/**
 * @license
 * Copyright 2020 Google Inc. 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, ForwardFunc} from '../engine';
import {AddNInputs} from '../kernel_names';
import {Tensor} from '../tensor';
import {NamedTensorMap} from '../tensor_types';
import {convertToTensor} from '../tensor_util_env';
import {TensorLike} from '../types';
import * as util from '../util';

import {op} from './operation';

/**
 * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.
 *
 * ```js
 * const a = tf.tensor1d([1, 2]);
 * const b = tf.tensor1d([3, 4]);
 * const c = tf.tensor1d([5, 6]);
 *
 * tf.addN([a, b, c]).print();
 * ```
 * @param tensors A list of tensors with the same shape and dtype.
 */
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
function addN_<T extends Tensor>(tensors: Array<T|TensorLike>): T {
  util.assert(
      Array.isArray(tensors),
      () => 'The argument passed to tf.addN() must be a list of tensors');
  util.assert(
      tensors.length >= 1,
      () => `Must pass at least one tensor to tf.addN(), but got ` +
          `${tensors.length}`);

  const $tensors =
      tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'addN'));

  const firstTensor = $tensors[0];
  $tensors.forEach(t => {
    if (t.dtype !== firstTensor.dtype) {
      throw new Error(
          'All tensors passed to tf.addN() must have the same dtype');
    }
  });

  $tensors.forEach(t => {
    if (!util.arraysEqual(t.shape, firstTensor.shape)) {
      throw new Error(
          'All tensors passed to tf.addN() must have the same shape');
    }
  });

  const forward: ForwardFunc<Tensor> = (backend, save) =>
      backend.addN($tensors);

  const inputs: AddNInputs = $tensors;

  return ENGINE.runKernelFunc(
             forward, inputs as {} as NamedTensorMap, null /* grad */,
             'AddN') as T;
}

export const addN = op({addN_});