#!/usr/bin/env python
# coding: utf-8

# In[ ]:


import heapq
from typing import List, Dict, Optional, Tuple
from strategiesPathfinding_strategy import PathFindingStrategy
from modelsMaze import Maze
from modelsCell import Cell

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

    @property
    def name(self) -> str:
        return "A*"

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

    def find_path(self, maze: Maze, start: Cell, exit_cell: Cell) -> List[Cell]:
        if start == exit_cell:
            return [start]

        # Приоритетная очередь: (f_score, counter, cell)
        open_set = [(0, 0, start)]
        counter = 1

        came_from: Dict[Cell, Optional[Cell]] = {}

        g_score: Dict[Cell, float] = {start: 0}
        f_score: Dict[Cell, float] = {start: self._heuristic(start, exit_cell)}

        visited_count = 0

        while open_set:
            current_f, _, current = heapq.heappop(open_set)
            visited_count += 1

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

            for neighbor in maze.get_neighbors(current):
                tentative_g_score = g_score.get(current, float('inf')) + 1

                if tentative_g_score < g_score.get(neighbor, float('inf')):
                    came_from[neighbor] = current
                    g_score[neighbor] = tentative_g_score
                    f_score[neighbor] = tentative_g_score + self._heuristic(neighbor, exit_cell)
                    heapq.heappush(open_set, (f_score[neighbor], counter, neighbor))
                    counter += 1

        self._last_visited_count = visited_count
        return []

    @property
    def last_visited_count(self) -> int:
        return getattr(self, '_last_visited_count', 0)

