/*
  ShashChess, a UCI chess playing engine derived from Stockfish
  Copyright (C) 2004-2024 Andrea Manzo, F. Ferraguti, K.Kiniama and ShashChess developers (see AUTHORS file)

  ShashChess is free software: you can redistribute it and/or modify
  it under the terms of the GNU General Public License as published by
  the Free Software Foundation, either version 3 of the License, or
  (at your option) any later version.

  ShashChess is distributed in the hope that it will be useful,
  but WITHOUT ANY WARRANTY; without even the implied warranty of
  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
  GNU General Public License for more details.

  You should have received a copy of the GNU General Public License
  along with this program.  If not, see <http://www.gnu.org/licenses/>.
*/

#include "search.h"
#include <cstddef>  //shashchess
#include <random>   //variety
#include <algorithm>
#include <array>
#include <atomic>
#include <cassert>
#include <chrono>
#include <cmath>
#include <cstdint>
#include <cstdlib>
#include <initializer_list>
#include <iostream>
#include <list>
#include <ratio>
#include <string>
#include <utility>

#include "evaluate.h"
#include "misc.h"
#include "movegen.h"
#include "movepick.h"
#include "nnue/network.h"
#include "nnue/nnue_accumulator.h"
#include "nnue/nnue_common.h"
#include "nnue/nnue_misc.h"
#include "position.h"
#include "syzygy/tbprobe.h"
#include "thread.h"
#include "timeman.h"
#include "tt.h"
#include "types.h"  //shashchess
#include "uci.h"
#include "ucioption.h"
#include "win_probability.h"  //livebook +shashin theory
#include "learn/learn.h"      //Khalid
#include "mcts/montecarlo.h"  //Montecarlo
//Livebook begin
#include "livebook/BaseLivebook.h"
#include "livebook/LichessEndgame.h"
#include "livebook/LichessMaster.h"
#include "livebook/ChessDb.h"
#include "livebook/LichessPlayer.h"
#include "livebook/LichessGames.h"
#include "livebook/Proxy.h"
#include "livebook/ChessDBContributor.h"
//Livebook end
namespace ShashChess {
// Kelly begin
bool                       useLearning          = true;
bool                       enabledLearningProbe = false;
std::vector<QLearningMove> qLearningTrajectory;
// Kelly end

namespace Search {
// from Shashin
bool highTal, middleTal, lowTal, capablanca, highPetrosian, middlePetrosian, lowPetrosian;
// end from Shashin
}

namespace TB = Tablebases;

void syzygy_extend_pv(const OptionsMap&             options,
                      const Search::LimitsType&     limits,
                      ShashChess::Position&         pos,
                      ShashChess::Search::RootMove& rootMove,
                      Value&                        v);

using Eval::evaluate;
using namespace Search;

namespace {

// Futility margin
Value futility_margin(Depth d, bool noTtCutNode, bool improving, bool oppWorsening) {
    Value futilityMult       = 118 - 33 * noTtCutNode;
    Value improvingDeduction = improving * futilityMult * 2;
    Value worseningDeduction = oppWorsening * futilityMult / 3;

    return futilityMult * d - improvingDeduction - worseningDeduction;
}

constexpr int futility_move_count(bool improving, Depth depth) {
    return (3 + depth * depth) / (2 - improving);
}

// Add correctionHistory value to raw staticEval and guarantee evaluation
// does not hit the tablebase range.
Value to_corrected_static_eval(Value v, const Worker& w, const Position& pos, Stack* ss) {
    const Color us    = pos.side_to_move();
    const auto  m     = (ss - 1)->currentMove;
    const auto  pcv   = w.pawnCorrectionHistory[us][pawn_structure_index<Correction>(pos)];
    const auto  mcv   = w.materialCorrectionHistory[us][material_index(pos)];
    const auto  macv  = w.majorPieceCorrectionHistory[us][major_piece_index(pos)];
    const auto  micv  = w.minorPieceCorrectionHistory[us][minor_piece_index(pos)];
    const auto  wnpcv = w.nonPawnCorrectionHistory[WHITE][us][non_pawn_index<WHITE>(pos)];
    const auto  bnpcv = w.nonPawnCorrectionHistory[BLACK][us][non_pawn_index<BLACK>(pos)];
    int         cntcv = 1;

    if (m.is_ok())
        cntcv = int((*(ss - 2)->continuationCorrectionHistory)[pos.piece_on(m.to_sq())][m.to_sq()]);

    const auto cv =
      (5932 * pcv + 2994 * mcv + 3269 * macv + 5660 * micv + 6237 * (wnpcv + bnpcv) + cntcv * 5555)
      / 131072;
    v += cv;
    return std::clamp(v, VALUE_TB_LOSS_IN_MAX_PLY + 1, VALUE_TB_WIN_IN_MAX_PLY - 1);
}

// History and stats update bonus, based on depth
int stat_bonus(Depth d) { return std::min(179 * d - 108, 1598); }

// History and stats update malus, based on depth
int stat_malus(Depth d) { return std::min(820 * d - 261, 2246); }

// Add a small random component to draw evaluations to avoid 3-fold blindness
Value value_draw(size_t nodes) { return VALUE_DRAW - 1 + Value(nodes & 0x2); }

int variety;  //variety


Value value_to_tt(Value v, int ply);
Value value_from_tt(Value v, int ply, int r50c);
void  update_pv(Move* pv, Move move, const Move* childPv);
void  update_continuation_histories(Stack* ss, Piece pc, Square to, int bonus);
void  update_quiet_histories(
   const Position& pos, Stack* ss, Search::Worker& workerThread, Move move, int bonus);
void update_all_stats(const Position&      pos,
                      Stack*               ss,
                      Search::Worker&      workerThread,
                      Move                 bestMove,
                      Square               prevSq,
                      ValueList<Move, 32>& quietsSearched,
                      ValueList<Move, 32>& capturesSearched,
                      Depth                depth);

}  // namespace


// livebook begin
#ifdef USE_LIVEBOOK
//livebook depth begin
int livebook_depth_count = 0;
int max_book_depth       = 255;
//livebook depth end

std::vector<Livebook::BaseLivebook*> opening_livebooks, endgame_livebooks;

std::string _proxy_url;
bool        _use_lichess_games   = false;
bool        _use_lichess_masters = false;
std::string _lichess_player;
std::string _lichess_player_color;
bool        _use_chess_db = false;

bool _use_chess_db_tablebase = false;
bool _use_lichess_tablebase  = false;

bool _chess_db_contribute = false;
bool _proxy_diversity     = false;

auto contributor = Livebook::ChessDBContributor();

void Search::set_livebook_depth(const int book_depth) { max_book_depth = book_depth; }

void Search::set_proxy_url(const std::string& proxy_url) {
    _proxy_url = proxy_url;
    update_livebooks();
}

void Search::set_use_lichess_games(const bool lichess_games) {
    _use_lichess_games = lichess_games;
    update_livebooks();
}

void Search::set_use_lichess_masters(const bool lichess_masters) {
    _use_lichess_masters = lichess_masters;
    update_livebooks();
}

void Search::set_lichess_player(const std::string& lichess_player) {
    _lichess_player = lichess_player;
    update_livebooks();
}

void Search::set_lichess_player_color(const std::string& lichess_player_color) {
    _lichess_player_color = lichess_player_color;
    update_livebooks();
}

void Search::set_use_chess_db(const bool chess_db) {
    _use_chess_db = chess_db;
    update_livebooks();
}

void Search::set_use_chess_db_tablebase(const bool chess_db) {
    _use_chess_db_tablebase = chess_db;
    update_online_tablebases();
}

void Search::set_use_lichess_tablebase(const bool lichess_tablebase) {
    _use_lichess_tablebase = lichess_tablebase;
    update_online_tablebases();
}

void Search::update_livebooks() {
    // Clear the existing opening livebooks
    opening_livebooks.clear();

    if (!_proxy_url.empty())
    {
        Livebook::Proxy* proxy = new Livebook::Proxy(_proxy_url);
        if (_proxy_diversity)
        {
            proxy->set_action(Livebook::Action::QUERY);
        }
        else
        {
            proxy->set_action(Livebook::Action::QUERY_BEST);
        }
        opening_livebooks.push_back(proxy);
    }

    if (!_lichess_player.empty())
    {
        if (_lichess_player_color.empty())
        {
            _lichess_player_color = "white";
        }

        opening_livebooks.push_back(
          new Livebook::LichessPlayer(_lichess_player, _lichess_player_color));
    }

    if (_use_lichess_games)
    {
        opening_livebooks.push_back(new Livebook::LichessGames());
    }

    if (_use_lichess_masters)
    {
        opening_livebooks.push_back(new Livebook::LichessMaster());
    }

    if (_use_chess_db)
    {
        opening_livebooks.push_back(new Livebook::ChessDb());
    }
}

void Search::update_online_tablebases() {
    // Clear the existing endgame livebooks
    endgame_livebooks.clear();

    if (_use_chess_db_tablebase)  // ChessDb tablebase
    {
        endgame_livebooks.push_back(new Livebook::ChessDb());
    }

    if (_use_lichess_tablebase)  // Lichess tablebase
    {
        endgame_livebooks.push_back(new Livebook::LichessEndgame());
    }
}

void Search::set_chess_db_contribute(const bool chess_db_contribute) {
    _chess_db_contribute = chess_db_contribute;
}

void Search::set_proxy_diversity(const bool proxy_diversity) {
    _proxy_diversity = proxy_diversity;
    update_livebooks();
}
#endif
// livebook end


//variety begin
void Search::set_variety(const std::string& varietyOption) {
    if (varietyOption == "Off")
    {
        variety = 0;
    };
    if (varietyOption == "Standard")
    {
        variety = 1;
    };
    if (varietyOption == "Psychological")
    {
        variety = 2;
    };
};
//variety end
//for learning begin
inline bool is_game_decided(const Position& pos, Value lastScore) {
    static constexpr const Value DecidedGameEvalThreeshold = PawnValue * 5;
    static constexpr const int   DecidedGameMaxPly         = 150;
    static constexpr const int   DecidedGameMaxPieceCount  = 5;

    //Assume game is decided if |last sent score| is above DecidedGameEvalThreeshold
    if (lastScore != VALUE_NONE && std::abs(lastScore) > DecidedGameEvalThreeshold)
        return true;

    //Assume game is decided (draw) if game ply is above 150
    if (pos.game_ply() > DecidedGameMaxPly)
        return true;

    if (pos.count<ALL_PIECES>() < DecidedGameMaxPieceCount)
        return true;

    //Assume game is not decided!
    return false;
}
//for learning end

// from Shashin
inline Value static_value(const Eval::NNUE::Networks&   networks,
                          Eval::NNUE::AccumulatorCaches refreshTable,
                          Position&                     pos,
                          Stack*                        ss,
                          int                           optimism) {
    // Check if MAX_PLY is reached
    if (ss->ply >= MAX_PLY)
        return VALUE_DRAW;

    // Check for immediate draw
    if (pos.is_draw(ss->ply) && !pos.checkers())
        return VALUE_DRAW;

    // Detect mate and stalemate situations
    if (MoveList<LEGAL>(pos).size() == 0)
        return pos.checkers() ? VALUE_MATE : VALUE_DRAW;

    //Should not call evaluate() if the side to move is under check!
    if (pos.checkers())
        return VALUE_DRAW;  //TODO: Not sure if VALUE_DRAW is correct!

    // Evaluate the position statically
    return evaluate(networks, pos, refreshTable, optimism);
}

inline int8_t getShashinRange(Value value, const Position& pos) {
    const uint8_t win_probability = WDLModel::get_win_probability(value, pos);

    if (win_probability <= SHASHIN_HIGH_PETROSIAN_THRESHOLD)
    {
        return SHASHIN_POSITION_HIGH_PETROSIAN;
    }
    if ((win_probability > SHASHIN_HIGH_PETROSIAN_THRESHOLD)
        && (win_probability <= SHASHIN_MIDDLE_HIGH_PETROSIAN_THRESHOLD))
    {
        return SHASHIN_POSITION_MIDDLE_HIGH_PETROSIAN;
    }
    if ((win_probability > SHASHIN_MIDDLE_HIGH_PETROSIAN_THRESHOLD)
        && (win_probability <= SHASHIN_MIDDLE_PETROSIAN_THRESHOLD))
    {
        return SHASHIN_POSITION_MIDDLE_PETROSIAN;
    }
    if ((win_probability > SHASHIN_MIDDLE_PETROSIAN_THRESHOLD)
        && (win_probability <= SHASHIN_MIDDLE_LOW_PETROSIAN_THRESHOLD))
    {
        return SHASHIN_POSITION_MIDDLE_LOW_PETROSIAN;
    }
    if ((win_probability > SHASHIN_MIDDLE_LOW_PETROSIAN_THRESHOLD)
        && (win_probability <= SHASHIN_LOW_PETROSIAN_THRESHOLD))
    {
        return SHASHIN_POSITION_LOW_PETROSIAN;
    }
    if ((win_probability > SHASHIN_LOW_PETROSIAN_THRESHOLD)
        && (win_probability <= 100 - SHASHIN_CAPABLANCA_THRESHOLD))
    {
        return SHASHIN_POSITION_CAPABLANCA_PETROSIAN;
    }
    if ((win_probability > (100 - SHASHIN_CAPABLANCA_THRESHOLD))
        && (win_probability < SHASHIN_CAPABLANCA_THRESHOLD))
    {
        return SHASHIN_POSITION_CAPABLANCA;
    }
    if ((win_probability < SHASHIN_LOW_TAL_THRESHOLD)
        && (win_probability >= SHASHIN_CAPABLANCA_THRESHOLD))
    {
        return SHASHIN_POSITION_CAPABLANCA_TAL;
    }
    if ((win_probability < SHASHIN_MIDDLE_LOW_TAL_THRESHOLD)
        && (win_probability >= SHASHIN_LOW_TAL_THRESHOLD))
    {
        return SHASHIN_POSITION_LOW_TAL;
    }
    if ((win_probability < SHASHIN_MIDDLE_TAL_THRESHOLD)
        && (win_probability >= SHASHIN_MIDDLE_LOW_TAL_THRESHOLD))
    {
        return SHASHIN_POSITION_MIDDLE_LOW_TAL;
    }
    if ((win_probability < SHASHIN_MIDDLE_HIGH_TAL_THRESHOLD)
        && (win_probability >= SHASHIN_MIDDLE_TAL_THRESHOLD))
    {
        return SHASHIN_POSITION_MIDDLE_TAL;
    }
    if ((win_probability < SHASHIN_HIGH_TAL_THRESHOLD)
        && (win_probability >= SHASHIN_MIDDLE_HIGH_TAL_THRESHOLD))
    {
        return SHASHIN_POSITION_MIDDLE_HIGH_TAL;
    }
    if (win_probability >= SHASHIN_HIGH_TAL_THRESHOLD)
    {
        return SHASHIN_POSITION_HIGH_TAL;
    }
    return SHASHIN_POSITION_TAL_CAPABLANCA_PETROSIAN;
}

inline bool isShashinTillHighMiddle(Worker* worker) {
    return (worker->shashinWinProbabilityRange != SHASHIN_POSITION_HIGH_PETROSIAN)
        && (worker->shashinWinProbabilityRange != SHASHIN_POSITION_HIGH_TAL);
}
inline bool isShashinTillMiddle(Worker* worker) {
    return isShashinTillHighMiddle(worker)
        && (worker->shashinWinProbabilityRange != SHASHIN_POSITION_MIDDLE_HIGH_PETROSIAN)
        && (worker->shashinWinProbabilityRange != SHASHIN_POSITION_MIDDLE_HIGH_TAL);
}
inline bool isShashinTillMiddleLow(Worker* worker) {
    return isShashinTillMiddle(worker)
        && (worker->shashinWinProbabilityRange != SHASHIN_POSITION_MIDDLE_PETROSIAN)
        && (worker->shashinWinProbabilityRange != SHASHIN_POSITION_MIDDLE_TAL);
}
inline bool isShashinTillLow(Worker* worker) {
    return isShashinTillMiddleLow(worker)
        && (worker->shashinWinProbabilityRange != SHASHIN_POSITION_MIDDLE_LOW_PETROSIAN)
        && (worker->shashinWinProbabilityRange != SHASHIN_POSITION_MIDDLE_LOW_TAL);
}
inline bool isShashinTillCaos(Worker* worker) {
    return isShashinTillLow(worker)
        && (worker->shashinWinProbabilityRange != SHASHIN_POSITION_LOW_PETROSIAN)
        && (worker->shashinWinProbabilityRange != SHASHIN_POSITION_LOW_TAL);
}

