#include <LightGBM/utils/common.h>
#include <LightGBM/bin.h>

#include "dense_bin.hpp"
#include "dense_nbits_bin.hpp"
#include "sparse_bin.hpp"
#include "ordered_sparse_bin.hpp"

#include <cmath>
#include <cstring>
#include <cstdint>

#include <limits>
#include <vector>
#include <algorithm>

namespace LightGBM {

BinMapper::BinMapper() {
}

// deep copy function for BinMapper
BinMapper::BinMapper(const BinMapper& other) {
  num_bin_ = other.num_bin_;
  missing_type_ = other.missing_type_;
  is_trival_ = other.is_trival_;
  sparse_rate_ = other.sparse_rate_;
  bin_type_ = other.bin_type_;
  if (bin_type_ == BinType::NumericalBin) {
    bin_upper_bound_ = other.bin_upper_bound_;
  } else {
    bin_2_categorical_ = other.bin_2_categorical_;
    categorical_2_bin_ = other.categorical_2_bin_;
  }
  min_val_ = other.min_val_;
  max_val_ = other.max_val_;
  default_bin_ = other.default_bin_;
}

BinMapper::BinMapper(const void* memory) {
  CopyFrom(reinterpret_cast<const char*>(memory));
}

BinMapper::~BinMapper() {

}

bool NeedFilter(std::vector<int>& cnt_in_bin, int total_cnt, int filter_cnt, BinType bin_type) {
  if (bin_type == BinType::NumericalBin) {
    int sum_left = 0;
    for (size_t i = 0; i < cnt_in_bin.size() - 1; ++i) {
      sum_left += cnt_in_bin[i];
      if (sum_left >= filter_cnt && total_cnt - sum_left >= filter_cnt) {
        return false;
      }
    }
  } else {
    for (size_t i = 0; i < cnt_in_bin.size() - 1; ++i) {
      int sum_left = cnt_in_bin[i];
      if (sum_left >= filter_cnt && total_cnt - sum_left >= filter_cnt) {
        return false;
      }
    }
  }
  return true;
}

std::vector<double> GreedyFindBin(const double* distinct_values, const int* counts, 
                                  int num_distinct_values, int max_bin, size_t total_cnt, int min_data_in_bin) {
  std::vector<double> bin_upper_bound;
  if (num_distinct_values <= max_bin) {
    bin_upper_bound.clear();
    int cur_cnt_inbin = 0;
    for (int i = 0; i < num_distinct_values - 1; ++i) {
      cur_cnt_inbin += counts[i];
      if (cur_cnt_inbin >= min_data_in_bin) {
        bin_upper_bound.push_back((distinct_values[i] + distinct_values[i + 1]) / 2);
        cur_cnt_inbin = 0;
      }
    }
    cur_cnt_inbin += counts[num_distinct_values - 1];
    bin_upper_bound.push_back(std::numeric_limits<double>::infinity());
  } else {
    if (min_data_in_bin > 0) {
      max_bin = std::min(max_bin, static_cast<int>(total_cnt / min_data_in_bin));
      max_bin = std::max(max_bin, 1);
    }
    double mean_bin_size = static_cast<double>(total_cnt) / max_bin;

    // mean size for one bin
    int rest_bin_cnt = max_bin;
    int rest_sample_cnt = static_cast<int>(total_cnt);
    std::vector<bool> is_big_count_value(num_distinct_values, false);
    for (int i = 0; i < num_distinct_values; ++i) {
      if (counts[i] >= mean_bin_size) {
        is_big_count_value[i] = true;
        --rest_bin_cnt;
        rest_sample_cnt -= counts[i];
      }
    }
    mean_bin_size = static_cast<double>(rest_sample_cnt) / rest_bin_cnt;
    std::vector<double> upper_bounds(max_bin, std::numeric_limits<double>::infinity());
    std::vector<double> lower_bounds(max_bin, std::numeric_limits<double>::infinity());

