from fnmatch import fnmatch
from heuristics.heuristic_base import Heuristic

def get_parts(fact):
    """Extract the components of a PDDL fact by removing parentheses and splitting the string."""
    return fact[1:-1].split()

def match(fact, *args):
    """
    Check if a PDDL fact matches a given pattern.

    - `fact`: The complete fact as a string, e.g., "(robot-at robot1 tile_0_1)".
    - `args`: The expected pattern (wildcards `*` allowed).
    - Returns `True` if the fact matches the pattern, else `False`.
    """
    parts = get_parts(fact)
    return all(fnmatch(part, arg) for part, arg in zip(parts, args))

class FloortileHeuristic(Heuristic):
    """
    A domain-dependent heuristic for the Floortile domain.

    # Summary
    This heuristic estimates the number of actions required to paint all tiles according to the goal conditions.
    It considers the distance robots need to travel, the number of tiles that need to be painted, and the color changes required.

    # Assumptions
    - Robots can move in four directions (up, down, left, right).
    - Robots can change their color, but this requires an action.
    - Each tile must be painted with the correct color as specified in the goal.

    # Heuristic Initialization
    - Extract the goal conditions for each tile (i.e., which color it should be painted).
    - Extract the static facts (e.g., adjacency relationships between tiles) to compute distances.

    # Step-By-Step Thinking for Computing Heuristic
    1. Identify the tiles that are not yet painted with the correct color.
    2. For each such tile, compute the minimum number of moves required for a robot to reach it.
    3. If the robot does not have the correct color, add a cost for changing the color.
    4. Sum the costs for all tiles to get the total heuristic value.
    """

    def __init__(self, task):
        """
        Initialize the heuristic by extracting:
        - Goal conditions for each tile.
        - Static facts (adjacency relationships between tiles).
        """
        self.goals = task.goals  # Goal conditions.
        static_facts = task.static  # Facts that are not affected by actions.

        # Map each tile to its goal color.
        self.goal_colors = {}
        for goal in self.goals:
            predicate, *args = get_parts(goal)
            if predicate == "painted":
                tile, color = args
                self.goal_colors[tile] = color

        # Build a graph of tile adjacencies.
        self.adjacency = {}
        for fact in static_facts:
            predicate, *args = get_parts(fact)
            if predicate in ["up", "down", "left", "right"]:
                tile1, tile2 = args
                if tile1 not in self.adjacency:
                    self.adjacency[tile1] = []
                self.adjacency[tile1].append(tile2)

    def __call__(self, node):
        """Compute an estimate of the minimal number of required actions."""
        state = node.state  # Current world state.

        # Track the current color of each robot.
        robot_colors = {}
        for fact in state:
            predicate, *args = get_parts(fact)
            if predicate == "robot-has":
                robot, color = args
                robot_colors[robot] = color

        # Track the current location of each robot.
        robot_locations = {}
        for fact in state:
            predicate, *args = get_parts(fact)
            if predicate == "robot-at":
                robot, tile = args
                robot_locations[robot] = tile

        total_cost = 0  # Initialize action cost counter.

        for tile, goal_color in self.goal_colors.items():
            # Check if the tile is already painted with the correct color.
            if f"(painted {tile} {goal_color})" in state:
                continue

            # Find the closest robot to this tile.
            min_distance = float('inf')
            closest_robot = None
            for robot, location in robot_locations.items():
                distance = self._compute_distance(location, tile)
                if distance < min_distance:
                    min_distance = distance
                    closest_robot = robot

            # Add the cost of moving the robot to the tile.
            total_cost += min_distance

            # Check if the robot has the correct color.
            if robot_colors[closest_robot] != goal_color:
                total_cost += 1  # Cost of changing color.

            # Add the cost of painting the tile.
            total_cost += 1

        return total_cost

    def _compute_distance(self, start_tile, goal_tile):
        """
        Compute the minimum number of moves required to go from `start_tile` to `goal_tile`.
        Uses a simple BFS to find the shortest path.
        """
        from collections import deque

        if start_tile == goal_tile:
            return 0

        visited = set()
        queue = deque([(start_tile, 0)])

        while queue:
            current_tile, distance = queue.popleft()
            if current_tile == goal_tile:
                return distance

            if current_tile in visited:
                continue
            visited.add(current_tile)

            for neighbor in self.adjacency.get(current_tile, []):
                queue.append((neighbor, distance + 1))

        return float('inf')  # If no path is found (should not happen in valid instances).
