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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

"""
Chess Environment for OpenEnv using moonfish.
This module provides a chess environment for reinforcement learning,
using python-chess for game logic and moonfish for position evaluation
and opponent play.
"""

import random
import threading
import uuid
from typing import Any, Dict, List, Optional, Tuple

import chess
from moonfish.lib import search_move
from moonfish.psqt import board_evaluation, BOARD_EVALUATION_CACHE
from openenv.core.env_server import Environment

from ..models import ChessAction, ChessObservation, ChessState
from .rubrics import ChessWinLossRubric

# moonfish keeps one evaluation cache per process, so sessions take turns with it.
_MOONFISH_LOCK = threading.Lock()


class ChessEnvironment(Environment):
    """
    Chess environment implementing the OpenEnv interface.
    Uses python-chess for game logic and moonfish for position evaluation.
    Designed for RL training where an agent plays as one color against
    an opponent (which can be random, moonfish engine, or self-play).
    """

    SUPPORTS_CONCURRENT_SESSIONS = True

    def __init__(
        self,
        opponent: str = "moonfish",
        opponent_depth: int = 2,
        max_moves: int = 500,
        agent_color: str | None = None,
        gamma: float = 0.99,
    ):
        """
        Initialize the chess environment.
        Args:
            opponent: Opponent type - "moonfish", "random", or None (self-play)
            opponent_depth: Search depth when using moonfish as opponent
            max_moves: Maximum half-moves before draw
            agent_color: Which color the agent plays - "white", "black", or None (alternate each episode)
            gamma: Discount factor for temporal credit assignment (0-1)
        """
        super().__init__(rubric=ChessWinLossRubric(gamma=gamma))
        self._opponent = opponent
        self._opponent_depth = opponent_depth
        self._max_moves = max_moves
        self._agent_color_setting = agent_color
        self._gamma = gamma
        self._board = None
        self._state = None
        self._agent_color = chess.WHITE
        self._agent_move_count = 0  # Track agent moves for discounting
        self.reset()

    def reset(self, fen: str = None):
        """
        Initialize a new chess game episode.
        Args:
            fen: Optional starting position in FEN notation.
        Returns:
            Initial observation of the board state.
        """
        self._reset_rubric()
        # moonfish's module-level evaluation cache is never trimmed.
        with _MOONFISH_LOCK:
            BOARD_EVALUATION_CACHE.clear()

        if fen:
            self._board = chess.Board(fen)
        else:
            self._board = chess.Board()

        # Generate episode ID once
        episode_id = str(uuid.uuid4())

        # Determine agent color
        if self._agent_color_setting == "white":
            self._agent_color = chess.WHITE
        elif self._agent_color_setting == "black":
            self._agent_color = chess.BLACK
        elif self._agent_color_setting is None:
            # Alternate each episode based on episode_id hash
            self._agent_color = (
                chess.WHITE if hash(episode_id) % 2 == 0 else chess.BLACK
            )
        else:
            self._agent_color = chess.WHITE

        self._state = ChessState(
            episode_id=episode_id,
            step_count=0,
            current_player="white" if self._board.turn else "black",
            fen=self._board.fen(),
            move_history=[],
        )
        self._agent_move_count = 0

        # If agent plays Black and opponent is configured, opponent moves first
        if self._opponent is not None and self._agent_color == chess.BLACK:
            self._make_opponent_move()

        # Check if starting position is already terminal (e.g., custom FEN with checkmate/stalemate)
        _, done = self._calculate_reward_and_done()
        return self._make_observation(done=done)

    def step(self, action: ChessAction):
        """
        Execute a chess move and return the resulting state.
        Args:
            action: The move to make in UCI format (e.g., "e2e4").
        Returns:
            Observation with reward and done flag.
        """
        if self._board is None:
            raise RuntimeError("Environment not initialized. Call reset() first.")

