/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */

#include "gtest/gtest.h"
#include "paddle/phi/api/lib/utils/allocator.h"
#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/core/sparse_csr_tensor.h"
#include "paddle/phi/tests/core/allocator.h"

namespace phi {
namespace tests {

TEST(sparse_csr_tensor, construct) {
  phi::CPUPlace cpu;
  auto dense_dims = phi::make_ddim({3, 3});
  std::vector<float> non_zero_data = {1.0, 2.0, 3.0};
  std::vector<int64_t> crows_data = {0, 1, 1, 3};
  std::vector<int64_t> cols_data = {1, 0, 2};

  auto fancy_allocator = std::unique_ptr<Allocator>(new FancyAllocator);
  auto alloc = fancy_allocator.get();
  // create non_zero_crows
  auto crows_dims = phi::make_ddim({static_cast<int>(crows_data.size())});
  DenseTensorMeta crows_meta(DataType::INT64, crows_dims, DataLayout::NCHW);
  DenseTensor crows(alloc, crows_meta);
  memcpy(crows.mutable_data<int64_t>(cpu),
         &crows_data[0],
         crows_data.size() * sizeof(int64_t));

  // create non_zero_cols
  auto cols_dims = phi::make_ddim({static_cast<int>(cols_data.size())});
  DenseTensorMeta cols_meta(DataType::INT64, cols_dims, DataLayout::NCHW);
  DenseTensor cols(alloc, cols_meta);
  memcpy(cols.mutable_data<int64_t>(cpu),
         &cols_data[0],
         cols_data.size() * sizeof(int64_t));

  // create non_zero_elements
  auto elements_dims = phi::make_ddim({static_cast<int>(non_zero_data.size())});
  DenseTensorMeta elements_meta(
      DataType::FLOAT32, elements_dims, DataLayout::NCHW);
  DenseTensor elements(alloc, elements_meta);
  memcpy(elements.mutable_data<float>(cpu),
         &non_zero_data[0],
         non_zero_data.size() * sizeof(float));

  SparseCsrTensor sparse(crows, cols, elements, dense_dims);

  CHECK_EQ(sparse.non_zero_cols().numel(),
           static_cast<int64_t>(non_zero_data.size()));
  CHECK_EQ(sparse.numel(), 9);
  CHECK(sparse.dims() == dense_dims);
  CHECK(sparse.dtype() == DataType::FLOAT32);
  CHECK(sparse.layout() == DataLayout::SPARSE_CSR);
  CHECK(sparse.place() == paddle::platform::CPUPlace());
  CHECK(sparse.initialized() == true);
}

TEST(sparse_csr_tensor, other_function) {
  auto fancy_allocator = std::unique_ptr<Allocator>(new FancyAllocator);
  auto alloc = fancy_allocator.get();
  auto dense_dims = phi::make_ddim({4, 4});
  auto crows_dims = phi::make_ddim({dense_dims[0] + 1});
  DenseTensorMeta crows_meta(DataType::INT64, crows_dims, DataLayout::NCHW);
  DenseTensor crows(alloc, crows_meta);

  const int64_t non_zero_num = 5;
  auto cols_dims = phi::make_ddim({non_zero_num});
  DenseTensorMeta cols_meta(DataType::INT64, cols_dims, DataLayout::NCHW);
  DenseTensor cols(alloc, cols_meta);
  DenseTensorMeta values_meta(DataType::FLOAT32, cols_dims, DataLayout::NCHW);
  DenseTensor values(alloc, values_meta);

  SparseCsrTensor csr(crows, cols, values, dense_dims);
  CHECK(csr.initialized());
  CHECK_EQ(csr.dims(), dense_dims);

  // Test Resize
  auto dense_dims_3d = phi::make_ddim({2, 4, 4});
  csr.Resize(dense_dims_3d, 2);
  CHECK_EQ(csr.non_zero_cols().numel(), 2);

  // Test shallow_copy
  SparseCsrTensor csr2(csr);
  CHECK(csr.dims() == csr2.dims());

  // Test shallow_copy_assignment
  SparseCsrTensor csr3 = csr2;
  CHECK(csr3.dims() == csr2.dims());
}

}  // namespace tests
}  // namespace phi
