import heapq
from abc import ABC, abstractmethod
from collections import deque
from typing import List

from .cell import Cell
from .maze import Maze


class PathFindingStrategy(ABC):
    def __init__(self):
        self.visited_count = 0

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

    def reconstruct_path(self, came_from: dict, exit_cell: Cell) -> List[Cell]:
        path = []
        current = exit_cell
        while current is not None:
            path.append(current)
            current = came_from.get(current)
        path.reverse()
        return path


class BFSStrategy(PathFindingStrategy):
    def find_path(self, maze: Maze, start: Cell, exit_cell: Cell) -> List[Cell]:
        queue = deque([start])
        came_from = {start: None}
        visited = {start}

        while queue:
            current = queue.popleft()
            if current == exit_cell:
                self.visited_count = len(visited)
                return self.reconstruct_path(came_from, exit_cell)
            for neighbor in maze.get_neighbors(current):
                if neighbor not in visited:
                    visited.add(neighbor)
                    came_from[neighbor] = current
                    queue.append(neighbor)
        self.visited_count = len(visited)
        return []


class DFSStrategy(PathFindingStrategy):
    def find_path(self, maze: Maze, start: Cell, exit_cell: Cell) -> List[Cell]:
        stack = [start]
        came_from = {start: None}
        visited = {start}

        while stack:
            current = stack.pop()
            if current == exit_cell:
                self.visited_count = len(visited)
                return self.reconstruct_path(came_from, exit_cell)
            for neighbor in maze.get_neighbors(current):
                if neighbor not in visited:
                    visited.add(neighbor)
                    came_from[neighbor] = current
                    stack.append(neighbor)
        self.visited_count = len(visited)
        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]:
        heap = []
        counter = 0
        start_f = self.heuristic(start, exit_cell)
        heapq.heappush(heap, (start_f, counter, start))
        counter += 1

        came_from = {}
        g_score = {start: 0}
        f_score = {start: start_f}
        visited = set()

        while heap:
            current_f, _, current = heapq.heappop(heap)
            visited.add(current)

            if current == exit_cell:
                self.visited_count = len(visited)
                return self.reconstruct_path(came_from, exit_cell)
            if current_f > f_score.get(current, float("inf")):
                continue
            for neighbor in maze.get_neighbors(current):
                tentative_g = g_score[current] + 1
                if tentative_g < g_score.get(neighbor, float("inf")):
                    came_from[neighbor] = current
                    g_score[neighbor] = tentative_g
                    new_f = tentative_g + self.heuristic(neighbor, exit_cell)
                    f_score[neighbor] = new_f
                    heapq.heappush(heap, (new_f, counter, neighbor))
                    counter += 1
        self.visited_count = len(visited)
        return []