/* Copyright (c) 2018 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

#if defined _WIN32 || defined __APPLE__
#else
#define _LINUX
#endif

#include <fstream>
#include <future>  // NOLINT
#include <memory>
#include <mutex>  // NOLINT
#include <sstream>
#include <string>
#include <thread>  // NOLINT
#include <unordered_map>
#include <utility>
#include <vector>

#include "paddle/fluid/framework/archive.h"
#include "paddle/fluid/framework/blocking_queue.h"
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/data_feed.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/reader.h"
#include "paddle/fluid/framework/variable.h"
#include "paddle/fluid/string/string_helper.h"

namespace paddle {
namespace framework {

// DataFeed is the base virtual class for all ohther DataFeeds.
// It is used to read files and parse the data for subsequent trainer.
// Example:
//   DataFeed* reader =
//   paddle::framework::DataFeedFactory::CreateDataFeed(data_feed_name);
//   reader->Init(data_feed_desc); // data_feed_desc is a protobuf object
//   reader->SetFileList(filelist);
//   const std::vector<std::string> & use_slot_alias =
//   reader->GetUseSlotAlias();
//   for (auto name: use_slot_alias){ // for binding memory
//     reader->AddFeedVar(scope->Var(name), name);
//   }
//   reader->Start();
//   while (reader->Next()) {
//      // trainer do something
//   }
union FeatureKey {
  uint64_t uint64_feasign_;
  float float_feasign_;
};

struct FeatureItem {
  FeatureItem() {}
  FeatureItem(FeatureKey sign, uint16_t slot) {
    this->sign() = sign;
    this->slot() = slot;
  }
  FeatureKey& sign() { return *(reinterpret_cast<FeatureKey*>(sign_buffer())); }
  const FeatureKey& sign() const {
    const FeatureKey* ret = reinterpret_cast<FeatureKey*>(sign_buffer());
    return *ret;
  }
  uint16_t& slot() { return slot_; }
  const uint16_t& slot() const { return slot_; }

 private:
  char* sign_buffer() const { return const_cast<char*>(sign_); }
  char sign_[sizeof(FeatureKey)];
  uint16_t slot_;
};

// sizeof Record is much less than std::vector<MultiSlotType>
struct Record {
  std::vector<FeatureItem> uint64_feasigns_;
  std::vector<FeatureItem> float_feasigns_;
  std::string ins_id_;
  std::string content_;
  uint64_t search_id;
  uint32_t rank;
  uint32_t cmatch;
};

struct PvInstanceObject {
  std::vector<Record*> ads;
  void merge_instance(Record* ins) { ads.push_back(ins); }
};

using PvInstance = PvInstanceObject*;

inline PvInstance make_pv_instance() { return new PvInstanceObject(); }

class DataFeed {
 public:
  DataFeed() {
    mutex_for_pick_file_ = nullptr;
    file_idx_ = nullptr;
  }
  virtual ~DataFeed() {}
  virtual void Init(const DataFeedDesc& data_feed_desc) = 0;
  virtual bool CheckFile(const char* filename) {
    PADDLE_THROW("This function(CheckFile) is not implemented.");
  }
  // Set filelist for DataFeed.
  // Pay attention that it must init all readers before call this function.
  // Otherwise, Init() function will init finish_set_filelist_ flag.
  virtual bool SetFileList(const std::vector<std::string>& files);
  virtual bool Start() = 0;

  // The trainer calls the Next() function, and the DataFeed will load a new
  // batch to the feed_vec. The return value of this function is the batch
  // size of the current batch.
  virtual int Next() = 0;
  // Get all slots' alias which defined in protofile
  virtual const std::vector<std::string>& GetAllSlotAlias() {
    return all_slots_;
  }
  // Get used slots' alias which defined in protofile
  virtual const std::vector<std::string>& GetUseSlotAlias() {
    return use_slots_;
  }
  // This function is used for binding feed_vec memory
  virtual void AddFeedVar(Variable* var, const std::string& name);

