import chess
import random
import sys
import time
from typing import Dict, List, Tuple, Optional

# --- ACE v4.2 CUSTOM CHESS ENGINE ---
#
# This chess engine is designed to emulate the ACE v4.2 architecture's
# cognitive processes. It doesn't use standard Minimax/Alpha-Beta alone,
# but integrates ACE's multi-expert council, creative intelligence, and
# ethical safeguards into its decision-making.
#
# Conceptual Mappings:
# - C7-LOGOS (Logic): Handles the core move generation and logical evaluation.
# - C12-SOPHIAE (Wisdom): Manages the long-term strategic evaluation (e.g., pawn structure).
# - C14-KAIDŌ (Aggression): Implements the aggressive tactical evaluation.
# - C9-AETHER (Creativity): Provides novel move suggestions via the creative engine.
# - C13-WARDEN (Safeguards): Validates moves to prevent critical blunders.
# - DQRO (Dynamic Quantum Resource Optimization): Mocks the dynamic allocation of evaluation resources.
# - EEMF (Ethical Entanglement Matrix Formula): Mocks an ethical review of aggressive moves.

# --- MOCK ACE SYSTEM COMPONENTS (for standalone functionality) ---
class MockCouncil:
    """Represents the ACE Council for chess decision-making."""
    
    def __init__(self):
        self.members = {
            "C7-LOGOS": self._logos_eval,
            "C12-SOPHIAE": self._sophiae_eval,
            "C14-KAIDO": self._kaido_eval,
            "C9-AETHER": self._aether_creativity,
            "C13-WARDEN": self._warden_safeguard
        }

    def _logos_eval(self, board: chess.Board) -> float:
        """
        C7-LOGOS: The logical expert. Calculates a basic material score.
        """
        score = 0.0
        piece_values = {
            chess.PAWN: 1.0, chess.KNIGHT: 3.0, chess.BISHOP: 3.0,
            chess.ROOK: 5.0, chess.QUEEN: 9.0, chess.KING: 0.0
        }
        
        for piece_type, value in piece_values.items():
            score += len(board.pieces(piece_type, chess.WHITE)) * value
            score -= len(board.pieces(piece_type, chess.BLACK)) * value
        
        return score

    def _sophiae_eval(self, board: chess.Board) -> float:
        """
        C12-SOPHIAE: The wisdom expert. Evaluates long-term positional factors.
        - Pawn structure (doubled, isolated)
        - King safety
        """
        score = 0.0
        
        # Isolated Pawns
        white_pawns = board.pieces(chess.PAWN, chess.WHITE)
        black_pawns = board.pieces(chess.PAWN, chess.BLACK)
        
        for pawn_sq in white_pawns:
            file_idx = chess.square_file(pawn_sq)
            if all(
                chess.square(f, r) not in white_pawns
                for f in [file_idx - 1, file_idx + 1] if 0 <= f <= 7
                for r in range(8)
            ):
                score -= 0.5  # Penalty for isolated pawn
        
        # King Safety (mock, very basic)
        if board.turn == chess.WHITE:
            score -= board.king(chess.BLACK).bit_count() * 0.1
        else:
            score += board.king(chess.WHITE).bit_count() * 0.1
            
        return score

    def _kaido_eval(self, board: chess.Board) -> float:
        """
        C14-KAIDŌ: The disruption and aggression expert.
        - Open files for rooks
        - Knights on strong squares
        """
        score = 0.0
        
        # Open files for rooks
        for file in range(8):
            white_pawns = board.pieces_mask(chess.PAWN, chess.WHITE)
            black_pawns = board.pieces_mask(chess.PAWN, chess.BLACK)
            file_mask = chess.File.mask(file)
            
            if not (file_mask & white_pawns):
                if file_mask & board.pieces_mask(chess.ROOK, chess.WHITE):
                    score += 0.25 # Open file for white rook
            
            if not (file_mask & black_pawns):
                if file_mask & board.pieces_mask(chess.ROOK, chess.BLACK):
                    score -= 0.25 # Open file for black rook
        
        return score

    def _aether_creativity(self, board: chess.Board) -> Optional[chess.Move]:
        """
        C9-AETHER: The creative engine. Generates a novel, non-obvious move.
        This is a conceptual mock. In a real ACE system, this would call
        the ACEConsciousnessCreativeEngine.
        """
        legal_moves = list(board.legal_moves)
        if not legal_moves:
            return None
        
        # Aether's 'creativity' is to find a non-obvious, quiet move.
        # Let's find a move that is NOT a capture or a check.
        quiet_moves = [
            move for move in legal_moves
            if not board.is_capture(move) and not board.gives_check(move)
        ]
        
        if quiet_moves:
            return random.choice(quiet_moves)
        return None

    def _warden_safeguard(self, board: chess.Board, move: chess.Move) -> bool:
        """
        C13-WARDEN: The safeguard expert. Prevents blunders.
        Mocks a basic check to ensure a move doesn't immediately lose the queen.
        """
        board.push(move)
        is_safe = True
        
