// Copyright (c) 2024 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.

#include "paddle/phi/kernels/funcs/math/unpooling.h"

namespace phi {
namespace math {
template <typename T>
class Unpool2dMaxFunctor<CPUContext, T> {
 public:
  void operator()(const CPUContext& context,
                  const DenseTensor& input,
                  const DenseTensor& indices,
                  DenseTensor* output) {
    const int batch_size = static_cast<int>(input.dims()[0]);
    const int input_height = static_cast<int>(input.dims()[2]);
    const int input_width = static_cast<int>(input.dims()[3]);
    const int output_channels = static_cast<int>(output->dims()[1]);
    const int output_height = static_cast<int>(output->dims()[2]);
    const int output_width = static_cast<int>(output->dims()[3]);
    int64_t input_feasize = static_cast<int64_t>(input_height) * input_width;
    int64_t output_feasize = static_cast<int64_t>(output_height) * output_width;
    const T* input_data = input.data<T>();
    const int* indices_data = indices.data<int>();
    T* output_data = context.template Alloc<T>(output);
    for (int b = 0; b < batch_size; ++b) {
      for (int c = 0; c < output_channels; ++c) {
        for (int64_t i = 0; i < input_feasize; ++i) {
          int index = indices_data[i];

          PADDLE_ENFORCE_LT(
              index,
              output_feasize,
              common::errors::InvalidArgument(
                  "index should less than output tensor height * output tensor "
                  "width. Expected %ld < %ld, but got "
                  "%ld >= %ld. Please check input value.",
                  index,
                  output_feasize,
                  index,
                  output_feasize));
          output_data[index] = input_data[i];
        }
        input_data += input_feasize;
        indices_data += input_feasize;
        output_data += output_feasize;
      }
    }
  }
};
template <class T>
class Unpool2dMaxGradFunctor<CPUContext, T> {
 public:
  void operator()(const CPUContext& context,
                  const DenseTensor& input,
                  const DenseTensor& indices,
                  const DenseTensor& output,
                  const DenseTensor& output_grad,
                  DenseTensor* input_grad) {
    const int batch_size = static_cast<int>(input.dims()[0]);
    const int input_height = static_cast<int>(input.dims()[2]);
    const int input_width = static_cast<int>(input.dims()[3]);
    const int output_channels = static_cast<int>(output.dims()[1]);
    const int output_height = static_cast<int>(output.dims()[2]);
    const int output_width = static_cast<int>(output.dims()[3]);
    int64_t input_feasize = static_cast<int64_t>(input_height) * input_width;
    int64_t output_feasize = static_cast<int64_t>(output_height) * output_width;
    const int* indices_data = indices.data<int>();
    const T* output_grad_data = output_grad.data<T>();
    T* input_grad_data = context.template Alloc<T>(input_grad);

    for (int b = 0; b < batch_size; ++b) {
      for (int c = 0; c < output_channels; ++c) {
        for (int64_t i = 0; i < input_feasize; ++i) {
          int index = indices_data[i];
          PADDLE_ENFORCE_LT(
              index,
              output_feasize,
              common::errors::InvalidArgument(
                  "index should less than output tensor height * output tensor "
                  "width. Expected %ld < %ld, but got "
                  "%ld >= %ld. Please check input value.",
                  index,
                  output_feasize,
                  index,
                  output_feasize));
          input_grad_data[i] = output_grad_data[index];
        }
        input_grad_data += input_feasize;
        indices_data += input_feasize;
        output_grad_data += output_feasize;
      }
    }
  }
};