inline void updateShashinValues(Value score, const Position& pos, Worker* worker) {
    int ply = pos.game_ply();
    if ((((ply > worker->shashinPly) || (ply == 0))))
    {
        worker->shashinWinProbabilityRange = getShashinRange(score, pos);
        worker->shashinPly                 = ply;
    }
}

inline bool isShashinPositionPetrosian(Worker* worker) {
    if ((worker->shashinWinProbabilityRange == SHASHIN_POSITION_HIGH_PETROSIAN)
        || (worker->shashinWinProbabilityRange == SHASHIN_POSITION_MIDDLE_HIGH_PETROSIAN)
        || (worker->shashinWinProbabilityRange == SHASHIN_POSITION_MIDDLE_PETROSIAN)
        || (worker->shashinWinProbabilityRange == SHASHIN_POSITION_MIDDLE_LOW_PETROSIAN)
        || (worker->shashinWinProbabilityRange == SHASHIN_POSITION_LOW_PETROSIAN))
    {
        return true;
    }
    return false;
}
inline bool isShashinPositionTal(Worker* worker) {
    if ((worker->shashinWinProbabilityRange == SHASHIN_POSITION_HIGH_TAL)
        || (worker->shashinWinProbabilityRange == SHASHIN_POSITION_MIDDLE_HIGH_TAL)
        || (worker->shashinWinProbabilityRange == SHASHIN_POSITION_MIDDLE_TAL)
        || (worker->shashinWinProbabilityRange == SHASHIN_POSITION_MIDDLE_LOW_TAL)
        || (worker->shashinWinProbabilityRange == SHASHIN_POSITION_LOW_TAL))
    {
        return true;
    }
    return false;
}
inline int8_t getInitialShashinWinProbabilityRange(const Eval::NNUE::Networks&   networks,
                                                   Eval::NNUE::AccumulatorCaches refreshTable,
                                                   Position&                     pos,
                                                   Stack*                        ss,
                                                   int                           optimism) {
    if (!highTal && !middleTal && !lowTal && !capablanca && !highPetrosian && !middlePetrosian
        && !lowPetrosian)
        return getShashinRange(static_value(networks, refreshTable, pos, ss, optimism), pos);
    if (highPetrosian && !middlePetrosian && !lowPetrosian && !capablanca && !lowTal && !middleTal
        && !highTal)
        return SHASHIN_POSITION_HIGH_PETROSIAN;
    if (highPetrosian && middlePetrosian && !lowPetrosian && !capablanca && !lowTal && !middleTal
        && !highTal)
        return SHASHIN_POSITION_MIDDLE_HIGH_PETROSIAN;
    if (!highPetrosian && middlePetrosian && !lowPetrosian && !capablanca && !lowTal && !middleTal
        && !highTal)
        return SHASHIN_POSITION_MIDDLE_PETROSIAN;
    if (!highPetrosian && middlePetrosian && lowPetrosian && !capablanca && !lowTal && !middleTal
        && !highTal)
        return SHASHIN_POSITION_MIDDLE_LOW_PETROSIAN;
    if (!highPetrosian && !middlePetrosian && lowPetrosian && !capablanca && !lowTal && !middleTal
        && !highTal)
        return SHASHIN_POSITION_LOW_PETROSIAN;
    if (!highPetrosian && !middlePetrosian && lowPetrosian && capablanca && !lowTal && !middleTal
        && !highTal)
        return SHASHIN_POSITION_CAPABLANCA_PETROSIAN;
    if (!highPetrosian && !middlePetrosian && !lowPetrosian && capablanca && !lowTal && !middleTal
        && !highTal)
        return SHASHIN_POSITION_CAPABLANCA;
    if (!highPetrosian && !middlePetrosian && !lowPetrosian && capablanca && lowTal && !middleTal
        && !highTal)
        return SHASHIN_POSITION_CAPABLANCA_TAL;
    if (!highPetrosian && !middlePetrosian && !lowPetrosian && !capablanca && lowTal && !middleTal
        && !highTal)
        return SHASHIN_POSITION_LOW_TAL;
    if (!highPetrosian && !middlePetrosian && !lowPetrosian && !capablanca && lowTal && middleTal
        && !highTal)
        return SHASHIN_POSITION_MIDDLE_LOW_TAL;
    if (!highPetrosian && !middlePetrosian && !lowPetrosian && !capablanca && !lowTal && middleTal
        && !highTal)
        return SHASHIN_POSITION_MIDDLE_TAL;
    if (!highPetrosian && !middlePetrosian && !lowPetrosian && !capablanca && !lowTal && middleTal
        && highTal)
        return SHASHIN_POSITION_MIDDLE_HIGH_TAL;
    if (!highPetrosian && !middlePetrosian && !lowPetrosian && !capablanca && !lowTal && !middleTal
        && highTal)
        return SHASHIN_POSITION_HIGH_TAL;
    return SHASHIN_POSITION_TAL_CAPABLANCA_PETROSIAN;
}

inline void initShashinValues(const Eval::NNUE::Networks&   networks,
                              Eval::NNUE::AccumulatorCaches refreshTable,
                              Position&                     pos,
                              Stack*                        ss,
                              Worker*                       worker,
                              int                           optimism) {
    worker->shashinPly = std::max(pos.game_ply(), ss->ply);
    worker->shashinWinProbabilityRange =
      getInitialShashinWinProbabilityRange(networks, refreshTable, pos, ss, optimism);
}
// end from Shashin

Search::Worker::Worker(SharedState&                    sharedState,
                       std::unique_ptr<ISearchManager> sm,
                       size_t                          threadId,
                       NumaReplicatedAccessToken       token) :
    // Unpack the SharedState struct into member variables
    threadIdx(threadId),
    numaAccessToken(token),
    manager(std::move(sm)),
    bookMan(sharedState.bookMan),  //from book management
    options(sharedState.options),
    threads(sharedState.threads),
    tt(sharedState.tt),
    networks(sharedState.networks),
    refreshTable(networks[token]) {
    clear();
}

void Search::Worker::ensure_network_replicated() {
    // Access once to force lazy initialization.
    // We do this because we want to avoid initialization during search.
    (void) (networks[numaAccessToken]);
}

void Search::Worker::start_searching() {

    // Non-main threads go directly to iterative_deepening()
    if (!is_mainthread())
    {
        iterative_deepening();
        return;
    }

    main_manager()->tm.init(limits, rootPos.side_to_move(), rootPos.game_ply(), options,
                            main_manager()->originalTimeAdjust);
    tt.new_search();
    // Kelly begin
    enabledLearningProbe = false;
    useLearning          = true;
    // Kelly end

    set_variety(options["Variety"]);  // variety
    // begin from Shashin
    highTal         = options["High Tal"];
    middleTal       = options["Middle Tal"];
    lowTal          = options["Low Tal"];
    capablanca      = options["Capablanca"];
    highPetrosian   = options["High Petrosian"];
    middlePetrosian = options["Middle Petrosian"];
    lowPetrosian    = options["Low Petrosian"];
    // end from Shashin
    Move bookMove = Move::none();  //Books management

    if (rootMoves.empty())
    {
        rootMoves.emplace_back(Move::none());
        main_manager()->updates.onUpdateNoMoves(
          {0, {rootPos.checkers() ? -VALUE_MATE : VALUE_DRAW, rootPos}});
    }
    else
    // Books management begin
    {

        bool think = true;
        if (!(limits.infinite || limits.mate || limits.depth || limits.nodes || limits.perft)
            && !main_manager()->ponder)
        {
            //Probe the configured books
            bookMove = bookMan.probe(rootPos, options);
            //Probe experience book begin
            if (bookMove == Move::none() && (bool) options["Experience Book"]
                && rootPos.game_ply() / 2 < (int) options["Experience Book Max Moves"])
            {
                Depth expBookMinDepth = (Depth) options["Experience Book Min Depth"];
                std::vector<LearningMove*> learningMoves = LD.probe(rootPos.key());
                if (!learningMoves.empty())
                {
                    LD.sortLearningMoves(learningMoves);
                    std::vector<LearningMove*> bestMoves;
                    Depth                      bestDepth = learningMoves[0]->depth;
                    if (bestDepth >= expBookMinDepth)
                    {
                        int   bestPerformance = learningMoves[0]->performance;
                        Value bestScore       = learningMoves[0]->score;
                        if (bestPerformance >= 50)
                        {
                            for (const auto& move : learningMoves)
                            {
                                if (move->depth == bestDepth && move->performance == bestPerformance
                                    && move->score == bestScore)
                                {
                                    bestMoves.push_back(move);
                                }
                                else
                                {
                                    break;
                                }
                            }
                            if (!bestMoves.empty())
                            {
                                std::random_device              rd;
                                std::mt19937                    gen(rd());
                                std::uniform_int_distribution<> distr(0, bestMoves.size() - 1);
                                bookMove = bestMoves[distr(gen)]->move;
                            }
                        }
                    }
                }
            }
            //Probe experience book end
            if (bookMove != Move::none()
                && std::find(rootMoves.begin(), rootMoves.end(), bookMove) != rootMoves.end())
            {
                think = false;

                for (auto&& th : threads)
                    std::swap(th->worker->rootMoves[0],
                              *std::find(th->worker->rootMoves.begin(), th->worker->rootMoves.end(),
                                         bookMove));
            }
        }
// Live Book begin
#ifdef USE_LIVEBOOK
        if (think && !bookMove)
        {
            if (!limits.infinite && !limits.mate)
            {
                livebook_depth_count = rootPos.game_ply();

                std::vector<Livebook::BaseLivebook*> livebooks;

                // If there are no more than 7 units on the board and there aren't the Syzygy Tbs, use the endgame livebooks
                const std::string& syzygyPath = static_cast<std::string>(options["SyzygyPath"]);

                int totalUnits = rootPos.count<ALL_PIECES>();

                if ((totalUnits <= 7)
                    && (syzygyPath.empty()
                        || ((!syzygyPath.empty())
                            && ((int(options["SyzygyProbeLimit"]) < totalUnits)))))
                {
                    livebooks = endgame_livebooks;
                }
                // If we are in the first max_book_depth plies, use the opening livebooks
                else if (livebook_depth_count < max_book_depth)
                {
                    livebooks = opening_livebooks;
                }

                for (const auto livebook : livebooks)
                {
                    if (std::vector<std::pair<std::string, Analysis>> output =
                          livebook->lookup(rootPos);
                        !output.empty())
                    {
                        std::string     uci;
                        const Analysis* best = nullptr;

                        // Iterate through the vector of moves and their corresponding analysis
                        for (auto& [move, analysis] : output)
                        {
                            if (best == nullptr || analysis > *best)
                            {
                                best = &analysis;
                                uci  = move;
                            }
                        }

                        if (best != nullptr)
                        {
                            bookMove = UCIEngine::to_move(rootPos, uci);

                            if (bookMove)
                            {
                                livebook_depth_count++;
                            }
                        }

                        break;
                    }
                }
            }


            if (bookMove && std::count(rootMoves.begin(), rootMoves.end(), bookMove))
            {
                think = false;

                auto best_thread = threads.get_best_thread()->worker.get();
                std::swap(best_thread->rootMoves[0],
                          *std::find(best_thread->rootMoves.begin(), best_thread->rootMoves.end(),
                                     bookMove));

                /*
                for (auto th : threads)
                {
                    std::swap(th->worker->rootMoves[0],
                              *std::find(th->worker->rootMoves.begin(), th->worker->rootMoves.end(),
                                         bookMove));
                }
                */
            }
            else
            {
                bookMove = Move::none();
            }
        }
#endif
        // Live Book end

        //from Book and live book management begin
        if (!bookMove && think)
        {
            //Initialize `mctsThreads` threads only once before any thread have begun searching
            mctsThreads        = size_t(int(options["MCTSThreads"]));
            mctsMultiStrategy  = size_t(int(options["MCTS Multi Strategy"]));
            mctsMultiMinVisits = double(int(options["MCTS Multi MinVisits"]));

            threads.start_searching();  // start non-main threads
            iterative_deepening();      // main thread start searching
        }
        //from Book and live book management end
    }
    // Books management end

    // When we reach the maximum depth, we can arrive here without a raise of
    // threads.stop. However, if we are pondering or in an infinite search,
    // the UCI protocol states that we shouldn't print the best move before the
    // GUI sends a "stop" or "ponderhit" command. We therefore simply wait here
    // until the GUI sends one of those commands.
    while (!threads.stop && (main_manager()->ponder || limits.infinite))
    {}  // Busy wait for a stop or a ponder reset

    // Stop the threads if not already stopped (also raise the stop if
    // "ponderhit" just reset threads.ponder)
    threads.stop = true;

    // Wait until all threads have finished
    threads.wait_for_search_finished();

    // When playing in 'nodes as time' mode, subtract the searched nodes from
    // the available ones before exiting.
    if (limits.npmsec)
        main_manager()->tm.advance_nodes_time(threads.nodes_searched()
                                              - limits.inc[rootPos.side_to_move()]);

    Worker* bestThread = this;
    //no Skill

    if (int(options["MultiPV"]) == 1 && !limits.depth && !limits.mate  //no skill
        && rootMoves[0].pv[0] != Move::none())
        bestThread = threads.get_best_thread()->worker.get();

    main_manager()->bestPreviousScore        = bestThread->rootMoves[0].score;
    main_manager()->bestPreviousAverageScore = bestThread->rootMoves[0].averageScore;


    // kelly begin
    if (!bookMove)
    {
        if (bestThread->completedDepth > 4 && LD.is_enabled() && !LD.is_paused())  // from Khalid
        {
            PersistedLearningMove plm;
            plm.key                = rootPos.key();
            plm.learningMove.depth = bestThread->completedDepth;
            plm.learningMove.move  = bestThread->rootMoves[0].pv[0];
            plm.learningMove.score = bestThread->rootMoves[0].score;
            plm.learningMove.performance =
              WDLModel::get_win_probability(bestThread->rootMoves[0].score, rootPos);

            if (LD.learning_mode() == LearningMode::Self)
            {
                const LearningMove* existingMove = LD.probe_move(plm.key, plm.learningMove.move);
                if (existingMove)
                    plm.learningMove.score = existingMove->score;
                QLearningMove qLearningMove;
                qLearningMove.persistedLearningMove = plm;
                const int qLearningMoveMaterial =
                  rootPos.count<PAWN>() + 3 * rootPos.count<KNIGHT>() + 3 * rootPos.count<BISHOP>()
                  + 5 * rootPos.count<ROOK>() + 9 * rootPos.count<QUEEN>();
                const int qLearningMoveMaterialClamp = std::clamp(qLearningMoveMaterial, 17, 78);
                qLearningMove.materialClamp          = qLearningMoveMaterialClamp;
                qLearningTrajectory.push_back(qLearningMove);
            }
            else
            {
                LD.add_new_learning(plm.key, plm.learningMove);
            }
        }
        if (!enabledLearningProbe)
        {
            useLearning = false;
        }
    }
    // Kelly end

    // Send again PV info if we have a new best thread
    if (bestThread != this)
        main_manager()->pv(*bestThread, threads, tt, bestThread->completedDepth);

    std::string ponder;

    if (bestThread->rootMoves[0].pv.size() > 1
        || bestThread->rootMoves[0].extract_ponder_from_tt(tt, rootPos))
        ponder = UCIEngine::move(bestThread->rootMoves[0].pv[1], rootPos.is_chess960());

    auto bestmove = UCIEngine::move(bestThread->rootMoves[0].pv[0], rootPos.is_chess960());
    main_manager()->updates.onBestmove(bestmove, ponder);
    // from Khalid begin
    // Save learning data if game is already decided
    if (!bookMove)
    {
        if (is_game_decided(rootPos, (bestThread->rootMoves[0].score)) && LD.is_enabled()
            && !LD.is_paused())
        {
            // Perform Q-learning if enabled
            if (LD.learning_mode() == LearningMode::Self)
            {
                putQLearningTrajectoryIntoLearningTable();
            }

