// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016
// Mehdi Goli    Codeplay Software Ltd.
// Ralph Potter  Codeplay Software Ltd.
// Luke Iwanski  Codeplay Software Ltd.
// Contact: <eigen@codeplay.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX

#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL

#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>

using Eigen::Tensor;
static const int DataLayout = ColMajor;

template <typename DataType, typename IndexType>
static void test_single_voxel_patch_sycl(const Eigen::SyclDevice& sycl_device) {
  IndexType sizeDim0 = 4;
  IndexType sizeDim1 = 2;
  IndexType sizeDim2 = 3;
  IndexType sizeDim3 = 5;
  IndexType sizeDim4 = 7;
  array<IndexType, 5> tensorColMajorRange = {{sizeDim0, sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
  array<IndexType, 5> tensorRowMajorRange = {{sizeDim4, sizeDim3, sizeDim2, sizeDim1, sizeDim0}};
  Tensor<DataType, 5, DataLayout, IndexType> tensor_col_major(tensorColMajorRange);
  Tensor<DataType, 5, RowMajor, IndexType> tensor_row_major(tensorRowMajorRange);
  tensor_col_major.setRandom();

  DataType* gpu_data_col_major =
      static_cast<DataType*>(sycl_device.allocate(tensor_col_major.size() * sizeof(DataType)));
  DataType* gpu_data_row_major =
      static_cast<DataType*>(sycl_device.allocate(tensor_row_major.size() * sizeof(DataType)));
  TensorMap<Tensor<DataType, 5, ColMajor, IndexType>> gpu_col_major(gpu_data_col_major, tensorColMajorRange);
  TensorMap<Tensor<DataType, 5, RowMajor, IndexType>> gpu_row_major(gpu_data_row_major, tensorRowMajorRange);

  sycl_device.memcpyHostToDevice(gpu_data_col_major, tensor_col_major.data(),
                                 (tensor_col_major.size()) * sizeof(DataType));
  gpu_row_major.device(sycl_device) = gpu_col_major.swap_layout();

  // single volume patch: ColMajor
  array<IndexType, 6> patchColMajorTensorRange = {{sizeDim0, 1, 1, 1, sizeDim1 * sizeDim2 * sizeDim3, sizeDim4}};
  Tensor<DataType, 6, DataLayout, IndexType> single_voxel_patch_col_major(patchColMajorTensorRange);
  size_t patchTensorBuffSize = single_voxel_patch_col_major.size() * sizeof(DataType);
  DataType* gpu_data_single_voxel_patch_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
  TensorMap<Tensor<DataType, 6, DataLayout, IndexType>> gpu_single_voxel_patch_col_major(
      gpu_data_single_voxel_patch_col_major, patchColMajorTensorRange);
  gpu_single_voxel_patch_col_major.device(sycl_device) = gpu_col_major.extract_volume_patches(1, 1, 1);
  sycl_device.memcpyDeviceToHost(single_voxel_patch_col_major.data(), gpu_data_single_voxel_patch_col_major,
                                 patchTensorBuffSize);

  VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(0), 4);
  VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(1), 1);
  VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(2), 1);
  VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(3), 1);
  VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(4), 2 * 3 * 5);
  VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(5), 7);

  array<IndexType, 6> patchRowMajorTensorRange = {{sizeDim4, sizeDim1 * sizeDim2 * sizeDim3, 1, 1, 1, sizeDim0}};
  Tensor<DataType, 6, RowMajor, IndexType> single_voxel_patch_row_major(patchRowMajorTensorRange);
  patchTensorBuffSize = single_voxel_patch_row_major.size() * sizeof(DataType);
  DataType* gpu_data_single_voxel_patch_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
  TensorMap<Tensor<DataType, 6, RowMajor, IndexType>> gpu_single_voxel_patch_row_major(
      gpu_data_single_voxel_patch_row_major, patchRowMajorTensorRange);
  gpu_single_voxel_patch_row_major.device(sycl_device) = gpu_row_major.extract_volume_patches(1, 1, 1);
  sycl_device.memcpyDeviceToHost(single_voxel_patch_row_major.data(), gpu_data_single_voxel_patch_row_major,
                                 patchTensorBuffSize);

  VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(0), 7);
  VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(1), 2 * 3 * 5);
  VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(2), 1);
  VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(3), 1);
  VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(4), 1);
  VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(5), 4);

  sycl_device.memcpyDeviceToHost(tensor_row_major.data(), gpu_data_row_major,
                                 (tensor_col_major.size()) * sizeof(DataType));
  for (IndexType i = 0; i < tensor_col_major.size(); ++i) {
    VERIFY_IS_EQUAL(tensor_col_major.data()[i], single_voxel_patch_col_major.data()[i]);
    VERIFY_IS_EQUAL(tensor_row_major.data()[i], single_voxel_patch_row_major.data()[i]);
    VERIFY_IS_EQUAL(tensor_col_major.data()[i], tensor_row_major.data()[i]);
  }

  sycl_device.deallocate(gpu_data_col_major);
  sycl_device.deallocate(gpu_data_row_major);
  sycl_device.deallocate(gpu_data_single_voxel_patch_col_major);
  sycl_device.deallocate(gpu_data_single_voxel_patch_row_major);
}

template <typename DataType, typename IndexType>
static void test_entire_volume_patch_sycl(const Eigen::SyclDevice& sycl_device) {
  const int depth = 4;
  const int patch_z = 2;
  const int patch_y = 3;
  const int patch_x = 5;
  const int batch = 7;

  array<IndexType, 5> tensorColMajorRange = {{depth, patch_z, patch_y, patch_x, batch}};
  array<IndexType, 5> tensorRowMajorRange = {{batch, patch_x, patch_y, patch_z, depth}};
  Tensor<DataType, 5, DataLayout, IndexType> tensor_col_major(tensorColMajorRange);
  Tensor<DataType, 5, RowMajor, IndexType> tensor_row_major(tensorRowMajorRange);
  tensor_col_major.setRandom();

  DataType* gpu_data_col_major =
      static_cast<DataType*>(sycl_device.allocate(tensor_col_major.size() * sizeof(DataType)));
  DataType* gpu_data_row_major =
      static_cast<DataType*>(sycl_device.allocate(tensor_row_major.size() * sizeof(DataType)));
  TensorMap<Tensor<DataType, 5, ColMajor, IndexType>> gpu_col_major(gpu_data_col_major, tensorColMajorRange);
  TensorMap<Tensor<DataType, 5, RowMajor, IndexType>> gpu_row_major(gpu_data_row_major, tensorRowMajorRange);

  sycl_device.memcpyHostToDevice(gpu_data_col_major, tensor_col_major.data(),
                                 (tensor_col_major.size()) * sizeof(DataType));
  gpu_row_major.device(sycl_device) = gpu_col_major.swap_layout();
  sycl_device.memcpyDeviceToHost(tensor_row_major.data(), gpu_data_row_major,
                                 (tensor_col_major.size()) * sizeof(DataType));

  // single volume patch: ColMajor
  array<IndexType, 6> patchColMajorTensorRange = {
      {depth, patch_z, patch_y, patch_x, patch_z * patch_y * patch_x, batch}};
  Tensor<DataType, 6, DataLayout, IndexType> entire_volume_patch_col_major(patchColMajorTensorRange);
  size_t patchTensorBuffSize = entire_volume_patch_col_major.size() * sizeof(DataType);
  DataType* gpu_data_entire_volume_patch_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
  TensorMap<Tensor<DataType, 6, DataLayout, IndexType>> gpu_entire_volume_patch_col_major(
      gpu_data_entire_volume_patch_col_major, patchColMajorTensorRange);
  gpu_entire_volume_patch_col_major.device(sycl_device) =
      gpu_col_major.extract_volume_patches(patch_z, patch_y, patch_x);
  sycl_device.memcpyDeviceToHost(entire_volume_patch_col_major.data(), gpu_data_entire_volume_patch_col_major,
                                 patchTensorBuffSize);

  //  Tensor<float, 5> tensor(depth, patch_z, patch_y, patch_x, batch);
  //  tensor.setRandom();
  //  Tensor<float, 5, RowMajor> tensor_row_major = tensor.swap_layout();

  // Tensor<float, 6> entire_volume_patch;
  // entire_volume_patch = tensor.extract_volume_patches(patch_z, patch_y, patch_x);
  VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(0), depth);
  VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(1), patch_z);
  VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(2), patch_y);
  VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(3), patch_x);
  VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(4), patch_z * patch_y * patch_x);
  VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(5), batch);

