/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

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. */

#pragma once

#include "paddle/framework/eigen.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/tensor.h"
#include "paddle/operators/math/im2col.h"
#include "paddle/operators/math/math_function.h"

namespace paddle {
namespace operators {
namespace math {

//    template <typename T, int MajorType = Eigen::RowMajor,
//            typename IndexType = Eigen::DenseIndex>
//    using EigenVector = framework::EigenVector<T, MajorType, IndexType>;

template <typename T, int MajorType = Eigen::RowMajor,
          typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
/*
 * \brief Converts the feature data of four dimensions(CDHW) into a colData of
 *        seven dimensions in the Vol2ColFunctor calculation,
 *        And in the Col2VolFunctor calculation, it is reversed.
 *
 * \param volData   Vol data.
 * \param volShape  The shape of volData,
 *                 [input_channels, input_depth, input_height, input_width].
 * \param colData  Column data.
 * \param colShape The shape of colData.
 *
 * The shape of colData is:
 * [input_channels, filter_depth, filter_height, filter_width, output_depth,
 * output_height, output_width]
 * So, it is easy to reshape into a convolution matrix for convolution
 * calculation based on matrix multiplication.
 * The shape of convolution matrix is [height, width], where the height is equal
 * input_channels * filter_depth * filter_height * filter_width, and the width
 * is equal output_depth * output_height * output_width.
 *
 * Reshape:
 *     shape of colData           shape of convolution matrix
 *     [input_channels,
 *      filter_depth,
 *      filter_height,
 *      filter_width,      ======>      [height, width]
 *      output_depth,
 *      output_height,
 *      output_width]
 *
 * \note The caller needs to ensure that volShape.inputChannels is equal to
 *       colShape.inputChannels.
 */

template <typename Place, typename T>
class SequenceProjectFunctor {
 public:
  void operator()(const platform::DeviceContext& context,
                  const framework::LoDTensor*& in,
                  const framework::LoDTensor* padding_data,
                  framework::LoDTensor* col, bool padding_trainable,
                  int context_start, int context_length, int context_stride,
                  int up_pad, int down_pad) {
    auto lod_level_0 = in->lod()[0];

    paddle::operators::math::Im2ColFunctor<
        paddle::operators::math::ColFormat::kOCF, Place, float>
        im2col_ocf;

    int input_row_begin, input_row_end;
    int sequence_height, sequence_width;
    sequence_width = in->dims()[1];

    for (int i = 0; i < static_cast<int>(lod_level_0.size()) - 1; ++i) {
      input_row_begin = (context_start > 0)
                            ? static_cast<int>(lod_level_0[i]) + context_start
                            : static_cast<int>(lod_level_0[i]);
      input_row_end = static_cast<int>(lod_level_0[i + 1]);

      framework::Tensor out_t =
          col->Slice(static_cast<int>(lod_level_0[i]),
                     static_cast<int>(lod_level_0[i + 1]));

      sequence_height = static_cast<int>(out_t.dims()[0]);

      std::vector<int64_t> output_shape(
          {sequence_height, 1, 1, context_length,
           sequence_width});  // output_height, output_width,
      // input_channels, filter_height, filter_width
      out_t.Resize(framework::make_ddim(output_shape));

      if (input_row_begin < input_row_end) {
        framework::Tensor in_t = in->Slice(input_row_begin, input_row_end);
        std::vector<int64_t> input_shape(
            {1, input_row_end - input_row_begin,
             sequence_width});  // input_channels, input_height, input_width
        in_t.Resize(framework::make_ddim(input_shape));

        im2col_ocf(context, in_t, out_t,
                   /*stride_height*/ context_stride, /*stride_width*/ 0, up_pad,
                   down_pad);
      }

      if (padding_trainable) {
        // add up trainable data
        out_t.Resize(framework::make_ddim(
            {sequence_height * context_length, sequence_width}));

        if (up_pad > 0) {  // add up pad
          int padding_rows = std::min(
              up_pad, static_cast<int>(lod_level_0[i + 1] - lod_level_0[i]));

          for (int k = 0; k < padding_rows; ++k) {
            int padding_size =
                k + context_length < up_pad ? context_length : up_pad - k;
            framework::Tensor out_t_sub = out_t.Slice(
                k * context_length, k * context_length + padding_size);
            framework::Tensor w_sub = padding_data->Slice(k, k + padding_size);
            // in this block, using EigenVector<T>::Flatten is ok too.
            auto out_t_sub_e = EigenMatrix<T>::From(out_t_sub);
            auto w_sub_e = EigenMatrix<T>::From(w_sub);
            out_t_sub_e.device(*context.GetEigenDevice<Place>()) = w_sub_e;
          }
        }
        if (down_pad > 0) {  // add down pad
          int down_pad_begin_row =
              std::max(0,
                       (sequence_height - context_start - context_length) + 1) +
              1;
          int padding_begin = std::max(0, context_start - sequence_height);
          int padding_size =
              sequence_height - context_start >= context_length
                  ? 1
                  : context_length - (sequence_height - context_start);
          if (context_start >= sequence_height) padding_size = context_length;
          int padding_idx = padding_begin;
          for (int t = 0; t + down_pad_begin_row <= sequence_height;
               ++t, ++padding_size) {
            if (context_start >= sequence_height) padding_size = context_length;
            if (padding_size > context_length) {
              padding_size = context_length;
              padding_idx++;
            }
            if (padding_begin > 0 || sequence_height == context_start)
              padding_idx = padding_begin + t;
            framework::Tensor out_t_sub = out_t.Slice(
                (down_pad_begin_row + t) * context_length - padding_size,
                (down_pad_begin_row + t) * context_length);
            framework::Tensor w_sub = padding_data->Slice(
                up_pad + padding_idx, up_pad + padding_idx + padding_size);
            auto out_t_sub_e = EigenMatrix<T>::From(out_t_sub);
            auto w_sub_e = EigenMatrix<T>::From(w_sub);
            out_t_sub_e.device(*context.GetEigenDevice<Place>()) = w_sub_e;
          }
        }
      }
      out_t.Resize(framework::make_ddim(
          {sequence_height, context_length * sequence_width}));
    }
  }
};

}  // namespace math
}  // namespace operators
}  // namespace paddle