2026-rff_mp/VaravinVV/docs/data/task2/main.py

import abc
import heapq
import time
from collections import deque
from dataclasses import dataclass
from typing import List, Optional, Dict, Set, Tuple, Any

class Cell:
    #тут что такое клетка
    def __init__(self, x: int, y: int, is_wall: bool = False,
                 is_exit: bool = False, is_start: bool = False):
        self.x = x
        self.y = y
        self.is_wall = is_wall
        self.is_exit = is_exit
        self.is_start = is_start

    def is_passable(self) -> bool: #можно ли пройти через клетку
        return not self.is_wall

    def __repr__(self) -> str:
        return f"Cell({self.x},{self.y})"


class Maze:
    def __init__(self, width: int, height: int): #что содержит лабиринт, начало конец и тд
        self.width = width
        self.height = height
        self.grid: List[List[Cell]] = []
        self.start_cell: Optional[Cell] = None
        self.exit_cell: Optional[Cell] = None

    def set_cell(self, x: int, y: int, cell: Cell) -> None: #ставим клетку куда надо или не ставим если в границы не попала
        if not (0 <= x < self.width and 0 <= y < self.height):
            raise IndexError("координаты вне границ лабиринта")
        self.grid[y][x] = cell

    def get_cell(self, x: int, y: int) -> Optional[Cell]: #тут уже из коррдинат клетку вытаскиваем
        if 0 <= x < self.width and 0 <= y < self.height:
            return self.grid[y][x]
        return None

    def get_neighbors(self, cell: Cell) -> List[Cell]: #если соседняя клетка проходима - добавляем
        neighbors = []
        for dx, dy in [(0, -1), (0, 1), (-1, 0), (1, 0)]:
            nx, ny = cell.x + dx, cell.y + dy
            neighbor = self.get_cell(nx, ny)
            if neighbor and neighbor.is_passable():
                neighbors.append(neighbor)
        return neighbors


class MazeBuilder(abc.ABC):
    @abc.abstractmethod
    def build_from_file(self, filename: str) -> Maze:
        pass


class TextFileMazeBuilder(MazeBuilder):
    def build_from_file(self, filename: str) -> Maze:
        lines = []
        with open(filename, 'r', encoding='utf-8') as f:
            for line in f:
                line = line.rstrip('\n')
                if line: #игнорируем пустые строки
                    lines.append(line)
        
        if not lines:
            raise ValueError("Файл пуст")
        
        height = len(lines)
        width = max(len(line) for line in lines)
        
        maze = Maze(width, height)
        #инициализируем сетку пустыми клетками,по умолчанию стенами
        maze.grid = [[Cell(x, y, is_wall=True) for x in range(width)] for y in range(height)]
        
        start_cell = None
        exit_cell = None
        
        for y, line in enumerate(lines):
            for x, ch in enumerate(line):
                if x >= width:
                    continue
                if ch == '#':
                    continue
                elif ch == ' ':
                    cell = Cell(x, y, is_wall=False) 
                elif ch == 'S':
                    cell = Cell(x, y, is_wall=False, is_start=True)
                    start_cell = cell
                elif ch == 'E':
                    cell = Cell(x, y, is_wall=False, is_exit=True)
                    exit_cell = cell
                else:
                    #любой другой символ считаем проходом
                    cell = Cell(x, y, is_wall=False)
                maze.set_cell(x, y, cell)
        
        if start_cell is None:
            raise ValueError("отсутствует стартовая клетка (S)") #invalid check
        if exit_cell is None:
            raise ValueError("отсутствует выход (E)")
        
        maze.start_cell = start_cell
        maze.exit_cell = exit_cell
        return maze

class PathFindingStrategy(abc.ABC):
    @abc.abstractmethod
    def find_path(self, maze: Maze, start: Cell, exit_: Cell) -> Tuple[List[Cell], int]:
        pass

#дальше скорее математика, методы вроде ещё в том семаке разбирали
class BFSStrategy(PathFindingStrategy):
    def find_path(self, maze: Maze, start: Cell, exit_: Cell) -> Tuple[List[Cell], int]:
        if start is exit_:
            return [start], 1
        
        queue = deque([start]) #используйте deque 👍
        visited: Set[Cell] = {start}
        parent: Dict[Cell, Optional[Cell]] = {start: None}
        
        while queue:
            current = queue.popleft()
            if current is exit_:
                #восстановление пути
                path = []
                cur = current
                while cur is not None:
                    path.append(cur)
                    cur = parent[cur]
                path.reverse()
                return path, len(visited)
            
            for neighbor in maze.get_neighbors(current):
                if neighbor not in visited:
                    visited.add(neighbor)
                    parent[neighbor] = current
                    queue.append(neighbor)
        
        return [], len(visited)


class DFSStrategy(PathFindingStrategy):
    def find_path(self, maze: Maze, start: Cell, exit_: Cell) -> Tuple[List[Cell], int]:
        if start is exit_:
            return [start], 1
        
        stack = [start]
        visited: Set[Cell] = {start}
        parent: Dict[Cell, Optional[Cell]] = {start: None}
        
        while stack:
            current = stack.pop()
            if current is exit_:
                path = []
                cur = current
                while cur is not None:
                    path.append(cur)
                    cur = parent[cur]
                path.reverse()
                return path, len(visited)
            
            for neighbor in maze.get_neighbors(current):
                if neighbor not in visited:
                    visited.add(neighbor)
                    parent[neighbor] = current
                    stack.append(neighbor)
        
        return [], len(visited)


class AStarStrategy(PathFindingStrategy):
    @staticmethod
    def _heuristic(cell: Cell, target: Cell) -> int:
        return abs(cell.x - target.x) + abs(cell.y - target.y) #самая простая эвристика
    
    def find_path(self, maze: Maze, start: Cell, exit_: Cell) -> Tuple[List[Cell], int]:
        if start is exit_:
            return [start], 1
        counter = 0
        open_set = [(0, counter, start)]
        g_score: Dict[Cell, int] = {start: 0}
        f_score: Dict[Cell, int] = {start: self._heuristic(start, exit_)}
        parent: Dict[Cell, Optional[Cell]] = {start: None}
        closed_set: Set[Cell] = set()
        visited_count = 0
        
        while open_set:
            _, _, current = heapq.heappop(open_set)
            if current in closed_set:
                continue
            closed_set.add(current)
            visited_count = len(closed_set)
            
            if current is exit_:
                path = []
                cur = current
                while cur is not None:
                    path.append(cur)
                    cur = parent[cur]
                path.reverse()
                return path, visited_count
            
            for neighbor in maze.get_neighbors(current):
                if neighbor in closed_set:
                    continue
                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
                    f = tentative_g + self._heuristic(neighbor, exit_)
                    f_score[neighbor] = f
                    counter += 1
                    heapq.heappush(open_set, (f, counter, neighbor))
        
        return [], visited_count

@dataclass
class SearchStats:
    time_ms: float          #время выполнения в мс
    visited_cells: int      #количество посещённых клеток
    path_length: int        #длина найденного пути (0 если пути нет)
    path_found: bool        #найден ли путь

class Observer(abc.ABC): #я забыл что я там писать хотел после наблюдателя удачи мне завтра разобрать
    @abc.abstractmethod #а я (гугл + хабр) разобрал снова балбесина!!!
    def update(self, event_type: str, data: Any = None) -> None:
        pass


class Subject:
    def __init__(self):
        self._observers: List[Observer] = []
    
    def attach(self, observer: Observer) -> None:
        if observer not in self._observers:
            self._observers.append(observer)
    
    def detach(self, observer: Observer) -> None:
        if observer in self._observers:
            self._observers.remove(observer)
    
    def notify(self, event_type: str, data: Any = None) -> None:
        for observer in self._observers:
            observer.update(event_type, data)

