/*
 * Copyright (c) 2018 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 "Winograd.h"

#include "tests/validation/Helpers.h"
#include "tests/validation/reference/Utils.h"

namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
namespace
{
template <typename T>
void winograd_input_transform3x3(const SimpleTensor<T> &src, SimpleTensor<T> &dst, const PadStrideInfo &conv_info)
{
    TensorShape shape4x4(4u, 4u);

    // Simple tensor for the 4x4 input tile
    SimpleTensor<T> src_tile{ shape4x4, src.data_type() };

    // Simple tensor for the 4x4 temporary tile
    SimpleTensor<T> tmp_tile{ shape4x4, src.data_type() };

    // Simple tensor for the 4x4 output tile
    SimpleTensor<T> dst_tile{ shape4x4, src.data_type() };

    // Simple tensor for the transformation matrix
    SimpleTensor<T> matrix{ shape4x4, src.data_type() };

    // Simple tensor for the transformation matrix transposed
    SimpleTensor<T> matrix_transposed{ shape4x4, src.data_type() };

    const float matrix_values[] = { 1.f, 0.f, -1.f, 0.f,
                                    0.f, 1.f, 1.f, 0.f,
                                    0.f, -1.f, 1.f, 0.f,
                                    0.f, 1.f, 0.f, -1.f
                                  };

    for(int i = 0; i < matrix.num_elements(); ++i)
    {
        matrix[i] = matrix_values[i];
    }

    transpose_matrix(matrix, matrix_transposed);

    const int in_w        = src.shape().x();
    const int in_h        = src.shape().y();
    const int in_d        = src.shape().z();
    const int num_batches = src.shape().total_size() / (in_w * in_h * in_d);
    const int num_tiles_x = std::ceil((in_w - 2 + conv_info.pad_left() + conv_info.pad_right()) / 2.0f);
    const int num_tiles_y = std::ceil((in_h - 2 + conv_info.pad_top() + conv_info.pad_bottom()) / 2.0f);

    ARM_COMPUTE_ERROR_ON((num_tiles_x * num_tiles_y) != static_cast<int>(dst.shape().y()));

    for(int b = 0; b < num_batches; ++b)
    {
        for(int z = 0; z < in_d; ++z)
        {
            for(int y = 0; y < num_tiles_y; ++y)
            {
                for(int x = 0; x < num_tiles_x; ++x)
                {
                    int xi = x * 2 - conv_info.pad_left();
                    int yi = y * 2 - conv_info.pad_top();

                    // Get the 4x4 tile from the input tensor
                    get_tile(src, src_tile, Coordinates(xi, yi, z, b));

                    // Compute the transformation
                    matrix_multiply(matrix, src_tile, tmp_tile);
                    matrix_multiply(tmp_tile, matrix_transposed, dst_tile);

                    // Store the 4x4 output tile across the 16 channels
                    for(int i = 0; i < 16; ++i)
                    {
                        int xo = z;
                        int yo = x + y * num_tiles_x;
                        dst[coords2index(dst.shape(), Coordinates(xo, yo, i, b))] = dst_tile[i];
                    }
                }
            }
        }
    }
}
} // namespace

template <typename T>
SimpleTensor<T> winograd_input_transform(const SimpleTensor<T> &src, const TensorShape &dst_shape, const PadStrideInfo &conv_info, const Size2D &kernel_dims)
{
    ARM_COMPUTE_ERROR_ON(kernel_dims.width != kernel_dims.height);
    ARM_COMPUTE_ERROR_ON(src.data_layout() != DataLayout::NCHW);

    SimpleTensor<T> dst{ dst_shape, src.data_type() };

    switch(kernel_dims.width)
    {
        case 3:
            winograd_input_transform3x3(src, dst, conv_info);
            break;
        default:
            ARM_COMPUTE_ERROR("Only 3x3 kernels are supported");
    }

    return dst;
}

template SimpleTensor<float> winograd_input_transform(const SimpleTensor<float> &src, const TensorShape &dst_shape, const PadStrideInfo &conv_info, const Size2D &kernel_dims);
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute