from abc import ABC, abstractmethod
from collections import deque
from heapq import heappush, heappop
from typing import List, Dict, Optional
from models import Cell, Maze


class PathFindingStrategy(ABC):
    
    @abstractmethod
    def find_path(self, maze: Maze, start: Cell, exit_cell: Cell) -> List[Cell]:
        pass


class BFSStrategy(PathFindingStrategy):
    
    def find_path(self, maze: Maze, start: Cell, exit_cell: Cell) -> List[Cell]:
        queue = deque([start])
        visited = {start}
        parent: Dict[Cell, Optional[Cell]] = {start: None}
        
        while queue:
            current = queue.popleft()
            
            if current == exit_cell:
                return self._reconstruct_path(parent, current)
            
            for neighbor in maze.get_neighbors(current):
                if neighbor not in visited:
                    visited.add(neighbor)
                    parent[neighbor] = current
                    queue.append(neighbor)
        
        return []
    
    def _reconstruct_path(self, parent: Dict[Cell, Optional[Cell]], current: Cell) -> List[Cell]:
        path = []
        while current is not None:
            path.append(current)
            current = parent.get(current)
        return list(reversed(path))


class DFSStrategy(PathFindingStrategy):
    
    def find_path(self, maze: Maze, start: Cell, exit_cell: Cell) -> List[Cell]:
        stack = [(start, [start])]
        visited = {start}
        
        while stack:
            current, path = stack.pop()
            
            if current == exit_cell:
                return path
            
            for neighbor in maze.get_neighbors(current):
                if neighbor not in visited:
                    visited.add(neighbor)
                    stack.append((neighbor, path + [neighbor]))
        
        return []


class AStarStrategy(PathFindingStrategy):
    
    def _heuristic(self, cell: Cell, exit_cell: Cell) -> int:
        return abs(cell.x - exit_cell.x) + abs(cell.y - exit_cell.y)
    
    def find_path(self, maze: Maze, start: Cell, exit_cell: Cell) -> List[Cell]:
        counter = 0
        open_set = [(self._heuristic(start, exit_cell), counter, start)]
        
        g_score: Dict[Cell, float] = {start: 0}
        parent: Dict[Cell, Optional[Cell]] = {start: None}
        
        while open_set:
            _, _, current = heappop(open_set)
            
            if current == exit_cell:
                return self._reconstruct_path(parent, current)
            
            for neighbor in maze.get_neighbors(current):
                tentative_g = g_score[current] + 1
                
                if neighbor not in g_score or tentative_g < g_score[neighbor]:
                    parent[neighbor] = current
                    g_score[neighbor] = tentative_g
                    counter += 1
                    f = tentative_g + self._heuristic(neighbor, exit_cell)
                    heappush(open_set, (f, counter, neighbor))
        
        return []
    
    def _reconstruct_path(self, parent: Dict[Cell, Optional[Cell]], current: Cell) -> List[Cell]:
        path = []
        while current is not None:
            path.append(current)
            current = parent.get(current)
        return list(reversed(path))