        # Parse the move
        try:
            move = chess.Move.from_uci(action.move)
        except ValueError:
            return self._make_observation(reward=-0.1, done=False)

        # Check if move is legal
        if move not in self._board.legal_moves:
            return self._make_observation(reward=-0.1, done=False)

        # Execute the move
        self._board.push(move)
        self._state.step_count += 1
        self._agent_move_count += 1
        self._state.move_history.append(action.move)
        self._state.current_player = "white" if self._board.turn else "black"
        self._state.fen = self._board.fen()

        # Calculate reward and check for game end
        reward, done = self._calculate_reward_and_done()

        # If game not over and opponent is configured, make opponent move
        if not done and self._opponent is not None:
            self._make_opponent_move()
            reward, done = self._calculate_reward_and_done()

        obs = self._make_observation(reward=reward, done=done)

        # Feed to rubric for trajectory accumulation
        self._apply_rubric(action, obs)

        # Note: obs.reward is kept from _calculate_reward_and_done() rather than
        # overridden by the rubric. The rubric here is a TrajectoryRubric that
        # returns 0.0 until done then the final score — the inline reward logic
        # already provides the correct per-step reward including penalties for
        # invalid/illegal moves. The rubric's value is in compute_step_rewards()
        # for training infrastructure, not in replacing obs.reward.

        return obs

    def _make_observation(self, reward: float = 0.0, done: bool = False):
        """Build observation from current board state."""
        legal_moves = [move.uci() for move in self._board.legal_moves]

        result = None
        if done:
            result = self._get_result_string()

        with _MOONFISH_LOCK:
            evaluation = board_evaluation(self._board)
        metadata = {
            "evaluation": evaluation,
            "fullmove_number": self._board.fullmove_number,
            "halfmove_clock": self._board.halfmove_clock,
        }

        # Compute discounted rewards for all agent moves when episode ends
        if done and self._agent_move_count > 0:
            discounted_rewards = self._compute_discounted_rewards(reward)
            metadata["discounted_rewards"] = discounted_rewards
            metadata["gamma"] = self._gamma

        return ChessObservation(
            fen=self._board.fen(),
            legal_moves=legal_moves,
            is_check=self._board.is_check(),
            done=done,
            reward=reward,
            result=result,
            metadata=metadata,
        )

    def _calculate_reward_and_done(self):
        """Calculate reward and check if episode is done."""
        if self._board.is_checkmate():
            winner = not self._board.turn
            if winner == self._agent_color:
                return 1.0, True
            else:
                return -1.0, True

        if self._board.is_stalemate():
            return 0.0, True

        if self._board.is_insufficient_material():
            return 0.0, True

        if self._board.is_fifty_moves():
            return 0.0, True

        if self._board.is_repetition(3):
            return 0.0, True

        if self._state.step_count >= self._max_moves:
            return 0.0, True

        return 0.0, False

    def _get_result_string(self):
        """Get the game result as a string."""
        if self._board.is_checkmate():
            return "1-0" if not self._board.turn else "0-1"
        return "1/2-1/2"

    def _compute_discounted_rewards(self, terminal_reward: float) -> list[float]:
        """
        Compute temporally discounted rewards for all agent moves.
        Uses exponential discounting: r_t = γ^(T-1-t) * R_final
        where T is total agent moves, t is move index, R_final is terminal reward.
        Earlier moves get less credit, later moves get more credit.
        This helps with credit assignment in long games.
        Args:
            terminal_reward: The final reward (+1 win, -1 loss, 0 draw)
        Returns:
            List of discounted rewards, one per agent move
        """
        T = self._agent_move_count
        discounted_rewards = []
        for t in range(T):
            # γ^(T-1-t) means last move (t=T-1) gets γ^0 = 1.0
            # First move (t=0) gets γ^(T-1)
            discount = self._gamma ** (T - 1 - t)
            discounted_rewards.append(discount * terminal_reward)
        return discounted_rewards

    def _make_opponent_move(self):
        """Make a move for the opponent using configured strategy."""
        if not self._board.legal_moves:
            return

        if self._opponent == "moonfish":
            with _MOONFISH_LOCK:
                move = search_move(self._board, depth=self._opponent_depth)
        elif self._opponent == "random":
            move = random.choice(list(self._board.legal_moves))
        else:
            return

        self._board.push(move)
        self._state.step_count += 1
        self._state.move_history.append(move.uci())
        self._state.current_player = "white" if self._board.turn else "black"
        self._state.fen = self._board.fen()

