/* 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/math/beam_search.h"
#include <algorithm>
#include <map>

namespace paddle {
namespace operators {
namespace math {

template <typename T>
class BeamSearchFunctor<platform::CPUDeviceContext, T> {
 public:
  void operator()(const platform::CPUDeviceContext &context,
                  const framework::LoDTensor *pre_ids,
                  const framework::LoDTensor *pre_scores,
                  const framework::LoDTensor *ids,
                  const framework::LoDTensor *scores,
                  framework::LoDTensor *selected_ids,
                  framework::LoDTensor *selected_scores,
                  framework::Tensor *parent_idx, size_t level, size_t beam_size,
                  int end_id, bool is_accumulated) {
    auto abs_lod = framework::ToAbsOffset(scores->lod());
    auto &high_level = abs_lod[level];

    auto items = SelectTopBeamSizeItems(pre_ids, pre_scores, ids, scores, level,
                                        beam_size, end_id, is_accumulated);
    auto selected_items = ToMap(items, high_level.back());
    if (FLAGS_v == 3) {
      VLOG(3) << "selected_items:";
      for (size_t i = 0; i < selected_items.size(); ++i) {
        VLOG(3) << "offset: " << i;
        for (auto &item : selected_items[i]) {
          VLOG(3) << item.ToString();
        }
      }
    }

    PruneEndBeams(pre_ids, abs_lod, &selected_items, level, end_id);
    // calculate the output tensor's height
    size_t num_instances = std::accumulate(
        std::begin(selected_items), std::end(selected_items), 0,
        [](size_t a, std::vector<Item> &b) { return a + b.size(); });
    // the output tensor shape should be [num_instances, 1]
    auto dims = framework::make_ddim(
        std::vector<int64_t>({static_cast<int>(num_instances), 1}));
    auto *selected_ids_data =
        selected_ids->mutable_data<int64_t>(dims, platform::CPUPlace());
    auto *selected_scores_data =
        selected_scores->mutable_data<float>(dims, platform::CPUPlace());
    auto *parent_idx_data =
        parent_idx
            ? parent_idx->mutable_data<int>(
                  {static_cast<int64_t>(num_instances)}, platform::CPUPlace())
            : nullptr;

    // fill in data
    std::vector<size_t> low_level;
    size_t low_offset = 0;
    for (auto &items : selected_items) {
      low_level.push_back(low_offset);
      for (auto &item : items) {
        if (parent_idx) {
          parent_idx_data[low_offset] = static_cast<int>(low_level.size() - 1);
        }
        selected_ids_data[low_offset] = item.id;
        selected_scores_data[low_offset] = item.score;
        low_offset++;
      }
    }
    low_level.push_back(low_offset);

    // fill lod
    framework::LoD lod(2);
    lod[0].assign(high_level.begin(), high_level.end());
    lod[1].assign(low_level.begin(), low_level.end());
    if (!framework::CheckLoD(lod)) {
      PADDLE_THROW("lod %s is not right", framework::LoDToString(lod));
    }
    selected_ids->set_lod(lod);
    selected_scores->set_lod(lod);
  }

  /*
   * The basic items help to sort.
   */
  struct Item {
    Item() {}
    Item(size_t offset, size_t id, float score)
        : offset(offset), id(id), score(score) {}
    // offset in the higher lod level.
    size_t offset;
    // prefix id in the lower lod level.
    // size_t prefix;
    // the candidate id
    size_t id;
    // the corresponding score
    float score;

    inline bool operator<(const Item &in) const {
      return (score < in.score) ||
             ((score == in.score) && (offset < in.offset));
    }

    inline void operator=(const Item &in) {
      offset = in.offset;
      id = in.id;
      score = in.score;
    }

    std::string ToString() {
      std::ostringstream os;
      os << "{";
      os << "offset: " << offset << ", ";
      os << "id: " << id << ", ";
      os << "score: " << score << "";
      os << "}";
      return os.str();
    }
  };

 protected:
  /*
   * Prune the source sentences all branchs finished, and it is optional.
   * Pruning must one step later than finishing (thus pre_ids is needed here),
   * since the end tokens must be writed out.
   */
  void PruneEndBeams(const framework::LoDTensor *pre_ids,
                     const framework::LoD &abs_lod,
                     std::vector<std::vector<Item>> *items, size_t lod_level,
                     int end_id) {
    auto *pre_ids_data = pre_ids->data<int64_t>();
    auto &high_level = abs_lod[lod_level];
    for (size_t src_idx = 0; src_idx < high_level.size() - 1; ++src_idx) {
      size_t src_prefix_start = high_level[src_idx];
      size_t src_prefix_end = high_level[src_idx + 1];
      bool finish_flag = true;
      for (size_t offset = src_prefix_start; offset < src_prefix_end;
           offset++) {
        for (auto &item : items->at(offset)) {
          if (item.id != static_cast<size_t>(end_id) ||
              pre_ids_data[offset] != end_id) {
            finish_flag = false;
            break;
          }
        }
        if (!finish_flag) break;
      }
      if (finish_flag) {  // all branchs of the beam (source sentence) end and
                          // prune this beam
        for (size_t offset = src_prefix_start; offset < src_prefix_end;
             offset++)
          items->at(offset).clear();
      }
    }
  }

