2026-rff_mp/KislyuninED/docks/data/2-st-exercize/maze-core.py

import sys
from collections import deque
import heapq
import time
import os


class Cell:
    def __init__(self, x, y):
        self._x = x
        self._y = y
        self._is_wall = False
        self._is_start = False
        self._is_exit = False
    
    @property
    def x(self):
        return self._x
    
    @property
    def y(self):
        return self._y
    
    @property
    def is_wall(self):
        return self._is_wall
    
    @is_wall.setter
    def is_wall(self, value):
        self._is_wall = value
    
    @property
    def is_start(self):
        return self._is_start
    
    @is_start.setter
    def is_start(self, value):
        self._is_start = value
    
    @property
    def is_exit(self):
        return self._is_exit
    
    @is_exit.setter
    def is_exit(self, value):
        self._is_exit = value
    
    def is_passable(self):
        return not self._is_wall


class Maze:
    def __init__(self, width, height):
        self._width = width
        self._height = height
        self._cells = [[Cell(x, y) for x in range(width)] for y in range(height)]
        self._start = None
        self._exit = None
    
    @property
    def width(self):
        return self._width
    
    @property
    def height(self):
        return self._height
    
    @property
    def start(self):
        return self._start
    
    @property
    def exit(self):
        return self._exit
    
    def get_cell(self, x, y):
        if 0 <= x < self._width and 0 <= y < self._height:
            return self._cells[y][x]
        return None
    
    def set_cell(self, x, y, cell_type):
        cell = self.get_cell(x, y)
        if cell is None:
            return
        
        if cell_type == 'wall':
            cell.is_wall = True
        elif cell_type == 'start':
            if self._start:
                self._start.is_start = False
            cell.is_start = True
            cell.is_wall = False
            self._start = cell
        elif cell_type == 'exit':
            if self._exit:
                self._exit.is_exit = False
            cell.is_exit = True
            cell.is_wall = False
            self._exit = cell
        elif cell_type == 'path':
            cell.is_wall = False
    
    def get_neighbors(self, cell):
        neighbors = []
        directions = [(0, -1), (0, 1), (-1, 0), (1, 0)]
        for dx, dy in directions:
            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:
    def build_from_file(self, filename):
        raise NotImplementedError("Need to realise in calss")


class TextFileMazeBuilder(MazeBuilder):
    def build_from_file(self, filename):
        with open(filename, 'r') as f:
            lines = [line.rstrip('\n') for line in f.readlines()]
        height = len(lines)
        width = max(len(line) for line in lines) if height > 0 else 0
        start_en = 0
        exit_en = 0
        maze = Maze(width, height)

        for y, line in enumerate(lines):
            for x, ch in enumerate(line):
                if ch == "#":
                    maze.set_cell(x, y, "wall")
                elif ch == "S":
                    maze.set_cell(x, y, "start")
                    start_en += 1
                elif ch == "E":
                    maze.set_cell(x, y, "exit")
                    exit_en += 1
                else:
                    maze.set_cell(x, y, 'path')
        if start_en != 1 or exit_en != 1:
            raise ValueError(f"Labirint must have one S and one E. Found: S={start_en}, E={exit_en}")
        return maze


class PathFindingStrategy:
    def find_path(self, maze, start, exit_cell):
        raise NotImplementedError
    
    def _reconstruct_path(self, came_from, start, exit_cell):
        path = []
        current = exit_cell
        while current is not None:
            path.append(current)
            current = came_from.get(current)
        path.reverse()
        return path
    
    def get_visited_count(self):
        return getattr(self, '_visited_count', 0)


class BFSStrategy(PathFindingStrategy):
    def find_path(self, maze, start, exit_cell):
        queue = deque()
        queue.append(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, start, 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, start, exit_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, start, 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, exit_cell):
        return abs(cell.x - exit_cell.x) + abs(cell.y - exit_cell.y)
    
    def find_path(self, maze, start, exit_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, start, 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 []


class SearchStats:
    def __init__(self, time_ms, visited_cells, path_length):
        self.time_ms = time_ms
        self.visited_cells = visited_cells
        self.path_length = path_length


class MazeSolver:
    def __init__(self, maze):
        self._maze = maze
        self._strategy = None
    
    def set_strategy(self, strategy):
        self._strategy = strategy
    
    def solve(self):
        if self._strategy is None:
            return None
        
        start_time = time.perf_counter()
        path = self._strategy.find_path(self._maze, self._maze.start, self._maze.exit)
        end_time = time.perf_counter()
        time_ms = (end_time - start_time) * 1000
        
        return SearchStats(time_ms, self._strategy.get_visited_count(), len(path))