve.map((Ae,Re)=>`coords.${se[Re+Be]}`),Ie[Fe-1]="i*2",Ie.join(", "),Le=Ne.map((Ae,Re)=>`coords.${se[Re+Ue]}`),Le[Me-2]="i*2",Le.join(", ");const ze=u.BroadcastUtil.getBroadcastDims(ve,be),He=u.BroadcastUtil.getBroadcastDims(Ne,be),Ke=ze.map(Ae=>`coords.${se[Ae+Be]} = 0;`).join(`
`),Ge=He.map(Ae=>`coords.${se[Ae+Ue]} = 0;`).join(`
`),Ve=`int lastDim = coords.${se[Oe-1]};
coords.${se[Oe-1]} = coords.${se[Oe-2]};
coords.${se[Oe-2]} = lastDim;`;return`
vec4 getAAtOutCoordsMatmul(int i) {
${Te} coords = getOutputCoords();
${Ve}
${Ke}
vec4 outputValue = getA(${Ie});
return outputValue;
}
vec4 getBAtOutCoordsMatmul(int i) {
${Te} coords = getOutputCoords();
${Ve}
${Ge}
vec4 outputValue = getB(${Le});
return outputValue;
}`}(k,C,m,O)}` : "",
J = E ? "getAAtOutCoordsMatmul(i)" : `getA(${function(Te,se){let ye="";for(let be=0;be<se-2;be++)ye+=`rc.${Te[be]}, `;return ye+=`rc.${Te[se-2]}, i*2`,ye}(C,L)})`,
ue = E ? "getBAtOutCoordsMatmul(i)" : `getB(${function(Te,se){let ye="";for(let be=0;be<se-2;be++)ye+=`rc.${Te[be]}, `;return ye+=`i*2, rc.${Te[se-1]}`,ye}(C,V)})`,
Se = `
${Z}
${z}
${$}
void main() {
${E?"":`${k} rc =
getOutputCoords(); int lastDim = rc.${C[Y-1]}; rc.${C[Y-1]} =
rc.${C[Y-2]}; rc.${C[Y-2]} = lastDim;
`}
vec4 value = vec4(0);
for (int i = 0; i < ${P}; i++) {
vec4 a = ${J};
vec4 b = ${ue};
value += (a.rrbb * b.rgrg);
value += (a.ggaa * b.baba);
}
${T}
${X}
${R.output} = value;
}`;
return Object.assign(Object.assign({}, g), {
output: {
dims: O,
type: m[0].type,
textureType: f.TextureType.packed
},
shaderSource: Se,
hasMain: !0
})
})(l, e, o, t)
})
}
},
5623: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.getBiasForMatmul = n.createMatmulProgramInfoLoader = n.parseMatMulAttributes = n.matMul = void 0;
const u = a(2517),
c = a(2039),
f = a(9390),
s = a(2823),
h = a(708);
function p(t, e) {
const r = (i = t.length > 2, d = e.activationCacheKey, {
name: "MatMul",
inputNames: i ? ["A", "B", "Bias"] : ["A", "B"],
inputTypes: i ? [c.TextureType.unpacked, c.TextureType.unpacked, c.TextureType.unpacked] : [c.TextureType.unpacked, c.TextureType.unpacked],
cacheHint: d
});
var i, d;
return Object.assign(Object.assign({}, r), {
get: () => function(g, m, _) {
const y = m[0].dims,
T = m[1].dims,
w = u.BroadcastUtil.calcShape(y, T, !0);
if (!w) throw new Error("Can't use matmul on the given tensors");
const S = (0, f.getCoordsDataType)(w.length),
O = (0, f.getGlChannels)(),
{
activationFunction: E,
applyActivation: v
} = (0, s.getActivationSnippet)(_),
P = m.length > 2,
L = P ? "value += getBiasForMatmul();" : "",
V = P ? `${o(S,O,m[2].dims,w,!1)}` : "",
R = w.length,
k = y.length,
Y = T.length,
C = `
${E}
${V}
float process(int indices[${R}]) {
int a[${k}];
int b[${Y}];
bcastMatmulIndices_A(indices, a);
bcastMatmulIndices_B(indices, b);
float value;
for (int k=0; k<${y[y.length-1]}; ++k) {
a[${k-1}] = k;
b[${Y-2}] = k;
value += _A(a) * _B(b);
}
${L}
${v}
return value;
}`;
return Object.assign(Object.assign({}, g), {
output: {
dims: w,
type: m[0].type,
textureType: c.TextureType.unpacked
},
shaderSource: C
})
}(r, t, e)
})
}
n.matMul = (t, e, r) => (l(e), t.session.pack ? [t.run((0, h.createPackedMatmulProgramInfoLoader)(t, e, r), e)] : [t.run(p(e, r), e)]), n.parseMatMulAttributes = t => (0, s.parseInternalActivationAttributes)(t.attributes), n.createMatmulProgramInfoLoader = p;
const l = t => {
if (!t || t.length !== 2) throw new Error("MatMul requires 2 inputs.");
if (t[0].dims[t[0].dims.length - 1] !== t[1].dims[t[1].dims.length - 2]) throw new Error("shared dimension does not match.");
if (t[0].type !== "float32" && t[0].type !== "float64" || t[1].type !== "float32" && t[1].type !== "float64") throw new Error("inputs should be float type");
if (t[0].type !== t[1].type) throw new Error("inputs types should match")
};
function o(t, e, r, i, d) {
let g = "";
const m = r.length,
_ = i.length,
y = _ - m;
g = _ < 2 && m > 0 ? "coords" : r.map((S, O) => `coords.${e[O+y]}`).join(", ");
const T = u.BroadcastUtil.getBroadcastDims(r, i).map(S => `coords.${e[S+y]} = 0;`).join(`
`);
let w = "vec4(outputValue.xx, outputValue.yy)";
return u.ShapeUtil.size(r) === 1 && (w = "vec4(outputValue.x)"), d ? `
vec4 getBiasForMatmul() {
${t} coords = getOutputCoords();
${T}
vec4 outputValue = getBias(${g});
return ${w};
}` : `
float getBiasForMatmul() {
${t} coords = getOutputCoords();
${T}
return getBias(coords.x);
}`
}
n.getBiasForMatmul = o
},
2403: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.createPackProgramInfoLoader = void 0;
const u = a(5060),
c = a(2039),
f = a(9390),
s = a(2827),
h = {
name: "pack",
inputNames: ["A"],
inputTypes: [c.TextureType.unpackedReversed]
};
n.createPackProgramInfoLoader = (p, l) => Object.assign(Object.assign({}, h), {
get: () => ((o, t) => {
const e = (0, u.getGlsl)(o.session.backend.glContext.version),
r = t.dims,
i = r.length,
d = t.dims.length,
g = (0, f.getCoordsDataType)(d),
m = (0, s.getChannels)("rc", d),
_ = (y = d, T = m, w = r[r.length - 2], S = r[r.length - 1], y === 0 || y === 1 ? "" : `
int r = ${T[y-2]};
int c = ${T[y-1]};
int rp1 = ${T[y-2]} + 1;
int cp1 = ${T[y-1]} + 1;
bool rEdge = rp1 >= ${S};
bool cEdge = cp1 >= ${w};
`);
var y, T, w, S;
let O;
O = i === 0 ? [1, 1] : i === 1 ? [r[0], 1] : [r[d - 1], r[d - 2]];
const E = function(L, V, R) {
if (L === 0) return "false";
if (L === 1) return `rc > ${V[0]}`;
let k = "";
for (let Y = L - 2; Y < L; Y++) k += `${R[Y]} >= ${V[Y-L+2]}`, Y < L - 1 && (k += "||");
return k
}(d, O, m),
v = function(L, V) {
const R = L.length;
if (R === 0) return "getA(), 0, 0, 0";
if (R === 1) return `getA(rc),
rc + 1 >= ${L[0]} ? 0. : getA(rc + 1),
0, 0`;
let k = "";
if (R > 2)
for (let Y = 0; Y < R - 2; ++Y) k += `${V[Y]},`;
return `getA(${k}r, c),
rEdge ? 0. : getA(${k}rp1, c),
cEdge ? 0. : getA(${k}r, cp1),
rEdge || cEdge ? 0. : getA(${k}rp1, cp1)`
}(r, m),
P = `
void main() {
${g} rc = getOutputCoords();
if(${E}) {
${e.output} = vec4(0);
} else {
${_}
${e.output} = vec4(${v});
}
}
`;
return Object.assign(Object.assign({}, h), {
hasMain: !0,
output: {
dims: t.dims,
type: t.type,
textureType: c.TextureType.packed
},
shaderSource: P
})
})(p, l)
})
},
2827: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.unpackFromChannel = n.getChannels = n.getVecChannels = void 0;
const u = a(9390);
function c(f, s) {
return (0, u.getGlChannels)(s).map(h => `${f}.${h}`)
}
n.getVecChannels = c, n.getChannels = function(f, s) {
return s === 1 ? [f] : c(f, s)
}, n.unpackFromChannel = function() {
return `
float getChannel(vec4 frag, int dim) {
int modCoord = imod(dim, 2);
return modCoord == 0 ? frag.r : frag.g;
}
float getChannel(vec4 frag, vec2 innerDims) {
vec2 modCoord = mod(innerDims, 2.);
return modCoord.x == 0. ?
(modCoord.y == 0. ? frag.r : frag.g) :
(modCoord.y == 0. ? frag.b : frag.a);
}
`
}
},
2870: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.parsePadAttributesV11 = n.padV11 = n.parsePadAttributesV2 = n.padV2 = void 0;
const u = a(246),
c = a(2517),
f = a(5060),
s = a(2039),
h = {
name: "Pad",
inputNames: ["A"],
inputTypes: [s.TextureType.unpacked]
};
n.padV2 = (g, m, _) => (o(m), [g.run(Object.assign(Object.assign({}, h), {
cacheHint: _.cacheKey,
get: () => l(g, m[0], _)
}), m)]), n.parsePadAttributesV2 = g => {
const m = g.attributes.getString("mode", "constant"),
_ = g.attributes.getFloat("value", 0),
y = g.attributes.getInts("pads");
return (0, u.createAttributeWithCacheKey)({
mode: m,
value: _,
pads: y
})
}, n.padV11 = (g, m, _) => {
t(m);
const y = p(g, m, _);
return (0, n.padV2)(g, [m[0]], y)
}, n.parsePadAttributesV11 = g => g.attributes.getString("mode", "constant");
const p = (g, m, _) => {
if (!g.session.isInitializer(m[1].dataId) || m.length >= 3 && !g.session.isInitializer(m[2].dataId)) throw new Error("dynamic pad attributes are not allowed");
const y = Array.from(m[1].integerData),
T = m.length >= 3 ? m[2].floatData[0] : 0;
return (0, u.createAttributeWithCacheKey)({
mode: _,
pads: y,
value: T
})
},
l = (g, m, _) => {
const y = c.ShapeUtil.padShape(m.dims.slice(), _.pads),
T = y.length,
w = `
${e(g,m,_)}
float process(int[${T}] indices) {
return padA(indices);
}`;
return {
name: "Pad",
inputNames: ["A"],
inputTypes: [s.TextureType.unpacked],
output: {
dims: y,
type: m.type,
textureType: s.TextureType.unpacked
},
shaderSource: w
}
},
o = g => {
if (!g || g.length !== 1) throw new Error("Pad requires 1 input");
if (g[0].type !== "float32" && g[0].type !== "float64") throw new Error("Invalid input type.")
},
t = g => {
if (!g || g.length !== 2 && g.length !== 3) throw new Error("Pad requires 2 or 3 inputs");
if (g[1].type !== "int32") throw new Error("Invalid input type.");
if (g.length >= 3 && g[2].type === "string") throw new Error("Invalid input type.")
},
e = (g, m, _) => {
const y = (0, f.getGlsl)(g.session.backend.glContext.version),
[T, w] = g.calculateTextureWidthAndHeight(m.dims, s.TextureType.unpacked),
S = c.ShapeUtil.computeStrides(m.dims);
switch (_.mode) {
case "constant":
return r(y, m.dims, S, T, w, _.pads, _.value);
case "reflect":
return i(y, m.dims, S, T, w, _.pads);
case "edge":
return d(y, m.dims, S, T, w, _.pads);
default:
throw new Error("Invalid mode")
}
},
r = (g, m, _, y, T, w, S) => {
const O = m.length;
let E = "";
for (let v = O - 1; v >= 0; --v) E += `
k = m[${v}] - ${w[v]};
if (k < 0) return constant;
if (k >= ${m[v]}) return constant;
offset += k * ${_[v]};
`;
return `
float padA(int m[${O}]) {
const float constant = float(${S});
int offset = 0;
int k = 0;
${E}
vec2 coords = offsetToCoords(offset, ${y}, ${T});
float value = getColorAsFloat(${g.texture2D}(A, coords));
return value;
}
`
},
i = (g, m, _, y, T, w) => {
const S = m.length;
let O = "";
for (let E = S - 1; E >= 0; --E) O += `
k = m[${E}] - ${w[E]};
if (k < 0) { k = -k; }
{
const int _2n_1 = ${2*(m[E]-1)};
k = int( mod( float(k), float(_2n_1) ) ) ;
if(k >= ${m[E]}) { k = _2n_1 - k; }
}
offset += k * ${_[E]};
`;
return `
float padA(int m[${S}]) {
int offset = 0;
int k = 0;
${O}
vec2 coords = offsetToCoords(offset, ${y}, ${T});
float value = getColorAsFloat(${g.texture2D}(A, coords));
return value;
}
`
},
d = (g, m, _, y, T, w) => {
const S = m.length;
let O = "";
for (let E = S - 1; E >= 0; --E) O += `
k = m[${E}] - ${w[E]};
if (k < 0) k = 0;
if (k >= ${m[E]}) k = ${m[E]-1};
offset += k * ${_[E]};
`;
return `
float padA(int m[${S}]) {
int offset = 0;
int k = 0;
${O}
vec2 coords = offsetToCoords(offset, ${y}, ${T});
float value = getColorAsFloat(${g.texture2D}(A, coords));
return value;
}
`
}
},
2143: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.globalMaxPool = n.parseMaxPoolAttributes = n.maxPool = n.parseGlobalAveragePoolAttributes = n.globalAveragePool = n.parseAveragePoolAttributes = n.averagePool = void 0;
const u = a(246),
c = a(2517),
f = a(2039);
n.averagePool = (d, g, m) => {
t(g);
const _ = {
name: "AveragePool",
inputNames: ["X"],
inputTypes: [f.TextureType.unpacked],
cacheHint: m.cacheKey
};
return [d.run(Object.assign(Object.assign({}, _), {
get: () => s(g, _, !1, m)
}), g)]
}, n.parseAveragePoolAttributes = d => {
const g = d.attributes.getString("auto_pad", "NOTSET"),
m = d.attributes.getInt("ceil_mode", 0),
_ = d.attributes.getInt("count_include_pad", 0) !== 0,
y = d.attributes.getInts("kernel_shape"),
T = d.attributes.getInts("strides", []),
w = d.attributes.getInts("pads", []);
if (m !== 0) throw new Error("using ceil() in shape computation is not yet supported for AveragePool");
return (0, u.createAttributeWithCacheKey)({
autoPad: g,
ceilMode: m,
countIncludePad: _,
kernelShape: y,
strides: T,
pads: w
})
};
const s = (d, g, m, _) => {
const [y, T] = p(d, _, m), w = c.ShapeUtil.size(y.kernelShape);
let S = "";
y.countIncludePad ? S += `value /= float(${w});` : S += `value /= float(${w} - pad);`;
const O = `
${e(d[0].dims,y,"value += _X(x);",S,"0.0")}
`;
return Object.assign(Object.assign({}, g), {
output: {
dims: T,
type: d[0].type,
textureType: f.TextureType.unpacked
},
shaderSource: O
})
};
n.globalAveragePool = (d, g, m) => {
t(g);
const _ = {
name: "GlobalAveragePool",
inputNames: ["X"],
inputTypes: [f.TextureType.unpacked],
cacheHint: `${m.countIncludePad}`
};
return [d.run(Object.assign(Object.assign({}, _), {
get: () => s(g, _, !0, m)
}), g)]
}, n.parseGlobalAveragePoolAttributes = d => {
const g = d.attributes.getInt("count_include_pad", 0) !== 0;
return (0, u.createAttributeWithCacheKey)({
autoPad: "",
ceilMode: 0,
countIncludePad: g,
kernelShape: [],
strides: [],
pads: []
})
}, n.maxPool = (d, g, m) => {
t(g);
const _ = {
name: "MaxPool",
inputNames: ["X"],
inputTypes: [f.TextureType.unpacked],
cacheHint: m.cacheKey
};
return [d.run(Object.assign(Object.assign({}, _), {
get: () => h(g, _, !1, m)
}), g)]
}, n.parseMaxPoolAttributes = d => {
const g = d.attributes.getString("auto_pad", "NOTSET"),
m = d.attributes.getInt("ceil_mode", 0),
_ = d.attributes.getInts("kernel_shape"),
y = d.attributes.getInts("strides", []),
T = d.attributes.getInts("pads", []),
w = d.attributes.getInt("storage_order", 0),
S = d.attributes.getInts("dilations", []);
if (w !== 0) throw new Error("column major storage order is not yet supported for MaxPool");
if (m !== 0) throw new Error("using ceil() in shape computation is not yet supported for MaxPool");
return (0, u.createAttributeWithCacheKey)({
autoPad: g,
ceilMode: m,
countIncludePad: !1,
kernelShape: _,
strides: y,
pads: T,
storageOrder: w,
dilations: S
})
};
const h = (d, g, m, _) => {
const [y, T] = p(d, _, m), w = `
${e(d[0].dims,y,`
value = max(_X(x), value);
`,"","-1e5")}
`;
return Object.assign(Object.assign({}, g), {
output: {
dims: T,
type: d[0].type,
textureType: f.TextureType.unpacked
},
shaderSource: w
})
},
p = (d, g, m) => {
const _ = d[0].dims.slice(),
y = Object.hasOwnProperty.call(g, "dilations"),
T = g.kernelShape.slice(),
w = g.strides.slice(),
S = y ? g.dilations.slice() : [],
O = g.pads.slice();
c.PoolConvUtil.adjustPoolAttributes(m, _, T, w, S, O);
const E = c.PoolConvUtil.computePoolOutputShape(m, _, w, S, T, O, g.autoPad),
v = Object.assign({}, g);
return y ? Object.assign(v, {
kernelShape: T,
strides: w,
pads: O,
dilations: S,
cacheKey: g.cacheKey
}) : Object.assign(v, {
kernelShape: T,
strides: w,
pads: O,
cacheKey: g.cacheKey
}), [v, E]
},
l = {
autoPad: "",
ceilMode: 0,
countIncludePad: !1,
kernelShape: [],
strides: [],
pads: [],
storageOrder: 0,
dilations: [],
cacheKey: ""
},
o = {
name: "GlobalMaxPool",
inputNames: ["X"],
inputTypes: [f.TextureType.unpacked]
};
n.globalMaxPool = (d, g) => (t(g), [d.run(Object.assign(Object.assign({}, o), {
get: () => h(g, o, !0, l)
}), g)]);
const t = d => {
if (!d || d.length !== 1) throw new Error("Pool ops requires 1 input.");
if (d[0].type !== "float32" && d[0].type !== "float64") throw new Error("Invalid input type.")
},
e = (d, g, m, _, y) => {
const T = d.length;
if (g.kernelShape.length <= 2) {
const w = g.kernelShape[g.kernelShape.length - 1],
S = g.strides[g.strides.length - 1],
O = g.pads[g.pads.length / 2 - 1],
E = g.pads[g.pads.length - 1],
v = d[T - 1];
let P = "",
L = "",
V = "";
if (P = O + E !== 0 ? `
for (int i = 0; i < ${w}; i++) {
x[${T} - 1] = indices[${T} - 1] * ${S} - ${O} + i;
if (x[${T} - 1] < 0 || x[${T} - 1] >= ${v}) {
pad++;
continue;
}
${m}
}` : `
for (int i = 0; i < ${w}; i++) {
x[${T} - 1] = indices[${T} - 1] * ${S} - ${O} + i;
${m}
}`, g.kernelShape.length === 2) {
const R = g.kernelShape[g.kernelShape.length - 2],
k = g.strides[g.strides.length - 2],
Y = g.pads[g.pads.length / 2 - 2],
C = g.pads[g.pads.length - 2],
$ = d[T - 2];
L = Y + C !== 0 ? `
for (int j = 0; j < ${R}; j++) {
x[${T} - 2] = indices[${T} - 2] * ${k} - ${Y} + j;
if (x[${T} - 2] < 0 || x[${T} - 2] >= ${$}) {
pad+= ${w};
continue;
}
` : `
for (int j = 0; j < ${R}; j++) {
x[${T} - 2] = indices[${T} - 2] * ${k} - ${Y} + j;
`, V = `
}
`
}
return `
float process(int indices[${T}]) {
int x[${T}];
copyVec(indices, x);
float value = ${y};
int pad = 0;
${L}
${P}
${V}
${_}
return value;
}
`
} {
const w = c.ShapeUtil.size(g.kernelShape),
S = c.ShapeUtil.computeStrides(g.kernelShape),
O = S.length,
E = g.pads.length,
v = i(O),
P = r(d, "inputDims"),
L = r(g.pads, "pads"),
V = r(S, "kernelStrides"),
R = r(g.strides, "strides");
let k = "";
return k = g.pads.reduce((Y, C) => Y + C) ? `
if (x[j] >= inputDims[j] || x[j] < 0) {
pad++;
isPad = true;
break;
}
}
if (!isPad) {
${m}
}` : `
}
${m}
`, `
${v}
float process(int indices[${T}]) {
int x[${T}];
copyVec(indices, x);
int offset[${O}];
int pads[${E}];
int inputDims[${T}];
int kernelStrides[${O}];
int strides[${O}];
${L}
${P}
${R}
${V}
float value = ${y};
int pad = 0;
bool isPad = false;
for (int i = 0; i < ${w}; i++) {
offsetToIndices(i, kernelStrides, offset);
isPad = false;
for (int j = ${T} - ${O}; j < ${T}; j++) {
x[j] = indices[j] * strides[j - ${T} + ${O}]
+ offset[j - ${T} + ${O}] - pads[j - 2];
${k}
}
${_}
return value;
}
`
}
},
r = (d, g) => {
let m = "";
for (let _ = 0; _ < d.length; _++) m += `
${g}[${_}] = ${d[_]};
`;
return m
},
i = d => `
void offsetToIndices(int offset, int[${d}] strides, out int[${d}] indices) {
if (${d} == 0) {
return;
}
for (int i = 0; i < ${d} - 1; ++i) {
indices[i] = offset / strides[i];
offset -= indices[i] * strides[i];
}
indices[${d} - 1] = offset;
}`
},
4939: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.reduceLogSumSquare = n.reduceLogSum = n.reduceProd = n.reduceMin = n.reduceMax = n.reduceMean = n.reduceSum = n.parseReduceAttributes = void 0;
const u = a(246),
c = a(782),
f = a(2517),
s = a(2039),
h = (o, t, e, r, i) => {
l(t);
const d = {
name: r,
inputNames: ["A"],
inputTypes: [s.TextureType.unpacked]
};
return [o.run(Object.assign(Object.assign({}, d), {
cacheHint: e.cacheKey,
get: () => p(o, t, e, r, i, d)
}), t)]
};
n.parseReduceAttributes = o => {
const t = o.attributes.getInts("axes", []),
e = o.attributes.getInt("keepdims", 1) === 1;
return (0, u.createAttributeWithCacheKey)({
axes: t,
keepDims: e
})
};
const p = (o, t, e, r, i, d) => {
const g = [],
m = t[0].dims.length || 1,
_ = [],
y = f.ShapeUtil.normalizeAxes(e.axes, t[0].dims.length),
T = i(t, y);
let w = T[1];
for (let O = 0; O < t[0].dims.length; O++) y.indexOf(O) >= 0 || y.length === 0 ? (e.keepDims && g.push(1), w = `
for(int j${O} = 0; j${O} < ${t[0].dims[O]}; j${O}++) {
inputIdx[${O}] = j${O};
${w}
}`) : (_.push(`inputIdx[${O}] = outputIdx[${g.length}];`), g.push(t[0].dims[O]));
const S = `
float process(int outputIdx[${g.length||1}]) {
float value; // final result
int inputIdx[${m}]; // addressing input data
${_.join(`
`)}
${T[0]} // init ops for reduce max/min
${w}
${T[2]} // final computation for reduce mean
return value;
}`;
return Object.assign(Object.assign({}, d), {
output: {
dims: g,
type: t[0].type,
textureType: s.TextureType.unpacked
},
shaderSource: S
})
},
l = o => {
if (!o || o.length !== 1) throw new Error("Reduce op requires 1 input.");
if (c.NUMBER_TYPES.indexOf(o[0].type) === -1) throw new Error("Invalid input type.")
};
n.reduceSum = (o, t, e) => h(o, t, e, "ReduceSum", () => ["value = 0.0;", "value += _A(inputIdx);", ""]), n.reduceMean = (o, t, e) => h(o, t, e, "ReduceMean", (r, i) => {
let d = 1;
for (let g = 0; g < r[0].dims.length; g++)(i.indexOf(g) >= 0 || i.length === 0) && (d *= r[0].dims[g]);
return ["value = 0.0;", "value += _A(inputIdx);", `value /= ${d}.;`]
}), n.reduceMax = (o, t, e) => h(o, t, e, "ReduceMax", (r, i) => {
const d = [];
for (let g = 0; g < r[0].dims.length; g++)(i.indexOf(g) >= 0 || i.length === 0) && d.push(`inputIdx[${g}] = 0;`);
return [`${d.join(`
`)}
value = _A(inputIdx);`, "value = max(value, _A(inputIdx));", ""]
}), n.reduceMin = (o, t, e) => h(o, t, e, "ReduceMin", (r, i) => {
const d = [];
for (let g = 0; g < r[0].dims.length; g++)(i.indexOf(g) >= 0 || i.length === 0) && d.push(`inputIdx[${g}] = 0;`);
return [`${d.join(`
`)}
value = _A(inputIdx);`, "value = min(value, _A(inputIdx));", ""]
}), n.reduceProd = (o, t, e) => h(o, t, e, "ReduceProd", () => ["value = 1.0;", "value *= _A(inputIdx);", ""]), n.reduceLogSum = (o, t, e) => h(o, t, e, "ReduceLogSum", () => ["value = 0.0;", "value += _A(inputIdx);", "value = log(value);"]), n.reduceLogSumSquare = (o, t, e) => h(o, t, e, "ReduceLogSumSquare", () => ["float t; value = 0.0;", "t = _A(inputIdx); value += t * t;", ""])
},
7019: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.isReshapeCheap = n.processDims3D = n.createPackedReshape3DProgramInfoLoader = void 0;
const u = a(2517),
c = a(5060),
f = a(2039),
s = a(2827);
n.createPackedReshape3DProgramInfoLoader = (h, p, l) => {
const o = (t => ({
name: "Reshape (packed)",
inputTypes: [f.TextureType.packed],
inputNames: ["A"],
cacheHint: `${t}`
}))(l);
return Object.assign(Object.assign({}, o), {
get: () => ((t, e, r, i) => {
const d = e.dims,
g = i;
let m = "";
for (let T = 0; T < 4; T++) {
let w = "";
switch (T) {
case 0:
w = "outputCoords = rc;";
break;
case 1:
w = "outputCoords = ivec3(rc.x, rc.y+1, rc.z);";
break;
case 2:
w = "outputCoords = ivec3(rc.x, rc.y, rc.z+1);";
break;
case 3:
w = "outputCoords = ivec3(rc.x, rc.y+1, rc.z+1);";
break;
default:
throw new Error
}
m += `
${w}
${T>0?"if(outputCoords.y < rows && outputCoords.z < cols){":""}
int flattenedIndex = getFlattenedIndex(outputCoords);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flattenedIndex);
vec2 innerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${T}] = getChannel(getA(inputRC.x, inputRC.y, inputRC.z), innerDims);
${T>0?"}":""}
`
}
const _ = (0, c.getGlsl)(t.session.backend.glContext.version),
y = `
${function(T){const w=u.ShapeUtil.computeStrides(T),S=["b","r","c"],O="index";return`
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${w.map((E,v)=>`int ${S[v]} = ${O} / ${E}; ${v===w.length-1?`int ${S[v+1]} = ${O} - ${S[v]} * ${E}`:`index -= ${S[v]} * ${E}`};`).join("")}
return ivec3(b, r, c);
}
`}(d)}
${function(T){const w=u.ShapeUtil.computeStrides(T);return`
int getFlattenedIndex(ivec3 coords) {
// reverse y, z order
return coords.x * ${w[0]} + coords.z * ${w[1]} + coords.y;
}
`}(g)}
${(0,s.unpackFromChannel)()}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.0);
ivec3 outputCoords;
int rows = ${g[2]};
int cols = ${g[1]};
${m}
${_.output} = result;
}
`;
return Object.assign(Object.assign({}, r), {
output: {
dims: g,
type: e.type,
textureType: f.TextureType.packed
},
shaderSource: y,
hasMain: !0
})
})(h, p, o, l)
})
}, n.processDims3D = function(h) {
if (h.length === 0) return [1, 1, 1];
let p = 1;
for (let l = 0; l < h.length - 2; ++l) p *= h[l];
return [p, h.length > 1 ? h[h.length - 2] : 1, h[h.length - 1]]
}, n.isReshapeCheap = function(h, p) {
let l = !1;
return l = h.length === 0 || p.length === 0 || (h.length < 2 || p.length < 2 ? h[h.length - 1] === p[p.length - 1] : h[h.length - 1] === p[p.length - 1] && h[h.length - 2] === p[p.length - 2]), l
}
},
718: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.reshape = void 0;
const u = a(2517);
n.reshape = (c, f) => {
const s = u.ShapeUtil.calculateReshapedDims(f[0].dims, f[1].integerData);
return c.session.pack ? [c.reshapePacked(f[0], s)] : [c.reshapeUnpacked(f[0], s)]
}
},
2268: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.parseResizeAttributesV11 = n.parseResizeAttributesV10 = n.resize = void 0;
const u = a(5060),
c = a(2039),
f = a(9390),
s = a(2827),
h = a(9793),
p = {
name: "Resize",
inputNames: ["A"],
inputTypes: [c.TextureType.packed]
};
n.resize = (r, i, d) => ((0, h.validateInputs)(i, d), [r.run(Object.assign(Object.assign({}, p), {
cacheHint: d.cacheKey,
get: () => l(r, i, d)
}), i)]), n.parseResizeAttributesV10 = r => (0, h.parseUpsampleAttributes)(r, 10), n.parseResizeAttributesV11 = r => (0, h.parseUpsampleAttributes)(r, 11);
const l = (r, i, d) => {
const g = (0, u.getGlsl)(r.session.backend.glContext.version),
[m, _] = o(i, d);
if (m.every(k => k === 1) && d.coordinateTransformMode !== "tf_crop_and_resize") return Object.assign(Object.assign({}, p), {
output: {
dims: _,
type: i[0].type,
textureType: c.TextureType.packed
},
hasMain: !0,
shaderSource: `void main() {
vec4 v = ${g.texture2D}(X, TexCoords);
${g.output} = v;
}`
});
const y = _.length;
if (y < 2) throw new Error(`output dimension should be at least 2, but got ${y}`);
const T = _[y - 2],
w = _[y - 1],
S = i[0].dims;
if (y !== S.length) throw new Error(`output dimension should match input ${S.length}, but got ${y}`);
const O = S[y - 2],
E = S[y - 1],
v = m[y - 2],
P = m[y - 1];
let L = "";
if (d.mode !== "linear") throw new Error(`resize (packed) does not support mode: '${d.mode}'`);
switch (d.coordinateTransformMode) {
case "asymmetric":
L = `
vec4 getSourceFracIndex(ivec4 coords) {
return vec4(coords) / scaleWHWH;
}
`;
break;
case "half_pixel":
L = `
vec4 getSourceFracIndex(ivec4 coords) {
return (vec4(coords) + 0.5) / scaleWHWH - 0.5;
}
`;
break;
case "pytorch_half_pixel":
L = `
vec4 getSourceFracIndex(ivec4 coords) {
vec4 fcoords = vec4(coords);
return vec4(
${w}.0 > 1.0 ? (fcoords.x + 0.5) / scaleWHWH.x - 0.5 : 0.0,
${T}.0 > 1.0 ? (fcoords.y + 0.5) / scaleWHWH.y - 0.5 : 0.0,
${w}.0 > 1.0 ? (fcoords.z + 0.5) / scaleWHWH.z - 0.5 : 0.0,
${T}.0 > 1.0 ? (fcoords.w + 0.5) / scaleWHWH.w - 0.5 : 0.0
);
}
`;
break;
case "align_corners":
L = `
vec4 getSourceFracIndex(ivec4 coords) {
vec4 resized = vec4(${w}.0 - 1.0, ${T}.0 - 1.0, ${w}.0 - 1.0,
${T}.0 - 1.0);
vec4 original = vec4(${E}.0 - 1.0, ${O}.0 - 1.0, ${E}.0 - 1.0,
${O}.0 - 1.0);
vec4 new_scale = original / resized;
return vec4(coords) * new_scale;
}
`;
break;
default:
throw new Error(`resize (packed) does not support coordinateTransformMode: '${d.coordinateTransformMode}'`)
}
const V = (0, f.getCoordsDataType)(y),
R = `
const vec2 inputWH = vec2(${O}.0, ${E}.0);
const vec4 scaleWHWH = vec4(float(${v}), float(${P}), float(${v}), float(${P}));
${(0,s.unpackFromChannel)()}
${L}
float getAValue(int x10, int r, int c, int d) {
return getChannel(getA(x10, r, c, d), vec2(c, d));
}
void main() {
${V} 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 < ${T-1};
bool hasNextCol = rc.z < ${w-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);
${g.output} = vec4(newValue);
}
`;
return Object.assign(Object.assign({}, p), {
output: {
dims: _,
type: i[0].type,
textureType: c.TextureType.packed
},
hasMain: !0,
shaderSource: R
})
},
o = (r, i) => {
const d = r[0].dims;
let g, m = i.scales;
if (m.length === 0) {
const y = r[i.scalesInputIdx];
if (y && y.size !== 0) {
if (r[i.sizesInputIdx]) throw new Error("Only one of scales or sizes must be provided as input.");
m = t(y, i.mode, i.isResize)
} else {
const T = r[i.sizesInputIdx];
if (!T || T.size === 0) throw new Error("Either scales or sizes MUST be provided as input.");
g = Array.from(T.integerData), m = e(g, d, i.mode, i.isResize)
}
} else if (r[i.sizesInputIdx]) throw new Error("Only one of scales or sizes must be provided as input.");
const _ = g || d.map((y, T) => Math.floor(y * m[T]));
return [m, _]
},
t = (r, i, d) => {
const g = Array.from(r.floatData);
return (0, h.scalesValidation)(g, i, d), g
},
e = (r, i, d, g) => {
const m = i.length,
_ = new Array(m);
for (let y = 0, T = m; y < T; y++)
if (i[y] === 0) {
if (r[y] !== 0) throw new Error("Input dim is zero but required output dim is non-zero.");
_[y] = 1
} else _[y] = r[y] / i[y];
return (0, h.scalesValidation)(_, d, g), _
}
},
8117: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.shape = void 0;
const u = a(9162);
n.shape = (f, s) => (c(s), [new u.Tensor([s[0].dims.length], "int32", void 0, void 0, new Int32Array(s[0].dims))]);
const c = f => {
if (!f || f.length !== 1) throw new Error("Shape requires 1 input.")
}
},
2278: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.sliceV10 = n.parseSliceAttributes = n.slice = void 0;
const u = a(246),
c = a(782),
f = a(2517),
s = a(2039),
h = {
name: "Slice",
inputNames: ["A"],
inputTypes: [s.TextureType.unpacked]
};
n.slice = (e, r, i) => (l(r), [e.run(Object.assign(Object.assign({}, h), {
cacheHint: i.cacheKey,
get: () => p(e, r[0], i)
}), r)]), n.parseSliceAttributes = e => {
const r = e.attributes.getInts("starts"),
i = e.attributes.getInts("ends"),
d = e.attributes.getInts("axes", []);
return (0, u.createAttributeWithCacheKey)({
starts: r,
ends: i,
axes: d
})
};
const p = (e, r, i) => {
const d = i.axes.length === 0 ? r.dims.slice(0).map((S, O) => O) : i.axes,
g = f.ShapeUtil.normalizeAxes(d, r.dims.length),
m = i.starts.map((S, O) => S > r.dims[g[O]] - 1 ? r.dims[g[O]] : f.ShapeUtil.normalizeAxis(S, r.dims[g[O]])),
_ = i.ends.map((S, O) => S > r.dims[g[O]] - 1 ? r.dims[g[O]] : f.ShapeUtil.normalizeAxis(S, r.dims[g[O]])),
y = r.dims.slice(),
T = [];
for (let S = 0; S < g.length; S++) y[g[S]] = _[S] - m[S], m[S] > 0 && T.push(`outputIdx[${g[S]}] += ${m[S]};`);
const w = `
float process(int outputIdx[${y.length}]) {
${T.join(`
`)}
return _A(outputIdx);
}`;
return Object.assign(Object.assign({}, h), {
output: {
dims: y,
type: r.type,
textureType: s.TextureType.unpacked
},
shaderSource: w
})
},
l = e => {
if (!e || e.length !== 1) throw new Error("Slice requires 1 input.");
if (c.NUMBER_TYPES.indexOf(e[0].type) === -1) throw new Error("Invalid input type.")
};
n.sliceV10 = (e, r) => {
t(r);
const i = o(e, r);
return [e.run(Object.assign(Object.assign({}, h), {
cacheHint: i.cacheKey,
get: () => p(e, r[0], i)
}), [r[0]])]
};
const o = (e, r) => {
if (!e.session.isInitializer(r[1].dataId) || !e.session.isInitializer(r[2].dataId) || r.length >= 4 && !e.session.isInitializer(r[3].dataId) || r.length >= 5 && !e.session.isInitializer(r[4].dataId)) throw new Error("dynamic slice attributes are not allowed");
if (r.length >= 5 && r[4].integerData.some(m => m !== 1)) throw new Error("currently non-1 steps is not supported for Slice");
const i = Array.from(r[1].integerData),
d = Array.from(r[2].integerData),
g = r.length >= 4 ? Array.from(r[3].integerData) : [];
return {
starts: i,
ends: d,
axes: g,
cacheKey: `${g};${i};${d}`
}
},
t = e => {
if (!e || e.length < 3 || e.length > 5) throw new Error("Invalid input number.");
if (e[1].type !== "int32" || e[1].dims.length !== 1) throw new Error("Invalid input type.");
if (e[2].type !== "int32" || e[2].dims.length !== 1) throw new Error("Invalid input type.");
if (e.length >= 4 && (e[3].type !== "int32" || e[3].dims.length !== 1)) throw new Error("Invalid input type.");
if (e.length >= 5 && (e[4].type !== "int32" || e[4].dims.length !== 1)) throw new Error("Invalid input type.")
}
},
5524: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.softmaxV13 = n.parseSoftmaxAttributesV13 = n.parseSoftmaxAttributes = n.softmax = void 0;
const u = a(246),
c = a(2517),
f = a(5060),
s = a(2039),
h = a(3738),
p = {
name: "SoftmaxComputeMax",
inputNames: ["A"],
inputTypes: [s.TextureType.unpacked]
},
l = {
name: "SoftmaxComputeScale",
inputNames: ["A", "Max"],
inputTypes: [s.TextureType.unpacked, s.TextureType.unpacked]
},
o = {
name: "SoftMax",
inputNames: ["A", "Max", "Norm"],
inputTypes: [s.TextureType.unpacked, s.TextureType.unpacked, s.TextureType.unpacked]
};
n.softmax = (g, m, _) => {
d(m);
const y = m[0].dims.slice(),
T = c.ShapeUtil.normalizeAxis(_.axis, y.length),
w = c.ShapeUtil.sizeToDimension(y, T),
S = c.ShapeUtil.sizeFromDimension(y, T);
return t(g, m, _, w, S)
}, n.parseSoftmaxAttributes = g => (0, u.createAttributeWithCacheKey)({
axis: g.attributes.getInt("axis", 1)
}), n.parseSoftmaxAttributesV13 = g => (0, u.createAttributeWithCacheKey)({
axis: g.attributes.getInt("axis", -1)
}), n.softmaxV13 = (g, m, _) => {
d(m);
const y = m[0].dims.slice(),
T = c.ShapeUtil.normalizeAxis(_.axis, y.length),
w = y.length,
S = T !== w - 1,
O = [];
let E, v = [],
P = [];
S && (v = Array.from({
length: w
}).map((k, Y) => Y), v[T] = w - 1, v[w - 1] = T, v.map(k => O.push(y[k])), E = (0, u.createAttributeWithCacheKey)({
perm: v
}), P = (0, h.transpose)(g, m, E));
const L = S ? c.ShapeUtil.sizeToDimension(O, w - 1) : c.ShapeUtil.sizeToDimension(y, w - 1),
V = S ? c.ShapeUtil.sizeFromDimension(O, w - 1) : c.ShapeUtil.sizeFromDimension(y, w - 1),
R = t(g, S ? P : m, _, L, V);
return S ? (0, h.transpose)(g, R, E) : R
};
const t = (g, m, _, y, T) => {
const w = e(g, m[0], y, T, [y]),
S = g.run(Object.assign(Object.assign({}, p), {
cacheHint: _.cacheKey,
get: () => w
}), m),
O = r(g, m[0], y, T, w.output.dims, [y]),
E = g.run(Object.assign(Object.assign({}, l), {
cacheHint: _.cacheKey,
get: () => O
}), [m[0], S]),
v = i(g, m[0], y, T, w.output.dims, O.output.dims);
return [g.run(Object.assign(Object.assign({}, o), {
cacheHint: _.cacheKey,
get: () => v
}), [m[0], S, E])]
},
e = (g, m, _, y, T) => {
const [w, S] = g.calculateTextureWidthAndHeight(m.dims, s.TextureType.unpacked), O = T.length;
if (_ < 1 || y < 1) throw new Error("Logical row count N and feature count D must be greater than or equal to 1");
if (T.length !== 1) throw new Error("Dimensionality of the output should be 1");
if (T[0] !== _) throw new Error("Shape of the output should be equal to logical row count");
const E = (0, f.getGlsl)(g.session.backend.glContext.version),
v = `
float process(int[${O}] indices) {
int logical_row_start_offset = indices[0] * ${y};
float max = getColorAsFloat(${E.texture2D}(A, offsetToCoords(logical_row_start_offset, ${w},
${S} )));
for(int i=1; i<${y}; ++i)
{
float current = getColorAsFloat(${E.texture2D}(A, offsetToCoords(logical_row_start_offset + i,
${w}, ${S})));
if(current > max)
max = current;
}
return max;
}`;
return Object.assign(Object.assign({}, p), {
output: {
dims: T,
type: m.type,
textureType: s.TextureType.unpacked
},
shaderSource: v
})
},
r = (g, m, _, y, T, w) => {
const [S, O] = g.calculateTextureWidthAndHeight(m.dims, s.TextureType.unpacked), E = w.length;
if (_ < 1 || y < 1) throw new Error("Logical row count N and feature count D must be greater than or equal to 1");
if (w.length !== 1) throw new Error("Dimensionality of the output should be 1");
if (w[0] !== _) throw new Error("Shape of the output should be equal to logical row count");
if (T.length !== 1) throw new Error("Dimensionality of the intermediate results should be 1");
if (T[0] !== _) throw new Error("Shape of the intermediate results should be equal to logical row count");
const v = `
float process(int[${E}] indices) {
int logical_row_start_offset = indices[0] * ${y};
float norm_factor = 0.0;
float max = _Max(indices);
for(int i=0; i<${y}; ++i)
{
norm_factor += exp(getColorAsFloat(${(0,f.getGlsl)(g.session.backend.glContext.version).texture2D}(A, offsetToCoords(logical_row_start_offset + i,
${S}, ${O}))) - max);
}
return norm_factor;
}`;
return Object.assign(Object.assign({}, l), {
output: {
dims: w,
type: m.type,
textureType: s.TextureType.unpacked
},
shaderSource: v
})
},
i = (g, m, _, y, T, w) => {
const [S, O] = g.calculateTextureWidthAndHeight(m.dims, s.TextureType.unpacked), E = m.dims.length;
if (_ < 1 || y < 1) throw new Error("Logical row count N and feature count D must be greater than or equal to 1");
if (T.length !== 1 || w.length !== 1) throw new Error("Dimensionality of the intermediate results should be 1");
if (T[0] !== _ || w[0] !== _) throw new Error("Shape of the intermediate results should be equal to logical row count");
const v = `
float process(int[${E}] indices) {
// get offset of current logical tensor index from the 2-D texture coordinates (TexCoords)
int offset = coordsToOffset(TexCoords, ${S}, ${O});
//determine the logical row for this index
int logical_row_index[1];
logical_row_index[0] = offset / ${y};
float norm_factor = _Norm(logical_row_index);
// avoid possible division by 0
// if norm_facor is 0, all elements are zero
// if so, return 0
if(norm_factor == 0.0)
return 0.0;
return exp(_A(indices) - _Max(logical_row_index)) / norm_factor;
}`;
return Object.assign(Object.assign({}, o), {
output: {
dims: m.dims,
type: m.type,
textureType: s.TextureType.unpacked
},
shaderSource: v
})
},
d = g => {
if (!g || g.length !== 1) throw new Error("Softmax requires 1 input.");
if (g[0].type !== "float32" && g[0].type !== "float64") throw new Error("Invalid input type")
}
},
5975: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.parseSplitAttributes = n.split = void 0;
const u = a(246),
c = a(2517),
f = a(2039),
s = {
name: "Split",
inputNames: ["A"],
inputTypes: [f.TextureType.unpacked]
};
n.split = (o, t, e) => {
l(t);
const r = c.ShapeUtil.normalizeAxis(e.axis, t[0].dims.length),
i = h(o, t, r, e),
d = [];
for (let g = 0; g < i; ++g) d.push(o.run(Object.assign(Object.assign({}, s), {
cacheHint: `${e.cacheKey};${g}`,
get: () => p(o, t[0], e, r, g)
}), t));
return d
}, n.parseSplitAttributes = o => {
const t = o.attributes.getInt("axis", 0),
e = o.attributes.getInts("split", []),
r = o.outputs.length;
return (0, u.createAttributeWithCacheKey)({
axis: t,
split: e,
numOutputs: r
})
};
const h = (o, t, e, r) => {
const [, i] = c.SplitUtil.splitShape(t[0].dims, e, r.split, r.numOutputs);
return i.length
},
p = (o, t, e, r, i) => {
const [d, g] = c.SplitUtil.splitShape(t.dims, r, e.split, e.numOutputs), m = g[i], _ = d[i], y = `
float process(int indices[${_.length}]) {
indices[${r}] += ${m};
return _A(indices);
}
`;
return Object.assign(Object.assign({}, s), {
cacheHint: `${e.cacheKey}:${i}`,
output: {
dims: _,
type: t.type,
textureType: f.TextureType.unpacked
},
shaderSource: y
})
},
l = o => {
if (!o || o.length !== 1) throw new Error("Split requires one input.");
if (o[0].type !== "int8" && o[0].type !== "uint8" && o[0].type !== "int16" && o[0].type !== "uint16" && o[0].type !== "int32" && o[0].type !== "uint32" && o[0].type !== "float32" && o[0].type !== "float64" && o[0].type !== "bool") throw new Error("Invalid input type.")
}
},
3933: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.parseSqueezeAttributes = n.squeezeV13 = n.squeeze = void 0;
const u = a(2517);
n.squeeze = (s, h, p) => {
c(h);
const l = u.ShapeUtil.squeezeShape(h[0].dims, p);
return [s.reshapeUnpacked(h[0], l)]
}, n.squeezeV13 = (s, h) => (f(h), (0, n.squeeze)(s, [h[0]], Array.from(h[1].integerData))), n.parseSqueezeAttributes = s => s.attributes.getInts("axes");
const c = s => {
if (!s || s.length !== 1) throw new Error("Squeeze requires 1 input.");
if (s[0].type === "string") throw new Error("invalid input tensor types.")
},
f = s => {
if (!s || s.length !== 2) throw new Error("Squeeze requires 2 inputs.");
if (s[1].type !== "int32") throw new Error("Invalid input type.")
}
},
6558: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.sum = void 0;
const u = a(5060),
c = a(2039);
n.sum = (h, p) => {
s(p);
const l = {
name: "Sum",
inputNames: p.map((o, t) => `X${t}`),
inputTypes: new Array(p.length).fill(c.TextureType.unpacked)
};
return [h.run(Object.assign(Object.assign({}, l), {
get: () => f(h, p, l)
}), p)]
};
const f = (h, p, l) => {
const o = (0, u.getGlsl)(h.session.backend.glContext.version),
t = p[0].dims.slice(),
e = `
void main() {
vec4 result = ${p.map((r,i)=>`${o.texture2D}(X${i},TexCoords)`).join(" + ")};
${o.output} = result;
}
`;
return Object.assign(Object.assign({}, l), {
output: {
dims: t,
type: p[0].type,
textureType: c.TextureType.unpacked
},
hasMain: !0,
shaderSource: e
})
},
s = h => {
if (!h || h.length === 0) throw new Error("Sum requires inputs.");
const p = h[0].dims.length;
for (let l = 1; l < h.length; l++) {
if (p !== h[l].dims.length) throw new Error("Input shapes are mismatched.");
for (let o = 0; o < p; o++)
if (h[0].dims[o] !== h[l].dims[o]) throw new Error("Input shapes are not matched.")
}
if (h[0].type !== "float32" && h[0].type !== "float64") throw new Error("Invalid input type.");
for (let l = 1; l < h.length; l++)
if (h[0].type !== h[l].type) throw new Error("Input types are not matched.")
}
},
5723: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.tile = void 0;
const u = a(782),
c = a(2039);
n.tile = (h, p) => {
s(p);
const l = {
name: "Tile",
inputNames: ["A"],
inputTypes: [c.TextureType.unpacked]
};
return [h.run(Object.assign(Object.assign({}, l), {
get: () => f(h, p, l)
}), p)]
};
const f = (h, p, l) => {
const o = p[0].dims.slice(),
t = new Array(o.length),
e = [];
for (let d = 0; d < o.length; d++) t[d] = o[d] * p[1].numberData[d], e.push(`inputIdx[${d}] = int(mod(float(outputIdx[${d}]), ${o[d]}.));`);
const r = t.length,
i = `
float process(int outputIdx[${r}]) {
int inputIdx[${r}];
${e.join(`
`)}
return _A(inputIdx);
}
`;
return Object.assign(Object.assign({}, l), {
output: {
dims: t,
type: p[0].type,
textureType: c.TextureType.unpacked
},
shaderSource: i
})
},
s = h => {
if (!h || h.length !== 2) throw new Error("Tile requires 2 input.");
if (h[1].dims.length !== 1) throw new Error("The second input shape must 1 dimension.");
if (h[1].dims[0] !== h[0].dims.length) throw new Error("Invalid input shape.");
if (u.NUMBER_TYPES.indexOf(h[0].type) === -1) throw new Error("Invalid input type.");
if (h[1].type !== "int32" && h[1].type !== "int16") throw new Error("Invalid repeat type.")