class MazeSolver(Subject):
    def __init__(self, maze: Maze, strategy: Optional[PathFindingStrategy] = None):
        super().__init__()
        self.maze = maze
        self._strategy = strategy
    
    def set_strategy(self, strategy: PathFindingStrategy) -> None: #смена алгоритма
        self._strategy = strategy
    
    def solve(self) -> Optional[SearchStats]:
        if self._strategy is None:
            print("где стратегия?")
            return None
        
        self.notify("solving_start", {"strategy": self._strategy.__class__.__name__})
        
        start_time = time.perf_counter()
        path, visited = self._strategy.find_path(self.maze, self.maze.start_cell, self.maze.exit_cell)
        end_time = time.perf_counter()
        
        time_ms = (end_time - start_time) * 1000.0
        path_found = len(path) > 0
        stats = SearchStats(
            time_ms=time_ms,
            visited_cells=visited,
            path_length=len(path) if path_found else 0,
            path_found=path_found
        )
        
        if path_found:
            self.notify("path_found", {"path": path, "stats": stats})
        else:
            self.notify("no_path", {"stats": stats})
        
        self.notify("solving_end", {"stats": stats})
        return stats

class ConsoleView(Observer): #красиво в консоль выводит лабиринт
    def __init__(self, maze: Maze):
        self.maze = maze
        self.last_path: List[Cell] = []
    
    def update(self, event_type: str, data: Any = None) -> None:
        if event_type == "path_found":
            self.last_path = data["path"]
            self.render()
        elif event_type == "no_path":
            self.last_path = []
            self.render(no_path=True)
        elif event_type == "solving_start":
            print(f"\nпоиск пути (алгоритм: {data['strategy']})\n")
    
    def render(self, no_path: bool = False) -> None:
        path_set = set(self.last_path) if self.last_path else set()
        
        for y in range(self.maze.height):
            row = []
            for x in range(self.maze.width):
                cell = self.maze.get_cell(x, y)
                if cell is None:
                    row.append('?')
                    continue
                if cell is self.maze.start_cell:
                    row.append('S')
                elif cell is self.maze.exit_cell:
                    row.append('E')
                elif cell in path_set:
                    row.append('*')
                elif cell.is_wall:
                    row.append('#')
                else:
                    row.append(' ')
            print(''.join(row))
        
        if no_path:
            print("\nнет пути (no way)")
        elif self.last_path:
            print(f"\nнайден путь длиной {len(self.last_path)} клеток")
        else:
            print("\nожидание решения")

def main():
    import sys
    
    if len(sys.argv) < 2:
        print("для запуска: python main.py <имя_лабиринта>.txt")
        return
    
    filename = sys.argv[1]
    
    #строится лабиринт из файла
    builder = TextFileMazeBuilder()
    try:
        maze = builder.build_from_file(filename)
    except Exception as e:
        print(f"ошибка загрузки лабиринта: {e}")
        return
    
    #наблюдатель и решатель прикрепляются
    solver = MazeSolver(maze)
    view = ConsoleView(maze)
    solver.attach(view)
    strategies = {
        "1": BFSStrategy(),
        "2": DFSStrategy(),
        "3": AStarStrategy()
    }
    
    print("\nвыберите алгоритм поиска:")
    print("1. BFS")
    print("2. DFS")
    print("3. A*")
    choice = input("введите (1/2/3): ").strip()
    
    strategy = strategies.get(choice)
    if not strategy:
        print("неверный выбор, по умолчанию используется BFS.")
        strategy = BFSStrategy()
    
    solver.set_strategy(strategy)
    stats = solver.solve()
    
    if stats:
        print("\nстатистика по поиску пути в данном лабиринте")
        print(f"выбранный алгоритм: {strategy.__class__.__name__}")
        print(f"время выполнения: {stats.time_ms:.3f} мс")
        print(f"посещено клеток: {stats.visited_cells}")
        print(f"путь найден?: {'да' if stats.path_found else 'нет'}")
        if stats.path_found:
            print(f"длина пути: {stats.path_length}")


if __name__ == "__main__":
    main()