  /*
   * Transform the items into a map whose key is offset, value is the items.
   * NOTE low performance.
   */
  std::vector<std::vector<Item>> ToMap(
      const std::vector<std::vector<Item>> &items, size_t element_num) {
    std::vector<std::vector<Item>> result;
    result.resize(element_num);
    for (auto &entries : items) {
      for (const auto &item : entries) {
        result[item.offset].push_back(item);
      }
    }
    return result;
  }

  void Insert(std::vector<Item> *top_beam_ptr, const Item &item,
              size_t beam_size) {
    std::vector<Item> &top_beam = *top_beam_ptr;

    size_t num_beams = top_beam.size();
    if (num_beams < beam_size) {
      top_beam.resize(num_beams + 1);
      num_beams++;
    } else {
      if (item < top_beam[beam_size - 1]) {
        return;
      }
    }

    for (int k = static_cast<int>(num_beams) - 2; k >= 0; --k) {
      if (top_beam[k] < item) {
        top_beam[k + 1] = top_beam[k];
      } else {
        top_beam[k + 1] = item;
        return;
      }
    }
    top_beam[0] = item;
  }

  /*
   * For each source, select top beam_size records.
   */
  std::vector<std::vector<Item>> SelectTopBeamSizeItems(
      const framework::LoDTensor *pre_ids,
      const framework::LoDTensor *pre_scores, const framework::LoDTensor *ids,
      const framework::LoDTensor *scores, size_t lod_level, size_t beam_size,
      int end_id, bool is_accumulated) {
    std::vector<std::vector<Item>> result;

    // find the current candidates
    auto abs_lod = framework::ToAbsOffset(scores->lod());

    auto *pre_ids_data = pre_ids->data<int64_t>();
    auto *pre_scores_data = pre_scores->data<float>();

    auto *ids_data = ids ? ids->data<int64_t>() : nullptr;
    auto *scores_data = scores->data<float>();

    size_t num_seqs = scores->NumElements(lod_level);
    size_t seq_width = 1;
    for (int i = 1; i < scores->dims().size(); i++) {
      seq_width *= scores->dims()[i];
    }

    for (size_t seq_id = 0; seq_id < num_seqs; ++seq_id) {
      size_t seq_offset_start = abs_lod[lod_level][seq_id];
      size_t seq_offset_end = abs_lod[lod_level][seq_id + 1];

      std::vector<Item> top_beam;
      top_beam.reserve(beam_size);

      for (size_t offset = seq_offset_start; offset < seq_offset_end;
           ++offset) {
        auto pre_id = pre_ids_data[offset];
        auto pre_score = pre_scores_data[offset];
        if (pre_id == end_id) {
          // Allocate all probability mass to end_id for finished branchs and
          // the other candidate ids can be ignored.
          Item item(offset, end_id, pre_score);
          Insert(&top_beam, item, beam_size);
        } else {
          size_t index = offset * seq_width;
          for (size_t d = 0; d < seq_width; d++, index++) {
            int64_t id = ids_data ? ids_data[index] : static_cast<int64_t>(d);
            float score = is_accumulated
                              ? scores_data[index]
                              : pre_score + std::log(scores_data[index]);
            Item item(offset, id, score);
            Insert(&top_beam, item, beam_size);
          }
        }
      }

      result.emplace_back(top_beam);
    }

    if (FLAGS_v == 3) {
      VLOG(3) << "SelectTopBeamSizeItems result size " << result.size();
      for (auto &items : result) {
        VLOG(3) << "item set:";
        for (auto &item : items) {
          VLOG(3) << item.ToString();
        }
      }
    }

    return result;
  }
};

template class BeamSearchFunctor<platform::CPUDeviceContext, int>;
template class BeamSearchFunctor<platform::CPUDeviceContext, int64_t>;
template class BeamSearchFunctor<platform::CPUDeviceContext, float>;
template class BeamSearchFunctor<platform::CPUDeviceContext, double>;

}  // namespace math
}  // namespace operators
}  // namespace paddle