"""
Connect4 Multi-Agent Environment — Server Side
Adapted for autonomous driving scenario:
  - Agent 1 = "Ego vehicle" (LLM being trained)
  - Agent 2 = "Opponent vehicle" (rule-based or another LLM)

The board represents a grid intersection control problem:
  - Winning = successfully navigating without collision
  - Rewards shaped for RL post-training
"""

import numpy as np
from typing import Optional
from openenv.core.environment import Environment
from ..models import (
    Connect4Action, Connect4Observation, Connect4State
)


ROWS = 6
COLS = 7
EMPTY = 0
AGENT1 = 1   # Ego vehicle / LLM under training
AGENT2 = 2   # Opponent / rule-based agent


class Connect4Environment(Environment):
    """
    Connect4 as a multi-agent driving coordination environment.

    Observation:
      - Board state (6x7 grid)
      - Current player turn
      - Legal moves
      - Last move played
      - Game status

    Reward shaping (for RL):
      +10.0  → Win (ego agent connects 4)
      -10.0  → Loss (opponent connects 4)
       +0.5  → Blocking opponent's winning move
       +0.2  → Creating a 3-in-a-row
       -0.1  → Invalid move attempt
        0.0  → Draw
    """

    def __init__(self):
        super().__init__()
        self.board: np.ndarray = np.zeros((ROWS, COLS), dtype=int)
        self.current_player: int = AGENT1
        self.done: bool = False
        self.winner: Optional[int] = None
        self.last_move: Optional[int] = None
        self.move_history: list = []

    # ------------------------------------------------------------------ #
    #  OpenEnv API                                                         #
    # ------------------------------------------------------------------ #

    def reset(self) -> Connect4Observation:
        self.board = np.zeros((ROWS, COLS), dtype=int)
        self.current_player = AGENT1
        self.done = False
        self.winner = None
        self.last_move = None
        self.move_history = []
        return self._make_observation("Game reset. Your turn — you are Player 1 (Ego Vehicle).")

    def step(self, action: Connect4Action) -> tuple[Connect4Observation, float, bool]:
        if self.done:
            obs = self._make_observation("Game already finished. Call reset() to start a new game.")
            return obs, 0.0, True

        col = action.column
        reward = 0.0

        # ---- validate move ----
        if col < 0 or col >= COLS or not self._is_valid(col):
            obs = self._make_observation(f"Invalid move: column {col} is full or out of range.")
            return obs, -0.1, False

        # ---- check for blocking bonus before placing ----
        reward += self._blocking_bonus(col)

        # ---- place piece ----
        row = self._drop_piece(col, self.current_player)
        self.last_move = col
        self.move_history.append((self.current_player, col))

        # ---- 3-in-a-row bonus ----
        if self._count_streak(row, col, self.current_player) >= 3:
            reward += 0.2

        # ---- check win ----
        if self._check_win(self.current_player):
            self.done = True
            self.winner = self.current_player
            reward += 10.0 if self.current_player == AGENT1 else -10.0
            msg = ("🏆 Ego vehicle wins! Successful navigation." 
                   if self.current_player == AGENT1 
                   else "💥 Opponent wins. Collision occurred.")
            obs = self._make_observation(msg)
            return obs, reward, True

        # ---- check draw ----
        if self._board_full():
            self.done = True
            obs = self._make_observation("🤝 Draw. Stalemate — no collision, no winner.")
            return obs, 0.0, True

        # ---- switch player ----
        self.current_player = AGENT2 if self.current_player == AGENT1 else AGENT1
        msg = f"Move accepted (col {col}). Now Player {self.current_player}'s turn."
        obs = self._make_observation(msg)
        return obs, reward, False

    def state(self) -> Connect4State:
        return Connect4State(
            episode_id=self._episode_id,
            step_count=self._step_count,
            current_player=self.current_player,
            done=self.done,
            winner=self.winner,
            move_history=self.move_history,
        )

    # ------------------------------------------------------------------ #
    #  Internal helpers                                                    #
    # ------------------------------------------------------------------ #

    def _make_observation(self, message: str) -> Connect4Observation:
        return Connect4Observation(
            board=self.board.tolist(),
            board_str=self._render_board(),
            current_player=self.current_player,
            legal_moves=self._legal_moves(),
            last_move=self.last_move,
            done=self.done,
            winner=self.winner,
            message=message,
        )

    def _render_board(self) -> str:
        symbols = {EMPTY: ".", AGENT1: "X", AGENT2: "O"}
        rows = []
        for r in range(ROWS):
            rows.append(" ".join(symbols[self.board[r][c]] for c in range(COLS)))
        rows.append("-" * (COLS * 2 - 1))
        rows.append(" ".join(str(c) for c in range(COLS)))
        return "\n".join(rows)

    def _is_valid(self, col: int) -> bool:
        return self.board[0][col] == EMPTY

    def _legal_moves(self) -> list[int]:
        return [c for c in range(COLS) if self._is_valid(c)]

    def _drop_piece(self, col: int, player: int) -> int:
        for row in range(ROWS - 1, -1, -1):
            if self.board[row][col] == EMPTY:
                self.board[row][col] = player
                return row
        return -1

    def _check_win(self, player: int) -> bool:
        b = self.board
        # Horizontal
        for r in range(ROWS):
            for c in range(COLS - 3):
                if all(b[r][c+i] == player for i in range(4)):
                    return True
        # Vertical
        for r in range(ROWS - 3):
            for c in range(COLS):
                if all(b[r+i][c] == player for i in range(4)):
                    return True
        # Diagonal /
        for r in range(3, ROWS):
            for c in range(COLS - 3):
                if all(b[r-i][c+i] == player for i in range(4)):
                    return True
        # Diagonal \
        for r in range(ROWS - 3):
            for c in range(COLS - 3):
                if all(b[r+i][c+i] == player for i in range(4)):
                    return True
        return False

    def _board_full(self) -> bool:
        return all(self.board[0][c] != EMPTY for c in range(COLS))

    def _count_streak(self, row: int, col: int, player: int) -> int:
        """Count max consecutive pieces for player around (row, col)."""
        directions = [(0,1),(1,0),(1,1),(1,-1)]
        best = 1
        for dr, dc in directions:
            count = 1
            for sign in [1, -1]:
                r, c = row + sign*dr, col + sign*dc
                while 0 <= r < ROWS and 0 <= c < COLS and self.board[r][c] == player:
                    count += 1
                    r += sign*dr
                    c += sign*dc
            best = max(best, count)
        return best

    def _blocking_bonus(self, col: int) -> float:
        """+0.5 if placing here blocks opponent's 4-in-a-row."""
        opponent = AGENT2 if self.current_player == AGENT1 else AGENT1
        test_board = self.board.copy()
        for row in range(ROWS - 1, -1, -1):
            if test_board[row][col] == EMPTY:
                test_board[row][col] = opponent
                break
        # Check if opponent would have won
        b = test_board
        for r in range(ROWS):
            for c in range(COLS - 3):
                if all(b[r][c+i] == opponent for i in range(4)):
                    return 0.5
        for r in range(ROWS - 3):
            for c in range(COLS):
                if all(b[r+i][c] == opponent for i in range(4)):
                    return 0.5
        return 0.0