    def web_actions(
        self, observation: Dict[str, Any]
    ) -> List[Tuple[str, Dict[str, Any]]]:
        """The legal moves (UCI) as buttons, sorted so moves of the same piece sit together."""
        return [
            (move, {"move": move})
            for move in sorted(observation.get("legal_moves") or [])
        ]

    def render_web(self, observation: Dict[str, Any]) -> Optional[str]:
        """Draw the board from the FEN, white at the bottom, the king in check highlighted."""
        if "fen" not in observation:
            return None
        board = chess.Board(observation["fen"])
        checked = board.king(board.turn) if observation["is_check"] else None
        cells = []
        for rank in range(7, -1, -1):
            cells.append(f'<span style="opacity:.6;font-size:12px">{rank + 1}</span>')
            for file in range(8):
                square = chess.square(file, rank)
                piece = board.piece_at(square)
                # Fixed board colours, so a1 stays dark in both themes.
                if square == checked:
                    bg = "var(--color-accent)"
                elif (file + rank) % 2:
                    bg = "hsl(37 50% 84%)"
                else:
                    bg = "hsl(28 33% 55%)"
                # U+FE0E keeps the pawn as a text glyph instead of an emoji.
                glyph = piece.unicode_symbol() + "\ufe0e" if piece else ""
                cells.append(
                    f'<span style="background:{bg};color:black">{glyph}</span>'
                )
        cells.append("<span></span>")
        cells += [
            f'<span style="opacity:.6;font-size:12px;line-height:20px">{f}</span>'
            for f in "abcdefgh"
        ]
        return (
            '<div role="img" aria-label="Chess board" style="display:inline-grid;'
            "grid-template-columns:16px repeat(8,40px);grid-template-rows:repeat(8,40px) 20px;"
            "line-height:40px;text-align:center;font-size:30px;color:var(--body-text-color);padding:10px;"
            'border:1px solid var(--border-color-primary);border-radius:10px;background:var(--background-fill-secondary)">'
            + "".join(cells)
            + "</div>"
        )

    @property
    def state(self):
        """Return the current episode state."""
        return self._state

	# Copyright (c) Meta Platforms, Inc. and affiliates.
	# All rights reserved.
	#
	# This source code is licensed under the BSD-style license found in the
	# LICENSE file in the root directory of this source tree.

	"""
	Chess Environment for OpenEnv using moonfish.

	This module provides a chess environment for reinforcement learning,
	using python-chess for game logic and moonfish for position evaluation
	and opponent play.
	"""

	import random
	import threading
	import uuid
	from typing import Any, Dict, List, Optional, Tuple

	import chess
	from moonfish.lib import search_move
	from moonfish.psqt import board_evaluation, BOARD_EVALUATION_CACHE
	from openenv.core.env_server import Environment

	from ..models import ChessAction, ChessObservation, ChessState
	from .rubrics import ChessWinLossRubric

	# moonfish keeps one evaluation cache per process, so sessions take turns with it.
	_MOONFISH_LOCK = threading.Lock()


	class ChessEnvironment(Environment):
	"""
	Chess environment implementing the OpenEnv interface.

	Uses python-chess for game logic and moonfish for position evaluation.
	Designed for RL training where an agent plays as one color against
	an opponent (which can be random, moonfish engine, or self-play).
	"""

	SUPPORTS_CONCURRENT_SESSIONS = True

	def __init__(
	self,
	opponent: str = "moonfish",
	opponent_depth: int = 2,
	max_moves: int = 500,
	agent_color: str \| None = None,
	gamma: float = 0.99,
	):
	"""
	Initialize the chess environment.

	Args:
	opponent: Opponent type - "moonfish", "random", or None (self-play)
	opponent_depth: Search depth when using moonfish as opponent
	max_moves: Maximum half-moves before draw
	agent_color: Which color the agent plays - "white", "black", or None (alternate each episode)
	gamma: Discount factor for temporal credit assignment (0-1)
	"""
	super().__init__(rubric=ChessWinLossRubric(gamma=gamma))
	self._opponent = opponent
	self._opponent_depth = opponent_depth
	self._max_moves = max_moves
	self._agent_color_setting = agent_color
	self._gamma = gamma
	self._board = None
	self._state = None
	self._agent_color = chess.WHITE
	self._agent_move_count = 0 # Track agent moves for discounting
	self.reset()

	def reset(self, fen: str = None):
	"""
	Initialize a new chess game episode.