            // Save to learning file
            if (!LD.is_readonly())
            {

                LD.persist(options);
            }
            // Stop learning until we receive *ucinewgame* command
            LD.pause();
        }
    }

    // from Khalid end

    // livebook begin
#ifdef USE_LIVEBOOK
    if (_chess_db_contribute)
    {
        contributor.contribute(rootPos, bestThread->rootMoves[0].pv[0]);
    }
#endif
    // livebook end
}

// Main iterative deepening loop. It calls search()
// repeatedly with increasing depth until the allocated thinking time has been
// consumed, the user stops the search, or the maximum search depth is reached.
void Search::Worker::iterative_deepening() {

    SearchManager* mainThread = (is_mainthread() ? main_manager() : nullptr);

    Move pv[MAX_PLY + 1];

    Depth lastBestMoveDepth = 0;
    Value lastBestScore     = -VALUE_INFINITE;
    auto  lastBestPV        = std::vector{Move::none()};

    Value  alpha, beta;
    Value  bestValue     = -VALUE_INFINITE;
    Color  us            = rootPos.side_to_move();
    double timeReduction = 1, totBestMoveChanges = 0;
    int    delta, iterIdx                        = 0;

    // Allocate stack with extra size to allow access from (ss - 7) to (ss + 2):
    // (ss - 7) is needed for update_continuation_histories(ss - 1) which accesses (ss - 6),
    // (ss + 2) is needed for initialization of cutOffCnt.
    Stack  stack[MAX_PLY + 10] = {};
    Stack* ss                  = stack + 7;

    for (int i = 7; i > 0; --i)
    {
        (ss - i)->continuationHistory =
          &this->continuationHistory[0][0][NO_PIECE][0];  // Use as a sentinel
        (ss - i)->continuationCorrectionHistory = &this->continuationCorrectionHistory[NO_PIECE][0];
        (ss - i)->staticEval                    = VALUE_NONE;
    }

    for (int i = 0; i <= MAX_PLY + 2; ++i)
        (ss + i)->ply = i;

    ss->pv = pv;

    if (mainThread)
    {
        if (mainThread->bestPreviousScore == VALUE_INFINITE)
            mainThread->iterValue.fill(VALUE_ZERO);
        else
            mainThread->iterValue.fill(mainThread->bestPreviousScore);
    }

    size_t multiPV = size_t(options["MultiPV"]);
    //no skill

    multiPV = std::min(multiPV, rootMoves.size());
    initShashinValues(networks[numaAccessToken], refreshTable, rootPos, ss, this,
                      VALUE_ZERO);  //from Shashin

    int searchAgainCounter = 0;

    lowPlyHistory.fill(0);
    // from mcts begin
    optimism[WHITE] = optimism[BLACK] =
      VALUE_ZERO;  //Must initialize optimism before calling static_value(). Not sure if 'VALUE_ZERO' is the right value

    bool  maybeDraw = rootPos.rule50_count() >= 90 || rootPos.upcoming_repetition(2);
    Value rootPosValue =
      static_value(networks[numaAccessToken], refreshTable, rootPos, ss, this->optimism[us]);
    bool possibleMCTSByValue =
      ((this->shashinWinProbabilityRange == SHASHIN_POSITION_HIGH_PETROSIAN)
       || (this->shashinWinProbabilityRange == SHASHIN_POSITION_MIDDLE_HIGH_PETROSIAN)
       || (this->shashinWinProbabilityRange == SHASHIN_POSITION_MIDDLE_PETROSIAN));
    if (!mainThread && bool(options["MCTS by Shashin"]) && multiPV == 1 && !maybeDraw
        && possibleMCTSByValue && (this->threadIdx) <= (size_t) (mctsThreads)
        && !is_game_decided(rootPos, rootPosValue))
    {
        MonteCarlo* monteCarlo = new MonteCarlo(rootPos, this, tt);

        if (monteCarlo)
        {
#if !defined(NDEBUG) && !defined(_NDEBUG)
            sync_cout << "info string *** Thread[" << threadIdx << "] is running MCTS search"
                      << sync_endl;
#endif

            monteCarlo->search(threads, limits, is_mainthread(), this);
            if ((this->threadIdx) == 1 && limits.infinite
                && threads.stop.load(std::memory_order_relaxed))
                monteCarlo->print_children();

            delete monteCarlo;

#if !defined(NDEBUG) && !defined(_NDEBUG)
            sync_cout << "info string *** Thread[" << threadIdx << "] finished MCTS search"
                      << sync_endl;
#endif

            return;
        }
    }

#if !defined(NDEBUG) && !defined(_NDEBUG)
    sync_cout << "info string *** Thread[" << threadIdx << "] is running A/B search" << sync_endl;
#endif

    // from mcts end
    // Iterative deepening loop until requested to stop or the target depth is reached
    while (++rootDepth < MAX_PLY && !threads.stop
           && !(limits.depth && mainThread && rootDepth > limits.depth))
    {
        // Age out PV variability metric
        if (mainThread)
            totBestMoveChanges /= 2;

        // Save the last iteration's scores before the first PV line is searched and
        // all the move scores except the (new) PV are set to -VALUE_INFINITE.
        for (RootMove& rm : rootMoves)
            rm.previousScore = rm.score;

        size_t pvFirst = 0;
        pvLast         = 0;

        if (!threads.increaseDepth)
            searchAgainCounter++;

        // MultiPV loop. We perform a full root search for each PV line
        for (pvIdx = 0; pvIdx < multiPV; ++pvIdx)
        {
            if (pvIdx == pvLast)
            {
                pvFirst = pvLast;
                for (pvLast++; pvLast < rootMoves.size(); pvLast++)
                    if (rootMoves[pvLast].tbRank != rootMoves[pvFirst].tbRank)
                        break;
            }

            // Reset UCI info selDepth for each depth and each PV line
            selDepth = 0;

            // Reset aspiration window starting size
            delta     = 5 + std::abs(rootMoves[pvIdx].meanSquaredScore) / 13797;
            Value avg = rootMoves[pvIdx].averageScore;
            alpha     = std::max(avg - delta, -VALUE_INFINITE);
            beta      = std::min(avg + delta, VALUE_INFINITE);

            // Adjust optimism based on root move's averageScore (~4 Elo)
            optimism[us]  = 132 * avg / (std::abs(avg) + 89);
            optimism[~us] = -optimism[us];

            // Start with a small aspiration window and, in the case of a fail
            // high/low, re-search with a bigger window until we don't fail
            // high/low anymore.
            int failedHighCnt = 0;
            while (true)
            {
                // Adjust the effective depth searched, but ensure at least one
                // effective increment for every four searchAgain steps (see issue #2717).
                Depth adjustedDepth =
                  (this->shashinWinProbabilityRange != SHASHIN_POSITION_HIGH_TAL)
                    ? std::max(1, rootDepth - failedHighCnt - 3 * (searchAgainCounter + 1) / 4)
                    : rootDepth;  //from Crystal yes:HT,TC, C, CP no:LT,MHP,P
                rootDelta = beta - alpha;
                bestValue = search<Root>(rootPos, ss, alpha, beta, adjustedDepth, false);

                // Bring the best move to the front. It is critical that sorting
                // is done with a stable algorithm because all the values but the
                // first and eventually the new best one is set to -VALUE_INFINITE
                // and we want to keep the same order for all the moves except the
                // new PV that goes to the front. Note that in the case of MultiPV
                // search the already searched PV lines are preserved.
                std::stable_sort(rootMoves.begin() + pvIdx, rootMoves.begin() + pvLast);

                // If search has been stopped, we break immediately. Sorting is
                // safe because RootMoves is still valid, although it refers to
                // the previous iteration.
                if (threads.stop)
                    break;

                // When failing high/low give some update before a re-search. To avoid
                // excessive output that could hang GUIs like Fritz 19, only start
                // at nodes > 10M (rather than depth N, which can be reached quickly)
                if (mainThread && multiPV == 1 && (bestValue <= alpha || bestValue >= beta)
                    && nodes > 10000000)
                    main_manager()->pv(*this, threads, tt, rootDepth);

                // In case of failing low/high increase aspiration window and re-search,
                // otherwise exit the loop.
                if (bestValue <= alpha)
                {
                    beta  = (alpha + beta) / 2;
                    alpha = std::max(bestValue - delta, -VALUE_INFINITE);

                    failedHighCnt = 0;
                    if (mainThread)
                        mainThread->stopOnPonderhit = false;
                }
                else if (bestValue >= beta)
                {
                    beta = std::min(bestValue + delta, VALUE_INFINITE);
                    ++failedHighCnt;
                }
                else
                    break;

                delta += delta / 3;

                assert(alpha >= -VALUE_INFINITE && beta <= VALUE_INFINITE);
            }

            // Sort the PV lines searched so far and update the GUI
            std::stable_sort(rootMoves.begin() + pvFirst, rootMoves.begin() + pvIdx + 1);

            if (mainThread
                && (threads.stop || pvIdx + 1 == multiPV || nodes > 10000000)
                // A thread that aborted search can have mated-in/TB-loss PV and
                // score that cannot be trusted, i.e. it can be delayed or refuted
                // if we would have had time to fully search other root-moves. Thus
                // we suppress this output and below pick a proven score/PV for this
                // thread (from the previous iteration).
                && !(threads.abortedSearch && rootMoves[0].uciScore <= VALUE_TB_LOSS_IN_MAX_PLY))
                main_manager()->pv(*this, threads, tt, rootDepth);

            if (threads.stop)
                break;
        }

        if (!threads.stop)
            completedDepth = rootDepth;

        // We make sure not to pick an unproven mated-in score,
        // in case this thread prematurely stopped search (aborted-search).
        if (threads.abortedSearch && rootMoves[0].score != -VALUE_INFINITE
            && rootMoves[0].score <= VALUE_TB_LOSS_IN_MAX_PLY)
        {
            // Bring the last best move to the front for best thread selection.
            Utility::move_to_front(rootMoves, [&lastBestPV = std::as_const(lastBestPV)](
                                                const auto& rm) { return rm == lastBestPV[0]; });
            rootMoves[0].pv    = lastBestPV;
            rootMoves[0].score = rootMoves[0].uciScore = lastBestScore;
        }
        else if (rootMoves[0].pv[0] != lastBestPV[0])
        {
            lastBestPV        = rootMoves[0].pv;
            lastBestScore     = rootMoves[0].score;
            lastBestMoveDepth = rootDepth;
        }

        if (!mainThread)
            continue;

        // Have we found a "mate in x"?
        if (limits.mate && rootMoves[0].score == rootMoves[0].uciScore
            && ((rootMoves[0].score >= VALUE_MATE_IN_MAX_PLY
                 && VALUE_MATE - rootMoves[0].score <= 2 * limits.mate)
                || (rootMoves[0].score != -VALUE_INFINITE
                    && rootMoves[0].score <= VALUE_MATED_IN_MAX_PLY
                    && VALUE_MATE + rootMoves[0].score <= 2 * limits.mate)))
            threads.stop = true;

        //no Skill

        // Use part of the gained time from a previous stable move for the current move
        for (auto&& th : threads)
        {
            totBestMoveChanges += th->worker->bestMoveChanges;
            th->worker->bestMoveChanges = 0;
        }

        // Do we have time for the next iteration? Can we stop searching now?
        if (limits.use_time_management() && !threads.stop && !mainThread->stopOnPonderhit)
        {
            int nodesEffort = rootMoves[0].effort * 100 / std::max(size_t(1), size_t(nodes));

            double fallingEval = (1067 + 223 * (mainThread->bestPreviousAverageScore - bestValue)
                                  + 97 * (mainThread->iterValue[iterIdx] - bestValue))
                               / 10000.0;
            fallingEval = std::clamp(fallingEval, 0.580, 1.667);

            // If the bestMove is stable over several iterations, reduce time accordingly
            timeReduction    = lastBestMoveDepth + 8 < completedDepth ? 1.495 : 0.687;
            double reduction = (1.48 + mainThread->previousTimeReduction) / (2.17 * timeReduction);
            double bestMoveInstability = 1 + 1.88 * totBestMoveChanges / threads.size();
            double recapture           = limits.capSq == rootMoves[0].pv[0].to_sq() ? 0.955 : 1.005;

            double totalTime =
              mainThread->tm.optimum() * fallingEval * reduction * bestMoveInstability * recapture;

            // Cap used time in case of a single legal move for a better viewer experience
            if (rootMoves.size() == 1)
                totalTime = std::min(500.0, totalTime);

            auto elapsedTime = elapsed();

            if (completedDepth >= 10 && nodesEffort >= 97 && elapsedTime > totalTime * 0.739
                && !mainThread->ponder)
                threads.stop = true;

            // Stop the search if we have exceeded the totalTime
            if (elapsedTime > totalTime)
            {
                // If we are allowed to ponder do not stop the search now but
                // keep pondering until the GUI sends "ponderhit" or "stop".
                if (mainThread->ponder)
                    mainThread->stopOnPonderhit = true;
                else
                    threads.stop = true;
            }
            else
                threads.increaseDepth = mainThread->ponder || elapsedTime <= totalTime * 0.506;
        }

        mainThread->iterValue[iterIdx] = bestValue;
        iterIdx                        = (iterIdx + 1) & 3;
    }

    if (!mainThread)
        return;

    mainThread->previousTimeReduction = timeReduction;
    //no Skill
}

// Reset histories, usually before a new game
void Search::Worker::clear() {
    mainHistory.fill(0);
    lowPlyHistory.fill(0);
    captureHistory.fill(-753);
    pawnHistory.fill(-1152);
    pawnCorrectionHistory.fill(0);
    materialCorrectionHistory.fill(0);
    majorPieceCorrectionHistory.fill(0);
    minorPieceCorrectionHistory.fill(0);
    nonPawnCorrectionHistory[WHITE].fill(0);
    nonPawnCorrectionHistory[BLACK].fill(0);

    for (auto& to : continuationCorrectionHistory)
        for (auto& h : to)
            h->fill(0);

    for (bool inCheck : {false, true})
        for (StatsType c : {NoCaptures, Captures})
            for (auto& to : continuationHistory[inCheck][c])
                for (auto& h : to)
                    h->fill(-678);

    for (size_t i = 1; i < reductions.size(); ++i)
        reductions[i] = int((18.43 + std::log(size_t(options["Threads"])) / 2) * std::log(i));

    refreshTable.clear(networks[numaAccessToken]);
}


// Main search function for both PV and non-PV nodes
template<NodeType nodeType>
Value Search::Worker::search(
  Position& pos, Stack* ss, Value alpha, Value beta, Depth depth, bool cutNode) {

    constexpr bool PvNode    = nodeType != NonPV;
    constexpr bool rootNode  = nodeType == Root;
    const bool     allNode   = !(PvNode || cutNode);
    bool           gameCycle = false;  // from Crystal

    // Dive into quiescence search when the depth reaches zero
    if (depth <= 0)
        return qsearch < PvNode ? PV : NonPV > (pos, ss, alpha, beta);

    // Limit the depth if extensions made it too large
    depth = std::min(depth, MAX_PLY - 1);

    // Check if we have an upcoming move that draws by repetition
    // from Crystal begin
    if (!rootNode)
    {
        if (pos.upcoming_repetition(ss->ply))
        {
            if (alpha < VALUE_DRAW)
            {

                alpha = value_draw(this->nodes);
                if (alpha >= beta)
                    return alpha;
            }
            gameCycle = true;
        }
    }
    // from Crystal end

    assert(-VALUE_INFINITE <= alpha && alpha < beta && beta <= VALUE_INFINITE);
    assert(PvNode || (alpha == beta - 1));
    assert(0 < depth && depth < MAX_PLY);
    assert(!(PvNode && cutNode));

    Move      pv[MAX_PLY + 1];
    StateInfo st;
    ASSERT_ALIGNED(&st, Eval::NNUE::CacheLineSize);

    Key  posKey;
    Move move, excludedMove = Move::none(), bestMove,
               expTTMove = Move::none();  // from Kelly
    Depth extension, newDepth;
    // from Kelly begin
    Value bestValue, value, eval = VALUE_NONE, maxValue, expTTValue = VALUE_NONE, probCutBeta;
    bool  givesCheck, improving, priorCapture, opponentWorsening = false, expTTHit = false;
    bool  isMate;  // from Crystal
    //from Kelly End
    bool  capture, ttCapture, kingDanger, nullParity;  // from Crystal
    Piece movedPiece;
    int   ourMove;  // from Crystal

