#include <torch/nn/modules/pooling.h>

#include <torch/expanding_array.h>

namespace F = torch::nn::functional;

namespace torch {
namespace nn {

template <size_t D, typename Derived>
AvgPoolImpl<D, Derived>::AvgPoolImpl(const AvgPoolOptions<D>& options_)
    : options(options_) {}

template <size_t D, typename Derived>
void AvgPoolImpl<D, Derived>::reset() {}

template <size_t D, typename Derived>
void AvgPoolImpl<D, Derived>::pretty_print(std::ostream& stream) const {
  stream << "torch::nn::AvgPool" << D << "d"
         << "(kernel_size=" << options.kernel_size()
         << ", stride=" << options.stride() << ", padding=" << options.padding()
         << ")";
}

Tensor AvgPool1dImpl::forward(const Tensor& input) {
  return F::detail::avg_pool1d(
      input,
      options.kernel_size(),
      options.stride(),
      options.padding(),
      options.ceil_mode(),
      options.count_include_pad());
}

Tensor AvgPool2dImpl::forward(const Tensor& input) {
  return F::detail::avg_pool2d(
      input,
      options.kernel_size(),
      options.stride(),
      options.padding(),
      options.ceil_mode(),
      options.count_include_pad(),
      options.divisor_override());
}

Tensor AvgPool3dImpl::forward(const Tensor& input) {
  return F::detail::avg_pool3d(
      input,
      options.kernel_size(),
      options.stride(),
      options.padding(),
      options.ceil_mode(),
      options.count_include_pad(),
      options.divisor_override());
}

template class AvgPoolImpl<1, AvgPool1dImpl>;
template class AvgPoolImpl<2, AvgPool2dImpl>;
template class AvgPoolImpl<3, AvgPool3dImpl>;

// ============================================================================

template <size_t D, typename Derived>
MaxPoolImpl<D, Derived>::MaxPoolImpl(const MaxPoolOptions<D>& options_)
    : options(options_) {}

template <size_t D, typename Derived>
void MaxPoolImpl<D, Derived>::reset() {}

template <size_t D, typename Derived>
void MaxPoolImpl<D, Derived>::pretty_print(std::ostream& stream) const {
  stream << std::boolalpha << "torch::nn::MaxPool" << D << "d"
         << "(kernel_size=" << options.kernel_size()
         << ", stride=" << options.stride() << ", padding=" << options.padding()
         << ", dilation=" << options.dilation()
         << ", ceil_mode=" << options.ceil_mode() << ")";
}

Tensor MaxPool1dImpl::forward(const Tensor& input) {
  return F::detail::max_pool1d(
      input,
      options.kernel_size(),
      options.stride(),
      options.padding(),
      options.dilation(),
      options.ceil_mode());
}

std::tuple<Tensor, Tensor> MaxPool1dImpl::forward_with_indices(
    const Tensor& input) {
  return F::detail::max_pool1d_with_indices(
      input,
      options.kernel_size(),
      options.stride(),
      options.padding(),
      options.dilation(),
      options.ceil_mode());
}

Tensor MaxPool2dImpl::forward(const Tensor& input) {
  return F::detail::max_pool2d(
      input,
      options.kernel_size(),
      options.stride(),
      options.padding(),
      options.dilation(),
      options.ceil_mode());
}

std::tuple<Tensor, Tensor> MaxPool2dImpl::forward_with_indices(
    const Tensor& input) {
  return F::detail::max_pool2d_with_indices(
      input,
      options.kernel_size(),
      options.stride(),
      options.padding(),
      options.dilation(),
      options.ceil_mode());
}

Tensor MaxPool3dImpl::forward(const Tensor& input) {
  return F::detail::max_pool3d(
      input,
      options.kernel_size(),
      options.stride(),
      options.padding(),
      options.dilation(),
      options.ceil_mode());
}

std::tuple<Tensor, Tensor> MaxPool3dImpl::forward_with_indices(
    const Tensor& input) {
  return F::detail::max_pool3d_with_indices(
      input,
      options.kernel_size(),
      options.stride(),
      options.padding(),
      options.dilation(),
      options.ceil_mode());
}

template class MaxPoolImpl<1, MaxPool1dImpl>;
template class MaxPoolImpl<2, MaxPool2dImpl>;
template class MaxPoolImpl<3, MaxPool3dImpl>;

// ============================================================================

Tensor AdaptiveMaxPool1dImpl::forward(const Tensor& input) {
  return F::detail::adaptive_max_pool1d(input, options.output_size());
}

std::tuple<Tensor, Tensor> AdaptiveMaxPool1dImpl::forward_with_indices(
    const Tensor& input) {
  return F::detail::adaptive_max_pool1d_with_indices(
      input, options.output_size());
}

