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2025-06-12 7b72ac13a83764a662159d4a49b7fffb90476ecb

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/**
 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
import { backend_util, util } from '@tensorflow/tfjs-core';
export function sparseReshapeImpl(inputIndices, inputIndicesShape, inputDType, inputShape, targetShape) {
    const denseSize = util.sizeFromShape(inputShape);
    const nnz = inputIndicesShape[0];
    const outputRank = targetShape.length;
    // Compute the output shape. Determine product of specified dimensions, and
    // find the index of the unspecified one.
    const outputShape = [];
    let product = 1;
    let unknownIndex = -1;
    for (let d = 0; d < outputRank; ++d) {
        const size = targetShape[d];
        if (size === -1) {
            if (unknownIndex !== -1) {
                throw new Error(backend_util
                    .getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(unknownIndex, d));
            }
            unknownIndex = d;
            outputShape.push(1);
        }
        else {
            if (size < 0) {
                throw new Error(backend_util.getSparseReshapeNegativeOutputDimErrorMessage(d, size));
            }
            product *= size;
            outputShape.push(size);
        }
    }
    if (unknownIndex !== -1) {
        if (product <= 0) {
            throw new Error(backend_util.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());
        }
        const missing = Math.trunc(denseSize / product);
        if (product * missing !== denseSize) {
            throw new Error(backend_util.getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape));
        }
        outputShape[unknownIndex] = missing;
    }
    const outputSize = util.sizeFromShape(outputShape);
    if (outputSize !== denseSize) {
        throw new Error(backend_util.getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape));
    }
    const inputRank = inputShape.length;
    const inputStrides = [];
    if (inputRank > 0) {
        inputStrides[inputRank - 1] = 1;
        for (let d = inputRank - 2; d >= 0; --d) {
            inputStrides[d] = inputStrides[d + 1] * inputShape[d + 1];
        }
    }
    const outputStrides = [];
    if (outputRank > 0) {
        outputStrides[outputRank - 1] = 1;
        for (let d = outputRank - 2; d >= 0; --d) {
            outputStrides[d] = outputStrides[d + 1] * outputShape[d + 1];
        }
    }
    const newIndices = util.getArrayFromDType(inputDType, nnz * outputRank);
    for (let i = 0; i < nnz; ++i) {
        let id = 0;
        for (let j = 0; j < inputRank; ++j) {
            // inputIndices is a 2d tensor with shape of [nnz, inputRank]
            id += inputIndices[i * inputRank + j] * inputStrides[j];
        }
        for (let j = 0; j < outputRank; ++j) {
            // newIndices is a 2d tensor with shape of [nnz, outputRank]
            newIndices[i * outputRank + j] = Math.trunc(id / outputStrides[j]);
            id %= outputStrides[j];
        }
    }
    return [newIndices, [nnz, outputRank], outputShape];
}
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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {backend_util, DataType, TypedArray, util} from '@tensorflow/tfjs-core';\n\nexport function sparseReshapeImpl(\n    inputIndices: TypedArray, inputIndicesShape: number[], inputDType: DataType,\n    inputShape: number[],\n    targetShape: number[]): [TypedArray, number[], number[]] {\n  const denseSize = util.sizeFromShape(inputShape);\n  const nnz = inputIndicesShape[0];\n  const outputRank = targetShape.length;\n\n  // Compute the output shape. Determine product of specified dimensions, and\n  // find the index of the unspecified one.\n  const outputShape: number[] = [];\n  let product = 1;\n  let unknownIndex = -1;\n  for (let d = 0; d < outputRank; ++d) {\n    const size = targetShape[d];\n    if (size === -1) {\n      if (unknownIndex !== -1) {\n        throw new Error(\n            backend_util\n                .getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(\n                    unknownIndex, d));\n      }\n      unknownIndex = d;\n      outputShape.push(1);\n    } else {\n      if (size < 0) {\n        throw new Error(\n            backend_util.getSparseReshapeNegativeOutputDimErrorMessage(\n                d, size));\n      }\n      product *= size;\n      outputShape.push(size);\n    }\n  }\n  if (unknownIndex !== -1) {\n    if (product <= 0) {\n      throw new Error(\n          backend_util.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());\n    }\n    const missing = Math.trunc(denseSize / product);\n    if (product * missing !== denseSize) {\n      throw new Error(\n          backend_util.getSparseReshapeInputOutputMultipleErrorMessage(\n              inputShape, outputShape));\n    }\n\n    outputShape[unknownIndex] = missing;\n  }\n  const outputSize = util.sizeFromShape(outputShape);\n  if (outputSize !== denseSize) {\n    throw new Error(\n        backend_util.getSparseReshapeInputOutputMismatchErrorMessage(\n            inputShape, outputShape));\n  }\n\n  const inputRank = inputShape.length;\n  const inputStrides: number[] = [];\n  if (inputRank > 0) {\n    inputStrides[inputRank - 1] = 1;\n    for (let d = inputRank - 2; d >= 0; --d) {\n      inputStrides[d] = inputStrides[d + 1] * inputShape[d + 1];\n    }\n  }\n\n  const outputStrides: number[] = [];\n  if (outputRank > 0) {\n    outputStrides[outputRank - 1] = 1;\n    for (let d = outputRank - 2; d >= 0; --d) {\n      outputStrides[d] = outputStrides[d + 1] * outputShape[d + 1];\n    }\n  }\n\n  const newIndices =\n      util.getArrayFromDType(inputDType, nnz * outputRank) as TypedArray;\n  for (let i = 0; i < nnz; ++i) {\n    let id = 0;\n    for (let j = 0; j < inputRank; ++j) {\n      // inputIndices is a 2d tensor with shape of [nnz, inputRank]\n      id += inputIndices[i * inputRank + j] * inputStrides[j];\n    }\n    for (let j = 0; j < outputRank; ++j) {\n      // newIndices is a 2d tensor with shape of [nnz, outputRank]\n      newIndices[i * outputRank + j] = Math.trunc(id / outputStrides[j]);\n      id %= outputStrides[j];\n    }\n  }\n  return [newIndices, [nnz, outputRank], outputShape];\n}\n"]}