chore: update node_modules with new binary files and dependencies

- Add new binary files for nodemon, onnxruntime-web, and xenova/transformers
- Update various JavaScript and TypeScript files in node_modules
- Remove unused files and dependencies
- Add new test fixtures and documentation files

node_modules/onnxruntime-web/lib/onnxjs/backends/webgl/ops/concat.js

@@ -0,0 +1,159 @@
+"use strict";
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+Object.defineProperty(exports, "__esModule", { value: true });
+exports.parseConcatAttributes = exports.concat = void 0;
+const attribute_with_cache_key_1 = require("../../../attribute-with-cache-key");
+const types_1 = require("../types");
+const concat_packed_1 = require("./concat-packed");
+const concat = (inferenceHandler, inputs, attributes) => {
+    validateInputs(inputs);
+    if (inferenceHandler.session.pack && inputs[0].dims.length > 1) {
+        const output = inferenceHandler.run((0, concat_packed_1.createPackedConcatProgramInfoLoader)(inferenceHandler, inputs, attributes), inputs);
+        return [output];
+    }
+    else {
+        const output = inferenceHandler.run(createUnpackedConcatProgramInfoLoader(inferenceHandler, inputs, attributes), inputs);
+        return [output];
+    }
+};
+exports.concat = concat;
+const createUnpackedConcatProgramMetadata = (inputCount, cacheHint) => ({
+    name: 'Concat',
+    inputNames: Array.from({ length: inputCount }, (v, i) => `X${i}`),
+    inputTypes: Array(inputCount).fill(types_1.TextureType.unpacked),
+    cacheHint
+});
+const createUnpackedConcatProgramInfo = (handler, metadata, inputs, axis) => {
+    const inputShape = inputs[0].dims.slice();
+    if (axis >= inputShape.length || axis < (-1 * inputShape.length)) {
+        throw new Error('axis specified for concat doesn\'t match input dimensionality');
+    }
+    if (axis < 0) {
+        axis = inputShape.length + axis;
+    }
+    // ensure all of the non-concatenated axes match each other
+    // calculate the shape of the output tensor while we do that
+    const outputShape = inputShape.slice(0);
+    for (let i = 1; i < inputs.length; i++) {
+        const dataNShape = inputs[i].dims.slice();
+        for (let axisIndex = 0; axisIndex < inputShape.length; axisIndex++) {
+            // add to the placeholder for computing output shape
+            if (axisIndex === axis) {
+                outputShape[axis] += dataNShape[axisIndex];
+            }
+            // ensure all non-cancatenated axes match each other
+            else if (inputShape[axisIndex] !== dataNShape[axisIndex]) {
+                throw new Error('non concat dimensions must match');
+            }
+        }
+    }
+    const rank = outputShape.length;
+    const sizeInConcatAxis = new Array(inputs.length);
+    let previousSum = 0;
+    for (let i = 0; i < sizeInConcatAxis.length; ++i) {
+        previousSum += inputs[i].dims[axis];
+        sizeInConcatAxis[i] = previousSum;
+    }
+    let getTextureIndexWhereDataResidesMethod = '';
+    // in most cases linear search is sufficient, as in most scenarios, only 2 tensors are concatenated
+    if (inputs.length < 5) {
+        getTextureIndexWhereDataResidesMethod = getTextureIndexWhereDataResidesLinearSearch(sizeInConcatAxis);
+    }
+    else {
+        getTextureIndexWhereDataResidesMethod = getTextureIndexWhereDataResidesBinarySearch(sizeInConcatAxis);
+    }
+    const fetchDataFromCorrectTextureMethod = getFetchDataFromCorrectTextureMethod(inputs.length, rank);
+    const getSizeInConcatAxisValueFromIndexMethod = getGetSizeInConcatAxisValueFromIndexMethod(sizeInConcatAxis);
+    const shaderSource = `
+        ${fetchDataFromCorrectTextureMethod}
+        ${getSizeInConcatAxisValueFromIndexMethod}
+        ${getTextureIndexWhereDataResidesMethod}
+        float process(int indices[${rank}]) {
