/**
 * @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 {DepthwiseConv2dNative, DepthwiseConv2dNativeAttrs, DepthwiseConv2dNativeInputs} from '../kernel_names';
import {NamedAttrMap} from '../kernel_registry';
import {Tensor3D, Tensor4D} 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';
import {reshape} from './reshape';

/**
 * Depthwise 2D convolution.
 *
 * Given a 4D `input` array and a `filter` array of shape
 * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing
 * `inChannels` convolutional filters of depth 1, this op applies a
 * different filter to each input channel (expanding from 1 channel to
 * `channelMultiplier` channels for each), then concatenates the results
 * together. The output has `inChannels * channelMultiplier` channels.
 *
 * See
 * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](
 *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)
 * for more details.
 *
 * @param x The input tensor, of rank 4 or rank 3, of shape
 *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
 * assumed.
 * @param filter The filter tensor, rank 4, of shape
 *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.
 * @param strides The strides of the convolution: `[strideHeight,
 * strideWidth]`. If strides is a single number, then `strideHeight ==
 * strideWidth`.
 * @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 dilations The dilation rates: `[dilationHeight, dilationWidth]`
 *     in which we sample input values across the height and width dimensions
 *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single
 *     number, then `dilationHeight == dilationWidth`. If it is greater than
 *     1, then all values of `strides` must be 1.
 * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
 *     "NHWC". Specify the data format of the input and output data. With the
 *     default format "NHWC", the data is stored in the order of: [batch,
 *     height, width, channels]. Only "NHWC" is currently supported.
 * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
 *     provided, it will default to truncate.
 *
 * @doc {heading: 'Operations', subheading: 'Convolution'}
 */
function depthwiseConv2d_<T extends Tensor3D|Tensor4D>(
    x: T|TensorLike, filter: Tensor4D|TensorLike,
    strides: [number, number]|number, pad: 'valid'|'same'|number,
    dataFormat: 'NHWC'|'NCHW' = 'NHWC',
    dilations: [number, number]|number = [1, 1],
    dimRoundingMode?: 'floor'|'round'|'ceil'): T {
  const $x = convertToTensor(x, 'x', 'depthwiseConv2d');
  const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');

  let x4D = $x as Tensor4D;
  let reshapedTo4D = false;
  if ($x.rank === 3) {
    reshapedTo4D = true;
    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
  }
  util.assert(
      x4D.rank === 4,
      () => `Error in depthwiseConv2d: input must be rank 4, but got ` +
          `rank ${x4D.rank}.`);
  util.assert(
      $filter.rank === 4,
      () => `Error in depthwiseConv2d: filter must be rank 4, but got rank ` +
          `${$filter.rank}.`);
  util.assert(
      x4D.shape[3] === $filter.shape[2],
      () => `Error in depthwiseConv2d: number of input channels ` +
          `(${x4D.shape[3]}) must match the inChannels dimension in ` +
          `filter ${$filter.shape[2]}.`);

  if (dimRoundingMode != null) {
    util.assert(
        util.isInt(pad as number),
        () => `Error in depthwiseConv2d: pad must be an integer when using, ` +
            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);
  }

  const inputs: DepthwiseConv2dNativeInputs = {x: x4D, filter: $filter};
  const attrs: DepthwiseConv2dNativeAttrs =
      {strides, pad, dataFormat, dilations, dimRoundingMode};

  // tslint:disable-next-line: no-unnecessary-type-assertion
  const res = ENGINE.runKernel(
                  DepthwiseConv2dNative, inputs as {} as NamedTensorMap,
                  attrs as {} as NamedAttrMap) as T;

  if (reshapedTo4D) {
    return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;
  }
  return res;
}

export const depthwiseConv2d = op({depthwiseConv2d_});