  // This function is used for binding feed_vec memory in a given scope
  virtual void AssignFeedVar(const Scope& scope);

  // This function will do nothing at default
  virtual void SetInputPvChannel(void* channel) {}
  // This function will do nothing at default
  virtual void SetOutputPvChannel(void* channel) {}
  // This function will do nothing at default
  virtual void SetConsumePvChannel(void* channel) {}

  // This function will do nothing at default
  virtual void SetInputChannel(void* channel) {}
  // This function will do nothing at default
  virtual void SetOutputChannel(void* channel) {}
  // This function will do nothing at default
  virtual void SetConsumeChannel(void* channel) {}
  // This function will do nothing at default
  virtual void SetThreadId(int thread_id) {}
  // This function will do nothing at default
  virtual void SetThreadNum(int thread_num) {}
  // This function will do nothing at default
  virtual void SetParseInsId(bool parse_ins_id) {}
  virtual void SetParseContent(bool parse_content) {}
  virtual void SetParseLogKey(bool parse_logkey) {}
  virtual void SetEnablePvMerge(bool enable_pv_merge) {}
  virtual void SetCurrentPhase(int current_phase) {}
  virtual void SetFileListMutex(std::mutex* mutex) {
    mutex_for_pick_file_ = mutex;
  }
  virtual void SetFileListIndex(size_t* file_index) { file_idx_ = file_index; }
  virtual const std::vector<std::string>& GetInsIdVec() const {
    return ins_id_vec_;
  }
  virtual const std::vector<std::string>& GetInsContentVec() const {
    return ins_content_vec_;
  }
  virtual int GetCurBatchSize() { return batch_size_; }
  virtual void LoadIntoMemory() {
    PADDLE_THROW("This function(LoadIntoMemory) is not implemented.");
  }
  virtual void SetPlace(const paddle::platform::Place& place) {
    place_ = place;
  }
  virtual const paddle::platform::Place& GetPlace() const { return place_; }

 protected:
  // The following three functions are used to check if it is executed in this
  // order:
  //   Init() -> SetFileList() -> Start() -> Next()
  virtual void CheckInit();
  virtual void CheckSetFileList();
  virtual void CheckStart();
  virtual void SetBatchSize(
      int batch);  // batch size will be set in Init() function
  // This function is used to pick one file from the global filelist(thread
  // safe).
  virtual bool PickOneFile(std::string* filename);
  virtual void CopyToFeedTensor(void* dst, const void* src, size_t size);

  std::vector<std::string> filelist_;
  size_t* file_idx_;
  std::mutex* mutex_for_pick_file_;

  // the alias of used slots, and its order is determined by
  // data_feed_desc(proto object)
  std::vector<std::string> use_slots_;
  std::vector<bool> use_slots_is_dense_;

  // the alias of all slots, and its order is determined by data_feed_desc(proto
  // object)
  std::vector<std::string> all_slots_;
  std::vector<std::string> all_slots_type_;
  std::vector<std::vector<int>> use_slots_shape_;
  std::vector<int> inductive_shape_index_;
  std::vector<int> total_dims_without_inductive_;
  // For the inductive shape passed within data
  std::vector<std::vector<int>> multi_inductive_shape_index_;
  std::vector<int>
      use_slots_index_;  // -1: not used; >=0: the index of use_slots_

  // The data read by DataFeed will be stored here
  std::vector<LoDTensor*> feed_vec_;

  LoDTensor* rank_offset_;

  // the batch size defined by user
  int default_batch_size_;
  // current batch size
  int batch_size_;

  bool finish_init_;
  bool finish_set_filelist_;
  bool finish_start_;
  std::string pipe_command_;
  std::vector<std::string> ins_id_vec_;
  std::vector<std::string> ins_content_vec_;
  platform::Place place_;
};