}
},
3738: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.parseTransposeAttributes = n.transpose = void 0;
const u = a(246),
c = a(2517),
f = a(2039),
s = {
name: "Transpose",
inputNames: ["A"],
inputTypes: [f.TextureType.unpacked]
};
n.transpose = (e, r, i) => (t(r), [e.run(Object.assign(Object.assign({}, s), {
cacheHint: i.cacheKey,
get: () => h(e, r[0], i.perm)
}), r)]), n.parseTransposeAttributes = e => (0, u.createAttributeWithCacheKey)({
perm: e.attributes.getInts("perm", [])
});
const h = (e, r, i) => {
const d = r.dims;
i = p(d, i);
const g = l(d, i),
m = d.length,
_ = `
${o("perm",i,m)}
float process(int indices[${m}]) {
int a[${m}];
perm(a, indices);
return _A(a);
}`;
return Object.assign(Object.assign({}, s), {
output: {
dims: g,
type: r.type,
textureType: f.TextureType.unpacked
},
shaderSource: _
})
},
p = (e, r) => (r && r.length !== e.length && (r = [...e.keys()].reverse()), r),
l = (e, r) => (r = p(e, r), c.ShapeUtil.sortBasedOnPerm(e, r)),
o = (e, r, i) => {
const d = [];
d.push(`void ${e}(out int a[${i}], int src[${i}]) {`);
for (let g = 0; g < i; ++g) d.push(` a[${r[g]}]=src[${g}];`);
return d.push(" }"), d.join(`
`)
},
t = e => {
if (!e || e.length !== 1) throw new Error("Transpose requires 1 input.");
if (e[0].type !== "float32" && e[0].type !== "float64") throw new Error("input should be float tensor")
}
},
8710: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.encodeAsUint8 = void 0;
const u = a(5060),
c = a(2039);
n.encodeAsUint8 = (f, s) => {
const h = s.shape,
p = (0, u.getGlsl)(f.session.backend.glContext.version),
l = `
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 = ${p.texture2D}(X,TexCoords).r;
${p.output} = encodeAsUint8(value);
}`,
o = {
name: "Uint8Encode",
inputTypes: [c.TextureType.unpacked],
inputNames: ["X"],
output: {
dims: h,
type: s.tensor.type,
textureType: c.TextureType.downloadUint8AsFloat
},
shaderSource: l,
hasMain: !0
};
return f.executeProgram(o, [s.tensor])
}
},
4909: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.tanh = n.tan = n.sqrt = n.sin = n.sigmoid = n.relu = n.not = n.neg = n.log = n.parseLeakyReluAttributes = n.leakyRelu = n.identity = n.floor = n.exp = n.parseEluAttributes = n.elu = n.cos = n.ceil = n.clipV11 = n.parseClipAttributes = n.clip = n.atan = n.asin = n.acos = n.abs = n.glslTanh = n.glslTan = n.glslSqrt = n.glslSigmoid = n.glslRelu = n.glslSin = n.glslNot = n.glslNeg = n.glslLog = n.glslLeakyRelu = n.glslIdentity = n.glslClip = n.glslFloor = n.glslExp = n.glslElu = n.glslCos = n.glslCeil = n.glslAtan = n.glslAsin = n.glslAcos = n.glslAbs = void 0;
const u = a(246),
c = a(2517),
f = a(8520),
s = a(5060),
h = a(2039);
function p() {
return R("abs")
}
function l() {
return R("acos")
}
function o() {
return R("asin")
}
function t() {
return R("atan")
}
function e() {
return R("ceil")
}
function r() {
return R("cos")
}
function i(C) {
const $ = "elu";
return {
body: `
const float alpha = float(${C});
float ${$}_(float a) {
return a >= 0.0 ? a: (exp(a) - 1.0) * alpha;
}
vec4 ${$}_(vec4 v) {
return vec4(${$}_(v.x), ${$}_(v.y), ${$}_(v.z), ${$}_(v.w));
}
`,
name: $,
type: f.FunctionType.ValueBased
}
}
function d() {
return R("exp")
}
function g() {
return R("floor")
}
function m(C, $) {
const X = "clip";
return {
body: `
const float min = float(${C});
const float max = float(${$});
float ${X}_(float a) {
return clamp(a, min, max);
}
vec4 ${X}_(vec4 v) {
return clamp(v, min, max);
}
`,
name: X,
type: f.FunctionType.ValueBased
}
}
function _() {
const C = "indentity";
return {
body: `
float ${C}_(float a) {
return a;
}
vec4 ${C}_(vec4 v) {
return v;
}
`,
name: C,
type: f.FunctionType.ValueBased
}
}
function y(C) {
const $ = "leakyRelu";
return {
body: `
const float alpha = float(${C});
float ${$}_(float a) {
return a < 0.0 ? a * alpha : a;
}
vec4 ${$}_(vec4 v) {
return vec4(${$}_(v.x), ${$}_(v.y), ${$}_(v.z), ${$}_(v.w));
}
`,
name: $,
type: f.FunctionType.ValueBased
}
}
function T() {
return R("log")
}
function w() {
const C = "neg";
return {
body: `
float ${C}_(float a) {
return -a;
}
vec4 ${C}_(vec4 v) {
return -v;
}
`,
name: C,
type: f.FunctionType.ValueBased
}
}
function S() {
const C = "not";
return {
body: `
float ${C}_(float a) {
return float( ! bool(a) );
}
bool ${C}_(bool a) {
return !a;
}
vec4 ${C}_(vec4 v) {
return vec4(!bool(v.x), !bool(v.y), !bool(v.z), !bool(v.w));
}
bvec4 ${C}_(bvec4 v) {
return bvec4(!v.x, !v.y, !v.z, !v.w);
}
`,
name: C,
type: f.FunctionType.ValueBased
}
}
function O() {
return R("sin")
}
function E() {
const C = "relu";
return {
body: `
float ${C}_(float a) {
return max( a, 0.0 );
}
vec4 ${C}_(vec4 v) {
return max( v, 0.0 );
}
`,
name: C,
type: f.FunctionType.ValueBased
}
}
function v() {
const C = "sigmoid";
return {
body: `
float ${C}_(float a) {
return 1.0 / (1.0 + exp(-a));
}
vec4 ${C}_(vec4 v) {
return 1.0 / (1.0 + exp(-v));
}
`,
name: C,
type: f.FunctionType.ValueBased
}
}
function P() {
return R("sqrt")
}
function L() {
return R("tan")
}
function V() {
const C = "tanh";
return {
body: `
float ${C}_(float a) {
a = clamp(a, -10., 10.);
a = exp(2.*a);
return (a - 1.) / (a + 1.);
}
vec4 ${C}_(vec4 v) {
v = clamp(v, -10., 10.);
v = exp(2.*v);
return (v - 1.) / (v + 1.);
}
`,
name: C,
type: f.FunctionType.ValueBased
}
}
function R(C) {
return {
body: `
float ${C}_(float a) {
return ${C}(a);
}
vec4 ${C}_(vec4 v) {
return ${C}(v);
}
`,
name: C,
type: f.FunctionType.ValueBased
}
}
n.glslAbs = p, n.glslAcos = l, n.glslAsin = o, n.glslAtan = t, n.glslCeil = e, n.glslCos = r, n.glslElu = i, n.glslExp = d, n.glslFloor = g, n.glslClip = m, n.glslIdentity = _, n.glslLeakyRelu = y, n.glslLog = T, n.glslNeg = w, n.glslNot = S, n.glslSin = O, n.glslRelu = E, n.glslSigmoid = v, n.glslSqrt = P, n.glslTan = L, n.glslTanh = V;
const k = (C, $, X, z) => {
const Z = C.session.pack ? h.TextureType.packed : h.TextureType.unpacked,
J = {
name: X.name,
inputTypes: [Z],
inputNames: ["A"],
cacheHint: z
};
return Object.assign(Object.assign({}, J), {
get: () => ((ue, Se, Te, se) => {
const ye = ue.session.pack ? h.TextureType.packed : h.TextureType.unpacked,
be = (0, s.getGlsl)(ue.session.backend.glContext.version);
return Object.assign(Object.assign({}, Se), {
output: {
dims: Te.dims,
type: Te.type,
textureType: ye
},
shaderSource: `
${se.body}
void main() {
vec4 v = ${be.texture2D}(A, TexCoords);
v = ${se.name}_(v);
${be.output} = v;
}
`,
hasMain: !0
})
})(C, J, $, X)
})
};
n.abs = (C, $) => [C.run(k(C, $[0], p()), $)], n.acos = (C, $) => [C.run(k(C, $[0], l()), $)], n.asin = (C, $) => [C.run(k(C, $[0], o()), $)], n.atan = (C, $) => [C.run(k(C, $[0], t()), $)], n.clip = (C, $, X) => [C.run(k(C, $[0], m(X.min, X.max), X.cacheKey), $)], n.parseClipAttributes = C => (0, u.createAttributeWithCacheKey)({
min: C.attributes.getFloat("min", c.MIN_CLIP),
max: C.attributes.getFloat("max", c.MAX_CLIP)
}), n.clipV11 = (C, $) => {
const X = Y(C, $);
return (0, n.clip)(C, [$[0]], X)
};
const Y = (C, $) => {
if ($.length >= 3 && (!C.session.isInitializer($[1].dataId) || !C.session.isInitializer($[2].dataId))) throw new Error("dynamic clip attributes are not allowed");
const X = $.length >= 3 ? $[1].numberData[0] : c.MIN_CLIP,
z = $.length >= 3 ? $[2].numberData[0] : c.MAX_CLIP;
return (0, u.createAttributeWithCacheKey)({
min: X,
max: z
})
};
n.ceil = (C, $) => [C.run(k(C, $[0], e()), $)], n.cos = (C, $) => [C.run(k(C, $[0], r()), $)], n.elu = (C, $, X) => [C.run(k(C, $[0], i(X.alpha), X.cacheKey), $)], n.parseEluAttributes = C => (0, u.createAttributeWithCacheKey)({
alpha: C.attributes.getFloat("alpha", 1)
}), n.exp = (C, $) => [C.run(k(C, $[0], d()), $)], n.floor = (C, $) => [C.run(k(C, $[0], g()), $)], n.identity = (C, $) => [C.run(k(C, $[0], _()), $)], n.leakyRelu = (C, $, X) => [C.run(k(C, $[0], y(X.alpha), X.cacheKey), $)], n.parseLeakyReluAttributes = C => (0, u.createAttributeWithCacheKey)({
alpha: C.attributes.getFloat("alpha", .01)
}), n.log = (C, $) => [C.run(k(C, $[0], T()), $)], n.neg = (C, $) => [C.run(k(C, $[0], w()), $)], n.not = (C, $) => [C.run(k(C, $[0], S()), $)], n.relu = (C, $) => [C.run(k(C, $[0], E()), $)], n.sigmoid = (C, $) => [C.run(k(C, $[0], v()), $)], n.sin = (C, $) => [C.run(k(C, $[0], O()), $)], n.sqrt = (C, $) => [C.run(k(C, $[0], P()), $)], n.tan = (C, $) => [C.run(k(C, $[0], L()), $)], n.tanh = (C, $) => [C.run(k(C, $[0], V()), $)]
},
5611: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.createUnpackProgramInfoLoader = n.createUnpackProgramInfo = void 0;
const u = a(5060),
c = a(2039),
f = a(9390),
s = a(2827),
h = {
name: "unpack",
inputNames: ["A"],
inputTypes: [c.TextureType.packed]
};
n.createUnpackProgramInfo = (p, l) => {
const o = l.dims.length,
t = (0, s.getChannels)("rc", o),
e = t.slice(-2),
r = (0, f.getCoordsDataType)(o),
i = (0, s.unpackFromChannel)(),
d = l.dims.length === 0 ? "" : function(_, y) {
if (_ === 1) return "rc";
let T = "";
for (let w = 0; w < _; w++) T += y[w], w < _ - 1 && (T += ",");
return T
}(o, t),
g = o <= 1 ? "rc" : `vec2(${e.join(",")})`,
m = `
${i}
void main() {
${r} rc = getOutputCoords();
// Sample the texture with the coords to get the rgba channel value.
vec4 packedInput = getA(${d});
${(0,u.getGlsl)(p.session.backend.glContext.version).output} = vec4(getChannel(packedInput, ${g}), 0, 0, 0);
}
`;
return Object.assign(Object.assign({}, h), {
hasMain: !0,
output: {
dims: l.dims,
type: l.type,
textureType: c.TextureType.unpacked
},
shaderSource: m
})
}, n.createUnpackProgramInfoLoader = (p, l) => Object.assign(Object.assign({}, h), {
get: () => (0, n.createUnpackProgramInfo)(p, l)
})
},
8428: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.parseUnsqueezeAttributes = n.unsqueezeV13 = n.unsqueeze = void 0;
const u = a(2517);
n.unsqueeze = (s, h, p) => {
c(h);
const l = u.ShapeUtil.unsqueezeShape(h[0].dims, p);
return [s.reshapeUnpacked(h[0], l)]
}, n.unsqueezeV13 = (s, h) => (f(h), (0, n.unsqueeze)(s, [h[0]], Array.from(h[1].integerData))), n.parseUnsqueezeAttributes = s => s.attributes.getInts("axes");
const c = s => {
if (!s || s.length !== 1) throw new Error("Unsqueeze requires 1 input.");
if (s[0].type === "string") throw new Error("invalid input tensor types.")
},
f = s => {
if (!s || s.length !== 2) throw new Error("Unsqueeze requires 2 inputs.");
if (s[1].type !== "int32") throw new Error("Invalid input type.")
}
},
9793: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.scalesValidation = n.validateInputs = n.parseUpsampleAttributes = n.parseUpsampleAttributesV9 = n.parseUpsampleAttributesV7 = n.upsample = void 0;
const u = a(246),
c = a(5060),
f = a(2039),
s = {
name: "Upsample",
inputNames: ["X"],
inputTypes: [f.TextureType.unpacked]
};
n.upsample = (p, l, o) => ((0, n.validateInputs)(l, o), [p.run(Object.assign(Object.assign({}, s), {
cacheHint: o.cacheKey,
get: () => h(p, l, o)
}), l)]), n.parseUpsampleAttributesV7 = p => (0, n.parseUpsampleAttributes)(p, 7), n.parseUpsampleAttributesV9 = p => (0, n.parseUpsampleAttributes)(p, 9), n.parseUpsampleAttributes = (p, l) => {
const o = l >= 10,
t = p.attributes.getString("mode", "nearest");
if (t !== "nearest" && t !== "linear" && (l < 11 || t !== "cubic")) throw new Error(`unrecognized mode: ${t}`);
let e = [];
l < 9 && (e = p.attributes.getFloats("scales"), (0, n.scalesValidation)(e, t, o));
const r = p.attributes.getFloat("extrapolation_value", 0),
i = l > 10 ? p.attributes.getString("coordinate_transformation_mode", "half_pixel") : "asymmetric";
if (["asymmetric", "pytorch_half_pixel", "tf_half_pixel_for_nn", "align_corners", "tf_crop_and_resize", "half_pixel"].indexOf(i) === -1) throw new Error(`coordinate_transform_mode '${i}' is not supported`);
const d = i === "tf_crop_and_resize",
g = d,
m = t === "nearest" && l >= 11 ? p.attributes.getString("nearest_mode", "round_prefer_floor") : "";
if (["round_prefer_floor", "round_prefer_ceil", "floor", "ceil", ""].indexOf(m) === -1) throw new Error(`nearest_mode '${m}' is not supported`);
const _ = p.attributes.getFloat("cubic_coeff_a", -.75),
y = p.attributes.getInt("exclude_outside", 0) !== 0;
if (y && t !== "cubic") throw new Error("exclude_outside can be set to 1 only when mode is CUBIC.");
const T = l < 11 || t === "nearest" && i === "asymmetric" && m === "floor";
let w = 0,
S = 0,
O = 0;
return l > 10 ? p.inputs.length > 2 ? (w = 1, S = 2, O = 3) : (S = 1, O = 2) : l === 9 && (S = 1), (0, u.createAttributeWithCacheKey)({
opset: l,
isResize: o,
mode: t,
scales: e,
extrapolationValue: r,
coordinateTransformMode: i,
useExtrapolation: g,
needRoiInput: d,
nearestMode: m,
cubicCoefficientA: _,
excludeOutside: y,
useNearest2xOptimization: T,
roiInputIdx: w,
scalesInputIdx: S,
sizesInputIdx: O
})
};
const h = (p, l, o) => {
const t = (0, c.getGlsl)(p.session.backend.glContext.version),
[e, r] = p.calculateTextureWidthAndHeight(l[0].dims, f.TextureType.unpacked),
i = l[0].dims.map((O, E) => Math.floor(O * o.scales[E])),
[d, g] = p.calculateTextureWidthAndHeight(i, f.TextureType.unpacked),
m = i.length,
_ = new Array(m),
y = new Array(m);
let T = `
int output_pitches[${m}];
int input_pitches[${m}];
`;
for (let O = m - 1; O >= 0; O--) _[O] = O === m - 1 ? 1 : _[O + 1] * i[O + 1], y[O] = O === m - 1 ? 1 : y[O + 1] * l[0].dims[O + 1], T += `
output_pitches[${O}] = ${_[O]};
input_pitches[${O}] = ${y[O]};
`;
const w = `
float getInputFloat(int index) {
vec2 coords = offsetToCoords(index, ${e}, ${r});
float value = getColorAsFloat(${t.texture2D}(X, coords));
return value;
}
`,
S = o.mode === "nearest" ? `
${w}
float process(int indices[${m}]) {
int input_index = 0;
int output_index = coordsToOffset(TexCoords, ${d}, ${g});
${T}
int d, m;
for (int dim = 0; dim < ${m}; ++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);
}` : m === 4 ? `
${w}
float process(int indices[4]) {
int input_index = 0;
int output_index = coordsToOffset(TexCoords, ${d}, ${g});
${T}
int m;
int index_of_dim0, index_of_dim1, index_of_dim2, index_of_dim3;
index_of_dim0 = output_index / output_pitches[0];
m = output_index - index_of_dim0 * output_pitches[0];
index_of_dim1 = m / output_pitches[1];
m = m - index_of_dim1 * output_pitches[1];
index_of_dim2 = m / output_pitches[2];
m = m - index_of_dim2 * output_pitches[2];
index_of_dim3 = m;
int index_of_input_dim2, index_of_input_dim3, x_offset, y_offset;
index_of_input_dim2 = index_of_dim2 / scales[2];
y_offset = index_of_dim2 - index_of_input_dim2 * scales[2];
index_of_input_dim3 = index_of_dim3 / scales[3];
x_offset = index_of_dim3 - index_of_input_dim3 * scales[3];
input_index = index_of_dim0 * input_pitches[0] +
index_of_dim1 * input_pitches[1] +
index_of_input_dim2 * input_pitches[2] +
index_of_input_dim3;
float x00 = getInputFloat(input_index);
float x10, x01, x11;
bool end_of_dim2 = false;
if (index_of_input_dim2 == (${l[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]);
}` : `
${w}
float process(int indices[2]) {
int input_index = 0;
int output_index = coordsToOffset(TexCoords, ${d}, ${g});
${T}
int m;
int index_of_dim0, index_of_dim1;
index_of_dim0 = output_index / output_pitches[0];
m = output_index - index_of_dim0 * output_pitches[0];
index_of_dim1 = m;
int index_of_input_dim0, index_of_input_dim1, x_offset, y_offset;
index_of_input_dim0 = index_of_dim0 / scales[0];
y_offset = index_of_dim0 - index_of_input_dim0 * scales[0];
index_of_input_dim1 = index_of_dim1 / scales[1];
x_offset = index_of_dim1 - index_of_input_dim1 * scales[1];
input_index = index_of_input_dim0 * input_pitches[0] + index_of_input_dim1;
float x00 = getInputFloat(input_index);
float x10, x01, x11;
bool end_of_dim0 = false;
if (index_of_input_dim0 == (${l[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({}, s), {
output: {
dims: i,
type: l[0].type,
textureType: f.TextureType.unpacked
},
shaderSource: S,
variables: [{
name: "scales",
type: "int",
arrayLength: o.scales.length,
data: o.scales.map(O => Math.ceil(O))
}]
})
};
n.validateInputs = (p, l) => {
if (!p || l.opset < 9 && p.length !== 1 || l.opset >= 9 && l.opset < 11 && p.length !== 2 || l.opset >= 11 && p.length < 2) throw new Error("invalid inputs.");
if (l.scales.length > 0 && p[0].dims.length !== l.scales.length) throw new Error("Invalid input shape.");
if (p[0].type === "string") throw new Error("Invalid input tensor types.")
}, n.scalesValidation = (p, l, o) => {
if (o) {
for (const t of p)
if (t <= 0) throw new Error("Scale value should be greater than 0.")
} else
for (const t of p)
if (t < 1) throw new Error("Scale value should be greater than or equal to 1.");
if (!(l !== "linear" && l !== "cubic" || p.length === 2 || p.length === 4 && p[0] === 1 && p[1] === 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 ${o?"Resize":"Upsample"} opeartor.`)
}
},
1958: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.ProgramManager = void 0;
const u = a(1670),
c = a(6231),
f = a(8879),
s = a(5060);
n.ProgramManager = class {
constructor(h, p, l) {
this.profiler = h, this.glContext = p, this.textureLayoutStrategy = l, this.repo = new Map, this.attributesBound = !1
}
getArtifact(h) {
return this.repo.get(h)
}
setArtifact(h, p) {
this.repo.set(h, p)
}
run(h, p, l) {
var o;
this.profiler.event("op", `ProgramManager.run ${(o=h.programInfo.name)!==null&&o!==void 0?o:"unknown kernel"}`, () => {
var t;
const e = this.glContext.gl,
r = h.program;
e.useProgram(r);
try {
this.bindOutput(l), this.attributesBound || this.bindAttributes(h.attribLocations), this.bindUniforms(h.uniformLocations, (t = h.programInfo.variables) !== null && t !== void 0 ? t : [], p)
} catch (i) {
throw c.Logger.error("ProgramManager", h.programInfo.shaderSource), i
}
this.profiler.event("backend", "GlContext.draw()", () => {
this.glContext.draw()
})
}, this.glContext)
}
dispose() {
this.vertexShader && this.glContext.deleteShader(this.vertexShader), this.repo.forEach(h => this.glContext.deleteProgram(h.program))
}
build(h, p, l) {
return this.profiler.event("backend", "ProgramManager.build", () => {
const o = new f.GlslPreprocessor(this.glContext, h, p, l),
t = o.preprocess(),
e = this.compile(t);
return {
programInfo: h,
program: e,
uniformLocations: this.getUniformLocations(e, o.context.programInfo.inputNames, o.context.programInfo.variables),
attribLocations: this.getAttribLocations(e)
}
})
}
compile(h) {
if (!this.vertexShader) {
c.Logger.verbose("ProrgramManager", "Compiling and caching Vertex shader for the first time");
const o = (0, s.getVertexShaderSource)(this.glContext.version);
this.vertexShader = this.glContext.compileShader(o, this.glContext.gl.VERTEX_SHADER)
}
u.env.debug && c.Logger.verbose("ProrgramManager", `FragShader:
${h}
`);
const p = this.glContext.compileShader(h, this.glContext.gl.FRAGMENT_SHADER),
l = this.glContext.createProgram(this.vertexShader, p);
return this.glContext.deleteShader(p), l
}
bindOutput(h) {
const p = h.width,
l = h.height;
c.Logger.verbose("ProrgramManager", `Binding output texture to Framebuffer: w/h=${p}/${l}, shape=${h.shape}, type=${h.tensor.type}`), this.glContext.attachFramebuffer(h.texture, p, l)
}
bindAttributes(h) {
const p = h.position,
l = h.textureCoord;
this.glContext.setVertexAttributes(p, l), this.attributesBound = !0
}
bindUniforms(h, p, l) {
var o;
const t = this.glContext.gl;
let e = 0;
for (const {
name: r,
type: i,
location: d,
arrayLength: g
}
of h) {
const m = (o = p.find(_ => _.name === r)) === null || o === void 0 ? void 0 : o.data;
if (i !== "sampler2D" && !m) throw new Error(`variable '${r}' does not have data defined in program info`);
switch (i) {
case "sampler2D":
this.bindTexture(l[e], d, e), e++;
break;
case "float":
g ? t.uniform1fv(d, m) : t.uniform1f(d, m);
break;
case "int":
g ? t.uniform1iv(d, m) : t.uniform1i(d, m);
break;
default:
throw new Error(`Uniform not implemented: ${i}`)
}
}
}
bindTexture(h, p, l) {
this.glContext.bindTextureToUniform(h.texture, l, p)
}
getAttribLocations(h) {
return {
position: this.getAttribLocation(h, "position"),
textureCoord: this.getAttribLocation(h, "textureCoord")
}
}
getUniformLocations(h, p, l) {
const o = [];
if (p)
for (const t of p) o.push({
name: t,
type: "sampler2D",
location: this.getUniformLocation(h, t)
});
if (l)
for (const t of l) o.push(Object.assign(Object.assign({}, t), {
location: this.getUniformLocation(h, t.name)
}));
return o
}
getUniformLocation(h, p) {
const l = this.glContext.gl.getUniformLocation(h, p);
if (l === null) throw new Error(`Uniform ${p} not found.`);
return l
}
getAttribLocation(h, p) {
return this.glContext.gl.getAttribLocation(h, p)
}
}
},
6416: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.WebGLSessionHandler = void 0;
const u = a(6231),
c = a(1047),
f = a(8316),
s = a(1640),
h = a(1958),
p = a(7859),
l = a(5702);
n.WebGLSessionHandler = class {
constructor(o, t) {
this.backend = o, this.context = t, this.layoutStrategy = new p.PreferLogicalStrategy(o.glContext.maxTextureSize), this.programManager = new h.ProgramManager(this.context.profiler, o.glContext, this.layoutStrategy), this.textureManager = new l.TextureManager(o.glContext, this.layoutStrategy, this.context.profiler, {
reuseTextures: o.textureCacheMode === "full"
}), this.packedTextureDataCache = new Map, this.unpackedTextureDataCache = new Map, this.pack = o.pack, this.pack2unpackMap = new Map, this.unpack2packMap = new Map
}
createInferenceHandler() {
return new f.WebGLInferenceHandler(this)
}
onGraphInitialized(o) {
const t = o.getValues().filter(e => e.from === -1 && e.tensor).map(e => e.tensor.dataId);
this.initializers = new Set(t)
}
isInitializer(o) {
return !!this.initializers && this.initializers.has(o)
}
addInitializer(o) {
this.initializers.add(o)
}
getTextureData(o, t) {
return t ? this.packedTextureDataCache.get(o) : this.unpackedTextureDataCache.get(o)
}
setTextureData(o, t, e = !1) {
u.Logger.verbose("WebGLSessionHandler", "Storing Texture data in cache"), e ? this.packedTextureDataCache.set(o, t) : this.unpackedTextureDataCache.set(o, t)
}
dispose() {
this.programManager.dispose(), this.textureManager.clearActiveTextures(), this.packedTextureDataCache.forEach(o => this.textureManager.releaseTexture(o, !0)), this.packedTextureDataCache = new Map, this.unpackedTextureDataCache.forEach(o => this.textureManager.releaseTexture(o, !0)), this.unpackedTextureDataCache = new Map
}
resolve(o, t, e) {
const r = (0, c.resolveOperator)(o, t, s.WEBGL_OP_RESOLVE_RULES);
return {
impl: r.opImpl,
context: r.opInit ? r.opInit(o, e) : o
}
}
}
},
7769: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.Uint8DataEncoder = n.RGBAFloatDataEncoder = n.RedFloat32DataEncoder = void 0;
const u = a(6231);
n.RedFloat32DataEncoder = class {
constructor(c, f = 1) {
if (f === 1) this.internalFormat = c.R32F, this.format = c.RED, this.textureType = c.FLOAT, this.channelSize = f;
else {
if (f !== 4) throw new Error(`Invalid number of channels: ${f}`);
this.internalFormat = c.RGBA32F, this.format = c.RGBA, this.textureType = c.FLOAT, this.channelSize = f
}
}
encode(c, f) {
let s, h;
return c.constructor !== Float32Array && (u.Logger.warning("Encoder", "data was not of type Float32; creating new Float32Array"), h = new Float32Array(c)), f * this.channelSize > c.length ? (u.Logger.warning("Encoder", "Source data too small. Allocating larger array"), h = c, s = this.allocate(f * this.channelSize), h.forEach((p, l) => s[l] = p)) : (h = c, s = h), s
}
allocate(c) {
return new Float32Array(4 * c)
}
decode(c, f) {
return this.channelSize === 1 ? c.filter((s, h) => h % 4 == 0).subarray(0, f) : c.subarray(0, f)
}
}, n.RGBAFloatDataEncoder = class {
constructor(c, f = 1, s) {
if (f !== 1 && f !== 4) throw new Error(`Invalid number of channels: ${f}`);
this.internalFormat = c.RGBA, this.format = c.RGBA, this.channelSize = f, this.textureType = s || c.FLOAT
}
encode(c, f) {
let s = c;
return this.channelSize === 1 && (u.Logger.verbose("Encoder", "Exploding into a larger array"), s = this.allocate(f), c.forEach((h, p) => s[4 * p] = h)), s
}
allocate(c) {
return new Float32Array(4 * c)
}
decode(c, f) {
return this.channelSize === 1 ? c.filter((s, h) => h % 4 == 0).subarray(0, f) : c.subarray(0, f)
}
}, n.Uint8DataEncoder = class {
constructor(c, f = 1) {
if (this.channelSize = 4, f === 1) this.internalFormat = c.ALPHA, this.format = c.ALPHA, this.textureType = c.UNSIGNED_BYTE, this.channelSize = f;
else {
if (f !== 4) throw new Error(`Invalid number of channels: ${f}`);
this.internalFormat = c.RGBA, this.format = c.RGBA, this.textureType = c.UNSIGNED_BYTE, this.channelSize = f
}
}
encode(c, f) {
return new Uint8Array(c.buffer, c.byteOffset, c.byteLength)
}
allocate(c) {
return new Uint8Array(c * this.channelSize)
}
decode(c, f) {
if (c instanceof Uint8Array) return c.subarray(0, f);
throw new Error(`Invalid array type: ${c.constructor}`)
}
}
},
7859: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.getBatchDim = n.sizeToSquarishShape = n.getRowsCols = n.sizeFromShape = n.isInt = n.parseAxisParam = n.squeezeShape = n.PreferLogicalStrategy = n.AlwaysKeepOriginalSizeStrategy = void 0;
const u = a(6231),
c = a(2517);
function f(o, t) {
const e = [],
r = [],
i = t != null && Array.isArray(t) && t.length === 0,
d = t == null || i ? null : s(t, o).sort();
let g = 0;
for (let m = 0; m < o.length; ++m) {
if (d != null) {
if (d[g] === m && o[m] !== 1) throw new Error(`Can't squeeze axis ${m} since its dim '${o[m]}' is not 1`);
(d[g] == null || d[g] > m) && o[m] === 1 && (e.push(o[m]), r.push(m)), d[g] <= m && g++
}
o[m] !== 1 && (e.push(o[m]), r.push(m))
}
return {
newShape: e,
keptDims: r
}
}
function s(o, t) {
const e = t.length;
return o = o == null ? t.map((r, i) => i) : [].concat(o), (0, c.assert)(o.every(r => r >= -e && r < e), () => `All values in axis param must be in range [-${e}, ${e}) but got axis ${o}`), (0, c.assert)(o.every(h), () => `All values in axis param must be integers but got axis ${o}`), o.map(r => r < 0 ? e + r : r)
}
function h(o) {
return o % 1 == 0
}
function p(o) {
if (o.length === 0) return 1;
let t = o[0];
for (let e = 1; e < o.length; e++) t *= o[e];
return t
}
function l(o) {
const t = Math.ceil(Math.sqrt(o));
return [t, Math.ceil(o / t)]
}
n.AlwaysKeepOriginalSizeStrategy = class {
constructor(o) {
this.maxTextureSize = o
}
computeTextureWH(o, t) {
if (o.length === 0) return [1, 1];
const e = this.maxTextureSize;
if (t && t.breakAxis !== void 0) {
const d = t.breakAxis >= o.length ? 1 : o.slice(t.breakAxis).reduce((m, _) => m * _),
g = t.breakAxis <= 0 ? 1 : o.slice(0, t.breakAxis).reduce((m, _) => m * _);
if (!(d > e || g > e)) return [d, g];
u.Logger.verbose("TextureLayout", `Given width/height preferences were unattainable: shape:${o}, breakAxis:${t.breakAxis}`)
}
const r = o.reduce((d, g) => d * g);
let i = Math.floor(Math.sqrt(r));
for (; i < e && i < r && r % i != 0; i++);
if (i >= e || r % i != 0) throw new Error(`The given dimensions are outside this GPU's boundaries: ${o}`);
return [i, r / i]
}
}, n.PreferLogicalStrategy = class {
constructor(o) {
this.maxTextureSize = o
}
computeTextureWH(o, t) {
const e = this.computeTexture(o, t);
return t && t.isPacked && (e[0] /= 2, e[1] /= 2), t && t.reverseWH ? [e[1], e[0]] : e
}
computeTexture(o, t) {
const e = t && t.isPacked;
if (o.length === 0) return e ? [2, 2] : [1, 1];
let r = this.maxTextureSize;
if (t && t.breakAxis !== void 0) {
const g = t.breakAxis >= o.length ? 1 : o.slice(t.breakAxis).reduce((_, y) => _ * y),
m = t.breakAxis <= 0 ? 1 : o.slice(0, t.breakAxis).reduce((_, y) => _ * y);
if (!(g > r || m > r)) return [g, m];
u.Logger.verbose("TextureLayout", `Given width/height preferences were unattainable: shape:${o}, breakAxis:${t.breakAxis}`)
}
let i = o.slice(0);
e && (r *= 2, i = i.map((g, m) => m >= i.length - 2 ? i[m] % 2 == 0 ? i[m] : i[m] + 1 : i[m]), i.length === 1 && (i = [2, i[0]])), i.length !== 2 && (i = f(i).newShape);
const d = p(i);
return i.length <= 1 && d <= r ? [1, d] : i.length === 2 && i[0] <= r && i[1] <= r ? i : i.length === 3 && i[0] * i[1] <= r && i[2] <= r ? [i[0] * i[1], i[2]] : i.length === 3 && i[0] <= r && i[1] * i[2] <= r ? [i[0], i[1] * i[2]] : i.length === 4 && i[0] * i[1] * i[2] <= r && i[3] <= r ? [i[0] * i[1] * i[2], i[3]] : i.length === 4 && i[0] <= r && i[1] * i[2] * i[3] <= r ? [i[0], i[1] * i[2] * i[3]] : e ? l(d / 4).map(g => 2 * g) : l(d)
}
}, n.squeezeShape = f, n.parseAxisParam = s, n.isInt = h, n.sizeFromShape = p, n.getRowsCols = function(o) {
if (o.length === 0) throw Error("Cannot get rows and columns of an empty shape array.");
return [o.length > 1 ? o[o.length - 2] : 1, o[o.length - 1]]
}, n.sizeToSquarishShape = l, n.getBatchDim = function(o, t = 2) {
return p(o.slice(0, o.length - t))
}
},
4057: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.createTextureLayoutFromShape = n.calculateTextureWidthAndHeight = n.createTextureLayoutFromTextureType = void 0;
const u = a(2517),
c = a(2039);
n.createTextureLayoutFromTextureType = (f, s, h) => {
const p = h === c.TextureType.unpacked || h === c.TextureType.unpackedReversed ? 1 : 4,
l = h === c.TextureType.packed,
o = h === c.TextureType.unpackedReversed || h === c.TextureType.packed,
t = h === c.TextureType.packedLastDimension ? s.length - 1 : void 0,
e = h === c.TextureType.packedLastDimension ? s.map((r, i) => i === s.length - 1 ? 4 * r : r) : void 0;
return (0, n.createTextureLayoutFromShape)(f, s, p, e, {
isPacked: l,
reverseWH: o,
breakAxis: t
})
}, n.calculateTextureWidthAndHeight = (f, s, h) => {
const p = (0, n.createTextureLayoutFromTextureType)(f, s, h);
return [p.width, p.height]
}, n.createTextureLayoutFromShape = (f, s, h = 1, p, l) => {
const o = !(!l || !l.isPacked),
[t, e] = f.computeTextureWH(o && p || s, l),
r = s.length;
let i = s.slice(0);
if (r === 0 && (i = [1]), h === 1) p = s;
else if (o) {
if (h !== 4) throw new Error("a packed texture must be 4-channel");
p = s, r > 0 && (i[r - 1] = Math.ceil(i[r - 1] / 2)), r > 1 && (i[r - 2] = Math.ceil(i[r - 2] / 2))
} else if (!p) throw new Error("Unpacked shape is needed when using channels > 1");
return {
width: t,
height: e,
channels: h,
isPacked: o,
shape: i,
strides: u.ShapeUtil.computeStrides(i),
unpackedShape: p,
reversedWH: l && l.reverseWH
}
}
},
5702: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.TextureManager = void 0;
const u = a(6231);
n.TextureManager = class {
constructor(c, f, s, h) {
this.glContext = c, this.layoutStrategy = f, this.profiler = s, this.config = h, this.pendingRead = new Map, h.reuseTextures && (this.inUseTextures = new Map, this.idleTextures = new Map, this.textureLookup = new Map)
}
createTextureFromLayout(c, f, s, h) {
const p = this.toEncoderType(c),
l = this.glContext.getEncoder(p, f.channels || 1, h);
if (f.isPacked && h === 1) throw new Error("not implemented");
const o = f.width,
t = f.height;
let e, r;
if (this.config.reuseTextures) {
e = `${o}x${t}_${l.format}_${l.internalFormat}_${l.textureType}`, r = this.inUseTextures.get(e), r || (r = [], this.inUseTextures.set(e, r));
const d = this.idleTextures.get(e);
if (d && d.length > 0) {
const g = d.pop();
return r.push(g), h === 1 && this.glContext.updateTexture(g, o, t, l, this.toTextureData(c, s)), g
}
}
u.Logger.verbose("TextureManager", `Creating new texture of size ${f.width}x${f.height}`);
const i = this.glContext.allocateTexture(o, t, l, this.toTextureData(c, s));
return this.config.reuseTextures && (r.push(i), this.textureLookup.set(i, e)), i
}
readTexture(c, f, s) {
return s || (s = 1), this.profiler.event("backend", "TextureManager.readTexture", () => {
const h = c.shape.reduce((l, o) => l * o) * s,
p = this.glContext.readTexture(c.texture, c.width, c.height, h, this.toEncoderType(f), s);
return this.toTensorData(f, p)
})
}
async readTextureAsync(c, f, s) {
const h = c.tensor.dataId;
if (s || (s = 1), this.pendingRead.has(h)) {
const p = this.pendingRead.get(h);
return new Promise(l => p?.push(l))
}
return this.profiler.event("backend", "TextureManager.readTextureAsync", async () => {
this.pendingRead.set(h, []);
const p = c.shape.reduce((e, r) => e * r) * s;
await this.glContext.createAndWaitForFence();
const l = this.glContext.readTexture(c.texture, c.width, c.height, p, this.toEncoderType(f), s),
o = this.toTensorData(f, l),
t = this.pendingRead.get(h);
return this.pendingRead.delete(h), t?.forEach(e => e(o)), o
})
}
readUint8TextureAsFloat(c) {
return this.profiler.event("backend", "TextureManager.readUint8TextureAsFloat", () => {
const f = c.shape.reduce((h, p) => h * p),
s = this.glContext.readTexture(c.texture, c.width, c.height, 4 * f, "byte", 4);
return new Float32Array(s.buffer, s.byteOffset, f)
})
}
releaseTexture(c, f) {
let s;
if (this.config.reuseTextures && (s = this.textureLookup.get(c.texture), s)) {
f && this.textureLookup.delete(s);
const h = this.inUseTextures.get(s);
if (h) {
const p = h.indexOf(c.texture);
if (p !== -1) {
h.splice(p, 1);
let l = this.idleTextures.get(s);
l || (l = [], this.idleTextures.set(s, l)), l.push(c.texture)
}
}
}
s && !f || (u.Logger.verbose("TextureManager", `Deleting texture of size ${c.width}x${c.height}`), this.glContext.deleteTexture(c.texture))
}
toTensorData(c, f) {
switch (c) {
case "int16":
return f instanceof Int16Array ? f : Int16Array.from(f);
case "int32":
return f instanceof Int32Array ? f : Int32Array.from(f);
case "int8":
return f instanceof Int8Array ? f : Int8Array.from(f);
case "uint16":
return f instanceof Uint16Array ? f : Uint16Array.from(f);
case "uint32":
return f instanceof Uint32Array ? f : Uint32Array.from(f);
case "uint8":
case "bool":
return f instanceof Uint8Array ? f : Uint8Array.from(f);
case "float32":
return f instanceof Float32Array ? f : Float32Array.from(f);
case "float64":
return f instanceof Float64Array ? f : Float64Array.from(f);
default:
throw new Error(`TensorData type ${c} is not supported`)
}
}
toTextureData(c, f) {
if (f) return f instanceof Float32Array ? f : new Float32Array(f)
}
toEncoderType(c) {
return "float"
}
clearActiveTextures() {
this.glContext.clearActiveTextures()
}
}
},
2039: (b, n) => {
var a;
Object.defineProperty(n, "__esModule", {
value: !0
}), n.TextureType = void 0, (a = n.TextureType || (n.TextureType = {}))[a.unpacked = 0] = "unpacked", a[a.unpackedReversed = 1] = "unpackedReversed", a[a.packed = 2] = "packed", a[a.downloadUint8AsFloat = 3] = "downloadUint8AsFloat", a[a.packedLastDimension = 4] = "packedLastDimension"
},
9390: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.getGlChannels = n.getCoordsDataType = n.getSqueezedParams = n.squeezeInputShape = n.generateShaderFuncNameFromInputSamplerNameAtOutCoords = n.generateShaderFuncNameFromInputSamplerName = n.repeatedTry = n.getPackedShape = void 0;
const u = a(2517);
n.getPackedShape = function(c) {
const f = c.length;
return c.slice(0, f - 1).concat(c[f - 1] / 4)
}, n.repeatedTry = async function(c, f = h => 0, s) {
return new Promise((h, p) => {
let l = 0;
const o = () => {
if (c()) return void h();
l++;
const t = f(l);
s != null && l >= s ? p() : setTimeout(o, t)
};
o()
})
}, n.generateShaderFuncNameFromInputSamplerName = function(c) {
return (0, u.assert)(c !== void 0 && c.length !== 0, () => "empty string found for sampler name"), "get" + c.charAt(0).toUpperCase() + c.slice(1)
}, n.generateShaderFuncNameFromInputSamplerNameAtOutCoords = function(c) {
return (0, u.assert)(c !== void 0 && c.length !== 0, () => "empty string found for sampler name"), "get" + c.charAt(0).toUpperCase() + c.slice(1) + "AtOutCoords"
}, n.squeezeInputShape = function(c, f) {
let s = JSON.parse(JSON.stringify(c));
return s = f, s
}, n.getSqueezedParams = function(c, f) {
return f.map(s => c[s]).join(", ")
}, n.getCoordsDataType = function(c) {
if (c <= 1) return "int";
if (c === 2) return "ivec2";
if (c === 3) return "ivec3";
if (c === 4) return "ivec4";
if (c === 5) return "ivec5";
if (c === 6) return "ivec6";
throw Error(`GPU for rank ${c} is not yet supported`)
}, n.getGlChannels = function(c = 6) {
return ["x", "y", "z", "w", "u", "v"].slice(0, c)
}
},
7305: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.createNewWebGLContext = n.createWebGLContext = void 0;
const u = a(6231),
c = a(1713),
f = {};
function s(h) {
const p = function() {
if (typeof document > "u") {
if (typeof OffscreenCanvas > "u") throw new TypeError("failed to create canvas: OffscreenCanvas is not supported");
return new OffscreenCanvas(1, 1)
}
const t = document.createElement("canvas");
return t.width = 1, t.height = 1, t
}();
let l;
const o = {
alpha: !1,
depth: !1,
antialias: !1,
stencil: !1,
preserveDrawingBuffer: !1,
premultipliedAlpha: !1,
failIfMajorPerformanceCaveat: !1
};
if ((!h || h === "webgl2") && (l = p.getContext("webgl2", o), l)) try {
return new c.WebGLContext(l, 2)
} catch (t) {
u.Logger.warning("GlContextFactory", `failed to create WebGLContext using contextId 'webgl2'. Error: ${t}`)
}
if ((!h || h === "webgl") && (l = p.getContext("webgl", o) || p.getContext("experimental-webgl", o), l)) try {
return new c.WebGLContext(l, 1)
} catch (t) {
u.Logger.warning("GlContextFactory", `failed to create WebGLContext using contextId 'webgl' or 'experimental-webgl'. Error: ${t}`)
}
throw new Error("WebGL is not supported")
}
n.createWebGLContext = function h(p) {
let l;
p && p !== "webgl2" || !("webgl2" in f) ? p && p !== "webgl" || !("webgl" in f) || (l = f.webgl) : l = f.webgl2, l = l || s(p), p = p || l.version === 1 ? "webgl" : "webgl2";
const o = l.gl;
return f[p] = l, o.isContextLost() ? (delete f[p], h(p)) : (o.disable(o.DEPTH_TEST), o.disable(o.STENCIL_TEST), o.disable(o.BLEND), o.disable(o.DITHER), o.disable(o.POLYGON_OFFSET_FILL), o.disable(o.SAMPLE_COVERAGE), o.enable(o.SCISSOR_TEST), o.enable(o.CULL_FACE), o.cullFace(o.BACK), l)
}, n.createNewWebGLContext = s
},
1713: function(b, n, a) {
var u = this && this.__createBinding || (Object.create ? function(o, t, e, r) {
r === void 0 && (r = e);
var i = Object.getOwnPropertyDescriptor(t, e);
i && !("get" in i ? !t.__esModule : i.writable || i.configurable) || (i = {
enumerable: !0,
get: function() {
return t[e]
}
}), Object.defineProperty(o, r, i)
} : function(o, t, e, r) {
r === void 0 && (r = e), o[r] = t[e]
}),
c = this && this.__setModuleDefault || (Object.create ? function(o, t) {
Object.defineProperty(o, "default", {
enumerable: !0,
value: t
})
} : function(o, t) {
o.default = t
}),
f = this && this.__importStar || function(o) {
if (o && o.__esModule) return o;
var t = {};
if (o != null)
for (var e in o) e !== "default" && Object.prototype.hasOwnProperty.call(o, e) && u(t, o, e);
return c(t, o), t
};
Object.defineProperty(n, "__esModule", {
value: !0
}), n.WebGLContext = n.linearSearchLastTrue = void 0;
const s = a(1670),
h = f(a(7769)),
p = a(9390);
function l(o) {
let t = 0;
for (; t < o.length && o[t](); ++t);
return t - 1
}
n.linearSearchLastTrue = l, n.WebGLContext = class {
constructor(o, t) {
this.frameBufferBound = !1, this.itemsToPoll = [], this.gl = o, this.version = t, this.getExtensions(), this.vertexbuffer = this.createVertexbuffer(), this.framebuffer = this.createFramebuffer(), this.queryVitalParameters()
}
allocateTexture(o, t, e, r) {
const i = this.gl,
d = i.createTexture();
i.bindTexture(i.TEXTURE_2D, d), i.texParameteri(i.TEXTURE_2D, i.TEXTURE_MIN_FILTER, i.NEAREST), i.texParameteri(i.TEXTURE_2D, i.TEXTURE_MAG_FILTER, i.NEAREST), i.texParameteri(i.TEXTURE_2D, i.TEXTURE_WRAP_S, i.CLAMP_TO_EDGE), i.texParameteri(i.TEXTURE_2D, i.TEXTURE_WRAP_T, i.CLAMP_TO_EDGE);
const g = r ? e.encode(r, o * t) : null;
return i.texImage2D(i.TEXTURE_2D, 0, e.internalFormat, o, t, 0, e.format, e.textureType, g), this.checkError(), d
}
updateTexture(o, t, e, r, i) {
const d = this.gl;
d.bindTexture(d.TEXTURE_2D, o);
const g = r.encode(i, t * e);
d.texSubImage2D(d.TEXTURE_2D, 0, 0, 0, t, e, r.format, r.textureType, g), this.checkError()
}
attachFramebuffer(o, t, e) {
const r = this.gl;
r.bindTexture(r.TEXTURE_2D, o), r.bindFramebuffer(r.FRAMEBUFFER, this.framebuffer), r.framebufferTexture2D(r.FRAMEBUFFER, r.COLOR_ATTACHMENT0, r.TEXTURE_2D, o, 0), this.checkError(), r.viewport(0, 0, t, e), r.scissor(0, 0, t, e)
}
readTexture(o, t, e, r, i, d) {
const g = this.gl;
d || (d = 1), this.frameBufferBound || this.attachFramebuffer(o, t, e);
const m = this.getEncoder(i, d),
_ = m.allocate(t * e);
return g.bindTexture(g.TEXTURE_2D, o), g.framebufferTexture2D(g.FRAMEBUFFER, g.COLOR_ATTACHMENT0, g.TEXTURE_2D, o, 0), g.readPixels(0, 0, t, e, g.RGBA, m.textureType, _), this.checkError(), m.decode(_, r)
}
isFramebufferReady() {
return !0
}
getActiveTexture() {
const o = this.gl;
return "TEXTURE" + (o.getParameter(this.gl.ACTIVE_TEXTURE) - o.TEXTURE0)
}
getTextureBinding() {
return this.gl.getParameter(this.gl.TEXTURE_BINDING_2D)
}
getFramebufferBinding() {
return this.gl.getParameter(this.gl.FRAMEBUFFER_BINDING)
}
setVertexAttributes(o, t) {
const e = this.gl;
e.vertexAttribPointer(o, 3, e.FLOAT, !1, 20, 0), e.enableVertexAttribArray(o), t !== -1 && (e.vertexAttribPointer(t, 2, e.FLOAT, !1, 20, 12), e.enableVertexAttribArray(t)), this.checkError()
}
createProgram(o, t) {
const e = this.gl,
r = e.createProgram();
return e.attachShader(r, o), e.attachShader(r, t), e.linkProgram(r), r
}
compileShader(o, t) {
const e = this.gl,
r = e.createShader(t);
if (!r) throw new Error(`createShader() returned null with type ${t}`);
if (e.shaderSource(r, o), e.compileShader(r), e.getShaderParameter(r, e.COMPILE_STATUS) === !1) throw new Error(`Failed to compile shader: ${e.getShaderInfoLog(r)}
Shader source:
${o}`);
return r
}
deleteShader(o) {
this.gl.deleteShader(o)
}
bindTextureToUniform(o, t, e) {
const r = this.gl;
r.activeTexture(r.TEXTURE0 + t), this.checkError(), r.bindTexture(r.TEXTURE_2D, o), this.checkError(), r.uniform1i(e, t), this.checkError()
}
draw() {
this.gl.drawArrays(this.gl.TRIANGLE_STRIP, 0, 4), this.checkError()
}
checkError() {
if (s.env.debug) {
const o = this.gl,
t = o.getError();
let e = "";
switch (t) {
case o.NO_ERROR:
return;
case o.INVALID_ENUM:
e = "INVALID_ENUM";
break;
case o.INVALID_VALUE:
e = "INVALID_VALUE";
break;
case o.INVALID_OPERATION:
e = "INVALID_OPERATION";
break;
case o.INVALID_FRAMEBUFFER_OPERATION:
e = "INVALID_FRAMEBUFFER_OPERATION";
break;
case o.OUT_OF_MEMORY:
e = "OUT_OF_MEMORY";
break;
case o.CONTEXT_LOST_WEBGL:
e = "CONTEXT_LOST_WEBGL";
break;
default:
e = `Unknown WebGL Error: ${t.toString(16)}`
}
throw new Error(e)
}
}
deleteTexture(o) {
this.gl.deleteTexture(o)
}
deleteProgram(o) {
this.gl.deleteProgram(o)
}
getEncoder(o, t, e = 0) {
if (this.version === 2) return new h.RedFloat32DataEncoder(this.gl, t);
switch (o) {
case "float":
return e === 1 || this.isRenderFloat32Supported ? new h.RGBAFloatDataEncoder(this.gl, t) : new h.RGBAFloatDataEncoder(this.gl, t, this.textureHalfFloatExtension.HALF_FLOAT_OES);
case "int":
throw new Error("not implemented");
case "byte":
return new h.Uint8DataEncoder(this.gl, t);
default:
throw new Error(`Invalid dataType: ${o}`)
}
}
clearActiveTextures() {
const o = this.gl;
for (let t = 0; t < this.maxTextureImageUnits; ++t) o.activeTexture(o.TEXTURE0 + t), o.bindTexture(o.TEXTURE_2D, null)
}
dispose() {
if (this.disposed) return;
const o = this.gl;
o.bindFramebuffer(o.FRAMEBUFFER, null), o.deleteFramebuffer(this.framebuffer), o.bindBuffer(o.ARRAY_BUFFER, null), o.deleteBuffer(this.vertexbuffer), o.bindBuffer(o.ELEMENT_ARRAY_BUFFER, null), o.finish(), this.disposed = !0
}
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 o = this.gl,
t = o.createBuffer();
if (!t) throw new Error("createBuffer() returned null");
const e = this.createDefaultGeometry();
return o.bindBuffer(o.ARRAY_BUFFER, t), o.bufferData(o.ARRAY_BUFFER, e, o.STATIC_DRAW), this.checkError(), t
}
createFramebuffer() {
const o = this.gl.createFramebuffer();
if (!o) throw new Error("createFramebuffer returned null");
return o
}
queryVitalParameters() {
const o = this.gl;
if (this.isFloatTextureAttachableToFrameBuffer = this.checkFloatTextureAttachableToFrameBuffer(), this.isRenderFloat32Supported = this.checkRenderFloat32(), this.isFloat32DownloadSupported = this.checkFloat32Download(), this.version === 1 && !this.textureHalfFloatExtension && !this.isRenderFloat32Supported) throw new Error("both float32 and float16 TextureType are not supported");
this.isBlendSupported = !this.isRenderFloat32Supported || this.checkFloat32Blend(), this.maxTextureSize = o.getParameter(o.MAX_TEXTURE_SIZE), this.maxTextureImageUnits = o.getParameter(o.MAX_TEXTURE_IMAGE_UNITS), this.version
}
getExtensions() {
this.version === 2 ? (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 o = this.gl,
t = o.createTexture();
o.bindTexture(o.TEXTURE_2D, t);
const e = this.version === 2 ? o.RGBA32F : o.RGBA;
o.texImage2D(o.TEXTURE_2D, 0, e, 1, 1, 0, o.RGBA, o.FLOAT, null);
const r = o.createFramebuffer();
o.bindFramebuffer(o.FRAMEBUFFER, r), o.framebufferTexture2D(o.FRAMEBUFFER, o.COLOR_ATTACHMENT0, o.TEXTURE_2D, t, 0);
const i = o.checkFramebufferStatus(o.FRAMEBUFFER) === o.FRAMEBUFFER_COMPLETE;
return o.bindTexture(o.TEXTURE_2D, null), o.bindFramebuffer(o.FRAMEBUFFER, null), o.deleteTexture(t), o.deleteFramebuffer(r), i
}
checkRenderFloat32() {
if (this.version === 2) {
if (!this.colorBufferFloatExtension) return !1
} else if (!this.textureFloatExtension) return !1;
return this.isFloatTextureAttachableToFrameBuffer
}
checkFloat32Download() {
if (this.version === 2) {
if (!this.colorBufferFloatExtension) return !1
} else if (!this.textureFloatExtension || !this.gl.getExtension("WEBGL_color_buffer_float")) return !1;
return this.isFloatTextureAttachableToFrameBuffer
}
checkFloat32Blend() {
const o = this.gl;
let t, e, r, i, d;
try {
t = o.createTexture(), e = o.createFramebuffer(), o.bindTexture(o.TEXTURE_2D, t);
const g = this.version === 2 ? o.RGBA32F : o.RGBA;
return o.texImage2D(o.TEXTURE_2D, 0, g, 1, 1, 0, o.RGBA, o.FLOAT, null), o.bindFramebuffer(o.FRAMEBUFFER, e), o.framebufferTexture2D(o.FRAMEBUFFER, o.COLOR_ATTACHMENT0, o.TEXTURE_2D, t, 0), o.enable(o.BLEND), r = o.createShader(o.VERTEX_SHADER), !!r && (o.shaderSource(r, "void main(){}"), o.compileShader(r), i = o.createShader(o.FRAGMENT_SHADER), !!i && (o.shaderSource(i, "precision highp float;void main(){gl_FragColor=vec4(0.5);}"), o.compileShader(i), d = o.createProgram(), !!d && (o.attachShader(d, r), o.attachShader(d, i), o.linkProgram(d), o.useProgram(d), o.drawArrays(o.POINTS, 0, 1), o.getError() === o.NO_ERROR)))
} finally {
o.disable(o.BLEND), d && o.deleteProgram(d), r && o.deleteShader(r), i && o.deleteShader(i), e && (o.bindFramebuffer(o.FRAMEBUFFER, null), o.deleteFramebuffer(e)), t && (o.bindTexture(o.TEXTURE_2D, null), o.deleteTexture(t))
}
}
beginTimer() {
if (this.version === 2 && this.disjointTimerQueryWebgl2Extension) {
const o = this.gl,
t = this.disjointTimerQueryWebgl2Extension,
e = o.createQuery();
return o.beginQuery(t.TIME_ELAPSED_EXT, e), e
}
throw new Error("WebGL1 profiling currently not supported.")
