// 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;
template <typename DataType, int DataLayout, typename IndexType>
void test_forced_eval_sycl(const Eigen::SyclDevice& sycl_device) {
  IndexType sizeDim1 = 100;
  IndexType sizeDim2 = 20;
  IndexType sizeDim3 = 20;
  Eigen::array<IndexType, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
  Eigen::Tensor<DataType, 3, DataLayout, IndexType> in1(tensorRange);
  Eigen::Tensor<DataType, 3, DataLayout, IndexType> in2(tensorRange);
  Eigen::Tensor<DataType, 3, DataLayout, IndexType> out(tensorRange);

  DataType* gpu_in1_data =
      static_cast<DataType*>(sycl_device.allocate(in1.dimensions().TotalSize() * sizeof(DataType)));
  DataType* gpu_in2_data =
      static_cast<DataType*>(sycl_device.allocate(in2.dimensions().TotalSize() * sizeof(DataType)));
  DataType* gpu_out_data =
      static_cast<DataType*>(sycl_device.allocate(out.dimensions().TotalSize() * sizeof(DataType)));

  in1 = in1.random() + in1.constant(static_cast<DataType>(10.0f));
  in2 = in2.random() + in2.constant(static_cast<DataType>(10.0f));

  // creating TensorMap from tensor
  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_in1(gpu_in1_data, tensorRange);
  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_in2(gpu_in2_data, tensorRange);
  Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_out(gpu_out_data, tensorRange);
  sycl_device.memcpyHostToDevice(gpu_in1_data, in1.data(), (in1.dimensions().TotalSize()) * sizeof(DataType));
  sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(), (in2.dimensions().TotalSize()) * sizeof(DataType));
  /// c=(a+b)*b
  gpu_out.device(sycl_device) = (gpu_in1 + gpu_in2).eval() * gpu_in2;
  sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.dimensions().TotalSize()) * sizeof(DataType));
  for (IndexType i = 0; i < sizeDim1; ++i) {
    for (IndexType j = 0; j < sizeDim2; ++j) {
      for (IndexType k = 0; k < sizeDim3; ++k) {
        VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) + in2(i, j, k)) * in2(i, j, k));
      }
    }
  }
  printf("(a+b)*b Test Passed\n");
  sycl_device.deallocate(gpu_in1_data);
  sycl_device.deallocate(gpu_in2_data);
  sycl_device.deallocate(gpu_out_data);
}

template <typename DataType, typename Dev_selector>
void tensorForced_evalperDevice(Dev_selector s) {
  QueueInterface queueInterface(s);
  auto sycl_device = Eigen::SyclDevice(&queueInterface);
  test_forced_eval_sycl<DataType, RowMajor, int64_t>(sycl_device);
  test_forced_eval_sycl<DataType, ColMajor, int64_t>(sycl_device);
}
EIGEN_DECLARE_TEST(cxx11_tensor_forced_eval_sycl) {
  for (const auto& device : Eigen::get_sycl_supported_devices()) {
    CALL_SUBTEST(tensorForced_evalperDevice<float>(device));
    CALL_SUBTEST(tensorForced_evalperDevice<half>(device));
  }
}
