// Copyright (c) 2024 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.

#pragma once

#include <algorithm>
#include <memory>
#include <vector>

#include "paddle/phi/backends/context_pool.h"
#include "paddle/phi/common/memory_utils.h"
#include "paddle/phi/core/enforce.h"
#include "paddle/phi/core/tensor_array.h"
#include "paddle/phi/core/tensor_utils.h"

namespace phi {
namespace funcs {
// all the lod have 2 levels.
// The first is source level, the second is sentence level.
// source level describe how many prefixes (branches) for each source sentence
// (beam). sentence level describe how these candidates belong to the prefixes.
const size_t kSourceLevel = 0;
const size_t kSentenceLevel = 1;

template <typename T>
struct Sentence {
  std::vector<int64_t> word_ids;
  std::vector<T> scores;
};

template <typename T>
using SentenceVector = std::vector<Sentence<T>>;

template <typename T>
struct BeamSearchDecoder {
  BeamSearchDecoder(size_t beam_size, int end_id)
      : beam_size_(beam_size), end_id_(end_id) {}

  /**
   * convert the result sentence_vector for each source sentence into two
   * DenseTensor.
   * One is all candidate sentences with word id, one is all candidate sentences
   * with word score.
   * Param:
   *  sentence_vector_list: sentence_vector for each source sentence.
   *  id_tensor: result DenseTensor for sentences of id.
   *  score_tensor: result DenseTensor for sentences of score.
   *  reverse: whether ids of sentence in sentence_vector_list is reversed
   *  sort_by_score: whether to sort hypotheses of each sentence by scores.
   */
  void ConvertSentenceVectorToDenseTensor(
      std::vector<SentenceVector<T>> sentence_vector_list,
      DenseTensor* id_tensor,
      DenseTensor* score_tensor,
      bool reverse = true,
      bool sort_by_score = true) const;

  /**
   * Gather the hypotheses for each source sentence by backtrace though the
   * TensorArray step_ids whose lods reserve the path in the tree.
   */
  void Backtrace(const TensorArray& step_ids,
                 const TensorArray& step_scores,
                 DenseTensor* id_tensor,
                 DenseTensor* score_tensor) const;

  size_t beam_size_;
  int end_id_;
};

template <typename T>
void BeamSearchDecoder<T>::ConvertSentenceVectorToDenseTensor(
    std::vector<SentenceVector<T>> sentence_vector_list,
    DenseTensor* id_tensor,
    DenseTensor* score_tensor,
    bool reverse,
    bool sort_by_score) const {
  size_t src_num = sentence_vector_list.size();

  PADDLE_ENFORCE_NE(
      src_num,
      0,
      common::errors::InvalidArgument(
          "src_num is the sequence number of the first decoding step"
          ", indicating by Input(Ids)[0].lod[0].size."
          "src_num has wrong value."
          "src_num should not be 0,"
          "But received %d.",
          src_num));

  std::vector<size_t> source_level_lod = {0};
  std::vector<size_t> sentence_level_lod = {0};
  std::vector<int64_t> id_data;
  std::vector<T> score_data;

  for (size_t src_idx = 0; src_idx < src_num; ++src_idx) {
    if (sort_by_score) {
      sort(sentence_vector_list[src_idx].begin(),
           sentence_vector_list[src_idx].end(),
           [reverse](const Sentence<T>& a, const Sentence<T>& b) {
             if (reverse)
               return a.scores.front() > b.scores.front();
             else
               return a.scores.back() > b.scores.back();
           });
    }
    for (Sentence<T>& sentence : sentence_vector_list[src_idx]) {
      if (reverse) {
        id_data.insert(id_data.end(),
                       sentence.word_ids.rbegin(),
                       sentence.word_ids.rend());
        score_data.insert(
            score_data.end(), sentence.scores.rbegin(), sentence.scores.rend());
      } else {
        id_data.insert(
            id_data.end(), sentence.word_ids.begin(), sentence.word_ids.end());
        score_data.insert(
            score_data.end(), sentence.scores.begin(), sentence.scores.end());
      }

      sentence_level_lod.push_back(sentence_level_lod.back() +
                                   sentence.word_ids.size());
    }
    source_level_lod.push_back(source_level_lod.back() +
                               sentence_vector_list[src_idx].size());
  }