// PrivateQueueDataFeed is the base virtual class for ohther DataFeeds.
// It use a read-thread to read file and parse data to a private-queue
// (thread level), and get data from this queue when trainer call Next().
template <typename T>
class PrivateQueueDataFeed : public DataFeed {
 public:
  PrivateQueueDataFeed() {}
  virtual ~PrivateQueueDataFeed() {}
  virtual bool Start();
  virtual int Next();

 protected:
  // The thread implementation function for reading file and parse.
  virtual void ReadThread();
  // This function is used to set private-queue size, and the most
  // efficient when the queue size is close to the batch size.
  virtual void SetQueueSize(int queue_size);
  // The reading and parsing method called in the ReadThread.
  virtual bool ParseOneInstance(T* instance) = 0;
  virtual bool ParseOneInstanceFromPipe(T* instance) = 0;
  // This function is used to put instance to vec_ins
  virtual void AddInstanceToInsVec(T* vec_ins, const T& instance,
                                   int index) = 0;
  // This function is used to put ins_vec to feed_vec
  virtual void PutToFeedVec(const T& ins_vec) = 0;

  // The thread for read files
  std::thread read_thread_;
  // using ifstream one line and one line parse is faster
  // than using fread one buffer and one buffer parse.
  //   for a 601M real data:
  //     ifstream one line and one line parse: 6034 ms
  //     fread one buffer and one buffer parse: 7097 ms
  std::ifstream file_;
  std::shared_ptr<FILE> fp_;
  size_t queue_size_;
  string::LineFileReader reader_;
  // The queue for store parsed data
  std::shared_ptr<paddle::framework::ChannelObject<T>> queue_;
};

template <typename T>
class InMemoryDataFeed : public DataFeed {
 public:
  InMemoryDataFeed();
  virtual ~InMemoryDataFeed() {}
  virtual void Init(const DataFeedDesc& data_feed_desc) = 0;
  virtual bool Start();
  virtual int Next();
  virtual void SetInputPvChannel(void* channel);
  virtual void SetOutputPvChannel(void* channel);
  virtual void SetConsumePvChannel(void* channel);

  virtual void SetInputChannel(void* channel);
  virtual void SetOutputChannel(void* channel);
  virtual void SetConsumeChannel(void* channel);
  virtual void SetThreadId(int thread_id);
  virtual void SetThreadNum(int thread_num);
  virtual void SetParseInsId(bool parse_ins_id);
  virtual void SetParseContent(bool parse_content);
  virtual void SetParseLogKey(bool parse_logkey);
  virtual void SetEnablePvMerge(bool enable_pv_merge);
  virtual void SetCurrentPhase(int current_phase);
  virtual void LoadIntoMemory();

 protected:
  virtual bool ParseOneInstance(T* instance) = 0;
  virtual bool ParseOneInstanceFromPipe(T* instance) = 0;
  virtual void PutToFeedVec(const std::vector<T>& ins_vec) = 0;

  int thread_id_;
  int thread_num_;
  bool parse_ins_id_;
  bool parse_content_;
  bool parse_logkey_;
  bool enable_pv_merge_;
  int current_phase_{-1};  // only for untest
  std::ifstream file_;
  std::shared_ptr<FILE> fp_;
  paddle::framework::ChannelObject<T>* input_channel_;
  paddle::framework::ChannelObject<T>* output_channel_;
  paddle::framework::ChannelObject<T>* consume_channel_;

  paddle::framework::ChannelObject<PvInstance>* input_pv_channel_;
  paddle::framework::ChannelObject<PvInstance>* output_pv_channel_;
  paddle::framework::ChannelObject<PvInstance>* consume_pv_channel_;
};

