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2025-02-12 ea42ff3ebee1eeb3fb29423aa848a249441db81c

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/**
 * @license
 * Copyright 2018 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.
 * =============================================================================
 */
/**
 * Gets the new shape of the input Tensor after it's been reshaped
 * to:
 * [blockShape[0], ..., blockShape[M-1], batch / prod(blockShape),
 * inputShape[1], ..., inputShape[N-1]]
 *
 * See step 1: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd
 */
export function getReshaped(inputShape, blockShape, prod, batchToSpace = true) {
    let reshaped = [];
    if (batchToSpace) {
        reshaped = reshaped.concat(blockShape.slice(0));
        reshaped.push(inputShape[0] / prod);
        reshaped = reshaped.concat(inputShape.slice(1));
    }
    else {
        reshaped = reshaped.concat(inputShape[0]);
        const spatialLength = blockShape.length;
        for (let i = 0; i < spatialLength; ++i) {
            reshaped =
                reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);
        }
        reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));
    }
    return reshaped;
}
/**
 * Gets the permutation that will transpose the dimensions of the
 * reshaped tensor to shape:
 *
 * [batch / prod(block_shape),inputShape[1], blockShape[0], ...,
 * inputShape[M], blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]
 *
 * see step 2: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd
 */
export function getPermuted(reshapedRank, blockShapeRank, batchToSpace = true) {
    const permuted = [];
    if (batchToSpace) {
        permuted.push(blockShapeRank);
        for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {
            if (i <= 2 * blockShapeRank) {
                permuted.push(i);
                permuted.push(i - (blockShapeRank + 1));
            }
            else {
                permuted.push(i);
            }
        }
    }
    else {
        const permutedBeforeBatch = [];
        const permutedAfterBatch = [];
        for (let i = 1; i < reshapedRank; ++i) {
            if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {
                permutedAfterBatch.push(i);
            }
            else {
                permutedBeforeBatch.push(i);
            }
        }
        permuted.push(...permutedBeforeBatch);
        permuted.push(0);
        permuted.push(...permutedAfterBatch);
    }
    return permuted;
}
/**
 * Gets the shape of the reshaped and permuted input Tensor before any cropping
 * is applied.  The new shape will be:
 *
 * [batch / prod(blockShape),inputShape[1] * blockShape[0], ...,
 * inputShape[M] * blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]
 *
 * See step 3: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd
 */
export function getReshapedPermuted(inputShape, blockShape, prod, batchToSpace = true) {
    const reshapedPermuted = [];
    if (batchToSpace) {
        reshapedPermuted.push(inputShape[0] / prod);
    }
    else {
        reshapedPermuted.push(inputShape[0] * prod);
    }
    for (let i = 1; i < inputShape.length; ++i) {
        if (i <= blockShape.length) {
            if (batchToSpace) {
                reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);
            }
            else {
                reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);
            }
        }
        else {
            reshapedPermuted.push(inputShape[i]);
        }
    }
    return reshapedPermuted;
}
/**
 * Converts the crops argument into the beginning coordinates of a slice
 * operation.