    ValueList<Move, 32> capturesSearched;
    ValueList<Move, 32> quietsSearched;
    // from Kelly begin
    bool updatedLearning = false;
    // flags to preserve node types
    bool disableNMAndPC = false;
    bool expectedPVNode = false;
    int  sibs           = 0;
    // from Kelly end
    // Step 1. Initialize node
    Worker* thisThread = this;
    ss->inCheck        = pos.checkers();
    priorCapture       = pos.captured_piece();
    Color us           = pos.side_to_move();
    ss->moveCount      = 0;
    bestValue          = -VALUE_INFINITE;
    maxValue           = VALUE_INFINITE;
    // from Crystal begin
    kingDanger        = false;
    ourMove           = !(ss->ply & 1);
    nullParity        = (ourMove == thisThread->nmpSide);
    ss->secondaryLine = false;
    ss->mainLine      = false;
    // from Crystal end
    // Full Threads patch begin
    if (thisThread->fullSearch)
        improving = true;
    // Full Threads patch end
    // Check for the available remaining time
    if (is_mainthread())
        main_manager()->check_time(*thisThread);

    // Used to send selDepth info to GUI (selDepth counts from 1, ply from 0)
    if (PvNode && thisThread->selDepth < ss->ply + 1)
        thisThread->selDepth = ss->ply + 1;

    if (!rootNode)
    {
        // Step 2. Check for aborted search and immediate draw
        if (threads.stop.load(std::memory_order_relaxed) || pos.is_draw(ss->ply)
            || ss->ply >= MAX_PLY)
            return (ss->ply >= MAX_PLY && !ss->inCheck) ? evaluate(
                     networks[numaAccessToken], pos, refreshTable, thisThread->optimism[us])
                                                        : value_draw(thisThread->nodes);

        // Step 3. Mate distance pruning. Even if we mate at the next move our score
        // would be at best mate_in(ss->ply + 1), but if alpha is already bigger because
        // a shorter mate was found upward in the tree then there is no need to search
        // because we will never beat the current alpha. Same logic but with reversed
        // signs apply also in the opposite condition of being mated instead of giving
        // mate. In this case, return a fail-high score.
        alpha = std::max(mated_in(ss->ply), alpha);
        beta  = std::min(mate_in(ss->ply + 1), beta);
        if (alpha >= beta)
            return alpha;
    }

    assert(0 <= ss->ply && ss->ply < MAX_PLY);

    bestMove            = Move::none();
    (ss + 2)->cutoffCnt = 0;
    Square prevSq = ((ss - 1)->currentMove).is_ok() ? ((ss - 1)->currentMove).to_sq() : SQ_NONE;
    ss->statScore = 0;

    // Step 4. Transposition table lookup
    excludedMove                   = ss->excludedMove;
    posKey                         = pos.key();
    auto [ttHit, ttData, ttWriter] = tt.probe(posKey);
    // Need further processing of the saved data
    ss->ttHit    = ttHit;
    ttData.move  = rootNode ? thisThread->rootMoves[thisThread->pvIdx].pv[0]
                 : ttHit    ? ttData.move
                            : Move::none();
    ttData.value = ttHit ? value_from_tt(ttData.value, ss->ply, pos.rule50_count()) : VALUE_NONE;
    ss->ttPv     = excludedMove ? ss->ttPv : PvNode || (ttHit && ttData.is_pv);
    ttCapture    = ttData.move && pos.capture_stage(ttData.move);

    // At this point, if excluded, skip straight to step 6, static eval. However,
    // to save indentation, we list the condition in all code between here and there.

    // At non-PV nodes we check for an early TT cutoff
    if (!PvNode && !excludedMove && ttData.depth > depth - (ttData.value <= beta)
        && ttData.value != VALUE_NONE  // Can happen when !ttHit or when access race in probe()
        // from Crystal begin  Yes: HT C CCT No:MHP, HP
        && (ttData.bound & (ttData.value >= beta ? BOUND_LOWER : BOUND_UPPER))
        && ((!gameCycle)
            || ((this->shashinWinProbabilityRange != SHASHIN_POSITION_HIGH_TAL)
                && (this->shashinWinProbabilityRange != SHASHIN_POSITION_CAPABLANCA))))
    // from Crystal end
    {
        // If ttMove is quiet, update move sorting heuristics on TT hit (~2 Elo)
        if (ttData.move && ttData.value >= beta)
        {
            // Bonus for a quiet ttMove that fails high (~2 Elo)
            if (!ttCapture)
                update_quiet_histories(pos, ss, *this, ttData.move, stat_bonus(depth));

            // Extra penalty for early quiet moves of
            // the previous ply (~1 Elo on STC, ~2 Elo on LTC)
            if (prevSq != SQ_NONE && (ss - 1)->moveCount <= 2 && !priorCapture)
                update_continuation_histories(ss - 1, pos.piece_on(prevSq), prevSq,
                                              -stat_malus(depth + 1));
        }

        // Partial workaround for the graph history interaction problem
        // For high rule50 counts don't produce transposition table cutoffs.
        if (pos.rule50_count() < 90)
            return ttData.value;
    }
    // from Kelly begin
    // Step 4Bis. Global Learning Table lookup
    expTTHit        = false;
    updatedLearning = false;

    if (!excludedMove && LD.is_enabled() && useLearning)
    {
        const LearningMove* learningMove = nullptr;
        sibs                             = LD.probeByMaxDepthAndScore(posKey, learningMove);
        if (learningMove)
        {
            assert(sibs);

            enabledLearningProbe = true;
            expTTHit             = true;
            if (!ttData.move)
            {
                ttData.move = learningMove->move;
            }

            if (learningMove->depth >= depth)
            {
                expTTMove       = learningMove->move;
                expTTValue      = learningMove->score;
                updatedLearning = true;
            }

            if ((learningMove->depth == 0))
                updatedLearning = false;

            if (updatedLearning && expTTValue != VALUE_NONE)


            {
                if (expTTValue < alpha)
                {
                    disableNMAndPC = true;
                }
                if (expTTValue > alpha && expTTValue < beta)
                {
                    expectedPVNode = true;
                    improving      = true;
                }
            }
            bool expTTCapture = learningMove->move && pos.capture_stage(expTTMove);
            // At this point, if excluded, skip straight to step 6, static eval. However,
            // to save indentation, we list the condition in all code between here and there.

            // At non-PV nodes we check for an early Global Learning Table cutoff
            if (!PvNode && updatedLearning && learningMove->depth > depth - (expTTValue <= beta)
                && expTTValue
                     != VALUE_NONE)  // Possible in case of Global Learning Table access race
            {
                // If expTTMove is quiet, update move sorting heuristics on Global learning table hit
                if (expTTMove && expTTValue >= beta)
                {
                    // Bonus for a quiet ttMove that fails high
                    if (!expTTCapture)
                        update_quiet_histories(pos, ss, *this, expTTMove, stat_bonus(depth));

                    // Extra penalty for early quiet moves of
                    // the previous ply
                    if (prevSq != SQ_NONE && (ss - 1)->moveCount <= 2 && !priorCapture)
                        update_continuation_histories(ss - 1, pos.piece_on(prevSq), prevSq,
                                                      -stat_malus(depth + 1));
                }

                // Partial workaround for the graph history interaction problem
                // For high rule50 counts don't produce transposition table cutoffs.
                if (pos.rule50_count() < 90)
                    return expTTValue;
            }
        }
    }
    // from Kelly end

    // Step 5. Tablebases probe
    if (!rootNode && !excludedMove && tbConfig.cardinality)
    {
        int piecesCount = pos.count<ALL_PIECES>();

        if (piecesCount <= tbConfig.cardinality
            && (piecesCount < tbConfig.cardinality || depth >= tbConfig.probeDepth)
            && pos.rule50_count() == 0 && !pos.can_castle(ANY_CASTLING))
        {
            TB::ProbeState err;
            TB::WDLScore   wdl = Tablebases::probe_wdl(pos, &err);

            // Force check of time on the next occasion
            if (is_mainthread())
                main_manager()->callsCnt = 0;

            if (err != TB::ProbeState::FAIL)
            {
                thisThread->tbHits.fetch_add(1, std::memory_order_relaxed);

                int drawScore = tbConfig.useRule50 ? 1 : 0;

                Value tbValue = VALUE_TB - ss->ply;

                // Use the range VALUE_TB to VALUE_TB_WIN_IN_MAX_PLY to score
                value = wdl < -drawScore ? -tbValue
                      : wdl > drawScore  ? tbValue
                                         : VALUE_DRAW + 2 * wdl * drawScore;

                Bound b = wdl < -drawScore ? BOUND_UPPER
                        : wdl > drawScore  ? BOUND_LOWER
                                           : BOUND_EXACT;

                if (b == BOUND_EXACT || (b == BOUND_LOWER ? value >= beta : value <= alpha))
                {
                    ttWriter.write(posKey, value_to_tt(value, ss->ply), ss->ttPv, b,
                                   std::min(MAX_PLY - 1, depth + 6), Move::none(), VALUE_NONE,
                                   tt.generation());

                    return value;
                }

                if (PvNode)
                {
                    if (b == BOUND_LOWER)
                        bestValue = value, alpha = std::max(alpha, bestValue);
                    else
                        maxValue = value;
                }
            }
        }
    }

    kingDanger = !ourMove && pos.king_danger(us);  //from crystal

    // Step 6. Static evaluation of the position
    Value unadjustedStaticEval = VALUE_NONE;
    if (ss->inCheck)
    {
        // Skip early pruning when in check
        ss->staticEval = eval = (ss - 2)->staticEval;
        improving             = false;
        goto moves_loop;
    }
    else if (excludedMove)
    {
        // Providing the hint that this node's accumulator will be used often
        // brings significant Elo gain (~13 Elo).
        Eval::NNUE::hint_common_parent_position(pos, networks[numaAccessToken], refreshTable);
        unadjustedStaticEval = eval = ss->staticEval;
    }
    else if (ss->ttHit)
    {
        // Never assume anything about values stored in TT
        unadjustedStaticEval = ttData.eval;
        if (unadjustedStaticEval == VALUE_NONE)
            unadjustedStaticEval =
              evaluate(networks[numaAccessToken], pos, refreshTable, thisThread->optimism[us]);
        else if (PvNode)
            Eval::NNUE::hint_common_parent_position(pos, networks[numaAccessToken], refreshTable);

        ss->staticEval = eval =
          to_corrected_static_eval(unadjustedStaticEval, *thisThread, pos, ss);

        // ttValue can be used as a better position evaluation (~7 Elo)
        if (ttData.value != VALUE_NONE
            && (ttData.bound & (ttData.value > eval ? BOUND_LOWER : BOUND_UPPER)))
            eval = ttData.value;
    }
    else
    {
        //Kelly begin
        unadjustedStaticEval =
          evaluate(networks[numaAccessToken], pos, refreshTable, thisThread->optimism[us]);

        if (!LD.is_enabled() || !expTTHit || !updatedLearning)
        {
            ss->staticEval = eval =
              to_corrected_static_eval(unadjustedStaticEval, *thisThread, pos, ss);

            // Static evaluation is saved as it was before adjustment by correction history
            ttWriter.write(posKey, VALUE_NONE, ss->ttPv, BOUND_NONE, DEPTH_UNSEARCHED, Move::none(),
                           unadjustedStaticEval, tt.generation());
        }
        else  // learning
        {
            // Never assume anything on values stored in Global Learning Table
            ss->staticEval = eval = expTTValue;
            if (eval == VALUE_NONE)
            {
                ss->staticEval = eval =
                  to_corrected_static_eval(unadjustedStaticEval, *thisThread, pos, ss);
            }
            if (eval == VALUE_DRAW)
            {
                eval = value_draw(this->nodes);
            }
            // Can expTTValue be used as a better position evaluation?
            if (expTTValue != VALUE_NONE)
            {
                eval = expTTValue;
            }
        }
    }
    // from Kelly end

    // Use static evaluation difference to improve quiet move ordering (~9 Elo)
    if (((ss - 1)->currentMove).is_ok() && !(ss - 1)->inCheck && !priorCapture)
    {
        int bonus = std::clamp(-10 * int((ss - 1)->staticEval + ss->staticEval), -1641, 1423) + 760;
        thisThread->mainHistory[~us][((ss - 1)->currentMove).from_to()] << bonus;
        if (type_of(pos.piece_on(prevSq)) != PAWN && ((ss - 1)->currentMove).type_of() != PROMOTION)
            thisThread->pawnHistory[pawn_structure_index(pos)][pos.piece_on(prevSq)][prevSq]
              << bonus / 2;
    }
    // full threads patch begin
    if (thisThread->fullSearch)
        goto moves_loop;  // full threads patch
    // full threads patch end
    // from Kelly begin
    if (!expectedPVNode)
    {
        // Set up the improving flag, which is true if current static evaluation is
        // bigger than the previous static evaluation at our turn (if we were in
        // check at our previous move we go back until we weren't in check) and is
        // false otherwise. The improving flag is used in various pruning heuristics.
        improving = ss->staticEval > (ss - 2)->staticEval;
    }
    opponentWorsening = ss->staticEval + (ss - 1)->staticEval > 2;

    // from Kelly end

    // Begin early pruning from Crystal by Shashin Yes: HT C No: MHP HP
    if ((!PvNode && (ourMove || !excludedMove) && !thisThread->nmpGuardV
         && abs(eval) < 2 * VALUE_KNOWN_WIN && abs(beta) < VALUE_MAX_EVAL && eval >= beta)
        || (this->shashinWinProbabilityRange != SHASHIN_POSITION_HIGH_TAL))
    {
        // Step 7. Razoring (~1 Elo)
        // If eval is really low, check with qsearch if we can exceed alpha. If the
        // search suggests we cannot exceed alpha, return a speculative fail low.
        if (((this->shashinWinProbabilityRange != SHASHIN_POSITION_HIGH_TAL)
             && (this->shashinWinProbabilityRange != SHASHIN_POSITION_CAPABLANCA))
            && (eval < alpha - 501 - 272 * depth * depth))  //from Crystal Yes: HT C No: MHP
        {
            value = qsearch<NonPV>(pos, ss, alpha - 1, alpha);
            if (value < alpha && std::abs(value) < VALUE_TB_WIN_IN_MAX_PLY)
                return value;
        }

        // Step 8. Futility pruning: child node (~40 Elo)
        // The depth condition is important for mate finding.
        // from Crystal begin yes HT HMT no C MHP
        bool fromCrystalFutilityPruning =
          ((!(isShashinTillMiddle(this))) && (isShashinPositionTal(this)));
        if (
          //common part begin
          !ss->ttPv
          && eval - futility_margin(depth, cutNode && !ss->ttHit, improving, opponentWorsening)
                 - (ss - 1)->statScore / 272
               >= beta
          //common part end
          && (((!fromCrystalFutilityPruning)
               && (depth < 13 && eval >= beta && (!ttData.move || ttCapture)
                   && beta > VALUE_TB_LOSS_IN_MAX_PLY && eval < VALUE_TB_WIN_IN_MAX_PLY))
              || (fromCrystalFutilityPruning
                  && (depth < (9
                               - 2
                                   * ((ss - 1)->mainLine || (ss - 1)->secondaryLine
                                      || (ttData.move && !ttCapture)))
                      && !kingDanger && !excludedMove && !gameCycle
                      && !(thisThread->nmpGuard && nullParity)))))
            return (!fromCrystalFutilityPruning) ? (beta + (eval - beta) / 3) : eval;
        // from Crystal end yes HT HMT no C MHP
        // Step 9. Null move search with verification search (~35 Elo)
        //from Crystal begin yes: HT HMT C No: LT TC HP
        bool nullMoveSearchFromCrystal =
          ((!(isShashinTillMiddle(this))) && isShashinPositionTal(this));
        if (ss->staticEval >= beta - 23 * depth + 400 && pos.non_pawn_material(us) && !excludedMove
            && !disableNMAndPC  //Kelly
            && ((!nullMoveSearchFromCrystal && cutNode && (ss - 1)->currentMove != Move::null()
                 && eval >= beta && ss->ply >= thisThread->nmpMinPly
                 && beta > VALUE_TB_LOSS_IN_MAX_PLY)
                || (nullMoveSearchFromCrystal && !thisThread->nmpGuard && !gameCycle
                    && eval >= ss->staticEval && !kingDanger
                    && (rootDepth < 11 || ourMove || MoveList<LEGAL>(pos).size() > 5))))
        //from Crystal end
        {
            assert(eval - beta >= 0);
            thisThread->nmpSide = ourMove;  // from Crystal
            // Null move dynamic reduction based on depth and eval
            Depth R = std::min(int(eval - beta) / 209, 6) + depth / 3 + 5;
            //from Crystal begin
            /*if (!ourMove && ((ss-1)->secondaryLine) && ((!(isShashinMiddle(this)))
                                             && isShashinPositionTal(this) ))
        R = std::min(R, 8);*/
            //from Crystal end
            /*if (   depth < 11
       	|| ttValue >= beta
        || (tte->depth()) < depth-R
        || !((tte->bound()) & BOUND_UPPER)        
        || (isShashinMiddle(this)) || (!isShashinPositionTal(this))) //from Crystal
    { */
            ss->currentMove         = Move::null();
            ss->continuationHistory = &thisThread->continuationHistory[0][0][NO_PIECE][0];
            ss->continuationCorrectionHistory =
              &thisThread->continuationCorrectionHistory[NO_PIECE][0];

            pos.do_null_move(st, tt);
            thisThread->nmpGuard = true;  // from Crystal
            Value nullValue      = -search<NonPV>(pos, ss + 1, -beta, -beta + 1, depth - R, false);
            thisThread->nmpGuard = false;  // from Crystal
            pos.undo_null_move();