}
endTimer() {
if (this.version !== 2 || !this.disjointTimerQueryWebgl2Extension) throw new Error("WebGL1 profiling currently not supported");
{
const o = this.gl,
t = this.disjointTimerQueryWebgl2Extension;
o.endQuery(t.TIME_ELAPSED_EXT)
}
}
isTimerResultAvailable(o) {
let t = !1,
e = !1;
if (this.version !== 2 || !this.disjointTimerQueryWebgl2Extension) throw new Error("WebGL1 profiling currently not supported");
{
const r = this.gl,
i = this.disjointTimerQueryWebgl2Extension;
t = r.getQueryParameter(o, r.QUERY_RESULT_AVAILABLE), e = r.getParameter(i.GPU_DISJOINT_EXT)
}
return t && !e
}
getTimerResult(o) {
let t = 0;
if (this.version !== 2) throw new Error("WebGL1 profiling currently not supported");
{
const e = this.gl;
t = e.getQueryParameter(o, e.QUERY_RESULT), e.deleteQuery(o)
}
return t / 1e6
}
async waitForQueryAndGetTime(o) {
return await (0, p.repeatedTry)(() => this.isTimerResultAvailable(o)), this.getTimerResult(o)
}
async createAndWaitForFence() {
const o = this.createFence(this.gl);
return this.pollFence(o)
}
createFence(o) {
let t;
const e = o,
r = e.fenceSync(e.SYNC_GPU_COMMANDS_COMPLETE, 0);
return o.flush(), t = r === null ? () => !0 : () => {
const i = e.clientWaitSync(r, 0, 0);
return i === e.ALREADY_SIGNALED || i === e.CONDITION_SATISFIED
}, {
query: r,
isFencePassed: t
}
}
async pollFence(o) {
return new Promise(t => {
this.addItemToPoll(() => o.isFencePassed(), () => t())
})
}
pollItems() {
const o = l(this.itemsToPoll.map(t => t.isDoneFn));
for (let t = 0; t <= o; ++t) {
const {
resolveFn: e
} = this.itemsToPoll[t];
e()
}
this.itemsToPoll = this.itemsToPoll.slice(o + 1)
}
async addItemToPoll(o, t) {
this.itemsToPoll.push({
isDoneFn: o,
resolveFn: t
}), this.itemsToPoll.length > 1 || await (0, p.repeatedTry)(() => (this.pollItems(), this.itemsToPoll.length === 0))
}
}
},
1036: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.ExecutionPlan = void 0;
const u = a(6231);
class c {
constructor(s, h) {
this.op = s, this.node = h
}
}
n.ExecutionPlan = class {
constructor(f, s, h) {
this.graph = f, this.profiler = h, this.initialize(s)
}
initialize(f) {
this.profiler.event("session", "ExecutionPlan.initialize", () => {
const s = this.graph.getNodes();
if (s.length !== f.length) throw new Error("The size of nodes and OPs do not match.");
this._ops = f.map((h, p) => new c(h, s[p])), this.reset(), this._starter = [], this._ops.forEach((h, p) => {
let l = !0;
for (const o of h.node.inputs)
if (!this._values[o] && this.graph.getInputIndices().indexOf(o) === -1) {
l = !1;
break
} l && this._starter.push(p)
})
})
}
reset() {
this._values = this.graph.getValues().map(f => f.tensor)
}
async execute(f, s) {
return this.profiler.event("session", "ExecutionPlan.execute", async () => {
this.reset();
const h = f.createInferenceHandler(),
p = this.graph.getInputIndices();
if (s.length !== p.length) throw new Error(`number of input tensors don't match the number of inputs to the model: actual: ${s.length} expected: ${p.length}`);
s.forEach((i, d) => {
const g = p[d];
this._values[g] = i
});
const l = this._starter.slice(0),
o = this.graph.getValues(),
t = this.graph.getNodes();
let e = 0;
for (; e < l.length;) {
const i = l[e++],
d = this._ops[i],
g = d.node.inputs.map(T => this._values[T]);
if (g.indexOf(void 0) !== -1) throw new Error(`unresolved input detected: op: ${d.node}`);
const m = g;
u.Logger.verbose("ExecPlan", `Runing op:${d.node.name} (${m.map((T,w)=>`'${d.node.inputs[w]}': ${T.type}[${T.dims.join(",")}]`).join(", ")})`);
const _ = await this.profiler.event("node", d.node.name, async () => d.op.impl(h, m, d.op.context));
if (_.length !== d.node.outputs.length) throw new Error("the size of output does not match model definition.");
_.forEach((T, w) => {
const S = d.node.outputs[w];
if (this._values[S]) throw new Error(`output [${S}] already has value: op:${d.node.name}`);
this._values[S] = T
});
const y = new Set;
_.forEach((T, w) => {
const S = d.node.outputs[w];
for (const O of o[S].to) {
const E = t[O];
let v = !0;
for (const P of E.inputs)
if (!this._values[P]) {
v = !1;
break
} v && y.add(O)
}
}), l.push(...y)
}
const r = [];
for (let i = 0; i < this.graph.getOutputIndices().length; i++) {
const d = this.graph.getOutputIndices()[i],
g = this._values[d];
if (g === void 0) throw new Error(`required output [${d}] does not have value`);
d === 0 ? await g.getData() : g.data, r.push(g)
}
return u.Logger.verbose("ExecPlan", "disposing of inferenceHandler"), h.dispose(), r
})
}
}
},
7070: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.Graph = void 0;
const u = a(1446),
c = a(7778),
f = a(9395),
s = a(9162),
h = a(2517);
var p = f.onnxruntime.experimental.fbs;
n.Graph = {
from: (e, r) => new t(e, r)
};
class l {
constructor(r) {
this._from = void 0, this._to = [], this.tensor = void 0, this.type = void 0, r && (this.type = h.ProtoUtil.tensorValueTypeFromProto(r.type.tensorType))
}
get from() {
return this._from
}
get to() {
return this._to
}
}
class o {
constructor(r, i) {
r instanceof u.onnx.NodeProto ? (this.name = r.name, this.opType = r.opType, this.attributes = new c.Attribute(r.attribute)) : r instanceof p.Node && (this.name = i ?? r.name(), this.opType = r.opType(), this.attributes = new c.Attribute(h.ProtoUtil.tensorAttributesFromORTFormat(r))), this.inputs = [], this.outputs = [], this.executeNode = !0
}
}
class t {
constructor(r, i) {
if (!r) throw new TypeError("graph is empty");
this.buildGraph(r), this.transformGraph(i), 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(r) {
if (r instanceof u.onnx.GraphProto) this.buildGraphFromOnnxFormat(r);
else {
if (!(r instanceof p.Graph)) throw new TypeError("Graph type is not supported.");
this.buildGraphFromOrtFormat(r)
}
}
buildGraphFromOnnxFormat(r) {
const i = new Map;
this._allData = [], this._allInputIndices = [], this._allInputNames = [], this._allOutputIndices = [], this._allOutputNames = [], this._nodes = [];
const d = new Map;
if (!r.input) throw new Error("missing information in graph: input");
const g = [];
for (const m of r.input) {
if (i.has(m.name)) throw new Error(`duplicated input name: ${m.name}`);
const _ = this._allData.push(new l(m)) - 1;
i.set(m.name, _), g.push(m.name)
}
if (!r.initializer) throw new Error("missing information in graph: initializer");
for (const m of r.initializer) {
let _ = i.get(m.name);
if (_ === void 0) {
const y = new l;
y.type = {
shape: {
dims: h.ProtoUtil.tensorDimsFromProto(m.dims)
},
tensorType: h.ProtoUtil.tensorDataTypeFromProto(m.dataType)
}, _ = this._allData.push(y) - 1, i.set(m.name, _)
}
this._allData[_]._from = -1, this._allData[_].tensor = s.Tensor.fromProto(m)
}
for (let m = 0; m < this._allData.length; m++) this._allData[m].tensor || (this._allInputIndices.push(m), this._allInputNames.push(g[m]));
if (!r.output) throw new Error("missing information in graph: output");
for (const m of r.output) {
if (i.has(m.name)) throw new Error(`duplicated output name: ${m.name}`);
const _ = this._allData.push(new l(m)) - 1;
i.set(m.name, _), this._allOutputIndices.push(_), this._allOutputNames.push(m.name)
}
if (!r.node) throw new Error("missing information in graph: node");
for (const m of r.node) {
if (!m.name)
for (let y = 0;; y++) {
const T = `unnamed_${m.opType}_${y}`;
if (!d.has(T)) {
m.name = T;
break
}
}
if (d.has(m.name)) throw new Error(`duplicated node name: ${m.name}`);
const _ = this._nodes.push(new o(m)) - 1;
d.set(m.name, _)
}
for (let m = 0; m < this._nodes.length; m++) {
const _ = this._nodes[m],
y = r.node[m];
if (!y.output) throw new Error(`missing output for node: ${y.name}`);
for (const T of y.output) {
let w = i.get(T);
if (w === void 0 && (w = this._allData.push(new l) - 1, i.set(T, w)), _.outputs.push(w), this._allData[w]._from !== void 0) throw new Error(`multiple nodes output to one data value: ${w}`);
if (this._allData[w]._from = m, y.opType === "Constant") {
if (!y.attribute || y.attribute.length !== 1 || !y.attribute[0].t) throw new Error("missing attributes or missing tensor value in attributes for this Constant operator");
if (!y.output || y.output.length !== 1) throw new Error("missing output or incorrect number of outputs for this Constant operator");
_.outputs.pop(), _.executeNode = !1, this._allData[w]._from = -1, this._allData[w].tensor = s.Tensor.fromProto(y.attribute[0].t)
}
}
}
for (let m = 0; m < this._nodes.length; m++) {
const _ = this._nodes[m],
y = r.node[m];
if (!y.input) throw new Error(`missing input for node: ${y.name}`);
for (const T of y.input) {
const w = i.get(T);
if (w === void 0) {
if (T === "" && y.input.length === 3 && y.opType === "Resize") continue;
throw new Error(`unrecognized input '${T}' for node: ${y.name}`)
}
_.inputs.push(w), this._allData[w]._to.push(m)
}
}
return !0
}
buildGraphFromOrtFormat(r) {
var i, d, g;
const m = new Map;
this._allData = [], this._allInputIndices = [], this._allInputNames = [], this._allOutputIndices = [], this._allOutputNames = [], this._nodes = [];
const _ = new Map,
y = [];
for (let T = 0; T < r.inputsLength(); T++) {
const w = r.inputs(T);
if (m.has(w)) throw new Error(`duplicated input name: ${w}`);
for (let S = 0; S < r.nodeArgsLength(); S++)
if (((i = r.nodeArgs(S)) === null || i === void 0 ? void 0 : i.name()) === w) {
const O = new l;
if (((g = (d = r.nodeArgs(S)) === null || d === void 0 ? void 0 : d.type()) === null || g === void 0 ? void 0 : g.valueType()) !== p.TypeInfoValue.tensor_type) throw new Error("Unexpected value type for the nodeArg.");
const E = r.nodeArgs(S).type().value(new p.TensorTypeAndShape),
v = h.ProtoUtil.tensorDataTypeFromProto(E.elemType()),
P = E.shape(),
L = [];
for (let R = 0; R < P.dimLength(); R++) L.push(h.LongUtil.longToNumber(P.dim(R).value().dimValue()));
O.type = {
shape: {
dims: L
},
tensorType: v
};
const V = this._allData.push(O) - 1;
m.set(w, V), y.push(w)
}
}
for (let T = 0; T < r.initializersLength(); T++) {
const w = r.initializers(T);
let S = m.get(w.name());
if (S === void 0) {
const O = new l,
E = h.ProtoUtil.tensorDimsFromORTFormat(w),
v = h.ProtoUtil.tensorDataTypeFromProto(w.dataType());
O.type = {
shape: {
dims: E
},
tensorType: v
}, S = this._allData.push(O) - 1, m.set(w.name(), S)
}
this._allData[S]._from = -1, this._allData[S].tensor = s.Tensor.fromOrtTensor(w)
}
for (let T = 0; T < this._allData.length; T++) this._allData[T].tensor || (this._allInputIndices.push(T), this._allInputNames.push(y[T]));
for (let T = 0; T < r.outputsLength(); T++) {
const w = r.outputs(T);
if (m.has(w)) throw new Error(`duplicated output name: ${w}`);
const S = this._allData.push(new l) - 1;
m.set(w, S), this._allOutputIndices.push(S), this._allOutputNames.push(w)
}
if (!r.nodes) throw new Error("missing information in graph: node");
for (let T = 0; T < r.nodesLength(); T++) {
const w = r.nodes(T);
let S = w.name();
if (!S)
for (let E = 0; S = `unnamed_${w.opType()}_${E}`, _.has(S); E++);
if (_.has(S)) throw new Error(`duplicated node name: ${S}`);
const O = this._nodes.push(new o(w, S)) - 1;
_.set(S, O)
}
for (let T = 0; T < this._nodes.length; T++) {
const w = this._nodes[T],
S = r.nodes(T);
if (S == null) throw new Error(`No node exists at index ${T}`);
if (S?.outputsLength() === 0) throw new Error(`missing output for node: ${S.name}`);
for (let O = 0; O < S?.outputsLength(); O++) {
const E = S?.outputs(O);
let v = m.get(E);
if (v === void 0 && (v = this._allData.push(new l) - 1, m.set(E, v)), w.outputs.push(v), this._allData[v]._from !== void 0) throw new Error(`multiple nodes output to one data value: ${v}`);
if (this._allData[v]._from = T, S.opType() === "Constant") {
if (S.attributesLength() !== 1 || !S.attributes(0).t()) throw new Error("missing attributes or missing tensor value in attributes for this Constant operator");
if (S.outputsLength() !== 1) throw new Error("missing output or incorrect number of outputs for this Constant operator");
w.outputs.pop(), w.executeNode = !1, this._allData[v]._from = -1, this._allData[v].tensor = s.Tensor.fromOrtTensor(S.attributes(0).t())
}
}
}
for (let T = 0; T < this._nodes.length; T++) {
const w = this._nodes[T],
S = r.nodes(T);
if (S.inputsLength() === 0) throw new Error(`missing input for node: ${S.name}`);
for (let O = 0; O < S.inputsLength(); O++) {
const E = S.inputs(O),
v = m.get(E);
if (v === void 0) throw new Error(`unrecognized input '${E}' for node: ${S.name()}`);
w.inputs.push(v), this._allData[v]._to.push(T)
}
}
}
checkIsAcyclic() {
const r = new Set;
this._allInputIndices.forEach(g => {
this._allData[g]._to.forEach(m => {
r.add(m)
})
});
const i = Array.from(r),
d = new Array(this._nodes.length).fill("white");
for (; i.length > 0;) {
const g = i.pop();
d[g] === "gray" ? d[g] = "black" : (i.push(g), d[g] = "gray", this._nodes[g].outputs.forEach(m => {
const _ = this._allData[m];
if (_.tensor !== void 0) throw new Error("node outputs should not be initialized");
if (_._from !== g) throw new Error("from property of the Value object doesn't match index of Node being processed");
_._to.forEach(y => {
if (d[y] === "gray") throw new Error("model graph is cyclic");
d[y] === "white" && i.push(y)
})
}))
}
}
transformGraph(r) {
this.removeAllIdentityNodes(), this.removeAllDropoutNodes(), this.fuseConvActivationNodes(), r && r.transformGraph(this), this.finalizeGraph()
}
finalizeGraph() {
let r = 0;
for (let i = 0; i < this._nodes.length; i++) this._nodes[i].executeNode ? r > 0 && (this._nodes[i].inputs.forEach(d => {
const g = this._allData[d]._to.indexOf(i + r);
g !== -1 && (this._allData[d]._to[g] = i)
}), this._nodes[i].outputs.forEach(d => {
this._allData[d]._from && this._allData[d]._from === i + r && (this._allData[d]._from = i)
})) : (r++, this._nodes[i].outputs.forEach(d => {
this._allData[d]._from = -2
}), this._nodes.splice(i, 1), i--);
r = 0;
for (let i = 0; i < this._allData.length; i++)
if (this._allData[i].from !== -2 || this._allOutputIndices.indexOf(i + r) !== -1) {
if (r > 0) {
let d = -1;
this._allData[i].from !== void 0 && this._allData[i].from !== -1 ? (d = this._nodes[this._allData[i].from].outputs.indexOf(i + r), d !== -1 && (this._nodes[this._allData[i].from].outputs[d] = i)) : (d = this._allInputIndices.indexOf(i + r), d !== -1 && (this._allInputIndices[d] = i)), this._allData[i].to.forEach(g => {
d = this._nodes[g].inputs.indexOf(i + r), d !== -1 && (this._nodes[g].inputs[d] = i)
}), this._allData[i].to.length === 0 && (d = this._allOutputIndices.indexOf(i + r), d !== -1 && (this._allOutputIndices[d] = i))
}
} else r++, this._allData.splice(i, 1), i--
}
deleteNode(r) {
const i = this._nodes[r];
if (i.outputs.length > 1) {
for (let T = 1; T < i.outputs.length; T++)
if (this._allData[i.outputs[T]].to.length > 0) throw new Error("Node deletion with more than one output connected to other nodes is not supported. ")
}
i.executeNode = !1;
const d = i.inputs[0],
g = i.outputs[0],
m = this._allData[g].to,
_ = this._allData[d].to.indexOf(r);
if (_ === -1) throw new Error("The Value object doesn't have the current Node in it's 'to' property ");
this._allData[d].to.splice(_, 1), this._allData[g]._to = [];
const y = this._allOutputIndices.indexOf(g);
if (y !== -1 && (this._allOutputIndices[y] = d), m && m.length > 0)
for (const T of m) {
const w = this._nodes[T].inputs.indexOf(g);
if (w === -1) throw new Error("The Node object doesn't have the output Value in it's 'inputs' property ");
this._nodes[T].inputs[w] = d, this._allData[d].to.push(T)
}
}
removeAllDropoutNodes() {
let r = 0;
for (const i of this._nodes) {
if (i.opType === "Dropout") {
if (i.inputs.length !== 1) throw new Error("Dropout nodes should only contain one input. ");
if (i.outputs.length !== 1 && i.outputs.length !== 2) throw new Error("Dropout nodes should contain either 1 or 2 output(s)");
if (i.outputs.length === 2 && this._allData[i.outputs[1]]._to.length !== 0) throw new Error("Dropout nodes's second output should not be referenced by other nodes");
this.deleteNode(r)
}
r++
}
}
removeAllIdentityNodes() {
let r = 0;
for (const i of this._nodes) i.opType === "Identity" && this.deleteNode(r), r++
}
isActivation(r) {
switch (r.opType) {
case "Relu":
case "Sigmoid":
case "Clip":
return !0;
default:
return !1
}
}
fuseConvActivationNodes() {
for (const r of this._nodes)
if (r.opType === "Conv") {
const i = this._allData[r.outputs[0]]._to;
if (i.length === 1 && this.isActivation(this._nodes[i[0]])) {
const d = this._nodes[i[0]];
if (d.opType === "Clip")
if (d.inputs.length === 1) try {
r.attributes.set("activation_params", "floats", [d.attributes.getFloat("min"), d.attributes.getFloat("max")])
} catch {
r.attributes.set("activation_params", "floats", [h.MIN_CLIP, h.MAX_CLIP])
} else {
if (!(d.inputs.length >= 3 && this._allData[d.inputs[1]].tensor !== void 0 && this._allData[d.inputs[2]].tensor !== void 0)) continue;
r.attributes.set("activation_params", "floats", [this._allData[d.inputs[1]].tensor.floatData[0], this._allData[d.inputs[2]].tensor.floatData[0]])
}
r.attributes.set("activation", "string", d.opType), this.deleteNode(i[0])
}
}
}
}
},
6231: (b, n) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.now = n.Profiler = n.Logger = void 0;
const a = {
verbose: 1e3,
info: 2e3,
warning: 4e3,
error: 5e3,
fatal: 6e3
},
u = {
none: new class {
log(o, t, e) {}
},
console: new class {
log(o, t, e) {
console.log(`${this.color(o)} ${e?"\x1B[35m"+e+"\x1B[0m ":""}${t}`)
}
color(o) {
switch (o) {
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: ${o}`)
}
}
}
},
c = {
provider: "console",
minimalSeverity: "warning",
logDateTime: !0,
logSourceLocation: !1
};
let f = {
"": c
};
function s(o, t, e, r) {
if (t === void 0) return i = o, {
verbose: s.verbose.bind(null, i),
info: s.info.bind(null, i),
warning: s.warning.bind(null, i),
error: s.error.bind(null, i),
fatal: s.fatal.bind(null, i)
};
if (e === void 0) h(o, t);
else if (typeof e == "number" && r === void 0) h(o, t);
else if (typeof e == "string" && r === void 0) h(o, e, 0, t);
else {
if (typeof e != "string" || typeof r != "number") throw new TypeError("input is valid");
h(o, e, 0, t)
}
var i
}
function h(o, t, e, r) {
const i = f[r || ""] || f[""];
a[o] < a[i.minimalSeverity] || (i.logDateTime && (t = `${new Date().toISOString()}|${t}`), i.logSourceLocation, u[i.provider].log(o, t, r))
}(function(o) {
function t(r) {
f = {}, e("", r || {})
}
function e(r, i) {
if (r === "*") t(i);
else {
const d = f[r] || c;
f[r] = {
provider: i.provider || d.provider,
minimalSeverity: i.minimalSeverity || d.minimalSeverity,
logDateTime: i.logDateTime === void 0 ? d.logDateTime : i.logDateTime,
logSourceLocation: i.logSourceLocation === void 0 ? d.logSourceLocation : i.logSourceLocation
}
}
}
o.verbose = function(r, i) {
o("verbose", r, i)
}, o.info = function(r, i) {
o("info", r, i)
}, o.warning = function(r, i) {
o("warning", r, i)
}, o.error = function(r, i) {
o("error", r, i)
}, o.fatal = function(r, i) {
o("fatal", r, i)
}, o.reset = t, o.set = e, o.setWithEnv = function(r) {
const i = {};
r.logLevel && (i.minimalSeverity = r.logLevel), e("", i)
}
})(s || (s = {})), n.Logger = s;
class p {
constructor(t, e, r, i, d, g) {
this.category = t, this.name = e, this.startTime = r, this.endCallback = i, this.timer = d, this.ctx = g
}
end() {
return this.endCallback(this)
}
async checkTimer() {
if (this.ctx === void 0 || this.timer === void 0) throw new Error("No webgl timer found");
return this.ctx.endTimer(), this.ctx.waitForQueryAndGetTime(this.timer)
}
}
class l {
constructor(t, e, r, i) {
this.category = t, this.name = e, this.startTime = r, this.endTime = i
}
}
n.Profiler = class {
static create(o) {
return o === void 0 ? new this : new this(o.maxNumberEvents, o.flushBatchSize, o.flushIntervalInMilliseconds)
}
constructor(o, t, e) {
this._started = !1, this._flushPointer = 0, this._started = !1, this._maxNumberEvents = o === void 0 ? 1e4 : o, this._flushBatchSize = t === void 0 ? 10 : t, this._flushIntervalInMilliseconds = e === void 0 ? 5e3 : e
}
start() {
this._started = !0, this._timingEvents = [], this._flushTime = (0, n.now)(), this._flushPointer = 0
}
stop() {
for (this._started = !1; this._flushPointer < this._timingEvents.length; this._flushPointer++) this.logOneEvent(this._timingEvents[this._flushPointer])
}
event(o, t, e, r) {
const i = this._started ? this.begin(o, t, r) : void 0;
let d = !1;
const g = e();
if (g && typeof g.then == "function") return d = !0, new Promise((m, _) => {
g.then(async y => {
i && await i.end(), m(y)
}, async y => {
i && await i.end(), _(y)
})
});
if (!d && i) {
const m = i.end();
if (m && typeof m.then == "function") return new Promise((_, y) => {
m.then(() => {
_(g)
}, T => {
y(T)
})
})
}
return g
}
begin(o, t, e) {
if (!this._started) throw new Error("profiler is not started yet");
if (e === void 0) {
const r = (0, n.now)();
return this.flush(r), new p(o, t, r, i => this.endSync(i))
} {
const r = e.beginTimer();
return new p(o, t, 0, async i => this.end(i), r, e)
}
}
async end(o) {
const t = await o.checkTimer();
this._timingEvents.length < this._maxNumberEvents && (this._timingEvents.push(new l(o.category, o.name, o.startTime, t)), this.flush(t))
}
endSync(o) {
const t = (0, n.now)();
this._timingEvents.length < this._maxNumberEvents && (this._timingEvents.push(new l(o.category, o.name, o.startTime, t)), this.flush(t))
}
logOneEvent(o) {
n.Logger.verbose(`Profiler.${o.category}`, `${(o.endTime-o.startTime).toFixed(2)}ms on event '${o.name}' at ${o.endTime.toFixed(2)}`)
}
flush(o) {
if (this._timingEvents.length - this._flushPointer >= this._flushBatchSize || o - this._flushTime >= this._flushIntervalInMilliseconds) {
for (const t = this._flushPointer; this._flushPointer < t + this._flushBatchSize && this._flushPointer < this._timingEvents.length; this._flushPointer++) this.logOneEvent(this._timingEvents[this._flushPointer]);
this._flushTime = (0, n.now)()
}
}
get started() {
return this._started
}
}, n.now = typeof performance < "u" && performance.now ? () => performance.now() : Date.now
},
2644: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.Model = void 0;
const u = a(5686),
c = a(1446),
f = a(7070),
s = a(9395),
h = a(2517);
var p = s.onnxruntime.experimental.fbs;
n.Model = class {
constructor() {}
load(l, o, t) {
if (!t) try {
return void this.loadFromOnnxFormat(l, o)
} catch (e) {
if (t !== void 0) throw e
}
this.loadFromOrtFormat(l, o)
}
loadFromOnnxFormat(l, o) {
const t = c.onnx.ModelProto.decode(l);
if (h.LongUtil.longToNumber(t.irVersion) < 3) throw new Error("only support ONNX model with IR_VERSION>=3");
this._opsets = t.opsetImport.map(e => ({
domain: e.domain,
version: h.LongUtil.longToNumber(e.version)
})), this._graph = f.Graph.from(t.graph, o)
}
loadFromOrtFormat(l, o) {
const t = new u.flatbuffers.ByteBuffer(l),
e = p.InferenceSession.getRootAsInferenceSession(t).model();
if (h.LongUtil.longToNumber(e.irVersion()) < 3) throw new Error("only support ONNX model with IR_VERSION>=3");
this._opsets = [];
for (let r = 0; r < e.opsetImportLength(); r++) {
const i = e.opsetImport(r);
this._opsets.push({
domain: i?.domain(),
version: h.LongUtil.longToNumber(i.version())
})
}
this._graph = f.Graph.from(e.graph(), o)
}
get graph() {
return this._graph
}
get opsets() {
return this._opsets
}
}
},
782: (b, n) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.FLOAT_TYPES = n.INT_TYPES = n.NUMBER_TYPES = void 0, n.NUMBER_TYPES = ["float32", "float64", "int32", "int16", "int8", "uint16", "uint32", "uint8"], n.INT_TYPES = ["int32", "int16", "int8", "uint16", "uint32", "uint8"], n.FLOAT_TYPES = ["float32", "float64"]
},
1047: (b, n) => {
function a(u, c) {
if (c.endsWith("+")) {
const f = Number.parseInt(c.substring(0, c.length - 1), 10);
return !isNaN(f) && f <= u
}
if (c.split("-").length === 2) {
const f = c.split("-"),
s = Number.parseInt(f[0], 10),
h = Number.parseInt(f[1], 10);
return !isNaN(s) && !isNaN(h) && s <= u && u <= h
}
return Number.parseInt(c, 10) === u
}
Object.defineProperty(n, "__esModule", {
value: !0
}), n.resolveOperator = void 0, n.resolveOperator = function(u, c, f) {
for (const s of f) {
const h = s[0],
p = s[1],
l = s[2],
o = s[3],
t = s[4];
if (u.opType === h) {
for (const e of c)
if ((e.domain === p || e.domain === "ai.onnx" && p === "") && a(e.version, l)) return {
opImpl: o,
opInit: t
}
}
}
throw new TypeError(`cannot resolve operator '${u.opType}' with opsets: ${c.map(s=>`${s.domain||"ai.onnx"} v${s.version}`).join(", ")}`)
}
},
9395: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.onnxruntime = void 0;
const u = a(5686);
var c, f;
c = n.onnxruntime || (n.onnxruntime = {}),
function(s) {
(function(h) {
h[h.UNDEFINED = 0] = "UNDEFINED", h[h.FLOAT = 1] = "FLOAT", h[h.INT = 2] = "INT", h[h.STRING = 3] = "STRING", h[h.TENSOR = 4] = "TENSOR", h[h.GRAPH = 5] = "GRAPH", h[h.FLOATS = 6] = "FLOATS", h[h.INTS = 7] = "INTS", h[h.STRINGS = 8] = "STRINGS", h[h.TENSORS = 9] = "TENSORS", h[h.GRAPHS = 10] = "GRAPHS", h[h.SPARSE_TENSOR = 11] = "SPARSE_TENSOR", h[h.SPARSE_TENSORS = 12] = "SPARSE_TENSORS"
})(s.AttributeType || (s.AttributeType = {}))
}((f = c.experimental || (c.experimental = {})).fbs || (f.fbs = {})),
function(s) {
(function(h) {
(function(p) {
(function(l) {
l[l.UNKNOWN = 0] = "UNKNOWN", l[l.VALUE = 1] = "VALUE", l[l.PARAM = 2] = "PARAM"
})(p.DimensionValueType || (p.DimensionValueType = {}))
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
(function(l) {
l[l.UNDEFINED = 0] = "UNDEFINED", l[l.FLOAT = 1] = "FLOAT", l[l.UINT8 = 2] = "UINT8", l[l.INT8 = 3] = "INT8", l[l.UINT16 = 4] = "UINT16", l[l.INT16 = 5] = "INT16", l[l.INT32 = 6] = "INT32", l[l.INT64 = 7] = "INT64", l[l.STRING = 8] = "STRING", l[l.BOOL = 9] = "BOOL", l[l.FLOAT16 = 10] = "FLOAT16", l[l.DOUBLE = 11] = "DOUBLE", l[l.UINT32 = 12] = "UINT32", l[l.UINT64 = 13] = "UINT64", l[l.COMPLEX64 = 14] = "COMPLEX64", l[l.COMPLEX128 = 15] = "COMPLEX128", l[l.BFLOAT16 = 16] = "BFLOAT16"
})(p.TensorDataType || (p.TensorDataType = {}))
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
(function(l) {
l[l.Primitive = 0] = "Primitive", l[l.Fused = 1] = "Fused"
})(p.NodeType || (p.NodeType = {}))
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
(function(l) {
l[l.NONE = 0] = "NONE", l[l.tensor_type = 1] = "tensor_type", l[l.sequence_type = 2] = "sequence_type", l[l.map_type = 3] = "map_type"
})(p.TypeInfoValue || (p.TypeInfoValue = {}))
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
class l {
constructor() {
this.bb = null, this.bb_pos = 0
}
__init(t, e) {
return this.bb_pos = t, this.bb = e, this
}
static getRootAsShape(t, e) {
return (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
static getSizePrefixedRootAsShape(t, e) {
return t.setPosition(t.position() + u.flatbuffers.SIZE_PREFIX_LENGTH), (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
dim(t, e) {
let r = this.bb.__offset(this.bb_pos, 4);
return r ? (e || new s.experimental.fbs.Dimension).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos + r) + 4 * t), this.bb) : null
}
dimLength() {
let t = this.bb.__offset(this.bb_pos, 4);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
static startShape(t) {
t.startObject(1)
}
static addDim(t, e) {
t.addFieldOffset(0, e, 0)
}
static createDimVector(t, e) {
t.startVector(4, e.length, 4);
for (let r = e.length - 1; r >= 0; r--) t.addOffset(e[r]);
return t.endVector()
}
static startDimVector(t, e) {
t.startVector(4, e, 4)
}
static endShape(t) {
return t.endObject()
}
static createShape(t, e) {
return l.startShape(t), l.addDim(t, e), l.endShape(t)
}
}
p.Shape = l
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
class l {
constructor() {
this.bb = null, this.bb_pos = 0
}
__init(t, e) {
return this.bb_pos = t, this.bb = e, this
}
static getRootAsDimension(t, e) {
return (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
static getSizePrefixedRootAsDimension(t, e) {
return t.setPosition(t.position() + u.flatbuffers.SIZE_PREFIX_LENGTH), (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
value(t) {
let e = this.bb.__offset(this.bb_pos, 4);
return e ? (t || new s.experimental.fbs.DimensionValue).__init(this.bb.__indirect(this.bb_pos + e), this.bb) : null
}
denotation(t) {
let e = this.bb.__offset(this.bb_pos, 6);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
static startDimension(t) {
t.startObject(2)
}
static addValue(t, e) {
t.addFieldOffset(0, e, 0)
}
static addDenotation(t, e) {
t.addFieldOffset(1, e, 0)
}
static endDimension(t) {
return t.endObject()
}
static createDimension(t, e, r) {
return l.startDimension(t), l.addValue(t, e), l.addDenotation(t, r), l.endDimension(t)
}
}
p.Dimension = l
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
class l {
constructor() {
this.bb = null, this.bb_pos = 0
}
__init(t, e) {
return this.bb_pos = t, this.bb = e, this
}
static getRootAsDimensionValue(t, e) {
return (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
static getSizePrefixedRootAsDimensionValue(t, e) {
return t.setPosition(t.position() + u.flatbuffers.SIZE_PREFIX_LENGTH), (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
dimType() {
let t = this.bb.__offset(this.bb_pos, 4);
return t ? this.bb.readInt8(this.bb_pos + t) : s.experimental.fbs.DimensionValueType.UNKNOWN
}
dimValue() {
let t = this.bb.__offset(this.bb_pos, 6);
return t ? this.bb.readInt64(this.bb_pos + t) : this.bb.createLong(0, 0)
}
dimParam(t) {
let e = this.bb.__offset(this.bb_pos, 8);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
static startDimensionValue(t) {
t.startObject(3)
}
static addDimType(t, e) {
t.addFieldInt8(0, e, s.experimental.fbs.DimensionValueType.UNKNOWN)
}
static addDimValue(t, e) {
t.addFieldInt64(1, e, t.createLong(0, 0))
}
static addDimParam(t, e) {
t.addFieldOffset(2, e, 0)
}
static endDimensionValue(t) {
return t.endObject()
}
static createDimensionValue(t, e, r, i) {
return l.startDimensionValue(t), l.addDimType(t, e), l.addDimValue(t, r), l.addDimParam(t, i), l.endDimensionValue(t)
}
}
p.DimensionValue = l
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
class l {
constructor() {
this.bb = null, this.bb_pos = 0
}
__init(t, e) {
return this.bb_pos = t, this.bb = e, this
}
static getRootAsTensorTypeAndShape(t, e) {
return (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
static getSizePrefixedRootAsTensorTypeAndShape(t, e) {
return t.setPosition(t.position() + u.flatbuffers.SIZE_PREFIX_LENGTH), (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
elemType() {
let t = this.bb.__offset(this.bb_pos, 4);
return t ? this.bb.readInt32(this.bb_pos + t) : s.experimental.fbs.TensorDataType.UNDEFINED
}
shape(t) {
let e = this.bb.__offset(this.bb_pos, 6);
return e ? (t || new s.experimental.fbs.Shape).__init(this.bb.__indirect(this.bb_pos + e), this.bb) : null
}
static startTensorTypeAndShape(t) {
t.startObject(2)
}
static addElemType(t, e) {
t.addFieldInt32(0, e, s.experimental.fbs.TensorDataType.UNDEFINED)
}
static addShape(t, e) {
t.addFieldOffset(1, e, 0)
}
static endTensorTypeAndShape(t) {
return t.endObject()
}
static createTensorTypeAndShape(t, e, r) {
return l.startTensorTypeAndShape(t), l.addElemType(t, e), l.addShape(t, r), l.endTensorTypeAndShape(t)
}
}
p.TensorTypeAndShape = l
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
class l {
constructor() {
this.bb = null, this.bb_pos = 0
}
__init(t, e) {
return this.bb_pos = t, this.bb = e, this
}
static getRootAsMapType(t, e) {
return (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
static getSizePrefixedRootAsMapType(t, e) {
return t.setPosition(t.position() + u.flatbuffers.SIZE_PREFIX_LENGTH), (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
keyType() {
let t = this.bb.__offset(this.bb_pos, 4);
return t ? this.bb.readInt32(this.bb_pos + t) : s.experimental.fbs.TensorDataType.UNDEFINED
}
valueType(t) {
let e = this.bb.__offset(this.bb_pos, 6);
return e ? (t || new s.experimental.fbs.TypeInfo).__init(this.bb.__indirect(this.bb_pos + e), this.bb) : null
}
static startMapType(t) {
t.startObject(2)
}
static addKeyType(t, e) {
t.addFieldInt32(0, e, s.experimental.fbs.TensorDataType.UNDEFINED)
}
static addValueType(t, e) {
t.addFieldOffset(1, e, 0)
}
static endMapType(t) {
return t.endObject()
}
static createMapType(t, e, r) {
return l.startMapType(t), l.addKeyType(t, e), l.addValueType(t, r), l.endMapType(t)
}
}
p.MapType = l
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
class l {
constructor() {
this.bb = null, this.bb_pos = 0
}
__init(t, e) {
return this.bb_pos = t, this.bb = e, this
}
static getRootAsSequenceType(t, e) {
return (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
static getSizePrefixedRootAsSequenceType(t, e) {
return t.setPosition(t.position() + u.flatbuffers.SIZE_PREFIX_LENGTH), (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
elemType(t) {
let e = this.bb.__offset(this.bb_pos, 4);
return e ? (t || new s.experimental.fbs.TypeInfo).__init(this.bb.__indirect(this.bb_pos + e), this.bb) : null
}
static startSequenceType(t) {
t.startObject(1)
}
static addElemType(t, e) {
t.addFieldOffset(0, e, 0)
}
static endSequenceType(t) {
return t.endObject()
}
static createSequenceType(t, e) {
return l.startSequenceType(t), l.addElemType(t, e), l.endSequenceType(t)
}
}
p.SequenceType = l
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(h.fbs || (h.fbs = {})).EdgeEnd = class {
constructor() {
this.bb = null, this.bb_pos = 0
}
__init(p, l) {
return this.bb_pos = p, this.bb = l, 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(p, l, o, t) {
return p.prep(4, 12), p.writeInt32(t), p.writeInt32(o), p.writeInt32(l), p.offset()
}
}
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
class l {
constructor() {
this.bb = null, this.bb_pos = 0
}
__init(t, e) {
return this.bb_pos = t, this.bb = e, this
}
static getRootAsNodeEdge(t, e) {
return (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
static getSizePrefixedRootAsNodeEdge(t, e) {
return t.setPosition(t.position() + u.flatbuffers.SIZE_PREFIX_LENGTH), (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
nodeIndex() {
let t = this.bb.__offset(this.bb_pos, 4);
return t ? this.bb.readUint32(this.bb_pos + t) : 0
}
inputEdges(t, e) {
let r = this.bb.__offset(this.bb_pos, 6);
return r ? (e || new s.experimental.fbs.EdgeEnd).__init(this.bb.__vector(this.bb_pos + r) + 12 * t, this.bb) : null
}
inputEdgesLength() {
let t = this.bb.__offset(this.bb_pos, 6);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
outputEdges(t, e) {
let r = this.bb.__offset(this.bb_pos, 8);
return r ? (e || new s.experimental.fbs.EdgeEnd).__init(this.bb.__vector(this.bb_pos + r) + 12 * t, this.bb) : null
}
outputEdgesLength() {
let t = this.bb.__offset(this.bb_pos, 8);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
static startNodeEdge(t) {
t.startObject(3)
}
static addNodeIndex(t, e) {
t.addFieldInt32(0, e, 0)
}
static addInputEdges(t, e) {
t.addFieldOffset(1, e, 0)
}
static startInputEdgesVector(t, e) {
t.startVector(12, e, 4)
}
static addOutputEdges(t, e) {
t.addFieldOffset(2, e, 0)
}
static startOutputEdgesVector(t, e) {
t.startVector(12, e, 4)
}
static endNodeEdge(t) {
return t.endObject()
}
static createNodeEdge(t, e, r, i) {
return l.startNodeEdge(t), l.addNodeIndex(t, e), l.addInputEdges(t, r), l.addOutputEdges(t, i), l.endNodeEdge(t)
}
}
p.NodeEdge = l
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
class l {
constructor() {
this.bb = null, this.bb_pos = 0
}
__init(t, e) {
return this.bb_pos = t, this.bb = e, this
}
static getRootAsNode(t, e) {
return (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
static getSizePrefixedRootAsNode(t, e) {
return t.setPosition(t.position() + u.flatbuffers.SIZE_PREFIX_LENGTH), (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
name(t) {
let e = this.bb.__offset(this.bb_pos, 4);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
docString(t) {
let e = this.bb.__offset(this.bb_pos, 6);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
domain(t) {
let e = this.bb.__offset(this.bb_pos, 8);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
sinceVersion() {
let t = this.bb.__offset(this.bb_pos, 10);
return t ? this.bb.readInt32(this.bb_pos + t) : 0
}
index() {
let t = this.bb.__offset(this.bb_pos, 12);
return t ? this.bb.readUint32(this.bb_pos + t) : 0
}
opType(t) {
let e = this.bb.__offset(this.bb_pos, 14);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
type() {
let t = this.bb.__offset(this.bb_pos, 16);
return t ? this.bb.readInt32(this.bb_pos + t) : s.experimental.fbs.NodeType.Primitive
}
executionProviderType(t) {
let e = this.bb.__offset(this.bb_pos, 18);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
inputs(t, e) {
let r = this.bb.__offset(this.bb_pos, 20);
return r ? this.bb.__string(this.bb.__vector(this.bb_pos + r) + 4 * t, e) : null
}
inputsLength() {
let t = this.bb.__offset(this.bb_pos, 20);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
outputs(t, e) {
let r = this.bb.__offset(this.bb_pos, 22);
return r ? this.bb.__string(this.bb.__vector(this.bb_pos + r) + 4 * t, e) : null
}
outputsLength() {
let t = this.bb.__offset(this.bb_pos, 22);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
attributes(t, e) {
let r = this.bb.__offset(this.bb_pos, 24);
return r ? (e || new s.experimental.fbs.Attribute).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos + r) + 4 * t), this.bb) : null
}
attributesLength() {
let t = this.bb.__offset(this.bb_pos, 24);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
inputArgCounts(t) {
let e = this.bb.__offset(this.bb_pos, 26);
return e ? this.bb.readInt32(this.bb.__vector(this.bb_pos + e) + 4 * t) : 0
}
inputArgCountsLength() {
let t = this.bb.__offset(this.bb_pos, 26);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
inputArgCountsArray() {
let t = this.bb.__offset(this.bb_pos, 26);
return t ? new Int32Array(this.bb.bytes().buffer, this.bb.bytes().byteOffset + this.bb.__vector(this.bb_pos + t), this.bb.__vector_len(this.bb_pos + t)) : null
}
implicitInputs(t, e) {
let r = this.bb.__offset(this.bb_pos, 28);
return r ? this.bb.__string(this.bb.__vector(this.bb_pos + r) + 4 * t, e) : null
}
implicitInputsLength() {
let t = this.bb.__offset(this.bb_pos, 28);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
static startNode(t) {
t.startObject(13)
}
static addName(t, e) {
t.addFieldOffset(0, e, 0)
}
static addDocString(t, e) {
t.addFieldOffset(1, e, 0)
}
static addDomain(t, e) {
t.addFieldOffset(2, e, 0)
}
static addSinceVersion(t, e) {
t.addFieldInt32(3, e, 0)
}
static addIndex(t, e) {
t.addFieldInt32(4, e, 0)
}
static addOpType(t, e) {
t.addFieldOffset(5, e, 0)
}
static addType(t, e) {
t.addFieldInt32(6, e, s.experimental.fbs.NodeType.Primitive)
}
static addExecutionProviderType(t, e) {
t.addFieldOffset(7, e, 0)
}
static addInputs(t, e) {
t.addFieldOffset(8, e, 0)
}
static createInputsVector(t, e) {
t.startVector(4, e.length, 4);
for (let r = e.length - 1; r >= 0; r--) t.addOffset(e[r]);
return t.endVector()
}
static startInputsVector(t, e) {
t.startVector(4, e, 4)
}
static addOutputs(t, e) {
t.addFieldOffset(9, e, 0)
}
static createOutputsVector(t, e) {
t.startVector(4, e.length, 4);
for (let r = e.length - 1; r >= 0; r--) t.addOffset(e[r]);
return t.endVector()
}
static startOutputsVector(t, e) {
t.startVector(4, e, 4)
}
static addAttributes(t, e) {
t.addFieldOffset(10, e, 0)
}
static createAttributesVector(t, e) {
t.startVector(4, e.length, 4);
for (let r = e.length - 1; r >= 0; r--) t.addOffset(e[r]);
return t.endVector()
}
static startAttributesVector(t, e) {
t.startVector(4, e, 4)
}
static addInputArgCounts(t, e) {
t.addFieldOffset(11, e, 0)
}
static createInputArgCountsVector(t, e) {
t.startVector(4, e.length, 4);
for (let r = e.length - 1; r >= 0; r--) t.addInt32(e[r]);
return t.endVector()
}
static startInputArgCountsVector(t, e) {
t.startVector(4, e, 4)
}
static addImplicitInputs(t, e) {
t.addFieldOffset(12, e, 0)
}
static createImplicitInputsVector(t, e) {
t.startVector(4, e.length, 4);
for (let r = e.length - 1; r >= 0; r--) t.addOffset(e[r]);
return t.endVector()
}
static startImplicitInputsVector(t, e) {
t.startVector(4, e, 4)
}
static endNode(t) {
return t.endObject()
}
static createNode(t, e, r, i, d, g, m, _, y, T, w, S, O, E) {
