#pragma once

#include <torch/nn/functional/pooling.h>
#include <torch/nn/options/upsampling.h>

#include <cmath>

namespace torch {
namespace nn {
namespace functional {

inline std::vector<int64_t> _interp_output_size(
  int64_t dim,
  std::tuple<
    Tensor,
    c10::optional<std::vector<int64_t>>,
    c10::optional<std::vector<double>>,
    c10::optional<bool>> closed_over_args) {
  Tensor input;
  c10::optional<std::vector<int64_t>> size;
  c10::optional<std::vector<double>> scale_factor;
  c10::optional<bool> recompute_scale_factor;
  std::tie(input, size, scale_factor, recompute_scale_factor) = closed_over_args;
  if (size == c10::nullopt && scale_factor == c10::nullopt) {
    TORCH_CHECK(false, "either size or scale_factor should be defined");
  }
  if (size != c10::nullopt && scale_factor != c10::nullopt) {
    TORCH_CHECK(false, "only one of size or scale_factor should be defined");
  }
  if (scale_factor != c10::nullopt) {
    if (static_cast<int64_t>(scale_factor.value().size()) != dim) {
      TORCH_CHECK(
        false,
        "scale_factor shape must match input shape. ",
        "Input is ", dim, "D, scale_factor size is ", torch::ArrayRef<double>(*scale_factor));
    }
  }
  if (size != c10::nullopt) {
    return *size;
  }

  TORCH_INTERNAL_ASSERT(scale_factor != c10::nullopt);
  auto scale_factors = *scale_factor;

  if (recompute_scale_factor == c10::nullopt) {
    // only warn when the scales have floating values since
    // the result for ints is the same with/without recompute_scale_factor
    bool is_float_scale_factor = false;
    for (double scale : scale_factors) {
      is_float_scale_factor = floor(scale) != scale;
      if (is_float_scale_factor) {
        break;
      }
    }
    if (is_float_scale_factor) {
      TORCH_WARN("The default behavior for interpolate/upsample with float scale_factor changed "
                 "in 1.6.0 to align with other frameworks/libraries, and uses scale_factor directly, "
                 "instead of relying on the computed output size. "
                 "If you wish to keep the old behavior, please set recompute_scale_factor=True. "
                 "See the documentation of nn.Upsample for details. ");
    }
  }

  std::vector<int64_t> ret;
  for (int64_t i = 0; i < dim; i++) {
    ret.emplace_back(static_cast<int64_t>(floor(input.size(i + 2) * scale_factors[i])));
  }
  return ret;
}

#ifndef DOXYGEN_SHOULD_SKIP_THIS
namespace detail {
inline Tensor interpolate(
  const Tensor& input,
  const c10::optional<std::vector<int64_t>>& size,
  const c10::optional<std::vector<double>>& scale_factor,
  InterpolateFuncOptions::mode_t mode,
  c10::optional<bool> align_corners,
  c10::optional<bool> recompute_scale_factor) {
  if (c10::get_if<enumtype::kNearest>(&mode) ||
      c10::get_if<enumtype::kArea>(&mode)) {
    if (align_corners != c10::nullopt) {
      TORCH_CHECK(
          false,
          "align_corners option can only be set with the "
          "interpolating modes: linear | bilinear | bicubic | trilinear");
    }
  } else {
    if (align_corners == c10::nullopt) {
      TORCH_WARN(
          "Default upsampling behavior when mode=", enumtype::get_enum_name(mode), " is changed "
          "to align_corners=False since 0.4.0. Please specify "
          "align_corners=True if the old behavior is desired. "
          "See the documentation of nn.Upsample for details.");
      align_corners = false;
    }
  }

  auto scale_factor_len = input.dim() - 2;
  std::vector<c10::optional<double>> scale_factor_list(scale_factor_len, c10::nullopt);
  if (scale_factor != c10::nullopt && !recompute_scale_factor.value_or(false)) {
    auto _scale_factor_repeated = *scale_factor;
    scale_factor_list = {};
    for (const auto& elem : _scale_factor_repeated) {
      scale_factor_list.emplace_back(elem);
    }
  }