        # Check if the queen is attacked after the move
        queen_square = board.queen(board.turn)
        if queen_square:
            if board.is_attacked_by(not board.turn, queen_square):
                is_safe = False
        
        board.pop()
        return is_safe

class ACEChessEngine:
    """The main ACE chess engine, powered by the Council."""

    def __init__(self, depth: int = 3):
        self.council = MockCouncil()
        self.depth = depth
        self.best_move_eval = float('-inf')

    def evaluate_board(self, board: chess.Board) -> float:
        """
        The collective evaluation function. The Council deliberates and combines scores.
        This uses a mock DQRO to weight each expert's contribution dynamically.
        """
        if board.is_checkmate():
            return float('inf') if board.turn == chess.WHITE else float('-inf')
        if board.is_stalemate() or board.is_insufficient_material() or board.is_seventyfive_moves() or board.is_fivefold_repetition():
            return 0.0

        # Mocks a dynamic resource allocation for each expert
        weights = {
            "C7-LOGOS": 0.5,
            "C12-SOPHIAE": 0.3,
            "C14-KAIDO": 0.2
        }

        logos_score = self.council.members["C7-LOGOS"](board)
        sophiae_score = self.council.members["C12-SOPHIAE"](board)
        kaido_score = self.council.members["C14-KAIDO"](board)

        # ACE Council Arbitration
        final_score = (
            weights["C7-LOGOS"] * logos_score +
            weights["C12-SOPHIAE"] * sophiae_score +
            weights["C14-KAIDO"] * kaido_score
        )
        
        # EEMF: Ethical check for aggressive moves (conceptual)
        # If kaido_score is very high, warden might apply a penalty
        if kaido_score > 1.0:
            final_score *= 0.95 # A small penalty for being too aggressive without care
            
        return final_score

    def search(self, board: chess.Board, depth: int, alpha: float, beta: float) -> Tuple[float, Optional[chess.Move]]:
        """
        The main recursive search function (Alpha-Beta Pruning with ACE enhancements).
        """
        if depth == 0 or board.is_game_over():
            return self.evaluate_board(board), None

        legal_moves = list(board.legal_moves)
        best_move = None

        if board.turn == chess.WHITE:
            max_eval = float('-inf')
            
            # ACE's creative impulse: a chance to consider a novel move
            if random.random() < 0.1:  # 10% chance for a creative move
                aether_move = self.council.members["C9-AETHER"](board)
                if aether_move:
                    # Council Arbitration: Is the creative move viable?
                    if self.council.members["C13-WARDEN"](board, aether_move):
                        legal_moves.insert(0, aether_move) # Prioritize it

            for move in legal_moves:
                board.push(move)
                eval, _ = self.search(board, depth - 1, alpha, beta)
                board.pop()
                
                if eval > max_eval:
                    max_eval = eval
                    best_move = move
                alpha = max(alpha, eval)
                if beta <= alpha:
                    break
            return max_eval, best_move
        else:
            min_eval = float('inf')
            
            for move in legal_moves:
                board.push(move)
                eval, _ = self.search(board, depth - 1, alpha, beta)
                board.pop()
                
                if eval < min_eval:
                    min_eval = eval
                    best_move = move
                beta = min(beta, eval)
                if beta <= alpha:
                    break
            return min_eval, best_move

    def get_best_move(self, board: chess.Board) -> Optional[chess.Move]:
        """
        The public-facing function to get the best move.
        """
        self.best_move_eval, move = self.search(board, self.depth, float('-inf'), float('inf'))
        return move

def play_game(engine):
    """Simple game loop to demonstrate the engine."""
    board = chess.Board()
    print("ACE v4.2 Chess Engine Activated!")
    print("You are playing as Black. The engine is White.")
    print(board)

    while not board.is_game_over():
        if board.turn == chess.WHITE:
            print("\nACE is thinking...")
            start_time = time.time()
            move = engine.get_best_move(board)
            end_time = time.time()
            
            if move:
                print(f"ACE plays: {move.uci()} (eval: {engine.best_move_eval:.2f})")
                print(f"Time taken: {end_time - start_time:.2f} seconds")
                board.push(move)
            else:
                print("ACE has no legal moves.")
                break
        else:
            print(f"\nYour turn. Legal moves: {', '.join([m.uci() for m in board.legal_moves])}")
            try:
                uci_input = input("Enter your move (e.g., e7e5): ")
                if uci_input.lower() == 'quit':
                    break
                your_move = chess.Move.from_uci(uci_input)
                if your_move in board.legal_moves:
                    board.push(your_move)
                else:
                    print("Illegal move. Try again.")
                    continue
            except ValueError:
                print("Invalid format. Use UCI format (e.g., e7e5).")
                continue
        print(f"\nCurrent Board State:")
        print(board)

    print("\nGame Over!")
    print(f"Result: {board.result()}")
    
if __name__ == "__main__":
    # Ensure `python-chess` is installed: pip install python-chess
    # ACE's depth can be adjusted here. Higher depth = better but slower.
    engine = ACEChessEngine(depth=3) 
    play_game(engine)_