  LegacyLoD lod;
  lod.push_back(source_level_lod);
  lod.push_back(sentence_level_lod);

  auto& cpu_ctx = *(DeviceContextPool::Instance().Get(CPUPlace()));

  id_tensor->set_lod(lod);
  id_tensor->Resize({static_cast<int64_t>(id_data.size())});
  cpu_ctx.Alloc<int64_t>(id_tensor);
  TensorFromVector<int64_t>(id_data, cpu_ctx, id_tensor);

  score_tensor->set_lod(lod);
  score_tensor->Resize({static_cast<int64_t>(score_data.size())});
  cpu_ctx.Alloc<int64_t>(score_tensor);
  TensorFromVector<T>(score_data, cpu_ctx, score_tensor);
}

template <typename T>
void BeamSearchDecoder<T>::Backtrace(const TensorArray& step_ids,
                                     const TensorArray& step_scores,
                                     DenseTensor* id_tensor,
                                     DenseTensor* score_tensor) const {
  PADDLE_ENFORCE_NE(
      step_ids.empty(),
      true,
      common::errors::InvalidArgument("Input(Ids) should not be empty."
                                      "But the Input(Ids) is empty."));
  PADDLE_ENFORCE_EQ(
      step_ids.size(),
      step_scores.size(),
      common::errors::InvalidArgument(
          "The size of Input(Ids) and Input(Scores) should be "
          "the same. But the size of Input(Ids) and Input(Scores) "
          "are not equal."));
  const size_t step_num = step_ids.size();
  const size_t src_num = step_ids.at(0).lod().at(kSourceLevel).size() - 1;
  std::vector<SentenceVector<T>> sentence_vector_list(
      src_num, SentenceVector<T>(beam_size_));
  std::vector<std::vector<size_t>> prefix_idx_vector_list(src_num);
  for (int step_id = step_num - 1; step_id >= 0; --step_id) {
    auto& cur_ids = step_ids.at(step_id);
    auto& cur_scores = step_scores.at(step_id);
    for (size_t src_idx = 0; src_idx < src_num; ++src_idx) {
      // for each source sentence
      auto& sentence_vector = sentence_vector_list.at(src_idx);
      auto& prefix_idx_vector = prefix_idx_vector_list.at(src_idx);
      size_t src_prefix_start = cur_ids.lod().at(kSourceLevel)[src_idx];
      size_t src_prefix_end = cur_ids.lod().at(kSourceLevel)[src_idx + 1];
      if (prefix_idx_vector.empty()) {  // be finished and pruned at this step
                                        // or the last time step
        for (size_t prefix_idx = src_prefix_start; prefix_idx < src_prefix_end;
             ++prefix_idx) {
          size_t candidate_start = cur_ids.lod().at(kSentenceLevel)[prefix_idx];
          size_t candidate_end =
              cur_ids.lod().at(kSentenceLevel)[prefix_idx + 1];
          for (size_t candidate_idx = candidate_start;
               candidate_idx < candidate_end;
               ++candidate_idx) {
            prefix_idx_vector.push_back(prefix_idx);
            size_t idx = prefix_idx_vector.size() - 1;
            auto cur_id = cur_ids.data<int64_t>()[candidate_idx];
            auto cur_score = cur_scores.data<T>()[candidate_idx];
            sentence_vector.at(idx).word_ids.push_back(cur_id);
            sentence_vector.at(idx).scores.push_back(cur_score);
          }
        }
      } else {  // use prefix_idx_vector to backtrace
        size_t src_candidate_start =
            cur_ids.lod().at(kSentenceLevel)[src_prefix_start];
        size_t prefix_idx = src_prefix_start;
        size_t candidate_num =
            cur_ids.lod().at(kSentenceLevel)[prefix_idx + 1] -
            cur_ids.lod().at(kSentenceLevel)[prefix_idx];
        for (size_t idx = 0; idx < prefix_idx_vector.size(); ++idx) {
          auto candidate_idx = prefix_idx_vector.at(idx);
          auto cur_id = cur_ids.data<int64_t>()[candidate_idx];
          auto cur_score = cur_scores.data<T>()[candidate_idx];
          if (cur_id != end_id_ || sentence_vector.at(idx).word_ids.empty()) {
            // to skip redundant end tokens
            sentence_vector.at(idx).word_ids.push_back(cur_id);
            sentence_vector.at(idx).scores.push_back(cur_score);
          }

          while (src_candidate_start + candidate_num <=
                 candidate_idx) {  // search the corresponding prefix
            prefix_idx++;
            candidate_num += cur_ids.lod().at(kSentenceLevel)[prefix_idx + 1] -
                             cur_ids.lod().at(kSentenceLevel)[prefix_idx];
          }
          prefix_idx_vector.at(idx) = prefix_idx;
        }
      }
    }
  }

  ConvertSentenceVectorToDenseTensor(
      sentence_vector_list, id_tensor, score_tensor, true, true);
}
}  // namespace funcs
}  // namespace phi