    int bin_cnt = 0;
    lower_bounds[bin_cnt] = distinct_values[0];
    int cur_cnt_inbin = 0;
    for (int i = 0; i < num_distinct_values - 1; ++i) {
      if (!is_big_count_value[i]) {
        rest_sample_cnt -= counts[i];
      }
      cur_cnt_inbin += counts[i];
      // need a new bin
      if (is_big_count_value[i] || cur_cnt_inbin >= mean_bin_size ||
        (is_big_count_value[i + 1] && cur_cnt_inbin >= std::max(1.0, mean_bin_size * 0.5f))) {
        upper_bounds[bin_cnt] = distinct_values[i];
        ++bin_cnt;
        lower_bounds[bin_cnt] = distinct_values[i + 1];
        if (bin_cnt >= max_bin - 1) { break; }
        cur_cnt_inbin = 0;
        if (!is_big_count_value[i]) {
          --rest_bin_cnt;
          mean_bin_size = rest_sample_cnt / static_cast<double>(rest_bin_cnt);
        }
      }
    }
    ++bin_cnt;
    // update bin upper bound
    bin_upper_bound.resize(bin_cnt);
    for (int i = 0; i < bin_cnt - 1; ++i) {
      bin_upper_bound[i] = (upper_bounds[i] + lower_bounds[i + 1]) / 2.0f;
    }
    // last bin upper bound
    bin_upper_bound[bin_cnt - 1] = std::numeric_limits<double>::infinity();
  }
  return bin_upper_bound;
}

std::vector<double> FindBinWithZeroAsMissing(const double* distinct_values, const int* counts,
                                             int num_distinct_values, int max_bin, size_t total_sample_cnt, int min_data_in_bin) {
  std::vector<double> bin_upper_bound;
  int left_cnt_data = 0;
  int cnt_missing = 0;
  int right_cnt_data = 0;
  for (int i = 0; i < num_distinct_values; ++i) {
    if (distinct_values[i] <= -kZeroAsMissingValueRange) {
      left_cnt_data += counts[i];
    } else if (distinct_values[i] > kZeroAsMissingValueRange) {
      right_cnt_data += counts[i];
    } else {
      cnt_missing += counts[i];
    }
  }

  int left_cnt = 0;
  for (int i = 0; i < num_distinct_values; ++i) {
    if (distinct_values[i] > -kZeroAsMissingValueRange) {
      left_cnt = i;
      break;
    }
  }

  if (left_cnt > 0) {
    int left_max_bin = static_cast<int>(static_cast<double>(left_cnt_data) / (total_sample_cnt - cnt_missing) * (max_bin - 1));
    bin_upper_bound = GreedyFindBin(distinct_values, counts, left_cnt, left_max_bin, left_cnt_data, min_data_in_bin);
    bin_upper_bound.back() = -kZeroAsMissingValueRange;
  }

  int right_start = -1;
  for (int i = left_cnt; i < num_distinct_values; ++i) {
    if (distinct_values[i] > kZeroAsMissingValueRange) {
      right_start = i;
      break;
    }
  }

  if (right_start >= 0) {
    int right_max_bin = max_bin - 1 - static_cast<int>(bin_upper_bound.size());
    auto right_bounds = GreedyFindBin(distinct_values + right_start, counts + right_start,
                                      num_distinct_values - right_start, right_max_bin, right_cnt_data, min_data_in_bin);
    bin_upper_bound.push_back(kZeroAsMissingValueRange);
    bin_upper_bound.insert(bin_upper_bound.end(), right_bounds.begin(), right_bounds.end());
  } else {
    bin_upper_bound.push_back(std::numeric_limits<double>::infinity());
  }
  return bin_upper_bound;
}

