#include <ATen/TypeDefault.h>
#include <ATen/native/IndexingUtils.h>
#include <ATen/native/TensorIterator.h>
#include <ATen/native/UpSample.h>
#include <cmath>
#include <vector>

#include <torch/library.h>

// Code temporary is in torchvision before merging it to PyTorch
namespace at {
namespace native {
namespace internal_upsample {

using scale_t = std::vector<c10::optional<double>>;

template <typename scalar_t, typename index_t>
static inline scalar_t interpolate_aa_single_dim_zero_strides(
    char* src,
    char** data,
    int64_t i,
    const index_t ids_stride) {
  const index_t ids_min = *(index_t*)&data[0][0];
  const index_t ids_size = *(index_t*)&data[1][0];

  char* src_min = src + ids_min;

  scalar_t t = *(scalar_t*)&src_min[0];
  index_t wts_idx = *(index_t*)&data[4][0];
  scalar_t* wts_ptr = (scalar_t*)&data[3][wts_idx];
  scalar_t wts = wts_ptr[0];

  scalar_t output = t * wts;
  int j = 1;
  for (; j < ids_size; j++) {
    wts = wts_ptr[j];
    t = *(scalar_t*)&src_min[j * ids_stride];
    output += t * wts;
  }
  return output;
}

template <typename scalar_t, typename index_t>
static inline scalar_t interpolate_aa_single_dim(
    char* src,
    char** data,
    const int64_t* strides,
    int64_t i,
    const index_t ids_stride) {
  index_t ids_min = *(index_t*)&data[0][i * strides[0]];
  index_t ids_size = *(index_t*)&data[1][i * strides[1]];

  char* src_min = src + ids_min;

  scalar_t t = *(scalar_t*)&src_min[0];
  index_t wts_idx = *(index_t*)&data[4][i * strides[4]];
  scalar_t* wts_ptr = (scalar_t*)&data[3][wts_idx];
  scalar_t wts = wts_ptr[0];

  scalar_t output = t * wts;
  int j = 1;
  for (; j < ids_size; j++) {
    wts = wts_ptr[j];
    t = *(scalar_t*)&src_min[j * ids_stride];
    output += t * wts;
  }
  return output;
}

template <typename scalar_t, typename index_t>
static inline void basic_loop_aa_single_dim_zero_strides(
    char** data,
    const int64_t* strides,
    int64_t n) {
  char* dst = data[0];
  char* src = data[1];
  // index stride is constant for the given dimension
  const index_t ids_stride = *(index_t*)&data[2 + 2][0];

  for (int64_t i = 0; i < n; i++) {
    *(scalar_t*)&dst[i * strides[0]] =
        interpolate_aa_single_dim_zero_strides<scalar_t, index_t>(
            src + i * strides[1], &data[2], i, ids_stride);
  }
}

template <typename scalar_t, typename index_t>
static inline void basic_loop_aa_single_dim_nonzero_strides(
    char** data,
    const int64_t* strides,
    int64_t n) {
  char* dst = data[0];
  char* src = data[1];
  // index stride is constant for the given dimension
  const index_t ids_stride = *(index_t*)&data[2 + 2][0];

  if (strides[1] == 0) {
    for (int64_t i = 0; i < n; i++) {
      *(scalar_t*)&dst[i * strides[0]] =
          interpolate_aa_single_dim<scalar_t, index_t>(
              src, &data[2], &strides[2], i, ids_stride);
    }
  } else {
    for (int64_t i = 0; i < n; i++) {
      *(scalar_t*)&dst[i * strides[0]] =
          interpolate_aa_single_dim<scalar_t, index_t>(
              src + i * strides[1], &data[2], &strides[2], i, ids_stride);
    }
  }
}

template <int m>
static inline bool is_zero_stride(const int64_t* strides) {
  bool output = strides[0] == 0;
  for (int i = 1; i < m; i++) {
    output &= (strides[i] == 0);
  }
  return output;
}

template <typename scalar_t, typename index_t, int out_ndims>
void ti_cpu_upsample_generic_aa(
    at::TensorIterator& iter,
    int interp_size = -1) {
  TORCH_INTERNAL_ASSERT(interp_size > 0);