  auto closed_over_args = std::make_tuple(input, size, scale_factor, recompute_scale_factor);
  if (input.dim() == 3 && c10::get_if<enumtype::kNearest>(&mode)) {
    return torch::upsample_nearest1d(input, _interp_output_size(1, closed_over_args), scale_factor_list.at(0));
  } else if (input.dim() == 4 && c10::get_if<enumtype::kNearest>(&mode)) {
    return torch::upsample_nearest2d(input, _interp_output_size(2, closed_over_args), 
                                     scale_factor_list.at(0), scale_factor_list.at(1));
  } else if (input.dim() == 5 && c10::get_if<enumtype::kNearest>(&mode)) {
    return torch::upsample_nearest3d(input, _interp_output_size(3, closed_over_args),
                                     scale_factor_list.at(0), scale_factor_list.at(1), scale_factor_list.at(2));
  } else if (input.dim() == 3 && c10::get_if<enumtype::kArea>(&mode)) {
    return detail::adaptive_avg_pool1d(input, _interp_output_size(1, closed_over_args));
  } else if (input.dim() == 4 && c10::get_if<enumtype::kArea>(&mode)) {
    return detail::adaptive_avg_pool2d(input, _interp_output_size(2, closed_over_args));
  } else if (input.dim() == 5 && c10::get_if<enumtype::kArea>(&mode)) {
    return detail::adaptive_avg_pool3d(input, _interp_output_size(3, closed_over_args));
  } else if (input.dim() == 3 && c10::get_if<enumtype::kLinear>(&mode)) {
    TORCH_INTERNAL_ASSERT(align_corners != c10::nullopt);
    return torch::upsample_linear1d(input, _interp_output_size(1, closed_over_args), *align_corners, scale_factor_list.at(0));
  } else if (input.dim() == 3 && c10::get_if<enumtype::kBilinear>(&mode)) {
    TORCH_CHECK(false, "Got 3D input, but bilinear mode needs 4D input");
  } else if (input.dim() == 3 && c10::get_if<enumtype::kTrilinear>(&mode)) {
    TORCH_CHECK(false, "Got 3D input, but trilinear mode needs 5D input");
  } else if (input.dim() == 4 && c10::get_if<enumtype::kLinear>(&mode)) {
    TORCH_CHECK(false, "Got 4D input, but linear mode needs 3D input");
  } else if (input.dim() == 4 && c10::get_if<enumtype::kBilinear>(&mode)) {
    TORCH_INTERNAL_ASSERT(align_corners != c10::nullopt);
    return torch::upsample_bilinear2d(input, _interp_output_size(2, closed_over_args), *align_corners, 
                                      scale_factor_list.at(0), scale_factor_list.at(1));
  } else if (input.dim() == 4 && c10::get_if<enumtype::kTrilinear>(&mode)) {
    TORCH_CHECK(false, "Got 4D input, but trilinear mode needs 5D input");
  } else if (input.dim() == 5 && c10::get_if<enumtype::kLinear>(&mode)) {
    TORCH_CHECK(false, "Got 5D input, but linear mode needs 3D input");
  } else if (input.dim() == 5 && c10::get_if<enumtype::kBilinear>(&mode)) {
    TORCH_CHECK(false, "Got 5D input, but bilinear mode needs 4D input");
  } else if (input.dim() == 5 && c10::get_if<enumtype::kTrilinear>(&mode)) {
    TORCH_INTERNAL_ASSERT(align_corners != c10::nullopt);
    return torch::upsample_trilinear3d(input, _interp_output_size(3, closed_over_args), *align_corners,
                                       scale_factor_list.at(0), scale_factor_list.at(1), scale_factor_list.at(2));
  } else if (input.dim() == 4 && c10::get_if<enumtype::kBicubic>(&mode)) {
    TORCH_INTERNAL_ASSERT(align_corners != c10::nullopt);
    return torch::upsample_bicubic2d(input, _interp_output_size(2, closed_over_args), *align_corners, 
                                     scale_factor_list.at(0), scale_factor_list.at(1));
  } else {
    TORCH_CHECK(
        false,
        "Input Error: Only 3D, 4D and 5D input Tensors supported "
        "(got ", input.dim(), "D) for the modes: nearest | linear | bilinear | bicubic | trilinear "
        "(got ", enumtype::get_enum_name(mode), ")");
  }
}
} // namespace detail
#endif /* DOXYGEN_SHOULD_SKIP_THIS */

/// See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.interpolate
/// about the exact behavior of this functional.
///
/// See the documentation for `torch::nn::functional::InterpolateFuncOptions` class to learn what
/// optional arguments are supported for this functional.
///
/// Example:
/// ```
/// namespace F = torch::nn::functional;
/// F::interpolate(input, F::InterpolateFuncOptions().size({4}).mode(torch::kNearest));
/// ```
inline Tensor interpolate(const Tensor& input, const InterpolateFuncOptions& options = {}) {
  return detail::interpolate(
    input,
    options.size(),
    options.scale_factor(),
    options.mode(),
    options.align_corners(),
    options.recompute_scale_factor());
}

} // namespace functional
} // namespace nn
} // namespace torch