void BinMapper::FindBin(double* values, int num_sample_values, size_t total_sample_cnt,
  int max_bin, int min_data_in_bin, int min_split_data, BinType bin_type, bool use_missing, bool zero_as_missing) {
  int na_cnt = 0;
  int tmp_num_sample_values = 0;
  for (int i = 0; i < num_sample_values; ++i) {
    if (!std::isnan(values[i])) {
      values[tmp_num_sample_values++] = values[i];
    }
  }
  if (!use_missing) {
    missing_type_ = MissingType::None;
  } else if (zero_as_missing) {
    missing_type_ = MissingType::Zero;
  } else {
    if (tmp_num_sample_values == num_sample_values) {
      missing_type_ = MissingType::None;
    } else {
      missing_type_ = MissingType::NaN;
    }
    na_cnt = num_sample_values - tmp_num_sample_values;
  }
  num_sample_values = tmp_num_sample_values;

  bin_type_ = bin_type;
  default_bin_ = 0;
  int zero_cnt = static_cast<int>(total_sample_cnt - num_sample_values - na_cnt);
  // find distinct_values first
  std::vector<double> distinct_values;
  std::vector<int> counts;

  std::sort(values, values + num_sample_values);

  // push zero in the front
  if (num_sample_values == 0 || (values[0] > 0.0f && zero_cnt > 0)) {
    distinct_values.push_back(0.0f);
    counts.push_back(zero_cnt);
  }

  if (num_sample_values > 0) {
    distinct_values.push_back(values[0]);
    counts.push_back(1);
  }

  for (int i = 1; i < num_sample_values; ++i) {
    if (values[i] != values[i - 1]) {
      if (values[i - 1] < 0.0f && values[i] > 0.0f) {
        distinct_values.push_back(0.0f);
        counts.push_back(zero_cnt);
      }
      distinct_values.push_back(values[i]);
      counts.push_back(1);
    } else {
      ++counts.back();
    }
  }

  // push zero in the back
  if (num_sample_values > 0 && values[num_sample_values - 1] < 0.0f && zero_cnt > 0) {
    distinct_values.push_back(0.0f);
    counts.push_back(zero_cnt);
  }
  min_val_ = distinct_values.front();
  max_val_ = distinct_values.back();
  std::vector<int> cnt_in_bin;
  int num_distinct_values = static_cast<int>(distinct_values.size());
  if (bin_type_ == BinType::NumericalBin) {
    if (missing_type_ == MissingType::Zero) {
      bin_upper_bound_ = FindBinWithZeroAsMissing(distinct_values.data(), counts.data(), num_distinct_values, max_bin, total_sample_cnt, min_data_in_bin);
      if (bin_upper_bound_.size() == 2) {
        missing_type_ = MissingType::None;
      }
    } else if (missing_type_ == MissingType::None) {
      bin_upper_bound_ = GreedyFindBin(distinct_values.data(), counts.data(), num_distinct_values, max_bin, total_sample_cnt, min_data_in_bin);
    } else {
      bin_upper_bound_ = GreedyFindBin(distinct_values.data(), counts.data(), num_distinct_values, max_bin - 1, total_sample_cnt - na_cnt, min_data_in_bin);
      bin_upper_bound_.push_back(NaN);
    }
    num_bin_ = static_cast<int>(bin_upper_bound_.size());
    {
      cnt_in_bin.resize(num_bin_, 0);
      int i_bin = 0;
      for (int i = 0; i < num_distinct_values; ++i) {
        if (distinct_values[i] > bin_upper_bound_[i_bin]) {
          ++i_bin;
        } 
        cnt_in_bin[i_bin] += counts[i];
      }
      if (missing_type_ == MissingType::NaN) {
        cnt_in_bin[num_bin_ - 1] = na_cnt;
      }
    }
    CHECK(num_bin_ <= max_bin);
  } else {
    // No missing handle for categorical features 
    missing_type_ = MissingType::None;
    // convert to int type first
    std::vector<int> distinct_values_int;
    std::vector<int> counts_int;
    distinct_values_int.push_back(static_cast<int>(distinct_values[0]));
    counts_int.push_back(counts[0]);
    for (size_t i = 1; i < distinct_values.size(); ++i) {
      if (static_cast<int>(distinct_values[i]) != distinct_values_int.back()) {
        distinct_values_int.push_back(static_cast<int>(distinct_values[i]));
        counts_int.push_back(counts[i]);
      } else {
        counts_int.back() += counts[i];
      }
    }
    // sort by counts
    Common::SortForPair<int, int>(counts_int, distinct_values_int, 0, true);
    // will ignore the categorical of small counts
    const int cut_cnt = static_cast<int>(total_sample_cnt * 0.98f);
    categorical_2_bin_.clear();
    bin_2_categorical_.clear();
    num_bin_ = 0;
    int used_cnt = 0;
    max_bin = std::min(static_cast<int>(distinct_values_int.size()), max_bin);
    while (used_cnt < cut_cnt || num_bin_ < max_bin) {
      bin_2_categorical_.push_back(distinct_values_int[num_bin_]);
      categorical_2_bin_[distinct_values_int[num_bin_]] = static_cast<unsigned int>(num_bin_);
      used_cnt += counts_int[num_bin_];
      ++num_bin_;
    }
    cnt_in_bin = counts_int;
    counts_int.resize(num_bin_);
    counts_int.back() += static_cast<int>(total_sample_cnt - used_cnt);
  }