  auto loop = [&](char** data, const int64_t* strides, int64_t n) {
    if ((strides[0] == sizeof(scalar_t)) && (strides[1] == sizeof(scalar_t)) &&
        is_zero_stride<3 + 2>(&strides[2])) {
      basic_loop_aa_single_dim_zero_strides<scalar_t, index_t>(
          data, strides, n);
    } else {
      basic_loop_aa_single_dim_nonzero_strides<scalar_t, index_t>(
          data, strides, n);
    }
  };

  iter.for_each(loop);
}

// Helper structs to use with ti_upsample_generic_Nd_kernel_impl
template <typename index_t, typename scalar_t>
struct HelperInterpBase {
  static inline void init_indices_weights(
      std::vector<Tensor>& output,
      int64_t output_size,
      int64_t ndims,
      int64_t reshape_dim,
      int interp_size) {
    auto new_shape = std::vector<int64_t>(ndims, 1);
    new_shape[reshape_dim] = output_size;

    for (int j = 0; j < interp_size; j++) {
      output.emplace_back(
          empty(new_shape, CPU(c10::CppTypeToScalarType<index_t>())));
      output.emplace_back(
          empty(new_shape, CPU(c10::CppTypeToScalarType<scalar_t>())));
    }
  }
};

template <typename index_t, typename scalar_t>
struct HelperInterpLinear : public HelperInterpBase<index_t, scalar_t> {
  static const int interp_size = 2;

  static inline std::vector<Tensor> compute_indices_weights(
      int64_t input_size,
      int64_t output_size,
      int64_t stride,
      int64_t ndims,
      int64_t reshape_dim,
      bool align_corners,
      const c10::optional<double> opt_scale,
      bool antialias,
      int& out_interp_size) {
    scalar_t scale = area_pixel_compute_scale<scalar_t>(
        input_size, output_size, align_corners, opt_scale);
    TORCH_INTERNAL_ASSERT(antialias);

    return _compute_indices_weights_aa(
        input_size,
        output_size,
        stride,
        ndims,
        reshape_dim,
        align_corners,
        scale,
        out_interp_size);
  }

  // taken from
  // https://github.com/python-pillow/Pillow/blob/6812205f18ca4ef54372e87e1a13ce4a859434df/
  // src/libImaging/Resample.c#L20-L29
  static inline scalar_t _filter(scalar_t x) {
    if (x < 0.0) {
      x = -x;
    }
    if (x < 1.0) {
      return 1.0 - x;
    }
    return 0.0;
  }

  static inline std::vector<Tensor> _compute_indices_weights_aa(
      int64_t input_size,
      int64_t output_size,
      int64_t stride,
      int64_t ndims,
      int64_t reshape_dim,
      bool align_corners,
      scalar_t scale,
      int& out_interp_size) {
    int interp_size = HelperInterpLinear<index_t, scalar_t>::interp_size;
    scalar_t support =
        (scale >= 1.0) ? (interp_size / 2) * scale : interp_size / 2 * 1.0;
    interp_size = (int)ceilf(support) * 2 + 1;

    // return interp_size
    out_interp_size = interp_size;

    std::vector<Tensor> output;
    auto new_shape = std::vector<int64_t>(ndims, 1);
    new_shape[reshape_dim] = output_size;

    // ---- Bounds approach as in PIL -----
    // bounds: xmin/xmax
    output.emplace_back(
        empty(new_shape, CPU(c10::CppTypeToScalarType<index_t>())));
    output.emplace_back(
        empty(new_shape, CPU(c10::CppTypeToScalarType<index_t>())));
    output.emplace_back(
        empty(new_shape, CPU(c10::CppTypeToScalarType<index_t>())));

