import time
import csv
import heapq
from collections import deque
from abc import ABC, abstractmethod
import matplotlib.pyplot as plt
import pandas as pd
from dataclasses import dataclass
import os


class Cell:
    """Клетка лабиринта"""
    def __init__(self, x, y, is_wall=False):
        self.x = x
        self.y = y
        self.is_wall = is_wall
        self.is_start = False
        self.is_exit = False

    def is_passable(self):
        return not self.is_wall


class Maze:
    """Лабиринт"""
    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

    def get_cell(self, x, y):
        if 0 <= x < self.width and 0 <= y < self.height:
            return self.cells[y][x]
        return None

    def get_neighbors(self, cell):
        neighbors = []
        for dx, dy in [(0, 1), (0, -1), (1, 0), (-1, 0)]:
            nx, ny = cell.x + dx, cell.y + dy
            nb = self.get_cell(nx, ny)
            if nb and nb.is_passable():
                neighbors.append(nb)
        return neighbors
    
    def __str__(self):
        result = ""
        for y in range(self.height):
            for x in range(self.width):
                cell = self.get_cell(x, y)
                if cell is None:
                    result += "?"
                elif cell.is_wall:
                    result += "#"
                elif cell.is_start:
                    result += "S"
                elif cell.is_exit:
                    result += "E"
                else:
                    result += " "
            result += "\n"
        return result

class MazeBuilder(ABC):
    @abstractmethod
    def build_from_file(self, filename):
        pass


class TextFileMazeBuilder(MazeBuilder):
    def build_from_file(self, filename):
        with open(filename, 'r', encoding='utf-8') as f:
            lines = [line.rstrip('\n') for line in f.readlines()]
        height = len(lines)
        width = max(len(line) for line in lines)
        maze = Maze(width, height)

        for y, line in enumerate(lines):
            for x, ch in enumerate(line):
                cell = maze.get_cell(x, y)
                if ch == '#':
                    cell.is_wall = True
                elif ch == 'S':
                    cell.is_start = True
                    maze.start = cell
                elif ch == 'E':
                    cell.is_exit = True
                    maze.exit = cell
                else:
                    cell.is_wall = False
        return maze


class PathFindingStrategy(ABC):
    @abstractmethod
    def find_path(self, maze, start, exit):
        pass


class BFSStrategy(PathFindingStrategy):
    """Поиск в ширину"""
    
    def find_path(self, maze, start, exit):
        visited = set()
        if start == exit:
            return [start], 1
        queue = deque([start])
        visited.add(start)
        parent = {start: None}
        while queue:
            current = queue.popleft()
            for nb in maze.get_neighbors(current):
                if nb not in visited:
                    visited.add(nb)
                    parent[nb] = current
                    if nb == exit:
                        path = []
                        node = nb
                        while node is not None:
                            path.append(node)
                            node = parent[node]
                        path.reverse()
                        return path, len(visited)
                    queue.append(nb)
        return [], len(visited)


class DFSStrategy(PathFindingStrategy):
    """Поиск в глубину"""
    
    def find_path(self, maze, start, exit):
        visited = set()
        stack = [(start, [start])]
        while stack:
            current, path = stack.pop()
            if current == exit:
                return path, len(visited)
            visited.add(current)
            for nb in maze.get_neighbors(current):
                if nb not in visited:
                    stack.append((nb, path + [nb]))
        return [], len(visited)


class AStarStrategy(PathFindingStrategy):
    """Алгоритм A"""
    
    def heuristic(self, cell, exit):
        return abs(cell.x - exit.x) + abs(cell.y - exit.y)

    def find_path(self, maze, start, exit):
        open_set = []
        counter = 0
        heapq.heappush(open_set, (0, counter, start))
        counter += 1
        came_from = {}
        g_score = {start: 0}
        f_score = {start: self.heuristic(start, exit)}
        visited = set()
        while open_set:
            _, _, current = heapq.heappop(open_set)
            visited.add(current)
            if current == exit:
                path = []
                node = current
                while node in came_from:
                    path.append(node)
                    node = came_from[node]
                path.append(start)
                path.reverse()
                return path, len(visited)
            for nb in maze.get_neighbors(current):
                tentative_g = g_score[current] + 1
                if tentative_g < g_score.get(nb, float('inf')):
                    came_from[nb] = current
                    g_score[nb] = tentative_g
                    f = tentative_g + self.heuristic(nb, exit)
                    heapq.heappush(open_set, (f, counter, nb))
                    counter += 1
        return [], len(visited)
    
@dataclass
class SearchStats:
    time_ms: float
    visited_cells: int
    path_length: int
    algorithm: str


class MazeSolver:
    def __init__(self, maze, strategy):
        self.maze = maze
        self.strategy = strategy

    def set_strategy(self, strategy):
        self.strategy = strategy

    def solve(self):
        if self.maze.start is None or self.maze.exit is None:
            raise ValueError("Лабиринт не имеет старта или выхода")
        start_time = time.perf_counter()
        path, visited = self.strategy.find_path(self.maze, self.maze.start, self.maze.exit)
        end_time = time.perf_counter()
        stats = SearchStats(
            time_ms=(end_time - start_time) * 1000,
            visited_cells=visited,
            path_length=len(path),
            algorithm=self.strategy.__class__.__name__
        )
        return path, stats