  // check trival(num_bin_ == 1) feature
  if (num_bin_ <= 1) {
    is_trival_ = true;
  } else {
    is_trival_ = false;
  }
  // check useless bin
  if (!is_trival_ && NeedFilter(cnt_in_bin, static_cast<int>(total_sample_cnt), min_split_data, bin_type_)) {
    is_trival_ = true;
  }

  if (!is_trival_) {
    default_bin_ = ValueToBin(0);
  }
  // calculate sparse rate
  sparse_rate_ = static_cast<double>(cnt_in_bin[default_bin_]) / static_cast<double>(total_sample_cnt);
}


int BinMapper::SizeForSpecificBin(int bin) {
  int size = 0;
  size += sizeof(int);
  size += sizeof(MissingType);
  size += sizeof(bool);
  size += sizeof(double);
  size += sizeof(BinType);
  size += 2 * sizeof(double);
  size += bin * sizeof(double);
  size += sizeof(uint32_t);
  return size;
}

void BinMapper::CopyTo(char * buffer) const {
  std::memcpy(buffer, &num_bin_, sizeof(num_bin_));
  buffer += sizeof(num_bin_);
  std::memcpy(buffer, &missing_type_, sizeof(missing_type_));
  buffer += sizeof(missing_type_);
  std::memcpy(buffer, &is_trival_, sizeof(is_trival_));
  buffer += sizeof(is_trival_);
  std::memcpy(buffer, &sparse_rate_, sizeof(sparse_rate_));
  buffer += sizeof(sparse_rate_);
  std::memcpy(buffer, &bin_type_, sizeof(bin_type_));
  buffer += sizeof(bin_type_);
  std::memcpy(buffer, &min_val_, sizeof(min_val_));
  buffer += sizeof(min_val_);
  std::memcpy(buffer, &max_val_, sizeof(max_val_));
  buffer += sizeof(max_val_);
  std::memcpy(buffer, &default_bin_, sizeof(default_bin_));
  buffer += sizeof(default_bin_);
  if (bin_type_ == BinType::NumericalBin) {
    std::memcpy(buffer, bin_upper_bound_.data(), num_bin_ * sizeof(double));
  } else {
    std::memcpy(buffer, bin_2_categorical_.data(), num_bin_ * sizeof(int));
  }
}