	Args:
	fen: Optional starting position in FEN notation.

	Returns:
	Initial observation of the board state.
	"""
	self._reset_rubric()
	# moonfish's module-level evaluation cache is never trimmed.
	with _MOONFISH_LOCK:
	BOARD_EVALUATION_CACHE.clear()

	if fen:
	self._board = chess.Board(fen)
	else:
	self._board = chess.Board()

	# Generate episode ID once
	episode_id = str(uuid.uuid4())

	# Determine agent color
	if self._agent_color_setting == "white":
	self._agent_color = chess.WHITE
	elif self._agent_color_setting == "black":
	self._agent_color = chess.BLACK
	elif self._agent_color_setting is None:
	# Alternate each episode based on episode_id hash
	self._agent_color = (
	chess.WHITE if hash(episode_id) % 2 == 0 else chess.BLACK
	)
	else:
	self._agent_color = chess.WHITE

	self._state = ChessState(
	episode_id=episode_id,
	step_count=0,
	current_player="white" if self._board.turn else "black",
	fen=self._board.fen(),
	move_history=[],
	)
	self._agent_move_count = 0

	# If agent plays Black and opponent is configured, opponent moves first
	if self._opponent is not None and self._agent_color == chess.BLACK:
	self._make_opponent_move()

	# Check if starting position is already terminal (e.g., custom FEN with checkmate/stalemate)
	_, done = self._calculate_reward_and_done()
	return self._make_observation(done=done)

	def step(self, action: ChessAction):
	"""
	Execute a chess move and return the resulting state.

	Args:
	action: The move to make in UCI format (e.g., "e2e4").

	Returns:
	Observation with reward and done flag.
	"""
	if self._board is None:
	raise RuntimeError("Environment not initialized. Call reset() first.")

	# Parse the move
	try:
	move = chess.Move.from_uci(action.move)
	except ValueError:
	return self._make_observation(reward=-0.1, done=False)

	# Check if move is legal
	if move not in self._board.legal_moves:
	return self._make_observation(reward=-0.1, done=False)

	# Execute the move
	self._board.push(move)
	self._state.step_count += 1
	self._agent_move_count += 1
	self._state.move_history.append(action.move)
	self._state.current_player = "white" if self._board.turn else "black"
	self._state.fen = self._board.fen()

	# Calculate reward and check for game end
	reward, done = self._calculate_reward_and_done()

	# If game not over and opponent is configured, make opponent move
	if not done and self._opponent is not None:
	self._make_opponent_move()
	reward, done = self._calculate_reward_and_done()

	obs = self._make_observation(reward=reward, done=done)

	# Feed to rubric for trajectory accumulation
	self._apply_rubric(action, obs)

	# Note: obs.reward is kept from _calculate_reward_and_done() rather than
	# overridden by the rubric. The rubric here is a TrajectoryRubric that
	# returns 0.0 until done then the final score — the inline reward logic
	# already provides the correct per-step reward including penalties for
	# invalid/illegal moves. The rubric's value is in compute_step_rewards()
	# for training infrastructure, not in replacing obs.reward.

	return obs

	def _make_observation(self, reward: float = 0.0, done: bool = False):
	"""Build observation from current board state."""
	legal_moves = [move.uci() for move in self._board.legal_moves]

	result = None
	if done:
	result = self._get_result_string()

	with _MOONFISH_LOCK:
	evaluation = board_evaluation(self._board)
	metadata = {
	"evaluation": evaluation,
	"fullmove_number": self._board.fullmove_number,
	"halfmove_clock": self._board.halfmove_clock,
	}