 */
export function getSliceBeginCoords(crops, blockShape) {
    const sliceBeginCoords = [0];
    for (let i = 0; i < blockShape; ++i) {
        sliceBeginCoords.push(crops[i][0]);
    }
    return sliceBeginCoords;
}
/**
 * Converts the crops argument into the size of a slice operation.  When
 * combined with getSliceBeginCoords this function allows the reshaped and
 * permuted Tensor to be cropped to its final output shape of:
 *
 * inputShape[1] * blockShape[0] - crops[0,0] - crops[0,1], ...,
 * inputShape[M] * blockShape[M-1] -crops[M-1,0] -
 * crops[M-1,1],inputShape[M+1], ..., inputShape[N-1]]
 *
 * See step 4: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd
 */
export function getSliceSize(uncroppedShape, crops, blockShape) {
    const sliceSize = uncroppedShape.slice(0, 1);
    for (let i = 0; i < blockShape; ++i) {
        sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);
    }
    return sliceSize;
}
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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\n/**\n * Gets the new shape of the input Tensor after it's been reshaped\n * to:\n * [blockShape[0], ..., blockShape[M-1], batch / prod(blockShape),\n * inputShape[1], ..., inputShape[N-1]]\n *\n * See step 1: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshaped(\n    inputShape: number[], blockShape: number[], prod: number,\n    batchToSpace = true): number[] {\n  let reshaped: number[] = [];\n  if (batchToSpace) {\n    reshaped = reshaped.concat(blockShape.slice(0));\n    reshaped.push(inputShape[0] / prod);\n    reshaped = reshaped.concat(inputShape.slice(1));\n  } else {\n    reshaped = reshaped.concat(inputShape[0]);\n    const spatialLength = blockShape.length;\n    for (let i = 0; i < spatialLength; ++i) {\n      reshaped =\n          reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);\n    }\n    reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));\n  }\n  return reshaped;\n}\n\n/**\n * Gets the permutation that will transpose the dimensions of the\n * reshaped tensor to shape:\n *\n * [batch / prod(block_shape),inputShape[1], blockShape[0], ...,\n * inputShape[M], blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * see step 2: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getPermuted(\n    reshapedRank: number, blockShapeRank: number,\n    batchToSpace = true): number[] {\n  const permuted = [];\n  if (batchToSpace) {\n    permuted.push(blockShapeRank);\n    for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {\n      if (i <= 2 * blockShapeRank) {\n        permuted.push(i);\n        permuted.push(i - (blockShapeRank + 1));\n      } else {\n        permuted.push(i);\n      }\n    }\n  } else {\n    const permutedBeforeBatch = [];\n    const permutedAfterBatch = [];\n    for (let i = 1; i < reshapedRank; ++i) {\n      if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {\n        permutedAfterBatch.push(i);\n      } else {\n        permutedBeforeBatch.push(i);\n      }\n    }\n    permuted.push(...permutedBeforeBatch);\n    permuted.push(0);\n    permuted.push(...permutedAfterBatch);\n  }\n  return permuted;\n}\n\n/**\n * Gets the shape of the reshaped and permuted input Tensor before any cropping\n * is applied.  The new shape will be:\n *\n * [batch / prod(blockShape),inputShape[1] * blockShape[0], ...,\n * inputShape[M] * blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 3: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshapedPermuted(\n    inputShape: number[], blockShape: number[], prod: number,\n    batchToSpace = true): number[] {\n  const reshapedPermuted = [];\n\n  if (batchToSpace) {\n    reshapedPermuted.push(inputShape[0] / prod);\n  } else {\n    reshapedPermuted.push(inputShape[0] * prod);\n  }\n\n  for (let i = 1; i < inputShape.length; ++i) {\n    if (i <= blockShape.length) {\n      if (batchToSpace) {\n        reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);\n      } else {\n        reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);\n      }\n    } else {\n      reshapedPermuted.push(inputShape[i]);\n    }\n  }\n\n  return reshapedPermuted;\n}\n\n/**\n * Converts the crops argument into the beginning coordinates of a slice\n * operation.\n */\nexport function getSliceBeginCoords(\n    crops: number[][], blockShape: number): number[] {\n  const sliceBeginCoords = [0];\n  for (let i = 0; i < blockShape; ++i) {\n    sliceBeginCoords.push(crops[i][0]);\n  }\n  return sliceBeginCoords;\n}\n\n/**\n * Converts the crops argument into the size of a slice operation.  When\n * combined with getSliceBeginCoords this function allows the reshaped and\n * permuted Tensor to be cropped to its final output shape of:\n *\n * inputShape[1] * blockShape[0] - crops[0,0] - crops[0,1], ...,\n * inputShape[M] * blockShape[M-1] -crops[M-1,0] -\n * crops[M-1,1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 4: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getSliceSize(\n    uncroppedShape: number[], crops: number[][], blockShape: number): number[] {\n  const sliceSize = uncroppedShape.slice(0, 1);\n  for (let i = 0; i < blockShape; ++i) {\n    sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);\n  }\n\n  return sliceSize;\n}\n"]}