return l.startNode(t), l.addName(t, e), l.addDocString(t, r), l.addDomain(t, i), l.addSinceVersion(t, d), l.addIndex(t, g), l.addOpType(t, m), l.addType(t, _), l.addExecutionProviderType(t, y), l.addInputs(t, T), l.addOutputs(t, w), l.addAttributes(t, S), l.addInputArgCounts(t, O), l.addImplicitInputs(t, E), l.endNode(t)
}
}
p.Node = l
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
class l {
constructor() {
this.bb = null, this.bb_pos = 0
}
__init(t, e) {
return this.bb_pos = t, this.bb = e, this
}
static getRootAsValueInfo(t, e) {
return (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
static getSizePrefixedRootAsValueInfo(t, e) {
return t.setPosition(t.position() + u.flatbuffers.SIZE_PREFIX_LENGTH), (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
name(t) {
let e = this.bb.__offset(this.bb_pos, 4);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
docString(t) {
let e = this.bb.__offset(this.bb_pos, 6);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
type(t) {
let e = this.bb.__offset(this.bb_pos, 8);
return e ? (t || new s.experimental.fbs.TypeInfo).__init(this.bb.__indirect(this.bb_pos + e), this.bb) : null
}
static startValueInfo(t) {
t.startObject(3)
}
static addName(t, e) {
t.addFieldOffset(0, e, 0)
}
static addDocString(t, e) {
t.addFieldOffset(1, e, 0)
}
static addType(t, e) {
t.addFieldOffset(2, e, 0)
}
static endValueInfo(t) {
return t.endObject()
}
static createValueInfo(t, e, r, i) {
return l.startValueInfo(t), l.addName(t, e), l.addDocString(t, r), l.addType(t, i), l.endValueInfo(t)
}
}
p.ValueInfo = l
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
class l {
constructor() {
this.bb = null, this.bb_pos = 0
}
__init(t, e) {
return this.bb_pos = t, this.bb = e, this
}
static getRootAsTypeInfo(t, e) {
return (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
static getSizePrefixedRootAsTypeInfo(t, e) {
return t.setPosition(t.position() + u.flatbuffers.SIZE_PREFIX_LENGTH), (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
denotation(t) {
let e = this.bb.__offset(this.bb_pos, 4);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
valueType() {
let t = this.bb.__offset(this.bb_pos, 6);
return t ? this.bb.readUint8(this.bb_pos + t) : s.experimental.fbs.TypeInfoValue.NONE
}
value(t) {
let e = this.bb.__offset(this.bb_pos, 8);
return e ? this.bb.__union(t, this.bb_pos + e) : null
}
static startTypeInfo(t) {
t.startObject(3)
}
static addDenotation(t, e) {
t.addFieldOffset(0, e, 0)
}
static addValueType(t, e) {
t.addFieldInt8(1, e, s.experimental.fbs.TypeInfoValue.NONE)
}
static addValue(t, e) {
t.addFieldOffset(2, e, 0)
}
static endTypeInfo(t) {
return t.endObject()
}
static createTypeInfo(t, e, r, i) {
return l.startTypeInfo(t), l.addDenotation(t, e), l.addValueType(t, r), l.addValue(t, i), l.endTypeInfo(t)
}
}
p.TypeInfo = l
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
class l {
constructor() {
this.bb = null, this.bb_pos = 0
}
__init(t, e) {
return this.bb_pos = t, this.bb = e, this
}
static getRootAsOperatorSetId(t, e) {
return (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
static getSizePrefixedRootAsOperatorSetId(t, e) {
return t.setPosition(t.position() + u.flatbuffers.SIZE_PREFIX_LENGTH), (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
domain(t) {
let e = this.bb.__offset(this.bb_pos, 4);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
version() {
let t = this.bb.__offset(this.bb_pos, 6);
return t ? this.bb.readInt64(this.bb_pos + t) : this.bb.createLong(0, 0)
}
static startOperatorSetId(t) {
t.startObject(2)
}
static addDomain(t, e) {
t.addFieldOffset(0, e, 0)
}
static addVersion(t, e) {
t.addFieldInt64(1, e, t.createLong(0, 0))
}
static endOperatorSetId(t) {
return t.endObject()
}
static createOperatorSetId(t, e, r) {
return l.startOperatorSetId(t), l.addDomain(t, e), l.addVersion(t, r), l.endOperatorSetId(t)
}
}
p.OperatorSetId = l
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
class l {
constructor() {
this.bb = null, this.bb_pos = 0
}
__init(t, e) {
return this.bb_pos = t, this.bb = e, this
}
static getRootAsTensor(t, e) {
return (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
static getSizePrefixedRootAsTensor(t, e) {
return t.setPosition(t.position() + u.flatbuffers.SIZE_PREFIX_LENGTH), (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
name(t) {
let e = this.bb.__offset(this.bb_pos, 4);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
docString(t) {
let e = this.bb.__offset(this.bb_pos, 6);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
dims(t) {
let e = this.bb.__offset(this.bb_pos, 8);
return e ? this.bb.readInt64(this.bb.__vector(this.bb_pos + e) + 8 * t) : this.bb.createLong(0, 0)
}
dimsLength() {
let t = this.bb.__offset(this.bb_pos, 8);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
dataType() {
let t = this.bb.__offset(this.bb_pos, 10);
return t ? this.bb.readInt32(this.bb_pos + t) : s.experimental.fbs.TensorDataType.UNDEFINED
}
rawData(t) {
let e = this.bb.__offset(this.bb_pos, 12);
return e ? this.bb.readUint8(this.bb.__vector(this.bb_pos + e) + t) : 0
}
rawDataLength() {
let t = this.bb.__offset(this.bb_pos, 12);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
rawDataArray() {
let t = this.bb.__offset(this.bb_pos, 12);
return t ? new Uint8Array(this.bb.bytes().buffer, this.bb.bytes().byteOffset + this.bb.__vector(this.bb_pos + t), this.bb.__vector_len(this.bb_pos + t)) : null
}
stringData(t, e) {
let r = this.bb.__offset(this.bb_pos, 14);
return r ? this.bb.__string(this.bb.__vector(this.bb_pos + r) + 4 * t, e) : null
}
stringDataLength() {
let t = this.bb.__offset(this.bb_pos, 14);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
static startTensor(t) {
t.startObject(6)
}
static addName(t, e) {
t.addFieldOffset(0, e, 0)
}
static addDocString(t, e) {
t.addFieldOffset(1, e, 0)
}
static addDims(t, e) {
t.addFieldOffset(2, e, 0)
}
static createDimsVector(t, e) {
t.startVector(8, e.length, 8);
for (let r = e.length - 1; r >= 0; r--) t.addInt64(e[r]);
return t.endVector()
}
static startDimsVector(t, e) {
t.startVector(8, e, 8)
}
static addDataType(t, e) {
t.addFieldInt32(3, e, s.experimental.fbs.TensorDataType.UNDEFINED)
}
static addRawData(t, e) {
t.addFieldOffset(4, e, 0)
}
static createRawDataVector(t, e) {
t.startVector(1, e.length, 1);
for (let r = e.length - 1; r >= 0; r--) t.addInt8(e[r]);
return t.endVector()
}
static startRawDataVector(t, e) {
t.startVector(1, e, 1)
}
static addStringData(t, e) {
t.addFieldOffset(5, e, 0)
}
static createStringDataVector(t, e) {
t.startVector(4, e.length, 4);
for (let r = e.length - 1; r >= 0; r--) t.addOffset(e[r]);
return t.endVector()
}
static startStringDataVector(t, e) {
t.startVector(4, e, 4)
}
static endTensor(t) {
return t.endObject()
}
static createTensor(t, e, r, i, d, g, m) {
return l.startTensor(t), l.addName(t, e), l.addDocString(t, r), l.addDims(t, i), l.addDataType(t, d), l.addRawData(t, g), l.addStringData(t, m), l.endTensor(t)
}
}
p.Tensor = l
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
class l {
constructor() {
this.bb = null, this.bb_pos = 0
}
__init(t, e) {
return this.bb_pos = t, this.bb = e, this
}
static getRootAsSparseTensor(t, e) {
return (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
static getSizePrefixedRootAsSparseTensor(t, e) {
return t.setPosition(t.position() + u.flatbuffers.SIZE_PREFIX_LENGTH), (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
values(t) {
let e = this.bb.__offset(this.bb_pos, 4);
return e ? (t || new s.experimental.fbs.Tensor).__init(this.bb.__indirect(this.bb_pos + e), this.bb) : null
}
indices(t) {
let e = this.bb.__offset(this.bb_pos, 6);
return e ? (t || new s.experimental.fbs.Tensor).__init(this.bb.__indirect(this.bb_pos + e), this.bb) : null
}
dims(t) {
let e = this.bb.__offset(this.bb_pos, 8);
return e ? this.bb.readInt64(this.bb.__vector(this.bb_pos + e) + 8 * t) : this.bb.createLong(0, 0)
}
dimsLength() {
let t = this.bb.__offset(this.bb_pos, 8);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
static startSparseTensor(t) {
t.startObject(3)
}
static addValues(t, e) {
t.addFieldOffset(0, e, 0)
}
static addIndices(t, e) {
t.addFieldOffset(1, e, 0)
}
static addDims(t, e) {
t.addFieldOffset(2, e, 0)
}
static createDimsVector(t, e) {
t.startVector(8, e.length, 8);
for (let r = e.length - 1; r >= 0; r--) t.addInt64(e[r]);
return t.endVector()
}
static startDimsVector(t, e) {
t.startVector(8, e, 8)
}
static endSparseTensor(t) {
return t.endObject()
}
static createSparseTensor(t, e, r, i) {
return l.startSparseTensor(t), l.addValues(t, e), l.addIndices(t, r), l.addDims(t, i), l.endSparseTensor(t)
}
}
p.SparseTensor = l
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
class l {
constructor() {
this.bb = null, this.bb_pos = 0
}
__init(t, e) {
return this.bb_pos = t, this.bb = e, this
}
static getRootAsAttribute(t, e) {
return (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
static getSizePrefixedRootAsAttribute(t, e) {
return t.setPosition(t.position() + u.flatbuffers.SIZE_PREFIX_LENGTH), (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
name(t) {
let e = this.bb.__offset(this.bb_pos, 4);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
docString(t) {
let e = this.bb.__offset(this.bb_pos, 6);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
type() {
let t = this.bb.__offset(this.bb_pos, 8);
return t ? this.bb.readInt32(this.bb_pos + t) : s.experimental.fbs.AttributeType.UNDEFINED
}
f() {
let t = this.bb.__offset(this.bb_pos, 10);
return t ? this.bb.readFloat32(this.bb_pos + t) : 0
}
i() {
let t = this.bb.__offset(this.bb_pos, 12);
return t ? this.bb.readInt64(this.bb_pos + t) : this.bb.createLong(0, 0)
}
s(t) {
let e = this.bb.__offset(this.bb_pos, 14);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
t(t) {
let e = this.bb.__offset(this.bb_pos, 16);
return e ? (t || new s.experimental.fbs.Tensor).__init(this.bb.__indirect(this.bb_pos + e), this.bb) : null
}
g(t) {
let e = this.bb.__offset(this.bb_pos, 18);
return e ? (t || new s.experimental.fbs.Graph).__init(this.bb.__indirect(this.bb_pos + e), this.bb) : null
}
floats(t) {
let e = this.bb.__offset(this.bb_pos, 20);
return e ? this.bb.readFloat32(this.bb.__vector(this.bb_pos + e) + 4 * t) : 0
}
floatsLength() {
let t = this.bb.__offset(this.bb_pos, 20);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
floatsArray() {
let t = this.bb.__offset(this.bb_pos, 20);
return t ? new Float32Array(this.bb.bytes().buffer, this.bb.bytes().byteOffset + this.bb.__vector(this.bb_pos + t), this.bb.__vector_len(this.bb_pos + t)) : null
}
ints(t) {
let e = this.bb.__offset(this.bb_pos, 22);
return e ? this.bb.readInt64(this.bb.__vector(this.bb_pos + e) + 8 * t) : this.bb.createLong(0, 0)
}
intsLength() {
let t = this.bb.__offset(this.bb_pos, 22);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
strings(t, e) {
let r = this.bb.__offset(this.bb_pos, 24);
return r ? this.bb.__string(this.bb.__vector(this.bb_pos + r) + 4 * t, e) : null
}
stringsLength() {
let t = this.bb.__offset(this.bb_pos, 24);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
tensors(t, e) {
let r = this.bb.__offset(this.bb_pos, 26);
return r ? (e || new s.experimental.fbs.Tensor).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos + r) + 4 * t), this.bb) : null
}
tensorsLength() {
let t = this.bb.__offset(this.bb_pos, 26);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
graphs(t, e) {
let r = this.bb.__offset(this.bb_pos, 28);
return r ? (e || new s.experimental.fbs.Graph).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos + r) + 4 * t), this.bb) : null
}
graphsLength() {
let t = this.bb.__offset(this.bb_pos, 28);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
static startAttribute(t) {
t.startObject(13)
}
static addName(t, e) {
t.addFieldOffset(0, e, 0)
}
static addDocString(t, e) {
t.addFieldOffset(1, e, 0)
}
static addType(t, e) {
t.addFieldInt32(2, e, s.experimental.fbs.AttributeType.UNDEFINED)
}
static addF(t, e) {
t.addFieldFloat32(3, e, 0)
}
static addI(t, e) {
t.addFieldInt64(4, e, t.createLong(0, 0))
}
static addS(t, e) {
t.addFieldOffset(5, e, 0)
}
static addT(t, e) {
t.addFieldOffset(6, e, 0)
}
static addG(t, e) {
t.addFieldOffset(7, e, 0)
}
static addFloats(t, e) {
t.addFieldOffset(8, e, 0)
}
static createFloatsVector(t, e) {
t.startVector(4, e.length, 4);
for (let r = e.length - 1; r >= 0; r--) t.addFloat32(e[r]);
return t.endVector()
}
static startFloatsVector(t, e) {
t.startVector(4, e, 4)
}
static addInts(t, e) {
t.addFieldOffset(9, e, 0)
}
static createIntsVector(t, e) {
t.startVector(8, e.length, 8);
for (let r = e.length - 1; r >= 0; r--) t.addInt64(e[r]);
return t.endVector()
}
static startIntsVector(t, e) {
t.startVector(8, e, 8)
}
static addStrings(t, e) {
t.addFieldOffset(10, e, 0)
}
static createStringsVector(t, e) {
t.startVector(4, e.length, 4);
for (let r = e.length - 1; r >= 0; r--) t.addOffset(e[r]);
return t.endVector()
}
static startStringsVector(t, e) {
t.startVector(4, e, 4)
}
static addTensors(t, e) {
t.addFieldOffset(11, e, 0)
}
static createTensorsVector(t, e) {
t.startVector(4, e.length, 4);
for (let r = e.length - 1; r >= 0; r--) t.addOffset(e[r]);
return t.endVector()
}
static startTensorsVector(t, e) {
t.startVector(4, e, 4)
}
static addGraphs(t, e) {
t.addFieldOffset(12, e, 0)
}
static createGraphsVector(t, e) {
t.startVector(4, e.length, 4);
for (let r = e.length - 1; r >= 0; r--) t.addOffset(e[r]);
return t.endVector()
}
static startGraphsVector(t, e) {
t.startVector(4, e, 4)
}
static endAttribute(t) {
return t.endObject()
}
static createAttribute(t, e, r, i, d, g, m, _, y, T, w, S, O, E) {
return l.startAttribute(t), l.addName(t, e), l.addDocString(t, r), l.addType(t, i), l.addF(t, d), l.addI(t, g), l.addS(t, m), l.addT(t, _), l.addG(t, y), l.addFloats(t, T), l.addInts(t, w), l.addStrings(t, S), l.addTensors(t, O), l.addGraphs(t, E), l.endAttribute(t)
}
}
p.Attribute = l
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
class l {
constructor() {
this.bb = null, this.bb_pos = 0
}
__init(t, e) {
return this.bb_pos = t, this.bb = e, this
}
static getRootAsGraph(t, e) {
return (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
static getSizePrefixedRootAsGraph(t, e) {
return t.setPosition(t.position() + u.flatbuffers.SIZE_PREFIX_LENGTH), (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
initializers(t, e) {
let r = this.bb.__offset(this.bb_pos, 4);
return r ? (e || new s.experimental.fbs.Tensor).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos + r) + 4 * t), this.bb) : null
}
initializersLength() {
let t = this.bb.__offset(this.bb_pos, 4);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
nodeArgs(t, e) {
let r = this.bb.__offset(this.bb_pos, 6);
return r ? (e || new s.experimental.fbs.ValueInfo).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos + r) + 4 * t), this.bb) : null
}
nodeArgsLength() {
let t = this.bb.__offset(this.bb_pos, 6);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
nodes(t, e) {
let r = this.bb.__offset(this.bb_pos, 8);
return r ? (e || new s.experimental.fbs.Node).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos + r) + 4 * t), this.bb) : null
}
nodesLength() {
let t = this.bb.__offset(this.bb_pos, 8);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
maxNodeIndex() {
let t = this.bb.__offset(this.bb_pos, 10);
return t ? this.bb.readUint32(this.bb_pos + t) : 0
}
nodeEdges(t, e) {
let r = this.bb.__offset(this.bb_pos, 12);
return r ? (e || new s.experimental.fbs.NodeEdge).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos + r) + 4 * t), this.bb) : null
}
nodeEdgesLength() {
let t = this.bb.__offset(this.bb_pos, 12);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
inputs(t, e) {
let r = this.bb.__offset(this.bb_pos, 14);
return r ? this.bb.__string(this.bb.__vector(this.bb_pos + r) + 4 * t, e) : null
}
inputsLength() {
let t = this.bb.__offset(this.bb_pos, 14);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
outputs(t, e) {
let r = this.bb.__offset(this.bb_pos, 16);
return r ? this.bb.__string(this.bb.__vector(this.bb_pos + r) + 4 * t, e) : null
}
outputsLength() {
let t = this.bb.__offset(this.bb_pos, 16);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
sparseInitializers(t, e) {
let r = this.bb.__offset(this.bb_pos, 18);
return r ? (e || new s.experimental.fbs.SparseTensor).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos + r) + 4 * t), this.bb) : null
}
sparseInitializersLength() {
let t = this.bb.__offset(this.bb_pos, 18);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
static startGraph(t) {
t.startObject(8)
}
static addInitializers(t, e) {
t.addFieldOffset(0, e, 0)
}
static createInitializersVector(t, e) {
t.startVector(4, e.length, 4);
for (let r = e.length - 1; r >= 0; r--) t.addOffset(e[r]);
return t.endVector()
}
static startInitializersVector(t, e) {
t.startVector(4, e, 4)
}
static addNodeArgs(t, e) {
t.addFieldOffset(1, e, 0)
}
static createNodeArgsVector(t, e) {
t.startVector(4, e.length, 4);
for (let r = e.length - 1; r >= 0; r--) t.addOffset(e[r]);
return t.endVector()
}
static startNodeArgsVector(t, e) {
t.startVector(4, e, 4)
}
static addNodes(t, e) {
t.addFieldOffset(2, e, 0)
}
static createNodesVector(t, e) {
t.startVector(4, e.length, 4);
for (let r = e.length - 1; r >= 0; r--) t.addOffset(e[r]);
return t.endVector()
}
static startNodesVector(t, e) {
t.startVector(4, e, 4)
}
static addMaxNodeIndex(t, e) {
t.addFieldInt32(3, e, 0)
}
static addNodeEdges(t, e) {
t.addFieldOffset(4, e, 0)
}
static createNodeEdgesVector(t, e) {
t.startVector(4, e.length, 4);
for (let r = e.length - 1; r >= 0; r--) t.addOffset(e[r]);
return t.endVector()
}
static startNodeEdgesVector(t, e) {
t.startVector(4, e, 4)
}
static addInputs(t, e) {
t.addFieldOffset(5, e, 0)
}
static createInputsVector(t, e) {
t.startVector(4, e.length, 4);
for (let r = e.length - 1; r >= 0; r--) t.addOffset(e[r]);
return t.endVector()
}
static startInputsVector(t, e) {
t.startVector(4, e, 4)
}
static addOutputs(t, e) {
t.addFieldOffset(6, e, 0)
}
static createOutputsVector(t, e) {
t.startVector(4, e.length, 4);
for (let r = e.length - 1; r >= 0; r--) t.addOffset(e[r]);
return t.endVector()
}
static startOutputsVector(t, e) {
t.startVector(4, e, 4)
}
static addSparseInitializers(t, e) {
t.addFieldOffset(7, e, 0)
}
static createSparseInitializersVector(t, e) {
t.startVector(4, e.length, 4);
for (let r = e.length - 1; r >= 0; r--) t.addOffset(e[r]);
return t.endVector()
}
static startSparseInitializersVector(t, e) {
t.startVector(4, e, 4)
}
static endGraph(t) {
return t.endObject()
}
static createGraph(t, e, r, i, d, g, m, _, y) {
return l.startGraph(t), l.addInitializers(t, e), l.addNodeArgs(t, r), l.addNodes(t, i), l.addMaxNodeIndex(t, d), l.addNodeEdges(t, g), l.addInputs(t, m), l.addOutputs(t, _), l.addSparseInitializers(t, y), l.endGraph(t)
}
}
p.Graph = l
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
class l {
constructor() {
this.bb = null, this.bb_pos = 0
}
__init(t, e) {
return this.bb_pos = t, this.bb = e, this
}
static getRootAsModel(t, e) {
return (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
static getSizePrefixedRootAsModel(t, e) {
return t.setPosition(t.position() + u.flatbuffers.SIZE_PREFIX_LENGTH), (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
irVersion() {
let t = this.bb.__offset(this.bb_pos, 4);
return t ? this.bb.readInt64(this.bb_pos + t) : this.bb.createLong(0, 0)
}
opsetImport(t, e) {
let r = this.bb.__offset(this.bb_pos, 6);
return r ? (e || new s.experimental.fbs.OperatorSetId).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos + r) + 4 * t), this.bb) : null
}
opsetImportLength() {
let t = this.bb.__offset(this.bb_pos, 6);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
producerName(t) {
let e = this.bb.__offset(this.bb_pos, 8);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
producerVersion(t) {
let e = this.bb.__offset(this.bb_pos, 10);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
domain(t) {
let e = this.bb.__offset(this.bb_pos, 12);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
modelVersion() {
let t = this.bb.__offset(this.bb_pos, 14);
return t ? this.bb.readInt64(this.bb_pos + t) : this.bb.createLong(0, 0)
}
docString(t) {
let e = this.bb.__offset(this.bb_pos, 16);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
graph(t) {
let e = this.bb.__offset(this.bb_pos, 18);
return e ? (t || new s.experimental.fbs.Graph).__init(this.bb.__indirect(this.bb_pos + e), this.bb) : null
}
graphDocString(t) {
let e = this.bb.__offset(this.bb_pos, 20);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
static startModel(t) {
t.startObject(9)
}
static addIrVersion(t, e) {
t.addFieldInt64(0, e, t.createLong(0, 0))
}
static addOpsetImport(t, e) {
t.addFieldOffset(1, e, 0)
}
static createOpsetImportVector(t, e) {
t.startVector(4, e.length, 4);
for (let r = e.length - 1; r >= 0; r--) t.addOffset(e[r]);
return t.endVector()
}
static startOpsetImportVector(t, e) {
t.startVector(4, e, 4)
}
static addProducerName(t, e) {
t.addFieldOffset(2, e, 0)
}
static addProducerVersion(t, e) {
t.addFieldOffset(3, e, 0)
}
static addDomain(t, e) {
t.addFieldOffset(4, e, 0)
}
static addModelVersion(t, e) {
t.addFieldInt64(5, e, t.createLong(0, 0))
}
static addDocString(t, e) {
t.addFieldOffset(6, e, 0)
}
static addGraph(t, e) {
t.addFieldOffset(7, e, 0)
}
static addGraphDocString(t, e) {
t.addFieldOffset(8, e, 0)
}
static endModel(t) {
return t.endObject()
}
static createModel(t, e, r, i, d, g, m, _, y, T) {
return l.startModel(t), l.addIrVersion(t, e), l.addOpsetImport(t, r), l.addProducerName(t, i), l.addProducerVersion(t, d), l.addDomain(t, g), l.addModelVersion(t, m), l.addDocString(t, _), l.addGraph(t, y), l.addGraphDocString(t, T), l.endModel(t)
}
}
p.Model = l
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
class l {
constructor() {
this.bb = null, this.bb_pos = 0
}
__init(t, e) {
return this.bb_pos = t, this.bb = e, this
}
static getRootAsKernelCreateInfos(t, e) {
return (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
static getSizePrefixedRootAsKernelCreateInfos(t, e) {
return t.setPosition(t.position() + u.flatbuffers.SIZE_PREFIX_LENGTH), (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
nodeIndices(t) {
let e = this.bb.__offset(this.bb_pos, 4);
return e ? this.bb.readUint32(this.bb.__vector(this.bb_pos + e) + 4 * t) : 0
}
nodeIndicesLength() {
let t = this.bb.__offset(this.bb_pos, 4);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
nodeIndicesArray() {
let t = this.bb.__offset(this.bb_pos, 4);
return t ? new Uint32Array(this.bb.bytes().buffer, this.bb.bytes().byteOffset + this.bb.__vector(this.bb_pos + t), this.bb.__vector_len(this.bb_pos + t)) : null
}
kernelDefHashes(t) {
let e = this.bb.__offset(this.bb_pos, 6);
return e ? this.bb.readUint64(this.bb.__vector(this.bb_pos + e) + 8 * t) : this.bb.createLong(0, 0)
}
kernelDefHashesLength() {
let t = this.bb.__offset(this.bb_pos, 6);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
static startKernelCreateInfos(t) {
t.startObject(2)
}
static addNodeIndices(t, e) {
t.addFieldOffset(0, e, 0)
}
static createNodeIndicesVector(t, e) {
t.startVector(4, e.length, 4);
for (let r = e.length - 1; r >= 0; r--) t.addInt32(e[r]);
return t.endVector()
}
static startNodeIndicesVector(t, e) {
t.startVector(4, e, 4)
}
static addKernelDefHashes(t, e) {
t.addFieldOffset(1, e, 0)
}
static createKernelDefHashesVector(t, e) {
t.startVector(8, e.length, 8);
for (let r = e.length - 1; r >= 0; r--) t.addInt64(e[r]);
return t.endVector()
}
static startKernelDefHashesVector(t, e) {
t.startVector(8, e, 8)
}
static endKernelCreateInfos(t) {
return t.endObject()
}
static createKernelCreateInfos(t, e, r) {
return l.startKernelCreateInfos(t), l.addNodeIndices(t, e), l.addKernelDefHashes(t, r), l.endKernelCreateInfos(t)
}
}
p.KernelCreateInfos = l
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
class l {
constructor() {
this.bb = null, this.bb_pos = 0
}
__init(t, e) {
return this.bb_pos = t, this.bb = e, this
}
static getRootAsSubGraphSessionState(t, e) {
return (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
static getSizePrefixedRootAsSubGraphSessionState(t, e) {
return t.setPosition(t.position() + u.flatbuffers.SIZE_PREFIX_LENGTH), (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
graphId(t) {
let e = this.bb.__offset(this.bb_pos, 4);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
sessionState(t) {
let e = this.bb.__offset(this.bb_pos, 6);
return e ? (t || new s.experimental.fbs.SessionState).__init(this.bb.__indirect(this.bb_pos + e), this.bb) : null
}
static startSubGraphSessionState(t) {
t.startObject(2)
}
static addGraphId(t, e) {
t.addFieldOffset(0, e, 0)
}
static addSessionState(t, e) {
t.addFieldOffset(1, e, 0)
}
static endSubGraphSessionState(t) {
let e = t.endObject();
return t.requiredField(e, 4), e
}
static createSubGraphSessionState(t, e, r) {
return l.startSubGraphSessionState(t), l.addGraphId(t, e), l.addSessionState(t, r), l.endSubGraphSessionState(t)
}
}
p.SubGraphSessionState = l
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
class l {
constructor() {
this.bb = null, this.bb_pos = 0
}
__init(t, e) {
return this.bb_pos = t, this.bb = e, this
}
static getRootAsSessionState(t, e) {
return (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
static getSizePrefixedRootAsSessionState(t, e) {
return t.setPosition(t.position() + u.flatbuffers.SIZE_PREFIX_LENGTH), (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
kernels(t) {
let e = this.bb.__offset(this.bb_pos, 4);
return e ? (t || new s.experimental.fbs.KernelCreateInfos).__init(this.bb.__indirect(this.bb_pos + e), this.bb) : null
}
subGraphSessionStates(t, e) {
let r = this.bb.__offset(this.bb_pos, 6);
return r ? (e || new s.experimental.fbs.SubGraphSessionState).__init(this.bb.__indirect(this.bb.__vector(this.bb_pos + r) + 4 * t), this.bb) : null
}
subGraphSessionStatesLength() {
let t = this.bb.__offset(this.bb_pos, 6);
return t ? this.bb.__vector_len(this.bb_pos + t) : 0
}
static startSessionState(t) {
t.startObject(2)
}
static addKernels(t, e) {
t.addFieldOffset(0, e, 0)
}
static addSubGraphSessionStates(t, e) {
t.addFieldOffset(1, e, 0)
}
static createSubGraphSessionStatesVector(t, e) {
t.startVector(4, e.length, 4);
for (let r = e.length - 1; r >= 0; r--) t.addOffset(e[r]);
return t.endVector()
}
static startSubGraphSessionStatesVector(t, e) {
t.startVector(4, e, 4)
}
static endSessionState(t) {
return t.endObject()
}
static createSessionState(t, e, r) {
return l.startSessionState(t), l.addKernels(t, e), l.addSubGraphSessionStates(t, r), l.endSessionState(t)
}
}
p.SessionState = l
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {})),
function(s) {
(function(h) {
(function(p) {
class l {
constructor() {
this.bb = null, this.bb_pos = 0
}
__init(t, e) {
return this.bb_pos = t, this.bb = e, this
}
static getRootAsInferenceSession(t, e) {
return (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
static getSizePrefixedRootAsInferenceSession(t, e) {
return t.setPosition(t.position() + u.flatbuffers.SIZE_PREFIX_LENGTH), (e || new l).__init(t.readInt32(t.position()) + t.position(), t)
}
static bufferHasIdentifier(t) {
return t.__has_identifier("ORTM")
}
ortVersion(t) {
let e = this.bb.__offset(this.bb_pos, 4);
return e ? this.bb.__string(this.bb_pos + e, t) : null
}
model(t) {
let e = this.bb.__offset(this.bb_pos, 6);
return e ? (t || new s.experimental.fbs.Model).__init(this.bb.__indirect(this.bb_pos + e), this.bb) : null
}
sessionState(t) {
let e = this.bb.__offset(this.bb_pos, 8);
return e ? (t || new s.experimental.fbs.SessionState).__init(this.bb.__indirect(this.bb_pos + e), this.bb) : null
}
static startInferenceSession(t) {
t.startObject(3)
}
static addOrtVersion(t, e) {
t.addFieldOffset(0, e, 0)
}
static addModel(t, e) {
t.addFieldOffset(1, e, 0)
}
static addSessionState(t, e) {
t.addFieldOffset(2, e, 0)
}
static endInferenceSession(t) {
return t.endObject()
}
static finishInferenceSessionBuffer(t, e) {
t.finish(e, "ORTM")
}
static finishSizePrefixedInferenceSessionBuffer(t, e) {
t.finish(e, "ORTM", !0)
}
static createInferenceSession(t, e, r, i) {
return l.startInferenceSession(t), l.addOrtVersion(t, e), l.addModel(t, r), l.addSessionState(t, i), l.endInferenceSession(t)
}
}
p.InferenceSession = l
})(h.fbs || (h.fbs = {}))
})(s.experimental || (s.experimental = {}))
}(n.onnxruntime || (n.onnxruntime = {}))
},
7448: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.OnnxjsSessionHandler = void 0;
const u = a(1670),
c = a(9162);
n.OnnxjsSessionHandler = class {
constructor(f) {
this.session = f, this.inputNames = this.session.inputNames, this.outputNames = this.session.outputNames
}
async dispose() {}
async run(f, s, h) {
const p = new Map;
for (const t in f)
if (Object.hasOwnProperty.call(f, t)) {
const e = f[t];
p.set(t, new c.Tensor(e.dims, e.type, void 0, void 0, e.data))
} const l = await this.session.run(p),
o = {};
return l.forEach((t, e) => {
o[e] = new u.Tensor(t.type, t.data, t.dims)
}), o
}
startProfiling() {
this.session.startProfiling()
}
endProfiling() {
this.session.endProfiling()
}
}
},
6919: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.Session = void 0;
const u = a(7067),
c = a(1296),
f = a(7091),
s = a(1036),
h = a(6231),
p = a(2644);
n.Session = class {
constructor(l = {}) {
this._initialized = !1, this.backendHint = l.backendHint, this.profiler = h.Profiler.create(l.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()
}
async loadModel(l, o, t) {
await this.profiler.event("session", "Session.loadModel", async () => {
const e = await (0, f.resolveBackend)(this.backendHint);
if (this.sessionHandler = e.createSessionHandler(this.context), this._model = new p.Model, typeof l == "string") {
const r = l.endsWith(".ort");
if (typeof fetch > "u") {
const i = await (0, c.promisify)(u.readFile)(l);
this.initialize(i, r)
} else {
const i = await fetch(l),
d = await i.arrayBuffer();
this.initialize(new Uint8Array(d), r)
}
} else if (ArrayBuffer.isView(l)) this.initialize(l);
else {
const r = new Uint8Array(l, o || 0, t || l.byteLength);
this.initialize(r)
}
})
}
initialize(l, o) {
if (this._initialized) throw new Error("already initialized");
this.profiler.event("session", "Session.initialize", () => {
const t = this.sessionHandler.transformGraph ? this.sessionHandler : void 0;
this._model.load(l, t, o), this.sessionHandler.onGraphInitialized && this.sessionHandler.onGraphInitialized(this._model.graph), this.initializeOps(this._model.graph), this._executionPlan = new s.ExecutionPlan(this._model.graph, this._ops, this.profiler)
}), this._initialized = !0
}
async run(l) {
if (!this._initialized) throw new Error("session not initialized yet");
return this.profiler.event("session", "Session.run", async () => {
const o = this.normalizeAndValidateInputs(l),
t = await this._executionPlan.execute(this.sessionHandler, o);
return this.createOutput(t)
})
}
normalizeAndValidateInputs(l) {
const o = this._model.graph.getInputNames();
if (Array.isArray(l)) {
if (l.length !== o.length) throw new Error(`incorrect input array length: expected ${o.length} but got ${l.length}`)
} else {
if (l.size !== o.length) throw new Error(`incorrect input map size: expected ${o.length} but got ${l.size}`);
const t = new Array(l.size);
let e = 0;
for (let r = 0; r < o.length; ++r) {
const i = l.get(o[r]);
if (!i) throw new Error(`missing input tensor for: '${name}'`);
t[e++] = i
}
l = t
}
if (this.context.graphInputTypes && this.context.graphInputTypes.length !== 0 && this.context.graphInputDims && this.context.graphInputDims.length !== 0) this.validateInputTensorDims(this.context.graphInputDims, l, !1);
else {
const t = this._model.graph.getInputIndices(),
e = this._model.graph.getValues(),
r = new Array(t.length);
for (let i = 0; i < t.length; ++i) {
const d = e[t[i]];
r[i] = d.type.shape.dims, this.context.graphInputTypes.push(d.type.tensorType), this.context.graphInputDims.push(l[i].dims)
}
this.validateInputTensorDims(r, l, !0)
}
return this.validateInputTensorTypes(this.context.graphInputTypes, l), l
}
validateInputTensorTypes(l, o) {
for (let t = 0; t < o.length; t++) {
const e = l[t],
r = o[t].type;
if (e !== r) throw new Error(`input tensor[${t}] check failed: expected type '${e}' but got ${r}`)
}
}
validateInputTensorDims(l, o, t) {
for (let e = 0; e < o.length; e++) {
const r = l[e],
i = o[e].dims;
if (!this.compareTensorDims(r, i, t)) throw new Error(`input tensor[${e}] check failed: expected shape '[${r.join(",")}]' but got [${i.join(",")}]`)
}
}
compareTensorDims(l, o, t) {
if (l.length !== o.length) return !1;
for (let e = 0; e < l.length; ++e)
if (l[e] !== o[e] && (!t || l[e] !== 0)) return !1;
return !0
}
createOutput(l) {
const o = this._model.graph.getOutputNames();
if (l.length !== o.length) throw new Error("expected number of outputs do not match number of generated outputs");
const t = new Map;
for (let e = 0; e < o.length; ++e) t.set(o[e], l[e]);
return t
}
initializeOps(l) {
const o = l.getNodes();
this._ops = new Array(o.length);
for (let t = 0; t < o.length; t++) this._ops[t] = this.sessionHandler.resolve(o[t], this._model.opsets, l)
}
}
},
9162: function(b, n, a) {
var u = this && this.__importDefault || function(d) {
return d && d.__esModule ? d : {
default: d
}
};
Object.defineProperty(n, "__esModule", {
value: !0
}), n.Tensor = void 0;
const c = a(3442),
f = u(a(3720)),
s = a(1446),
h = a(9395),
p = a(2517);
var l = h.onnxruntime.experimental.fbs;
class o {
get data() {
if (this.cache === void 0) {
const g = this.dataProvider(this.dataId);
if (g.length !== this.size) throw new Error("Length of data provided by the Data Provider is inconsistent with the dims of this Tensor.");
this.cache = g
}
return this.cache
}
get stringData() {
if (this.type !== "string") 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 (this.type !== "string") return this.data;
throw new TypeError("type cannot be non-number (string)")
}
get(g) {
return this.data[p.ShapeUtil.indicesToOffset(g, this.strides)]
}
set(g, m) {
this.data[p.ShapeUtil.indicesToOffset(g, this.strides)] = m
}
async getData() {
return this.cache === void 0 && (this.cache = await this.asyncDataProvider(this.dataId)), this.cache
}
get strides() {
return this._strides || (this._strides = p.ShapeUtil.computeStrides(this.dims)), this._strides
}
constructor(g, m, _, y, T, w = c.Guid.create()) {
this.dims = g, this.type = m, this.dataProvider = _, this.asyncDataProvider = y, this.cache = T, this.dataId = w, this.size = p.ShapeUtil.validateDimsAndCalcSize(g);
const S = this.size,
O = _ === void 0 && y === void 0 && T === void 0;
if (T !== void 0 && T.length !== S) throw new RangeError("Input dims doesn't match data length.");
if (m === "string") {
if (!(T === void 0 || Array.isArray(T) && T.every(E => typeof E == "string"))) throw new TypeError("cache should be a string array");
O && (this.cache = new Array(S))
} else {
if (T !== void 0) {
const E = e(m);
if (!(T instanceof E)) throw new TypeError(`cache should be type ${E.name}`)
}
if (O) {
const E = new ArrayBuffer(S * function(v) {
switch (v) {
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 ${v}`)
}
}(m));
this.cache = function(v, P) {
return new(e(P))(v)
}(E, m)
}
}
}
static fromProto(g) {
if (!g) throw new Error("cannot construct Value from an empty tensor");
const m = p.ProtoUtil.tensorDataTypeFromProto(g.dataType),
_ = p.ProtoUtil.tensorDimsFromProto(g.dims),
y = new o(_, m);
if (m === "string") g.stringData.forEach((T, w) => {
y.data[w] = (0, p.decodeUtf8String)(T)
});
else if (g.rawData && typeof g.rawData.byteLength == "number" && g.rawData.byteLength > 0) {
const T = y.data,
w = new DataView(g.rawData.buffer, g.rawData.byteOffset, g.rawData.byteLength),
S = t(g.dataType),
O = g.rawData.byteLength / S;
if (g.rawData.byteLength % S != 0) throw new Error("invalid buffer length");
if (T.length !== O) throw new Error("buffer length mismatch");
for (let E = 0; E < O; E++) {
const v = i(w, g.dataType, E * S);
T[E] = v
}
} else {
let T;
switch (g.dataType) {
case s.onnx.TensorProto.DataType.FLOAT:
T = g.floatData;
break;
case s.onnx.TensorProto.DataType.INT32:
case s.onnx.TensorProto.DataType.INT16:
case s.onnx.TensorProto.DataType.UINT16:
case s.onnx.TensorProto.DataType.INT8:
case s.onnx.TensorProto.DataType.UINT8:
case s.onnx.TensorProto.DataType.BOOL:
T = g.int32Data;
break;
case s.onnx.TensorProto.DataType.INT64:
T = g.int64Data;
break;
case s.onnx.TensorProto.DataType.DOUBLE:
T = g.doubleData;
break;
case s.onnx.TensorProto.DataType.UINT32:
case s.onnx.TensorProto.DataType.UINT64:
T = g.uint64Data;
break;
default:
throw new Error("unspecific error")
}
if (T == null) throw new Error("failed to populate data from a tensorproto value");
const w = y.data;
if (w.length !== T.length) throw new Error("array length mismatch");
for (let S = 0; S < T.length; S++) {
const O = T[S];
f.default.isLong(O) ? w[S] = r(O, g.dataType) : w[S] = O
}
}
return y
}
static fromData(g, m, _) {
return new o(m, _, void 0, void 0, g)
}
static fromOrtTensor(g) {
if (!g) throw new Error("cannot construct Value from an empty tensor");
const m = p.ProtoUtil.tensorDimsFromORTFormat(g),
_ = p.ProtoUtil.tensorDataTypeFromProto(g.dataType()),
y = new o(m, _);
if (_ === "string")
for (let T = 0; T < g.stringDataLength(); T++) y.data[T] = g.stringData(T);
else if (g.rawDataArray() && typeof g.rawDataLength() == "number" && g.rawDataLength() > 0) {
const T = y.data,
w = new DataView(g.rawDataArray().buffer, g.rawDataArray().byteOffset, g.rawDataLength()),
S = t(g.dataType()),
O = g.rawDataLength() / S;
if (g.rawDataLength() % S != 0) throw new Error("invalid buffer length");
if (T.length !== O) throw new Error("buffer length mismatch");
for (let E = 0; E < O; E++) {
const v = i(w, g.dataType(), E * S);
T[E] = v
}
}
return y
}
}
function t(d) {
switch (d) {
case s.onnx.TensorProto.DataType.UINT8:
case s.onnx.TensorProto.DataType.INT8:
case s.onnx.TensorProto.DataType.BOOL:
return 1;
case s.onnx.TensorProto.DataType.UINT16:
case s.onnx.TensorProto.DataType.INT16:
return 2;
case s.onnx.TensorProto.DataType.FLOAT:
case s.onnx.TensorProto.DataType.INT32:
case s.onnx.TensorProto.DataType.UINT32:
return 4;
case s.onnx.TensorProto.DataType.INT64:
case s.onnx.TensorProto.DataType.DOUBLE:
case s.onnx.TensorProto.DataType.UINT64:
return 8;
default:
throw new Error(`cannot calculate sizeof() on type ${s.onnx.TensorProto.DataType[d]}`)
}
}
function e(d) {
switch (d) {
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 r(d, g) {
if (g === s.onnx.TensorProto.DataType.INT64 || g === l.TensorDataType.INT64) {
if (d.greaterThanOrEqual(2147483648) || d.lessThan(-2147483648)) throw new TypeError("int64 is not supported")
} else {
if (g !== s.onnx.TensorProto.DataType.UINT32 && g !== l.TensorDataType.UINT32 && g !== s.onnx.TensorProto.DataType.UINT64 && g !== l.TensorDataType.UINT64) throw new TypeError(`not a LONG type: ${s.onnx.TensorProto.DataType[g]}`);
if (d.greaterThanOrEqual(4294967296) || d.lessThan(0)) throw new TypeError("uint64 is not supported")
}
return d.toNumber()
}
function i(d, g, m) {
switch (g) {
case s.onnx.TensorProto.DataType.BOOL:
case s.onnx.TensorProto.DataType.UINT8:
return d.getUint8(m);
case s.onnx.TensorProto.DataType.INT8:
return d.getInt8(m);
case s.onnx.TensorProto.DataType.UINT16:
return d.getUint16(m, !0);
case s.onnx.TensorProto.DataType.INT16:
return d.getInt16(m, !0);
case s.onnx.TensorProto.DataType.FLOAT:
return d.getFloat32(m, !0);
case s.onnx.TensorProto.DataType.INT32:
return d.getInt32(m, !0);
case s.onnx.TensorProto.DataType.UINT32:
return d.getUint32(m, !0);
case s.onnx.TensorProto.DataType.INT64:
return r(f.default.fromBits(d.getUint32(m, !0), d.getUint32(m + 4, !0), !1), g);
case s.onnx.TensorProto.DataType.DOUBLE:
return d.getFloat64(m, !0);
case s.onnx.TensorProto.DataType.UINT64:
return r(f.default.fromBits(d.getUint32(m, !0), d.getUint32(m + 4, !0), !0), g);
default:
throw new Error(`cannot read from DataView for type ${s.onnx.TensorProto.DataType[g]}`)
}
}
n.Tensor = o
},
2517: function(b, n, a) {
var u = this && this.__importDefault || function(g) {
return g && g.__esModule ? g : {
default: g
}
};
Object.defineProperty(n, "__esModule", {
value: !0