// This class define the data type of instance(ins_vec) in MultiSlotDataFeed
class MultiSlotType {
 public:
  MultiSlotType() {}
  ~MultiSlotType() {}
  void Init(const std::string& type, size_t reserved_size = 0) {
    CheckType(type);
    if (type_[0] == 'f') {
      float_feasign_.clear();
      if (reserved_size) {
        float_feasign_.reserve(reserved_size);
      }
    } else if (type_[0] == 'u') {
      uint64_feasign_.clear();
      if (reserved_size) {
        uint64_feasign_.reserve(reserved_size);
      }
    }
    type_ = type;
  }
  void InitOffset(size_t max_batch_size = 0) {
    if (max_batch_size > 0) {
      offset_.reserve(max_batch_size + 1);
    }
    offset_.resize(1);
    // LoDTensor' lod is counted from 0, the size of lod
    // is one size larger than the size of data.
    offset_[0] = 0;
  }
  const std::vector<size_t>& GetOffset() const { return offset_; }
  std::vector<size_t>& MutableOffset() { return offset_; }
  void AddValue(const float v) {
    CheckFloat();
    float_feasign_.push_back(v);
  }
  void AddValue(const uint64_t v) {
    CheckUint64();
    uint64_feasign_.push_back(v);
  }
  void CopyValues(const float* input, size_t size) {
    CheckFloat();
    float_feasign_.resize(size);
    memcpy(float_feasign_.data(), input, size * sizeof(float));
  }
  void CopyValues(const uint64_t* input, size_t size) {
    CheckUint64();
    uint64_feasign_.resize(size);
    memcpy(uint64_feasign_.data(), input, size * sizeof(uint64_t));
  }
  void AddIns(const MultiSlotType& ins) {
    if (ins.GetType()[0] == 'f') {  // float
      CheckFloat();
      auto& vec = ins.GetFloatData();
      offset_.push_back(offset_.back() + vec.size());
      float_feasign_.insert(float_feasign_.end(), vec.begin(), vec.end());
    } else if (ins.GetType()[0] == 'u') {  // uint64
      CheckUint64();
      auto& vec = ins.GetUint64Data();
      offset_.push_back(offset_.back() + vec.size());
      uint64_feasign_.insert(uint64_feasign_.end(), vec.begin(), vec.end());
    }
  }
  void AppendValues(const uint64_t* input, size_t size) {
    CheckUint64();
    offset_.push_back(offset_.back() + size);
    uint64_feasign_.insert(uint64_feasign_.end(), input, input + size);
  }
  void AppendValues(const float* input, size_t size) {
    CheckFloat();
    offset_.push_back(offset_.back() + size);
    float_feasign_.insert(float_feasign_.end(), input, input + size);
  }
  const std::vector<float>& GetFloatData() const { return float_feasign_; }
  std::vector<float>& MutableFloatData() { return float_feasign_; }
  const std::vector<uint64_t>& GetUint64Data() const { return uint64_feasign_; }
  std::vector<uint64_t>& MutableUint64Data() { return uint64_feasign_; }
  const std::string& GetType() const { return type_; }
  size_t GetBatchSize() { return offset_.size() - 1; }
  std::string& MutableType() { return type_; }

  std::string DebugString() {
    std::stringstream ss;
    ss << "\ntype: " << type_ << "\n";
    ss << "offset: ";
    ss << "[";
    for (const size_t& i : offset_) {
      ss << offset_[i] << ",";
    }
    ss << "]\ndata: [";
    if (type_[0] == 'f') {
      for (const float& i : float_feasign_) {
        ss << i << ",";
      }
    } else {
      for (const uint64_t& i : uint64_feasign_) {
        ss << i << ",";
      }
    }
    ss << "]\n";
    return ss.str();
  }

 private:
  void CheckType(const std::string& type) const {
    PADDLE_ENFORCE((type == "uint64") || (type == "float"),
                   "There is no this type<%s>.", type);
  }
  void CheckFloat() const {
    PADDLE_ENFORCE(type_[0] == 'f', "Add %s value to float slot.", type_);
  }
  void CheckUint64() const {
    PADDLE_ENFORCE(type_[0] == 'u', "Add %s value to uint64 slot.", type_);
  }
  std::vector<float> float_feasign_;
  std::vector<uint64_t> uint64_feasign_;
  std::string type_;
  std::vector<size_t> offset_;
};