    {
      // Weights
      new_shape[reshape_dim] = output_size * interp_size;
      auto wts = empty(new_shape, CPU(c10::CppTypeToScalarType<scalar_t>()));
      auto strides = wts.strides().vec();
      strides[reshape_dim] = 0;
      new_shape[reshape_dim] = output_size;
      wts = wts.as_strided(new_shape, strides);
      output.emplace_back(wts);
      // Weights indices
      output.emplace_back(
          empty(new_shape, CPU(c10::CppTypeToScalarType<index_t>())));
    }

    scalar_t center, total_w, invscale = (scale >= 1.0) ? 1.0 / scale : 1.0;
    index_t zero = static_cast<index_t>(0);
    int64_t* idx_ptr_xmin = output[0].data_ptr<index_t>();
    int64_t* idx_ptr_size = output[1].data_ptr<index_t>();
    int64_t* idx_ptr_stride = output[2].data_ptr<index_t>();
    scalar_t* wt_ptr = output[3].data_ptr<scalar_t>();
    int64_t* wt_idx_ptr = output[4].data_ptr<index_t>();

    int64_t xmin, xmax, j;

    for (int64_t i = 0; i < output_size; i++) {
      center = scale * (i + 0.5);
      xmin = std::max(static_cast<int64_t>(center - support + 0.5), zero);
      xmax =
          std::min(static_cast<int64_t>(center + support + 0.5), input_size) -
          xmin;
      idx_ptr_xmin[i] = xmin * stride;
      idx_ptr_size[i] = xmax;
      idx_ptr_stride[i] = stride;

      wt_idx_ptr[i] = i * interp_size * sizeof(scalar_t);

      total_w = 0.0;
      for (j = 0; j < xmax; j++) {
        scalar_t w = _filter((j + xmin - center + 0.5) * invscale);
        wt_ptr[i * interp_size + j] = w;
        total_w += w;
      }
      for (j = 0; j < xmax; j++) {
        if (total_w != 0.0) {
          wt_ptr[i * interp_size + j] /= total_w;
        }
      }

      for (; j < interp_size; j++) {
        wt_ptr[i * interp_size + j] = static_cast<scalar_t>(0.0);
      }
    }
    return output;
  }
};

template <
    typename index_t,
    int out_ndims,
    typename scale_type,
    template <typename, typename>
    class F>
void _ti_separable_upsample_generic_Nd_kernel_impl_single_dim(
    Tensor& output,
    const Tensor& input,
    int interp_dim,
    bool align_corners,
    const scale_type& scales,
    bool antialias) {
  // input can be NCHW, NCL or NCKHW
  auto shape = input.sizes().vec();
  auto strides = input.strides().vec();
  auto oshape = output.sizes();

  TORCH_INTERNAL_ASSERT(
      shape.size() == oshape.size() && shape.size() == 2 + out_ndims);
  TORCH_INTERNAL_ASSERT(strides.size() == 2 + out_ndims);
  TORCH_INTERNAL_ASSERT(antialias);

  for (int i = 0; i < out_ndims; i++) {
    shape[i + 2] = oshape[i + 2];
  }
  strides[interp_dim] = 0;
  auto restrided_input = input.as_strided(shape, strides);

  std::vector<std::vector<Tensor>> indices_weights;

  int interp_size = F<index_t, float>::interp_size;
  auto input_scalar_type = input.scalar_type();

  if (interp_size == 1 && input_scalar_type == at::ScalarType::Byte) {
    // nearest also supports uint8 tensor, but we have to use float
    // with compute_indices_weights
    input_scalar_type = at::ScalarType::Float;
  }

  AT_DISPATCH_FLOATING_TYPES_AND(
      at::ScalarType::Byte,
      input_scalar_type,
      "compute_indices_weights_generic",
      [&] {
        indices_weights.emplace_back(
            F<index_t, scalar_t>::compute_indices_weights(
                input.size(interp_dim),
                oshape[interp_dim],
                input.stride(interp_dim) * input.element_size(),
                input.dim(),
                interp_dim,
                align_corners,
                scales[interp_dim - 2],
                antialias,
                interp_size));
      });

