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chenyc

2025-02-12 ea42ff3ebee1eeb3fb29423aa848a249441db81c

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import { computeStrides, sizeFromShape } from '../util';
/**
 * Check whether updates.shape = indices.shape[:batchDim] +
 * shape[sliceDim:]
 *
 * @param x The input tensor.
 */
export function validateUpdateShape(shape, indices, updates) {
    const sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;
    const batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;
    const shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +
        `shape[sliceDim:], got updates.shape: ${updates.shape}` +
        `, indices.shape: ${indices.shape}, shape: ${shape}` +
        `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;
    if (updates.rank < batchDim) {
        throw new Error(shapeError + ` update.rank < ${batchDim}. `);
    }
    if (shape.length < sliceDim + (updates.rank - batchDim)) {
        throw new Error(shapeError +
            ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);
    }
    if (updates.rank !== batchDim + shape.length - sliceDim) {
        throw new Error(shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);
    }
    for (let d = 0; d < batchDim; ++d) {
        if (updates.shape[d] !== indices.shape[d]) {
            throw new Error(shapeError +
                ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${indices.shape[d]}).`);
        }
    }
    for (let d = 0; d < updates.rank - batchDim; ++d) {
        if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {
            throw new Error(shapeError +
                ` updates.shape[${d + batchDim}] (${updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${shape[d + batchDim]})`);
        }
    }
}
/**
 * Validate scatter nd inputs.
 *
 * @param update The tensor contains the update values.
 * @param indices The tensor contains the indices for the update values.
 * @param shape The shape of the output tensor.
 */
export function validateInput(updates, indices, shape) {
    if (indices.rank < 1) {
        throw new Error('tf.scatterND() expects the indices to be rank 1 or higher,' +
            ` but the rank was ${indices.rank}.`);
    }
    if (updates.rank < 1) {
        throw new Error('tf.scatterND() expects the updates to be rank 1 or higher,' +
            ` but the rank was ${updates.rank}.`);
    }
    if (indices.dtype !== 'int32') {
        throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${indices.dtype}`);
    }
    if (shape.length < 1) {
        throw new Error(`Output rank must be greater or equal to 1, but got shape: ${shape}`);
    }
    if (shape.length === 0) {
        if (indices.size === 0) {
            throw new Error(`Indices specified for empty output. indices shape: ${indices.shape}`);
        }
        if (updates.size === 0) {
            throw new Error(`Updates specified for empty output. updates shape: ${updates.shape}`);
        }
    }
    validateUpdateShape(shape, indices, updates);
}
/**
 * Calculate the shape information for the output.
 *
 * @param update The tensor contains the update values.
 * @param indices The tensor contains the indices for the update values.
 * @param shape The shape of the output tensor.
 *
 * @returns ScatterShapeInfo
 */
export function calculateShapes(updates, indices, shape) {
    // Calculate the number of dimensions in indices
    const indicesRank = indices.shape.length;
    const sliceRank = (indicesRank > 1) ? indices.shape[indicesRank - 1] : 1;
    // Calculate the number of elements that make up each slice of our updated
    // tensor. This allows us to work with flattened tensors and copy over whole
    // slices at a time.
    const totalNd = shape.length;
    let sliceSize = 1;
    for (let i = sliceRank; i < totalNd; ++i) {
        sliceSize *= shape[i];
    }
    const safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;
    const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;
    const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];
    const outputSize = sizeFromShape(shape);
    return { sliceRank, numUpdates, sliceSize, strides, outputSize };
}
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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 */\nimport { TensorInfo } from '../tensor_info';\nimport {Tensor} from '../tensor';\nimport {computeStrides, sizeFromShape} from '../util';\n\n/**\n * Check whether updates.shape = indices.shape[:batchDim] +\n * shape[sliceDim:]\n *\n * @param x The input tensor.\n */\nexport function validateUpdateShape(\n    shape: number[], indices: Tensor, updates: Tensor) {\n  const sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n  const batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;\n\n  const shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +\n      `shape[sliceDim:], got updates.shape: ${updates.shape}` +\n      `, indices.shape: ${indices.shape}, shape: ${shape}` +\n      `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n\n  if (updates.rank < batchDim) {\n    throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n  }\n  if (shape.length < sliceDim + (updates.rank - batchDim)) {\n    throw new Error(\n        shapeError +\n        ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n  }\n  if (updates.rank !== batchDim + shape.length - sliceDim) {\n    throw new Error(\n        shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n  }\n  for (let d = 0; d < batchDim; ++d) {\n    if (updates.shape[d] !== indices.shape[d]) {\n      throw new Error(\n          shapeError +\n          ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${\n              indices.shape[d]}).`);\n    }\n  }\n  for (let d = 0; d < updates.rank - batchDim; ++d) {\n    if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n      throw new Error(\n          shapeError +\n          ` updates.shape[${d + batchDim}] (${\n              updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${\n              shape[d + batchDim]})`);\n    }\n  }\n}\n\nexport interface ScatterShapeInfo {\n  sliceRank: number;\n  numUpdates: number;\n  sliceSize: number;\n  strides: number[];\n  outputSize: number;\n}\n/**\n * Validate scatter nd inputs.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n */\nexport function validateInput(\n    updates: Tensor, indices: Tensor, shape: number[]) {\n  if (indices.rank < 1) {\n    throw new Error(\n        'tf.scatterND() expects the indices to be rank 1 or higher,' +\n        ` but the rank was ${indices.rank}.`);\n  }\n  if (updates.rank < 1) {\n    throw new Error(\n        'tf.scatterND() expects the updates to be rank 1 or higher,' +\n        ` but the rank was ${updates.rank}.`);\n  }\n  if (indices.dtype !== 'int32') {\n    throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${\n        indices.dtype}`);\n  }\n  if (shape.length < 1) {\n    throw new Error(\n        `Output rank must be greater or equal to 1, but got shape: ${shape}`);\n  }\n\n  if (shape.length === 0) {\n    if (indices.size === 0) {\n      throw new Error(`Indices specified for empty output. indices shape: ${\n          indices.shape}`);\n    }\n    if (updates.size === 0) {\n      throw new Error(`Updates specified for empty output. updates shape: ${\n          updates.shape}`);\n    }\n  }\n\n  validateUpdateShape(shape, indices, updates);\n}\n\n/**\n * Calculate the shape information for the output.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n *\n * @returns ScatterShapeInfo\n */\nexport function calculateShapes(\n    updates: TensorInfo, indices: TensorInfo,\n    shape: number[]): ScatterShapeInfo {\n  // Calculate the number of dimensions in indices\n  const indicesRank = indices.shape.length;\n  const sliceRank = (indicesRank > 1) ? indices.shape[indicesRank - 1] : 1;\n\n  // Calculate the number of elements that make up each slice of our updated\n  // tensor. This allows us to work with flattened tensors and copy over whole\n  // slices at a time.\n  const totalNd = shape.length;\n\n  let sliceSize = 1;\n  for (let i = sliceRank; i < totalNd; ++i) {\n    sliceSize *= shape[i];\n  }\n\n  const safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;\n  const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;\n\n  const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];\n  const outputSize = sizeFromShape(shape);\n  return {sliceRank, numUpdates, sliceSize, strides, outputSize};\n}\n"]}