}), n.decodeUtf8String = n.MAX_CLIP = n.MIN_CLIP = n.PoolConvUtil = n.ReduceUtil = n.SplitUtil = n.MathUtil = n.ShapeUtil = n.LongUtil = n.ProtoUtil = n.GemmUtil = n.arrayCopyHelper = n.BroadcastUtil = n.MatMulUtil = n.ArrayUtil = n.assert = n.checkInputsShape = void 0;
const c = a(5686),
f = u(a(3720)),
s = a(1446),
h = a(9162);
n.checkInputsShape = function(g, ...m) {
if (!g || g.length !== m.length) return !1;
for (let _ = 0; _ < g.length; _++)
if (!g[_].dims || g[_].dims.length !== m[_]) return !1;
return !0
}, n.assert = function(g, m) {
if (!g) throw new Error(typeof m == "string" ? m : m())
}, n.ArrayUtil = class {
static arraysEqual(g, m) {
if (g.length !== m.length) return !1;
for (let _ = 0; _ < g.length; _++)
if (g[_] !== m[_]) return !1;
return !0
}
};
class p {
static preprocessInputShapes(m, _) {
return [m.length === 1 ? [1, m[0]] : m, _.length === 1 ? [_[0], 1] : _]
}
static postprocessOutputShape(m, _, y) {
_ === 1 && m.splice(m.length - 2, 1), y === 1 && m.pop()
}
static calcMatMulShape(m, _) {
return m[1] !== _[0] ? void 0 : [m[0], _[1]]
}
}
n.MatMulUtil = p;
class l {
static calcShape(m, _, y = !1) {
const T = m.length,
w = _.length;
if (T === 0) return _;
if (w === 0) return m;
const S = Math.max(m.length, _.length),
O = new Array(S);
if (y) {
if (T < 2 || w < 2) return;
const E = p.calcMatMulShape([m[T - 2], m[T - 1]], [_[w - 2], _[w - 1]]);
if (E === void 0) return;
[O[S - 2], O[S - 1]] = E
}
for (let E = y ? 3 : 1; E <= S; E++) {
const v = T - E < 0 ? 1 : m[T - E],
P = w - E < 0 ? 1 : _[w - E];
if (v !== P && v > 1 && P > 1) return;
O[S - E] = Math.max(v, P)
}
return O
}
static index(m, _) {
const y = new Array(_.length);
return l.fillIndex(m, _, y), y
}
static fillIndex(m, _, y) {
const T = m.length - _.length;
for (let w = 0; w < _.length; w++) y[w] = m[T + w] % _[w]
}
static calc(m, _, y, T, w) {
const S = l.calcShape(m.dims, _.dims);
if (S) {
if (T && !e.areEqual(S, m.dims)) return;
const O = e.size(S),
E = T ? m : new h.Tensor(S, w || m.type);
if (S.length === 0) E.set([], y(m.get([]), _.get([])));
else {
const v = new Array(S.length),
P = new Array(m.dims.length),
L = new Array(_.dims.length);
let V, R = 0,
k = 0,
Y = !1,
C = !1;
m.dims.length === 0 && (R = m.get([]), Y = !0), _.dims.length === 0 && (k = _.get([]), C = !0);
for (let $ = 0; $ < O; $++) {
V = $;
for (let X = S.length - 1; X >= 0; X--) v[X] = V % S[X], V = Math.floor(V / S[X]);
Y || (l.fillIndex(v, m.dims, P), R = m.get(P)), C || (l.fillIndex(v, _.dims, L), k = _.get(L)), E.set(v, y(R, k))
}
}
return E
}
}
static isValidBroadcast(m, _) {
const y = m.length,
T = _.length;
if (y > T) return !1;
for (let w = 1; w <= y; w++)
if (m[y - w] !== 1 && m[y - w] !== _[T - w]) return !1;
return !0
}
static getBroadcastDims(m, _) {
const y = m.length,
T = [];
for (let w = 0; w < y; w++) {
const S = y - 1 - w,
O = m[S] || 1;
(_[_.length - 1 - w] || 1) > 1 && O === 1 && T.unshift(S)
}
return T
}
}
n.BroadcastUtil = l, n.arrayCopyHelper = function(g, m, _, y, T) {
if (y < 0 || y >= m.length) throw new Error("sourceIndex out of bounds");
if (_ < 0 || _ >= g.length) throw new Error("targetIndex out of bounds");
if (y + T > m.length) throw new Error("source indices to be copied are outside bounds");
if (_ + T > g.length) throw new Error("target array is too small to hold result");
for (let w = 0; w < T; w++) g[_ + w] = m[y + w]
}, n.GemmUtil = class {
static getShapeOfGemmResult(g, m, _, y, T) {
if (g.length !== 2 || _.length !== 2) throw new Error("shape need to be of size 2");
let w, S, O;
m ? (w = g[1], S = g[0]) : (w = g[0], S = g[1]);
let E = -1;
if (y ? (O = _[0], E = 1) : (O = _[1], E = 0), _[E] !== S) throw new Error("dimension mismatch");
if (w <= 0 || O <= 0 || S <= 0) throw new Error("invalid shape specified");
if (T && !l.isValidBroadcast(T, [w, O])) throw new Error("gemm: invalid bias shape for broadcast");
return [w, O, S]
}
};
class o {
static tensorDataTypeFromProto(m) {
switch (m) {
case s.onnx.TensorProto.DataType.INT8:
return "int8";
case s.onnx.TensorProto.DataType.UINT8:
return "uint8";
case s.onnx.TensorProto.DataType.BOOL:
return "bool";
case s.onnx.TensorProto.DataType.INT16:
return "int16";
case s.onnx.TensorProto.DataType.UINT16:
return "uint16";
case s.onnx.TensorProto.DataType.INT32:
return "int32";
case s.onnx.TensorProto.DataType.UINT32:
return "uint32";
case s.onnx.TensorProto.DataType.FLOAT:
return "float32";
case s.onnx.TensorProto.DataType.DOUBLE:
return "float64";
case s.onnx.TensorProto.DataType.STRING:
return "string";
case s.onnx.TensorProto.DataType.INT64:
return "int32";
case s.onnx.TensorProto.DataType.UINT64:
return "uint32";
default:
throw new Error(`unsupported data type: ${s.onnx.TensorProto.DataType[m]}`)
}
}
static tensorDataTypeStringToEnum(m) {
switch (m) {
case "int8":
return s.onnx.TensorProto.DataType.INT8;
case "uint8":
return s.onnx.TensorProto.DataType.UINT8;
case "bool":
return s.onnx.TensorProto.DataType.BOOL;
case "int16":
return s.onnx.TensorProto.DataType.INT16;
case "uint16":
return s.onnx.TensorProto.DataType.UINT16;
case "int32":
return s.onnx.TensorProto.DataType.INT32;
case "uint32":
return s.onnx.TensorProto.DataType.UINT32;
case "float32":
return s.onnx.TensorProto.DataType.FLOAT;
case "float64":
return s.onnx.TensorProto.DataType.DOUBLE;
case "string":
return s.onnx.TensorProto.DataType.STRING;
case "int64":
return s.onnx.TensorProto.DataType.INT64;
case "uint64":
return s.onnx.TensorProto.DataType.UINT64;
default:
throw new Error(`unsupported data type: ${m}`)
}
}
static tensorDimsFromProto(m) {
return m.map(_ => f.default.isLong(_) ? _.toNumber() : _)
}
static tensorValueTypeFromProto(m) {
return {
tensorType: o.tensorDataTypeFromProto(m.elemType),
shape: {
dims: o.tensorDimsFromProto(m.shape.dim.map(_ => _.dimValue))
}
}
}
static tensorDimsFromORTFormat(m) {
const _ = [];
for (let y = 0; y < m.dimsLength(); y++) _.push(t.longToNumber(m.dims(y)));
return _
}
static tensorAttributesFromORTFormat(m) {
const _ = [];
for (let y = 0; y < m.attributesLength(); y++) _.push(m.attributes(y));
return _
}
}
n.ProtoUtil = o;
class t {
static longToNumber(m, _) {
return f.default.isLong(m) ? m.toNumber() : m instanceof c.flatbuffers.Long ? f.default.fromValue({
low: m.low,
high: m.high,
unsigned: _ != null && _
}).toNumber() : m
}
static isLong(m) {
return f.default.isLong(m) || m instanceof c.flatbuffers.Long
}
}
n.LongUtil = t;
class e {
static size(m) {
return e.getSizeFromDimensionRange(m, 0, m.length)
}
static sizeFromDimension(m, _) {
if (_ < 0 || _ > m.length) throw new Error(`invalid dimension of ${_} for sizeFromDimension as Tensor has ${m.length} dimensions.`);
return e.getSizeFromDimensionRange(m, _, m.length)
}
static sizeToDimension(m, _) {
if (_ < 0 || _ > m.length) throw new Error(`invalid dimension of ${_} for sizeToDimension as Tensor has ${m.length} dimensions.`);
return e.getSizeFromDimensionRange(m, 0, _)
}
static getSizeFromDimensionRange(m, _, y) {
let T = 1;
for (let w = _; w < y; w++) {
if (m[w] <= 0) throw new Error("cannot get valid size from specified dimension range. Most likely the range contains 0 or negative values in them.");
T *= m[w]
}
return T
}
static computeStrides(m) {
const _ = m.length;
if (_ === 0) return [];
if (_ === 1) return [1];
const y = new Array(_);
y[_ - 1] = 1, y[_ - 2] = m[_ - 1];
for (let T = _ - 3; T >= 0; --T) y[T] = y[T + 1] * m[T + 1];
return y
}
static transpose(m) {
return m.slice().reverse()
}
static indicesToOffset(m, _, y) {
y === void 0 && (y = m.length);
let T = 0;
for (let w = 0; w < y; ++w) T += _[w] * m[w];
return T
}
static offsetToIndices(m, _) {
const y = _.length;
if (y === 0) return [];
if (y === 1) return [m * _[0]];
const T = new Array(_.length);
for (let w = 0; w < T.length - 1; ++w) T[w] = Math.floor(m / _[w]), m -= T[w] * _[w];
return T[T.length - 1] = m, T
}
static normalizeAxis(m, _) {
if (m < -_ && m >= _) throw new Error("unsupported axis for this operation.");
return m < 0 ? m + _ : m
}
static normalizeAxes(m, _) {
return m.map(y => this.normalizeAxis(y, _))
}
static incrementIndex(m, _, y) {
if (_.length === 0 || m.length === 0) throw new Error("Index incrementing unsupported for scalar Tensor");
if (y === void 0) y = _.length;
else if (y <= 0 || y > _.length) throw new Error("Incorrect axis to increment on");
for (let T = y - 1; T >= 0 && (m[T]++, !(m[T] < _[T])); --T) m[T] = 0
}
static calculateReshapedDims(m, _) {
if (_.length === 0) {
if (m.length === 0 || e.size(m) === 1) return [];
throw new Error("cannot reshape to a scalar Tensor")
}
const y = _.length,
T = new Array(y);
let w = -1,
S = 1;
for (let E = 0; E < y; E++) {
if (_[E] < -1) throw new Error("a dimension in shape hints cannot be less than -1");
if (_[E] === -1) {
if (w !== -1) throw new Error("at most one dimension in shape hints can be -1");
w = E
} else {
if (_[E] === 0) {
if (E >= m.length) throw new Error("the dimension with value zero exceeds the dimension size of the input tensor");
T[E] = m[E]
} else T[E] = _[E];
S *= T[E]
}
}
const O = e.size(m);
if (w !== -1) {
if (O % S != 0) throw new Error(`the input tensor cannot be reshaped to the requested shape. Input shape: [${m}] Output shape: [${_}]`);
T[w] = O / S
} else if (S !== O) throw new Error("reshapedDims and originalDims don't have matching sizes");
return T
}
static sortBasedOnPerm(m, _) {
return _ ? _.map(y => m[y]) : m.slice().reverse()
}
static padShape(m, _) {
const y = m.length;
return m.map((T, w) => T + _[w] + _[w + y])
}
static areEqual(m, _) {
return m.length === _.length && m.every((y, T) => y === _[T])
}
static validateDimsAndCalcSize(m) {
if (m.length > 6) throw new TypeError("Only rank 0 to 6 is supported for tensor shape.");
let _ = 1;
for (const y of m) {
if (!Number.isInteger(y)) throw new TypeError(`Invalid shape: ${y} is not an integer`);
if (y < 0 || y > 2147483647) throw new TypeError(`Invalid shape: length ${y} is not allowed`);
_ *= y
}
return _
}
static flattenShape(m, _) {
_ < 0 && (_ += m.length);
const y = m.reduce((w, S) => w * S, 1),
T = m.slice(_).reduce((w, S) => w * S, 1);
return [y / T, T]
}
static squeezeShape(m, _) {
const y = new Array;
_ = e.normalizeAxes(_, m.length);
for (let T = 0; T < m.length; T++) {
const w = _.indexOf(T) >= 0;
if (w && m[T] !== 1) throw new Error("squeeze an axis of size different than 1");
(_.length === 0 && m[T] > 1 || _.length > 0 && !w) && y.push(m[T])
}
return y
}
static unsqueezeShape(m, _) {
const y = new Array(m.length + _.length);
y.fill(0);
for (let w = 0; w < _.length; w++) {
const S = e.normalizeAxis(_[w], y.length);
if (S >= y.length) throw new Error("'axes' has an out of range axis");
if (y[S] !== 0) throw new Error("'axes' has a duplicate axis");
y[S] = 1
}
let T = 0;
for (let w = 0; w < y.length; w++) y[w] === 0 && (y[w] = m[T++]);
if (T !== m.length) throw new Error("the unsqueezed dimension could not be established");
return y
}
}
n.ShapeUtil = e, n.MathUtil = class {
static sqr(g, m, _, y, T) {
if (y < 0 || y >= m.length) throw new Error("sourceIndex out of bounds");
if (_ < 0 || _ >= g.length) throw new Error("targetIndex out of bounds");
if (y + T > m.length) throw new Error("source indices to be copied are outside bounds");
if (_ + T > g.length) throw new Error("target array is too small to hold result");
for (let w = 0; w < T; w++) g[_ + w] += Math.pow(m[y + w], 2)
}
static axpy(g, m, _, y, T, w) {
if (y < 0 || y >= m.length) throw new Error("sourceIndex out of bounds");
if (_ < 0 || _ >= g.length) throw new Error("targetIndex out of bounds");
if (y + T > m.length) throw new Error("source indices to be copied are outside bounds");
if (_ + T > g.length) throw new Error("target array is too small to hold result");
for (let S = 0; S < T; S++) g[_ + S] += w * m[y + S]
}
static powx(g, m, _, y, T, w) {
if (y < 0 || y >= m.length) throw new Error("sourceIndex out of bounds");
if (_ < 0 || _ >= g.length) throw new Error("targetIndex out of bounds");
if (y + T > m.length) throw new Error("source indices to be copied are outside bounds");
if (_ + T > g.length) throw new Error("target array is too small to hold result");
for (let S = 0; S < T; S++) g[_ + S] = Math.pow(m[y + S], w)
}
static mul(g, m, _, y, T) {
if (y < 0 || y >= m.length) throw new Error("sourceIndex out of bounds");
if (_ < 0 || _ >= g.length) throw new Error("targetIndex out of bounds");
if (y + T > m.length) throw new Error("source indices to be copied are outside bounds");
if (_ + T > g.length) throw new Error("target array is too small to hold result");
for (let w = 0; w < T; w++) g[_ + w] = m[y + w] * g[_ + w]
}
};
class r {
static splitShape(m, _, y, T) {
if (y.length === 0) {
if (!T) throw new Error("need to know number of outputs when the 'split' attribute is not specified");
r.determineSplit(m[_], T, y)
}
const w = [],
S = [0];
for (let O = 0; O < y.length; ++O) {
O !== 0 && S.push(S[O - 1] + y[O - 1]);
const E = m.slice();
E[_] = y[O], w.push(E)
}
return [w, S]
}
static determineSplit(m, _, y) {
if (m % _ != 0) throw new Error("cannot split tensor to equal sized parts");
for (let T = 0; T < _; ++T) y.push(m / _)
}
}
n.SplitUtil = r;
class i {
static calcReduce(m, _, y, T, w) {
const S = m.dims.slice(0);
_.length === 0 && S.forEach((R, k) => _.push(k));
const O = i.calcReduceShape(S, _, !0),
E = e.size(O),
v = new h.Tensor(O, m.type),
P = e.computeStrides(O),
L = e.computeStrides(S),
V = new Array(S.length);
for (let R = 0; R < E; R++) {
const k = e.offsetToIndices(R, P);
l.fillIndex(k, S, V), v.set(k, i.calcReduceByAxis(m.numberData, _, S, 0, e.indicesToOffset(V, L), T, w))
}
return y ? v : new h.Tensor(i.calcReduceShape(S, _, y), v.type, void 0, void 0, v.data, v.dataId)
}
static calcReduceByAxis(m, _, y, T, w, S, O) {
let E = 0;
if (T >= _.length) return S(m[w]);
const v = _[T],
P = v >= y.length ? 1 : e.size(y.slice(v + 1));
for (let L = 0; L < y[v]; L++) E = L === 0 ? i.calcReduceByAxis(m, _, y, T + 1, w, S, O) : O(E, i.calcReduceByAxis(m, _, y, T + 1, w, S, O)), w += P;
return E
}
static calcReduceShape(m, _, y) {
const T = m.slice();
for (let w = 0; w < _.length; w++) T[_[w]] = y ? 1 : 0;
return T.filter(w => w !== 0)
}
}
n.ReduceUtil = i;
class d {
static adjustPoolAttributes(m, _, y, T, w, S) {
if (!m && y.length !== _.length - 2) throw new Error("length of specified kernel shapes should be 2 less than length of input dimensions");
if (m)
for (let O = 0; O < _.length - 2; O++) O >= y.length ? y.push(_[O + 2]) : y[O] = _[O + 2];
for (let O = 0; O < y.length; O++)
if (O < T.length) {
if (T[O] < 0) throw new Error("strides should be greater than or equal to 1")
} else T.push(1);
for (let O = 0; O < y.length; O++)
if (O < w.length) {
if (w[O] < 0) throw new Error("dilations should be greater than or equal to 1")
} else w.push(1);
for (let O = 0; O < 2 * y.length; O++)
if (O < S.length) {
if (S[O] < 0) throw new Error("pad should be greater than or equal to 1")
} else S.push(0);
for (let O = 0; O < y.length; O++) {
if (y[O] <= 0) throw new Error("kernel shapes need to be greater than 0");
if (S[O] >= y[O] || S[O + y.length] >= y[O]) throw new Error("pads should be smaller than kernel")
}
}
static adjustPadsBasedOnAutoPad(m, _, y, T, w, S) {
if (S) {
if (w.length !== 2 * (m.length - 2)) throw new Error("length of pads should be twice the length of data dimensions");
if (_.length !== m.length - 2) throw new Error("length of strides should be the length of data dimensions");
if (T.length !== m.length - 2) throw new Error("length of kernel shapes should be the length of data dimensions");
for (let O = 0; O < m.length - 2; O++) d.adjustPadAndReturnShape(m[O + 2], _[O], y[O], T[O], w, O, O + m.length - 2, S)
}
}
static computePoolOutputShape(m, _, y, T, w, S, O) {
if (_.length <= 0) throw new Error("input shape must be of size greater than 0");
const E = [_[0], _[1]];
return d.computeShapeHelper(m, _, E, y, T, w, S, O), E
}
static computeConvOutputShape(m, _, y, T, w, S, O) {
if (m.length <= 0 || _.length <= 0) throw new Error("invalid input tensor dims or invalid filter tensor dims");
const E = [m[0], _[0]];
return d.computeShapeHelper(!1, m, E, y, T, w, S, O), E
}
static computeShapeHelper(m, _, y, T, w, S, O, E) {
if (m)
for (let v = 0; v < _.length - 2; v++) y.push(1);
else
for (let v = 0; v < _.length - 2; v++) y.push(d.adjustPadAndReturnShape(_[v + 2], T[v], w[v], S[v], O, v, v + _.length - 2, E))
}
static adjustPadAndReturnShape(m, _, y, T, w, S, O, E) {
const v = y * (T - 1) + 1;
if (!E || E === "NOTSET") return Math.floor((m + w[S] + w[O] - v) / _ + 1);
switch (E) {
case "VALID":
return w[S] = 0, w[O] = 0, Math.floor((m - v) / _ + 1);
case "SAME_LOWER":
case "SAME_UPPER":
if (y !== 1) throw new Error("Dilation not supported for SAME_UPPER or SAME_LOWER");
{
const P = ((m + _ - 1) / _ - 1) * _ + T - m;
return w[S] = Math.floor(E === "SAME_LOWER" ? (P + 1) / 2 : P / 2), w[O] = P - w[S], Math.floor((m + P - T) / _ + 1)
}
default:
throw new Error("Unsupported AutoPad type")
}
}
}
n.PoolConvUtil = d, n.MIN_CLIP = -34028234663852886e22, n.MAX_CLIP = 34028234663852886e22, n.decodeUtf8String = function(g) {
return new TextDecoder().decode(g)
}
},
7967: (b, n) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.iterateExtraOptions = void 0, n.iterateExtraOptions = (a, u, c, f) => {
if (typeof a == "object" && a !== null) {
if (c.has(a)) throw new Error("Circular reference in options");
c.add(a)
}
Object.entries(a).forEach(([s, h]) => {
const p = u ? u + s : s;
if (typeof h == "object")(0, n.iterateExtraOptions)(h, p + ".", c, f);
else if (typeof h == "string" || typeof h == "number") f(p, h.toString());
else {
if (typeof h != "boolean") throw new Error("Can't handle extra config type: " + typeof h);
f(p, h ? "1" : "0")
}
})
}
},
2157: function(b, n, a) {
var u, c = this && this.__createBinding || (Object.create ? function(P, L, V, R) {
R === void 0 && (R = V);
var k = Object.getOwnPropertyDescriptor(L, V);
k && !("get" in k ? !L.__esModule : k.writable || k.configurable) || (k = {
enumerable: !0,
get: function() {
return L[V]
}
}), Object.defineProperty(P, R, k)
} : function(P, L, V, R) {
R === void 0 && (R = V), P[R] = L[V]
}),
f = this && this.__setModuleDefault || (Object.create ? function(P, L) {
Object.defineProperty(P, "default", {
enumerable: !0,
value: L
})
} : function(P, L) {
P.default = L
}),
s = this && this.__importStar || function(P) {
if (P && P.__esModule) return P;
var L = {};
if (P != null)
for (var V in P) V !== "default" && Object.prototype.hasOwnProperty.call(P, V) && c(L, P, V);
return f(L, P), L
};
Object.defineProperty(n, "__esModule", {
value: !0
}), n.endProfiling = n.run = n.releaseSession = n.createSession = n.createSessionFinalize = n.createSessionAllocate = n.initOrt = n.initWasm = void 0;
const h = a(1670),
p = s(a(349)),
l = a(6361),
o = () => !!h.env.wasm.proxy && typeof document < "u";
let t, e, r, i = !1,
d = !1,
g = !1;
const m = [],
_ = [],
y = [],
T = [],
w = [],
S = [],
O = () => {
if (i || !d || g || !t) throw new Error("worker not ready")
},
E = P => {
switch (P.data.type) {
case "init-wasm":
i = !1, P.data.err ? (g = !0, e[1](P.data.err)) : (d = !0, e[0]());
break;
case "init-ort":
P.data.err ? r[1](P.data.err) : r[0]();
break;
case "create_allocate":
P.data.err ? m.shift()[1](P.data.err) : m.shift()[0](P.data.out);
break;
case "create_finalize":
P.data.err ? _.shift()[1](P.data.err) : _.shift()[0](P.data.out);
break;
case "create":
P.data.err ? y.shift()[1](P.data.err) : y.shift()[0](P.data.out);
break;
case "release":
P.data.err ? T.shift()[1](P.data.err) : T.shift()[0]();
break;
case "run":
P.data.err ? w.shift()[1](P.data.err) : w.shift()[0](P.data.out);
break;
case "end-profiling":
P.data.err ? S.shift()[1](P.data.err) : S.shift()[0]()
}
},
v = typeof document < "u" ? (u = document?.currentScript) === null || u === void 0 ? void 0 : u.src : void 0;
n.initWasm = async () => {
if (o()) {
if (d) return;
if (i) throw new Error("multiple calls to 'initWasm()' detected.");
if (g) throw new Error("previous call to 'initWasm()' failed.");
return i = !0, h.env.wasm.wasmPaths === void 0 && v && v.indexOf("blob:") !== 0 && (h.env.wasm.wasmPaths = v.substr(0, +v.lastIndexOf("/") + 1)), new Promise((P, L) => {
t?.terminate(), t = a(9710).Z(), t.onmessage = E, e = [P, L];
const V = {
type: "init-wasm",
in: h.env.wasm
};
t.postMessage(V)
})
}
return (0, l.initializeWebAssembly)(h.env.wasm)
}, n.initOrt = async (P, L) => {
if (o()) return O(), new Promise((V, R) => {
r = [V, R];
const k = {
type: "init-ort",
in: {
numThreads: P,
loggingLevel: L
}
};
t.postMessage(k)
});
p.initOrt(P, L)
}, n.createSessionAllocate = async P => o() ? (O(), new Promise((L, V) => {
m.push([L, V]);
const R = {
type: "create_allocate",
in: {
model: P
}
};
t.postMessage(R, [P.buffer])
})) : p.createSessionAllocate(P), n.createSessionFinalize = async (P, L) => o() ? (O(), new Promise((V, R) => {
_.push([V, R]);
const k = {
type: "create_finalize",
in: {
modeldata: P,
options: L
}
};
t.postMessage(k)
})) : p.createSessionFinalize(P, L), n.createSession = async (P, L) => o() ? (O(), new Promise((V, R) => {
y.push([V, R]);
const k = {
type: "create",
in: {
model: P,
options: L
}
};
t.postMessage(k, [P.buffer])
})) : p.createSession(P, L), n.releaseSession = async P => {
if (o()) return O(), new Promise((L, V) => {
T.push([L, V]);
const R = {
type: "release",
in: P
};
t.postMessage(R)
});
p.releaseSession(P)
}, n.run = async (P, L, V, R, k) => o() ? (O(), new Promise((Y, C) => {
w.push([Y, C]);
const $ = {
type: "run",
in: {
sessionId: P,
inputIndices: L,
inputs: V,
outputIndices: R,
options: k
}
};
t.postMessage($, p.extractTransferableBuffers(V))
})) : p.run(P, L, V, R, k), n.endProfiling = async P => {
if (o()) return O(), new Promise((L, V) => {
S.push([L, V]);
const R = {
type: "end-profiling",
in: P
};
t.postMessage(R)
});
p.endProfiling(P)
}
},
586: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.setRunOptions = void 0;
const u = a(7967),
c = a(4983),
f = a(6361);
n.setRunOptions = s => {
const h = (0, f.getInstance)();
let p = 0;
const l = [],
o = s || {};
try {
if (s?.logSeverityLevel === void 0) o.logSeverityLevel = 2;
else if (typeof s.logSeverityLevel != "number" || !Number.isInteger(s.logSeverityLevel) || s.logSeverityLevel < 0 || s.logSeverityLevel > 4) throw new Error(`log serverity level is not valid: ${s.logSeverityLevel}`);
if (s?.logVerbosityLevel === void 0) o.logVerbosityLevel = 0;
else if (typeof s.logVerbosityLevel != "number" || !Number.isInteger(s.logVerbosityLevel)) throw new Error(`log verbosity level is not valid: ${s.logVerbosityLevel}`);
s?.terminate === void 0 && (o.terminate = !1);
let t = 0;
if (s?.tag !== void 0 && (t = (0, c.allocWasmString)(s.tag, l)), p = h._OrtCreateRunOptions(o.logSeverityLevel, o.logVerbosityLevel, !!o.terminate, t), p === 0) throw new Error("Can't create run options");
return s?.extra !== void 0 && (0, u.iterateExtraOptions)(s.extra, "", new WeakSet, (e, r) => {
const i = (0, c.allocWasmString)(e, l),
d = (0, c.allocWasmString)(r, l);
if (h._OrtAddRunConfigEntry(p, i, d) !== 0) throw new Error(`Can't set a run config entry: ${e} - ${r}`)
}), [p, l]
} catch (t) {
throw p !== 0 && h._OrtReleaseRunOptions(p), l.forEach(h._free), t
}
}
},
2306: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.OnnxruntimeWebAssemblySessionHandler = void 0;
const u = a(2806),
c = a(1670),
f = a(2850),
s = a(2157);
let h;
n.OnnxruntimeWebAssemblySessionHandler = class {
async createSessionAllocate(p) {
const l = await fetch(p),
o = await l.arrayBuffer();
return (0, s.createSessionAllocate)(new Uint8Array(o))
}
async loadModel(p, l) {
if (h || (await (0, s.initOrt)(c.env.wasm.numThreads, (o => {
switch (o) {
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: ${o}`)
}
})(c.env.logLevel)), h = !0), typeof p == "string")
if (typeof fetch > "u") {
const o = await (0, f.promisify)(u.readFile)(p);
[this.sessionId, this.inputNames, this.outputNames] = await (0, s.createSession)(o, l)
} else {
const o = await this.createSessionAllocate(p);
[this.sessionId, this.inputNames, this.outputNames] = await (0, s.createSessionFinalize)(o, l)
}
else [this.sessionId, this.inputNames, this.outputNames] = await (0, s.createSession)(p, l)
}
async dispose() {
return (0, s.releaseSession)(this.sessionId)
}
async run(p, l, o) {
const t = [],
e = [];
Object.entries(p).forEach(g => {
const m = g[0],
_ = g[1],
y = this.inputNames.indexOf(m);
if (y === -1) throw new Error(`invalid input '${m}'`);
t.push(_), e.push(y)
});
const r = [];
Object.entries(l).forEach(g => {
const m = g[0],
_ = this.outputNames.indexOf(m);
if (_ === -1) throw new Error(`invalid output '${m}'`);
r.push(_)
});
const i = await (0, s.run)(this.sessionId, e, t.map(g => [g.type, g.dims, g.data]), r, o),
d = {};
for (let g = 0; g < i.length; g++) d[this.outputNames[r[g]]] = new c.Tensor(i[g][0], i[g][2], i[g][1]);
return d
}
startProfiling() {}
endProfiling() {
(0, s.endProfiling)(this.sessionId)
}
}
},
4919: (b, n, a) => {
Object.defineProperty(n, "__esModule", {
value: !0
}), n.setSessionOptions = void 0;
const u = a(7967),
c = a(4983),
f = a(6361);
n.setSessionOptions = s => {
const h = (0, f.getInstance)();
let p = 0;
const l = [],
o = s || {};
(t => {
t.extra || (t.extra = {}), t.extra.session || (t.extra.session = {});
const e = t.extra.session;
e.use_ort_model_bytes_directly || (e.use_ort_model_bytes_directly = "1")
})(o);
try {
s?.graphOptimizationLevel === void 0 && (o.graphOptimizationLevel = "all");
const t = (i => {
switch (i) {
case "disabled":
return 0;
case "basic":
return 1;
case "extended":
return 2;
case "all":
return 99;
default:
throw new Error(`unsupported graph optimization level: ${i}`)
}
})(o.graphOptimizationLevel);
s?.enableCpuMemArena === void 0 && (o.enableCpuMemArena = !0), s?.enableMemPattern === void 0 && (o.enableMemPattern = !0), s?.executionMode === void 0 && (o.executionMode = "sequential");
const e = (i => {
switch (i) {
case "sequential":
return 0;
case "parallel":
return 1;
default:
throw new Error(`unsupported execution mode: ${i}`)
}
})(o.executionMode);
let r = 0;
if (s?.logId !== void 0 && (r = (0, c.allocWasmString)(s.logId, l)), s?.logSeverityLevel === void 0) o.logSeverityLevel = 2;
else if (typeof s.logSeverityLevel != "number" || !Number.isInteger(s.logSeverityLevel) || s.logSeverityLevel < 0 || s.logSeverityLevel > 4) throw new Error(`log serverity level is not valid: ${s.logSeverityLevel}`);
if (s?.logVerbosityLevel === void 0) o.logVerbosityLevel = 0;
else if (typeof s.logVerbosityLevel != "number" || !Number.isInteger(s.logVerbosityLevel)) throw new Error(`log verbosity level is not valid: ${s.logVerbosityLevel}`);
if (s?.enableProfiling === void 0 && (o.enableProfiling = !1), p = h._OrtCreateSessionOptions(t, !!o.enableCpuMemArena, !!o.enableMemPattern, e, !!o.enableProfiling, 0, r, o.logSeverityLevel, o.logVerbosityLevel), p === 0) throw new Error("Can't create session options");
return s?.executionProviders && ((i, d, g) => {
for (const m of d) {
let _ = typeof m == "string" ? m : m.name;
switch (_) {
case "xnnpack":
_ = "XNNPACK";
break;
case "wasm":
case "cpu":
continue;
default:
throw new Error(`not supported EP: ${_}`)
}
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const g = (0, c.allocWasmString)(i, l),
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k = g.stackAlloc(4 * S),
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let C = V / 4,
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for (let ue = 0; ue < S; ue++) {
const Se = g.HEAPU32[k / 4 + ue],
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},
477: b => {
b.exports = function(n, a, u, c) {
var f = self || window;
try {
try {
var s;
try {
s = new f.Blob([n])
} catch {
(s = new(f.BlobBuilder || f.WebKitBlobBuilder || f.MozBlobBuilder || f.MSBlobBuilder)).append(n), s = s.getBlob()
}
var h = f.URL || f.webkitURL,
p = h.createObjectURL(s),
l = new f[a](p, u);
return h.revokeObjectURL(p), l
} catch {
return new f[a]("data:application/javascript,".concat(encodeURIComponent(n)), u)
}
} catch {
if (!c) throw Error("Inline worker is not supported");
return new f[a](c, u)
}
}
},
4154: b => {
b.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}};
`
},
1670: b => {
b.exports = __WEBPACK_EXTERNAL_MODULE__1670__
},
7067: () => {},
1296: () => {},
1384: () => {},
3993: () => {},
908: () => {},
6953: () => {},
9925: () => {},
2806: () => {},
6449: () => {},
2850: () => {},
5381: () => {},
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flatbuffers: () => u
});
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u.Offset, u.Table, u.SIZEOF_SHORT = 2, u.SIZEOF_INT = 4, u.FILE_IDENTIFIER_LENGTH = 4, u.SIZE_PREFIX_LENGTH = 4, u.Encoding = {
UTF8_BYTES: 1,
UTF16_STRING: 2
}, u.int32 = new Int32Array(2), u.float32 = new Float32Array(u.int32.buffer), u.float64 = new Float64Array(u.int32.buffer), u.isLittleEndian = new Uint16Array(new Uint8Array([1, 0]).buffer)[0] === 1, u.Long = function(c, f) {
this.low = 0 | c, this.high = 0 | f
}, u.Long.create = function(c, f) {
return c == 0 && f == 0 ? u.Long.ZERO : new u.Long(c, f)
}, u.Long.prototype.toFloat64 = function() {
return (this.low >>> 0) + 4294967296 * this.high
}, u.Long.prototype.equals = function(c) {
return this.low == c.low && this.high == c.high
}, u.Long.ZERO = new u.Long(0, 0), u.Builder = function(c) {
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else var f = 1024;
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}, u.Builder.prototype.forceDefaults = function(c) {
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}, u.Builder.prototype.prep = function(c, f) {
c > this.minalign && (this.minalign = c);
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var h = this.bb.capacity();
this.bb = u.Builder.growByteBuffer(this.bb), this.space += this.bb.capacity() - h
}
this.pad(s)
}, u.Builder.prototype.pad = function(c) {
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}, u.Builder.prototype.writeInt8 = function(c) {
this.bb.writeInt8(this.space -= 1, c)
}, u.Builder.prototype.writeInt16 = function(c) {
this.bb.writeInt16(this.space -= 2, c)
}, u.Builder.prototype.writeInt32 = function(c) {
this.bb.writeInt32(this.space -= 4, c)
}, u.Builder.prototype.writeInt64 = function(c) {
this.bb.writeInt64(this.space -= 8, c)
}, u.Builder.prototype.writeFloat32 = function(c) {
this.bb.writeFloat32(this.space -= 4, c)
}, u.Builder.prototype.writeFloat64 = function(c) {
this.bb.writeFloat64(this.space -= 8, c)
}, u.Builder.prototype.addInt8 = function(c) {
this.prep(1, 0), this.writeInt8(c)
}, u.Builder.prototype.addInt16 = function(c) {
this.prep(2, 0), this.writeInt16(c)
}, u.Builder.prototype.addInt32 = function(c) {
this.prep(4, 0), this.writeInt32(c)
}, u.Builder.prototype.addInt64 = function(c) {
this.prep(8, 0), this.writeInt64(c)
}, u.Builder.prototype.addFloat32 = function(c) {
this.prep(4, 0), this.writeFloat32(c)
}, u.Builder.prototype.addFloat64 = function(c) {
this.prep(8, 0), this.writeFloat64(c)
}, u.Builder.prototype.addFieldInt8 = function(c, f, s) {
(this.force_defaults || f != s) && (this.addInt8(f), this.slot(c))
}, u.Builder.prototype.addFieldInt16 = function(c, f, s) {
(this.force_defaults || f != s) && (this.addInt16(f), this.slot(c))
}, u.Builder.prototype.addFieldInt32 = function(c, f, s) {
(this.force_defaults || f != s) && (this.addInt32(f), this.slot(c))
}, u.Builder.prototype.addFieldInt64 = function(c, f, s) {
!this.force_defaults && f.equals(s) || (this.addInt64(f), this.slot(c))
}, u.Builder.prototype.addFieldFloat32 = function(c, f, s) {
(this.force_defaults || f != s) && (this.addFloat32(f), this.slot(c))
}, u.Builder.prototype.addFieldFloat64 = function(c, f, s) {
(this.force_defaults || f != s) && (this.addFloat64(f), this.slot(c))
}, u.Builder.prototype.addFieldOffset = function(c, f, s) {
(this.force_defaults || f != s) && (this.addOffset(f), this.slot(c))
}, u.Builder.prototype.addFieldStruct = function(c, f, s) {
f != s && (this.nested(f), this.slot(c))
}, u.Builder.prototype.nested = function(c) {
if (c != this.offset()) throw new Error("FlatBuffers: struct must be serialized inline.")
}, u.Builder.prototype.notNested = function() {
if (this.isNested) throw new Error("FlatBuffers: object serialization must not be nested.")
}, u.Builder.prototype.slot = function(c) {
this.vtable[c] = this.offset()
}, u.Builder.prototype.offset = function() {
return this.bb.capacity() - this.space
}, u.Builder.growByteBuffer = function(c) {
var f = c.capacity();
if (3221225472 & f) throw new Error("FlatBuffers: cannot grow buffer beyond 2 gigabytes.");
var s = f << 1,
h = u.ByteBuffer.allocate(s);
return h.setPosition(s - f), h.bytes().set(c.bytes(), s - f), h
}, u.Builder.prototype.addOffset = function(c) {
this.prep(u.SIZEOF_INT, 0), this.writeInt32(this.offset() - c + u.SIZEOF_INT)
}, u.Builder.prototype.startObject = function(c) {
this.notNested(), this.vtable == null && (this.vtable = []), this.vtable_in_use = c;
for (var f = 0; f < c; f++) this.vtable[f] = 0;
this.isNested = !0, this.object_start = this.offset()
}, u.Builder.prototype.endObject = function() {
if (this.vtable == null || !this.isNested) throw new Error("FlatBuffers: endObject called without startObject");
this.addInt32(0);
for (var c = this.offset(), f = this.vtable_in_use - 1; f >= 0 && this.vtable[f] == 0; f--);
for (var s = f + 1; f >= 0; f--) this.addInt16(this.vtable[f] != 0 ? c - this.vtable[f] : 0);
this.addInt16(c - this.object_start);
var h = (s + 2) * u.SIZEOF_SHORT;
this.addInt16(h);
var p = 0,
l = this.space;
e: for (f = 0; f < this.vtables.length; f++) {
var o = this.bb.capacity() - this.vtables[f];
if (h == this.bb.readInt16(o)) {
for (var t = u.SIZEOF_SHORT; t < h; t += u.SIZEOF_SHORT)
if (this.bb.readInt16(l + t) != this.bb.readInt16(o + t)) continue e;
p = this.vtables[f];
break
}
}
return p ? (this.space = this.bb.capacity() - c, this.bb.writeInt32(this.space, p - c)) : (this.vtables.push(this.offset()), this.bb.writeInt32(this.bb.capacity() - c, this.offset() - c)), this.isNested = !1, c
}, u.Builder.prototype.finish = function(c, f, s) {
var h = s ? u.SIZE_PREFIX_LENGTH : 0;
if (f) {
var p = f;
if (this.prep(this.minalign, u.SIZEOF_INT + u.FILE_IDENTIFIER_LENGTH + h), p.length != u.FILE_IDENTIFIER_LENGTH) throw new Error("FlatBuffers: file identifier must be length " + u.FILE_IDENTIFIER_LENGTH);
for (var l = u.FILE_IDENTIFIER_LENGTH - 1; l >= 0; l--) this.writeInt8(p.charCodeAt(l))
}
this.prep(this.minalign, u.SIZEOF_INT + h), this.addOffset(c), h && this.addInt32(this.bb.capacity() - this.space), this.bb.setPosition(this.space)
}, u.Builder.prototype.finishSizePrefixed = function(c, f) {
this.finish(c, f, !0)
}, u.Builder.prototype.requiredField = function(c, f) {
var s = this.bb.capacity() - c,
h = s - this.bb.readInt32(s);
if (this.bb.readInt16(h + f) == 0) throw new Error("FlatBuffers: field " + f + " must be set")
}, u.Builder.prototype.startVector = function(c, f, s) {
this.notNested(), this.vector_num_elems = f, this.prep(u.SIZEOF_INT, c * f), this.prep(s, c * f)
}, u.Builder.prototype.endVector = function() {
return this.writeInt32(this.vector_num_elems), this.offset()
}, u.Builder.prototype.createString = function(c) {
if (c instanceof Uint8Array) var f = c;
else {
f = [];
for (var s = 0; s < c.length;) {
var h, p = c.charCodeAt(s++);
(h = p < 55296 || p >= 56320 ? p : (p << 10) + c.charCodeAt(s++) + -56613888) < 128 ? f.push(h) : (h < 2048 ? f.push(h >> 6 & 31 | 192) : (h < 65536 ? f.push(h >> 12 & 15 | 224) : f.push(h >> 18 & 7 | 240, h >> 12 & 63 | 128), f.push(h >> 6 & 63 | 128)), f.push(63 & h | 128))
}
}
this.addInt8(0), this.startVector(1, f.length, 1), this.bb.setPosition(this.space -= f.length), s = 0;
for (var l = this.space, o = this.bb.bytes(); s < f.length; s++) o[l++] = f[s];
return this.endVector()
}, u.Builder.prototype.createLong = function(c, f) {
return u.Long.create(c, f)
}, u.ByteBuffer = function(c) {
this.bytes_ = c, this.position_ = 0
}, u.ByteBuffer.allocate = function(c) {
return new u.ByteBuffer(new Uint8Array(c))
}, u.ByteBuffer.prototype.clear = function() {
this.position_ = 0
}, u.ByteBuffer.prototype.bytes = function() {
return this.bytes_
}, u.ByteBuffer.prototype.position = function() {
return this.position_
}, u.ByteBuffer.prototype.setPosition = function(c) {
this.position_ = c
}, u.ByteBuffer.prototype.capacity = function() {
return this.bytes_.length
}, u.ByteBuffer.prototype.readInt8 = function(c) {
return this.readUint8(c) << 24 >> 24
}, u.ByteBuffer.prototype.readUint8 = function(c) {
return this.bytes_[c]
}, u.ByteBuffer.prototype.readInt16 = function(c) {
return this.readUint16(c) << 16 >> 16
}, u.ByteBuffer.prototype.readUint16 = function(c) {
return this.bytes_[c] | this.bytes_[c + 1] << 8
}, u.ByteBuffer.prototype.readInt32 = function(c) {
return this.bytes_[c] | this.bytes_[c + 1] << 8 | this.bytes_[c + 2] << 16 | this.bytes_[c + 3] << 24
}, u.ByteBuffer.prototype.readUint32 = function(c) {
return this.readInt32(c) >>> 0
}, u.ByteBuffer.prototype.readInt64 = function(c) {
return new u.Long(this.readInt32(c), this.readInt32(c + 4))
}, u.ByteBuffer.prototype.readUint64 = function(c) {
return new u.Long(this.readUint32(c), this.readUint32(c + 4))
}, u.ByteBuffer.prototype.readFloat32 = function(c) {
return u.int32[0] = this.readInt32(c), u.float32[0]
}, u.ByteBuffer.prototype.readFloat64 = function(c) {
return u.int32[u.isLittleEndian ? 0 : 1] = this.readInt32(c), u.int32[u.isLittleEndian ? 1 : 0] = this.readInt32(c + 4), u.float64[0]
}, u.ByteBuffer.prototype.writeInt8 = function(c, f) {
this.bytes_[c] = f
}, u.ByteBuffer.prototype.writeUint8 = function(c, f) {
this.bytes_[c] = f
}, u.ByteBuffer.prototype.writeInt16 = function(c, f) {
this.bytes_[c] = f, this.bytes_[c + 1] = f >> 8
}, u.ByteBuffer.prototype.writeUint16 = function(c, f) {
this.bytes_[c] = f, this.bytes_[c + 1] = f >> 8
}, u.ByteBuffer.prototype.writeInt32 = function(c, f) {
this.bytes_[c] = f, this.bytes_[c + 1] = f >> 8, this.bytes_[c + 2] = f >> 16, this.bytes_[c + 3] = f >> 24
}, u.ByteBuffer.prototype.writeUint32 = function(c, f) {
this.bytes_[c] = f, this.bytes_[c + 1] = f >> 8, this.bytes_[c + 2] = f >> 16, this.bytes_[c + 3] = f >> 24
}, u.ByteBuffer.prototype.writeInt64 = function(c, f) {
this.writeInt32(c, f.low), this.writeInt32(c + 4, f.high)
}, u.ByteBuffer.prototype.writeUint64 = function(c, f) {
this.writeUint32(c, f.low), this.writeUint32(c + 4, f.high)
}, u.ByteBuffer.prototype.writeFloat32 = function(c, f) {
u.float32[0] = f, this.writeInt32(c, u.int32[0])
}, u.ByteBuffer.prototype.writeFloat64 = function(c, f) {
u.float64[0] = f, this.writeInt32(c, u.int32[u.isLittleEndian ? 0 : 1]), this.writeInt32(c + 4, u.int32[u.isLittleEndian ? 1 : 0])
}, u.ByteBuffer.prototype.getBufferIdentifier = function() {
if (this.bytes_.length < this.position_ + u.SIZEOF_INT + u.FILE_IDENTIFIER_LENGTH) throw new Error("FlatBuffers: ByteBuffer is too short to contain an identifier.");
for (var c = "", f = 0; f < u.FILE_IDENTIFIER_LENGTH; f++) c += String.fromCharCode(this.readInt8(this.position_ + u.SIZEOF_INT + f));
return c
}, u.ByteBuffer.prototype.__offset = function(c, f) {
var s = c - this.readInt32(c);
return f < this.readInt16(s) ? this.readInt16(s + f) : 0
}, u.ByteBuffer.prototype.__union = function(c, f) {
return c.bb_pos = f + this.readInt32(f), c.bb = this, c
}, u.ByteBuffer.prototype.__string = function(c, f) {
c += this.readInt32(c);
var s = this.readInt32(c),
h = "",
p = 0;
if (c += u.SIZEOF_INT, f === u.Encoding.UTF8_BYTES) return this.bytes_.subarray(c, c + s);
for (; p < s;) {
var l, o = this.readUint8(c + p++);
if (o < 192) l = o;
else {
var t = this.readUint8(c + p++);
if (o < 224) l = (31 & o) << 6 | 63 & t;
else {
var e = this.readUint8(c + p++);
l = o < 240 ? (15 & o) << 12 | (63 & t) << 6 | 63 & e : (7 & o) << 18 | (63 & t) << 12 | (63 & e) << 6 | 63 & this.readUint8(c + p++)
}
}
l < 65536 ? h += String.fromCharCode(l) : (l -= 65536, h += String.fromCharCode(55296 + (l >> 10), 56320 + (1023 & l)))
}
return h
}, u.ByteBuffer.prototype.__indirect = function(c) {
return c + this.readInt32(c)
}, u.ByteBuffer.prototype.__vector = function(c) {
return c + this.readInt32(c) + u.SIZEOF_INT
}, u.ByteBuffer.prototype.__vector_len = function(c) {
return this.readInt32(c + this.readInt32(c))
}, u.ByteBuffer.prototype.__has_identifier = function(c) {
