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2025-02-12 ea42ff3ebee1eeb3fb29423aa848a249441db81c

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/**
 * @license
 * Copyright 2020 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 { convertToTensor } from '../../tensor_util_env';
import { assert } from '../../util';
import { greaterEqual } from '../greater_equal';
import { less } from '../less';
import { lessEqual } from '../less_equal';
import { logicalAnd } from '../logical_and';
import { minimum } from '../minimum';
import { neg } from '../neg';
import { op } from '../operation';
import { range } from '../range';
import { reshape } from '../reshape';
import { stack } from '../stack';
import { sub } from '../sub';
import { unstack } from '../unstack';
import { where } from '../where';
import { zeros } from '../zeros';
/**
 * Copy a tensor setting everything outside a central band in each innermost
 * matrix to zero.
 *
 * The band part is computed as follows: Assume input has `k` dimensions
 * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where
 * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.
 * The indicator function
 * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower)`
 * `&& (num_upper < 0 || (n-m) <= num_upper)`
 *
 * ```js
 * const x = tf.tensor2d([[ 0,  1,  2, 3],
 *                        [-1,  0,  1, 2],
 *                        [-2, -1,  0, 1],
 *                        [-3, -2, -1, 0]]);
 * let y = tf.linalg.bandPart(x, 1, -1);
 * y.print(); // [[ 0,  1,  2, 3],
 *            //  [-1,  0,  1, 2],
 *            //  [ 0, -1,  0, 1],
 *            //  [ 0, 0 , -1, 0]]
 * let z = tf.linalg.bandPart(x, 2, 1);
 * z.print(); // [[ 0,  1,  0, 0],
 *            //  [-1,  0,  1, 0],
 *            //  [-2, -1,  0, 1],
 *            //  [ 0, -2, -1, 0]]
 * ```
 *
 * @param x Rank `k` tensor
 * @param numLower Number of subdiagonals to keep.
 *   If negative, keep entire lower triangle.
 * @param numUpper Number of subdiagonals to keep.
 *   If negative, keep entire upper triangle.
 * @returns Rank `k` tensor of the same shape as input.
 *   The extracted banded tensor.
 *
 * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}
 */
function bandPart_(a, numLower, numUpper) {
    const $a = convertToTensor(a, 'a', 'bandPart');
    assert($a.rank >= 2, () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);
    const shape = $a.shape;
    const [M, N] = $a.shape.slice(-2);
    let $numLower;
    let $numUpper;
    if (typeof numLower === 'number') {
        assert(numLower % 1 === 0, () => `bandPart(): numLower must be an integer, got ${numLower}.`);
        assert(numLower <= M, () => `bandPart(): numLower (${numLower})` +
            ` must not be greater than the number of rows (${M}).`);
        $numLower =
            convertToTensor(numLower < 0 ? M : numLower, 'numLower', 'bandPart');
    }
    else {
        assert(numLower.dtype === 'int32', () => `bandPart(): numLower's dtype must be an int32.`);
        // If numLower is a Scalar, checking `numLower <= M` could hurt performance,
        // but minimum(numLower, M) could avoid unexpected results.
        $numLower = where(less(numLower, 0), M, minimum(numLower, M));
    }
    if (typeof numUpper === 'number') {
        assert(numUpper % 1 === 0, () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);
        assert(numUpper <= N, () => `bandPart(): numUpper (${numUpper})` +
            ` must not be greater than the number of columns (${N}).`);
        $numUpper =
            convertToTensor(numUpper < 0 ? N : numUpper, 'numUpper', 'bandPart');
    }
    else {
        assert(numUpper.dtype === 'int32', () => `bandPart(): numUpper's dtype must be an int32.`);
        $numUpper = where(less(numUpper, 0), N, minimum(numUpper, N));
    }
    const i = reshape(range(0, M, 1, 'int32'), [-1, 1]);
    const j = range(0, N, 1, 'int32');
    const ij = sub(i, j);
    const inBand = logicalAnd(lessEqual(ij, $numLower), greaterEqual(ij, neg($numUpper)));
    const zero = zeros([M, N], $a.dtype);
    return reshape(stack(unstack(reshape($a, [-1, M, N]))
        .map(mat => where(inBand, mat, zero))), shape);
}
export const bandPart = /* @__PURE__ */ op({ bandPart_ });
