from fnmatch import fnmatch
from heuristics.heuristic_base import Heuristic

class RoversHeuristic(Heuristic):
    """
    A domain-dependent heuristic for the Rovers domain.

    # Summary
    This heuristic estimates the number of actions needed to achieve all required communications (soil, rock, and image data) for each objective and mode.

    # Assumptions:
    - The rover must collect samples (soil or rock) or take images before communicating the data.
    - Navigation between visible waypoints is required to reach the necessary locations.
    - Communication actions require the data to be collected and the rover to be in a visible range of the lander.

    # Heuristic Initialization
    - Extracts goal conditions and static facts (e.g., visibility and traversability between waypoints).
    - Maps each objective and mode to their required communication data.

    # Step-by-Step Thinking for Computing Heuristic
    1. Identify all required communications (soil, rock, image data) for each objective and mode.
    2. For each communication, check if it is already achieved.
    3. For each unachieved communication:
       a. Determine if the required data (sample or image) has been collected.
       b. If not collected, estimate the actions needed to collect it, including navigation to the sample location.
       c. Estimate the actions needed to communicate the data, including navigation to the lander's location.
       d. If the data is already collected but not communicated, estimate the actions needed for communication.
    4. Sum the estimated actions for all unachieved communications to get the total heuristic value.
    """

    def __init__(self, task):
        """Initialize the heuristic by extracting goal conditions and static facts."""
        self.goals = task.goals
        self.static = task.static

        # Extract static information into useful data structures
        self.visible = self._extract_visible_waypoints()
        self.can_traverse = self._extract_traversable_paths()
        self communicable = self._extract_communicable_pairs()

    def _extract_visible_waypoints(self):
        """Extract visible waypoints for each waypoint from static facts."""
        visible = {}
        for fact in self.static:
            if fnmatch(fact, '(visible ?w ?p)'):
                w, p = fact[1:-1].split()[1], fact[1:-1].split()[2]
                if w not in visible:
                    visible[w] = []
                visible[w].append(p)
        return visible

    def _extract_traversable_paths(self):
        """Extract can_traverse relations into a dictionary."""
        can_traverse = {}
        for fact in self.static:
            if fnmatch(fact, '(can_traverse ?r ?x ?y)'):
                r, x, y = fact[1:-1].split()[1], fact[1:-1].split()[2], fact[1:-1].split()[3]
                if (r, x) not in can_traverse:
                    can_traverse[(r, x)] = []
                can_traverse[(r, x)].append(y)
        return can_traverse

    def _extract_communicable_pairs(self):
        """Extract communicable pairs (rover to lander) from static facts."""
        communicable = set()
        for fact in self.static:
            if fnmatch(fact, '(at_lander ?l ?y)'):
                l, y = fact[1:-1].split()[1], fact[1:-1].split()[2]
                communicable.add((l, y))
        return communicable

    def __call__(self, node):
        """Estimate the minimum cost to achieve all required communications."""
        state = node.state

        def match(fact, *args):
            """Check if a PDDL fact matches a given pattern."""
            parts = fact[1:-1].split()
            return all(fnmatch(part, arg) for part, arg in zip(parts, args))

        # Extract current state information
        current_communications = set()
        current_samples = set()
        current_images = set()
        rover_locations = {}

        for fact in state:
            if match(fact, 'communicated_'):
                current_communications.add(fact)
            elif match(fact, 'have_soil_analysis'):
                current_samples.add(('soil', fact.split()[1], fact.split()[2]))
            elif match(fact, 'have_rock_analysis'):
                current_samples.add(('rock', fact.split()[1], fact.split()[2]))
            elif match(fact, 'have_image'):
                current_images.add(('image', fact.split()[1], fact.split()[2], fact.split()[3]))
            elif match(fact, 'at'):
                rover = fact.split()[1]
                loc = fact.split()[2]
                rover_locations[rover] = loc

        # Extract goal information
        goal_communications = {}
        for goal in self.goals:
            if match(goal, '(communicated_'):
                obj = goal.split()[1]
                mode = goal.split()[2] if len(goal.split()) > 2 else None
                key = (obj, mode)
                if key not in goal_communications:
                    goal_communications[key] = []
                goal_communications[key].append(goal)

        # For each goal communication, check if it's already done
        total_actions = 0

        # Process soil and rock communications
        for (sample_type, obj, loc) in current_samples:
            pass  # Placeholder for actual logic

        # Process image communications
        for (image_type, rover, obj, mode) in current_images:
            pass  # Placeholder for actual logic

        # Calculate navigation steps between waypoints
        def get_navigation_steps(from_loc, to_loc):
            steps = 0
            # Implement BFS or similar to find minimal steps
            return steps

        # Sum up all required actions
        return total_actions
