/* Copyright (c) 2019 PaddlePaddle Authors. 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. */

#pragma once

#include <memory>
#include <vector>

#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/im2col.h"
#include "paddle/fluid/operators/math/math_function.h"

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;

inline int CalcOutputSize(int input_size, int filter_size, int dilation,
                          int padding1, int padding2, int stride) {
  const int dkernel = dilation * (filter_size - 1) + 1;
  int output_size = (input_size + padding1 + padding2 - dkernel) / stride + 1;
  return output_size;
}

template <typename DeviceContext, typename T>
class UnfoldOpKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    const Tensor* input = ctx.Input<Tensor>("X");
    const int batch_size = static_cast<int>(input->dims()[0]);
    Tensor* output = ctx.Output<Tensor>("Y");
    output->mutable_data<T>(ctx.GetPlace());

    std::vector<int> kernel_sizes = ctx.Attr<std::vector<int>>("kernel_sizes");
    std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
    std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
    std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");

    math::Im2ColFunctor<math::ColFormat::kCFO, DeviceContext, T> im2col;
    auto& dev_ctx = ctx.template device_context<DeviceContext>();

    auto input_dims = input->dims();

    int output_height =
        CalcOutputSize(input_dims[2], kernel_sizes[0], dilations[0],
                       paddings[0], paddings[2], strides[0]);
    int output_width =
        CalcOutputSize(input_dims[3], kernel_sizes[1], dilations[1],
                       paddings[1], paddings[3], strides[1]);

    framework::DDim input_shape({input_dims[1], input_dims[2], input_dims[3]});
    framework::DDim output_matrix_shape({input_dims[1], kernel_sizes[0],
                                         kernel_sizes[1], output_height,
                                         output_width});

    for (int i = 0; i < batch_size; i++) {
      Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
      Tensor out_batch = output->Slice(i, i + 1).Resize(output_matrix_shape);
      im2col(dev_ctx, in_batch, dilations, strides, paddings, &out_batch);
    }
  }
};

template <typename DeviceContext, typename T>
class UnfoldGradOpKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    const Tensor* output_grad = ctx.Input<Tensor>(framework::GradVarName("Y"));
    Tensor* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
    input_grad->mutable_data<T>(ctx.GetPlace());

    if ((!output_grad) || (!input_grad)) return;

    std::vector<int> kernel_sizes = ctx.Attr<std::vector<int>>("kernel_sizes");
    std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
    std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
    std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");

    const int batch_size = static_cast<int>(input_grad->dims()[0]);

    auto input_dims = input_grad->dims();

    int output_height =
        CalcOutputSize(input_dims[2], kernel_sizes[0], dilations[0],
                       paddings[0], paddings[2], strides[0]);
    int output_width =
        CalcOutputSize(input_dims[3], kernel_sizes[1], dilations[1],
                       paddings[1], paddings[3], strides[1]);

    framework::DDim input_shape({input_dims[1], input_dims[2], input_dims[3]});
    framework::DDim output_matrix_shape({input_dims[1], kernel_sizes[0],
                                         kernel_sizes[1], output_height,
                                         output_width});

    math::Col2ImFunctor<math::ColFormat::kCFO, DeviceContext, T> col2im;
    auto& dev_ctx = ctx.template device_context<DeviceContext>();

    math::SetConstant<DeviceContext, T> set_zero;
    set_zero(dev_ctx, input_grad, static_cast<T>(0));
    for (int i = 0; i < batch_size; i++) {
      Tensor out_grad_batch =
          output_grad->Slice(i, i + 1).Resize(output_matrix_shape);
      Tensor in_grad_batch = input_grad->Slice(i, i + 1).Resize(input_shape);
      col2im(dev_ctx, out_grad_batch, dilations, strides, paddings,
             &in_grad_batch);
    }
  }
};
}  // namespace operators
}  // namespace paddle