            // Do not return unproven mate or TB scores
            if (nullValue >= beta && nullValue < VALUE_TB_WIN_IN_MAX_PLY)
            {
                if (thisThread->nmpMinPly || depth < 16)
                    return nullValue;

                assert(!thisThread->nmpMinPly);  // Recursive verification is not allowed

                // Do verification search at high depths, with null move pruning disabled
                // until ply exceeds nmpMinPly.
                //from Crystal begin yes TC C No HT MLT MHP HP
                if ((this->shashinWinProbabilityRange != SHASHIN_POSITION_CAPABLANCA))
                {
                    thisThread->nmpMinPly = ss->ply + 3 * (depth - R) / 4;
                }
                //from Crystal end
                thisThread->nmpGuardV = true;  // from Crystal
                Value v               = search<NonPV>(pos, ss, beta - 1, beta, depth - R, false);
                thisThread->nmpGuardV = false;  // from Crystal
                thisThread->nmpMinPly = 0;
                if (v >= beta)
                    return nullValue;
            }
        }

        // Step 10. Internal iterative reductions (~9 Elo)
        // For PV nodes without a ttMove, we decrease depth
        if (PvNode && !ttData.move
            && ((!gameCycle && depth >= 3 && (ss - 1)->moveCount > 1)
                || ((this->shashinWinProbabilityRange
                     != SHASHIN_POSITION_HIGH_TAL))))  //from Crystal yes HT C No MLT TC MHP HP
        {
            depth -= (this->shashinWinProbabilityRange != SHASHIN_POSITION_HIGH_TAL)
                     ? 3
                     : 0;  //from Crystal yes HT CCT No CCP MHP HP
        }
        // Use qsearch if depth <= 0.
        if (depth <= 0)
        {
            return qsearch<PV>(pos, ss, alpha, beta);
        }
        // For cutNodes, if depth is high enough, decrease depth by 2 if there is no ttMove, or
        // by 1 if there is a ttMove with an upper bound.
        if (cutNode && depth >= 7 && (!ttData.move || ttData.bound == BOUND_UPPER))
            depth -= 1 + !ttData.move;


        // Step 11. ProbCut (~10 Elo)
        // If we have a good enough capture (or queen promotion) and a reduced search
        // returns a value much above beta, we can (almost) safely prune the previous move.
        probCutBeta = beta + 189 - 53 * improving - 30 * opponentWorsening;
        if (!disableNMAndPC  // Kelly
            && ((!PvNode && depth > 3
                 && std::abs(beta) < VALUE_TB_WIN_IN_MAX_PLY
                 // If value from transposition table is lower than probCutBeta, don't attempt
                 // probCut there and in further interactions with transposition table cutoff
                 // depth is set to depth - 3 because probCut search has depth set to depth - 4
                 // but we also do a move before it. So effective depth is equal to depth - 3.
                 && !(ttData.depth >= depth - 3 && ttData.value != VALUE_NONE
                      && ttData.value < probCutBeta))))
        {
            assert(probCutBeta < VALUE_INFINITE && probCutBeta > beta);

            MovePicker mp(pos, ttData.move, probCutBeta - ss->staticEval,
                          &thisThread->captureHistory);
            Piece      captured;

            while ((move = mp.next_move()) != Move::none())
            {
                assert(move.is_ok());

                if (move == excludedMove)
                    continue;

                if (!pos.legal(move))
                    continue;

                assert(pos.capture_stage(move));

                movedPiece = pos.moved_piece(move);
                captured   = pos.piece_on(move.to_sq());

                // Prefetch the TT entry for the resulting position
                prefetch(tt.first_entry(pos.key_after(move)));

                ss->currentMove = move;
                ss->continuationHistory =
                  &this
                     ->continuationHistory[ss->inCheck][true][pos.moved_piece(move)][move.to_sq()];
                ss->continuationCorrectionHistory =
                  &this->continuationCorrectionHistory[pos.moved_piece(move)][move.to_sq()];

                thisThread->nodes.fetch_add(1, std::memory_order_relaxed);
                pos.do_move(move, st);

                // Perform a preliminary qsearch to verify that the move holds
                value = -qsearch<NonPV>(pos, ss + 1, -probCutBeta, -probCutBeta + 1);

                // If the qsearch held, perform the regular search
                if (value >= probCutBeta)
                    value = -search<NonPV>(pos, ss + 1, -probCutBeta, -probCutBeta + 1, depth - 4,
                                           !cutNode);

                pos.undo_move(move);

                if (value >= probCutBeta)
                {
                    thisThread->captureHistory[movedPiece][move.to_sq()][type_of(captured)]
                      << stat_bonus(depth - 2);

                    // Save ProbCut data into transposition table
                    ttWriter.write(posKey, value_to_tt(value, ss->ply), ss->ttPv, BOUND_LOWER,
                                   depth - 3, move, unadjustedStaticEval, tt.generation());
                    return std::abs(value) < VALUE_TB_WIN_IN_MAX_PLY ? value - (probCutBeta - beta)
                                                                     : value;
                }
            }

            Eval::NNUE::hint_common_parent_position(pos, networks[numaAccessToken], refreshTable);
        }
    }        // End early Pruning from Crystal by Shashin
moves_loop:  // When in check, search starts here

    // Step 12. A small Probcut idea (~4 Elo)
    probCutBeta = beta + 379;
    //from Crystal begin
    bool probCutFromCrystal =
      (((this->shashinWinProbabilityRange == SHASHIN_POSITION_HIGH_TAL)
        || (this->shashinWinProbabilityRange == SHASHIN_POSITION_CAPABLANCA)));
    if ((ttData.bound & BOUND_LOWER) && ttData.depth >= depth - 4 && ttData.value >= probCutBeta
        && ((probCutFromCrystal
             && (ss->inCheck && !gameCycle && !kingDanger && !(ss - 1)->secondaryLine
                 && !(thisThread->nmpGuard && nullParity) && !(thisThread->nmpGuardV && nullParity)
                 && ttCapture && ourMove && !excludedMove && abs(ttData.value) < VALUE_MAX_EVAL
                 && abs(beta) < VALUE_MAX_EVAL))
            || (!probCutFromCrystal && std::abs(beta) < VALUE_TB_WIN_IN_MAX_PLY
                && std::abs(ttData.value) < VALUE_TB_WIN_IN_MAX_PLY)))
        //from Crystal end
        return probCutBeta;

    const PieceToHistory* contHist[] = {(ss - 1)->continuationHistory,
                                        (ss - 2)->continuationHistory,
                                        (ss - 3)->continuationHistory,
                                        (ss - 4)->continuationHistory,
                                        nullptr,
                                        (ss - 6)->continuationHistory};


    MovePicker mp(pos, ttData.move, depth, &thisThread->mainHistory, &thisThread->lowPlyHistory,
                  &thisThread->captureHistory, contHist, &thisThread->pawnHistory, ss->ply);

    value = bestValue;

    int  moveCount        = 0;
    bool moveCountPruning = false;

    //from Crystal begin
    // Indicate PvNodes that will probably fail low if the node was searched
    // at a depth equal to or greater than the current depth, and the result
    // of this search was a fail low.
    bool lmrCapture = cutNode && (ss - 1)->moveCount > 1;

    bool kingDangerThem = ourMove && pos.king_danger(~us);

    bool lmPrunable = (!ourMove || ss->ply > 6 || (ss - 1)->moveCount > 1 || (ss - 3)->moveCount > 1
                       || (ss - 5)->moveCount > 1);

    bool allowLMR =
      depth > 1 && !gameCycle && (!kingDangerThem || ss->ply > 6) && (!PvNode || ss->ply > 1);

    bool doLMP = !PvNode && (lmPrunable || ss->ply > 2) && pos.non_pawn_material(us);
    //from Crystal end

    // Step 13. Loop through all pseudo-legal moves until no moves remain
    // or a beta cutoff occurs.
    while ((move = mp.next_move(moveCountPruning)) != Move::none())
    {
        assert(move.is_ok());

        if (move == excludedMove)
            continue;

        // Check for legality
        if (!pos.legal(move))
            continue;

        // At root obey the "searchmoves" option and skip moves not listed in Root
        // Move List. In MultiPV mode we also skip PV moves that have been already
        // searched and those of lower "TB rank" if we are in a TB root position.
        if (rootNode
            && !std::count(thisThread->rootMoves.begin() + thisThread->pvIdx,
                           thisThread->rootMoves.begin() + thisThread->pvLast, move))
            continue;

        ss->moveCount = ++moveCount;

        if (rootNode && is_mainthread() && nodes > 10000000)
        {
            main_manager()->updates.onIter(
              {depth, UCIEngine::move(move, pos.is_chess960()), moveCount + thisThread->pvIdx});
        }
        if (PvNode)
            (ss + 1)->pv = nullptr;

        extension  = 0;
        capture    = pos.capture_stage(move);
        movedPiece = pos.moved_piece(move);
        givesCheck = pos.gives_check(move);
        // from Crystal begin Yes HMT C no HT TC MHP HP

        isMate = false;

        // This tracks all of our possible responses to our opponent's best moves outside of the PV.
        // The reasoning here is that while we look for flaws in the PV, we must otherwise find an improvement
        // in a secondary root move in order to change the PV. Such an improvement must occur on the path of
        // our opponent's best moves or else it is meaningless.
        ss->secondaryLine =
          ((rootNode && moveCount > 1)
           || (!ourMove && (ss - 1)->secondaryLine && !excludedMove && moveCount == 1)
           || (ourMove && (ss - 1)->secondaryLine));
        ss->mainLine = ((rootNode && moveCount == 1) || (!ourMove && (ss - 1)->mainLine)
                        || (ourMove && (ss - 1)->mainLine && moveCount == 1 && !excludedMove));
        if (this->shashinWinProbabilityRange == SHASHIN_POSITION_MIDDLE_HIGH_TAL)
        {
            if (givesCheck)
            {
                pos.do_move(move, st, givesCheck);
                isMate = MoveList<LEGAL>(pos).size() == 0;
                pos.undo_move(move);
            }

            if (isMate)
            {
                ss->currentMove = move;
                ss->continuationHistory =
                  &thisThread->continuationHistory[ss->inCheck][capture][movedPiece][move.to_sq()];
                value = mate_in(ss->ply + 1);

                if (PvNode && (moveCount == 1 || value > alpha))
                {
                    (ss + 1)->pv    = pv;
                    (ss + 1)->pv[0] = Move::none();
                }
            }
            else
            {
                // If we already have a mate in 1 from the current position and the current
                // move isn't a mate in 1, continue as there is no point to searching it.
                if (bestValue >= mate_in(ss->ply + 1))
                    continue;
            }
        }
        // end from Crystal
        // Calculate new depth for this move
        newDepth = depth - 1;

        int delta = beta - alpha;

        Depth r = reduction(improving, depth, moveCount, delta);
        // full threads patch begin
        if (thisThread->fullSearch)
        {
            goto skipExtensionAndPruning;
        }
        // full threads patch end

        // Step 14. Pruning at shallow depth (~120 Elo).
        // Depth conditions are important for mate finding.
        if (  // from Crystal
          (((this->shashinWinProbabilityRange != SHASHIN_POSITION_HIGH_TAL) && !rootNode
            && pos.non_pawn_material(us) && bestValue > VALUE_TB_LOSS_IN_MAX_PLY)
           || ((this->shashinWinProbabilityRange == SHASHIN_POSITION_HIGH_TAL) && doLMP
               && bestValue > VALUE_MATED_IN_MAX_PLY))
          // end from Crystal
        )
        {
            // Skip quiet moves if movecount exceeds our FutilityMoveCount threshold (~8 Elo)
            moveCountPruning = moveCount >= futility_move_count(improving, depth);
            //from Crystal begin yes HT MHT C NO TC HP MHP
            if (lmPrunable || (this->shashinWinProbabilityRange != SHASHIN_POSITION_HIGH_TAL))
            {
                // Reduced depth of the next LMR search
                int lmrDepth = newDepth - r;

                if (capture || givesCheck)
                {
                    Piece capturedPiece = pos.piece_on(move.to_sq());
                    int   captHist =
                      thisThread->captureHistory[movedPiece][move.to_sq()][type_of(capturedPiece)];
                    // Futility pruning for captures (~2 Elo)
                    if (!givesCheck && lmrDepth < 7 && !ss->inCheck)
                    {
                        Value futilityValue = ss->staticEval + 300 + 238 * lmrDepth
                                            + PieceValue[capturedPiece] + captHist / 7;
                        if (futilityValue <= alpha)
                            continue;
                    }

                    // SEE based pruning for captures and checks (~11 Elo)
                    int seeHist = std::clamp(captHist / 32, -159 * depth, 160 * depth);
                    if (!pos.see_ge(move, -167 * depth - seeHist))
                        continue;
                }
                else
                {
                    int history =
                      (*contHist[0])[movedPiece][move.to_sq()]
                      + (*contHist[1])[movedPiece][move.to_sq()]
                      + thisThread
                          ->pawnHistory[pawn_structure_index(pos)][movedPiece][move.to_sq()];

                    // Continuation history based pruning (~2 Elo)
                    if (history < -4071 * depth)
                        continue;

                    history += 2 * thisThread->mainHistory[us][move.from_to()];

                    lmrDepth += history / 3653;

                    Value futilityValue = ss->staticEval
                                        + (bestValue < ss->staticEval - 51 ? 145 : 49)
                                        + 144 * lmrDepth;

                    // Futility pruning: parent node (~13 Elo)
                    //begin from Crystal
                    bool futilityPruningParentNodeFromCrystal =
                      ((this->shashinWinProbabilityRange != SHASHIN_POSITION_HIGH_TAL)
                       && (this->shashinWinProbabilityRange
                           != SHASHIN_POSITION_CAPABLANCA_PETROSIAN));
                    if (!ss->inCheck)
                    {
                        if (!futilityPruningParentNodeFromCrystal)
                        {
                            // Futility pruning: parent node (~13 Elo)
                            if (lmrDepth < 12 && futilityValue <= alpha)
                            {
                                if (bestValue <= futilityValue
                                    && std::abs(bestValue) < VALUE_TB_WIN_IN_MAX_PLY
                                    && futilityValue < VALUE_TB_WIN_IN_MAX_PLY)
                                    bestValue = futilityValue;
                                continue;
                            }
                        }
                        else
                        {
                            // Futility pruning: parent node (~13 Elo)
                            if (lmrDepth < (5 * (2 - (ourMove && (ss - 1)->secondaryLine)))
                                && history < 20500 - 3875 * (depth - 1) && futilityValue <= alpha)
                                continue;
                        }
                    }
                    //end from Crystal

                    lmrDepth = std::max(lmrDepth, 0);

                    // Prune moves with negative SEE (~4 Elo)
                    if (!pos.see_ge(move, -24 * lmrDepth * lmrDepth))
                        continue;
                }
            }
            //from Crystal end
        }

        // Step 15. Extensions (~100 Elo)
        // We take care to not overdo to avoid search getting stuck.
        if (ss->ply < thisThread->rootDepth * 2)
        {
            // Singular extension search (~76 Elo, ~170 nElo). If all moves but one
            // fail low on a search of (alpha-s, beta-s), and just one fails high on
            // (alpha, beta), then that move is singular and should be extended. To
            // verify this we do a reduced search on the position excluding the ttMove
            // and if the result is lower than ttValue minus a margin, then we will
            // extend the ttMove. Recursive singular search is avoided.