Tensor AdaptiveMaxPool2dImpl::forward(const Tensor& input) {
  return F::detail::adaptive_max_pool2d(input, options.output_size());
}

std::tuple<Tensor, Tensor> AdaptiveMaxPool2dImpl::forward_with_indices(
    const Tensor& input) {
  return F::detail::adaptive_max_pool2d_with_indices(
      input, options.output_size());
}

Tensor AdaptiveMaxPool3dImpl::forward(const Tensor& input) {
  return F::detail::adaptive_max_pool3d(input, options.output_size());
}

std::tuple<Tensor, Tensor> AdaptiveMaxPool3dImpl::forward_with_indices(
    const Tensor& input) {
  return F::detail::adaptive_max_pool3d_with_indices(
      input, options.output_size());
}

template class AdaptiveMaxPoolImpl<1, ExpandingArray<1>, AdaptiveMaxPool1dImpl>;
template class AdaptiveMaxPoolImpl<
    2,
    ExpandingArrayWithOptionalElem<2>,
    AdaptiveMaxPool2dImpl>;
template class AdaptiveMaxPoolImpl<
    3,
    ExpandingArrayWithOptionalElem<3>,
    AdaptiveMaxPool3dImpl>;

// ============================================================================

Tensor AdaptiveAvgPool1dImpl::forward(const Tensor& input) {
  return F::detail::adaptive_avg_pool1d(input, options.output_size());
}

Tensor AdaptiveAvgPool2dImpl::forward(const Tensor& input) {
  return F::detail::adaptive_avg_pool2d(input, options.output_size());
}

Tensor AdaptiveAvgPool3dImpl::forward(const Tensor& input) {
  return F::detail::adaptive_avg_pool3d(input, options.output_size());
}

template class AdaptiveAvgPoolImpl<1, ExpandingArray<1>, AdaptiveAvgPool1dImpl>;
template class AdaptiveAvgPoolImpl<
    2,
    ExpandingArrayWithOptionalElem<2>,
    AdaptiveAvgPool2dImpl>;
template class AdaptiveAvgPoolImpl<
    3,
    ExpandingArrayWithOptionalElem<3>,
    AdaptiveAvgPool3dImpl>;

// ============================================================================

template <size_t D, typename Derived>
MaxUnpoolImpl<D, Derived>::MaxUnpoolImpl(const MaxUnpoolOptions<D>& options_)
    : options(options_) {}

template <size_t D, typename Derived>
void MaxUnpoolImpl<D, Derived>::reset() {}

template <size_t D, typename Derived>
void MaxUnpoolImpl<D, Derived>::pretty_print(std::ostream& stream) const {
  stream << std::boolalpha << "torch::nn::MaxUnpool" << D << "d"
         << "(kernel_size=" << options.kernel_size()
         << ", stride=" << options.stride() << ", padding=" << options.padding()
         << ")";
}

Tensor MaxUnpool1dImpl::forward(
    const Tensor& input,
    const Tensor& indices,
    const c10::optional<std::vector<int64_t>>& output_size) {
  return F::detail::max_unpool1d(
      input,
      indices,
      options.kernel_size(),
      options.stride(),
      options.padding(),
      output_size);
}

Tensor MaxUnpool2dImpl::forward(
    const Tensor& input,
    const Tensor& indices,
    const c10::optional<std::vector<int64_t>>& output_size) {
  return F::detail::max_unpool2d(
      input,
      indices,
      options.kernel_size(),
      options.stride(),
      options.padding(),
      output_size);
}

Tensor MaxUnpool3dImpl::forward(
    const Tensor& input,
    const Tensor& indices,
    const c10::optional<std::vector<int64_t>>& output_size) {
  return F::detail::max_unpool3d(
      input,
      indices,
      options.kernel_size(),
      options.stride(),
      options.padding(),
      output_size);
}

template class MaxUnpoolImpl<1, MaxUnpool1dImpl>;
template class MaxUnpoolImpl<2, MaxUnpool2dImpl>;
template class MaxUnpoolImpl<3, MaxUnpool3dImpl>;

// ============================================================================

FractionalMaxPool2dImpl::FractionalMaxPool2dImpl(
    FractionalMaxPool2dOptions options_)
    : options(std::move(options_)) {
  // NOLINTNEXTLINE(clang-analyzer-optin.cplusplus.VirtualCall)
  reset();
}

void FractionalMaxPool2dImpl::reset() {
  _random_samples =
      register_buffer("_random_samples", options._random_samples());
  if (options.output_size() == c10::nullopt &&
      options.output_ratio() == c10::nullopt) {
    TORCH_CHECK(
        false,
        "FractionalMaxPool2d requires specifying either ",
        "an output size, or a pooling ratio");
  }
  if (options.output_size() != c10::nullopt &&
      options.output_ratio() != c10::nullopt) {
    TORCH_CHECK(
        false, "only one of output_size and output_ratio may be specified");
  }
  if (options.output_ratio() != c10::nullopt) {
    at::ArrayRef<double> output_ratio =
        at::ArrayRef<double>(options.output_ratio().value());
    if (!(0 < output_ratio[0] && output_ratio[0] < 1 && 0 < output_ratio[1] &&
          output_ratio[1] < 1)) {
      TORCH_CHECK(
          false,
          "output_ratio must be between 0 and 1 (got ",
          output_ratio,
          ")");
    }
  }
}