  TensorIteratorConfig config;
  config.check_all_same_dtype(false)
      .declare_static_dtype_and_device(input.scalar_type(), input.device())
      .add_output(output)
      .add_input(restrided_input);

  for (auto& idx_weight : indices_weights) {
    for (auto& tensor : idx_weight) {
      config.add_input(tensor);
    }
  }

  auto iter = config.build();

  if (interp_size > 1) {
    // Nearest also supports uint8 tensor, so need to handle it separately
    AT_DISPATCH_FLOATING_TYPES(iter.dtype(), "upsample_generic_Nd", [&] {
      ti_cpu_upsample_generic_aa<scalar_t, index_t, out_ndims>(
          iter, interp_size);
    });
  } else {
    AT_DISPATCH_FLOATING_TYPES_AND(
        at::ScalarType::Byte, iter.dtype(), "upsample_generic_Nd", [&] {
          ti_cpu_upsample_generic_aa<scalar_t, index_t, out_ndims>(
              iter, interp_size);
        });
  }
}

template <
    typename index_t,
    int out_ndims,
    typename scale_type,
    template <typename, typename>
    class F>
void ti_separable_upsample_generic_Nd_kernel_impl(
    Tensor& output,
    const Tensor& input,
    bool align_corners,
    const scale_type& scales,
    bool antialias) {
  auto temp_oshape = input.sizes().vec();
  at::Tensor temp_output, temp_input = input;
  for (int i = 0; i < out_ndims - 1; i++) {
    int interp_dim = 2 + out_ndims - 1 - i;
    temp_oshape[interp_dim] = output.sizes()[interp_dim];
    temp_output = at::empty(temp_oshape, input.options());
    _ti_separable_upsample_generic_Nd_kernel_impl_single_dim<
        index_t,
        out_ndims,
        scale_t,
        HelperInterpLinear>(
        temp_output, temp_input, interp_dim, align_corners, scales, antialias);
    temp_input = temp_output;
  }
  _ti_separable_upsample_generic_Nd_kernel_impl_single_dim<
      index_t,
      out_ndims,
      scale_t,
      HelperInterpLinear>(
      output, temp_input, 2, align_corners, scales, antialias);
}

void _ti_upsample_bilinear2d_kernel_impl(
    Tensor& output,
    const Tensor& input,
    bool align_corners,
    c10::optional<double> scales_h,
    c10::optional<double> scales_w,
    bool antialias) {
  ti_separable_upsample_generic_Nd_kernel_impl<
      int64_t,
      2,
      scale_t,
      HelperInterpLinear>(
      output, input, align_corners, {scales_h, scales_w}, antialias);
}

} // namespace internal_upsample
} // namespace native
} // namespace at

namespace vision {
namespace ops {

namespace {

at::Tensor interpolate_linear_aa_forward_kernel(
    const at::Tensor& input,
    at::IntArrayRef output_size,
    bool align_corners) {
  TORCH_CHECK(input.device().is_cpu(), "input must be a CPU tensor");

  c10::optional<c10::ArrayRef<double>> scale_factors = {};

  // Copied from UpSampleBilinear2d.cpp
  auto output = at::empty({0}, input.options());
  auto osize = at::native::upsample::compute_output_size(
      input.sizes(), output_size, scale_factors);
  auto scale_h = at::native::upsample::get_scale_value(scale_factors, 0);
  auto scale_w = at::native::upsample::get_scale_value(scale_factors, 1);
  auto full_output_size =
      at::native::upsample_2d_common_check(input.sizes(), osize);

  // Allow for empty batch size but not other dimensions
  TORCH_CHECK(
      input.numel() != 0 ||
          c10::multiply_integers(
              input.sizes().begin() + 1, input.sizes().end()),
      "Non-empty 4D data tensor expected but got a tensor with sizes ",
      input.sizes());

  output.resize_(full_output_size, input.suggest_memory_format());
  at::native::internal_upsample::_ti_upsample_bilinear2d_kernel_impl(
      output, input, align_corners, scale_h, scale_w, /*antialias=*/true);
  return output;
}

// TODO: Implement backward function
// at::Tensor interpolate_linear_aa_backward_kernel(
//     const at::Tensor& grad) {
//   return grad_input;
// }

} // namespace

TORCH_LIBRARY_IMPL(torchvision, CPU, m) {
  m.impl(
      TORCH_SELECTIVE_NAME("torchvision::_interpolate_linear_aa"),
      TORCH_FN(interpolate_linear_aa_forward_kernel));
  // TODO: Implement backward function
  //   m.impl(
  //       TORCH_SELECTIVE_NAME("torchvision::_interpolate_linear_aa_backward"),
  //       TORCH_FN(interpolate_linear_aa_backward_kernel));
}

} // namespace ops
} // namespace vision
