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

from classes import Cell, Maze


class PathFindingStrategy(ABC):
    """Интерфейс стратегии поиска пути."""

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

    # Вспомогательный метод восстановления пути по словарю предшественников
    @staticmethod
    def _reconstruct_path(came_from: dict, start: Cell, goal: Cell) -> list[Cell]:
        path = []
        current = goal
        while current != start:
            path.append(current)
            current = came_from[current]
        path.append(start)
        path.reverse()
        return path


# ── BFS ──────────────────────────────────────────────────────────────────────

class BFSStrategy(PathFindingStrategy):


    def find_path(self, maze: Maze, start: Cell, exit_cell: Cell) -> list[Cell]:
        queue = deque([start])
        came_from: dict[Cell, Cell | None] = {start: None}
        self.visited_count = 0

        while queue:
            current = queue.popleft()
            self.visited_count += 1

            if current == exit_cell:
                return self._reconstruct_path(came_from, start, exit_cell)

            for neighbor in maze.get_neighbors(current):
                if neighbor not in came_from:
                    came_from[neighbor] = current
                    queue.append(neighbor)

        return []  # путь не найден


# ── DFS ──────────────────────────────────────────────────────────────────────

class DFSStrategy(PathFindingStrategy):


    def find_path(self, maze: Maze, start: Cell, exit_cell: Cell) -> list[Cell]:
        stack = [start]
        came_from: dict[Cell, Cell | None] = {start: None}
        self.visited_count = 0

        while stack:
            current = stack.pop()
            self.visited_count += 1

            if current == exit_cell:
                return self._reconstruct_path(came_from, start, exit_cell)

            for neighbor in maze.get_neighbors(current):
                if neighbor not in came_from:
                    came_from[neighbor] = current
                    stack.append(neighbor)

        return []


# ── A* ───────────────────────────────────────────────────────────────────────

class AStarStrategy(PathFindingStrategy):
    """A* с манхэттенской эвристикой"""

    def __init__(self):
        self.visited_count = 0

    def _heuristic(self, a: Cell, b: Cell) -> int:
        return abs(a.x - b.x) + abs(a.y - b.y)

    def find_path(self, maze: Maze, start: Cell, exit_cell: Cell) -> list[Cell]:
        g_score = {start: 0}
        parent: dict[Cell] = {start: None}
        open_heap = [(self._heuristic(start, exit_cell), 0, start)]
        closed_set: set[Cell] = set()  # уже обработанные клетки
        self.visited_count = 0
        counter = 0  # счётчик для устранения неоднозначности

        while open_heap:
            _, _, current = heapq.heappop(open_heap)

            if current in closed_set:
                continue
            closed_set.add(current)
            self.visited_count += 1

            if current == exit_cell:
                return self._reconstruct_path(parent, start, exit_cell)

            for neighbor in maze.get_neighbors(current):
                if neighbor in closed_set:
                    continue
                tentative_g = g_score[current]
                if tentative_g < g_score.get(neighbor, float('inf')):
                    g_score[neighbor] = tentative_g
                    parent[neighbor] = current
                    f = tentative_g + self._heuristic(neighbor, exit_cell)
                    counter += 1
                    heapq.heappush(open_heap, (f, counter, neighbor))

        return []