/*
 * Copyright (c) 2017 ARM Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
 * deal in the Software without restriction, including without limitation the
 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
 * sell copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */

#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp"
#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp"
#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp"

namespace winograd
{

using Tiles = InputTransformImplTiles<3, 3, 4, 4, float>;

namespace
{


template <bool Specialized, int PadTop=0, int PadLeft=0, int PadBottom=0, int PadRight=0>
void winograd_input_transform_4x4_fp32_process_tile(
  int n_channels,
  const float* const input_base,
  const int input_row_stride,
  const int input_col_stride,
  float* const matrix_base,
    const int matrix_stride,
     const int _pad_top,
     const int _pad_left,
     const int _pad_bottom,
     const int _pad_right
  )
{
const int pad_top = Specialized ? PadTop : _pad_top;
  const int pad_left = Specialized ? PadLeft : _pad_left;
  const int pad_bottom = Specialized ? PadBottom : _pad_bottom;
  const int pad_right = Specialized ? PadRight : _pad_right;

  constexpr int inner_tile_i = 4, inner_tile_j = 4;
  const int cells_i = inner_tile_i - pad_bottom;
  const int cells_j = inner_tile_i - pad_right;



  float *outptr = matrix_base;

  // Get pointers into the input tile
  const float *x_ptrs[inner_tile_i][inner_tile_j];
  for (int i = pad_top, xi = 0; i < cells_i; i++, xi++)
  {
    // Get a pointer into the row
    const float* const row_ptr = input_base + xi*input_row_stride;

    for (int j = pad_left, xj = 0; j < cells_j; j++, xj++)
    {
      x_ptrs[i][j] = row_ptr + xj*input_col_stride;
    }
  }

  // Matrices used/computed in this kernel.
  float x[inner_tile_i][inner_tile_j];
  float XTx[inner_tile_i][inner_tile_j];
  float U[inner_tile_i][inner_tile_j];

  for (int i = 0; i < inner_tile_i; i++)
  {
    for (int j = 0; j < inner_tile_j; j++)
    {
      x[i][j] = XTx[i][j] = 0.0f;
    }
  }

  // Perform the Winograd input transformation for each channel in the input
  // tensor.
  int channels_remaining = n_channels;
#ifdef __aarch64__
  for (; channels_remaining >= 4; channels_remaining -= 4)
  {
    // Matrices used/computed in this kernel.
    float32x4_t x[inner_tile_i][inner_tile_j];
    float32x4_t XTx[inner_tile_i][inner_tile_j];
    float32x4_t U[inner_tile_i][inner_tile_j];

    for (int i = 0; i < inner_tile_i; i++)
    {
      for (int j = 0; j < inner_tile_j; j++)
      {
        x[i][j] = vdupq_n_f32(0.0f);
        XTx[i][j] = vdupq_n_f32(0.0f);
      }
    }

    // Load x
    for (int i = pad_top; i < cells_i; i++)
    {
      for (int j = pad_left; j < cells_j; j++)
      {
        x[i][j] = vld1q_f32(x_ptrs[i][j]);
        x_ptrs[i][j] += 4;
      }
    }

    // Compute XT . x
    for (int j = pad_left; j < cells_j; j++)
    {
      // XTx[0][j] = x[0][j] - x[2][j];
      XTx[0][j] = vsubq_f32(x[0][j], x[2][j]);

      // XTx[1][j] = x[1][j] + x[2][j];
      XTx[1][j] = vaddq_f32(x[1][j], x[2][j]);

      // XTx[2][j] = x[2][j] - x[1][j];
      XTx[2][j] = vsubq_f32(x[2][j], x[1][j]);

      // XTx[3][j] = x[1][j] - x[3][j];
      XTx[3][j] = vsubq_f32(x[1][j], x[3][j]);
    }

    // Compute U = XT . x . X
    for (int i = 0; i < inner_tile_i; i++)
    {
      // U[i][0] = XTx[i][0] - XTx[i][2];
      U[i][0] = vsubq_f32(XTx[i][0], XTx[i][2]);

      // U[i][1] = XTx[i][1] + XTx[i][2];
      U[i][1] = vaddq_f32(XTx[i][1], XTx[i][2]);

      // U[i][2] = XTx[i][2] - XTx[i][1];
      U[i][2] = vsubq_f32(XTx[i][2], XTx[i][1]);

      // U[i][3] = XTx[i][1] - XTx[i][3];
      U[i][3] = vsubq_f32(XTx[i][1], XTx[i][3]);
    }

    // Store the transformed matrix
    for (int i = 0, m = 0; i < inner_tile_i; i++)
    {
      for (int j = 0; j < inner_tile_j; j++, m++)
      {
        vst1q_f32(outptr + m*matrix_stride, U[i][j]);
      }
    }
    outptr += 4;
  }
#endif  // __aarch64__
#ifdef __arm_any__
  for (; channels_remaining >= 2; channels_remaining -= 2)
  {
    // Matrices used/computed in this kernel.
    float32x2_t x[inner_tile_i][inner_tile_j];
    float32x2_t XTx[inner_tile_i][inner_tile_j];
    float32x2_t U[inner_tile_i][inner_tile_j];

    for (int i = 0; i < inner_tile_i; i++)
    {
      for (int j = 0; j < inner_tile_j; j++)
      {
        x[i][j] = vdup_n_f32(0.0f);
        XTx[i][j] = vdup_n_f32(0.0f);
      }
    }