Tensor FractionalMaxPool2dImpl::forward(const Tensor& input) {
  return F::detail::fractional_max_pool2d(
      input,
      options.kernel_size(),
      options.output_size(),
      options.output_ratio(),
      _random_samples);
}

std::tuple<Tensor, Tensor> FractionalMaxPool2dImpl::forward_with_indices(
    const Tensor& input) {
  return F::detail::fractional_max_pool2d_with_indices(
      input,
      options.kernel_size(),
      options.output_size(),
      options.output_ratio(),
      _random_samples);
}

void FractionalMaxPool2dImpl::pretty_print(std::ostream& stream) const {
  stream << "torch::nn::FractionalMaxPool2d()";
}

FractionalMaxPool3dImpl::FractionalMaxPool3dImpl(
    FractionalMaxPool3dOptions options_)
    : options(std::move(options_)) {
  // NOLINTNEXTLINE(clang-analyzer-optin.cplusplus.VirtualCall)
  reset();
}

void FractionalMaxPool3dImpl::reset() {
  _random_samples =
      register_buffer("_random_samples", options._random_samples());
  if (options.output_size() == c10::nullopt &&
      options.output_ratio() == c10::nullopt) {
    TORCH_CHECK(
        false,
        "FractionalMaxPool3d requires specifying either ",
        "an output size, or a pooling ratio");
  }
  if (options.output_size() != c10::nullopt &&
      options.output_ratio() != c10::nullopt) {
    TORCH_CHECK(
        false, "only one of output_size and output_ratio may be specified");
  }
  if (options.output_ratio() != c10::nullopt) {
    at::ArrayRef<double> output_ratio =
        at::ArrayRef<double>(options.output_ratio().value());
    if (!(0 < output_ratio[0] && output_ratio[0] < 1 && 0 < output_ratio[1] &&
          output_ratio[1] < 1 && 0 < output_ratio[2] && output_ratio[2] < 1)) {
      TORCH_CHECK(
          false,
          "output_ratio must be between 0 and 1 (got ",
          output_ratio,
          ")");
    }
  }
}

Tensor FractionalMaxPool3dImpl::forward(const Tensor& input) {
  return F::detail::fractional_max_pool3d(
      input,
      options.kernel_size(),
      options.output_size(),
      options.output_ratio(),
      _random_samples);
}

std::tuple<Tensor, Tensor> FractionalMaxPool3dImpl::forward_with_indices(
    const Tensor& input) {
  return F::detail::fractional_max_pool3d_with_indices(
      input,
      options.kernel_size(),
      options.output_size(),
      options.output_ratio(),
      _random_samples);
}

void FractionalMaxPool3dImpl::pretty_print(std::ostream& stream) const {
  stream << "torch::nn::FractionalMaxPool3d()";
}

// ============================================================================

template <size_t D, typename Derived>
LPPoolImpl<D, Derived>::LPPoolImpl(const LPPoolOptions<D>& options_)
    : options(options_) {}

template <size_t D, typename Derived>
void LPPoolImpl<D, Derived>::reset() {}

template <size_t D, typename Derived>
void LPPoolImpl<D, Derived>::pretty_print(std::ostream& stream) const {
  stream << std::boolalpha << "torch::nn::LPPool" << D << "d("
         << "norm_type=" << options.norm_type() << ", "
         << "kernel_size=" << options.kernel_size() << ", "
         << "stride=" << options.stride() << ", "
         << "ceil_mode=" << options.ceil_mode() << ")";
}

Tensor LPPool1dImpl::forward(const Tensor& input) {
  return F::detail::lp_pool1d(
      input,
      options.norm_type(),
      options.kernel_size(),
      options.stride(),
      options.ceil_mode());
}

template class LPPoolImpl<1, LPPool1dImpl>;

Tensor LPPool2dImpl::forward(const Tensor& input) {
  return F::detail::lp_pool2d(
      input,
      options.norm_type(),
      options.kernel_size(),
      options.stride(),
      options.ceil_mode());
}

template class LPPoolImpl<2, LPPool2dImpl>;

} // namespace nn
} // namespace torch