template <class AR>
paddle::framework::Archive<AR>& operator<<(paddle::framework::Archive<AR>& ar,
                                           const MultiSlotType& ins) {
  ar << ins.GetType();
#ifdef _LINUX
  ar << ins.GetOffset();
#else
  const auto& offset = ins.GetOffset();
  ar << (uint64_t)offset.size();
  for (const size_t& x : offset) {
    ar << (const uint64_t)x;
  }
#endif
  ar << ins.GetFloatData();
  ar << ins.GetUint64Data();
  return ar;
}

template <class AR>
paddle::framework::Archive<AR>& operator>>(paddle::framework::Archive<AR>& ar,
                                           MultiSlotType& ins) {
  ar >> ins.MutableType();
#ifdef _LINUX
  ar >> ins.MutableOffset();
#else
  auto& offset = ins.MutableOffset();
  offset.resize(ar.template Get<uint64_t>());
  for (size_t& x : offset) {
    uint64_t t;
    ar >> t;
    x = (size_t)t;
  }
#endif
  ar >> ins.MutableFloatData();
  ar >> ins.MutableUint64Data();
  return ar;
}

struct RecordCandidate {
  std::string ins_id_;
  std::unordered_multimap<uint16_t, FeatureKey> feas;

  RecordCandidate& operator=(const Record& rec) {
    feas.clear();
    ins_id_ = rec.ins_id_;
    for (auto& fea : rec.uint64_feasigns_) {
      feas.insert({fea.slot(), fea.sign()});
    }
    return *this;
  }
};

class RecordCandidateList {
 public:
  RecordCandidateList() = default;
  RecordCandidateList(const RecordCandidateList&) = delete;
  RecordCandidateList& operator=(const RecordCandidateList&) = delete;

  void ReSize(size_t length);

  void ReInit();

  void AddAndGet(const Record& record, RecordCandidate* result);

 private:
  size_t _capacity = 0;
  std::mutex _mutex;
  bool _full = false;
  size_t _cur_size = 0;
  size_t _total_size = 0;
  std::vector<RecordCandidate> _candidate_list;
};

template <class AR>
paddle::framework::Archive<AR>& operator<<(paddle::framework::Archive<AR>& ar,
                                           const FeatureKey& fk) {
  ar << fk.uint64_feasign_;
  ar << fk.float_feasign_;
  return ar;
}

template <class AR>
paddle::framework::Archive<AR>& operator>>(paddle::framework::Archive<AR>& ar,
                                           FeatureKey& fk) {
  ar >> fk.uint64_feasign_;
  ar >> fk.float_feasign_;
  return ar;
}

template <class AR>
paddle::framework::Archive<AR>& operator<<(paddle::framework::Archive<AR>& ar,
                                           const FeatureItem& fi) {
  ar << fi.sign();
  ar << fi.slot();
  return ar;
}

template <class AR>
paddle::framework::Archive<AR>& operator>>(paddle::framework::Archive<AR>& ar,
                                           FeatureItem& fi) {
  ar >> fi.sign();
  ar >> fi.slot();
  return ar;
}

template <class AR>
paddle::framework::Archive<AR>& operator<<(paddle::framework::Archive<AR>& ar,
                                           const Record& r) {
  ar << r.uint64_feasigns_;
  ar << r.float_feasigns_;
  ar << r.ins_id_;
  return ar;
}

template <class AR>
paddle::framework::Archive<AR>& operator>>(paddle::framework::Archive<AR>& ar,
                                           Record& r) {
  ar >> r.uint64_feasigns_;
  ar >> r.float_feasigns_;
  ar >> r.ins_id_;
  return ar;
}