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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 {Scalar, Tensor} from '../../tensor';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport {assert} from '../../util';\n\nimport {greaterEqual} from '../greater_equal';\nimport {less} from '../less';\nimport {lessEqual} from '../less_equal';\nimport {logicalAnd} from '../logical_and';\nimport {minimum} from '../minimum';\nimport {neg} from '../neg';\nimport {op} from '../operation';\nimport {range} from '../range';\nimport {reshape} from '../reshape';\nimport {stack} from '../stack';\nimport {sub} from '../sub';\nimport {unstack} from '../unstack';\nimport {where} from '../where';\nimport {zeros} from '../zeros';\n\n/**\n * Copy a tensor setting everything outside a central band in each innermost\n * matrix to zero.\n *\n * The band part is computed as follows: Assume input has `k` dimensions\n * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where\n * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.\n * The indicator function\n * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower)`\n * `&& (num_upper < 0 || (n-m) <= num_upper)`\n *\n * ```js\n * const x = tf.tensor2d([[ 0,  1,  2, 3],\n *                        [-1,  0,  1, 2],\n *                        [-2, -1,  0, 1],\n *                        [-3, -2, -1, 0]]);\n * let y = tf.linalg.bandPart(x, 1, -1);\n * y.print(); // [[ 0,  1,  2, 3],\n *            //  [-1,  0,  1, 2],\n *            //  [ 0, -1,  0, 1],\n *            //  [ 0, 0 , -1, 0]]\n * let z = tf.linalg.bandPart(x, 2, 1);\n * z.print(); // [[ 0,  1,  0, 0],\n *            //  [-1,  0,  1, 0],\n *            //  [-2, -1,  0, 1],\n *            //  [ 0, -2, -1, 0]]\n * ```\n *\n * @param x Rank `k` tensor\n * @param numLower Number of subdiagonals to keep.\n *   If negative, keep entire lower triangle.\n * @param numUpper Number of subdiagonals to keep.\n *   If negative, keep entire upper triangle.\n * @returns Rank `k` tensor of the same shape as input.\n *   The extracted banded tensor.\n *\n * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'}\n */\nfunction bandPart_<T extends Tensor>(\n    a: T|TensorLike, numLower: number|Scalar, numUpper: number|Scalar): T {\n  const $a = convertToTensor(a, 'a', 'bandPart');\n  assert(\n      $a.rank >= 2,\n      () => `bandPart(): Rank must be at least 2, got ${$a.rank}.`);\n\n  const shape = $a.shape;\n  const [M, N] = $a.shape.slice(-2);\n\n  let $numLower: Scalar;\n  let $numUpper: Scalar;\n  if (typeof numLower === 'number') {\n    assert(\n        numLower % 1 === 0,\n        () => `bandPart(): numLower must be an integer, got ${numLower}.`);\n    assert(\n        numLower <= M,\n        () => `bandPart(): numLower (${numLower})` +\n            ` must not be greater than the number of rows (${M}).`);\n    $numLower =\n        convertToTensor(numLower < 0 ? M : numLower, 'numLower', 'bandPart') as\n        Scalar;\n  } else {\n    assert(\n        numLower.dtype === 'int32',\n        () => `bandPart(): numLower's dtype must be an int32.`);\n    // If numLower is a Scalar, checking `numLower <= M` could hurt performance,\n    // but minimum(numLower, M) could avoid unexpected results.\n    $numLower = where(less(numLower, 0), M, minimum(numLower, M)) as Scalar;\n  }\n\n  if (typeof numUpper === 'number') {\n    assert(\n        numUpper % 1 === 0,\n        () => `bandPart(): numUpper must be an integer, got ${numUpper}.`);\n    assert(\n        numUpper <= N,\n        () => `bandPart(): numUpper (${numUpper})` +\n            ` must not be greater than the number of columns (${N}).`);\n    $numUpper =\n        convertToTensor(numUpper < 0 ? N : numUpper, 'numUpper', 'bandPart') as\n        Scalar;\n  } else {\n    assert(\n        numUpper.dtype === 'int32',\n        () => `bandPart(): numUpper's dtype must be an int32.`);\n    $numUpper = where(less(numUpper, 0), N, minimum(numUpper, N)) as Scalar;\n  }\n\n  const i = reshape(range(0, M, 1, 'int32'), [-1, 1]);\n  const j = range(0, N, 1, 'int32');\n  const ij = sub(i, j);\n\n  const inBand =\n      logicalAnd(lessEqual(ij, $numLower), greaterEqual(ij, neg($numUpper)));\n\n  const zero = zeros([M, N], $a.dtype);\n\n  return reshape(\n             stack(unstack(reshape($a, [-1, M, N]))\n                       .map(mat => where(inBand, mat, zero))),\n             shape) as T;\n}\n\nexport const bandPart = /* @__PURE__ */ op({bandPart_});\n"]}