    // Load x
    for (int i = pad_top; i < cells_i; i++)
    {
      for (int j = pad_left; j < cells_j; j++)
      {
        x[i][j] = vld1_f32(x_ptrs[i][j]);
        x_ptrs[i][j] += 2;
      }
    }

    // Compute XT . x
    for (int j = pad_left; j < cells_j; j++)
    {
      // XTx[0][j] = x[0][j] - x[2][j];
      XTx[0][j] = vsub_f32(x[0][j], x[2][j]);

      // XTx[1][j] = x[1][j] + x[2][j];
      XTx[1][j] = vadd_f32(x[1][j], x[2][j]);

      // XTx[2][j] = x[2][j] - x[1][j];
      XTx[2][j] = vsub_f32(x[2][j], x[1][j]);

      // XTx[3][j] = x[1][j] - x[3][j];
      XTx[3][j] = vsub_f32(x[1][j], x[3][j]);
    }

    // Compute U = XT . x . X
    for (int i = 0; i < inner_tile_i; i++)
    {
      // U[i][0] = XTx[i][0] - XTx[i][2];
      U[i][0] = vsub_f32(XTx[i][0], XTx[i][2]);

      // U[i][1] = XTx[i][1] + XTx[i][2];
      U[i][1] = vadd_f32(XTx[i][1], XTx[i][2]);

      // U[i][2] = XTx[i][2] - XTx[i][1];
      U[i][2] = vsub_f32(XTx[i][2], XTx[i][1]);

      // U[i][3] = XTx[i][1] - XTx[i][3];
      U[i][3] = vsub_f32(XTx[i][1], XTx[i][3]);
    }

    // Store the transformed matrix
    for (int i = 0, m = 0; i < inner_tile_i; i++)
    {
      for (int j = 0; j < inner_tile_j; j++, m++)
      {
        vst1_f32(outptr + m*matrix_stride, U[i][j]);
      }
    }
    outptr += 2;
  }
#endif  // __arm_any__
  for (; channels_remaining; channels_remaining--)
  {
    // Load x
    for (int i = pad_top; i < cells_i; i++)
    {
      for (int j = pad_left; j < cells_j; j++)
      {
        x[i][j] = *(x_ptrs[i][j]++);
      }
    }

    // Compute XT . x
    for (int j = pad_left; j < cells_j; j++)
    {
      XTx[0][j] = x[0][j] - x[2][j];
      XTx[1][j] = x[1][j] + x[2][j];
      XTx[2][j] = x[2][j] - x[1][j];
      XTx[3][j] = x[1][j] - x[3][j];
    }

    // Compute U = XT . x . X
    for (int i = 0; i < inner_tile_i; i++)
    {
      U[i][0] = XTx[i][0] - XTx[i][2];
      U[i][1] = XTx[i][1] + XTx[i][2];
      U[i][2] = XTx[i][2] - XTx[i][1];
      U[i][3] = XTx[i][1] - XTx[i][3];
    }

    // Store the transformed matrix
    for (int i = 0, m = 0; i < inner_tile_i; i++)
    {
      for (int j = 0; j < inner_tile_j; j++, m++)
      {
        *(outptr + m*matrix_stride) = U[i][j];
      }
    }
    outptr++;
  }
}

}  // namespace (anonymous)

template <>
const Tiles::TileFn Tiles::tilefn_generic = winograd_input_transform_4x4_fp32_process_tile<false>;

template <>
const Tiles::TileFn Tiles::tilefn_unpadded = winograd_input_transform_4x4_fp32_process_tile<true>;


template <>
const Tiles::TileFn Tiles::tilefn_top_padded[n_pad_top] = {
  winograd_input_transform_4x4_fp32_process_tile<true, 1, 0, 0, 0>,
};

template <>
const Tiles::TileFn Tiles::tilefn_left_padded[n_pad_left] = {
  winograd_input_transform_4x4_fp32_process_tile<true, 0, 1, 0, 0>,
};

template <>
const Tiles::TileFn Tiles::tilefn_bottom_padded[n_pad_bottom] = {
  winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 1, 0>,
  winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 2, 0>,
  winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 3, 0>,
  winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 4, 0>,
};

template <>
const Tiles::TileFn Tiles::tilefn_right_padded[n_pad_right] = {
  winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 0, 1>,
  winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 0, 2>,
  winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 0, 3>,
  winograd_input_transform_4x4_fp32_process_tile<true, 0, 0, 0, 4>,
};

template class InputTransform<3, 3, 4, 4, float>;
}  // namespace winograd