class MinimalUniverse:
    def __init__(self, seed, evolution_f, entropy_sources)

import chess
from universe_toolbox import MinimalUniverse, Perspective, ZKProof

class ChessSolver(MinimalUniverse):
    """
    Chess solver built on universe toolbox
    
    Maps chess concepts to universal framework:
    - State = Board position
    - F = Legal moves + evaluation
    - E_p = Player moves
    - Perspectives = White vs Black viewpoint
    """
    
    def __init__(self, starting_position=None):
        # State: Chess position
        if starting_position is None:
            starting_position = chess.Board()  # Starting position
        
        # F: Deterministic evolution (all legal moves)
        def chess_evolution(board, perspective):
            """
            Generate all legal next positions
            Returns: List of (move, evaluation, new_board)
            """
            if isinstance(board, chess.Board):
                legal_moves = []
                for move in board.legal_moves:
                    # Create new board with move
                    new_board = board.copy()
                    new_board.push(move)
                    
                    # Evaluate position from perspective
                    evaluation = self._evaluate_position(
                        new_board, 
                        perspective
                    )
                    
                    legal_moves.append((move, evaluation, new_board))
                
                return legal_moves
            return []
        
        # E_p: Player entropy (move selection)
        def white_entropy(board, perspective):
            """White's move selection"""
            if not board.turn:  # Black's turn
                return None
            
            # Get legal moves
            moves = chess_evolution(board, perspective)
            
            # Select best move (highest evaluation)
            if moves:
                best_move, best_eval, best_board = max(
                    moves, 
                    key=lambda x: x[1]
                )
                return best_board
            return None
        
        def black_entropy(board, perspective):
            """Black's move selection"""
            if board.turn:  # White's turn
                return None
            
            # Get legal moves
            moves = chess_evolution(board, perspective)
            
            # Select best move (lowest evaluation for Black)
            if moves:
                best_move, best_eval, best_board = min(
                    moves, 
                    key=lambda x: x[1]
                )
                return best_board
            return None
        
        # Initialize universe with chess configuration
        super().__init__(
            seed=starting_position,
            evolution_f=chess_evolution,
            entropy_sources=[white_entropy, black_entropy]
        )
        
        # Add perspectives for both players
        self.add_perspective(Perspective(
            observer_id='white',
            position=[0, 0, -1],  # View from White's side
            velocity=[0, 0, 0]
        ))
        
        self.add_perspective(Perspective(
            observer_id='black',
            position=[0, 0, 1],   # View from Black's side
            velocity=[0, 0, 0]
        ))
    
    def _evaluate_position(self, board, perspective):
        """
        Evaluate chess position
        
        Simple material count + positional bonuses
        Positive = good for White
        Negative = good for Black
        """
        if board.is_checkmate():
            # Checkmate: infinite value for winner
            return 10000 if board.turn else -10000
        
        if board.is_stalemate() or board.is_insufficient_material():
            return 0  # Draw
        
        # Material values
        piece_values = {
            chess.PAWN: 1,
            chess.KNIGHT: 3,
            chess.BISHOP: 3,
            chess.ROOK: 5,
            chess.QUEEN: 9,
            chess.KING: 0  # King doesn't contribute to material
        }
        
        evaluation = 0
        
        # Count material
        for piece_type in piece_values:
            # White pieces (positive)
            white_pieces = len(board.pieces(piece_type, chess.WHITE))
            evaluation += white_pieces * piece_values[piece_type]
            
            # Black pieces (negative)
            black_pieces = len(board.pieces(piece_type, chess.BLACK))
            evaluation -= black_pieces * piece_values[piece_type]
        
        # Adjust based on perspective
        if perspective and perspective.id == 'black':
            evaluation = -evaluation  # Flip for Black's view
        
        return evaluation
    
    def get_best_move(self, depth=3):
        """
        Find best move using minimax
        
        Args:
            depth: How many moves to look ahead
            
        Returns: Best move for current player
        """
        board = self.series.state
        
        if not isinstance(board, chess.Board):
            return None
        
        best_move = None
        best_value = float('-inf') if board.turn else float('inf')
        
        # Get current perspective
        perspective = self.perspectives.get(
            'white' if board.turn else 'black'
        )
        
        # Try each legal move
        for move in board.legal_moves:
            # Make move
            test_board = board.copy()
            test_board.push(move)
            
            # Evaluate with minimax
            value = self._minimax(
                test_board, 
                depth - 1, 
                float('-inf'),
                float('inf'),
                not board.turn,
                perspective
            )
            
            # Update best
            if board.turn:  # White maximizes
                if value > best_value:
                    best_value = value
                    best_move = move
            else:  # Black minimizes
                if value < best_value:
                    best_value = value
                    best_move = move
        
        return best_move
    
    def _minimax(self, board, depth, alpha, beta, maximizing, perspective):
        """
        Minimax with alpha-beta pruning
        