+          int textureIndex = getTextureWhereDataResides (indices[${axis}]);
+
+          if(textureIndex != 0) {
+            indices[${axis}] = indices[${axis}] - int(getSizeInConcatAxisValueFromIndex(textureIndex-int(1)));
+          }
+
+          return fetchDataFromCorrectTexture(textureIndex, indices);
+        }`;
+    return Object.assign(Object.assign({}, metadata), { output: { dims: outputShape, type: inputs[0].type, textureType: types_1.TextureType.unpacked }, shaderSource });
+};
+const createUnpackedConcatProgramInfoLoader = (handler, inputs, attributes) => {
+    const metadata = createUnpackedConcatProgramMetadata(inputs.length, attributes.cacheKey);
+    return Object.assign(Object.assign({}, metadata), { get: () => createUnpackedConcatProgramInfo(handler, metadata, inputs, attributes.axis) });
+};
+const getTextureIndexWhereDataResidesLinearSearch = (sizeInConcatAxis) => {
+    const searchAxis = sizeInConcatAxis.map((size, i) => `if(index<${size}) {return ${i};}
+`);
+    return `int getTextureWhereDataResides(int index) {
+      ${searchAxis.join('')}
+    }`;
+};
+// TODO: Implement BinarySearch in GLSL
+const getTextureIndexWhereDataResidesBinarySearch = (sizeInConcatAxis) => getTextureIndexWhereDataResidesLinearSearch(sizeInConcatAxis);
+const getFetchDataFromCorrectTextureMethod = (numberOfTensors, tensorRank) => {
+    const codeLines = [`float fetchDataFromCorrectTexture(int textureIndex, int indices[${tensorRank}]) {`];
+    for (let i = 0; i < numberOfTensors; ++i) {
+        if (i === 0) {
+            codeLines.push('\t' +
+                `if (textureIndex == ${i}) { return _X${i}(indices); }`);
+        }
+        else if (i === numberOfTensors - 1) {
+            codeLines.push('\t' +
+                `else { return _X${i}(indices); }`);
+        }
+        else {
+            codeLines.push('\t' +
+                `else if (textureIndex == ${i}) { return _X${i}(indices); }`);
+        }
+    }
+    codeLines.push('\t' +
+        '}');
+    return codeLines.join('\n');
+};
+const getGetSizeInConcatAxisValueFromIndexMethod = (sizeInConcatAxis) => {
+    const codeLines = ['int getSizeInConcatAxisValueFromIndex(int index) {'];
+    for (let i = 0; i < sizeInConcatAxis.length; ++i) {
+        if (i === 0) {
+            codeLines.push('\t' +
+                `if (index == ${i}) { return ${sizeInConcatAxis[i]}; }`);
+        }
+        else if (i === sizeInConcatAxis.length - 1) {
+            codeLines.push('\t' +
+                `else { return ${sizeInConcatAxis[i]}; }`);
+        }
+        else {
+            codeLines.push('\t' +
+                `else if (index == ${i}) { return ${sizeInConcatAxis[i]}; }`);
+        }
+    }
+    codeLines.push('\t' +
+        '}');
+    return codeLines.join('\n');
+};
+const parseConcatAttributes = (node) => (0, attribute_with_cache_key_1.createAttributeWithCacheKey)({ axis: node.attributes.getInt('axis') });
+exports.parseConcatAttributes = parseConcatAttributes;
+const validateInputs = (inputs) => {
+    if (!inputs || inputs.length < 1) {
+        throw new Error('too few inputs');
+    }
+    const inputType = inputs[0].type;
+    const inputDimensionality = inputs[0].dims.length;
+    // TODO: Support string concat
+    if (inputType === 'string') {
+        throw new Error('string tensor is not supported yet');
+    }
+    for (const input of inputs) {
+        // make sure types of all inputs match
+        if (input.type !== inputType) {
+            throw new Error('input tensors should be one type');
+        }
+        // make sure the dimensionality of all inputs are the same
+        if (input.dims.length !== inputDimensionality) {
+            throw new Error('input tensors should have the same shape');
+        }
+    }
+};
+//# sourceMappingURL=concat.js.map