/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

   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 "paddle/framework/tensor_array.h"

#include <gtest/gtest.h>

namespace paddle {
namespace framework {

class TensorArrayTester : public ::testing::Test {
 protected:
  void SetUp() override {
    LoDTensor source;
    source.Resize(make_ddim({batch_size, dim}));
    int* data = source.mutable_data<int>(platform::CPUPlace());
    for (int i = 0; i < 16 * 32; i++) {
      data[i] = i;
    }
    ta.Unstack(source);
  }

  TensorArray ta;
  const int batch_size = 16;
  const int dim = 32;
};

TEST_F(TensorArrayTester, Read) {
  for (int i = 0; i < batch_size; i++) {
    const auto& tensor = ta.Read(i);
    ASSERT_EQ(tensor.dims()[0], 1);
    ASSERT_EQ(tensor.dims()[1], dim);
  }
}

TEST_F(TensorArrayTester, Write) {
  LoDTensor source;
  source.Resize(make_ddim({1, dim}));
  for (int i = 0; i < dim; i++) {
    *(source.mutable_data<int>(platform::CPUPlace()) + i) = i;
  }

  ta.Write(2, source);

  const auto& tensor = ta.Read(2);
  for (int i = 0; i < dim; i++) {
    EXPECT_EQ(*(tensor.data<int>() + i), *(source.data<int>() + i));
  }
}

TEST_F(TensorArrayTester, WriteShared) {
  LoDTensor source;
  source.Resize(make_ddim({1, dim}));
  for (int i = 0; i < dim; i++) {
    *(source.mutable_data<int>(platform::CPUPlace()) + i) = i;
  }

  ta.WriteShared(2, source);

  const auto& tensor = ta.Read(2);
  for (int i = 0; i < dim; i++) {
    EXPECT_EQ(*(tensor.data<int>() + i), *(source.data<int>() + i));
  }

  EXPECT_EQ(source.data<int>(), tensor.data<int>());
}

class TensorArrayPackTester : public ::testing::Test {
 protected:
  virtual void SetUp() override {
    lod.push_back(std::vector<size_t>{0, 2, 9, 13});

    source.set_lod(lod);
    source.Resize(make_ddim({13, 128}));
    source.mutable_data<int>(platform::CPUPlace());

    // content of each setence: 0 1 2 3 4
    const auto& level = lod.front();
    for (size_t i = 0; i < level.size() - 1; i++) {
      size_t begin = level[i];
      size_t end = level[i + 1];
      for (size_t j = begin; j < end; j++) {
        auto record = source.Slice(j, j + 1);
        for (int dim = 0; dim < 128; dim++) {
          record.mutable_data<int>(platform::CPUPlace())[dim] = j - begin;
        }
      }
    }

    // unpack
    meta = ta.Unpack(source, 0, true);
  }

  LoD lod;
  TensorArray ta;
  LoDTensor source;
  std::vector<DySeqMeta> meta;
};

TEST_F(TensorArrayPackTester, Unpack) {
  ASSERT_EQ(ta.size(), 7UL);

  const auto& t0 = ta.Read(0);
  const auto& t1 = ta.Read(1);

  ASSERT_EQ(t0.data<int>()[0], int(0));
  ASSERT_EQ(t1.data<int>()[0], int(1));
}

TEST_F(TensorArrayPackTester, Pack) {
  LoDTensor packed = ta.Pack(0, meta, lod);
}

TEST_F(TensorArrayTester, size) {
  ASSERT_EQ(ta.size(), static_cast<size_t>(batch_size));
}

TEST(TensorArray, LodPack) {
  // three time steps, each step stores a LoDTensors
  // - [0] [1]
  // - [2 3], [4 5]
  // - [6 7] [] [8], [9, 10]
  // try to get a LoDTensor with content:
  // - [0 2 6]
  // - [0 2 7]
  // - [0 3]
  // - [1 4 8]
  // - [1 5 9]
  // - [1 5 10]
  std::array<LoDTensor, 3> tensors;
  tensors[0].Resize(make_ddim({2, 1}));
  tensors[1].Resize(make_ddim({4, 1}));
  tensors[2].Resize(make_ddim({5, 1}));
  int index = 0;
  for (auto& t : tensors) {
    t.mutable_data<int>(platform::CPUPlace());
    for (int i = 0; i < t.dims()[0]; i++) {
      t.data<int>()[i] = index;
      index++;
    }
  }

  std::array<LoD, 3> lods;
  std::vector<std::vector<size_t>> levels{
      {0, 1, 2}, {0, 2, 4}, {0, 2, 2, 3, 5}};
  for (int i = 0; i < 3; i++) {
    lods[i].emplace_back(levels[i].begin(), levels[i].end());
  }

  TensorArray ta;
  for (int i = 0; i < 3; i++) {
    tensors[i].set_lod(lods[i]);
    ta.Write(i, tensors[i]);
  }

  auto merged = ta.LodPack(0);

  std::vector<int> target_tensor_data{{0, 2, 6,  // 0
                                       0, 2, 7,  // 1
                                       0, 3,     // 2
                                       1, 4, 8,  // 3
                                       1, 5, 9,  // 5
                                       1, 5, 10}};
  EXPECT_EQ(merged.dims()[0], (int)target_tensor_data.size());
  for (size_t i = 0; i < target_tensor_data.size(); i++) {
    EXPECT_EQ(target_tensor_data[i], merged.data<int>()[i]);
  }
}

}  // namespace framework
}  // namespace paddle