void BinMapper::CopyFrom(const char * buffer) {
  std::memcpy(&num_bin_, buffer, sizeof(num_bin_));
  buffer += sizeof(num_bin_);
  std::memcpy(&missing_type_, buffer, sizeof(missing_type_));
  buffer += sizeof(missing_type_);
  std::memcpy(&is_trival_, buffer, sizeof(is_trival_));
  buffer += sizeof(is_trival_);
  std::memcpy(&sparse_rate_, buffer, sizeof(sparse_rate_));
  buffer += sizeof(sparse_rate_);
  std::memcpy(&bin_type_, buffer, sizeof(bin_type_));
  buffer += sizeof(bin_type_);
  std::memcpy(&min_val_, buffer, sizeof(min_val_));
  buffer += sizeof(min_val_);
  std::memcpy(&max_val_, buffer, sizeof(max_val_));
  buffer += sizeof(max_val_);
  std::memcpy(&default_bin_, buffer, sizeof(default_bin_));
  buffer += sizeof(default_bin_);
  if (bin_type_ == BinType::NumericalBin) {
    bin_upper_bound_ = std::vector<double>(num_bin_);
    std::memcpy(bin_upper_bound_.data(), buffer, num_bin_ * sizeof(double));
  } else {
    bin_2_categorical_ = std::vector<int>(num_bin_);
    std::memcpy(bin_2_categorical_.data(), buffer, num_bin_ * sizeof(int));
    categorical_2_bin_.clear();
    for (int i = 0; i < num_bin_; ++i) {
      categorical_2_bin_[bin_2_categorical_[i]] = static_cast<unsigned int>(i);
    }
  }
}

void BinMapper::SaveBinaryToFile(FILE* file) const {
  fwrite(&num_bin_, sizeof(num_bin_), 1, file);
  fwrite(&missing_type_, sizeof(missing_type_), 1, file);
  fwrite(&is_trival_, sizeof(is_trival_), 1, file);
  fwrite(&sparse_rate_, sizeof(sparse_rate_), 1, file);
  fwrite(&bin_type_, sizeof(bin_type_), 1, file);
  fwrite(&min_val_, sizeof(min_val_), 1, file);
  fwrite(&max_val_, sizeof(max_val_), 1, file);
  fwrite(&default_bin_, sizeof(default_bin_), 1, file);
  if (bin_type_ == BinType::NumericalBin) {
    fwrite(bin_upper_bound_.data(), sizeof(double), num_bin_, file);
  } else {
    fwrite(bin_2_categorical_.data(), sizeof(int), num_bin_, file);
  }
}

size_t BinMapper::SizesInByte() const {
  size_t ret = sizeof(num_bin_) + sizeof(missing_type_) + sizeof(is_trival_) + sizeof(sparse_rate_)
    + sizeof(bin_type_) + sizeof(min_val_) + sizeof(max_val_) + sizeof(default_bin_);
  if (bin_type_ == BinType::NumericalBin) {
    ret += sizeof(double) *  num_bin_;
  } else {
    ret += sizeof(int) * num_bin_;
  }
  return ret;
}

template class DenseBin<uint8_t>;
template class DenseBin<uint16_t>;
template class DenseBin<uint32_t>;

template class SparseBin<uint8_t>;
template class SparseBin<uint16_t>;
template class SparseBin<uint32_t>;

template class OrderedSparseBin<uint8_t>;
template class OrderedSparseBin<uint16_t>;
template class OrderedSparseBin<uint32_t>;

Bin* Bin::CreateBin(data_size_t num_data, int num_bin, double sparse_rate, 
  bool is_enable_sparse, double sparse_threshold, bool* is_sparse) {
  // sparse threshold
  if (sparse_rate >= sparse_threshold && is_enable_sparse) {
    *is_sparse = true;
    return CreateSparseBin(num_data, num_bin);
  } else {
    *is_sparse = false;
    return CreateDenseBin(num_data, num_bin);
  }
}

Bin* Bin::CreateDenseBin(data_size_t num_data, int num_bin) {
  if (num_bin <= 16) {
    return new Dense4bitsBin(num_data);
  } else if (num_bin <= 256) {
    return new DenseBin<uint8_t>(num_data);
  } else if (num_bin <= 65536) {
    return new DenseBin<uint16_t>(num_data);
  } else {
    return new DenseBin<uint32_t>(num_data);
  }
}

Bin* Bin::CreateSparseBin(data_size_t num_data, int num_bin) {
  if (num_bin <= 256) {
    return new SparseBin<uint8_t>(num_data);
  } else if (num_bin <= 65536) {
    return new SparseBin<uint16_t>(num_data);
  } else {
    return new SparseBin<uint32_t>(num_data);
  }
}

}  // namespace LightGBM