            // Note: the depth margin and singularBeta margin are known for having
            // non-linear scaling. Their values are optimized to time controls of
            // 180+1.8 and longer so changing them requires tests at these types of
            // time controls. Generally, higher singularBeta (i.e closer to ttValue)
            // and lower extension margins scale well.

            if (!rootNode && move == ttData.move && !excludedMove
                && depth >= 4 - (thisThread->completedDepth > 36) + ss->ttPv
                && std::abs(ttData.value) < VALUE_TB_WIN_IN_MAX_PLY && (ttData.bound & BOUND_LOWER)
                && ttData.depth >= depth - 3)
            {
                Value singularBeta  = ttData.value - (54 + 77 * (ss->ttPv && !PvNode)) * depth / 64;
                Depth singularDepth = newDepth / 2;

                ss->excludedMove = move;
                value =
                  search<NonPV>(pos, ss, singularBeta - 1, singularBeta, singularDepth, cutNode);
                ss->excludedMove = Move::none();

                if (value < singularBeta)
                {
                    int doubleMargin = 262 * PvNode - 204 * !ttCapture;
                    int tripleMargin = 97 + 266 * PvNode - 255 * !ttCapture + 94 * ss->ttPv;

                    extension = 1 + (value < singularBeta - doubleMargin)
                              + (value < singularBeta - tripleMargin);

                    depth += ((!PvNode) && (depth < 14));
                }

                // Multi-cut pruning
                // Our ttMove is assumed to fail high based on the bound of the TT entry,
                // and if after excluding the ttMove with a reduced search we fail high
                // over the original beta, we assume this expected cut-node is not
                // singular (multiple moves fail high), and we can prune the whole
                // subtree by returning a softbound.
                else if (value >= beta && std::abs(value) < VALUE_TB_WIN_IN_MAX_PLY)
                    return value;

                // Negative extensions
                // If other moves failed high over (ttValue - margin) without the
                // ttMove on a reduced search, but we cannot do multi-cut because
                // (ttValue - margin) is lower than the original beta, we do not know
                // if the ttMove is singular or can do a multi-cut, so we reduce the
                // ttMove in favor of other moves based on some conditions:

                // If the ttMove is assumed to fail high over current beta (~7 Elo)
                else if (ttData.value >= beta)
                    extension = -3;

                // If we are on a cutNode but the ttMove is not assumed to fail high
                // over current beta (~1 Elo)
                else if (cutNode)
                    extension = -2;
            }

            // Extension for capturing the previous moved piece (~1 Elo at LTC)
            else if (PvNode && move.to_sq() == prevSq
                     && thisThread->captureHistory[movedPiece][move.to_sq()]
                                                  [type_of(pos.piece_on(move.to_sq()))]
                          > 4299)
                extension = 1;
        }

        // Add extension to new depth
        newDepth += extension;

skipExtensionAndPruning:  // full threads search patch

        // Speculative prefetch as early as possible
        prefetch(tt.first_entry(pos.key_after(move)));

        // Update the current move (this must be done after singular extension search)
        ss->currentMove = move;
        ss->continuationHistory =
          &thisThread->continuationHistory[ss->inCheck][capture][movedPiece][move.to_sq()];
        ss->continuationCorrectionHistory =
          &thisThread->continuationCorrectionHistory[movedPiece][move.to_sq()];
        uint64_t nodeCount = rootNode ? uint64_t(nodes) : 0;

        // Step 16. Make the move
        thisThread->nodes.fetch_add(1, std::memory_order_relaxed);
        pos.do_move(move, st, givesCheck);
        bool doLMRStep = !(thisThread->fullSearch);  // full threads patch
        // These reduction adjustments have proven non-linear scaling.
        // They are optimized to time controls of 180 + 1.8 and longer,
        // so changing them or adding conditions that are similar requires
        // tests at these types of time controls.

        // Decrease reduction if position is or has been on the PV (~7 Elo)
        if (ss->ttPv)
            r -= 1 + (ttData.value > alpha) + (ttData.depth >= depth);

        // Decrease reduction for PvNodes (~0 Elo on STC, ~2 Elo on LTC)
        if (PvNode)
            r--;

        // These reduction adjustments have no proven non-linear scaling

        // Increase reduction for cut nodes (~4 Elo)
        if (cutNode)
            r += 2 - (ttData.depth >= depth && ss->ttPv);

        // Increase reduction if ttMove is a capture but the current move is not a capture (~3 Elo)
        if (ttCapture && !capture)
            r += 1 + (depth < 8);

        // Increase reduction if next ply has a lot of fail high (~5 Elo)
        if ((ss + 1)->cutoffCnt > 3)
            r += 1 + allNode;

        // For first picked move (ttMove) reduce reduction (~3 Elo)
        else if (move == ttData.move)
            r -= 2;

        ss->statScore = 2 * thisThread->mainHistory[us][move.from_to()]
                      + (*contHist[0])[movedPiece][move.to_sq()]
                      + (*contHist[1])[movedPiece][move.to_sq()] - 4410;

        // Decrease/increase reduction for moves with a good/bad history (~8 Elo)
        r -= ss->statScore / 11016;

        // Step 17. Late moves reduction / extension (LMR, ~117 Elo)
        //from Crystal begin
        bool lmrFromCrystal = (this->shashinWinProbabilityRange == SHASHIN_POSITION_CAPABLANCA_TAL);
        if (doLMRStep && moveCount > sibs &&  //full threads patch + kelly
            ((lmrFromCrystal && (allowLMR && moveCount > 1 && (!capture || lmrCapture)))
             || ((!lmrFromCrystal) && (depth >= 2 && moveCount > 1))))
        //from Crystal end
        {
            // In general we want to cap the LMR depth search at newDepth, but when
            // reduction is negative, we allow this move a limited search extension
            // beyond the first move depth.
            // To prevent problems when the max value is less than the min value,
            // std::clamp has been replaced by a more robust implementation.
            Depth d = std::max(1, std::min(newDepth - r, newDepth + !allNode));

            value = -search<NonPV>(pos, ss + 1, -(alpha + 1), -alpha, d, true);

            // Do a full-depth search when reduced LMR search fails high
            if (value > alpha && d < newDepth)
            {
                // Adjust full-depth search based on LMR results - if the result was
                // good enough search deeper, if it was bad enough search shallower.
                const bool doDeeperSearch    = value > (bestValue + 38 + 2 * newDepth);  // (~1 Elo)
                const bool doShallowerSearch = value < bestValue + 8;                    // (~2 Elo)

                newDepth += doDeeperSearch - doShallowerSearch;

                if (newDepth > d)
                    value = -search<NonPV>(pos, ss + 1, -(alpha + 1), -alpha, newDepth, !cutNode);

                // Post LMR continuation history updates (~1 Elo)
                int bonus = value >= beta ? 3 * stat_bonus(newDepth) : -stat_malus(newDepth);
                update_continuation_histories(ss, movedPiece, move.to_sq(), bonus);
            }
        }

        // Step 18. Full-depth search when LMR is skipped
        else if (!PvNode || moveCount > 1)
        {
            // Increase reduction if ttMove is not present (~6 Elo)
            if (!ttData.move)
                r += 2;

            // Note that if expected reduction is high, we reduce search depth by 1 here (~9 Elo)
            value = -search<NonPV>(pos, ss + 1, -(alpha + 1), -alpha, newDepth - (r > 3), !cutNode);
        }

        // For PV nodes only, do a full PV search on the first move or after a fail high,
        // otherwise let the parent node fail low with value <= alpha and try another move.
        if (PvNode && (moveCount == 1 || value > alpha))
        {
            (ss + 1)->pv    = pv;
            (ss + 1)->pv[0] = Move::none();

            // Extend move from transposition table if we are about to dive into qsearch.
            if (move == ttData.move && ss->ply <= thisThread->rootDepth * 2)
                newDepth = std::max(newDepth, 1);

            value = -search<PV>(pos, ss + 1, -beta, -alpha, newDepth, false);
        }

        // Step 19. Undo move
        pos.undo_move(move);

        assert(value > -VALUE_INFINITE && value < VALUE_INFINITE);

        // Step 20. Check for a new best move
        // Finished searching the move. If a stop occurred, the return value of
        // the search cannot be trusted, and we return immediately without updating
        // best move, principal variation nor transposition table.
        if (threads.stop.load(std::memory_order_relaxed))
            return VALUE_ZERO;

        if (rootNode)
        {
            RootMove& rm =
              *std::find(thisThread->rootMoves.begin(), thisThread->rootMoves.end(), move);

            rm.effort += nodes - nodeCount;

            rm.averageScore =
              rm.averageScore != -VALUE_INFINITE ? (value + rm.averageScore) / 2 : value;

            rm.meanSquaredScore = rm.meanSquaredScore != -VALUE_INFINITE * VALUE_INFINITE
                                  ? (value * std::abs(value) + rm.meanSquaredScore) / 2
                                  : value * std::abs(value);

            // PV move or new best move?
            if (moveCount == 1 || value > alpha)
            {
                rm.score = rm.uciScore = value;
                rm.selDepth            = thisThread->selDepth;
                rm.scoreLowerbound = rm.scoreUpperbound = false;

                if (value >= beta)
                {
                    rm.scoreLowerbound = true;
                    rm.uciScore        = beta;
                }
                else if (value <= alpha)
                {
                    rm.scoreUpperbound = true;
                    rm.uciScore        = alpha;
                }

                rm.pv.resize(1);

                assert((ss + 1)->pv);

                for (Move* m = (ss + 1)->pv; *m != Move::none(); ++m)
                    rm.pv.push_back(*m);

                // We record how often the best move has been changed in each iteration.
                // This information is used for time management. In MultiPV mode,
                // we must take care to only do this for the first PV line.
                if (moveCount > 1 && !thisThread->pvIdx)
                    ++thisThread->bestMoveChanges;
            }
            else
                // All other moves but the PV, are set to the lowest value: this
                // is not a problem when sorting because the sort is stable and the
                // move position in the list is preserved - just the PV is pushed up.
                rm.score = -VALUE_INFINITE;
        }

        // In case we have an alternative move equal in eval to the current bestmove,
        // promote it to bestmove by pretending it just exceeds alpha (but not beta).
        int inc =
          (value == bestValue && (int(nodes) & 15) == 0 && ss->ply + 2 >= thisThread->rootDepth
           && std::abs(value) + 1 < VALUE_TB_WIN_IN_MAX_PLY);

        if (value + inc > bestValue)
        {
            bestValue = value;

            if (value + inc > alpha)
            {
                bestMove = move;

                if (PvNode && !rootNode)  // Update pv even in fail-high case
                    update_pv(ss->pv, move, (ss + 1)->pv);

                if (value >= beta)
                {
                    ss->cutoffCnt += !ttData.move + (extension < 2);
                    assert(value >= beta);  // Fail high
                    break;
                }
                else
                {
                    // Reduce other moves if we have found at least one score improvement (~2 Elo)
                    if (depth > 2 && depth < 14
                        && ((!gameCycle)
                            || (this->shashinWinProbabilityRange
                                != SHASHIN_POSITION_CAPABLANCA))  //from Crystal
                        && std::abs(value) < VALUE_TB_WIN_IN_MAX_PLY)
                        depth -= 2;

                    assert(depth > 0);
                    alpha = value;  // Update alpha! Always alpha < beta
                }
            }
        }

        // If the move is worse than some previously searched move,
        // remember it, to update its stats later.
        if (move != bestMove && moveCount <= 32)
        {
            if (capture)
                capturesSearched.push_back(move);
            else
                quietsSearched.push_back(move);
        }
    }

    // Step 21. Check for mate and stalemate
    // All legal moves have been searched and if there are no legal moves, it
    // must be a mate or a stalemate. If we are in a singular extension search then
    // return a fail low score.

    assert(moveCount || !ss->inCheck || excludedMove || !MoveList<LEGAL>(pos).size());

    // Adjust best value for fail high cases at non-pv nodes
    if (!PvNode && bestValue >= beta && std::abs(bestValue) < VALUE_TB_WIN_IN_MAX_PLY
        && std::abs(beta) < VALUE_TB_WIN_IN_MAX_PLY && std::abs(alpha) < VALUE_TB_WIN_IN_MAX_PLY)
        bestValue = (bestValue * depth + beta) / (depth + 1);

    if (!moveCount)
        bestValue = excludedMove ? alpha : ss->inCheck ? mated_in(ss->ply) : VALUE_DRAW;

    // If there is a move that produces search value greater than alpha,
    // we update the stats of searched moves.
    else if (bestMove)
        update_all_stats(pos, ss, *this, bestMove, prevSq, quietsSearched, capturesSearched, depth);

    // Bonus for prior countermove that caused the fail low
    else if (!priorCapture && prevSq != SQ_NONE)
    {
        int bonus = (118 * (depth > 5) + 38 * !allNode + 169 * ((ss - 1)->moveCount > 8)
                     + 116 * (!ss->inCheck && bestValue <= ss->staticEval - 101)
                     + 133 * (!(ss - 1)->inCheck && bestValue <= -(ss - 1)->staticEval - 92));

        // Proportional to "how much damage we have to undo"
        bonus += std::min(-(ss - 1)->statScore / 102, 305);

        bonus = std::max(bonus, 0);

        update_continuation_histories(ss - 1, pos.piece_on(prevSq), prevSq,
                                      stat_bonus(depth) * bonus / 107);
        thisThread->mainHistory[~us][((ss - 1)->currentMove).from_to()]
          << stat_bonus(depth) * bonus / 174;


        if (type_of(pos.piece_on(prevSq)) != PAWN && ((ss - 1)->currentMove).type_of() != PROMOTION)
            thisThread->pawnHistory[pawn_structure_index(pos)][pos.piece_on(prevSq)][prevSq]
              << stat_bonus(depth) * bonus / 25;
    }

    // Bonus when search fails low and there is a TT move
    else if (ttData.move && !allNode)
        thisThread->mainHistory[us][ttData.move.from_to()] << stat_bonus(depth) / 4;

    if (PvNode
        && (this->shashinWinProbabilityRange
            != SHASHIN_POSITION_CAPABLANCA))  //From Crystal yes C No TC
        bestValue = std::min(bestValue, maxValue);

    // If no good move is found and the previous position was ttPv, then the previous
    // opponent move is probably good and the new position is added to the search tree. (~7 Elo)
    if (bestValue <= alpha)
        ss->ttPv = ss->ttPv || ((ss - 1)->ttPv && depth > 3);

    // Write gathered information in transposition table. Note that the
    // static evaluation is saved as it was before correction history.
    if (!excludedMove && !(rootNode && thisThread->pvIdx))
        ttWriter.write(posKey, value_to_tt(bestValue, ss->ply), ss->ttPv,
                       bestValue >= beta    ? BOUND_LOWER
                       : PvNode && bestMove ? BOUND_EXACT
                                            : BOUND_UPPER,
                       depth, bestMove, unadjustedStaticEval, tt.generation());