if (c.length != u.FILE_IDENTIFIER_LENGTH) throw new Error("FlatBuffers: file identifier must be length " + u.FILE_IDENTIFIER_LENGTH);
for (var f = 0; f < u.FILE_IDENTIFIER_LENGTH; f++)
if (c.charCodeAt(f) != this.readInt8(this.position_ + u.SIZEOF_INT + f)) return !1;
return !0
}, u.ByteBuffer.prototype.createLong = function(c, f) {
return u.Long.create(c, f)
}
}
},
__webpack_module_cache__ = {};
function __webpack_require__(b) {
var n = __webpack_module_cache__[b];
if (n !== void 0) return n.exports;
var a = __webpack_module_cache__[b] = {
exports: {}
};
return __webpack_modules__[b].call(a.exports, a, a.exports, __webpack_require__), a.exports
}
__webpack_require__.n = b => {
var n = b && b.__esModule ? () => b.default : () => b;
return __webpack_require__.d(n, {
a: n
}), n
}, __webpack_require__.d = (b, n) => {
for (var a in n) __webpack_require__.o(n, a) && !__webpack_require__.o(b, a) && Object.defineProperty(b, a, {
enumerable: !0,
get: n[a]
})
}, __webpack_require__.g = function() {
if (typeof globalThis == "object") return globalThis;
try {
return this || new Function("return this")()
} catch {
if (typeof window == "object") return window
}
}(), __webpack_require__.o = (b, n) => Object.prototype.hasOwnProperty.call(b, n), __webpack_require__.r = b => {
typeof Symbol < "u" && Symbol.toStringTag && Object.defineProperty(b, Symbol.toStringTag, {
value: "Module"
}), Object.defineProperty(b, "__esModule", {
value: !0
})
};
var __webpack_exports__ = __webpack_require__(6018);
return __webpack_exports__
})())
})(ortWeb_min$1);
var ortWeb_minExports = ortWeb_min$1.exports;
const ortWeb_min = getDefaultExportFromCjs(ortWeb_minExports),
ONNX_WEB = _mergeNamespaces({
__proto__: null,
default: ortWeb_min
}, [ortWeb_minExports]);
let ONNX;
const executionProviders = ["wasm"];
typeof process < "u" && process?.release?.name === "node" ? (ONNX = sharp ?? ONNX_NODE, executionProviders.unshift("cpu")) : (ONNX = ortWeb_min ?? ONNX_WEB, typeof navigator < "u" && /iP(hone|od|ad).+16_4.+AppleWebKit/.test(navigator.userAgent) && (ONNX.env.wasm.simd = !1));
const {
env: onnx_env
} = ONNX, VERSION = "2.15.0", WEB_CACHE_AVAILABLE = typeof self < "u" && "caches" in self, FS_AVAILABLE = !isEmpty(sharp), PATH_AVAILABLE = !isEmpty(sharp), RUNNING_LOCALLY = FS_AVAILABLE && PATH_AVAILABLE, __dirname = RUNNING_LOCALLY ? sharp.dirname(sharp.dirname(sharp.fileURLToPath(import.meta.url))) : "./", DEFAULT_CACHE_DIR = RUNNING_LOCALLY ? sharp.join(__dirname, "/.cache/") : null, DEFAULT_LOCAL_MODEL_PATH = "/models/", localModelPath = RUNNING_LOCALLY ? sharp.join(__dirname, DEFAULT_LOCAL_MODEL_PATH) : DEFAULT_LOCAL_MODEL_PATH;
onnx_env.wasm.wasmPaths = RUNNING_LOCALLY ? sharp.join(__dirname, "/dist/") : `https://cdn.jsdelivr.net/npm/@xenova/transformers@${VERSION}/dist/`;
const env$1 = {
backends: {
onnx: onnx_env,
tfjs: {}
},
__dirname,
version: VERSION,
allowRemoteModels: !0,
remoteHost: "https://huggingface.co/",
remotePathTemplate: "{model}/resolve/{revision}/",
allowLocalModels: !0,
localModelPath,
useFS: FS_AVAILABLE,
useBrowserCache: WEB_CACHE_AVAILABLE,
useFSCache: FS_AVAILABLE,
cacheDir: DEFAULT_CACHE_DIR,
useCustomCache: !1,
customCache: null
};
function isEmpty(b) {
return Object.keys(b).length === 0
}
var define_process_env_default = {};
globalThis.ReadableStream || (globalThis.ReadableStream = sharp.ReadableStream);
class FileResponse {
_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"
};
constructor(n) {
if (this.filePath = n, this.headers = new Headers, this.exists = sharp.existsSync(n), this.exists) {
this.status = 200, this.statusText = "OK";
let a = sharp.statSync(n);
this.headers.set("content-length", a.size.toString()), this.updateContentType();
let u = this;
this.body = new ReadableStream({
start(c) {
u.arrayBuffer().then(f => {
c.enqueue(new Uint8Array(f)), c.close()
})
}
})
} else this.status = 404, this.statusText = "Not Found", this.body = null
}
updateContentType() {
const n = this.filePath.toString().split(".").pop().toLowerCase();
this.headers.set("content-type", this._CONTENT_TYPE_MAP[n] ?? "application/octet-stream")
}
clone() {
let n = new FileResponse(this.filePath);
return n.exists = this.exists, n.status = this.status, n.statusText = this.statusText, n.headers = new Headers(this.headers), n
}
async arrayBuffer() {
return (await sharp.promises.readFile(this.filePath)).buffer
}
async blob() {
const n = await sharp.promises.readFile(this.filePath);
return new Blob([n], {
type: this.headers.get("content-type")
})
}
async text() {
return await sharp.promises.readFile(this.filePath, "utf8")
}
async json() {
return JSON.parse(await this.text())
}
}
function isValidHttpUrl(b, n = null) {
let a;
try {
a = new URL(b)
} catch {
return !1
}
return n && !n.includes(a.hostname) ? !1 : a.protocol === "http:" || a.protocol === "https:"
}
async function getFile(b) {
if (env$1.useFS && !isValidHttpUrl(b)) return new FileResponse(b);
if (typeof process < "u" && process?.release?.name === "node") {
const n = !!define_process_env_default?.TESTING_REMOTELY,
a = env$1.version,
u = new Headers;
if (u.set("User-Agent", `transformers.js/${a}; is_ci/${n};`), isValidHttpUrl(b, ["huggingface.co", "hf.co"])) {
const f = define_process_env_default?.HF_TOKEN ?? define_process_env_default?.HF_ACCESS_TOKEN;
f && u.set("Authorization", `Bearer ${f}`)
}
return fetch(b, {
headers: u
})
} else return fetch(b)
}
const ERROR_MAPPING = {
400: "Bad request error occurred while trying to load file",
401: "Unauthorized access to file",
403: "Forbidden access to file",
404: "Could not locate file",
408: "Request timeout error occurred while trying to load file",
500: "Internal server error error occurred while trying to load file",
502: "Bad gateway error occurred while trying to load file",
503: "Service unavailable error occurred while trying to load file",
504: "Gateway timeout error occurred while trying to load file"
};
function handleError(b, n, a) {
if (!a) return null;
const u = ERROR_MAPPING[b] ?? `Error (${b}) occurred while trying to load file`;
throw Error(`${u}: "${n}".`)
}
class FileCache {
constructor(n) {
this.path = n
}
async match(n) {
let a = sharp.join(this.path, n),
u = new FileResponse(a);
if (u.exists) return u
}
async put(n, a) {
const u = Buffer.from(await a.arrayBuffer());
let c = sharp.join(this.path, n);
try {
await sharp.promises.mkdir(sharp.dirname(c), {
recursive: !0
}), await sharp.promises.writeFile(c, u)
} catch (f) {
console.warn("An error occurred while writing the file to cache:", f)
}
}
}
async function tryCache(b, ...n) {
for (let a of n) try {
let u = await b.match(a);
if (u) return u
} catch {
continue
}
}
async function getModelFile(b, n, a = !0, u = {}) {
if (!env$1.allowLocalModels && u.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`).");
dispatchCallback(u.progress_callback, {
status: "initiate",
name: b,
file: n
});
let c;
if (!c && env$1.useBrowserCache) {
if (typeof caches > "u") throw Error("Browser cache is not available in this environment.");
try {
c = await caches.open("transformers-cache")
} catch (m) {
console.warn("An error occurred while opening the browser cache:", m)
}
}
if (!c && env$1.useFSCache && (c = new FileCache(u.cache_dir ?? env$1.cacheDir)), !c && env$1.useCustomCache) throw Error("`env.useCustomCache=true`, but `env.customCache` is not defined.");
const f = u.revision ?? "main";
let s = pathJoin(b, n),
h = pathJoin(env$1.localModelPath, s),
p = pathJoin(env$1.remoteHost, env$1.remotePathTemplate.replaceAll("{model}", b).replaceAll("{revision}", encodeURIComponent(f)), n),
l = f === "main" ? s : pathJoin(b, f, n),
o, t = c instanceof FileCache ? l : p,
e = !1,
r;
c && (r = await tryCache(c, h, t));
const i = r !== void 0;
if (r === void 0) {
if (env$1.allowLocalModels)
if (isValidHttpUrl(s)) {
if (u.local_files_only) throw new Error(`\`local_files_only=true\`, but attempted to load a remote file from: ${s}.`)
} else try {
r = await getFile(h), o = h
} catch (_) {
console.warn(`Unable to load from local path "${h}": "${_}"`)
}
if (r === void 0 || r.status === 404) {
if (u.local_files_only || !env$1.allowRemoteModels) {
if (a) throw Error(`\`local_files_only=true\` or \`env.allowRemoteModels=false\` and file was not found locally at "${h}".`);
return null
}
if (r = await getFile(p), r.status !== 200) return handleError(r.status, p, a);
o = t
}
e = c && typeof Response < "u" && r instanceof Response && r.status === 200
}
dispatchCallback(u.progress_callback, {
status: "download",
name: b,
file: n
});
const d = {
status: "progress",
name: b,
file: n
};
let g;
return u.progress_callback ? i && typeof navigator < "u" && /firefox/i.test(navigator.userAgent) ? (g = new Uint8Array(await r.arrayBuffer()), dispatchCallback(u.progress_callback, {
...d,
progress: 100,
loaded: g.length,
total: g.length
})) : g = await readResponse(r, m => {
dispatchCallback(u.progress_callback, {
...d,
...m
})
}) : g = new Uint8Array(await r.arrayBuffer()), e && o && await c.match(o) === void 0 && await c.put(o, new Response(g, {
headers: r.headers
})).catch(m => {
console.warn(`Unable to add response to browser cache: ${m}.`)
}), dispatchCallback(u.progress_callback, {
status: "done",
name: b,
file: n
}), g
}
async function getModelJSON(b, n, a = !0, u = {}) {
let c = await getModelFile(b, n, a, u);
if (c === null) return {};
let s = new TextDecoder("utf-8").decode(c);
return JSON.parse(s)
}
async function readResponse(b, n) {
const a = b.headers.get("Content-Length");
a === null && console.warn("Unable to determine content-length from response headers. Will expand buffer when needed.");
let u = parseInt(a ?? "0"),
c = new Uint8Array(u),
f = 0;
const s = b.body.getReader();
async function h() {
const {
done: p,
value: l
} = await s.read();
if (p) return;
let o = f + l.length;
if (o > u) {
u = o;
let e = new Uint8Array(u);
e.set(c), c = e
}
c.set(l, f), f = o;
const t = f / u * 100;
return n({
progress: t,
loaded: f,
total: u
}), h()
}
return await h(), c
}
function pathJoin(...b) {
return b = b.map((n, a) => (a && (n = n.replace(new RegExp("^/"), "")), a !== b.length - 1 && (n = n.replace(new RegExp("/$"), "")), n)), b.join("/")
}
function interpolate_data(b, [n, a, u], [c, f], s = "bilinear", h = !1) {
const p = f / u,
l = c / a,
o = new b.constructor(c * f * n),
t = a * u,
e = c * f;
for (let r = 0; r < c; ++r)
for (let i = 0; i < f; ++i) {
const d = r * f + i,
g = (i + .5) / p - .5,
m = (r + .5) / l - .5;
let _ = Math.floor(g),
y = Math.floor(m);
const T = Math.min(_ + 1, u - 1),
w = Math.min(y + 1, a - 1);
_ = Math.max(_, 0), y = Math.max(y, 0);
const S = g - _,
O = m - y,
E = (1 - S) * (1 - O),
v = S * (1 - O),
P = (1 - S) * O,
L = S * O,
V = y * u,
R = w * u,
k = V + _,
Y = V + T,
C = R + _,
$ = R + T;
for (let X = 0; X < n; ++X) {
const z = X * t;
o[X * e + d] = E * b[z + k] + v * b[z + Y] + P * b[z + C] + L * b[z + $]
}
}
return o
}
function transpose_data(b, n, a) {
const u = new Array(a.length),
c = new Array(a.length);
for (let h = a.length - 1, p = 1; h >= 0; --h) c[h] = p, u[h] = n[a[h]], p *= u[h];
const f = a.map((h, p) => c[a.indexOf(p)]),
s = new b.constructor(b.length);
for (let h = 0; h < b.length; ++h) {
let p = 0;
for (let l = n.length - 1, o = h; l >= 0; --l) p += o % n[l] * f[l], o = Math.floor(o / n[l]);
s[p] = b[h]
}
return [s, u]
}
function softmax(b) {
const n = max(b)[0],
a = b.map(f => Math.exp(f - n)),
u = a.reduce((f, s) => f + s, 0);
return a.map(f => f / u)
}
function log_softmax(b) {
return softmax(b).map(u => Math.log(u))
}
function getTopItems(b, n = 0) {
return b = Array.from(b).map((a, u) => [u, a]).sort((a, u) => u[1] - a[1]), n !== null && n > 0 && (b = b.slice(0, n)), b
}
function min(b) {
if (b.length === 0) throw Error("Array must not be empty");
let n = b[0],
a = 0;
for (let u = 1; u < b.length; ++u) b[u] < n && (n = b[u], a = u);
return [n, a]
}
function max(b) {
if (b.length === 0) throw Error("Array must not be empty");
let n = b[0],
a = 0;
for (let u = 1; u < b.length; ++u) b[u] > n && (n = b[u], a = u);
return [Number(n), a]
}
function isPowerOfTwo(b) {
return b > 0 && (b & b - 1) === 0
}
class P2FFT {
constructor(n) {
if (this.size = n | 0, this.size <= 1 || !isPowerOfTwo(this.size)) throw new Error("FFT size must be a power of two larger than 1");
this._csize = n << 1, this.table = new Float64Array(this.size * 2);
for (let u = 0; u < this.table.length; u += 2) {
const c = Math.PI * u / this.size;
this.table[u] = Math.cos(c), this.table[u + 1] = -Math.sin(c)
}
let a = 0;
for (let u = 1; this.size > u; u <<= 1) ++a;
this._width = a % 2 === 0 ? a - 1 : a, this._bitrev = new Int32Array(1 << this._width);
for (let u = 0; u < this._bitrev.length; ++u) {
this._bitrev[u] = 0;
for (let c = 0; c < this._width; c += 2) {
const f = this._width - c - 2;
this._bitrev[u] |= (u >>> c & 3) << f
}
}
}
createComplexArray() {
return new Float64Array(this._csize)
}
fromComplexArray(n, a) {
const u = a || new Array(n.length >>> 1);
for (let c = 0; c < n.length; c += 2) u[c >>> 1] = n[c];
return u
}
toComplexArray(n, a) {
const u = a || this.createComplexArray();
for (let c = 0; c < u.length; c += 2) u[c] = n[c >>> 1], u[c + 1] = 0;
return u
}
completeSpectrum(n) {
const a = this._csize,
u = a >>> 1;
for (let c = 2; c < u; c += 2) n[a - c] = n[c], n[a - c + 1] = -n[c + 1]
}
transform(n, a) {
if (n === a) throw new Error("Input and output buffers must be different");
this._transform4(n, a, 1)
}
realTransform(n, a) {
if (n === a) throw new Error("Input and output buffers must be different");
this._realTransform4(n, a, 1)
}
inverseTransform(n, a) {
if (n === a) throw new Error("Input and output buffers must be different");
this._transform4(n, a, -1);
for (let u = 0; u < n.length; ++u) n[u] /= this.size
}
_transform4(n, a, u) {
const c = this._csize;
let s = 1 << this._width,
h = c / s << 1,
p, l;
const o = this._bitrev;
if (h === 4)
for (p = 0, l = 0; p < c; p += h, ++l) {
const t = o[l];
this._singleTransform2(a, n, p, t, s)
} else
for (p = 0, l = 0; p < c; p += h, ++l) {
const t = o[l];
this._singleTransform4(a, n, p, t, s, u)
}
for (s >>= 2; s >= 2; s >>= 2) {
h = c / s << 1;
const t = h >>> 2;
for (p = 0; p < c; p += h) {
const e = p + t - 1;
for (let r = p, i = 0; r < e; r += 2, i += s) {
const d = r,
g = d + t,
m = g + t,
_ = m + t,
y = n[d],
T = n[d + 1],
w = n[g],
S = n[g + 1],
O = n[m],
E = n[m + 1],
v = n[_],
P = n[_ + 1],
L = this.table[i],
V = u * this.table[i + 1],
R = w * L - S * V,
k = w * V + S * L,
Y = this.table[2 * i],
C = u * this.table[2 * i + 1],
$ = O * Y - E * C,
X = O * C + E * Y,
z = this.table[3 * i],
Z = u * this.table[3 * i + 1],
J = v * z - P * Z,
ue = v * Z + P * z,
Se = y + $,
Te = T + X,
se = y - $,
ye = T - X,
be = R + J,
Ie = k + ue,
Le = u * (R - J),
ve = u * (k - ue);
n[d] = Se + be, n[d + 1] = Te + Ie, n[g] = se + ve, n[g + 1] = ye - Le, n[m] = Se - be, n[m + 1] = Te - Ie, n[_] = se - ve, n[_ + 1] = ye + Le
}
}
}
}
_singleTransform2(n, a, u, c, f) {
const s = n[c],
h = n[c + 1],
p = n[c + f],
l = n[c + f + 1];
a[u] = s + p, a[u + 1] = h + l, a[u + 2] = s - p, a[u + 3] = h - l
}
_singleTransform4(n, a, u, c, f, s) {
const h = f * 2,
p = f * 3,
l = n[c],
o = n[c + 1],
t = n[c + f],
e = n[c + f + 1],
r = n[c + h],
i = n[c + h + 1],
d = n[c + p],
g = n[c + p + 1],
m = l + r,
_ = o + i,
y = l - r,
T = o - i,
w = t + d,
S = e + g,
O = s * (t - d),
E = s * (e - g);
a[u] = m + w, a[u + 1] = _ + S, a[u + 2] = y + E, a[u + 3] = T - O, a[u + 4] = m - w, a[u + 5] = _ - S, a[u + 6] = y - E, a[u + 7] = T + O
}
_realTransform4(n, a, u) {
const c = this._csize;
let s = 1 << this._width,
h = c / s << 1,
p, l;
const o = this._bitrev;
if (h === 4)
for (p = 0, l = 0; p < c; p += h, ++l) {
const t = o[l];
this._singleRealTransform2(a, n, p, t >>> 1, s >>> 1)
} else
for (p = 0, l = 0; p < c; p += h, ++l) {
const t = o[l];
this._singleRealTransform4(a, n, p, t >>> 1, s >>> 1, u)
}
for (s >>= 2; s >= 2; s >>= 2) {
h = c / s << 1;
const t = h >>> 2;
for (p = 0; p < c; p += h) {
const e = p + t - 1;
for (let r = p, i = 0; r < e; r += 2, i += s) {
const d = r,
g = d + t,
m = g + t,
_ = m + t,
y = n[d],
T = n[d + 1],
w = n[g],
S = n[g + 1],
O = n[m],
E = n[m + 1],
v = n[_],
P = n[_ + 1],
L = this.table[i],
V = u * this.table[i + 1],
R = w * L - S * V,
k = w * V + S * L,
Y = this.table[2 * i],
C = u * this.table[2 * i + 1],
$ = O * Y - E * C,
X = O * C + E * Y,
z = this.table[3 * i],
Z = u * this.table[3 * i + 1],
J = v * z - P * Z,
ue = v * Z + P * z,
Se = y + $,
Te = T + X,
se = y - $,
ye = T - X,
be = R + J,
Ie = k + ue,
Le = u * (R - J),
ve = u * (k - ue);
n[d] = Se + be, n[d + 1] = Te + Ie, n[g] = se + ve, n[g + 1] = ye - Le, n[m] = Se - be, n[m + 1] = Te - Ie, n[_] = se - ve, n[_ + 1] = ye + Le
}
}
}
}
_singleRealTransform2(n, a, u, c, f) {
const s = n[c],
h = n[c + f];
a[u] = s + h, a[u + 1] = 0, a[u + 2] = s - h, a[u + 3] = 0
}
_singleRealTransform4(n, a, u, c, f, s) {
const h = f * 2,
p = f * 3,
l = n[c],
o = n[c + f],
t = n[c + h],
e = n[c + p],
r = l + t,
i = l - t,
d = o + e,
g = s * (o - e);
a[u] = r + d, a[u + 1] = 0, a[u + 2] = i, a[u + 3] = -g, a[u + 4] = r - d, a[u + 5] = 0, a[u + 6] = i, a[u + 7] = g
}
}
class NP2FFT {
constructor(n) {
const a = 2 * (n - 1),
u = 2 * (2 * n - 1),
c = 2 ** Math.ceil(Math.log2(u));
this.bufferSize = c, this._a = a;
const f = new Float64Array(u),
s = new Float64Array(c);
this._chirpBuffer = new Float64Array(c), this._buffer1 = new Float64Array(c), this._buffer2 = new Float64Array(c), this._outBuffer1 = new Float64Array(c), this._outBuffer2 = new Float64Array(c);
const h = -2 * Math.PI / n,
p = Math.cos(h),
l = Math.sin(h);
for (let o = 0; o < u >> 1; ++o) {
const t = (o + 1 - n) ** 2 / 2,
e = Math.sqrt(p ** 2 + l ** 2) ** t,
r = t * Math.atan2(l, p),
i = 2 * o;
f[i] = e * Math.cos(r), f[i + 1] = e * Math.sin(r), s[i] = f[i], s[i + 1] = -f[i + 1]
}
this._slicedChirpBuffer = f.subarray(a, u), this._f = new P2FFT(c >> 1), this._f.transform(this._chirpBuffer, s)
}
_transform(n, a, u) {
const c = this._buffer1,
f = this._buffer2,
s = this._outBuffer1,
h = this._outBuffer2,
p = this._chirpBuffer,
l = this._slicedChirpBuffer,
o = this._a;
if (u)
for (let t = 0; t < l.length; t += 2) {
const e = t + 1,
r = t >> 1,
i = a[r];
c[t] = i * l[t], c[e] = i * l[e]
} else
for (let t = 0; t < l.length; t += 2) {
const e = t + 1;
c[t] = a[t] * l[t] - a[e] * l[e], c[e] = a[t] * l[e] + a[e] * l[t]
}
this._f.transform(s, c);
for (let t = 0; t < p.length; t += 2) {
const e = t + 1;
f[t] = s[t] * p[t] - s[e] * p[e], f[e] = s[t] * p[e] + s[e] * p[t]
}
this._f.inverseTransform(h, f);
for (let t = 0; t < h.length; t += 2) {
const e = h[t + o],
r = h[t + o + 1],
i = l[t],
d = l[t + 1];
n[t] = e * i - r * d, n[t + 1] = e * d + r * i
}
}
transform(n, a) {
this._transform(n, a, !1)
}
realTransform(n, a) {
this._transform(n, a, !0)
}
}
class FFT {
constructor(n) {
this.fft_length = n, this.isPowerOfTwo = isPowerOfTwo(n), this.isPowerOfTwo ? (this.fft = new P2FFT(n), this.outputBufferSize = 2 * n) : (this.fft = new NP2FFT(n), this.outputBufferSize = this.fft.bufferSize)
}
realTransform(n, a) {
this.fft.realTransform(n, a)
}
transform(n, a) {
this.fft.transform(n, a)
}
}
function medianFilter(b, n) {
if (n % 2 === 0 || n <= 0) throw new Error("Window size must be a positive odd number");
const a = new b.constructor(b.length),
u = new b.constructor(n),
c = Math.floor(n / 2);
for (let f = 0; f < b.length; ++f) {
let s = 0;
for (let h = -c; h <= c; ++h) {
let p = f + h;
p < 0 ? p = Math.abs(p) : p >= b.length && (p = 2 * (b.length - 1) - p), u[s++] = b[p]
}
u.sort(), a[f] = u[c]
}
return a
}
const DataTypeMap = Object.freeze({
float32: Float32Array,
float64: Float64Array,
string: Array,
int8: Int8Array,
uint8: Uint8Array,
int16: Int16Array,
uint16: Uint16Array,
int32: Int32Array,
uint32: Uint32Array,
int64: BigInt64Array,
uint64: BigUint64Array,
bool: Uint8Array
}),
ONNXTensor$1 = ONNX.Tensor;
class Tensor {
dims;
type;
data;
size;
constructor(...n) {
return n[0] instanceof ONNXTensor$1 ? Object.assign(this, n[0]) : Object.assign(this, new ONNXTensor$1(n[0], n[1], n[2])), new Proxy(this, {
get: (a, u) => {
if (typeof u == "string") {
let c = Number(u);
if (Number.isInteger(c)) return a._getitem(c)
}
return a[u]
},
set: (a, u, c) => a[u] = c
})
}*[Symbol.iterator]() {
const [n, ...a] = this.dims;
if (a.length > 0) {
const u = a.reduce((c, f) => c * f);
for (let c = 0; c < n; ++c) yield this._subarray(c, u, a)
} else yield* this.data
}
_getitem(n) {
const [a, ...u] = this.dims;
if (n = safeIndex(n, a), u.length > 0) {
const c = u.reduce((f, s) => f * s);
return this._subarray(n, c, u)
} else return new Tensor(this.type, [this.data[n]], u)
}
indexOf(n) {
for (let a = 0; a < this.data.length; ++a)
if (this.data[a] == n) return a;
return -1
}
_subarray(n, a, u) {
const c = n * a,
f = (n + 1) * a,
s = "subarray" in this.data ? this.data.subarray(c, f) : this.data.slice(c, f);
return new Tensor(this.type, s, u)
}
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]
}
tolist() {
return reshape(this.data, this.dims)
}
sigmoid() {
return this.clone().sigmoid_()
}
sigmoid_() {
for (let n = 0; n < this.data.length; ++n) this.data[n] = 1 / (1 + Math.exp(-this.data[n]));
return this
}
mul(n) {
return this.clone().mul_(n)
}
mul_(n) {
for (let a = 0; a < this.data.length; ++a) this.data[a] *= n;
return this
}
add(n) {
return this.clone().add_(n)
}
add_(n) {
for (let a = 0; a < this.data.length; ++a) this.data[a] += n;
return this
}
clone() {
return new Tensor(this.type, this.data.slice(), this.dims.slice())
}
slice(...n) {
let a = [],
u = [];
for (let p = 0; p < this.dims.length; ++p) {
let l = n[p];
if (l == null) u.push([0, this.dims[p]]), a.push(this.dims[p]);
else if (typeof l == "number") l = safeIndex(l, this.dims[p], p), u.push([l, l + 1]);
else if (Array.isArray(l) && l.length === 2) {
if (l[0] > l[1]) throw new Error(`Invalid slice: ${l}`);
let o = [Math.max(l[0], 0), Math.min(l[1], this.dims[p])];
u.push(o), a.push(o[1] - o[0])
} else throw new Error(`Invalid slice: ${l}`)
}
let c = u.map(([p, l]) => l - p),
f = c.reduce((p, l) => p * l),
s = new this.data.constructor(f);
const h = this.stride();
for (let p = 0; p < f; ++p) {
let l = 0;
for (let o = c.length - 1, t = p; o >= 0; --o) {
const e = c[o];
l += (t % e + u[o][0]) * h[o], t = Math.floor(t / e)
}
s[p] = this.data[l]
}
return new Tensor(this.type, s, a)
}
transpose(...n) {
return transpose(this, n)
}
sum(n = null, a = !1) {
return this.norm(1, n, a)
}
norm(n = "fro", a = null, u = !1) {
if (n === "fro") n = 2;
else if (typeof n == "string") throw Error(`Unsupported norm: ${n}`);
if (a === null) {
let s = this.data.reduce((h, p) => h + p ** n, 0) ** (1 / n);
return new Tensor(this.type, [s], [])
}
a = safeIndex(a, this.dims.length);
const c = this.dims.slice();
c[a] = 1;
const f = new this.data.constructor(this.data.length / this.dims[a]);
for (let s = 0; s < this.data.length; ++s) {
let h = 0;
for (let p = this.dims.length - 1, l = s, o = 1; p >= 0; --p) {
const t = this.dims[p];
if (p !== a) {
const e = l % t;
h += e * o, o *= c[p]
}
l = Math.floor(l / t)
}
f[h] += this.data[s] ** n
}
if (n !== 1)
for (let s = 0; s < f.length; ++s) f[s] = f[s] ** (1 / n);
return u || c.splice(a, 1), new Tensor(this.type, f, c)
}
normalize_(n = 2, a = 1) {
a = safeIndex(a, this.dims.length);
const u = this.norm(n, a, !0);
for (let c = 0; c < this.data.length; ++c) {
let f = 0;
for (let s = this.dims.length - 1, h = c, p = 1; s >= 0; --s) {
const l = this.dims[s];
if (s !== a) {
const o = h % l;
f += o * p, p *= this.dims[s]
}
h = Math.floor(h / l)
}
this.data[c] /= u.data[f]
}
return this
}
normalize(n = 2, a = 1) {
return this.clone().normalize_(n, a)
}
stride() {
return dimsToStride(this.dims)
}
squeeze(n = null) {
return new Tensor(this.type, this.data, calc_squeeze_dims(this.dims, n))
}
squeeze_(n = null) {
return this.dims = calc_squeeze_dims(this.dims, n), this
}
unsqueeze(n = null) {
return new Tensor(this.type, this.data, calc_unsqueeze_dims(this.dims, n))
}
unsqueeze_(n = null) {
return this.dims = calc_unsqueeze_dims(this.dims, n), this
}
flatten_(n = 0, a = -1) {
a = (a + this.dims.length) % this.dims.length;
let u = this.dims.slice(0, n),
c = this.dims.slice(n, a + 1),
f = this.dims.slice(a + 1);
return this.dims = [...u, c.reduce((s, h) => s * h, 1), ...f], this
}
flatten(n = 0, a = -1) {
return this.clone().flatten_(n, a)
}
view(...n) {
let a = -1;
for (let u = 0; u < n.length; ++u)
if (n[u] === -1) {
if (a !== -1) throw new Error("Only one dimension can be inferred");
a = u
} if (a !== -1) {
const u = n.reduce((c, f, s) => s !== a ? c * f : c, 1);
n[a] = this.data.length / u
}
return new Tensor(this.type, this.data, n)
}
neg_() {
for (let n = 0; n < this.data.length; ++n) this.data[n] = -this.data[n];
return this
}
neg() {
return this.clone().neg_()
}
clamp_(n, a) {
for (let u = 0; u < this.data.length; ++u) this.data[u] = Math.min(Math.max(this.data[u], n), a);
return this
}
clamp(n, a) {
return this.clone().clamp_(n, a)
}
round_() {
for (let n = 0; n < this.data.length; ++n) this.data[n] = Math.round(this.data[n]);
return this
}
round() {
return this.clone().round_()
}
to(n) {
if (this.type === n) return this;
if (!DataTypeMap.hasOwnProperty(n)) throw new Error(`Unsupported type: ${n}`);
return new Tensor(n, DataTypeMap[n].from(this.data), this.dims)
}
}
function reshape(b, n) {
const a = b.length,
u = n.reduce((f, s) => f * s);
if (a !== u) throw Error(`cannot reshape array of size ${a} into shape (${n})`);
let c = b;
for (let f = n.length - 1; f >= 0; f--) c = c.reduce((s, h) => {
let p = s[s.length - 1];
return p.length < n[f] ? p.push(h) : s.push([h]), s
}, [
[]
]);
return c[0]
}
function transpose(b, n) {
const [a, u] = transpose_data(b.data, b.dims, n);
return new Tensor(b.type, a, u)
}
function interpolate(b, [n, a], u = "bilinear", c = !1) {
const f = b.dims.at(-3) ?? 1,
s = b.dims.at(-2),
h = b.dims.at(-1);
let p = interpolate_data(b.data, [f, s, h], [n, a], u, c);
return new Tensor(b.type, p, [f, n, a])
}
function calc_squeeze_dims(b, n) {
return b = b.slice(), n === null ? b = b.filter(a => a !== 1) : typeof n == "number" ? b[n] === 1 && b.splice(n, 1) : Array.isArray(n) && (b = b.filter((a, u) => a !== 1 || !n.includes(u))), b
}
function calc_unsqueeze_dims(b, n) {
return n = safeIndex(n, b.length + 1), b = b.slice(), b.splice(n, 0, 1), b
}
function safeIndex(b, n, a = null) {
if (b < -n || b >= n) throw new Error(`IndexError: index ${b} is out of bounds for dimension${a===null?"":" "+a} with size ${n}`);
return b < 0 && (b = (b % n + n) % n), b
}
function cat(b, n = 0) {
n = safeIndex(n, b[0].dims.length);
const a = b[0].dims.slice();
a[n] = b.reduce((s, h) => s + h.dims[n], 0);
const u = a.reduce((s, h) => s * h, 1),
c = new b[0].data.constructor(u),
f = b[0].type;
if (n === 0) {
let s = 0;
for (let h of b) c.set(h.data, s), s += h.data.length
} else {
let s = 0;
for (let h = 0; h < b.length; ++h) {
let p = b[h];
for (let l = 0; l < p.data.length; ++l) {
let o = 0;
for (let t = p.dims.length - 1, e = l, r = 1; t >= 0; --t) {
const i = p.dims[t];
let d = e % i;
t === n && (d += s), o += d * r, r *= a[t], e = Math.floor(e / i)
}
c[o] = p.data[l]
}
s += p.dims[n]
}
}
return new Tensor(f, c, a)
}
function stack(b, n = 0) {
return cat(b.map(a => a.unsqueeze(n)), n)
}
function std_mean(b, n = null, a = 1, u = !1) {
if (n === null) {
const l = b.data.reduce((r, i) => r + i, 0) / b.data.length,
o = Math.sqrt(b.data.reduce((r, i) => r + (i - l) ** 2, 0) / (b.data.length - a)),
t = new Tensor(b.type, [l], []);
return [new Tensor(b.type, [o], []), t]
}
n = safeIndex(n, b.dims.length);
const c = mean(b, n, u),
f = b.dims.slice();
f[n] = 1;
const s = new b.data.constructor(b.data.length / b.dims[n]);
for (let p = 0; p < b.data.length; ++p) {
let l = 0;
for (let o = b.dims.length - 1, t = p, e = 1; o >= 0; --o) {
const r = b.dims[o];
if (o !== n) {
const i = t % r;
l += i * e, e *= f[o]
}
t = Math.floor(t / r)
}
s[l] += (b.data[p] - c.data[l]) ** 2
}
for (let p = 0; p < s.length; ++p) s[p] = Math.sqrt(s[p] / (b.dims[n] - a));
return u || f.splice(n, 1), [new Tensor(b.type, s, f), c]
}
function mean(b, n = null, a = !1) {
if (n === null) {
let f = b.data.reduce((s, h) => s + h, 0);
return new Tensor(b.type, [f / b.data.length], [])
}
n = safeIndex(n, b.dims.length);
const u = b.dims.slice();
u[n] = 1;
const c = new b.data.constructor(b.data.length / b.dims[n]);
for (let f = 0; f < b.data.length; ++f) {
let s = 0;
for (let h = b.dims.length - 1, p = f, l = 1; h >= 0; --h) {
const o = b.dims[h];
if (h !== n) {
const t = p % o;
s += t * l, l *= u[h]
}
p = Math.floor(p / o)
}
c[s] += b.data[f]
}
if (b.dims[n] !== 1)
for (let f = 0; f < c.length; ++f) c[f] = c[f] / b.dims[n];
return a || u.splice(n, 1), new Tensor(b.type, c, u)
}
function dynamicTimeWarping(b) {
const [n, a] = b.dims, u = [n + 1, a + 1], c = new Tensor("float32", new Float32Array(u[0] * u[1]).fill(1 / 0), u), f = new Tensor("float32", new Float32Array(u[0] * u[1]).fill(-1), u);
c[0].data[0] = 0;
for (let o = 1; o < a + 1; ++o)
for (let t = 1; t < n + 1; ++t) {
const e = c[t - 1][o - 1].item(),
r = c[t - 1][o].item(),
i = c[t][o - 1].item();
let d, g;
e < r && e < i ? (d = e, g = 0) : r < e && r < i ? (d = r, g = 1) : (d = i, g = 2), c[t].data[o] = b[t - 1][o - 1].item() + d, f[t].data[o] = g
}
let s = n,
h = a;
f.data.fill(2, 0, u[1]);
for (let o = 0; o < u[0]; ++o) f[o].data[0] = 1;
let p = [],
l = [];
for (; s > 0 || h > 0;) switch (p.push(s - 1), l.push(h - 1), f[s][h].item()) {
case 0:
--s, --h;
break;
case 1:
--s;
break;
case 2:
--h;
break;
default:
throw new Error(`Internal error in dynamic time warping. Unexpected trace[${s}, ${h}]. Please file a bug report.`)
}
return p.reverse(), l.reverse(), [p, l]
}
function dimsToStride(b) {
const n = new Array(b.length);
for (let a = b.length - 1, u = 1; a >= 0; --a) n[a] = u, u *= b[a];
return n
}
function ones(b) {
const n = b.reduce((a, u) => a * u, 1);
return new Tensor("int64", new BigInt64Array(n).fill(1n), b)
}
function ones_like(b) {
return ones(b.dims)
}
var TOKEN_TYPES = Object.freeze({
Text: "Text",
NumericLiteral: "NumericLiteral",
BooleanLiteral: "BooleanLiteral",
StringLiteral: "StringLiteral",
Identifier: "Identifier",
Equals: "Equals",
OpenParen: "OpenParen",
CloseParen: "CloseParen",
OpenStatement: "OpenStatement",
CloseStatement: "CloseStatement",
OpenExpression: "OpenExpression",
CloseExpression: "CloseExpression",
OpenSquareBracket: "OpenSquareBracket",
CloseSquareBracket: "CloseSquareBracket",
Comma: "Comma",
Dot: "Dot",
Colon: "Colon",
Pipe: "Pipe",
CallOperator: "CallOperator",
AdditiveBinaryOperator: "AdditiveBinaryOperator",
MultiplicativeBinaryOperator: "MultiplicativeBinaryOperator",
ComparisonBinaryOperator: "ComparisonBinaryOperator",
UnaryOperator: "UnaryOperator",
Set: "Set",
If: "If",
For: "For",
In: "In",
NotIn: "NotIn",
Else: "Else",
EndIf: "EndIf",
ElseIf: "ElseIf",
EndFor: "EndFor",
And: "And",
Or: "Or",
Not: "UnaryOperator"
});
Object.freeze({
set: TOKEN_TYPES.Set,
for: TOKEN_TYPES.For,
in: TOKEN_TYPES.In,
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,
true: TOKEN_TYPES.BooleanLiteral,
false: TOKEN_TYPES.BooleanLiteral
});
TOKEN_TYPES.OpenStatement, TOKEN_TYPES.CloseStatement, TOKEN_TYPES.OpenExpression, TOKEN_TYPES.CloseExpression, TOKEN_TYPES.OpenParen, TOKEN_TYPES.CloseParen, TOKEN_TYPES.OpenSquareBracket, TOKEN_TYPES.CloseSquareBracket, TOKEN_TYPES.Comma, TOKEN_TYPES.Dot, TOKEN_TYPES.Colon, TOKEN_TYPES.Pipe, TOKEN_TYPES.ComparisonBinaryOperator, TOKEN_TYPES.ComparisonBinaryOperator, TOKEN_TYPES.ComparisonBinaryOperator, TOKEN_TYPES.ComparisonBinaryOperator, TOKEN_TYPES.ComparisonBinaryOperator, TOKEN_TYPES.ComparisonBinaryOperator, TOKEN_TYPES.AdditiveBinaryOperator, TOKEN_TYPES.AdditiveBinaryOperator, TOKEN_TYPES.MultiplicativeBinaryOperator, TOKEN_TYPES.MultiplicativeBinaryOperator, TOKEN_TYPES.MultiplicativeBinaryOperator, TOKEN_TYPES.Equals;
const BYTES_TO_UNICODE = (() => {
const b = [...Array.from({
length: 94
}, (c, f) => f + 33), ...Array.from({
length: 12
}, (c, f) => f + 161), ...Array.from({
length: 82
}, (c, f) => f + 174)],
n = b.slice();
let a = 0;
for (let c = 0; c < 256; ++c) b.includes(c) || (b.push(c), n.push(256 + a), a += 1);
const u = n.map(c => String.fromCharCode(c));
return Object.fromEntries(b.map((c, f) => [c, u[f]]))
})();
reverseDictionary(BYTES_TO_UNICODE);
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"]
];
new Map(WHISPER_LANGUAGES);
async function loadConfig(b, n) {
return await getModelJSON(b, "config.json", !0, n)
}
class PretrainedConfig {
constructor(n) {
this.model_type = null, this.is_encoder_decoder = !1, Object.assign(this, n)
}
static async from_pretrained(n, {
progress_callback: a = null,
config: u = null,
cache_dir: c = null,
local_files_only: f = !1,
revision: s = "main"
} = {}) {
let h = u ?? await loadConfig(n, {
progress_callback: a,
config: u,
cache_dir: c,
local_files_only: f,
revision: s
});
return new this(h)
}
}
class AutoConfig {
static async from_pretrained(...n) {
return PretrainedConfig.from_pretrained(...n)
}
}
class LogitsProcessorList extends Callable {
constructor() {
super(), this.processors = []
}
push(n) {
this.processors.push(n)
}
extend(n) {
this.processors.push(...n)
}
_call(n, a) {
for (let u of a) this.processors.forEach(c => c(n, u))
} [Symbol.iterator]() {
return this.processors.values()
}
}
class LogitsProcessor extends Callable {
_call(n, a) {
throw Error("`_call` should be implemented in a subclass")
}
}
class ForceTokensLogitsProcessor extends LogitsProcessor {
constructor(n) {
super(), this.force_token_map = Object.fromEntries(n ?? [])
}
_call(n, a) {
let u = this.force_token_map[n.length];
return exists(u) && (a.data.fill(-1 / 0), a.data[u] = 0), a
}
}
class ForcedBOSTokenLogitsProcessor extends LogitsProcessor {
constructor(n) {
super(), this.bos_token_id = n
}
_call(n, a) {
return n.length === 1 && (a.data.fill(-1 / 0), a.data[this.bos_token_id] = 0), a
}
}
class ForcedEOSTokenLogitsProcessor extends LogitsProcessor {
constructor(n, a) {
super(), this.max_length = n, this.forced_eos_token_id = a
}
_call(n, a) {}
}
class SuppressTokensAtBeginLogitsProcessor extends LogitsProcessor {
constructor(n, a) {
super(), this.begin_suppress_tokens = n, this.begin_index = a
}
_call(n, a) {
if (n.length === this.begin_index)
for (let u of this.begin_suppress_tokens) a.data[u] = -1 / 0;
return a
}
}
class WhisperTimeStampLogitsProcessor extends LogitsProcessor {
constructor(n) {
super(), this.eos_token_id = n.eos_token_id, this.no_timestamps_token_id = n.no_timestamps_token_id, this.timestamp_begin = this.no_timestamps_token_id + 1, this.begin_index = (n.forced_decoder_ids || []).length + 2, n.forced_decoder_ids.slice(-1)[0][1] === this.no_timestamps_token_id && (this.begin_index -= 1), this.max_initial_timestamp_index = n.max_initial_timestamp_index
}
_call(n, a) {
const u = a.data;
if (u[this.no_timestamps_token_id] = -1 / 0, n.length === this.begin_index - 1) return u.fill(-1 / 0), u[this.timestamp_begin] = 0, a;
const c = n.slice(this.begin_index),
f = c.length >= 1 && c[c.length - 1] >= this.timestamp_begin,
s = c.length < 2 || c[c.length - 2] >= this.timestamp_begin;
if (f && (s ? u.subarray(this.timestamp_begin).fill(-1 / 0) : u.subarray(0, this.eos_token_id).fill(-1 / 0)), n.length === this.begin_index && this.max_initial_timestamp_index !== null) {
const o = this.timestamp_begin + this.max_initial_timestamp_index;
u.subarray(o + 1).fill(-1 / 0)
}
const h = log_softmax(u),
p = Math.log(h.subarray(this.timestamp_begin).map(Math.exp).reduce((o, t) => o + t)),
l = max(h.subarray(0, this.timestamp_begin))[0];
return p > l && u.subarray(0, this.timestamp_begin).fill(-1 / 0), a
}
}
class NoRepeatNGramLogitsProcessor extends LogitsProcessor {
constructor(n) {
super(), this.no_repeat_ngram_size = n
}
getNgrams(n) {
const a = n.length,
u = [];
for (let f = 0; f < a + 1 - this.no_repeat_ngram_size; ++f) {
const s = [];
for (let h = 0; h < this.no_repeat_ngram_size; ++h) s.push(n[f + h]);
u.push(s)
}
const c = new Map;
for (const f of u) {
const s = f.slice(0, f.length - 1),
h = JSON.stringify(s),
p = c.get(h) ?? [];
p.push(f[f.length - 1]), c.set(h, p)
}
return c
}
getGeneratedNgrams(n, a) {
const u = a.slice(a.length + 1 - this.no_repeat_ngram_size, a.length);
return n.get(JSON.stringify(u)) ?? []
}
calcBannedNgramTokens(n) {
const a = [];
if (n.length + 1 < this.no_repeat_ngram_size) return a;
{
const u = this.getNgrams(n);
return this.getGeneratedNgrams(u, n)
}
}
_call(n, a) {
const u = this.calcBannedNgramTokens(n);
for (const c of u) a.data[c] = -1 / 0;
return a
}
}
class RepetitionPenaltyLogitsProcessor extends LogitsProcessor {
constructor(n) {
super(), this.penalty = n
}
_call(n, a) {
for (const u of n) a.data[u] < 0 ? a.data[u] *= this.penalty : a.data[u] /= this.penalty;
return a
}
}
class MinLengthLogitsProcessor extends LogitsProcessor {
constructor(n, a) {
super(), this.min_length = n, this.eos_token_id = Array.isArray(a) ? a : [a]
}
_call(n, a) {
if (n.length < this.min_length)
for (const u of this.eos_token_id) a.data[u] = -1 / 0;
return a
}
}
class MinNewTokensLengthLogitsProcessor extends LogitsProcessor {
constructor(n, a, u) {
super(), this.prompt_length_to_skip = n, this.min_new_tokens = a, this.eos_token_id = Array.isArray(u) ? u : [u]
}
_call(n, a) {
if (n.length - this.prompt_length_to_skip < this.min_new_tokens)
for (const c of this.eos_token_id) a.data[c] = -1 / 0;
return a
}
}
class NoBadWordsLogitsProcessor extends LogitsProcessor {
constructor(n, a) {
super(), this.bad_words_ids = n, this.eos_token_id = Array.isArray(a) ? a : [a]
}
_call(n, a) {
for (const u of this.bad_words_ids) {
let c = !0;
for (let f = 1; f <= u.length - 1 && u.length < n.length; ++f)
if (u.at(-f - 1) !== n.at(-f)) {
c = !1;
break
} c && (a.data[u.at(-1)] = -1 / 0)
}
return a
}
}
const GenerationConfig = class {
constructor(b = {}) {
this.max_length = b.max_length ?? 20, this.max_new_tokens = b.max_new_tokens ?? null, this.min_length = b.min_length ?? 0, this.min_new_tokens = b.min_new_tokens ?? null, this.early_stopping = b.early_stopping ?? !1, this.max_time = b.max_time ?? null, this.do_sample = b.do_sample ?? !1, this.num_beams = b.num_beams ?? 1, this.num_beam_groups = b.num_beam_groups ?? 1, this.penalty_alpha = b.penalty_alpha ?? null, this.use_cache = b.use_cache ?? !0, this.temperature = b.temperature ?? 1, this.top_k = b.top_k ?? 50, this.top_p = b.top_p ?? 1, this.typical_p = b.typical_p ?? 1, this.epsilon_cutoff = b.epsilon_cutoff ?? 0, this.eta_cutoff = b.eta_cutoff ?? 0, this.diversity_penalty = b.diversity_penalty ?? 0, this.repetition_penalty = b.repetition_penalty ?? 1, this.encoder_repetition_penalty = b.encoder_repetition_penalty ?? 1, this.length_penalty = b.length_penalty ?? 1, this.no_repeat_ngram_size = b.no_repeat_ngram_size ?? 0, this.bad_words_ids = b.bad_words_ids ?? null, this.force_words_ids = b.force_words_ids ?? null, this.renormalize_logits = b.renormalize_logits ?? !1, this.constraints = b.constraints ?? null, this.forced_bos_token_id = b.forced_bos_token_id ?? null, this.forced_eos_token_id = b.forced_eos_token_id ?? null, this.remove_invalid_values = b.remove_invalid_values ?? !1, this.exponential_decay_length_penalty = b.exponential_decay_length_penalty ?? null, this.suppress_tokens = b.suppress_tokens ?? null, this.begin_suppress_tokens = b.begin_suppress_tokens ?? null, this.forced_decoder_ids = b.forced_decoder_ids ?? null, this.num_return_sequences = b.num_return_sequences ?? 1, this.output_attentions = b.output_attentions ?? !1, this.output_hidden_states = b.output_hidden_states ?? !1, this.output_scores = b.output_scores ?? !1, this.return_dict_in_generate = b.return_dict_in_generate ?? !1, this.pad_token_id = b.pad_token_id ?? null, this.bos_token_id = b.bos_token_id ?? null, this.eos_token_id = b.eos_token_id ?? null, this.encoder_no_repeat_ngram_size = b.encoder_no_repeat_ngram_size ?? 0, this.decoder_start_token_id = b.decoder_start_token_id ?? null, this.generation_kwargs = b.generation_kwargs ?? {}
}
};
class Sampler extends Callable {
constructor(n) {
super(), this.generation_config = n
}
_call(n, a = -1) {
return this.sample(n, a)
}
sample(n, a) {
throw Error("sample should be implemented in subclasses.")
