/*
  Stockfish, a UCI chess playing engine derived from Glaurung 2.1
  Copyright (C) 2004-2024 The Stockfish developers (see AUTHORS file)

  Stockfish 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.

  Stockfish 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 "evaluate.h"

#include <algorithm>
#include <cassert>
#include <cmath>
#include <cstdlib>
#include <iomanip>
#include <iostream>
#include <memory>
#include <sstream>
#include <tuple>

#include "nnue/network.h"
#include "nnue/nnue_misc.h"
#include "position.h"
#include "types.h"
#include "uci.h"
#include "nnue/nnue_accumulator.h"

namespace Stockfish {

// Returns a static, purely materialistic evaluation of the position from
// the point of view of the given color. It can be divided by PawnValue to get
// an approximation of the material advantage on the board in terms of pawns.
int Eval::simple_eval(const Position& pos, Color c) {
    return PawnValue * (pos.count<PAWN>(c) - pos.count<PAWN>(~c))
         + (pos.non_pawn_material(c) - pos.non_pawn_material(~c));
}

bool Eval::use_smallnet(const Position& pos) {
    int simpleEval = simple_eval(pos, pos.side_to_move());
    return std::abs(simpleEval) > 962;
}

// Evaluate is the evaluator for the outer world. It returns a static evaluation
// of the position from the point of view of the side to move.
Value Eval::evaluate(const Eval::NNUE::Networks&    networks,
                     const Position&                pos,
                     Eval::NNUE::AccumulatorCaches& caches,
                     int                            optimism) {

    assert(!pos.checkers());

    int  simpleEval = simple_eval(pos, pos.side_to_move());
    bool smallNet   = use_smallnet(pos);
    int  v;

    auto [psqt, positional] = smallNet ? networks.small.evaluate(pos, &caches.small)
                                       : networks.big.evaluate(pos, &caches.big);

    Value nnue           = (125 * psqt + 131 * positional) / 128;
    int   nnueComplexity = std::abs(psqt - positional);

    // Re-evaluate the position when higher eval accuracy is worth the time spent
    if (smallNet && (nnue * simpleEval < 0 || std::abs(nnue) < 227))
    {
        std::tie(psqt, positional) = networks.big.evaluate(pos, &caches.big);
        nnue                       = (125 * psqt + 131 * positional) / 128;
        nnueComplexity             = std::abs(psqt - positional);
        smallNet                   = false;
    }

    // Blend optimism and eval with nnue complexity
    optimism += optimism * nnueComplexity / 457;
    nnue -= nnue * nnueComplexity / 19157;

    int material = 554 * pos.count<PAWN>() + pos.non_pawn_material();
    v            = (nnue * (73921 + material) + optimism * (8112 + material)) / 73260;

    // Damp down the evaluation linearly when shuffling
    v -= v * pos.rule50_count() / 212;

    // Guarantee evaluation does not hit the tablebase range
    v = std::clamp(v, VALUE_TB_LOSS_IN_MAX_PLY + 1, VALUE_TB_WIN_IN_MAX_PLY - 1);

    return v;
}

// Like evaluate(), but instead of returning a value, it returns
// a string (suitable for outputting to stdout) that contains the detailed
// descriptions and values of each evaluation term. Useful for debugging.
// Trace scores are from white's point of view
std::string Eval::trace(Position& pos, const Eval::NNUE::Networks& networks) {

    if (pos.checkers())
        return "Final evaluation: none (in check)";

    auto caches = std::make_unique<Eval::NNUE::AccumulatorCaches>(networks);

    std::stringstream ss;
    ss << std::showpoint << std::noshowpos << std::fixed << std::setprecision(2);
    ss << '\n' << NNUE::trace(pos, networks, *caches) << '\n';

    ss << std::showpoint << std::showpos << std::fixed << std::setprecision(2) << std::setw(15);

    auto [psqt, positional] = networks.big.evaluate(pos, &caches->big);
    Value v                 = psqt + positional;
    v                       = pos.side_to_move() == WHITE ? v : -v;
    ss << "NNUE evaluation        " << 0.01 * UCIEngine::to_cp(v, pos) << " (white side)\n";

    v = evaluate(networks, pos, *caches, VALUE_ZERO);
    v = pos.side_to_move() == WHITE ? v : -v;
    ss << "Final evaluation       " << 0.01 * UCIEngine::to_cp(v, pos) << " (white side)";
    ss << " [with scaled NNUE, ...]";
    ss << "\n";

    return ss.str();
}

}  // namespace Stockfish