// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2015
// 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_TEST_FUNC cxx11_tensor_reverse_sycl
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL

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


template <typename DataType, int DataLayout, typename IndexType>
static void test_simple_reverse(const Eigen::SyclDevice&  sycl_device) {

  IndexType dim1 = 2;
  IndexType dim2 = 3;
  IndexType dim3 = 5;
  IndexType dim4 = 7;

  array<IndexType, 4> tensorRange = {{dim1, dim2, dim3, dim4}};
  Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange);
  Tensor<DataType, 4, DataLayout, IndexType> reversed_tensor(tensorRange);
  tensor.setRandom();

  array<bool, 4> dim_rev;
  dim_rev[0] = false;
  dim_rev[1] = true;
  dim_rev[2] = true;
  dim_rev[3] = false;

  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(tensor.dimensions().TotalSize()*sizeof(DataType)));
  DataType* gpu_out_data =static_cast<DataType*>(sycl_device.allocate(reversed_tensor.dimensions().TotalSize()*sizeof(DataType)));

  TensorMap<Tensor<DataType, 4, DataLayout, IndexType> >  in_gpu(gpu_in_data, tensorRange);
  TensorMap<Tensor<DataType, 4, DataLayout, IndexType> >  out_gpu(gpu_out_data, tensorRange);

  sycl_device.memcpyHostToDevice(gpu_in_data, tensor.data(),(tensor.dimensions().TotalSize())*sizeof(DataType));
  out_gpu.device(sycl_device) = in_gpu.reverse(dim_rev);
  sycl_device.memcpyDeviceToHost(reversed_tensor.data(), gpu_out_data, reversed_tensor.dimensions().TotalSize()*sizeof(DataType));
  // Check that the CPU and GPU reductions return the same result.
  for (IndexType i = 0; i < 2; ++i) {
    for (IndexType j = 0; j < 3; ++j) {
      for (IndexType k = 0; k < 5; ++k) {
        for (IndexType l = 0; l < 7; ++l) {
          VERIFY_IS_EQUAL(tensor(i,j,k,l), reversed_tensor(i,2-j,4-k,l));
        }
      }
    }
  }
  dim_rev[0] = true;
  dim_rev[1] = false;
  dim_rev[2] = false;
  dim_rev[3] = false;

  out_gpu.device(sycl_device) = in_gpu.reverse(dim_rev);
  sycl_device.memcpyDeviceToHost(reversed_tensor.data(), gpu_out_data, reversed_tensor.dimensions().TotalSize()*sizeof(DataType));

  for (IndexType i = 0; i < 2; ++i) {
    for (IndexType j = 0; j < 3; ++j) {
      for (IndexType k = 0; k < 5; ++k) {
        for (IndexType l = 0; l < 7; ++l) {
          VERIFY_IS_EQUAL(tensor(i,j,k,l), reversed_tensor(1-i,j,k,l));
        }
      }
    }
  }

  dim_rev[0] = true;
  dim_rev[1] = false;
  dim_rev[2] = false;
  dim_rev[3] = true;
  out_gpu.device(sycl_device) = in_gpu.reverse(dim_rev);
  sycl_device.memcpyDeviceToHost(reversed_tensor.data(), gpu_out_data, reversed_tensor.dimensions().TotalSize()*sizeof(DataType));

  for (IndexType i = 0; i < 2; ++i) {
    for (IndexType j = 0; j < 3; ++j) {
      for (IndexType k = 0; k < 5; ++k) {
        for (IndexType l = 0; l < 7; ++l) {
          VERIFY_IS_EQUAL(tensor(i,j,k,l), reversed_tensor(1-i,j,k,6-l));
        }
      }
    }
  }

  sycl_device.deallocate(gpu_in_data);
  sycl_device.deallocate(gpu_out_data);
}



template <typename DataType, int DataLayout, typename IndexType>
static void test_expr_reverse(const Eigen::SyclDevice&  sycl_device, bool LValue)
{
  IndexType dim1 = 2;
  IndexType dim2 = 3;
  IndexType dim3 = 5;
  IndexType dim4 = 7;

  array<IndexType, 4> tensorRange = {{dim1, dim2, dim3, dim4}};
  Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange);
  Tensor<DataType, 4, DataLayout, IndexType> expected(tensorRange);
  Tensor<DataType, 4, DataLayout, IndexType> result(tensorRange);
  tensor.setRandom();

  array<bool, 4> dim_rev;
  dim_rev[0] = false;
  dim_rev[1] = true;
  dim_rev[2] = false;
  dim_rev[3] = true;

  DataType* gpu_in_data = static_cast<DataType*>(sycl_device.allocate(tensor.dimensions().TotalSize()*sizeof(DataType)));
  DataType* gpu_out_data_expected =static_cast<DataType*>(sycl_device.allocate(expected.dimensions().TotalSize()*sizeof(DataType)));
  DataType* gpu_out_data_result =static_cast<DataType*>(sycl_device.allocate(result.dimensions().TotalSize()*sizeof(DataType)));

