/* Copyright (c) 2016 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 "paddle/fluid/operators/beam_search_decode_op.h"
#include "gtest/gtest.h"

using CPUPlace = paddle::platform::CPUPlace;
using LoD = paddle::framework::LoD;
using LoDTensor = paddle::framework::LoDTensor;
using LoDTensorArray = paddle::framework::LoDTensorArray;

template <typename T>
using BeamSearchDecoder = paddle::operators::BeamSearchDecoder<T>;
template <typename T>
using Sentence = paddle::operators::Sentence<T>;
template <typename T>
using SentenceVector = paddle::operators::SentenceVector<T>;

namespace paddle {
namespace test {

void GenerateExample(const std::vector<size_t>& level_0,
                     const std::vector<size_t>& level_1,
                     const std::vector<int>& data, LoDTensorArray* ids,
                     LoDTensorArray* scores) {
  PADDLE_ENFORCE_EQ(level_0.back(), level_1.size() - 1,
                    "source level is used to describe candidate set");
  PADDLE_ENFORCE_EQ(level_1.back(), data.size(),
                    "the lowest level is used to describe data"
                    ", so it's last element should be data length");

  CPUPlace place;

  LoD lod;
  lod.push_back(level_0);
  lod.push_back(level_1);

  // Ids
  LoDTensor tensor_id;
  tensor_id.set_lod(lod);
  tensor_id.Resize({static_cast<int64_t>(data.size())});
  // malloc memory
  int64_t* id_ptr = tensor_id.mutable_data<int64_t>(place);
  for (size_t i = 0; i < data.size(); ++i) {
    id_ptr[i] = static_cast<int64_t>(data.at(i));
  }

  // Scores
  LoDTensor tensor_score;
  tensor_score.set_lod(lod);
  tensor_score.Resize({static_cast<int64_t>(data.size())});
  // malloc memory
  float* score_ptr = tensor_score.mutable_data<float>(place);
  for (size_t i = 0; i < data.size(); ++i) {
    score_ptr[i] = static_cast<float>(data.at(i));
  }

  ids->push_back(tensor_id);
  scores->push_back(tensor_score);
}

}  // namespace test
}  // namespace paddle

TEST(BeamSearchDecodeOp, Backtrace) {
  CPUPlace place;

  // we will constuct a sample data with 4 steps and 2 source sentences
  // beam_size = 2, start_id = 0, end_id = 1
  LoDTensorArray ids;
  LoDTensorArray scores;

  paddle::test::GenerateExample(
      std::vector<size_t>{0, 1, 2}, std::vector<size_t>{0, 1, 2},
      std::vector<int>{0, 0}, &ids, &scores);  // start with start_id
  paddle::test::GenerateExample(std::vector<size_t>{0, 1, 2},
                                std::vector<size_t>{0, 2, 4},
                                std::vector<int>{2, 3, 4, 5}, &ids, &scores);
  paddle::test::GenerateExample(std::vector<size_t>{0, 2, 4},
                                std::vector<size_t>{0, 2, 2, 4, 4},
                                std::vector<int>{3, 1, 5, 4}, &ids, &scores);
  paddle::test::GenerateExample(std::vector<size_t>{0, 2, 4},
                                std::vector<size_t>{0, 1, 2, 3, 4},
                                std::vector<int>{1, 1, 3, 5}, &ids, &scores);
  paddle::test::GenerateExample(
      std::vector<size_t>{0, 2, 4},
      std::vector<size_t>{0, 0, 0, 2,
                          2},  // the branchs of the first source sentence
                               // are pruned since finished
      std::vector<int>{5, 1},
      &ids, &scores);

  ASSERT_EQ(ids.size(), 5UL);
  ASSERT_EQ(scores.size(), 5UL);

  BeamSearchDecoder<float> helper(2, 1);  // beam_size = 2, end_id = 1

  LoDTensor id_tensor;
  LoDTensor score_tensor;
  helper.Backtrace(ids, scores, &id_tensor, &score_tensor);

  LoD lod = id_tensor.lod();
  std::vector<size_t> expect_source_lod = {0, 2, 4};
  EXPECT_EQ(lod[0], expect_source_lod);
  std::vector<size_t> expect_sentence_lod = {0, 4, 7, 12, 17};
  EXPECT_EQ(lod[1], expect_sentence_lod);
  std::vector<int> expect_data = {0, 2, 3, 1, 0, 2, 1, 0, 4,
                                  5, 3, 5, 0, 4, 5, 3, 1};
  ASSERT_EQ(id_tensor.dims()[0], static_cast<int64_t>(expect_data.size()));
  for (size_t i = 0; i < expect_data.size(); ++i) {
    ASSERT_EQ(id_tensor.data<int64_t>()[i],
              static_cast<int64_t>(expect_data[i]));
  }
  for (int64_t i = 0; i < id_tensor.dims()[0]; ++i) {
    ASSERT_EQ(score_tensor.data<float>()[i],
              static_cast<float>(id_tensor.data<int64_t>()[i]));
  }
}