/**
 * @license
 * Copyright 2019 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 {ENGINE} from '../engine';
import {Tensor} from '../tensor';
import {convertToTensor} from '../tensor_util_env';
import {op} from './operation';

/**
 * Returns a diagonal tensor with a given diagonal values.
 *
 * Given a diagonal, this operation returns a tensor with the diagonal and
 * everything else padded with zeros.
 *
 * Assume the input has dimensions `[D1,..., Dk]`, then the output is a tensor
 * of rank 2k with dimensions `[D1,..., Dk, D1,..., Dk]`
 *
 * ```js
 * const x = tf.tensor1d([1, 2, 3, 4]);
 *
 * tf.diag(x).print()
 * ```
 * ```js
 * const x = tf.tensor1d([1, 2, 3, 4, 5, 6, 6, 8], [4, 2])
 *
 * tf.diag(x).print()
 * ```
 * @param x The input tensor.
 */
function diag_(x: Tensor): Tensor {
  const $x = convertToTensor(x, 'x', 'diag').flatten();
  const outShape = [...x.shape, ...x.shape];
  return ENGINE.runKernelFunc(backend => backend.diag($x), {$x})
      .reshape(outShape);
}

export const diag = op({diag_});