  TensorMap<Tensor<DataType, 4, DataLayout, IndexType> >  in_gpu(gpu_in_data, tensorRange);
  TensorMap<Tensor<DataType, 4, DataLayout, IndexType> >  out_gpu_expected(gpu_out_data_expected, tensorRange);
  TensorMap<Tensor<DataType, 4, DataLayout, IndexType> >  out_gpu_result(gpu_out_data_result, tensorRange);


  sycl_device.memcpyHostToDevice(gpu_in_data, tensor.data(),(tensor.dimensions().TotalSize())*sizeof(DataType));

  if (LValue) {
    out_gpu_expected.reverse(dim_rev).device(sycl_device) = in_gpu;
  } else {
    out_gpu_expected.device(sycl_device) = in_gpu.reverse(dim_rev);
  }
  sycl_device.memcpyDeviceToHost(expected.data(), gpu_out_data_expected, expected.dimensions().TotalSize()*sizeof(DataType));


  array<IndexType, 4> src_slice_dim;
  src_slice_dim[0] = 2;
  src_slice_dim[1] = 3;
  src_slice_dim[2] = 1;
  src_slice_dim[3] = 7;
  array<IndexType, 4> src_slice_start;
  src_slice_start[0] = 0;
  src_slice_start[1] = 0;
  src_slice_start[2] = 0;
  src_slice_start[3] = 0;
  array<IndexType, 4> dst_slice_dim = src_slice_dim;
  array<IndexType, 4> dst_slice_start = src_slice_start;

  for (IndexType i = 0; i < 5; ++i) {
    if (LValue) {
        out_gpu_result.slice(dst_slice_start, dst_slice_dim).reverse(dim_rev).device(sycl_device) =
          in_gpu.slice(src_slice_start, src_slice_dim);
    } else {
      out_gpu_result.slice(dst_slice_start, dst_slice_dim).device(sycl_device) =
          in_gpu.slice(src_slice_start, src_slice_dim).reverse(dim_rev);
    }
    src_slice_start[2] += 1;
    dst_slice_start[2] += 1;
  }
  sycl_device.memcpyDeviceToHost(result.data(), gpu_out_data_result, result.dimensions().TotalSize()*sizeof(DataType));

  for (IndexType i = 0; i < expected.dimension(0); ++i) {
    for (IndexType j = 0; j < expected.dimension(1); ++j) {
      for (IndexType k = 0; k < expected.dimension(2); ++k) {
        for (IndexType l = 0; l < expected.dimension(3); ++l) {
          VERIFY_IS_EQUAL(result(i,j,k,l), expected(i,j,k,l));
        }
      }
    }
  }

  dst_slice_start[2] = 0;
  result.setRandom();
  sycl_device.memcpyHostToDevice(gpu_out_data_result, result.data(),(result.dimensions().TotalSize())*sizeof(DataType));
  for (IndexType i = 0; i < 5; ++i) {
     if (LValue) {
       out_gpu_result.slice(dst_slice_start, dst_slice_dim).reverse(dim_rev).device(sycl_device) =
           in_gpu.slice(dst_slice_start, dst_slice_dim);
     } else {
       out_gpu_result.slice(dst_slice_start, dst_slice_dim).device(sycl_device) =
           in_gpu.reverse(dim_rev).slice(dst_slice_start, dst_slice_dim);
     }
    dst_slice_start[2] += 1;
  }
  sycl_device.memcpyDeviceToHost(result.data(), gpu_out_data_result, result.dimensions().TotalSize()*sizeof(DataType));

  for (IndexType i = 0; i < expected.dimension(0); ++i) {
    for (IndexType j = 0; j < expected.dimension(1); ++j) {
      for (IndexType k = 0; k < expected.dimension(2); ++k) {
        for (IndexType l = 0; l < expected.dimension(3); ++l) {
          VERIFY_IS_EQUAL(result(i,j,k,l), expected(i,j,k,l));
        }
      }
    }
  }
}



template<typename DataType> void sycl_reverse_test_per_device(const cl::sycl::device& d){
  std::cout << "Running on " << d.template get_info<cl::sycl::info::device::name>() << std::endl;
  QueueInterface queueInterface(d);
  auto sycl_device = Eigen::SyclDevice(&queueInterface);
  test_simple_reverse<DataType, RowMajor, int64_t>(sycl_device);
  test_simple_reverse<DataType, ColMajor, int64_t>(sycl_device);
  test_expr_reverse<DataType, RowMajor, int64_t>(sycl_device, false);
  test_expr_reverse<DataType, ColMajor, int64_t>(sycl_device, false);
  test_expr_reverse<DataType, RowMajor, int64_t>(sycl_device, true);
  test_expr_reverse<DataType, ColMajor, int64_t>(sycl_device, true);
}
void test_cxx11_tensor_reverse_sycl() {
  for (const auto& device :Eigen::get_sycl_supported_devices()) {
    CALL_SUBTEST(sycl_reverse_test_per_device<float>(device));
  }
}