/**
 * @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 {Tensor2D, Tensor3D, Tensor4D} from '../tensor';
import {convertToTensor} from '../tensor_util_env';
import {TensorLike} from '../types';
import * as util from '../util';

import {conv2d} from './conv2d';
import * as conv_util from './conv_util';
import {op} from './operation';
import {reshape} from './reshape';

/**
 * Computes a 1D convolution over the input x.
 *
 * @param x The input tensor, of rank 3 or rank 2, of shape
 *     `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.
 * @param filter The filter, rank 3, of shape
 *     `[filterWidth, inDepth, outDepth]`.
 * @param stride The number of entries by which the filter is moved right at
 *     each step.
 * @param pad The type of padding algorithm.
 *    - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *    - `valid`: output will be smaller than input if filter is larger
 *       than 1x1.
 *   - For more info, see this guide:
 *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](
 *          https://www.tensorflow.org/api_guides/python/nn#Convolution)
 * @param dataFormat An optional string from "NWC", "NCW". Defaults to "NWC",
 *     the data is stored in the order of [batch, in_width, in_channels]. Only
 *     "NWC" is currently supported.
 * @param dilation The dilation rate in which we sample input values in
 *     atrous convolution. Defaults to `1`. If it is greater than 1, then
 *     stride must be `1`.
 * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
 *     provided, it will default to truncate.
 *
 * @doc {heading: 'Operations', subheading: 'Convolution'}
 */
function conv1d_<T extends Tensor2D|Tensor3D>(
    x: T|TensorLike, filter: Tensor3D|TensorLike, stride: number,
    pad: 'valid'|'same'|number|conv_util.ExplicitPadding,
    dataFormat: 'NWC'|'NCW' = 'NWC', dilation = 1,
    dimRoundingMode?: 'floor'|'round'|'ceil'): T {
  const $x = convertToTensor(x, 'x', 'conv1d');
  const $filter = convertToTensor(filter, 'filter', 'conv1d');

  let x3D = $x as Tensor3D;
  let reshapedTo3D = false;
  if ($x.rank === 2) {
    reshapedTo3D = true;
    x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]);
  }

  util.assert(
      x3D.rank === 3,
      () => `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);
  util.assert(
      $filter.rank === 3,
      () => `Error in conv1d: filter must be rank 3, but got rank ` +
          `${$filter.rank}.`);
  if (dimRoundingMode != null) {
    util.assert(
        util.isInt(pad as number),
        () => `Error in conv1d: pad must be an integer when using, ` +
            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);
  }

  util.assert(
      x3D.shape[2] === $filter.shape[1],
      () => `Error in conv1d: depth of input (${x3D.shape[2]}) must match ` +
          `input depth for filter ${$filter.shape[1]}.`);
  util.assert(
      conv_util.eitherStridesOrDilationsAreOne(stride, dilation),
      () => 'Error in conv1D: Either stride or dilation must be 1. ' +
          `Got stride ${stride} and dilation '${dilation}'`);
  util.assert(
      dataFormat === 'NWC',
      () => `Error in conv1d: got dataFormat of ${
          dataFormat} but only NWC is currently supported.`);

  const filter4D = reshape(
      $filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]);
  const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);
  const strides: [number, number] = [1, stride];
  const dilations: [number, number] = [1, dilation];

  const conv2dDataFormat = 'NHWC';

  const res = conv2d(
      (input4D as Tensor4D), (filter4D as Tensor4D), strides, pad,
      conv2dDataFormat, dilations, dimRoundingMode);

  if (reshapedTo3D) {
    return reshape(res, [res.shape[2], res.shape[3]]) as T;
  }

  return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]) as T;
}

export const conv1d = op({conv1d_});