    // Adjust correction history
    if (!ss->inCheck && (!bestMove || !pos.capture(bestMove))
        && !(bestValue >= beta && bestValue <= ss->staticEval)
        && !(!bestMove && bestValue >= ss->staticEval))
    {
        const auto m = (ss - 1)->currentMove;

        auto bonus = std::clamp(int(bestValue - ss->staticEval) * depth / 8,
                                -CORRECTION_HISTORY_LIMIT / 4, CORRECTION_HISTORY_LIMIT / 4);
        thisThread->pawnCorrectionHistory[us][pawn_structure_index<Correction>(pos)]
          << bonus * 101 / 128;
        thisThread->materialCorrectionHistory[us][material_index(pos)] << bonus * 99 / 128;
        thisThread->majorPieceCorrectionHistory[us][major_piece_index(pos)] << bonus * 157 / 128;
        thisThread->minorPieceCorrectionHistory[us][minor_piece_index(pos)] << bonus * 153 / 128;
        thisThread->nonPawnCorrectionHistory[WHITE][us][non_pawn_index<WHITE>(pos)]
          << bonus * 123 / 128;
        thisThread->nonPawnCorrectionHistory[BLACK][us][non_pawn_index<BLACK>(pos)]
          << bonus * 165 / 128;

        if (m.is_ok())
            (*(ss - 2)->continuationCorrectionHistory)[pos.piece_on(m.to_sq())][m.to_sq()] << bonus;
    }

    assert(bestValue > -VALUE_INFINITE && bestValue < VALUE_INFINITE);

    return bestValue;
}


// Quiescence search function, which is called by the main search function with
// depth zero, or recursively with further decreasing depth. With depth <= 0, we
// "should" be using static eval only, but tactical moves may confuse the static eval.
// To fight this horizon effect, we implement this qsearch of tactical moves (~155 Elo).
// See https://www.chessprogramming.org/Horizon_Effect
// and https://www.chessprogramming.org/Quiescence_Search
template<NodeType nodeType>
Value Search::Worker::qsearch(Position& pos, Stack* ss, Value alpha, Value beta) {

    static_assert(nodeType != Root);
    constexpr bool PvNode = nodeType == PV;

    assert(alpha >= -VALUE_INFINITE && alpha < beta && beta <= VALUE_INFINITE);
    assert(PvNode || (alpha == beta - 1));

    // Check if we have an upcoming move that draws by repetition (~1 Elo)
    if (alpha < VALUE_DRAW && pos.upcoming_repetition(ss->ply))
    {
        alpha = value_draw(this->nodes);
        if (alpha >= beta)
            return alpha;
    }

    Move      pv[MAX_PLY + 1];
    StateInfo st;
    ASSERT_ALIGNED(&st, Eval::NNUE::CacheLineSize);

    Key   posKey;
    Move  move, bestMove;
    Value bestValue, value, futilityBase;
    bool  pvHit, givesCheck, capture, gameCycle = false;  // from Crystal
    int   moveCount;
    Color us = pos.side_to_move();

    // Step 1. Initialize node
    if (PvNode)
    {
        (ss + 1)->pv = pv;
        ss->pv[0]    = Move::none();
    }

    Worker* thisThread = this;
    bestMove           = Move::none();
    ss->inCheck        = pos.checkers();
    moveCount          = 0;

    // Used to send selDepth info to GUI (selDepth counts from 1, ply from 0)
    if (PvNode && thisThread->selDepth < ss->ply + 1)
        thisThread->selDepth = ss->ply + 1;

    // from Crystal begin
    if (pos.upcoming_repetition(ss->ply))
    {
        gameCycle = true;
    }
    // from Crystal end

    // Step 2. Check for an immediate draw or maximum ply reached
    if (pos.is_draw(ss->ply) || ss->ply >= MAX_PLY)
        return (ss->ply >= MAX_PLY && !ss->inCheck)
               ? evaluate(networks[numaAccessToken], pos, refreshTable, thisThread->optimism[us])
               : VALUE_DRAW;

    assert(0 <= ss->ply && ss->ply < MAX_PLY);

    // Step 3. Transposition table lookup
    posKey                         = pos.key();
    auto [ttHit, ttData, ttWriter] = tt.probe(posKey);
    // Need further processing of the saved data
    ss->ttHit    = ttHit;
    ttData.move  = ttHit ? ttData.move : Move::none();
    ttData.value = ttHit ? value_from_tt(ttData.value, ss->ply, pos.rule50_count()) : VALUE_NONE;
    pvHit        = ttHit && ttData.is_pv;

    // At non-PV nodes we check for an early TT cutoff
    //from Crystal begin
    bool earlyTTCutOffFromCrystal = (this->shashinWinProbabilityRange == SHASHIN_POSITION_HIGH_TAL);
    if (!PvNode
        && ttData.value != VALUE_NONE  // Can happen when !ttHit or when access race in probe()
        && (ttData.bound & (ttData.value >= beta ? BOUND_LOWER : BOUND_UPPER))
        && (ttData.depth >= DEPTH_QS)
        && (((!earlyTTCutOffFromCrystal)
             || ((earlyTTCutOffFromCrystal) && (ss->ttHit && !gameCycle)))))
        return ttData.value;
    //from Crystal end

    // Step 4. Static evaluation of the position
    Value unadjustedStaticEval = VALUE_NONE;
    if (ss->inCheck)
        bestValue = futilityBase = -VALUE_INFINITE;
    else
    {
        if (ss->ttHit)
        {
            // Never assume anything about values stored in TT
            unadjustedStaticEval = ttData.eval;
            if (unadjustedStaticEval == VALUE_NONE)
                unadjustedStaticEval =
                  evaluate(networks[numaAccessToken], pos, refreshTable, thisThread->optimism[us]);
            ss->staticEval = bestValue =
              to_corrected_static_eval(unadjustedStaticEval, *thisThread, pos, ss);

            // ttValue can be used as a better position evaluation (~13 Elo)
            if (std::abs(ttData.value) < VALUE_TB_WIN_IN_MAX_PLY
                && (ttData.bound & (ttData.value > bestValue ? BOUND_LOWER : BOUND_UPPER)))
                bestValue = ttData.value;
        }
        else
        {
            // In case of null move search, use previous static eval with opposite sign
            unadjustedStaticEval =
              (ss - 1)->currentMove != Move::null()
                ? evaluate(networks[numaAccessToken], pos, refreshTable, thisThread->optimism[us])
                : -(ss - 1)->staticEval;
            ss->staticEval = bestValue =
              to_corrected_static_eval(unadjustedStaticEval, *thisThread, pos, ss);
        }

        // Stand pat. Return immediately if static value is at least beta
        if (bestValue >= beta)
        {
            if (std::abs(bestValue) < VALUE_TB_WIN_IN_MAX_PLY)
                bestValue = (3 * bestValue + beta) / 4;
            if (!ss->ttHit)
                ttWriter.write(posKey, value_to_tt(bestValue, ss->ply), false, BOUND_LOWER,
                               DEPTH_UNSEARCHED, Move::none(), unadjustedStaticEval,
                               tt.generation());
            return bestValue;
        }

        if (bestValue > alpha)
            alpha = bestValue;

        futilityBase = ss->staticEval + 280;
    }

    const PieceToHistory* contHist[] = {(ss - 1)->continuationHistory,
                                        (ss - 2)->continuationHistory};

    Square prevSq = ((ss - 1)->currentMove).is_ok() ? ((ss - 1)->currentMove).to_sq() : SQ_NONE;

    // Initialize a MovePicker object for the current position, and prepare to search
    // the moves. We presently use two stages of move generator in quiescence search:
    // captures, or evasions only when in check.
    MovePicker mp(pos, ttData.move, DEPTH_QS, &thisThread->mainHistory, &thisThread->lowPlyHistory,
                  &thisThread->captureHistory, contHist, &thisThread->pawnHistory, ss->ply);

    // Step 5. Loop through all pseudo-legal moves until no moves remain or a beta
    // cutoff occurs.
    while ((move = mp.next_move()) != Move::none())
    {
        assert(move.is_ok());

        if (!pos.legal(move))
            continue;

        givesCheck = pos.gives_check(move);
        capture    = pos.capture_stage(move);

        moveCount++;

        // Step 6. Pruning
        if (bestValue > VALUE_TB_LOSS_IN_MAX_PLY && pos.non_pawn_material(us))
        {
            // Futility pruning and moveCount pruning (~10 Elo)
            if (!givesCheck && move.to_sq() != prevSq && futilityBase > VALUE_TB_LOSS_IN_MAX_PLY
                && move.type_of() != PROMOTION)
            {
                if (moveCount > 2
                                  + ((this->shashinWinProbabilityRange != SHASHIN_POSITION_HIGH_TAL)
                                       ? 0
                                       : PvNode))  //from Crystal yes HT CP no LT MHP HP
                    continue;

                Value futilityValue = futilityBase + PieceValue[pos.piece_on(move.to_sq())];

                // If static eval + value of piece we are going to capture is
                // much lower than alpha, we can prune this move. (~2 Elo)
                if (futilityValue <= alpha)
                {
                    bestValue = std::max(bestValue, futilityValue);
                    continue;
                }

                // If static exchange evaluation is low enough
                // we can prune this move. (~2 Elo)
                if (!pos.see_ge(move, alpha - futilityBase))
                {
                    bestValue = std::min(alpha, futilityBase);
                    continue;
                }
            }

            // Continuation history based pruning (~3 Elo)
            if (!capture
                && (*contHist[0])[pos.moved_piece(move)][move.to_sq()]
                       + (*contHist[1])[pos.moved_piece(move)][move.to_sq()]
                       + thisThread->pawnHistory[pawn_structure_index(pos)][pos.moved_piece(move)]
                                                [move.to_sq()]
                     <= 5036)
                continue;

            // Do not search moves with bad enough SEE values (~5 Elo)
            if (!pos.see_ge(move, -82))
                continue;
        }

        // Speculative prefetch as early as possible
        prefetch(tt.first_entry(pos.key_after(move)));

        // Update the current move
        ss->currentMove = move;
        ss->continuationHistory =
          &thisThread
             ->continuationHistory[ss->inCheck][capture][pos.moved_piece(move)][move.to_sq()];
        ss->continuationCorrectionHistory =
          &thisThread->continuationCorrectionHistory[pos.moved_piece(move)][move.to_sq()];

        // Step 7. Make and search the move
        thisThread->nodes.fetch_add(1, std::memory_order_relaxed);
        pos.do_move(move, st, givesCheck);
        value = -qsearch<nodeType>(pos, ss + 1, -beta, -alpha);
        pos.undo_move(move);

        assert(value > -VALUE_INFINITE && value < VALUE_INFINITE);

        // Step 8. Check for a new best move
        if (value > bestValue)
        {
            bestValue = value;

            if (value > alpha)
            {
                bestMove = move;

                if (PvNode)  // Update pv even in fail-high case
                    update_pv(ss->pv, move, (ss + 1)->pv);

                if (value < beta)  // Update alpha here!
                    alpha = value;
                else
                    break;  // Fail high
            }
        }
    }
    // new variety begin
    if (std::abs(bestValue) == VALUE_INFINITE)
    {
        variety = 0;
    }

    if (variety != 0)
    {
        Value       maxIncrement = (variety == 1) ? 13 : 309;
        static PRNG rng(now());
        if (((variety == 2) && (bestValue <= 309) && (bestValue >= -309))
            || ((variety == 1) && (bestValue <= 13) && (bestValue >= -13)))
        {
            int maxValidIncrement = maxIncrement - std::abs(bestValue);
            if (maxValidIncrement < 0)
            {
                maxValidIncrement = 0;
            }
            int increment = static_cast<int>(rng.rand<uint64_t>() % (maxValidIncrement + 1));
            bestValue += increment;
        }
    }
    // end new variety
    // Step 9. Check for mate
    // All legal moves have been searched. A special case: if we are
    // in check and no legal moves were found, it is checkmate.
    if (ss->inCheck && bestValue == -VALUE_INFINITE)
    {
        assert(!MoveList<LEGAL>(pos).size());
        return mated_in(ss->ply);  // Plies to mate from the root
    }

    if (std::abs(bestValue) < VALUE_TB_WIN_IN_MAX_PLY && bestValue >= beta)
        bestValue = (3 * bestValue + beta) / 4;

    // Save gathered info in transposition table. The static evaluation
    // is saved as it was before adjustment by correction history.
    ttWriter.write(posKey, value_to_tt(bestValue, ss->ply), pvHit,
                   bestValue >= beta ? BOUND_LOWER : BOUND_UPPER, DEPTH_QS, bestMove,
                   unadjustedStaticEval, tt.generation());

    assert(bestValue > -VALUE_INFINITE && bestValue < VALUE_INFINITE);

    return bestValue;
}

Depth Search::Worker::reduction(bool i, Depth d, int mn, int delta) const {
    int reductionScale = reductions[d] * reductions[mn];
    //mcts begin
    if (rootDelta != 0)
        return (reductionScale + 1239 - delta * 795 / rootDelta) / 1024
             + (!i && reductionScale > 1293);
    else  // avoid divide by zero error
        return (reductionScale + 1239 - delta * 795) / 1024 + (!i && reductionScale > 1341);
    //mcts end
}

// elapsed() returns the time elapsed since the search started. If the
// 'nodestime' option is enabled, it will return the count of nodes searched
// instead. This function is called to check whether the search should be
// stopped based on predefined thresholds like time limits or nodes searched.
//
// elapsed_time() returns the actual time elapsed since the start of the search.
// This function is intended for use only when printing PV outputs, and not used
// for making decisions within the search algorithm itself.
TimePoint Search::Worker::elapsed() const {
    return main_manager()->tm.elapsed([this]() { return threads.nodes_searched(); });
}

TimePoint Search::Worker::elapsed_time() const { return main_manager()->tm.elapsed_time(); }


namespace {
// Adjusts a mate or TB score from "plies to mate from the root" to
// "plies to mate from the current position". Standard scores are unchanged.
// The function is called before storing a value in the transposition table.
Value value_to_tt(Value v, int ply) {

    assert(v != VALUE_NONE);
    return v >= VALUE_TB_WIN_IN_MAX_PLY ? v + ply : v <= VALUE_TB_LOSS_IN_MAX_PLY ? v - ply : v;
}


// Inverse of value_to_tt(): it adjusts a mate or TB score from the transposition
// table (which refers to the plies to mate/be mated from current position) to
// "plies to mate/be mated (TB win/loss) from the root". However, to avoid
// potentially false mate or TB scores related to the 50 moves rule and the
// graph history interaction, we return the highest non-TB score instead.
Value value_from_tt(Value v, int ply, int r50c) {

    if (v == VALUE_NONE)
        return VALUE_NONE;

    // handle TB win or better
    if (v >= VALUE_TB_WIN_IN_MAX_PLY)
    {
        // Downgrade a potentially false mate score
        if (v >= VALUE_MATE_IN_MAX_PLY && VALUE_MATE - v > 100 - r50c)
            return VALUE_TB_WIN_IN_MAX_PLY - 1;

        // Downgrade a potentially false TB score.
        if (VALUE_TB - v > 100 - r50c)
            return VALUE_TB_WIN_IN_MAX_PLY - 1;

        return v - ply;
    }

    // handle TB loss or worse
    if (v <= VALUE_TB_LOSS_IN_MAX_PLY)
    {
        // Downgrade a potentially false mate score.
        if (v <= VALUE_MATED_IN_MAX_PLY && VALUE_MATE + v > 100 - r50c)
            return VALUE_TB_LOSS_IN_MAX_PLY + 1;

        // Downgrade a potentially false TB score.
        if (VALUE_TB + v > 100 - r50c)
            return VALUE_TB_LOSS_IN_MAX_PLY + 1;

        return v + ply;
    }

    return v;
}


// Adds current move and appends child pv[]
void update_pv(Move* pv, Move move, const Move* childPv) {

    for (*pv++ = move; childPv && *childPv != Move::none();)
        *pv++ = *childPv++;
    *pv = Move::none();
}


// Updates stats at the end of search() when a bestMove is found
void update_all_stats(const Position&      pos,
                      Stack*               ss,
                      Search::Worker&      workerThread,
                      Move                 bestMove,
                      Square               prevSq,
                      ValueList<Move, 32>& quietsSearched,
                      ValueList<Move, 32>& capturesSearched,
                      Depth                depth) {

    CapturePieceToHistory& captureHistory = workerThread.captureHistory;
    Piece                  moved_piece    = pos.moved_piece(bestMove);
    PieceType              captured;

    int quietMoveBonus = stat_bonus(depth);
    int quietMoveMalus = stat_malus(depth);

    if (!pos.capture_stage(bestMove))
    {
        update_quiet_histories(pos, ss, workerThread, bestMove, quietMoveBonus);

        // Decrease stats for all non-best quiet moves
        for (Move move : quietsSearched)
            update_quiet_histories(pos, ss, workerThread, move, -quietMoveMalus);
    }
    else
    {
        // Increase stats for the best move in case it was a capture move
        captured = type_of(pos.piece_on(bestMove.to_sq()));
        captureHistory[moved_piece][bestMove.to_sq()][captured] << quietMoveBonus;
    }

    // Extra penalty for a quiet early move that was not a TT move in
    // previous ply when it gets refuted.
    if (prevSq != SQ_NONE && ((ss - 1)->moveCount == 1 + (ss - 1)->ttHit) && !pos.captured_piece())
        update_continuation_histories(ss - 1, pos.piece_on(prevSq), prevSq, -quietMoveMalus);

    // Decrease stats for all non-best capture moves
    for (Move move : capturesSearched)
    {
        moved_piece = pos.moved_piece(move);
        captured    = type_of(pos.piece_on(move.to_sq()));
        captureHistory[moved_piece][move.to_sq()][captured] << -quietMoveMalus;
    }
}


// Updates histories of the move pairs formed by moves
// at ply -1, -2, -3, -4, and -6 with current move.
void update_continuation_histories(Stack* ss, Piece pc, Square to, int bonus) {

    bonus = bonus * 53 / 64;

    for (int i : {1, 2, 3, 4, 6})
    {
        // Only update the first 2 continuation histories if we are in check
        if (ss->inCheck && i > 2)
            break;
        if (((ss - i)->currentMove).is_ok())
            (*(ss - i)->continuationHistory)[pc][to] << bonus / (1 + (i == 3));
    }
}

// Updates move sorting heuristics

void update_quiet_histories(
  const Position& pos, Stack* ss, Search::Worker& workerThread, Move move, int bonus) {

    Color us = pos.side_to_move();
    workerThread.mainHistory[us][move.from_to()] << bonus;
    if (ss->ply < LOW_PLY_HISTORY_SIZE)
        workerThread.lowPlyHistory[ss->ply][move.from_to()] << bonus;

    update_continuation_histories(ss, pos.moved_piece(move), move.to_sq(), bonus);

    int pIndex = pawn_structure_index(pos);
    workerThread.pawnHistory[pIndex][pos.moved_piece(move)][move.to_sq()] << bonus / 2;
}

}
// no skill


// mcts begin
//  minimax_value() is a wrapper around the search() and qsearch() functions
//  used to compute the minimax evaluation of a position at the given depth,
//  from the point of view of the side to move. It does not compute PV nor
//  emit anything on the output stream. Note: you can call this function
//  with depth == DEPTH_ZERO to compute the quiescence value of the position.