  //  Tensor<float, 6, RowMajor> entire_volume_patch_row_major;
  // entire_volume_patch_row_major = tensor_row_major.extract_volume_patches(patch_z, patch_y, patch_x);

  array<IndexType, 6> patchRowMajorTensorRange = {
      {batch, patch_z * patch_y * patch_x, patch_x, patch_y, patch_z, depth}};
  Tensor<DataType, 6, RowMajor, IndexType> entire_volume_patch_row_major(patchRowMajorTensorRange);
  patchTensorBuffSize = entire_volume_patch_row_major.size() * sizeof(DataType);
  DataType* gpu_data_entire_volume_patch_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
  TensorMap<Tensor<DataType, 6, RowMajor, IndexType>> gpu_entire_volume_patch_row_major(
      gpu_data_entire_volume_patch_row_major, patchRowMajorTensorRange);
  gpu_entire_volume_patch_row_major.device(sycl_device) =
      gpu_row_major.extract_volume_patches(patch_z, patch_y, patch_x);
  sycl_device.memcpyDeviceToHost(entire_volume_patch_row_major.data(), gpu_data_entire_volume_patch_row_major,
                                 patchTensorBuffSize);

  VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(0), batch);
  VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(1), patch_z * patch_y * patch_x);
  VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(2), patch_x);
  VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(3), patch_y);
  VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(4), patch_z);
  VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(5), depth);

  const int dz = patch_z - 1;
  const int dy = patch_y - 1;
  const int dx = patch_x - 1;

  const int forward_pad_z = dz / 2;
  const int forward_pad_y = dy / 2;
  const int forward_pad_x = dx / 2;

  for (int pz = 0; pz < patch_z; pz++) {
    for (int py = 0; py < patch_y; py++) {
      for (int px = 0; px < patch_x; px++) {
        const int patchId = pz + patch_z * (py + px * patch_y);
        for (int z = 0; z < patch_z; z++) {
          for (int y = 0; y < patch_y; y++) {
            for (int x = 0; x < patch_x; x++) {
              for (int b = 0; b < batch; b++) {
                for (int d = 0; d < depth; d++) {
                  float expected = 0.0f;
                  float expected_row_major = 0.0f;
                  const int eff_z = z - forward_pad_z + pz;
                  const int eff_y = y - forward_pad_y + py;
                  const int eff_x = x - forward_pad_x + px;
                  if (eff_z >= 0 && eff_y >= 0 && eff_x >= 0 && eff_z < patch_z && eff_y < patch_y && eff_x < patch_x) {
                    expected = tensor_col_major(d, eff_z, eff_y, eff_x, b);
                    expected_row_major = tensor_row_major(b, eff_x, eff_y, eff_z, d);
                  }
                  VERIFY_IS_EQUAL(entire_volume_patch_col_major(d, z, y, x, patchId, b), expected);
                  VERIFY_IS_EQUAL(entire_volume_patch_row_major(b, patchId, x, y, z, d), expected_row_major);
                }
              }
            }
          }
        }
      }
    }
  }
  sycl_device.deallocate(gpu_data_col_major);
  sycl_device.deallocate(gpu_data_row_major);
  sycl_device.deallocate(gpu_data_entire_volume_patch_col_major);
  sycl_device.deallocate(gpu_data_entire_volume_patch_row_major);
}

template <typename DataType, typename dev_Selector>
void sycl_tensor_volume_patch_test_per_device(dev_Selector s) {
  QueueInterface queueInterface(s);
  auto sycl_device = Eigen::SyclDevice(&queueInterface);
  std::cout << "Running on " << s.template get_info<cl::sycl::info::device::name>() << std::endl;
  test_single_voxel_patch_sycl<DataType, int64_t>(sycl_device);
  test_entire_volume_patch_sycl<DataType, int64_t>(sycl_device);
}
EIGEN_DECLARE_TEST(cxx11_tensor_volume_patch_sycl) {
  for (const auto& device : Eigen::get_sycl_supported_devices()) {
    CALL_SUBTEST(sycl_tensor_volume_patch_test_per_device<half>(device));
    CALL_SUBTEST(sycl_tensor_volume_patch_test_per_device<float>(device));
  }
}