/**
 * @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 {ENGINE} from '../engine';
import {Maximum, MaximumInputs} from '../kernel_names';
import {Tensor} from '../tensor';
import {NamedTensorMap} from '../tensor_types';
import {makeTypesMatch} from '../tensor_util';
import {convertToTensor} from '../tensor_util_env';
import {TensorLike} from '../types';

import {assertAndGetBroadcastShape} from './broadcast_util';
import {cast} from './cast';
import {op} from './operation';

/**
 * Returns the max of a and b (`a > b ? a : b`) element-wise.
 * Supports broadcasting.
 *
 * We also expose `tf.maximumStrict` which has the same signature as this op and
 * asserts that `a` and `b` are the same shape (does not broadcast).
 *
 * ```js
 * const a = tf.tensor1d([1, 4, 3, 16]);
 * const b = tf.tensor1d([1, 2, 9, 4]);
 *
 * a.maximum(b).print();  // or tf.maximum(a, b)
 * ```
 *
 * ```js
 * // Broadcast maximum a with b.
 * const a = tf.tensor1d([2, 4, 6, 8]);
 * const b = tf.scalar(5);
 *
 * a.maximum(b).print();  // or tf.maximum(a, b)
 * ```
 *
 * @param a The first tensor.
 * @param b The second tensor. Must have the same type as `a`.
 *
 * @doc {heading: 'Operations', subheading: 'Arithmetic'}
 */
function maximum_<T extends Tensor>(
    a: Tensor|TensorLike, b: Tensor|TensorLike): T {
  let $a = convertToTensor(a, 'a', 'maximum');
  let $b = convertToTensor(b, 'b', 'maximum');
  [$a, $b] = makeTypesMatch($a, $b);

  if ($a.dtype === 'bool') {
    $a = cast($a, 'int32');
    $b = cast($b, 'int32');
  }
  assertAndGetBroadcastShape($a.shape, $b.shape);

  const inputs: MaximumInputs = {a: $a, b: $b};

  return ENGINE.runKernel(Maximum, inputs as {} as NamedTensorMap);
}

export const maximum = op({maximum_});