}
getLogits(n, a) {
let u = n.dims.at(-1),
c = n.data;
if (a === -1) c = c.slice(-u);
else {
let f = a * u;
c = c.slice(f, f + u)
}
return this.generation_config.temperature > 0 && (c = c.map(f => f / this.generation_config.temperature)), c
}
randomSelect(n) {
let a = n.reduce((c, f) => c + f, 0),
u = Math.random() * a;
for (let c = 0; c < n.length; ++c)
if (u -= n[c], u <= 0) return c;
return 0
}
static getSampler(n) {
if (n.do_sample) return new MultinomialSampler(n);
if (n.num_beams > 1) return new BeamSearchSampler(n);
if (n.num_return_sequences > 1) throw Error(`num_return_sequences has to be 1 when doing greedy search, but is ${n.num_return_sequences}.`);
return new GreedySampler(n)
}
}
class GreedySampler extends Sampler {
sample(n, a = -1) {
let u = this.getLogits(n, a);
return [
[max(u)[1], 0]
]
}
}
class MultinomialSampler extends Sampler {
sample(n, a = -1) {
let u = n.dims.at(-1);
this.generation_config.top_k > 0 && (u = Math.min(this.generation_config.top_k, u));
const c = this.getLogits(n, a),
f = getTopItems(c, u),
s = softmax(f.map(h => h[1]));
return Array.from({
length: this.generation_config.num_beams
}, () => {
const h = this.randomSelect(s);
return [f[h][0], Math.log(s[h])]
})
}
}
class BeamSearchSampler extends Sampler {
sample(n, a = -1) {
let u = n.dims.at(-1);
this.generation_config.top_k > 0 && (u = Math.min(this.generation_config.top_k, u));
const c = this.getLogits(n, a),
f = getTopItems(c, u),
s = softmax(f.map(h => h[1]));
return Array.from({
length: this.generation_config.num_beams
}, (h, p) => [f[p][0], Math.log(s[p])])
}
}
const {
InferenceSession,
Tensor: ONNXTensor,
env
} = ONNX, MODEL_TYPES = {
EncoderOnly: 0,
EncoderDecoder: 1,
Seq2Seq: 2,
Vision2Seq: 3,
DecoderOnly: 4,
MaskGeneration: 5
}, MODEL_TYPE_MAPPING = new Map, MODEL_NAME_TO_CLASS_MAPPING = new Map, MODEL_CLASS_TO_NAME_MAPPING = new Map;
async function constructSession(b, n, a) {
let u = `onnx/${n}${a.quantized?"_quantized":""}.onnx`,
c = await getModelFile(b, u, !0, a);
try {
return await InferenceSession.create(c, {
executionProviders
})
} catch (f) {
if (executionProviders.length === 1 && executionProviders[0] === "wasm") throw f;
return console.warn(f), console.warn("Something went wrong during model construction (most likely a missing operation). Using `wasm` as a fallback. "), await InferenceSession.create(c, {
executionProviders: ["wasm"]
})
}
}
function validateInputs(b, n) {
const a = Object.create(null),
u = [];
for (const s of b.inputNames) {
const h = n[s];
if (!(h instanceof Tensor)) {
u.push(s);
continue
}
a[s] = env.wasm.proxy ? h.clone() : h
}
if (u.length > 0) throw new Error(`An error occurred during model execution: "Missing the following inputs: ${u.join(", ")}.`);
const c = Object.keys(n).length,
f = b.inputNames.length;
if (c > f) {
let s = Object.keys(n).filter(h => !b.inputNames.includes(h));
console.warn(`WARNING: Too many inputs were provided (${c} > ${f}). The following inputs will be ignored: "${s.join(", ")}".`)
}
return a
}
async function sessionRun(b, n) {
const a = validateInputs(b, n);
try {
let u = await b.run(a);
return u = replaceTensors(u), u
} catch (u) {
throw console.error(`An error occurred during model execution: "${u}".`), console.error("Inputs given to model:", a), u
}
}
function replaceTensors(b) {
for (let n in b) b[n] instanceof ONNXTensor ? b[n] = new Tensor(b[n]) : typeof b[n] == "object" && replaceTensors(b[n]);
return b
}
function toI64Tensor(b) {
if (b instanceof Tensor) return b;
if (b.length === 0) throw Error("items must be non-empty");
if (Array.isArray(b[0])) {
if (b.some(n => n.length !== b[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(b.flat().map(n => BigInt(n))), [b.length, b[0].length])
} else return new Tensor("int64", BigInt64Array.from(b.map(n => BigInt(n))), [1, b.length])
}
function prepareAttentionMask(b, n) {
let a = b.config.pad_token_id ?? null,
u = b.config.eos_token_id ?? null;
isIntegralNumber(u) && (u = [u]);
let c = n.indexOf(a) !== -1,
f = u === null || !u.includes(a);
if (c && f) {
let s = BigInt64Array.from(n.data.map(h => h != a));
return new Tensor("int64", s, n.dims)
} else return ones_like(n)
}
function preparePositionIds(b, n, a) {
if (!b.inputNames.includes("position_ids")) return;
const u = new BigInt64Array(n.attention_mask.data.length);
for (let c = 0; c < n.attention_mask.dims[0]; ++c) {
let f = c * n.attention_mask.dims[1],
s = BigInt(0);
for (let h = 0; h < n.attention_mask.dims[1]; ++h) {
const p = f + h;
n.attention_mask.data[p] === 0n ? u[p] = BigInt(1) : (u[p] = s, s += n.attention_mask.data[p])
}
}
n.position_ids = new Tensor("int64", u, n.attention_mask.dims), a && (n.position_ids = n.position_ids.slice(null, -1).unsqueeze_(-1))
}
function boolTensor(b) {
return new Tensor("bool", [b], [1])
}
async function seq2seqForward(b, n) {
let {
encoder_outputs: a,
past_key_values: u
} = n;
a || (a = (await encoderForward(b, n)).last_hidden_state);
let c = {
input_ids: n.decoder_input_ids,
encoder_hidden_states: a
};
const f = !!u;
b.decoder_merged_session.inputNames.includes("use_cache_branch") && (c.use_cache_branch = boolTensor(f)), b.decoder_merged_session.inputNames.includes("encoder_attention_mask") && (c.encoder_attention_mask = n.attention_mask), preparePositionIds(b.decoder_merged_session, c, f), b.addPastKeyValues(c, u);
const s = await sessionRun(b.decoder_merged_session, c);
let h = s.logits;
u = b.getPastKeyValues(s, u);
const p = b.getAttentions(s);
return new Seq2SeqLMOutput({
logits: h,
past_key_values: u,
encoder_outputs: a,
...p
})
}
function seq2seqStartBeams(b, n, a, u) {
let c = [],
f = 0;
const s = b.requires_attention_mask ?? !0;
let h = a.decoder_input_ids ?? a.decoder_start_token_id ?? a.bos_token_id ?? a.eos_token_id;
h instanceof Tensor ? h = h.tolist().flat() : Array.isArray(h) || (h = [h]);
for (let p of n) {
p.dims = [1, ...p.dims];
let l = {
inputs: p,
encoder_outputs: null,
prev_model_outputs: null,
output_token_ids: h,
done: !1,
score: 0,
id: f++
};
s && (l.attention_mask = prepareAttentionMask(b, p)), c.push(l)
}
return c
}
async function seq2seqRunBeam(b, n) {
const a = b.main_input_name;
let u = n.output_token_ids;
n.prev_model_outputs && (u = u.slice(-1));
let c = {
[a]: n.inputs,
decoder_input_ids: toI64Tensor(u),
encoder_outputs: n.encoder_outputs,
past_key_values: n.prev_model_outputs?.past_key_values
};
n.attention_mask && (c.attention_mask = n.attention_mask);
let f = await b.forward(c);
return n.prev_model_outputs = f, n.encoder_outputs = f.encoder_outputs, f
}
function seq2seqUpdatebeam(b, n) {
b.output_token_ids = [...b.output_token_ids, n]
}
async function encoderForward(b, n) {
const a = Object.create(null);
for (const u of b.session.inputNames) a[u] = n[u];
return b.session.inputNames.includes("token_type_ids") && !a.token_type_ids && (a.token_type_ids = new Tensor("int64", new BigInt64Array(a.input_ids.data.length), a.input_ids.dims)), await sessionRun(b.session, a)
}
async function decoderForward(b, n) {
let {
input_ids: a,
past_key_values: u,
attention_mask: c
} = n, f = {
input_ids: a,
attention_mask: c ?? prepareAttentionMask(b, a)
};
const s = !!u;
b.session.inputNames.includes("use_cache_branch") && (f.use_cache_branch = boolTensor(s)), preparePositionIds(b.session, f, s), b.addPastKeyValues(f, u);
let h = await sessionRun(b.session, f),
p = h.logits;
return u = b.getPastKeyValues(h, u), {
logits: p,
past_key_values: u
}
}
function decoderStartBeams(b, n, a, u, c) {
let f = [],
s = 0;
for (let h of n) {
let p = h.tolist().map(Number);
h.dims = [1, ...h.dims];
let l;
c ? (l = c[s], l.dims = [1, ...l.dims]) : l = prepareAttentionMask(b, h);
let o = {
input: h,
model_input_ids: h,
attention_mask: l,
prev_model_outputs: null,
output_token_ids: p,
num_output_tokens: u,
done: !1,
score: 0,
id: s++
};
f.push(o)
}
return f
}
async function decoderRunBeam(b, n) {
let a = new BigInt64Array(n.output_token_ids.length).fill(1n),
u = {
input_ids: n.model_input_ids,
attention_mask: new Tensor("int64", a, [1, a.length]),
past_key_values: n.prev_model_outputs?.past_key_values
},
c = await b.forward(u);
return n.prev_model_outputs = c, c
}
function decoderUpdatebeam(b, n) {
b.output_token_ids = [...b.output_token_ids, n], b.model_input_ids = new Tensor("int64", [BigInt(n)], [1, 1])
}
class PreTrainedModel extends Callable {
main_input_name = "input_ids";
constructor(n, a) {
super(), this.config = n, this.session = a;
const u = MODEL_CLASS_TO_NAME_MAPPING.get(this.constructor),
c = MODEL_TYPE_MAPPING.get(u);
this.can_generate = !1, this._runBeam = null, this._getStartBeams = null, this._updateBeam = null, this._forward = null, c === MODEL_TYPES.DecoderOnly ? (this.can_generate = !0, this._runBeam = decoderRunBeam, this._getStartBeams = decoderStartBeams, this._updateBeam = decoderUpdatebeam, this._forward = decoderForward) : c === MODEL_TYPES.Seq2Seq || c === MODEL_TYPES.Vision2Seq ? (this.can_generate = !0, this._runBeam = seq2seqRunBeam, this._getStartBeams = seq2seqStartBeams, this._updateBeam = seq2seqUpdatebeam, this._forward = seq2seqForward) : c === MODEL_TYPES.EncoderDecoder ? this._forward = encoderForward : this._forward = encoderForward
}
async dispose() {
const n = [];
for (let a of Object.keys(this)) {
const u = this[a];
u instanceof InferenceSession && n.push(u.handler.dispose())
}
return await Promise.all(n)
}
static async from_pretrained(n, {
quantized: a = !0,
progress_callback: u = null,
config: c = null,
cache_dir: f = null,
local_files_only: s = !1,
revision: h = "main",
model_file_name: p = null
} = {}) {
let l = {
quantized: a,
progress_callback: u,
config: c,
cache_dir: f,
local_files_only: s,
revision: h,
model_file_name: p
};
const o = MODEL_CLASS_TO_NAME_MAPPING.get(this),
t = MODEL_TYPE_MAPPING.get(o);
let e;
return t === MODEL_TYPES.DecoderOnly ? e = await Promise.all([AutoConfig.from_pretrained(n, l), constructSession(n, l.model_file_name ?? "decoder_model_merged", l), getModelJSON(n, "generation_config.json", !1, l)]) : t === MODEL_TYPES.Seq2Seq || t === MODEL_TYPES.Vision2Seq ? e = await Promise.all([AutoConfig.from_pretrained(n, l), constructSession(n, "encoder_model", l), constructSession(n, "decoder_model_merged", l), getModelJSON(n, "generation_config.json", !1, l)]) : t === MODEL_TYPES.MaskGeneration ? e = await Promise.all([AutoConfig.from_pretrained(n, l), constructSession(n, "vision_encoder", l), constructSession(n, "prompt_encoder_mask_decoder", l)]) : t === MODEL_TYPES.EncoderDecoder ? e = await Promise.all([AutoConfig.from_pretrained(n, l), constructSession(n, "encoder_model", l), constructSession(n, "decoder_model_merged", l)]) : (t !== MODEL_TYPES.EncoderOnly && console.warn(`Model type for '${o??c?.model_type}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`), e = await Promise.all([AutoConfig.from_pretrained(n, l), constructSession(n, l.model_file_name ?? "model", l)])), new this(...e)
}
async _call(n) {
return await this.forward(n)
}
async forward(n) {
return await this._forward(this, n)
}
_get_logits_processor(n, a, u = null) {
const c = new LogitsProcessorList;
if (n.repetition_penalty !== null && n.repetition_penalty !== 1 && c.push(new RepetitionPenaltyLogitsProcessor(n.repetition_penalty)), n.no_repeat_ngram_size !== null && n.no_repeat_ngram_size > 0 && c.push(new NoRepeatNGramLogitsProcessor(n.no_repeat_ngram_size)), n.bad_words_ids !== null && c.push(new NoBadWordsLogitsProcessor(n.bad_words_ids, n.eos_token_id)), n.min_length !== null && n.eos_token_id !== null && n.min_length > 0 && c.push(new MinLengthLogitsProcessor(n.min_length, n.eos_token_id)), n.min_new_tokens !== null && n.eos_token_id !== null && n.min_new_tokens > 0 && c.push(new MinNewTokensLengthLogitsProcessor(a, n.min_new_tokens, n.eos_token_id)), n.forced_bos_token_id !== null && c.push(new ForcedBOSTokenLogitsProcessor(n.forced_bos_token_id)), n.forced_eos_token_id !== null && c.push(new ForcedEOSTokenLogitsProcessor(n.max_length, n.forced_eos_token_id)), n.begin_suppress_tokens !== null) {
let f = a > 1 || n.forced_bos_token_id === null ? a : a + 1;
n.forced_decoder_ids !== null && (f += n.forced_decoder_ids[n.forced_decoder_ids.length - 1][0]), c.push(new SuppressTokensAtBeginLogitsProcessor(n.begin_suppress_tokens, f))
}
return n.forced_decoder_ids !== null && c.push(new ForceTokensLogitsProcessor(n.forced_decoder_ids)), u !== null && c.extend(u), c
}
_get_generation_config(n) {
let a = new GenerationConfig(this.config);
return "generation_config" in this && Object.assign(a, this.generation_config), n !== null && Object.assign(a, n), a
}
async generate(n, a = null, u = null, {
inputs_attention_mask: c = null
} = {}) {
if (!this.can_generate) {
let g = `The current model class (${MODEL_CLASS_TO_NAME_MAPPING.get(this.constructor)}) is not compatible with \`.generate()\`, as it doesn't have a language model head.`;
const m = this.config.model_type,
_ = MODEL_WITH_LM_HEAD_MAPPING_NAMES.get(m) ?? MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES.get(m) ?? MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES.get(m) ?? MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES.get(m);
throw _ && (g += ` Please use the following class instead: '${_[0]}'`), Error(g)
}
if (!(n instanceof Tensor) && !isTypedArray(n) && !Array.isArray(n)) throw Error(`\`inputs\` must be a Tensor, TypedArray, or Array, but is "${n.constructor.name}".`);
let f;
if (this.config.is_encoder_decoder) f = 0;
else if (f = n instanceof Tensor ? n.dims.at(-1) : n.length, f === 0) throw Error("Must supply a non-empty array of input token ids.");
a = this._get_generation_config(a), u = u ?? new LogitsProcessorList, u = this._get_logits_processor(a, f, u);
let s = a.eos_token_id;
s !== null && !Array.isArray(s) && (s = [s]);
let h = 1;
const p = h + (a.max_new_tokens ?? 1 / 0),
l = Number.isInteger(a.max_length) && (a.max_new_tokens ?? null) === null;
let o = Sampler.getSampler(a),
t = this.getStartBeams(n, a, h, c);
for (; t.some(d => !d.done) && h < p;) {
let d = [];
for (let g of t) {
if (g.done) {
d.push(g);
continue
}
if (l && g.output_token_ids.length >= a.max_length) {
g.done = !0, d.push(g);
continue
}
let m = await this.runBeam(g);
a.output_attentions && this.addAttentionsToBeam(g, m), a.output_scores;
let _ = m.logits.slice(null, -1, null);
u(g.output_token_ids, _);
let y = o(_);
for (let [T, w] of y) {
let S = {
...g
};
this.updateBeam(S, T), S.score += w, s && s.includes(T) && (S.done = !0), d.push(S)
}
}++h, d = this.groupBeams(d).map(g => g.sort((m, _) => _.score - m.score).slice(0, a.num_beams)), t = d.flat(), a.callback_function && a.callback_function(t)
}
const e = this.groupBeams(t),
r = d => e.map(g => a.num_return_sequences > 1 ? g.slice(0, a.num_return_sequences).map(m => m[d]) : [g[0][d]]).flat(),
i = r("output_token_ids");
if (a.return_dict_in_generate) {
const d = r("decoder_attentions"),
g = r("cross_attentions");
return {
sequences: i,
decoder_attentions: d,
cross_attentions: g
}
} else return i
}
addAttentionsToBeam(n, a) {
if (this.config.is_encoder_decoder) {
if (!a.cross_attentions || a.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`.");
n.cross_attentions || (n.cross_attentions = []), n.cross_attentions.push(a.cross_attentions)
}
if (!a.decoder_attentions || a.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`.");
n.decoder_attentions || (n.decoder_attentions = []), n.decoder_attentions.push(a.decoder_attentions)
}
groupBeams(n) {
const a = Object.create(null);
for (const u of n) a[u.id] === void 0 ? a[u.id] = [u] : a[u.id].push(u);
return Object.values(a)
}
getPastKeyValues(n, a) {
const u = Object.create(null);
for (const c in n)
if (c.startsWith("present")) {
let f = c.replace("present", "past_key_values");
a && c.includes("encoder") ? u[f] = a[f] : u[f] = n[c]
} return u
}
getAttentions(n) {
const a = Object.create(null);
for (const u of ["cross_attentions", "decoder_attentions"]) {
const c = [];
for (const f in n)
if (f.startsWith(u)) {
const s = f.split(".").pop();
c[s] = n[f]
} a[u] = c
}
return a
}
addPastKeyValues(n, a) {
if (a) Object.assign(n, a);
else if (this.config.is_encoder_decoder && (this.add_encoder_pkv ?? !0)) {
let c = [1, this.num_encoder_heads, 0, this.encoder_dim_kv],
f = [1, this.num_decoder_heads, 0, this.decoder_dim_kv];
for (let s = 0; s < this.num_decoder_layers; ++s) n[`past_key_values.${s}.encoder.key`] = new Tensor("float32", [], c), n[`past_key_values.${s}.encoder.value`] = new Tensor("float32", [], c), n[`past_key_values.${s}.decoder.key`] = new Tensor("float32", [], f), n[`past_key_values.${s}.decoder.value`] = new Tensor("float32", [], f)
} else if (this.config.model_type === "falcon") {
let c = [1 * this.num_heads, 0, this.dim_kv];
for (let f = 0; f < this.num_layers; ++f) n[`past_key_values.${f}.key`] = new Tensor("float32", [], c), n[`past_key_values.${f}.value`] = new Tensor("float32", [], c)
} else if (this.config.multi_query) {
let c = [1 * this.num_heads, 0, 2 * this.dim_kv];
for (let f = 0; f < this.num_layers; ++f) n[`past_key_values.${f}.key_value`] = new Tensor("float32", [], c)
} else if (this.config.model_type === "bloom") {
let c = [1 * this.num_heads, this.dim_kv, 0],
f = [1 * this.num_heads, 0, this.dim_kv];
for (let s = 0; s < this.num_layers; ++s) n[`past_key_values.${s}.key`] = new Tensor("float32", [], c), n[`past_key_values.${s}.value`] = new Tensor("float32", [], f)
} else {
let c = [1, this.num_heads, 0, this.dim_kv];
for (let f = 0; f < this.num_layers; ++f) n[`past_key_values.${f}.key`] = new Tensor("float32", [], c), n[`past_key_values.${f}.value`] = new Tensor("float32", [], c)
}
}
getStartBeams(n, a, u, c) {
return this._getStartBeams(this, n, a, u, c)
}
async runBeam(n) {
return await this._runBeam(this, n)
}
updateBeam(n, a) {
return this._updateBeam(n, a)
}
}
class ModelOutput {}
class BertPreTrainedModel extends PreTrainedModel {}
class BertModel extends BertPreTrainedModel {}
class BertForMaskedLM extends BertPreTrainedModel {
async _call(n) {
return new MaskedLMOutput(await super._call(n))
}
}
class BertForSequenceClassification extends BertPreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class BertForTokenClassification extends BertPreTrainedModel {
async _call(n) {
return new TokenClassifierOutput(await super._call(n))
}
}
class BertForQuestionAnswering extends BertPreTrainedModel {
async _call(n) {
return new QuestionAnsweringModelOutput(await super._call(n))
}
}
class RoFormerPreTrainedModel extends PreTrainedModel {}
class RoFormerModel extends RoFormerPreTrainedModel {}
class RoFormerForMaskedLM extends RoFormerPreTrainedModel {
async _call(n) {
return new MaskedLMOutput(await super._call(n))
}
}
class RoFormerForSequenceClassification extends RoFormerPreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class RoFormerForTokenClassification extends RoFormerPreTrainedModel {
async _call(n) {
return new TokenClassifierOutput(await super._call(n))
}
}
class RoFormerForQuestionAnswering extends RoFormerPreTrainedModel {
async _call(n) {
return new QuestionAnsweringModelOutput(await super._call(n))
}
}
class ConvBertPreTrainedModel extends PreTrainedModel {}
class ConvBertModel extends ConvBertPreTrainedModel {}
class ConvBertForMaskedLM extends ConvBertPreTrainedModel {
async _call(n) {
return new MaskedLMOutput(await super._call(n))
}
}
class ConvBertForSequenceClassification extends ConvBertPreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class ConvBertForTokenClassification extends ConvBertPreTrainedModel {
async _call(n) {
return new TokenClassifierOutput(await super._call(n))
}
}
class ConvBertForQuestionAnswering extends ConvBertPreTrainedModel {
async _call(n) {
return new QuestionAnsweringModelOutput(await super._call(n))
}
}
class ElectraPreTrainedModel extends PreTrainedModel {}
class ElectraModel extends ElectraPreTrainedModel {}
class ElectraForMaskedLM extends ElectraPreTrainedModel {
async _call(n) {
return new MaskedLMOutput(await super._call(n))
}
}
class ElectraForSequenceClassification extends ElectraPreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class ElectraForTokenClassification extends ElectraPreTrainedModel {
async _call(n) {
return new TokenClassifierOutput(await super._call(n))
}
}
class ElectraForQuestionAnswering extends ElectraPreTrainedModel {
async _call(n) {
return new QuestionAnsweringModelOutput(await super._call(n))
}
}
class CamembertPreTrainedModel extends PreTrainedModel {}
class CamembertModel extends CamembertPreTrainedModel {}
class CamembertForMaskedLM extends CamembertPreTrainedModel {
async _call(n) {
return new MaskedLMOutput(await super._call(n))
}
}
class CamembertForSequenceClassification extends CamembertPreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class CamembertForTokenClassification extends CamembertPreTrainedModel {
async _call(n) {
return new TokenClassifierOutput(await super._call(n))
}
}
class CamembertForQuestionAnswering extends CamembertPreTrainedModel {
async _call(n) {
return new QuestionAnsweringModelOutput(await super._call(n))
}
}
class DebertaPreTrainedModel extends PreTrainedModel {}
class DebertaModel extends DebertaPreTrainedModel {}
class DebertaForMaskedLM extends DebertaPreTrainedModel {
async _call(n) {
return new MaskedLMOutput(await super._call(n))
}
}
class DebertaForSequenceClassification extends DebertaPreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class DebertaForTokenClassification extends DebertaPreTrainedModel {
async _call(n) {
return new TokenClassifierOutput(await super._call(n))
}
}
class DebertaForQuestionAnswering extends DebertaPreTrainedModel {
async _call(n) {
return new QuestionAnsweringModelOutput(await super._call(n))
}
}
class DebertaV2PreTrainedModel extends PreTrainedModel {}
class DebertaV2Model extends DebertaV2PreTrainedModel {}
class DebertaV2ForMaskedLM extends DebertaV2PreTrainedModel {
async _call(n) {
return new MaskedLMOutput(await super._call(n))
}
}
class DebertaV2ForSequenceClassification extends DebertaV2PreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class DebertaV2ForTokenClassification extends DebertaV2PreTrainedModel {
async _call(n) {
return new TokenClassifierOutput(await super._call(n))
}
}
class DebertaV2ForQuestionAnswering extends DebertaV2PreTrainedModel {
async _call(n) {
return new QuestionAnsweringModelOutput(await super._call(n))
}
}
class DistilBertPreTrainedModel extends PreTrainedModel {}
class DistilBertModel extends DistilBertPreTrainedModel {}
class DistilBertForSequenceClassification extends DistilBertPreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class DistilBertForTokenClassification extends DistilBertPreTrainedModel {
async _call(n) {
return new TokenClassifierOutput(await super._call(n))
}
}
class DistilBertForQuestionAnswering extends DistilBertPreTrainedModel {
async _call(n) {
return new QuestionAnsweringModelOutput(await super._call(n))
}
}
class DistilBertForMaskedLM extends DistilBertPreTrainedModel {
async _call(n) {
return new MaskedLMOutput(await super._call(n))
}
}
class EsmPreTrainedModel extends PreTrainedModel {}
class EsmModel extends EsmPreTrainedModel {}
class EsmForMaskedLM extends EsmPreTrainedModel {
async _call(n) {
return new MaskedLMOutput(await super._call(n))
}
}
class EsmForSequenceClassification extends EsmPreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class EsmForTokenClassification extends EsmPreTrainedModel {
async _call(n) {
return new TokenClassifierOutput(await super._call(n))
}
}
class MobileBertPreTrainedModel extends PreTrainedModel {}
class MobileBertModel extends MobileBertPreTrainedModel {}
class MobileBertForMaskedLM extends MobileBertPreTrainedModel {
async _call(n) {
return new MaskedLMOutput(await super._call(n))
}
}
class MobileBertForSequenceClassification extends MobileBertPreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class MobileBertForQuestionAnswering extends MobileBertPreTrainedModel {
async _call(n) {
return new QuestionAnsweringModelOutput(await super._call(n))
}
}
class MPNetPreTrainedModel extends PreTrainedModel {}
class MPNetModel extends MPNetPreTrainedModel {}
class MPNetForMaskedLM extends MPNetPreTrainedModel {
async _call(n) {
return new MaskedLMOutput(await super._call(n))
}
}
class MPNetForSequenceClassification extends MPNetPreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class MPNetForTokenClassification extends MPNetPreTrainedModel {
async _call(n) {
return new TokenClassifierOutput(await super._call(n))
}
}
class MPNetForQuestionAnswering extends MPNetPreTrainedModel {
async _call(n) {
return new QuestionAnsweringModelOutput(await super._call(n))
}
}
class SqueezeBertPreTrainedModel extends PreTrainedModel {}
class SqueezeBertModel extends SqueezeBertPreTrainedModel {}
class SqueezeBertForMaskedLM extends SqueezeBertPreTrainedModel {
async _call(n) {
return new MaskedLMOutput(await super._call(n))
}
}
class SqueezeBertForSequenceClassification extends SqueezeBertPreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class SqueezeBertForQuestionAnswering extends SqueezeBertPreTrainedModel {
async _call(n) {
return new QuestionAnsweringModelOutput(await super._call(n))
}
}
class AlbertPreTrainedModel extends PreTrainedModel {}
class AlbertModel extends AlbertPreTrainedModel {}
class AlbertForSequenceClassification extends AlbertPreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class AlbertForQuestionAnswering extends AlbertPreTrainedModel {
async _call(n) {
return new QuestionAnsweringModelOutput(await super._call(n))
}
}
class AlbertForMaskedLM extends AlbertPreTrainedModel {
async _call(n) {
return new MaskedLMOutput(await super._call(n))
}
}
class T5PreTrainedModel extends PreTrainedModel {}
class T5Model extends T5PreTrainedModel {}
class T5ForConditionalGeneration extends T5PreTrainedModel {
constructor(n, a, u, c) {
super(n, a), this.decoder_merged_session = u, this.generation_config = c, 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 {
constructor(n, a, u, c) {
super(n, a), this.decoder_merged_session = u, this.generation_config = c, 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 {
constructor(n, a, u, c) {
super(n, a), this.decoder_merged_session = u, this.generation_config = c, 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 {
constructor(n, a, u, c) {
super(n, a), this.decoder_merged_session = u, this.generation_config = c, 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 {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class MBartPreTrainedModel extends PreTrainedModel {}
class MBartModel extends MBartPreTrainedModel {}
class MBartForConditionalGeneration extends MBartPreTrainedModel {
constructor(n, a, u, c) {
super(n, a), this.decoder_merged_session = u, this.generation_config = c, 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 {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class MBartForCausalLM extends MBartPreTrainedModel {
constructor(n, a, u) {
super(n, a), this.generation_config = u, 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 {
constructor(n, a, u, c) {
super(n, a), this.decoder_merged_session = u, this.generation_config = c, 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 {
constructor(n, a, u, c) {
super(n, a), this.decoder_merged_session = u, this.generation_config = c, 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 {
async _call(n) {
return new MaskedLMOutput(await super._call(n))
}
}
class RobertaForSequenceClassification extends RobertaPreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class RobertaForTokenClassification extends RobertaPreTrainedModel {
async _call(n) {
return new TokenClassifierOutput(await super._call(n))
}
}
class RobertaForQuestionAnswering extends RobertaPreTrainedModel {
async _call(n) {
return new QuestionAnsweringModelOutput(await super._call(n))
}
}
class XLMPreTrainedModel extends PreTrainedModel {}
class XLMModel extends XLMPreTrainedModel {}
class XLMWithLMHeadModel extends XLMPreTrainedModel {
async _call(n) {
return new MaskedLMOutput(await super._call(n))
}
}
class XLMForSequenceClassification extends XLMPreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class XLMForTokenClassification extends XLMPreTrainedModel {
async _call(n) {
return new TokenClassifierOutput(await super._call(n))
}
}
class XLMForQuestionAnswering extends XLMPreTrainedModel {
async _call(n) {
return new QuestionAnsweringModelOutput(await super._call(n))
}
}
class XLMRobertaPreTrainedModel extends PreTrainedModel {}
class XLMRobertaModel extends XLMRobertaPreTrainedModel {}
class XLMRobertaForMaskedLM extends XLMRobertaPreTrainedModel {
async _call(n) {
return new MaskedLMOutput(await super._call(n))
}
}
class XLMRobertaForSequenceClassification extends XLMRobertaPreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class XLMRobertaForTokenClassification extends XLMRobertaPreTrainedModel {
async _call(n) {
return new TokenClassifierOutput(await super._call(n))
}
}
class XLMRobertaForQuestionAnswering extends XLMRobertaPreTrainedModel {
async _call(n) {
return new QuestionAnsweringModelOutput(await super._call(n))
}
}
class ASTPreTrainedModel extends PreTrainedModel {}
class ASTModel extends ASTPreTrainedModel {}
class ASTForAudioClassification extends ASTPreTrainedModel {}
class WhisperPreTrainedModel extends PreTrainedModel {}
class WhisperModel extends WhisperPreTrainedModel {}
class WhisperForConditionalGeneration extends WhisperPreTrainedModel {
requires_attention_mask = !1;
main_input_name = "input_features";
constructor(n, a, u, c) {
super(n, a), this.decoder_merged_session = u, this.generation_config = c, 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
}
async generate(n, a = null, u = null) {
if (a = this._get_generation_config(a), a.return_timestamps ??= !1, a.return_timestamps && (u = [new WhisperTimeStampLogitsProcessor(a)]), a.return_token_timestamps && (a.output_attentions = !0, a.return_dict_in_generate = !0, a.task === "translate" && console.warn("Token-level timestamps may not be reliable for task 'translate'."), !a.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 c = await super.generate(n, a, u);
return a.return_token_timestamps && a.alignment_heads && (c.token_timestamps = this._extract_token_timestamps(c, a.alignment_heads, a.num_frames)), c
}
_extract_token_timestamps(n, a, u = null, c = .02) {
if (!n.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 f = this.config.median_filter_width;
f === void 0 && (console.warn("Model config has no `median_filter_width`, using default value of 7."), f = 7);
const s = n.cross_attentions.map(l => {
let o = Array.from({
length: this.config.decoder_layers
}, (g, m) => cat(l.map(_ => _[m]), 2)),
t = stack(a.map(([g, m]) => u ? o[g].slice(null, m, null, [0, u]) : o[g].slice(null, m)));
t = t.transpose(1, 0, 2, 3);
let [e, r] = std_mean(t, -2, 0, !0), i = t.clone();
for (let g = 0; g < i.dims[0]; ++g) {
let m = i[g];
for (let _ = 0; _ < m.dims[0]; ++_) {
let y = m[_];
const T = e[g][_][0],
w = r[g][_][0];
for (let S = 0; S < y.dims[0]; ++S) {
let O = y[S];
for (let E = 0; E < O.data.length; ++E) O.data[E] = (O.data[E] - w.data[E]) / T.data[E];
O.data.set(medianFilter(O.data, f))
}
}
}
return mean(i, 1)
}),
h = [n.sequences.length, n.sequences[0].length],
p = new Tensor("float32", new Float32Array(h[0] * h[1]), h);
for (let l = 0; l < h[0]; ++l) {
const o = s[l].neg().squeeze_(0);
let [t, e] = dynamicTimeWarping(o), r = Array.from({
length: t.length - 1
}, (g, m) => t[m + 1] - t[m]), i = mergeArrays([1], r).map(g => !!g), d = [];
for (let g = 0; g < i.length; ++g) i[g] && d.push(e[g] * c);
p[l].data.set(d, 1)
}
return p
}
}
class VisionEncoderDecoderModel extends PreTrainedModel {
main_input_name = "pixel_values";
constructor(n, a, u, c) {
super(n, a), this.decoder_merged_session = u, this.generation_config = c;
const f = this.config.encoder,
s = this.config.decoder,
h = f.model_type;
(MODEL_MAPPING_NAMES_ENCODER_ONLY.get(h) ?? MODEL_MAPPING_NAMES_ENCODER_DECODER.get(h)) || console.warn(`Model type for encoder '${h}' not found, assuming encoder-only architecture. Please report this at https://github.com/xenova/transformers.js/issues/new/choose.`);
const l = MODEL_WITH_LM_HEAD_MAPPING_NAMES.get(s.model_type);
if (!l) throw new Error(`Unable to construct \`VisionEncoderDecoder\` due to unsupported decoder: "${this.config.decoder.model_type}"`);
const o = l[1],
t = new o(s, u, c);
this.add_encoder_pkv = "num_decoder_layers" in t, this.add_encoder_pkv ? (this.num_decoder_layers = t.num_decoder_layers, this.num_decoder_heads = t.num_decoder_heads, this.decoder_dim_kv = t.decoder_dim_kv, this.num_encoder_layers = t.num_encoder_layers, this.num_encoder_heads = t.num_encoder_heads, this.encoder_dim_kv = t.encoder_dim_kv) : (this.num_layers = t.num_layers, this.num_heads = t.num_heads, this.dim_kv = t.dim_kv)
}
}
class CLIPPreTrainedModel extends PreTrainedModel {}
class CLIPModel extends CLIPPreTrainedModel {}
class CLIPTextModelWithProjection extends CLIPPreTrainedModel {
static async from_pretrained(n, a = {}) {
return a.model_file_name ??= "text_model", super.from_pretrained(n, a)
}
}
class CLIPVisionModelWithProjection extends CLIPPreTrainedModel {
static async from_pretrained(n, a = {}) {
return a.model_file_name ??= "vision_model", super.from_pretrained(n, a)
}
}
class SiglipPreTrainedModel extends PreTrainedModel {}
class SiglipModel extends SiglipPreTrainedModel {}
class SiglipTextModel extends SiglipPreTrainedModel {
static async from_pretrained(n, a = {}) {
return a.model_file_name ??= "text_model", super.from_pretrained(n, a)
}
}
class SiglipVisionModel extends CLIPPreTrainedModel {
static async from_pretrained(n, a = {}) {
return a.model_file_name ??= "vision_model", super.from_pretrained(n, a)
}
}
class ChineseCLIPPreTrainedModel extends PreTrainedModel {}
class ChineseCLIPModel extends ChineseCLIPPreTrainedModel {}
class CLIPSegPreTrainedModel extends PreTrainedModel {}
class CLIPSegModel extends CLIPSegPreTrainedModel {}
class CLIPSegForImageSegmentation extends CLIPSegPreTrainedModel {}
class GPT2PreTrainedModel extends PreTrainedModel {
constructor(n, a, u) {
super(n, a), this.generation_config = u, 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 {
constructor(n, a, u) {
super(n, a), this.generation_config = u, 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 {
constructor(n, a, u) {
super(n, a), this.generation_config = u, 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 {
constructor(n, a, u) {
super(n, a), this.generation_config = u, 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 {
constructor(n, a, u) {
super(n, a), this.generation_config = u, 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 {
constructor(n, a, u) {
super(n, a), this.generation_config = u, 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 {
constructor(n, a, u) {
super(n, a), this.generation_config = u, this.config.pad_token_id = this.config.eos_token_id, this.num_heads = this.config.num_key_value_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 LlamaModel extends LlamaPreTrainedModel {}
class LlamaForCausalLM extends LlamaPreTrainedModel {}
class Qwen2PreTrainedModel extends PreTrainedModel {
constructor(n, a, u) {
super(n, a), this.generation_config = u, this.config.pad_token_id = this.config.eos_token_id, this.num_heads = this.config.num_key_value_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 Qwen2Model extends Qwen2PreTrainedModel {}
class Qwen2ForCausalLM extends Qwen2PreTrainedModel {}
class PhiPreTrainedModel extends PreTrainedModel {
constructor(n, a, u) {
super(n, a), this.generation_config = u, 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 {
constructor(n, a, u) {
super(n, a), this.generation_config = u, 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 {
constructor(n, a, u) {
super(n, a), this.generation_config = u, 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 {
constructor(n, a, u) {
super(n, a), this.generation_config = u, 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 {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class VitMattePreTrainedModel extends PreTrainedModel {}
class VitMatteForImageMatting extends VitMattePreTrainedModel {
async _call(n) {
return new ImageMattingOutput(await super._call(n))
}
}
class MobileViTPreTrainedModel extends PreTrainedModel {}
class MobileViTModel extends MobileViTPreTrainedModel {}
class MobileViTForImageClassification extends MobileViTPreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class OwlViTPreTrainedModel extends PreTrainedModel {}
class OwlViTModel extends OwlViTPreTrainedModel {}
class OwlViTForObjectDetection extends OwlViTPreTrainedModel {}
class BeitPreTrainedModel extends PreTrainedModel {}
class BeitModel extends BeitPreTrainedModel {}
class BeitForImageClassification extends BeitPreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class DetrPreTrainedModel extends PreTrainedModel {}
class DetrModel extends DetrPreTrainedModel {}
class DetrForObjectDetection extends DetrPreTrainedModel {
async _call(n) {
return new DetrObjectDetectionOutput(await super._call(n))
}
}
class DetrForSegmentation extends DetrPreTrainedModel {
async _call(n) {
return new DetrSegmentationOutput(await super._call(n))
}
}
class DetrObjectDetectionOutput extends ModelOutput {
constructor({
logits: n,
pred_boxes: a
}) {
super(), this.logits = n, this.pred_boxes = a
}
}
class DetrSegmentationOutput extends ModelOutput {
constructor({
logits: n,
pred_boxes: a,
pred_masks: u
}) {
super(), this.logits = n, this.pred_boxes = a, this.pred_masks = u
}
}
class TableTransformerPreTrainedModel extends PreTrainedModel {}
class TableTransformerModel extends TableTransformerPreTrainedModel {}
class TableTransformerForObjectDetection extends TableTransformerPreTrainedModel {
async _call(n) {
return new TableTransformerObjectDetectionOutput(await super._call(n))
}
}
class TableTransformerObjectDetectionOutput extends DetrObjectDetectionOutput {}
class DeiTPreTrainedModel extends PreTrainedModel {}
class DeiTModel extends DeiTPreTrainedModel {}
class DeiTForImageClassification extends DeiTPreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class ResNetPreTrainedModel extends PreTrainedModel {}
class ResNetModel extends ResNetPreTrainedModel {}
class ResNetForImageClassification extends ResNetPreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class SwinPreTrainedModel extends PreTrainedModel {}
class SwinModel extends SwinPreTrainedModel {}
class SwinForImageClassification extends SwinPreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
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 {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class ConvNextV2PreTrainedModel extends PreTrainedModel {}
class ConvNextV2Model extends ConvNextV2PreTrainedModel {}
class ConvNextV2ForImageClassification extends ConvNextV2PreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class Dinov2PreTrainedModel extends PreTrainedModel {}
class Dinov2Model extends Dinov2PreTrainedModel {}
class Dinov2ForImageClassification extends Dinov2PreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class YolosPreTrainedModel extends PreTrainedModel {}
class YolosModel extends YolosPreTrainedModel {}
class YolosForObjectDetection extends YolosPreTrainedModel {
async _call(n) {
return new YolosObjectDetectionOutput(await super._call(n))
}
}
class YolosObjectDetectionOutput extends ModelOutput {
constructor({
logits: n,
pred_boxes: a
}) {
super(), this.logits = n, this.pred_boxes = a
}
}
class SamPreTrainedModel extends PreTrainedModel {}
class SamModel extends SamPreTrainedModel {
constructor(n, a, u) {
super(n, a), this.prompt_encoder_mask_decoder = u
}
async get_image_embeddings({
pixel_values: n
}) {
return await encoderForward(this, {
pixel_values: n
})
}
async forward(n) {
if ((!n.image_embeddings || !n.image_positional_embeddings) && (n = {
...n,
...await this.get_image_embeddings(n)
}), !n.input_labels) {
const a = n.input_points.dims.slice(0, -1),
u = a.reduce((c, f) => c * f, 1);
n.input_labels = new Tensor("int64", new BigInt64Array(u).fill(1n), a)
}
return await sessionRun(this.prompt_encoder_mask_decoder, {
input_points: n.input_points,
input_labels: n.input_labels,
image_embeddings: n.image_embeddings,
image_positional_embeddings: n.image_positional_embeddings
})
}
async _call(n) {
return new SamImageSegmentationOutput(await super._call(n))
}
}
class SamImageSegmentationOutput extends ModelOutput {
constructor({
iou_scores: n,
pred_masks: a
}) {
super(), this.iou_scores = n, this.pred_masks = a
}
}
class MarianPreTrainedModel extends PreTrainedModel {}
class MarianModel extends MarianPreTrainedModel {}
class MarianMTModel extends MarianPreTrainedModel {
constructor(n, a, u, c) {
super(n, a), this.decoder_merged_session = u, this.generation_config = c, 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 {
constructor(n, a, u, c) {
super(n, a), this.decoder_merged_session = u, this.generation_config = c, 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 {
async _call(n) {
return new CausalLMOutput(await super._call(n))
}
}
class Wav2Vec2ForSequenceClassification extends Wav2Vec2PreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class Wav2Vec2BertPreTrainedModel extends PreTrainedModel {}
class Wav2Vec2BertModel extends Wav2Vec2BertPreTrainedModel {}
class Wav2Vec2BertForCTC extends Wav2Vec2BertPreTrainedModel {
async _call(n) {
return new CausalLMOutput(await super._call(n))
}
}
class Wav2Vec2BertForSequenceClassification extends Wav2Vec2BertPreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class HubertModel extends Wav2Vec2PreTrainedModel {}
class HubertForCTC extends Wav2Vec2PreTrainedModel {
async _call(n) {
return new CausalLMOutput(await super._call(n))
}
}
class HubertForSequenceClassification extends Wav2Vec2PreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class WavLMPreTrainedModel extends PreTrainedModel {}
class WavLMModel extends WavLMPreTrainedModel {}
class WavLMForCTC extends WavLMPreTrainedModel {
async _call(n) {
return new CausalLMOutput(await super._call(n))
}
}
class WavLMForSequenceClassification extends WavLMPreTrainedModel {
async _call(n) {
return new SequenceClassifierOutput(await super._call(n))
}
}
class SpeechT5PreTrainedModel extends PreTrainedModel {}
class SpeechT5ForSpeechToText extends SpeechT5PreTrainedModel {}
class SpeechT5ForTextToSpeech extends SpeechT5PreTrainedModel {
constructor(n, a, u, c) {
super(n, a), this.decoder_merged_session = u, this.generation_config = c, 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
}
async generate_speech(n, a, {
threshold: u = .5,
minlenratio: c = 0,
maxlenratio: f = 20,
vocoder: s = null
} = {}) {
const h = {
input_ids: n
},
{
encoder_outputs: p,
encoder_attention_mask: l
} = await encoderForward(this, h),
o = p.dims[1] / this.config.reduction_factor,
t = Math.floor(o * f),
e = Math.floor(o * c),
r = this.config.num_mel_bins;
let i = [],
d = null,
g = null,
m = 0;
for (;;) {
++m;
const T = boolTensor(!!g);
let w;
g ? w = g.output_sequence_out : w = new Tensor("float32", new Float32Array(r), [1, 1, r]);
let S = {
use_cache_branch: T,
output_sequence: w,
encoder_attention_mask: l,
speaker_embeddings: a,
encoder_hidden_states: p
};
this.addPastKeyValues(S, d), g = await sessionRun(this.decoder_merged_session, S), d = this.getPastKeyValues(g, d);
const {
prob: O,
spectrum: E
} = g;
if (i.push(E), m >= e && (Array.from(O.data).filter(v => v >= u).length > 0 || m >= t)) break
}
const _ = cat(i),
{
waveform: y
} = await sessionRun(s.session, {
spectrogram: _
});
return {
spectrogram: _,
waveform: y
}
}
}
class SpeechT5HifiGan extends PreTrainedModel {
main_input_name = "spectrogram"
}
class TrOCRPreTrainedModel extends PreTrainedModel {
constructor(n, a, u) {
super(n, a), this.generation_config = u, 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 {
constructor(n, a, u) {
super(n, a), this.generation_config = u, 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 FalconPreTrainedModel extends PreTrainedModel {
constructor(n, a, u) {
super(n, a), this.generation_config = u, 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 {
static async from_pretrained(n, a = {}) {
return a.model_file_name ??= "text_model", super.from_pretrained(n, a)
}
}
class ClapAudioModelWithProjection extends ClapPreTrainedModel {
static async from_pretrained(n, a = {}) {
return a.model_file_name ??= "audio_model", super.from_pretrained(n, a)
}
}
class VitsPreTrainedModel extends PreTrainedModel {}
class VitsModel extends VitsPreTrainedModel {
async _call(n) {
return new VitsModelOutput(await super._call(n))
}
}
class SegformerPreTrainedModel extends PreTrainedModel {}
class SegformerForImageClassification extends SegformerPreTrainedModel {}
class SegformerForSemanticSegmentation extends SegformerPreTrainedModel {}
class PretrainedMixin {
static MODEL_CLASS_MAPPINGS = null;
static BASE_IF_FAIL = !1;
static async from_pretrained(n, {
quantized: a = !0,
progress_callback: u = null,
config: c = null,
cache_dir: f = null,
local_files_only: s = !1,
revision: h = "main",
model_file_name: p = null
} = {}) {
let l = {
quantized: a,
progress_callback: u,
config: c,
cache_dir: f,
local_files_only: s,
revision: h,
model_file_name: p
};
if (c = await AutoConfig.from_pretrained(n, l), l.config || (l.config = c), !this.MODEL_CLASS_MAPPINGS) throw new Error("`MODEL_CLASS_MAPPINGS` not implemented for this type of `AutoClass`: " + this.name);
for (let o of this.MODEL_CLASS_MAPPINGS) {
const t = o.get(c.model_type);
if (t) return await t[1].from_pretrained(n, l)
}
if (this.BASE_IF_FAIL) return console.warn(`Unknown model class "${c.model_type}", attempting to construct from base class.`), await PreTrainedModel.from_pretrained(n, l);
throw Error(`Unsupported model type: ${c.model_type}`)
}
}
const MODEL_MAPPING_NAMES_ENCODER_ONLY = new Map([
["bert", ["BertModel", BertModel]],
["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]],
["hubert", ["HubertModel", HubertModel]],
["wavlm", ["WavLMModel", WavLMModel]],
["audio-spectrogram-transformer", ["ASTModel", ASTModel]],
["vits", ["VitsModel", VitsModel]],
["detr", ["DetrModel", DetrModel]],
["table-transformer", ["TableTransformerModel", TableTransformerModel]],
["vit", ["ViTModel", ViTModel]],
["mobilevit", ["MobileViTModel", MobileViTModel]],
["owlvit", ["OwlViTModel", OwlViTModel]],
["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]]
]),
MODEL_MAPPING_NAMES_ENCODER_DECODER = 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]]
]),
MODEL_MAPPING_NAMES_DECODER_ONLY = 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]],
["falcon", ["FalconModel", FalconModel]]
]),
MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES = new Map([
["speecht5", ["SpeechT5ForSpeechToText", SpeechT5ForSpeechToText]],
["whisper", ["WhisperForConditionalGeneration", WhisperForConditionalGeneration]]
]),
MODEL_FOR_TEXT_TO_SPECTROGRAM_MAPPING_NAMES = new Map([
["speecht5", ["SpeechT5ForTextToSpeech", SpeechT5ForTextToSpeech]]
]),
MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING_NAMES = new Map([
["vits", ["VitsModel", VitsModel]]
]),
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = 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]]
]),
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = 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]]
]),
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES = 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]]
]),
MODEL_WITH_LM_HEAD_MAPPING_NAMES = 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]],
["falcon", ["FalconForCausalLM", FalconForCausalLM]],
["trocr", ["TrOCRForCausalLM", TrOCRForCausalLM]]
]),
MODEL_FOR_MASKED_LM_MAPPING_NAMES = 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]]
]),
MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = 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]]
]),
MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES = new Map([
["vision-encoder-decoder", ["VisionEncoderDecoderModel", VisionEncoderDecoderModel]]
]),
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = new Map([
["vit", ["ViTForImageClassification", ViTForImageClassification]],
["mobilevit", ["MobileViTForImageClassification", MobileViTForImageClassification]],
["beit", ["BeitForImageClassification", BeitForImageClassification]],
["deit", ["DeiTForImageClassification", DeiTForImageClassification]],
["convnext", ["ConvNextForImageClassification", ConvNextForImageClassification]],
["convnextv2", ["ConvNextV2ForImageClassification", ConvNextV2ForImageClassification]],
["dinov2", ["Dinov2ForImageClassification", Dinov2ForImageClassification]],
["resnet", ["ResNetForImageClassification", ResNetForImageClassification]],
["swin", ["SwinForImageClassification", SwinForImageClassification]],
["segformer", ["SegformerForImageClassification", SegformerForImageClassification]]
]),
MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES = new Map([
["detr", ["DetrForObjectDetection", DetrForObjectDetection]],
["table-transformer", ["TableTransformerForObjectDetection", TableTransformerForObjectDetection]],
["yolos", ["YolosForObjectDetection", YolosForObjectDetection]]
]),
MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES = new Map([
["owlvit", ["OwlViTForObjectDetection", OwlViTForObjectDetection]]
]),
MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES = new Map([
["detr", ["DetrForSegmentation", DetrForSegmentation]],
["clipseg", ["CLIPSegForImageSegmentation", CLIPSegForImageSegmentation]]
]),
MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES = new Map([
["segformer", ["SegformerForSemanticSegmentation", SegformerForSemanticSegmentation]]
]),
MODEL_FOR_MASK_GENERATION_MAPPING_NAMES = new Map([
["sam", ["SamModel", SamModel]]
]),
MODEL_FOR_CTC_MAPPING_NAMES = new Map([
["wav2vec2", ["Wav2Vec2ForCTC", Wav2Vec2ForCTC]],
["wav2vec2-bert", ["Wav2Vec2BertForCTC", Wav2Vec2BertForCTC]],
["wavlm", ["WavLMForCTC", WavLMForCTC]],
["hubert", ["HubertForCTC", HubertForCTC]]
]),
MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES = new Map([
["wav2vec2", ["Wav2Vec2ForSequenceClassification", Wav2Vec2ForSequenceClassification]],
["wav2vec2-bert", ["Wav2Vec2BertForSequenceClassification", Wav2Vec2BertForSequenceClassification]],
["wavlm", ["WavLMForSequenceClassification", WavLMForSequenceClassification]],
["hubert", ["HubertForSequenceClassification", HubertForSequenceClassification]],
["audio-spectrogram-transformer", ["ASTForAudioClassification", ASTForAudioClassification]]
]),
MODEL_FOR_IMAGE_MATTING_MAPPING_NAMES = new Map([
["vitmatte", ["VitMatteForImageMatting", VitMatteForImageMatting]]
]),
MODEL_FOR_IMAGE_TO_IMAGE_MAPPING_NAMES = new Map([
["swin2sr", ["Swin2SRForImageSuperResolution", Swin2SRForImageSuperResolution]]
]),
MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES = new Map([
["dpt", ["DPTForDepthEstimation", DPTForDepthEstimation]],
["depth_anything", ["DepthAnythingForDepthEstimation", DepthAnythingForDepthEstimation]],
["glpn", ["GLPNForDepthEstimation", GLPNForDepthEstimation]]
]),
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]
];
for (const [b, n] of MODEL_CLASS_TYPE_MAPPING)
for (const [a, u] of b.values()) MODEL_TYPE_MAPPING.set(a, n), MODEL_CLASS_TO_NAME_MAPPING.set(u, a), MODEL_NAME_TO_CLASS_MAPPING.set(a, u);
const CUSTOM_MAPPING = [
["CLIPTextModelWithProjection", CLIPTextModelWithProjection, MODEL_TYPES.EncoderOnly],
["CLIPVisionModelWithProjection", CLIPVisionModelWithProjection, MODEL_TYPES.EncoderOnly],
["SiglipTextModel", SiglipTextModel, MODEL_TYPES.EncoderOnly],
["SiglipVisionModel", SiglipVisionModel, MODEL_TYPES.EncoderOnly],
["ClapTextModelWithProjection", ClapTextModelWithProjection, MODEL_TYPES.EncoderOnly],
["ClapAudioModelWithProjection", ClapAudioModelWithProjection, MODEL_TYPES.EncoderOnly]
];
for (const [b, n, a] of CUSTOM_MAPPING) MODEL_TYPE_MAPPING.set(b, a), MODEL_CLASS_TO_NAME_MAPPING.set(n, b), MODEL_NAME_TO_CLASS_MAPPING.set(b, n);
class AutoModel extends PretrainedMixin {
static MODEL_CLASS_MAPPINGS = MODEL_CLASS_TYPE_MAPPING.map(n => n[0]);
static BASE_IF_FAIL = !0
}
class Seq2SeqLMOutput extends ModelOutput {
constructor({
logits: n,
past_key_values: a,
encoder_outputs: u,
decoder_attentions: c = null,
cross_attentions: f = null
}) {
super(), this.logits = n, this.past_key_values = a, this.encoder_outputs = u, this.decoder_attentions = c, this.cross_attentions = f
}
}
class SequenceClassifierOutput extends ModelOutput {
constructor({
logits: n
}) {
super(), this.logits = n
}
}
class TokenClassifierOutput extends ModelOutput {
constructor({
logits: n
}) {
super(), this.logits = n
}
}
class MaskedLMOutput extends ModelOutput {
constructor({
logits: n
}) {
super(), this.logits = n
}
}
class QuestionAnsweringModelOutput extends ModelOutput {
constructor({
start_logits: n,
end_logits: a
}) {
super(), this.start_logits = n, this.end_logits = a
}
}
class CausalLMOutput extends ModelOutput {
constructor({
logits: n
}) {
super(), this.logits = n
}
}
class ImageMattingOutput extends ModelOutput {
constructor({
alphas: n
}) {
super(), this.alphas = n
}
}
class VitsModelOutput extends ModelOutput {
constructor({
waveform: n,
spectrogram: a
}) {
super(), this.waveform = n, this.spectrogram = a
}
}
const BROWSER_ENV = typeof self < "u",
WEBWORKER_ENV = BROWSER_ENV && self.constructor.name === "DedicatedWorkerGlobalScope";
let createCanvasFunction, ImageDataClass, loadImageFunction;
if (BROWSER_ENV) createCanvasFunction = (b, n) => {
if (!self.OffscreenCanvas) throw new Error("OffscreenCanvas not supported by this browser.");
return new self.OffscreenCanvas(b, n)
}, loadImageFunction = self.createImageBitmap, ImageDataClass = self.ImageData;
else if (sharp) loadImageFunction = async b => {
const a = (await b.metadata()).channels;
let {
data: u,
info: c
} = await b.raw().toBuffer({
resolveWithObject: !0
});
const f = new RawImage(new Uint8ClampedArray(u), c.width, c.height, c.channels);
return a !== void 0 && a !== c.channels && f.convert(a), f
};
else throw new Error("Unable to load image processing library.");
const RESAMPLING_MAPPING = {
0: "nearest",
1: "lanczos",
2: "bilinear",
3: "bicubic",
4: "box",
5: "hamming"
},
CONTENT_TYPE_MAP = new Map([
["png", "image/png"],
["jpg", "image/jpeg"],
["jpeg", "image/jpeg"],
["gif", "image/gif"]
]);
class RawImage {
constructor(n, a, u, c) {
this.data = n, this.width = a, this.height = u, this.channels = c
}
get size() {
return [this.width, this.height]
}
static async read(n) {
if (n instanceof RawImage) return n;
if (typeof n == "string" || n instanceof URL) return await this.fromURL(n);
throw new Error(`Unsupported input type: ${typeof n}`)
}
static async fromURL(n) {
let a = await getFile(n);
if (a.status !== 200) throw new Error(`Unable to read image from "${n}" (${a.status} ${a.statusText})`);
let u = await a.blob();
return this.fromBlob(u)
}
static async fromBlob(n) {
if (BROWSER_ENV) {
let a = await loadImageFunction(n);
const u = createCanvasFunction(a.width, a.height).getContext("2d");
return u.drawImage(a, 0, 0), new this(u.getImageData(0, 0, a.width, a.height).data, a.width, a.height, 4)
} else {
let a = sharp(await n.arrayBuffer());
return await loadImageFunction(a)
}
}
static fromTensor(n, a = "CHW") {
if (n.dims.length !== 3) throw new Error(`Tensor should have 3 dimensions, but has ${n.dims.length} dimensions.`);
if (a === "CHW") n = n.transpose(1, 2, 0);
else if (a !== "HWC") throw new Error(`Unsupported channel format: ${a}`);
if (!(n.data instanceof Uint8ClampedArray || n.data instanceof Uint8Array)) throw new Error(`Unsupported tensor type: ${n.type}`);
switch (n.dims[2]) {
case 1:
case 2:
case 3:
case 4:
return new RawImage(n.data, n.dims[1], n.dims[0], n.dims[2]);
default:
throw new Error(`Unsupported number of channels: ${n.dims[2]}`)
}
}
grayscale() {
if (this.channels === 1) return this;
let n = new Uint8ClampedArray(this.width * this.height * 1);
switch (this.channels) {
case 3:
case 4:
for (let a = 0, u = 0; a < this.data.length; a += this.channels) {
const c = this.data[a],
f = this.data[a + 1],
s = this.data[a + 2];
n[u++] = Math.round(.2989 * c + .587 * f + .114 * s)
}
break;
default:
throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)
}
return this._update(n, this.width, this.height, 1)
}
rgb() {
if (this.channels === 3) return this;
let n = new Uint8ClampedArray(this.width * this.height * 3);
switch (this.channels) {
case 1:
for (let a = 0, u = 0; a < this.data.length; ++a) n[u++] = this.data[a], n[u++] = this.data[a], n[u++] = this.data[a];
break;
case 4:
for (let a = 0, u = 0; a < this.data.length; a += 4) n[u++] = this.data[a], n[u++] = this.data[a + 1], n[u++] = this.data[a + 2];
break;
default:
throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)
}
return this._update(n, this.width, this.height, 3)
}
rgba() {
if (this.channels === 4) return this;
let n = new Uint8ClampedArray(this.width * this.height * 4);
switch (this.channels) {
case 1:
for (let a = 0, u = 0; a < this.data.length; ++a) n[u++] = this.data[a], n[u++] = this.data[a], n[u++] = this.data[a], n[u++] = 255;
break;
case 3:
for (let a = 0, u = 0; a < this.data.length; a += 3) n[u++] = this.data[a], n[u++] = this.data[a + 1], n[u++] = this.data[a + 2], n[u++] = 255;
break;
default:
throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)
}
return this._update(n, this.width, this.height, 4)
}
async resize(n, a, {
resample: u = 2
} = {}) {
let c = RESAMPLING_MAPPING[u] ?? u;
if (BROWSER_ENV) {
let f = this.channels,
s = this.toCanvas();
const h = createCanvasFunction(n, a).getContext("2d");
return h.drawImage(s, 0, 0, n, a), new RawImage(h.getImageData(0, 0, n, a).data, n, a, 4).convert(f)
} else {
let f = this.toSharp();
switch (c) {
case "box":
case "hamming":
(c === "box" || c === "hamming") && (console.warn(`Resampling method ${c} is not yet supported. Using bilinear instead.`), c = "bilinear");
case "nearest":
case "bilinear":
case "bicubic":
f = f.affine([n / this.width, 0, 0, a / this.height], {
interpolator: c
});
break;
case "lanczos":
f = f.resize({
width: n,
height: a,
fit: "fill",
kernel: "lanczos3"
});
break;
default:
throw new Error(`Resampling method ${c} is not supported.`)
}
return await loadImageFunction(f)
}
}
async pad([n, a, u, c]) {
if (n = Math.max(n, 0), a = Math.max(a, 0), u = Math.max(u, 0), c = Math.max(c, 0), n === 0 && a === 0 && u === 0 && c === 0) return this;
if (BROWSER_ENV) {
let f = this.channels,
s = this.toCanvas(),
h = this.width + n + a,
p = this.height + u + c;
const l = createCanvasFunction(h, p).getContext("2d");
return l.drawImage(s, 0, 0, this.width, this.height, n, u, h, p), new RawImage(l.getImageData(0, 0, h, p).data, h, p, 4).convert(f)
} else {
let f = this.toSharp().extend({
left: n,
right: a,
top: u,
bottom: c
});
return await loadImageFunction(f)
}
}
async crop([n, a, u, c]) {
if (n = Math.max(n, 0), a = Math.max(a, 0), u = Math.min(u, this.width - 1), c = Math.min(c, this.height - 1), n === 0 && a === 0 && u === this.width - 1 && c === this.height - 1) return this;
const f = u - n + 1,
s = c - a + 1;
if (BROWSER_ENV) {
const h = this.channels,
p = this.toCanvas(),
l = createCanvasFunction(f, s).getContext("2d");
return l.drawImage(p, n, a, f, s, 0, 0, f, s), new RawImage(l.getImageData(0, 0, f, s).data, f, s, 4).convert(h)
} else {
const h = this.toSharp().extract({
left: n,
top: a,
width: f,
height: s
});
return await loadImageFunction(h)
}
}
async center_crop(n, a) {
if (this.width === n && this.height === a) return this;
let u = (this.width - n) / 2,
c = (this.height - a) / 2;
if (BROWSER_ENV) {
let f = this.channels,
s = this.toCanvas();
const h = createCanvasFunction(n, a).getContext("2d");
let p = 0,
l = 0,
o = 0,
t = 0;
return u >= 0 ? p = u : o = -u, c >= 0 ? l = c : t = -c, h.drawImage(s, p, l, n, a, o, t, n, a), new RawImage(h.getImageData(0, 0, n, a).data, n, a, 4).convert(f)
} else {
let f = this.toSharp();
if (u >= 0 && c >= 0) f = f.extract({
left: Math.floor(u),
top: Math.floor(c),
width: n,
height: a
});
else if (u <= 0 && c <= 0) {
let s = Math.floor(-c),
h = Math.floor(-u);
f = f.extend({
top: s,
left: h,
right: n - this.width - h,
bottom: a - this.height - s
})
} else {
let s = [0, 0],
h = 0;
c < 0 ? (s[0] = Math.floor(-c), s[1] = a - this.height - s[0]) : h = Math.floor(c);
let p = [0, 0],
l = 0;
u < 0 ? (p[0] = Math.floor(-u), p[1] = n - this.width - p[0]) : l = Math.floor(u), f = f.extend({
top: s[0],
bottom: s[1],
left: p[0],
right: p[1]
}).extract({
left: l,
top: h,
width: n,
height: a
})
}
return await loadImageFunction(f)
}
}
async toBlob(n = "image/png", a = 1) {
if (!BROWSER_ENV) throw new Error("toBlob() is only supported in browser environments.");
return await this.toCanvas().convertToBlob({
type: n,
quality: a
})
}
toCanvas() {
if (!BROWSER_ENV) throw new Error("toCanvas() is only supported in browser environments.");
let n = this.clone().rgba(),
a = createCanvasFunction(n.width, n.height),
u = new ImageDataClass(n.data, n.width, n.height);
return a.getContext("2d").putImageData(u, 0, 0), a
}
_update(n, a, u, c = null) {
return this.data = n, this.width = a, this.height = u, c !== null && (this.channels = c), this
}
clone() {
return new RawImage(this.data.slice(), this.width, this.height, this.channels)
}
convert(n) {
if (this.channels === n) return this;
switch (n) {
case 1:
this.grayscale();
break;
case 3:
this.rgb();
break;
case 4:
this.rgba();
break;
default:
throw new Error(`Conversion failed due to unsupported number of channels: ${this.channels}`)
}
return this
}
async save(n) {
if (BROWSER_ENV) {
if (WEBWORKER_ENV) throw new Error("Unable to save an image from a Web Worker.");
const a = n.split(".").pop().toLowerCase(),
u = CONTENT_TYPE_MAP.get(a) ?? "image/png",
c = await this.toBlob(u),
f = URL.createObjectURL(c),
s = document.createElement("a");
s.href = f, s.download = n, s.click(), s.remove()
} else {
if (env$1.useFS) return await this.toSharp().toFile(n);
throw new Error("Unable to save the image because filesystem is disabled in this environment.")