// This DataFeed is used to feed multi-slot type data.
// The format of multi-slot type data:
//   [n feasign_0 feasign_1 ... feasign_n]*
class MultiSlotDataFeed
    : public PrivateQueueDataFeed<std::vector<MultiSlotType>> {
 public:
  MultiSlotDataFeed() {}
  virtual ~MultiSlotDataFeed() {}
  virtual void Init(const DataFeedDesc& data_feed_desc);
  virtual bool CheckFile(const char* filename);

 protected:
  virtual void ReadThread();
  virtual void AddInstanceToInsVec(std::vector<MultiSlotType>* vec_ins,
                                   const std::vector<MultiSlotType>& instance,
                                   int index);
  virtual bool ParseOneInstance(std::vector<MultiSlotType>* instance);
  virtual bool ParseOneInstanceFromPipe(std::vector<MultiSlotType>* instance);
  virtual void PutToFeedVec(const std::vector<MultiSlotType>& ins_vec);
};

class MultiSlotInMemoryDataFeed : public InMemoryDataFeed<Record> {
 public:
  MultiSlotInMemoryDataFeed() {}
  virtual ~MultiSlotInMemoryDataFeed() {}
  virtual void Init(const DataFeedDesc& data_feed_desc);

 protected:
  virtual bool ParseOneInstance(Record* instance);
  virtual bool ParseOneInstanceFromPipe(Record* instance);
  virtual void PutToFeedVec(const std::vector<Record>& ins_vec);
  virtual void GetMsgFromLogKey(const std::string& log_key, uint64_t* search_id,
                                uint32_t* cmatch, uint32_t* rank);
  std::vector<std::vector<float>> batch_float_feasigns_;
  std::vector<std::vector<uint64_t>> batch_uint64_feasigns_;
  std::vector<std::vector<size_t>> offset_;
  std::vector<bool> visit_;
};

class PaddleBoxDataFeed : public MultiSlotInMemoryDataFeed {
 public:
  PaddleBoxDataFeed() {}
  virtual ~PaddleBoxDataFeed() {}

 protected:
  virtual void Init(const DataFeedDesc& data_feed_desc);
  virtual bool Start();
  virtual int Next();
  virtual void AssignFeedVar(const Scope& scope);
  virtual void PutToFeedVec(const std::vector<PvInstance>& pv_vec);
  virtual void PutToFeedVec(const std::vector<Record*>& ins_vec);
  virtual int GetCurrentPhase();
  virtual void GetRankOffset(const std::vector<PvInstance>& pv_vec,
                             int ins_number);
  std::string rank_offset_name_;
  int pv_batch_size_;
};

#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
template <typename T>
class PrivateInstantDataFeed : public DataFeed {
 public:
  PrivateInstantDataFeed() {}
  virtual ~PrivateInstantDataFeed() {}
  void Init(const DataFeedDesc& data_feed_desc) override;
  bool Start() override { return true; }
  int Next() override;

 protected:
  // The batched data buffer
  std::vector<MultiSlotType> ins_vec_;

  // This function is used to preprocess with a given filename, e.g. open it or
  // mmap
  virtual bool Preprocess(const std::string& filename) = 0;

  // This function is used to postprocess system resource such as closing file
  // NOTICE: Ensure that it is safe to call before Preprocess
  virtual bool Postprocess() = 0;

  // The reading and parsing method.
  virtual bool ParseOneMiniBatch() = 0;

  // This function is used to put ins_vec to feed_vec
  virtual void PutToFeedVec();
};

class MultiSlotFileInstantDataFeed
    : public PrivateInstantDataFeed<std::vector<MultiSlotType>> {
 public:
  MultiSlotFileInstantDataFeed() {}
  virtual ~MultiSlotFileInstantDataFeed() {}

 protected:
  int fd_{-1};
  char* buffer_{nullptr};
  size_t end_{0};
  size_t offset_{0};

  bool Preprocess(const std::string& filename) override;

  bool Postprocess() override;

  bool ParseOneMiniBatch() override;
};
#endif

}  // namespace framework
}  // namespace paddle