template <typename T>
class Unpool3dMaxFunctor<CPUContext, T> {
 public:
  void operator()(const CPUContext& context,
                  const DenseTensor& input,
                  const DenseTensor& indices,
                  DenseTensor* output) {
    const int batch_size = static_cast<int>(input.dims()[0]);
    const int input_depth = static_cast<int>(input.dims()[2]);
    const int input_height = static_cast<int>(input.dims()[3]);
    const int input_width = static_cast<int>(input.dims()[4]);
    const int output_channels = static_cast<int>(output->dims()[1]);
    const int output_depth = static_cast<int>(output->dims()[2]);
    const int output_height = static_cast<int>(output->dims()[3]);
    const int output_width = static_cast<int>(output->dims()[4]);
    int64_t input_feasize =
        static_cast<int64_t>(input_depth) * input_height * input_width;
    int64_t output_feasize =
        static_cast<int64_t>(output_depth) * output_height * output_width;
    const T* input_data = input.data<T>();
    const int* indices_data = indices.data<int>();
    T* output_data = context.template Alloc<T>(output);
    for (int b = 0; b < batch_size; ++b) {
      for (int c = 0; c < output_channels; ++c) {
        for (int64_t i = 0; i < input_feasize; ++i) {
          int index = indices_data[i];

          PADDLE_ENFORCE_LT(
              index,
              output_feasize,
              common::errors::InvalidArgument(
                  "index should less than output tensor depth * output tensor "
                  "height "
                  "* output tensor width. Expected %ld < %ld, but got "
                  "%ld >= %ld. Please check input value.",
                  index,
                  output_feasize,
                  index,
                  output_feasize));
          output_data[index] = input_data[i];
        }
        input_data += input_feasize;
        indices_data += input_feasize;
        output_data += output_feasize;
      }
    }
  }
};
template <class T>
class Unpool3dMaxGradFunctor<CPUContext, T> {
 public:
  void operator()(const CPUContext& context,
                  const DenseTensor& input,
                  const DenseTensor& indices,
                  const DenseTensor& output,
                  const DenseTensor& output_grad,
                  DenseTensor* input_grad) {
    const int batch_size = static_cast<int>(input.dims()[0]);
    const int input_depth = static_cast<int>(input.dims()[2]);
    const int input_height = static_cast<int>(input.dims()[3]);
    const int input_width = static_cast<int>(input.dims()[4]);
    const int output_channels = static_cast<int>(output.dims()[1]);
    const int output_depth = static_cast<int>(output.dims()[2]);
    const int output_height = static_cast<int>(output.dims()[3]);
    const int output_width = static_cast<int>(output.dims()[4]);
    int64_t input_feasize =
        static_cast<int64_t>(input_depth) * input_height * input_width;
    int64_t output_feasize =
        static_cast<int64_t>(output_depth) * output_height * output_width;
    const int* indices_data = indices.data<int>();
    const T* output_grad_data = output_grad.data<T>();
    T* input_grad_data = context.template Alloc<T>(input_grad);

    for (int b = 0; b < batch_size; ++b) {
      for (int c = 0; c < output_channels; ++c) {
        for (int64_t i = 0; i < input_feasize; ++i) {
          int index = indices_data[i];
          PADDLE_ENFORCE_LT(
              index,
              output_feasize,
              common::errors::InvalidArgument(
                  "index should less than output tensor depth * output tensor "
                  "height "
                  "* output tensor width. Expected %ld < %ld, but got "
                  "%ld >= %ld. Please check input value.",
                  index,
                  output_feasize,
                  index,
                  output_feasize));
          input_grad_data[i] = output_grad_data[index];
        }
        input_grad_data += input_feasize;
        indices_data += input_feasize;
        output_grad_data += output_feasize;
      }
    }
  }
};

template class Unpool2dMaxGradFunctor<CPUContext, float>;
template class Unpool2dMaxGradFunctor<CPUContext, double>;
template class Unpool2dMaxFunctor<CPUContext, float>;
template class Unpool2dMaxFunctor<CPUContext, double>;
template class Unpool3dMaxGradFunctor<CPUContext, float>;
template class Unpool3dMaxGradFunctor<CPUContext, double>;
template class Unpool3dMaxFunctor<CPUContext, float>;
template class Unpool3dMaxFunctor<CPUContext, double>;
}  // namespace math
}  // namespace phi