Value Search::Worker::minimax_value(Position& pos, Search::Stack* ss, Depth depth) {

    //    threads.stopOnPonderhit = threads.stop = false;
    Value alpha = -VALUE_INFINITE;
    Value beta  = VALUE_INFINITE;
    Move  pv[MAX_PLY + 1];
    ss->pv = pv;

    /*   if (pos.should_debug())
      {
          debug << "Entering minimax_value() for the following position:" << std::endl;
          debug << pos << std::endl;
          hit_any_key();
      }*/
    Value value = search<PV>(pos, ss, alpha, beta, depth, false);

    // Have we found a "mate in x"?
    if (limits.mate && value >= VALUE_MATE_IN_MAX_PLY && VALUE_MATE - value <= 2 * limits.mate)
        threads.stop = true;

    /*    if (pos.should_debug())
      {
          debug << pos << std::endl;
          debug << "... exiting minimax_value() with value = " << value << std::endl;
          hit_any_key();
      }
    */
    return value;
}

// minimax_value() is a wrapper around the search() and qsearch() functions
// used to compute the minimax evaluation of a position at the given depth,
// from the point of view of the side to move. It does not compute PV nor
// emit anything on the output stream. Note: you can call this function
// with depth == DEPTH_ZERO to compute the quiescence value of the position.

Value Search::Worker::minimax_value(
  Position& pos, Search::Stack* ss, Depth depth, Value alpha, Value beta) {

    //    threads.stopOnPonderhit = threads.stop = false;
    //   alpha = -VALUE_INFINITE;
    //   beta = VALUE_INFINITE;
    Move pv[MAX_PLY + 1];
    ss->pv = pv;

    /*   if (pos.should_debug())
      {
          debug << "Entering minimax_value() for the following position:" << std::endl;
          debug << pos << std::endl;
          hit_any_key();
      }*/
    Value value = VALUE_ZERO;
    Value delta = Value(18);

    while (!threads.stop.load(std::memory_order_relaxed))
    {
        value = search<PV>(pos, ss, alpha, beta, depth, false);
        if (value <= alpha)
        {
            beta  = (alpha + beta) / 2;
            alpha = std::max(value - delta, -VALUE_INFINITE);
        }
        else if (value >= beta)
        {
            beta = std::min(value + delta, VALUE_INFINITE);
            // ++failedHighCnt;
        }
        else
        {
            //++rootMoves[pvIdx].bestMoveCount;
            break;
        }
    }

    // Have we found a "mate in x"?
    if (limits.mate && value >= VALUE_MATE_IN_MAX_PLY && VALUE_MATE - value <= 2 * limits.mate)
        threads.stop = true;

    /*    if (pos.should_debug())
      {
          debug << pos << std::endl;
          debug << "... exiting minimax_value() with value = " << value << std::endl;
          hit_any_key();
      }
    */
    return value;
}
// mcts end


// Used to print debug info and, more importantly, to detect
// when we are out of available time and thus stop the search.
void SearchManager::check_time(Search::Worker& worker) {
    if (--callsCnt > 0)
        return;

    // When using nodes, ensure checking rate is not lower than 0.1% of nodes
    callsCnt = worker.limits.nodes ? std::min(512, int(worker.limits.nodes / 1024)) : 512;

    static TimePoint lastInfoTime = now();

    TimePoint elapsed = tm.elapsed([&worker]() { return worker.threads.nodes_searched(); });
    TimePoint tick    = worker.limits.startTime + elapsed;

    if (tick - lastInfoTime >= 1000)
    {
        lastInfoTime = tick;
        dbg_print();
    }

    // We should not stop pondering until told so by the GUI
    if (ponder)
        return;

    if (
      // Later we rely on the fact that we can at least use the mainthread previous
      // root-search score and PV in a multithreaded environment to prove mated-in scores.
      worker.completedDepth >= 1
      && ((worker.limits.use_time_management() && (elapsed > tm.maximum() || stopOnPonderhit))
          || (worker.limits.movetime && elapsed >= worker.limits.movetime)
          || (worker.limits.nodes && worker.threads.nodes_searched() >= worker.limits.nodes)))
        worker.threads.stop = worker.threads.abortedSearch = true;
}

// Used to correct and extend PVs for moves that have a TB (but not a mate) score.
// Keeps the search based PV for as long as it is verified to maintain the game
// outcome, truncates afterwards. Finally, extends to mate the PV, providing a
// possible continuation (but not a proven mating line).
void syzygy_extend_pv(const OptionsMap&         options,
                      const Search::LimitsType& limits,
                      Position&                 pos,
                      RootMove&                 rootMove,
                      Value&                    v) {

    auto t_start      = std::chrono::steady_clock::now();
    int  moveOverhead = int(options["Move Overhead"]);

    // Do not use more than moveOverhead / 2 time, if time management is active
    auto time_abort = [&t_start, &moveOverhead, &limits]() -> bool {
        auto t_end = std::chrono::steady_clock::now();
        return limits.use_time_management()
            && 2 * std::chrono::duration<double, std::milli>(t_end - t_start).count()
                 > moveOverhead;
    };

    std::list<StateInfo> sts;

    // Step 0, do the rootMove, no correction allowed, as needed for MultiPV in TB.
    auto& stRoot = sts.emplace_back();
    pos.do_move(rootMove.pv[0], stRoot);
    int ply = 1;

    // Step 1, walk the PV to the last position in TB with correct decisive score
    while (size_t(ply) < rootMove.pv.size())
    {
        Move& pvMove = rootMove.pv[ply];

        RootMoves legalMoves;
        for (const auto& m : MoveList<LEGAL>(pos))
            legalMoves.emplace_back(m);

        Tablebases::Config config = Tablebases::rank_root_moves(options, pos, legalMoves);
        RootMove&          rm     = *std::find(legalMoves.begin(), legalMoves.end(), pvMove);

        if (legalMoves[0].tbRank != rm.tbRank)
            break;

        ply++;

        auto& st = sts.emplace_back();
        pos.do_move(pvMove, st);

        // Do not allow for repetitions or drawing moves along the PV in TB regime
        if (config.rootInTB && pos.is_draw(ply))
        {
            pos.undo_move(pvMove);
            ply--;
            break;
        }

        // Full PV shown will thus be validated and end in TB.
        // If we cannot validate the full PV in time, we do not show it.
        if (config.rootInTB && time_abort())
            break;
    }

    // Resize the PV to the correct part
    rootMove.pv.resize(ply);

    // Step 2, now extend the PV to mate, as if the user explored syzygy-tables.info
    // using top ranked moves (minimal DTZ), which gives optimal mates only for simple
    // endgames e.g. KRvK.
    while (!pos.is_draw(0))
    {
        if (time_abort())
            break;

        RootMoves legalMoves;
        for (const auto& m : MoveList<LEGAL>(pos))
        {
            auto&     rm = legalMoves.emplace_back(m);
            StateInfo tmpSI;
            pos.do_move(m, tmpSI);
            // Give a score of each move to break DTZ ties restricting opponent mobility,
            // but not giving the opponent a capture.
            for (const auto& mOpp : MoveList<LEGAL>(pos))
                rm.tbRank -= pos.capture(mOpp) ? 100 : 1;
            pos.undo_move(m);
        }

        // Mate found
        if (legalMoves.size() == 0)
            break;

        // Sort moves according to their above assigned rank.
        // This will break ties for moves with equal DTZ in rank_root_moves.
        std::stable_sort(
          legalMoves.begin(), legalMoves.end(),
          [](const Search::RootMove& a, const Search::RootMove& b) { return a.tbRank > b.tbRank; });

        // The winning side tries to minimize DTZ, the losing side maximizes it
        Tablebases::Config config = Tablebases::rank_root_moves(options, pos, legalMoves, true);

        // If DTZ is not available we might not find a mate, so we bail out
        if (!config.rootInTB || config.cardinality > 0)
            break;

        ply++;

        Move& pvMove = legalMoves[0].pv[0];
        rootMove.pv.push_back(pvMove);
        auto& st = sts.emplace_back();
        pos.do_move(pvMove, st);
    }

    // Finding a draw in this function is an exceptional case, that cannot happen
    // during engine game play, since we have a winning score, and play correctly
    // with TB support. However, it can be that a position is draw due to the 50 move
    // rule if it has been been reached on the board with a non-optimal 50 move counter
    // (e.g. 8/8/6k1/3B4/3K4/4N3/8/8 w - - 54 106 ) which TB with dtz counter rounding
    // cannot always correctly rank. See also
    // https://github.com/official-stockfish/Stockfish/issues/5175#issuecomment-2058893495
    // We adjust the score to match the found PV. Note that a TB loss score can be
    // displayed if the engine did not find a drawing move yet, but eventually search
    // will figure it out (e.g. 1kq5/q2r4/5K2/8/8/8/8/7Q w - - 96 1 )
    if (pos.is_draw(0))
        v = VALUE_DRAW;

    // Undo the PV moves
    for (auto it = rootMove.pv.rbegin(); it != rootMove.pv.rend(); ++it)
        pos.undo_move(*it);

    // Inform if we couldn't get a full extension in time
    if (time_abort())
        sync_cout
          << "info string Syzygy based PV extension requires more time, increase Move Overhead as needed."
          << sync_endl;
}

void SearchManager::pv(Search::Worker&           worker,
                       const ThreadPool&         threads,
                       const TranspositionTable& tt,
                       Depth                     depth) {

    const auto nodes     = threads.nodes_searched();
    auto&      rootMoves = worker.rootMoves;
    auto&      pos       = worker.rootPos;
    size_t     pvIdx     = worker.pvIdx;
    size_t     multiPV   = std::min(size_t(worker.options["MultiPV"]), rootMoves.size());
    uint64_t   tbHits    = threads.tb_hits() + (worker.tbConfig.rootInTB ? rootMoves.size() : 0);

    for (size_t i = 0; i < multiPV; ++i)
    {
        bool updated = rootMoves[i].score != -VALUE_INFINITE;

        if (depth == 1 && !updated && i > 0)
            continue;

        Depth d = updated ? depth : std::max(1, depth - 1);
        Value v = updated ? rootMoves[i].uciScore : rootMoves[i].previousScore;

        if (v == -VALUE_INFINITE)
            v = VALUE_ZERO;

        bool tb = worker.tbConfig.rootInTB && std::abs(v) <= VALUE_TB;
        v       = tb ? rootMoves[i].tbScore : v;

        bool isExact = i != pvIdx || tb || !updated;  // tablebase- and previous-scores are exact

        // Potentially correct and extend the PV, and in exceptional cases v
        if (std::abs(v) >= VALUE_TB_WIN_IN_MAX_PLY && std::abs(v) < VALUE_MATE_IN_MAX_PLY
            && ((!rootMoves[i].scoreLowerbound && !rootMoves[i].scoreUpperbound) || isExact))
            syzygy_extend_pv(worker.options, worker.limits, pos, rootMoves[i], v);

        updateShashinValues((Value) v, pos, &worker);  //by shashin
        std::string pv;
        for (Move m : rootMoves[i].pv)
            pv += UCIEngine::move(m, pos.is_chess960()) + " ";

        // Remove last whitespace
        if (!pv.empty())
            pv.pop_back();

        auto wdl   = worker.options["UCI_ShowWDL"] ? UCIEngine::wdl(v, pos) : "";
        auto bound = rootMoves[i].scoreLowerbound
                     ? "lowerbound"
                     : (rootMoves[i].scoreUpperbound ? "upperbound" : "");

        InfoFull info;

        info.depth    = d;
        info.selDepth = rootMoves[i].selDepth;
        info.multiPV  = i + 1;
        info.score    = {v, pos};
        info.wdl      = wdl;

        if (!isExact)
            info.bound = bound;

        TimePoint time = tm.elapsed_time() + 1;
        info.timeMs    = time;
        info.nodes     = nodes;
        info.nps       = nodes * 1000 / time;
        info.tbHits    = tbHits;
        info.pv        = pv;
        info.hashfull  = tt.hashfull();

        updates.onUpdateFull(info);
    }
}

// Called in case we have no ponder move before exiting the search,
// for instance, in case we stop the search during a fail high at root.
// We try hard to have a ponder move to return to the GUI,
// otherwise in case of 'ponder on' we have nothing to think about.
bool RootMove::extract_ponder_from_tt(const TranspositionTable& tt, Position& pos) {

    StateInfo st;
    ASSERT_ALIGNED(&st, Eval::NNUE::CacheLineSize);

    assert(pv.size() == 1);
    if (pv[0] == Move::none())
        return false;

    pos.do_move(pv[0], st);

    auto [ttHit, ttData, ttWriter] = tt.probe(pos.key());
    if (ttHit)
    {
        if (MoveList<LEGAL>(pos).contains(ttData.move))
            pv.push_back(ttData.move);
    }

    pos.undo_move(pv[0]);
    return pv.size() > 1;
}


// Kelly begin
void putQLearningTrajectoryIntoLearningTable() {
    const double learning_rate = 0.5;
    const double gamma         = 0.99;

    if (qLearningTrajectory.size() <= 1)
        return;

    for (size_t index = qLearningTrajectory.size() - 1; index > 0; --index)
    {
        LearningMove prevLearningMove =
          qLearningTrajectory[index - 1].persistedLearningMove.learningMove;
        const LearningMove currentLearningMove =
          qLearningTrajectory[index].persistedLearningMove.learningMove;

        prevLearningMove.score = prevLearningMove.score * (1 - learning_rate)
                               + learning_rate * (gamma * currentLearningMove.score);
        prevLearningMove.performance = WDLModel::get_win_probability_by_material(
          prevLearningMove.score, qLearningTrajectory[index - 1].materialClamp);

        LD.add_new_learning(qLearningTrajectory[index - 1].persistedLearningMove.key,
                            prevLearningMove);
    }

    qLearningTrajectory.clear();
}

void setStartPoint() {
    useLearning = true;
    LD.resume();
}
// Kelly end

}  // namespace ShashChess