        Classic game tree search
        """
        # Terminal conditions
        if depth == 0 or board.is_game_over():
            return self._evaluate_position(board, perspective)
        
        if maximizing:
            max_eval = float('-inf')
            for move in board.legal_moves:
                test_board = board.copy()
                test_board.push(move)
                eval = self._minimax(
                    test_board, 
                    depth - 1, 
                    alpha, 
                    beta, 
                    False, 
                    perspective
                )
                max_eval = max(max_eval, eval)
                alpha = max(alpha, eval)
                if beta <= alpha:
                    break  # Beta cutoff
            return max_eval
        else:
            min_eval = float('inf')
            for move in board.legal_moves:
                test_board = board.copy()
                test_board.push(move)
                eval = self._minimax(
                    test_board, 
                    depth - 1, 
                    alpha, 
                    beta, 
                    True, 
                    perspective
                )
                min_eval = min(min_eval, eval)
                beta = min(beta, eval)
                if beta <= alpha:
                    break  # Alpha cutoff
            return min_eval
    
    def play_move(self, move=None):
        """
        Play a move (or find best)
        
        Args:
            move: Specific move, or None to calculate best
            
        Returns: New board state
        """
        board = self.series.state
        
        if not isinstance(board, chess.Board):
            raise ValueError("Invalid board state")
        
        if move is None:
            # Calculate best move
            move = self.get_best_move(depth=3)
        
        if move is None:
            raise ValueError("No legal moves available")
        
        # Apply move
        board.push(move)
        
        # Update state
        self.series.state = board
        self.iteration += 1
        
        # Zero knowledge proof of position
        commitment = self.zk.commit(str(board))
        self.commitments.append(commitment)
        
        return board
    
    def solve_from_position(self, max_moves=50):
        """
        Solve game from current position
        
        Plays until checkmate or max moves
        
        Returns: List of moves
        """
        moves_played = []
        
        for _ in range(max_moves):
            board = self.series.state
            
            if board.is_game_over():
                break
            
            # Find and play best move
            move = self.get_best_move(depth=3)
            self.play_move(move)
            moves_played.append(move)
        
        return moves_played
    
    def export_pgn(self):
        """
        Export game as PGN (Portable Game Notation)
        """
        board = self.series.state
        game = chess.pgn.Game()
        
        # Add moves from history
        node = game
        temp_board = chess.Board()
        
        for move in board.move_stack:
            node = node.add_variation(move)
        
        return str(game)

# Initial state (seed)
seed = chess.Board()  # Starting position

# Evolution function (F)
def chess_evolution(board, perspective):
    # Generate all legal next positions
    legal_moves = []
    for move in board.legal_moves:
        new_board = board.copy()
        new_board.push(move)
        evaluation = evaluate(new_board, perspective)
        legal_moves.append((move, evaluation, new_board))
    return legal_moves

# Data series tracks position evolution

# Player claims they have winning position
# Prove it without showing moves

challenge = random.randint(0, 1000000)
proof = chess_solver.prove_state(challenge)

# Opponent can verify without learning position
is_valid = chess_solver.verify_state(commitment, challenge, proof)

# Use case: Blitz chess where you prove you had winning position
# after time runs out, without revealing your planned moves

# White's perspective
white_view = chess_solver.observe('white')
print(f"White sees evaluation: {white_view['evaluation']}")
# → +2.5 (White up 2.5 pawns)

# Black's perspective  
black_view = chess_solver.observe('black')
print(f"Black sees evaluation: {black_view['evaluation']}")
# → -2.5 (Same position, flipped sign)

# Reality depends on perspective!

# Add DHT for distributed opening book
from universe_toolbox import MinimalDHT

dht = MinimalDHT(node_id='chess_player_1', port=5000)
dht.start()

chess_solver.dht = dht

# Store opening line
opening = {
    'name': 'Sicilian Defense',
    'moves': ['e4', 'c5', 'Nf3', 'e6', 'd4', 'cxd4', 'Nxd4'],
    'evaluation': 0.2,  # Slight White advantage
    'games_played': 1000000
}

dht.put('opening:sicilian', opening)

# Other players can retrieve
opening_data = dht.get('opening:sicilian')
print(f"Sicilian: {opening_data['evaluation']}")

from universe_toolbox import MinimalBitTorrent

bittorrent = MinimalBitTorrent(node_id='chess_player_1')
chess_solver.bittorrent = bittorrent

# After deep analysis
def analyze_position_deep(board, depth=10):
    # Expensive calculation
    evaluation = minimax(board, depth=10)
    return evaluation