}
}
toSharp() {
if (BROWSER_ENV) throw new Error("toSharp() is only supported in server-side environments.");
return sharp(this.data, {
raw: {
width: this.width,
height: this.height,
channels: this.channels
}
})
}
}
function hanning(b) {
if (b < 1) return new Float64Array;
if (b === 1) return new Float64Array([1]);
const n = b - 1,
a = Math.PI / n,
u = new Float64Array(b);
for (let c = 0; c < b; ++c) {
const f = 2 * c - n;
u[c] = .5 + .5 * Math.cos(a * f)
}
return u
}
const HERTZ_TO_MEL_MAPPING = {
htk: b => 2595 * Math.log10(1 + b / 700),
kaldi: b => 1127 * Math.log(1 + b / 700),
slaney: (b, n = 1e3, a = 15, u = 27 / Math.log(6.4)) => b >= n ? a + Math.log(b / n) * u : 3 * b / 200
};
function hertz_to_mel(b, n = "htk") {
const a = HERTZ_TO_MEL_MAPPING[n];
if (!a) throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');
return typeof b == "number" ? a(b) : b.map(u => a(u))
}
const MEL_TO_HERTZ_MAPPING = {
htk: b => 700 * (10 ** (b / 2595) - 1),
kaldi: b => 700 * (Math.exp(b / 1127) - 1),
slaney: (b, n = 1e3, a = 15, u = Math.log(6.4) / 27) => b >= a ? n * Math.exp(u * (b - a)) : 200 * b / 3
};
function mel_to_hertz(b, n = "htk") {
const a = MEL_TO_HERTZ_MAPPING[n];
if (!a) throw new Error('mel_scale should be one of "htk", "slaney" or "kaldi".');
return typeof b == "number" ? a(b) : b.map(u => a(u))
}
function _create_triangular_filter_bank(b, n) {
const a = Float64Array.from({
length: n.length - 1
}, (s, h) => n[h + 1] - n[h]),
u = Array.from({
length: b.length
}, () => new Array(n.length));
for (let s = 0; s < b.length; ++s) {
const h = u[s];
for (let p = 0; p < n.length; ++p) h[p] = n[p] - b[s]
}
const c = n.length - 2,
f = Array.from({
length: c
}, () => new Array(b.length));
for (let s = 0; s < b.length; ++s) {
const h = u[s];
for (let p = 0; p < c; ++p) {
const l = -h[p] / a[p],
o = h[p + 2] / a[p + 1];
f[p][s] = Math.max(0, Math.min(l, o))
}
}
return f
}
function linspace(b, n, a) {
const u = (n - b) / (a - 1);
return Float64Array.from({
length: a
}, (c, f) => b + u * f)
}
function mel_filter_bank(b, n, a, u, c, f = null, s = "htk", h = !1) {
if (f !== null && f !== "slaney") throw new Error('norm must be one of null or "slaney"');
const p = hertz_to_mel(a, s),
l = hertz_to_mel(u, s),
o = linspace(p, l, n + 2);
let t = mel_to_hertz(o, s),
e;
if (h) {
const i = c / (b * 2);
e = hertz_to_mel(Float64Array.from({
length: b
}, (d, g) => g * i), s), t = o
} else e = linspace(0, Math.floor(c / 2), b);
const r = _create_triangular_filter_bank(e, t);
if (f !== null && f === "slaney")
for (let i = 0; i < n; ++i) {
const d = r[i],
g = 2 / (t[i + 2] - t[i]);
for (let m = 0; m < b; ++m) d[m] *= g
}
return r
}
function padReflect(b, n, a) {
const u = new b.constructor(b.length + n + a),
c = b.length - 1;
for (let f = 0; f < b.length; ++f) u[n + f] = b[f];
for (let f = 1; f <= n; ++f) u[n - f] = b[calculateReflectOffset(f, c)];
for (let f = 1; f <= a; ++f) u[c + n + f] = b[calculateReflectOffset(c - f, c)];
return u
}
function _db_conversion_helper(b, n, a, u, c) {
if (a <= 0) throw new Error("reference must be greater than zero");
if (u <= 0) throw new Error("min_value must be greater than zero");
a = Math.max(u, a);
const f = Math.log10(a);
for (let s = 0; s < b.length; ++s) b[s] = n * Math.log10(Math.max(u, b[s]) - f);
if (c !== null) {
if (c <= 0) throw new Error("db_range must be greater than zero");
const s = max(b)[0] - c;
for (let h = 0; h < b.length; ++h) b[h] = Math.max(b[h], s)
}
return b
}
function amplitude_to_db(b, n = 1, a = 1e-5, u = null) {
return _db_conversion_helper(b, 20, n, a, u)
}
function power_to_db(b, n = 1, a = 1e-10, u = null) {
return _db_conversion_helper(b, 10, n, a, u)
}
function spectrogram(b, n, a, u, {
fft_length: c = null,
power: f = 1,
center: s = !0,
pad_mode: h = "reflect",
onesided: p = !0,
preemphasis: l = null,
mel_filters: o = null,
mel_floor: t = 1e-10,
log_mel: e = null,
reference: r = 1,
min_value: i = 1e-10,
db_range: d = null,
remove_dc_offset: g = null,
max_num_frames: m = null,
do_pad: _ = !0,
transpose: y = !1
} = {}) {
const T = n.length;
if (c === null && (c = a), a > c) throw Error(`frame_length (${a}) may not be larger than fft_length (${c})`);
if (T !== a) throw new Error(`Length of the window (${T}) must equal frame_length (${a})`);
if (u <= 0) throw new Error("hop_length must be greater than zero");
if (s) {
if (h !== "reflect") throw new Error(`pad_mode="${h}" not implemented yet.`);
const C = Math.floor((c - 1) / 2) + 1;
b = padReflect(b, C, C)
}
const w = Math.floor(1 + Math.floor((b.length - a) / u)),
S = p ? Math.floor(c / 2) + 1 : c;
let O = w,
E = w;
m !== null && (m > w ? _ && (E = m) : E = O = m);
const v = new FFT(c),
P = new Float64Array(c),
L = new Float64Array(v.outputBufferSize),
V = new Array(O);
for (let C = 0; C < O; ++C) {
const $ = C * u;
for (let z = 0; z < a; ++z) P[z] = b[$ + z];
if (g) {
let z = 0;
for (let J = 0; J < a; ++J) z += P[J];
const Z = z / a;
for (let J = 0; J < a; ++J) P[J] -= Z
}
if (l !== null) {
for (let z = a - 1; z >= 1; --z) P[z] -= l * P[z - 1];
P[0] *= 1 - l
}
for (let z = 0; z < n.length; ++z) P[z] *= n[z];
v.realTransform(L, P);
const X = new Array(S);
for (let z = 0; z < X.length; ++z) {
const Z = z << 1;
X[z] = L[Z] ** 2 + L[Z + 1] ** 2
}
V[C] = X
}
if (f !== null && f !== 2) {
const C = 2 / f;
for (let $ = 0; $ < V.length; ++$) {
const X = V[$];
for (let z = 0; z < X.length; ++z) X[z] **= C
}
}
const R = o.length,
k = new Float32Array(R * E),
Y = y ? [E, R] : [R, E];
for (let C = 0; C < R; ++C) {
const $ = o[C];
for (let X = 0; X < O; ++X) {
const z = V[X];
let Z = 0;
for (let J = 0; J < S; ++J) Z += $[J] * z[J];
k[y ? X * R + C : C * O + X] = Math.max(t, Z)
}
}
if (f !== null && e !== null) {
const C = Math.min(k.length, O * R);
switch (e) {
case "log":
for (let $ = 0; $ < C; ++$) k[$] = Math.log(k[$]);
break;
case "log10":
for (let $ = 0; $ < C; ++$) k[$] = Math.log10(k[$]);
break;
case "dB":
if (f === 1) amplitude_to_db(k, r, i, d);
else if (f === 2) power_to_db(k, r, i, d);
else throw new Error(`Cannot use log_mel option '${e}' with power ${f}`);
break;
default:
throw new Error(`log_mel must be one of null, 'log', 'log10' or 'dB'. Got '${e}'`)
}
}
return {
data: k,
dims: Y
}
}
function window_function(b, n, {
periodic: a = !0,
frame_length: u = null,
center: c = !0
} = {}) {
const f = a ? b + 1 : b;
let s;
switch (n) {
case "boxcar":
s = new Float64Array(f).fill(1);
break;
case "hann":
case "hann_window":
s = hanning(f);
break;
case "povey":
s = hanning(f).map(h => Math.pow(h, .85));
break;
default:
throw new Error(`Unknown window type ${n}.`)
}
if (a && (s = s.subarray(0, b)), u === null) return s;
if (b > u) throw new Error(`Length of the window (${b}) may not be larger than frame_length (${u})`);
return s
}
function center_to_corners_format([b, n, a, u]) {
return [b - a / 2, n - u / 2, b + a / 2, n + u / 2]
}
function post_process_object_detection(b, n = .5, a = null, u = !1) {
const c = b.logits,
f = b.pred_boxes,
[s, h, p] = c.dims;
if (a !== null && a.length !== s) throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");
let l = [];
for (let o = 0; o < s; ++o) {
let t = a !== null ? a[o] : null,
e = {
boxes: [],
classes: [],
scores: []
},
r = c[o],
i = f[o];
for (let d = 0; d < h; ++d) {
let g = r[d],
m = [],
_;
if (u) {
_ = g.sigmoid().data;
for (let y = 0; y < _.length; ++y) _[y] > n && m.push(y)
} else {
let y = max(g.data)[1];
if (y === p - 1) continue;
m.push(y), _ = softmax(g.data)
}
for (const y of m) {
let T = i[d].data;
T = center_to_corners_format(T), t !== null && (T = T.map((w, S) => w * t[(S + 1) % 2])), e.boxes.push(T), e.classes.push(y), e.scores.push(_[y])
}
}
l.push(e)
}
return l
}
function validate_audio_inputs(b, n) {
if (!(b instanceof Float32Array || b instanceof Float64Array)) throw new Error(`${n} expects input to be a Float32Array or a Float64Array, but got ${b?.constructor?.name??typeof b} instead.If using the feature extractor directly, remember to use \`read_audio(url, sampling_rate)\` to obtain the raw audio data of the file/url.`)
}
function constraint_to_multiple_of(b, n, a = 0, u = null) {
let c = Math.round(b / n) * n;
return u !== null && c > u && (c = Math.floor(b / n) * n), c < a && (c = Math.ceil(b / n) * n), c
}
function enforce_size_divisibility([b, n], a) {
return [Math.floor(b / a) * a, Math.floor(n / a) * a]
}
class FeatureExtractor extends Callable {
constructor(n) {
super(), this.config = n
}
}
class ImageFeatureExtractor extends FeatureExtractor {
constructor(n) {
super(n), this.image_mean = this.config.image_mean ?? this.config.mean, this.image_std = this.config.image_std ?? this.config.std, this.resample = this.config.resample ?? 2, this.do_rescale = this.config.do_rescale ?? !0, this.rescale_factor = this.config.rescale_factor ?? 1 / 255, this.do_normalize = this.config.do_normalize, this.do_resize = this.config.do_resize, this.do_thumbnail = this.config.do_thumbnail, this.size = this.config.size, this.size_divisibility = this.config.size_divisibility ?? this.config.size_divisor, this.do_center_crop = this.config.do_center_crop, this.crop_size = this.config.crop_size, this.do_convert_rgb = this.config.do_convert_rgb ?? !0, this.do_crop_margin = this.config.do_crop_margin, this.pad_size = this.config.pad_size, this.do_pad = this.config.do_pad, this.do_pad && !this.pad_size && this.size && this.size.width !== void 0 && this.size.height !== void 0 && (this.pad_size = this.size)
}
async thumbnail(n, a, u = 2) {
const c = n.height,
f = n.width,
s = a.height,
h = a.width;
let p = Math.min(c, s),
l = Math.min(f, h);
return p === c && l === f ? n : (c > f ? l = Math.floor(f * p / c) : f > c && (p = Math.floor(c * l / f)), await n.resize(l, p, {
resample: u
}))
}
async crop_margin(n, a = 200) {
const u = n.clone().grayscale(),
c = min(u.data)[0],
s = max(u.data)[0] - c;
if (s === 0) return n;
const h = a / 255;
let p = u.width,
l = u.height,
o = 0,
t = 0;
for (let e = 0; e < u.height; ++e) {
const r = e * u.width;
for (let i = 0; i < u.width; ++i)(u.data[r + i] - c) / s < h && (p = Math.min(p, i), l = Math.min(l, e), o = Math.max(o, i), t = Math.max(t, e))
}
return n = await n.crop([p, l, o, t]), n
}
pad_image(n, a, u, {
mode: c = "constant",
center: f = !1,
constant_values: s = 0
} = {}) {
const [h, p, l] = a;
let o, t;
if (typeof u == "number" ? (o = u, t = u) : (o = u.width, t = u.height), o !== h || t !== p) {
const e = new Float32Array(o * t * l);
if (Array.isArray(s))
for (let d = 0; d < e.length; ++d) e[d] = s[d % l];
else s !== 0 && e.fill(s);
const [r, i] = f ? [Math.floor((o - h) / 2), Math.floor((t - p) / 2)] : [0, 0];
for (let d = 0; d < p; ++d) {
const g = (d + i) * o,
m = d * h;
for (let _ = 0; _ < h; ++_) {
const y = (g + _ + r) * l,
T = (m + _) * l;
for (let w = 0; w < l; ++w) e[y + w] = n[T + w]
}
}
if (c === "symmetric") {
if (f) throw new Error("`center` padding is not supported when `mode` is set to `symmetric`.");
const d = p - 1,
g = h - 1;
for (let m = 0; m < t; ++m) {
const _ = m * o,
y = calculateReflectOffset(m, d) * h;
for (let T = 0; T < o; ++T) {
if (m < p && T < h) continue;
const w = (_ + T) * l,
S = (y + calculateReflectOffset(T, g)) * l;
for (let O = 0; O < l; ++O) e[w + O] = n[S + O]
}
}
}
n = e, a = [t, o, l]
}
return [n, a]
}
rescale(n) {
for (let a = 0; a < n.length; ++a) n[a] = this.rescale_factor * n[a]
}
get_resize_output_image_size(n, a) {
const [u, c] = n.size;
let f, s;
if (this.do_thumbnail) {
const {
height: h,
width: p
} = a;
f = Math.min(h, p)
} else Number.isInteger(a) ? (f = a, s = this.config.max_size ?? f) : a !== void 0 && (f = a.shortest_edge, s = a.longest_edge);
if (f !== void 0 || s !== void 0) {
const h = f === void 0 ? 1 : Math.max(f / u, f / c),
p = u * h,
l = c * h,
o = s === void 0 ? 1 : Math.min(s / p, s / l);
let t = Math.floor(Number((p * o).toFixed(2))),
e = Math.floor(Number((l * o).toFixed(2)));
return this.size_divisibility !== void 0 && ([t, e] = enforce_size_divisibility([t, e], this.size_divisibility)), [t, e]
} else if (a !== void 0 && a.width !== void 0 && a.height !== void 0) {
let h = a.width,
p = a.height;
if (this.config.keep_aspect_ratio && this.config.ensure_multiple_of) {
let l = a.height / c,
o = a.width / u;
Math.abs(1 - o) < Math.abs(1 - l) ? l = o : o = l, p = constraint_to_multiple_of(l * c, this.config.ensure_multiple_of), h = constraint_to_multiple_of(o * u, this.config.ensure_multiple_of)
}
return [h, p]
} else {
if (this.size_divisibility !== void 0) return enforce_size_divisibility([u, c], this.size_divisibility);
throw new Error(`Could not resize image due to unsupported \`this.size\` option in config: ${JSON.stringify(a)}`)
}
}
async resize(n) {
const [a, u] = this.get_resize_output_image_size(n, this.size);
return await n.resize(a, u, {
resample: this.resample
})
}
async preprocess(n, {
do_normalize: a = null,
do_pad: u = null,
do_convert_rgb: c = null,
do_convert_grayscale: f = null
} = {}) {
this.do_crop_margin && (n = await this.crop_margin(n));
const [s, h] = n.size;
if (c ?? this.do_convert_rgb ? n = n.rgb() : f && (n = n.grayscale()), this.do_resize && (n = await this.resize(n)), this.do_thumbnail && (n = await this.thumbnail(n, this.size, this.resample)), this.do_center_crop) {
let r, i;
Number.isInteger(this.crop_size) ? (r = this.crop_size, i = this.crop_size) : (r = this.crop_size.width, i = this.crop_size.height), n = await n.center_crop(r, i)
}
const p = [n.height, n.width];
let l = Float32Array.from(n.data),
o = [n.height, n.width, n.channels];
if (this.do_rescale && this.rescale(l), a ?? this.do_normalize) {
let r = this.image_mean;
Array.isArray(this.image_mean) || (r = new Array(n.channels).fill(r));
let i = this.image_std;
if (Array.isArray(this.image_std) || (i = new Array(n.channels).fill(r)), r.length !== n.channels || i.length !== n.channels) throw new Error(`When set to arrays, the length of \`image_mean\` (${r.length}) and \`image_std\` (${i.length}) must match the number of channels in the image (${n.channels}).`);
for (let d = 0; d < l.length; d += n.channels)
for (let g = 0; g < n.channels; ++g) l[d + g] = (l[d + g] - this.image_mean[g]) / this.image_std[g]
}(u ?? (this.do_pad && this.pad_size)) && ([l, o] = this.pad_image(l, [n.width, n.height, n.channels], this.pad_size));
const t = new Tensor("float32", l, o),
e = transpose(t, [2, 0, 1]);
return {
original_size: [h, s],
reshaped_input_size: p,
pixel_values: e
}
}
async _call(n, ...a) {
Array.isArray(n) || (n = [n]);
const u = await Promise.all(n.map(f => this.preprocess(f)));
return {
pixel_values: stack(u.map(f => f.pixel_values), 0),
original_sizes: u.map(f => f.original_size),
reshaped_input_sizes: u.map(f => f.reshaped_input_size)
}
}
}
class SegformerFeatureExtractor extends ImageFeatureExtractor {
post_process_semantic_segmentation(n, a = null) {
const u = n.logits,
c = u.dims[0];
if (a !== null && a.length !== c) throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");
const f = [];
for (let s = 0; s < c; ++s) {
const h = a !== null ? a[s] : null;
let p = u[s];
h !== null && (p = interpolate(p, h, "bilinear", !1));
const [l, o] = h ?? p.dims.slice(-2), t = new Tensor("int32", new Int32Array(l * o), [l, o]), e = p[0].data;
for (let g = 1; g < p.dims[0]; ++g) {
const m = p[g].data;
for (let _ = 0; _ < m.length; ++_) m[_] > e[_] && (e[_] = m[_], t.data[_] = g)
}
const r = new Array(p.dims[0]),
i = t.data;
for (let g = 0; g < i.length; ++g) {
const m = i[g];
r[m] = m
}
const d = r.filter(g => g !== void 0);
f.push({
segmentation: t,
labels: d
})
}
return f
}
}
class DPTImageProcessor extends ImageFeatureExtractor {}
class BitImageProcessor extends ImageFeatureExtractor {}
class DPTFeatureExtractor extends ImageFeatureExtractor {}
class GLPNFeatureExtractor extends ImageFeatureExtractor {}
class CLIPFeatureExtractor extends ImageFeatureExtractor {}
class ChineseCLIPFeatureExtractor extends ImageFeatureExtractor {}
class SiglipImageProcessor extends ImageFeatureExtractor {}
class ConvNextFeatureExtractor extends ImageFeatureExtractor {
constructor(n) {
super(n), this.crop_pct = this.config.crop_pct ?? 224 / 256
}
async resize(n) {
const a = this.size?.shortest_edge;
if (a === void 0) throw new Error("Size dictionary must contain 'shortest_edge' key.");
if (a < 384) {
const u = Math.floor(a / this.crop_pct),
[c, f] = this.get_resize_output_image_size(n, {
shortest_edge: u
});
n = await n.resize(c, f, {
resample: this.resample
}), n = await n.center_crop(a, a)
} else n = await n.resize(a, a, {
resample: this.resample
});
return n
}
}
class ConvNextImageProcessor extends ConvNextFeatureExtractor {}
class ViTFeatureExtractor extends ImageFeatureExtractor {}
class ViTImageProcessor extends ImageFeatureExtractor {}
class MobileViTFeatureExtractor extends ImageFeatureExtractor {}
class OwlViTFeatureExtractor extends ImageFeatureExtractor {
post_process_object_detection(...n) {
return post_process_object_detection(...n)
}
}
class DeiTFeatureExtractor extends ImageFeatureExtractor {}
class BeitFeatureExtractor extends ImageFeatureExtractor {}
class DonutFeatureExtractor extends ImageFeatureExtractor {
pad_image(n, a, u, c = {}) {
const [f, s, h] = a;
let p = this.image_mean;
Array.isArray(this.image_mean) || (p = new Array(h).fill(p));
let l = this.image_std;
Array.isArray(l) || (l = new Array(h).fill(p));
const o = p.map((t, e) => -t / this.image_std[e]);
return super.pad_image(n, a, u, {
center: !0,
constant_values: o,
...c
})
}
}
class NougatImageProcessor extends DonutFeatureExtractor {}
class DetrFeatureExtractor extends ImageFeatureExtractor {
async _call(n) {
const a = await super._call(n),
u = [a.pixel_values.dims[0], 64, 64],
c = new Tensor("int64", new BigInt64Array(u.reduce((f, s) => f * s)).fill(1n), u);
return {
...a,
pixel_mask: c
}
}
post_process_object_detection(...n) {
return post_process_object_detection(...n)
}
remove_low_and_no_objects(n, a, u, c) {
let f = [],
s = [],
h = [];
for (let p = 0; p < n.dims[0]; ++p) {
let l = n[p],
o = a[p],
t = max(l.data)[1];
if (t === c) continue;
let r = softmax(l.data)[t];
r > u && (f.push(o), s.push(r), h.push(t))
}
return [f, s, h]
}
check_segment_validity(n, a, u, c = .5, f = .8) {
let s = [],
h = 0,
p = 0;
for (let o = 0; o < n.length; ++o) n[o] === u && (s.push(o), ++h), a[u].data[o] >= c && ++p;
let l = h > 0 && p > 0;
return l && (l = h / p > f), [l, s]
}
compute_segments(n, a, u, c, f, s = null, h = null) {
let [p, l] = h ?? n[0].dims, o = new Tensor("int32", new Int32Array(p * l), [p, l]), t = [];
if (h !== null)
for (let d = 0; d < n.length; ++d) n[d] = interpolate(n[d], h, "bilinear", !1);
let e = new Int32Array(n[0].data.length),
r = new Float32Array(n[0].data.length);
for (let d = 0; d < n.length; ++d) {
let g = a[d];
for (let m = 0; m < n[d].data.length; ++m) n[d].data[m] *= g, n[d].data[m] > r[m] && (e[m] = d, r[m] = n[d].data[m])
}
let i = 0;
for (let d = 0; d < u.length; ++d) {
let g = u[d],
[m, _] = this.check_segment_validity(e, n, d, c, f);
if (m) {
++i;
for (let y of _) o.data[y] = i;
t.push({
id: i,
label_id: g,
score: a[d]
})
}
}
return [o, t]
}
post_process_panoptic_segmentation(n, a = .5, u = .5, c = .8, f = null, s = null) {
f === null && (console.warn("`label_ids_to_fuse` unset. No instance will be fused."), f = new Set);
const h = n.logits,
l = n.pred_masks.sigmoid();
let [o, t, e] = h.dims;
if (e -= 1, s !== null && s.length !== o) throw Error("Make sure that you pass in as many target sizes as the batch dimension of the logits");
let r = [];
for (let i = 0; i < o; ++i) {
let d = s !== null ? s[i] : null,
g = h[i],
m = l[i],
[_, y, T] = this.remove_low_and_no_objects(g, m, a, e);
if (T.length === 0) {
let [O, E] = d ?? m.dims.slice(-2), v = new Tensor("int32", new Int32Array(O * E).fill(-1), [O, E]);
r.push({
segmentation: v,
segments_info: []
});
continue
}
let [w, S] = this.compute_segments(_, y, T, u, c, f, d);
r.push({
segmentation: w,
segments_info: S
})
}
return r
}
post_process_instance_segmentation() {
throw Error("Not implemented yet")
}
}
class YolosFeatureExtractor extends ImageFeatureExtractor {
post_process_object_detection(...n) {
return post_process_object_detection(...n)
}
}
class SamImageProcessor extends ImageFeatureExtractor {
reshape_input_points(n, a, u) {
n = structuredClone(n);
let c = calculateDimensions(n);
if (c.length === 3) c = [1, ...c], n = [n];
else if (c.length !== 4) throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");
for (let f = 0; f < n.length; ++f) {
let s = a[f],
h = u[f],
p = [h[0] / s[0], h[1] / s[1]];
for (let l = 0; l < n[f].length; ++l)
for (let o = 0; o < n[f][l].length; ++o)
for (let t = 0; t < n[f][l][o].length; ++t) n[f][l][o][t] *= p[t]
}
return new Tensor("float32", Float32Array.from(n.flat(1 / 0)), c)
}
add_input_labels(n, a) {
let u = calculateDimensions(n);
if (u.length === 2) u = [1, ...u], n = [n];
else if (u.length !== 3) throw Error("The input_points must be a 4D tensor of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.");
if (u.some((c, f) => c !== a.dims[f])) throw Error(`The first ${u.length} dimensions of 'input_points' and 'input_labels' must be the same.`);
return new Tensor("int64", n.flat(1 / 0).map(BigInt), u)
}
async _call(n, a = null, u = null) {
const c = await super._call(n);
if (a && (c.input_points = this.reshape_input_points(a, c.original_sizes, c.reshaped_input_sizes)), u) {
if (!c.input_points) throw Error("`input_points` must be provided if `input_labels` are provided.");
c.input_labels = this.add_input_labels(u, c.input_points)
}
return c
}
post_process_masks(n, a, u, {
mask_threshold: c = 0,
binarize: f = !0,
pad_size: s = null
} = {}) {
const h = [];
s = s ?? this.pad_size;
const p = [s.height, s.width];
for (let l = 0; l < a.length; ++l) {
const o = a[l],
t = u[l],
e = n[l],
r = [];
for (let i = 0; i < e.dims[0]; ++i) {
const d = e[i];
let g = interpolate(d, p, "bilinear", !1);
if (g = g.slice(null, [0, t[0]], [0, t[1]]), g = interpolate(g, o, "bilinear", !1), f) {
const m = new Uint8Array(g.data.length);
for (let _ = 0; _ < g.data.length; ++_) g.data[_] > c && (m[_] = 1);
g = new Tensor("bool", m, g.dims)
}
r.push(g)
}
h.push(stack(r))
}
return h
}
}
class Swin2SRImageProcessor extends ImageFeatureExtractor {
pad_image(n, a, u, c = {}) {
const [f, s, h] = a;
return super.pad_image(n, a, {
width: f + (u - f % u) % u,
height: s + (u - s % u) % u
}, {
mode: "symmetric",
center: !1,
constant_values: -1,
...c
})
}
}
class VitMatteImageProcessor extends ImageFeatureExtractor {
async _call(n, a) {
Array.isArray(n) || (n = [n]), Array.isArray(a) || (a = [a]);
const u = await Promise.all(n.map(s => this.preprocess(s))),
c = await Promise.all(a.map(s => this.preprocess(s, {
do_normalize: !1,
do_convert_rgb: !1,
do_convert_grayscale: !0
})));
return {
pixel_values: stack(u.map((s, h) => cat([s.pixel_values, c[h].pixel_values], 0)), 0),
original_sizes: u.map(s => s.original_size),
reshaped_input_sizes: u.map(s => s.reshaped_input_size)
}
}
}
class WhisperFeatureExtractor extends FeatureExtractor {
constructor(n) {
super(n), this.config.mel_filters ??= mel_filter_bank(Math.floor(1 + this.config.n_fft / 2), this.config.feature_size, 0, 8e3, this.config.sampling_rate, "slaney", "slaney"), this.window = window_function(this.config.n_fft, "hann")
}
_extract_fbank_features(n) {
const {
data: a,
dims: u
} = spectrogram(n, this.window, this.config.n_fft, this.config.hop_length, {
power: 2,
mel_filters: this.config.mel_filters,
log_mel: "log10",
max_num_frames: this.config.nb_max_frames
}), c = max(a)[0];
for (let f = 0; f < a.length; ++f) a[f] = (Math.max(a[f], c - 8) + 4) / 4;
return {
data: a,
dims: u
}
}
async _call(n) {
validate_audio_inputs(n, "WhisperFeatureExtractor");
let a;
n.length > this.config.n_samples ? (console.warn("Attempting to extract features for audio longer than 30 seconds. If using a pipeline to extract transcript from a long audio clip, remember to specify `chunk_length_s` and/or `stride_length_s`."), a = n.slice(0, this.config.n_samples)) : (a = new Float32Array(this.config.n_samples), a.set(n));
const {
data: u,
dims: c
} = this._extract_fbank_features(a);
return {
input_features: new Tensor("float32", u, [1, ...c])
}
}
}
class Wav2Vec2FeatureExtractor extends FeatureExtractor {
_zero_mean_unit_var_norm(n) {
const u = n.reduce((f, s) => f + s, 0) / n.length,
c = n.reduce((f, s) => f + (s - u) ** 2, 0) / n.length;
return n.map(f => (f - u) / Math.sqrt(c + 1e-7))
}
async _call(n) {
validate_audio_inputs(n, "Wav2Vec2FeatureExtractor"), n instanceof Float64Array && (n = new Float32Array(n));
let a = n;
this.config.do_normalize && (a = this._zero_mean_unit_var_norm(a));
const u = [1, a.length];
return {
input_values: new Tensor("float32", a, u),
attention_mask: new Tensor("int64", new BigInt64Array(a.length).fill(1n), u)
}
}
}
class SeamlessM4TFeatureExtractor extends FeatureExtractor {
constructor(n) {
super(n);
const a = this.config.sampling_rate,
u = mel_filter_bank(256, this.config.num_mel_bins, 20, Math.floor(a / 2), a, null, "kaldi", !0);
for (let c = 0; c < u.length; ++c) u[c].push(0);
this.mel_filters = u, this.window = window_function(400, "povey", {
periodic: !1
})
}
_extract_fbank_features(n, a) {
return n = n.map(u => u * 32768), spectrogram(n, this.window, 400, 160, {
fft_length: 512,
power: 2,
center: !1,
preemphasis: .97,
mel_filters: this.mel_filters,
log_mel: "log",
mel_floor: 1192092955078125e-22,
remove_dc_offset: !0,
max_num_frames: a,
transpose: !0
})
}
async _call(n, {
padding: a = !0,
pad_to_multiple_of: u = 2,
do_normalize_per_mel_bins: c = !0,
return_attention_mask: f = !0
} = {}) {
validate_audio_inputs(n, "SeamlessM4TFeatureExtractor");
let s = this._extract_fbank_features(n, this.config.max_length);
if (c) {
const [i, d] = s.dims;
for (let g = 0; g < d; ++g) {
let m = 0;
for (let w = 0; w < i; ++w) m += s.data[w * d + g];
const _ = m / i;
let y = 0;
for (let w = 0; w < i; ++w) y += (s.data[w * d + g] - _) ** 2;
y /= i - 1;
const T = Math.sqrt(y + 1e-7);
for (let w = 0; w < i; ++w) {
const S = w * d + g;
s.data[S] = (s.data[S] - _) / T
}
}
}
let h;
if (a) {
const [i, d] = s.dims, g = i % u;
if (g > 0) {
const m = new Float32Array(d * (i + g));
m.set(s.data), m.fill(this.config.padding_value, s.data.length);
const _ = i + g;
s = {
data: m,
dims: [_, d]
}, f && (h = new Tensor("int64", new BigInt64Array(_), [1, _]), h.data.fill(1n, 0, i))
}
}
const [p, l] = s.dims, o = this.config.stride;
if (p % o !== 0) throw new Error(`The number of frames (${p}) must be a multiple of the stride (${o}).`);
const e = new Tensor("float32", s.data, s.dims).view(1, Math.floor(p / o), l * o),
r = {
input_features: e
};
if (f) {
const i = e.dims[1],
d = new Tensor("int64", new BigInt64Array(i), [1, i]);
if (h)
for (let g = 1, m = 0; g < p; g += o, ++m) d.data[m] = h.data[g];
else d.data.fill(1n);
r.attention_mask = d
}
return r
}
}
class ASTFeatureExtractor extends FeatureExtractor {
constructor(n) {
super(n);
const a = this.config.sampling_rate,
u = mel_filter_bank(256, this.config.num_mel_bins, 20, Math.floor(a / 2), a, null, "kaldi", !0);
for (let c = 0; c < u.length; ++c) u[c].push(0);
this.mel_filters = u, this.window = window_function(400, "hann", {
periodic: !1
}), this.mean = this.config.mean, this.std = this.config.std
}
_extract_fbank_features(n, a) {
return spectrogram(n, this.window, 400, 160, {
fft_length: 512,
power: 2,
center: !1,
preemphasis: .97,
mel_filters: this.mel_filters,
log_mel: "log",
mel_floor: 1192092955078125e-22,
remove_dc_offset: !0,
max_num_frames: a,
transpose: !0
})
}
async _call(n) {
validate_audio_inputs(n, "ASTFeatureExtractor");
const a = this._extract_fbank_features(n, this.config.max_length);
if (this.config.do_normalize) {
const u = this.std * 2;
for (let c = 0; c < a.data.length; ++c) a.data[c] = (a.data[c] - this.mean) / u
}
return {
input_values: new Tensor("float32", a.data, [1, ...a.dims])
}
}
}
class ClapFeatureExtractor extends FeatureExtractor {
constructor(n) {
super(n), this.mel_filters = mel_filter_bank(this.config.nb_frequency_bins, this.config.feature_size, this.config.frequency_min, this.config.frequency_max, this.config.sampling_rate, null, "htk"), this.mel_filters_slaney = mel_filter_bank(this.config.nb_frequency_bins, this.config.feature_size, this.config.frequency_min, this.config.frequency_max, this.config.sampling_rate, "slaney", "slaney"), this.window = window_function(this.config.fft_window_size, "hann")
}
_get_input_mel(n, a, u, c) {
let f, s = !1;
const h = n.length - a;
if (h > 0)
if (u === "rand_trunc") {
s = !0;
const p = Math.floor(Math.random() * (h + 1));
n = n.subarray(p, p + a), f = this._extract_fbank_features(n, this.mel_filters_slaney, this.config.nb_max_samples), f.dims = [1, ...f.dims]
} else throw new Error(`Truncation strategy "${u}" not implemented`);
else {
if (h < 0) {
let p = new Float64Array(a);
if (p.set(n), c === "repeat")
for (let l = n.length; l < a; l += n.length) p.set(n.subarray(0, Math.min(n.length, a - l)), l);
else if (c === "repeatpad")
for (let l = n.length; l < -h; l += n.length) p.set(n, l);
n = p
}
if (u === "fusion") throw new Error(`Truncation strategy "${u}" not implemented`);
f = this._extract_fbank_features(n, this.mel_filters_slaney, this.config.nb_max_samples), f.dims = [1, ...f.dims]
}
return {
...f,
longer: s
}
}
_extract_fbank_features(n, a, u = null) {
return spectrogram(n, this.window, this.config.fft_window_size, this.config.hop_length, {
power: 2,
mel_filters: a,
log_mel: "dB",
max_num_frames: u,
do_pad: !1,
transpose: !0
})
}
async _call(n, {
max_length: a = null
} = {}) {
validate_audio_inputs(n, "ClapFeatureExtractor");
const u = this._get_input_mel(n, a ?? this.config.nb_max_samples, this.config.truncation, this.config.padding);
return {
input_features: new Tensor("float32", u.data, [1, ...u.dims])
}
}
}
class SpeechT5FeatureExtractor extends FeatureExtractor {}
class Processor extends Callable {
constructor(n) {
super(), this.feature_extractor = n
}
async _call(n, ...a) {
return await this.feature_extractor(n, ...a)
}
}
class SamProcessor extends Processor {
async _call(...n) {
return await this.feature_extractor(...n)
}
post_process_masks(...n) {
return this.feature_extractor.post_process_masks(...n)
}
reshape_input_points(...n) {
return this.feature_extractor.reshape_input_points(...n)
}
}
class WhisperProcessor extends Processor {
async _call(n) {
return await this.feature_extractor(n)
}
}
class Wav2Vec2ProcessorWithLM extends Processor {
async _call(n) {
return await this.feature_extractor(n)
}
}
class SpeechT5Processor extends Processor {
async _call(n) {
return await this.feature_extractor(n)
}
}
class OwlViTProcessor extends Processor {}
class AutoProcessor {
static FEATURE_EXTRACTOR_CLASS_MAPPING = {
ImageFeatureExtractor,
WhisperFeatureExtractor,
ViTFeatureExtractor,
MobileViTFeatureExtractor,
OwlViTFeatureExtractor,
CLIPFeatureExtractor,
ChineseCLIPFeatureExtractor,
SiglipImageProcessor,
ConvNextFeatureExtractor,
ConvNextImageProcessor,
SegformerFeatureExtractor,
BitImageProcessor,
DPTImageProcessor,
DPTFeatureExtractor,
GLPNFeatureExtractor,
BeitFeatureExtractor,
DeiTFeatureExtractor,
DetrFeatureExtractor,
YolosFeatureExtractor,
DonutFeatureExtractor,
NougatImageProcessor,
ViTImageProcessor,
VitMatteImageProcessor,
SamImageProcessor,
Swin2SRImageProcessor,
Wav2Vec2FeatureExtractor,
SeamlessM4TFeatureExtractor,
SpeechT5FeatureExtractor,
ASTFeatureExtractor,
ClapFeatureExtractor
};
static PROCESSOR_CLASS_MAPPING = {
WhisperProcessor,
Wav2Vec2ProcessorWithLM,
SamProcessor,
SpeechT5Processor,
OwlViTProcessor
};
static async from_pretrained(n, {
progress_callback: a = null,
config: u = null,
cache_dir: c = null,
local_files_only: f = !1,
revision: s = "main"
} = {}) {
let h = u ?? await getModelJSON(n, "preprocessor_config.json", !0, {
progress_callback: a,
config: u,
cache_dir: c,
local_files_only: f,
revision: s
}),
p = h.feature_extractor_type ?? h.image_processor_type,
l = this.FEATURE_EXTRACTOR_CLASS_MAPPING[p];
if (!l)
if (h.size !== void 0) console.warn(`Feature extractor type "${p}" not found, assuming ImageFeatureExtractor due to size parameter in config.`), l = ImageFeatureExtractor;
else throw new Error(`Unknown Feature Extractor type: ${p}`);
let o = this.PROCESSOR_CLASS_MAPPING[h.processor_class] ?? Processor,
t = new l(h);
return new o(t)
}
}
env$1.allowLocalModels = !1;
env$1.backends.onnx.wasm.proxy = !0;
const EXAMPLE_URL = "https://images.pexels.com/photos/5965592/pexels-photo-5965592.jpeg?auto=compress&cs=tinysrgb&w=1024",
status = document.getElementById("status"),
fileUpload = document.getElementById("upload"),
imageContainer = document.getElementById("container"),
example = document.getElementById("example");
status.textContent = "Loading model...";
const model = await AutoModel.from_pretrained("briaai/RMBG-1.4", {
config: {
model_type: "custom"
}
}),
processor = await AutoProcessor.from_pretrained("briaai/RMBG-1.4", {
config: {
do_normalize: !0,
do_pad: !1,
do_rescale: !0,
do_resize: !0,
image_mean: [.5, .5, .5],
feature_extractor_type: "ImageFeatureExtractor",
image_std: [1, 1, 1],
resample: 2,
rescale_factor: .00392156862745098,
size: {
width: 1024,
height: 1024
}
}
});
status.textContent = "Ready";
example.addEventListener("click", b => {
b.preventDefault(), predict(EXAMPLE_URL)
});
fileUpload.addEventListener("change", function(b) {
const n = b.target.files[0];
if (!n) return;
const a = new FileReader;
a.onload = u => predict(u.target.result), a.readAsDataURL(n)
});
async function predict(b) {
const n = await RawImage.fromURL(b);
imageContainer.innerHTML = "", imageContainer.style.backgroundImage = `url(${b})`;
const a = n.width / n.height,
[u, c] = a > 720 / 480 ? [720, 720 / a] : [480 * a, 480];
imageContainer.style.width = `${u}px`, imageContainer.style.height = `${c}px`, status.textContent = "Analysing...";
const {
pixel_values: f
} = await processor(n), {
output: s
} = await model({
input: f
}), h = await RawImage.fromTensor(s[0].mul(255).to("uint8")).resize(n.width, n.height), p = document.createElement("canvas");
p.width = n.width, p.height = n.height;
const l = p.getContext("2d");
l.drawImage(n.toCanvas(), 0, 0);
const o = l.getImageData(0, 0, n.width, n.height);
for (let t = 0; t < h.data.length; ++t) o.data[4 * t + 3] = h.data[t];
l.putImageData(o, 0, 0), imageContainer.append(p), imageContainer.style.removeProperty("background-image"), imageContainer.style.background = 'url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQBAMAAADt3eJSAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAGUExURb+/v////5nD/3QAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAAUSURBVBjTYwABQSCglEENMxgYGAAynwRB8BEAgQAAAABJRU5ErkJggg==")', status.textContent = "Done!";
// Create download button
const downloadButton = document.createElement("button");
downloadButton.textContent = "Download";
downloadButton.addEventListener("click", () => {
const downloadLink = document.createElement("a");
downloadLink.href = p.toDataURL(); // Convert canvas to data URL
downloadLink.download = "image.png"; // Specify download file name
downloadLink.click(); // Trigger download
});
imageContainer.appendChild(downloadButton);
}