	# Compute discounted rewards for all agent moves when episode ends
	if done and self._agent_move_count > 0:
	discounted_rewards = self._compute_discounted_rewards(reward)
	metadata["discounted_rewards"] = discounted_rewards
	metadata["gamma"] = self._gamma

	return ChessObservation(
	fen=self._board.fen(),
	legal_moves=legal_moves,
	is_check=self._board.is_check(),
	done=done,
	reward=reward,
	result=result,
	metadata=metadata,
	)

	def _calculate_reward_and_done(self):
	"""Calculate reward and check if episode is done."""
	if self._board.is_checkmate():
	winner = not self._board.turn
	if winner == self._agent_color:
	return 1.0, True
	else:
	return -1.0, True

	if self._board.is_stalemate():
	return 0.0, True

	if self._board.is_insufficient_material():
	return 0.0, True

	if self._board.is_fifty_moves():
	return 0.0, True

	if self._board.is_repetition(3):
	return 0.0, True

	if self._state.step_count >= self._max_moves:
	return 0.0, True

	return 0.0, False

	def _get_result_string(self):
	"""Get the game result as a string."""
	if self._board.is_checkmate():
	return "1-0" if not self._board.turn else "0-1"
	return "1/2-1/2"

	def _compute_discounted_rewards(self, terminal_reward: float) -> list[float]:
	"""
	Compute temporally discounted rewards for all agent moves.

	Uses exponential discounting: r_t = γ^(T-1-t) * R_final
	where T is total agent moves, t is move index, R_final is terminal reward.

	Earlier moves get less credit, later moves get more credit.
	This helps with credit assignment in long games.

	Args:
	terminal_reward: The final reward (+1 win, -1 loss, 0 draw)

	Returns:
	List of discounted rewards, one per agent move
	"""
	T = self._agent_move_count
	discounted_rewards = []
	for t in range(T):
	# γ^(T-1-t) means last move (t=T-1) gets γ^0 = 1.0
	# First move (t=0) gets γ^(T-1)
	discount = self._gamma ** (T - 1 - t)
	discounted_rewards.append(discount * terminal_reward)
	return discounted_rewards

	def _make_opponent_move(self):
	"""Make a move for the opponent using configured strategy."""
	if not self._board.legal_moves:
	return

	if self._opponent == "moonfish":
	with _MOONFISH_LOCK:
	move = search_move(self._board, depth=self._opponent_depth)
	elif self._opponent == "random":
	move = random.choice(list(self._board.legal_moves))
	else:
	return

	self._board.push(move)
	self._state.step_count += 1
	self._state.move_history.append(move.uci())
	self._state.current_player = "white" if self._board.turn else "black"
	self._state.fen = self._board.fen()

	def web_actions(
	self, observation: Dict[str, Any]
	) -> List[Tuple[str, Dict[str, Any]]]:
	"""The legal moves (UCI) as buttons, sorted so moves of the same piece sit together."""
	return [
	(move, {"move": move})
	for move in sorted(observation.get("legal_moves") or [])
	]

	def render_web(self, observation: Dict[str, Any]) -> Optional[str]:
	"""Draw the board from the FEN, white at the bottom, the king in check highlighted."""
	if "fen" not in observation:
	return None
	board = chess.Board(observation["fen"])
	checked = board.king(board.turn) if observation["is_check"] else None
	cells = []
	for rank in range(7, -1, -1):
	cells.append(f'<span style="opacity:.6;font-size:12px">{rank + 1}</span>')
	for file in range(8):
	square = chess.square(file, rank)
	piece = board.piece_at(square)
	# Fixed board colours, so a1 stays dark in both themes.
	if square == checked:
	bg = "var(--color-accent)"
	elif (file + rank) % 2:
	bg = "hsl(37 50% 84%)"
	else:
	bg = "hsl(28 33% 55%)"
	# U+FE0E keeps the pawn as a text glyph instead of an emoji.
	glyph = piece.unicode_symbol() + "\ufe0e" if piece else ""
	cells.append(
	f'<span style="background:{bg};color:black">{glyph}</span>'
	)
	cells.append("<span></span>")
	cells += [
	f'<span style="opacity:.6;font-size:12px;line-height:20px">{f}</span>'
	for f in "abcdefgh"
	]
	return (
	'<div role="img" aria-label="Chess board" style="display:inline-grid;'
	"grid-template-columns:16px repeat(8,40px);grid-template-rows:repeat(8,40px) 20px;"
	"line-height:40px;text-align:center;font-size:30px;color:var(--body-text-color);padding:10px;"
	'border:1px solid var(--border-color-primary);border-radius:10px;background:var(--background-fill-secondary)">'
	+ "".join(cells)
	+ "</div>"
	)

	@property
	def state(self):
	"""Return the current episode state."""
	return self._state