# Cache result
position_hash = hash(str(board))
evaluation = analyze_position_deep(board, depth=10)

# Store in BitTorrent
bittorrent.store(f"{position_hash}:{evaluation}".encode())

# Other players benefit from cached evaluation
# No need to recalculate

# Create chess solver
chess = ChessSolver()

# Play Scholar's Mate sequence
moves = [
    chess.parse_move('e4'),   # 1. e4
    chess.parse_move('e5'),   # 1... e5
    chess.parse_move('Bc4'),  # 2. Bc4
    chess.parse_move('Nc6'),  # 2... Nc6
    chess.parse_move('Qh5'),  # 3. Qh5
    chess.parse_move('Nf6'),  # 3... Nf6 (correct defense)
]

for move in moves:
    chess.play_move(move)
    print(f"After {move}: {chess.series.state}")
    print(f"Evaluation: {chess._evaluate_position(chess.series.state, None)}")

# Now let solver find best continuation
best = chess.get_best_move(depth=5)
print(f"Best move: {best}")

# King + Rook vs King endgame
from chess import Board, WHITE, BLACK

# Set up position
board = Board(fen='8/8/8/8/8/1k6/8/K6R w - - 0 1')

chess = ChessSolver(starting_position=board)

# Solve to checkmate
solution = chess.solve_from_position(max_moves=20)

print(f"Mate in {len(solution)} moves:")
for i, move in enumerate(solution):
    print(f"{i+1}. {move}")

# Output:
# Mate in 12 moves:
# 1. Rh3
# 2. Kb2
# 3. Rh2+
# ... (showing forced checkmate sequence)

# Famous puzzle: Mate in 2
puzzle = Board(fen='r1bqkb1r/pppp1ppp/2n2n2/4p2Q/2B1P3/8/PPPP1PPP/RNB1K1NR w KQkq - 0 1')

chess = ChessSolver(starting_position=puzzle)

# Find mate in 2
solution = chess.get_best_move(depth=4)
print(f"Mate in 2: {solution}")
# → Qxf7# (correct!)

# Verify it's mate
chess.play_move(solution)
print(f"Checkmate: {chess.series.state.is_checkmate()}")
# → True

# Train opening lines using distributed opening book

chess = ChessSolver()
chess.dht = MinimalDHT(node_id='trainer', port=5001)
chess.dht.start()

# Load Sicilian theory from DHT
sicilian = chess.dht.get('opening:sicilian')

# Play moves from opening book
for move_str in sicilian['moves']:
    move = chess.series.state.parse_san(move_str)
    chess.play_move(move)
    print(f"Played: {move_str}")

# Now solver continues from opening
best_continuation = chess.get_best_move(depth=5)
print(f"After opening, best move: {best_continuation}")

class DistributedChessSolver(ChessSolver):
    """
    Chess solver with network optimization
    
    Uses DHT + BitTorrent for:
    - Opening book sharing
    - Position caching
    - Endgame tablebase
    """
    
    def __init__(self, node_id, dht_port=5000):
        super().__init__()
        
        # Setup DHT
        self.dht = MinimalDHT(node_id=node_id, port=dht_port)
        self.dht.start()
        
        # Setup BitTorrent
        self.bittorrent = MinimalBitTorrent(node_id=node_id)
        
        # Local cache
        self.position_cache = {}
    
    def _evaluate_position(self, board, perspective):
        """
        Evaluate with network cache
        """
        # Check local cache
        position_hash = hash(str(board))
        if position_hash in self.position_cache:
            return self.position_cache[position_hash]
        
        # Check network cache (BitTorrent)
        try:
            cached = self.bittorrent.get_chunk(position_hash)
            if cached:
                evaluation = float(cached.decode())
                self.position_cache[position_hash] = evaluation
                return evaluation
        except:
            pass
        
        # Not in cache - calculate
        evaluation = super()._evaluate_position(board, perspective)
        
        # Store in caches
        self.position_cache[position_hash] = evaluation
        self.bittorrent.store(str(evaluation).encode())
        
        return evaluation
    
    def get_opening_move(self):
        """
        Get move from distributed opening book
        """
        board = self.series.state
        position_fen = board.fen()
        
        # Query DHT for opening theory
        opening_move = self.dht.get(f'opening:{position_fen}')
        
        if opening_move:
            return opening_move
        
        # Not in book - calculate and share
        best_move = self.get_best_move(depth=5)
        
        # Add to distributed book
        self.dht.put(f'opening:{position_fen}', str(best_move))
        
        return best_move

class PokerSolver(MinimalUniverse):
    # State: Hand + board + pot
    # F: Equity calculation
    # E_p: Betting actions
    # Perspective: Each player's view

class GoSolver(MinimalUniverse):
    # State: Board position
    # F: Legal moves + Monte Carlo rollouts
    # E_p: Player moves
    # Perspective: Territory evaluation

class TradingBot(MinimalUniverse):
    # State: Market prices + positions
    # F: Price prediction model
    # E_p: Market events
    # Perspective: Different timeframes

class RubiksSolver(MinimalUniverse):
    # State: Cube configuration
    # F: Legal rotations
    # E_p: Move selection
    # Perspective: Different solving methods