735 lines
28 KiB
Python
735 lines
28 KiB
Python
import math
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from maze import *
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import math
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import copy
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from collections import deque
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class Greedy3x3ResourceCollector:
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"""
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基于3x3视野的贪心资源收集器
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每次移动时选择3x3视野范围内最高价值的资源
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只能进行上下左右移动
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"""
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def __init__(self, map_data, start=None, end=None):
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"""
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初始化3x3视野贪心资源收集器
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Args:
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map_data: 迷宫地图，2D列表 (注意：这里是[y][x]格式)
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start: 起始位置 (x, y)，如果为None则自动寻找
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end: 目标位置 (x, y)，如果为None则自动寻找
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"""
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self.original_map = copy.deepcopy(map_data)
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self.map_data = copy.deepcopy(map_data)
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self.rows = len(map_data)
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self.cols = len(map_data[0]) if self.rows > 0 else 0
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# 寻找起始位置和目标位置
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self.start = start or self._find_position('s')
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self.end = end or self._find_position('e')
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if not self.start:
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raise ValueError("无法找到起始位置 's'")
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if not self.end:
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raise ValueError("无法找到目标位置 'e'")
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self.current_pos = self.start
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self.path = [self.start]
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self.collected_resources = []
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self.total_value = 0
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self.visited_resources = set()
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self.explored_positions = set([self.start])
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# 增加历史移动记录和死胡同检测相关变量
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self.position_visit_count = {self.start: 1} # 记录每个位置的访问次数
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self.deadend_positions = set() # 记录已知的死胡同位置
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self.backtrack_points = [] # 记录可能的回溯点
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self.oscillation_detection = [] # 用于检测来回走动的历史
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self.max_oscillation_length = 6 # 检测来回走动的最大长度
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print(f"3x3视野贪心算法初始化")
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print(f"起始位置: {self.start}")
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print(f"目标位置: {self.end}")
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def _find_position(self, target):
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"""寻找地图中指定字符的位置，返回(x, y)格式"""
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for y in range(self.rows):
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for x in range(self.cols):
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if self.map_data[y][x].lower() == target.lower():
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return (x, y)
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return None
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def get_3x3_vision(self, pos):
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"""
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获取以pos为中心的3x3视野范围内的所有单元格
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Args:
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pos: 当前位置 (x, y)
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Returns:
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dict: {(x, y): cell_value} 形式的字典
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"""
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x, y = pos
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vision = {}
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# 遍历3x3范围
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for dx in range(-1, 2):
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for dy in range(-1, 2):
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new_x, new_y = x + dx, y + dy
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# 检查边界
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if 0 <= new_x < self.cols and 0 <= new_y < self.rows:
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vision[(new_x, new_y)] = self.map_data[new_y][new_x]
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return vision
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def get_adjacent_cells(self, pos):
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"""
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获取当前位置的上下左右四个相邻位置
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Args:
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pos: 当前位置 (x, y)
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Returns:
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list: 可移动的相邻位置列表
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"""
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x, y = pos
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adjacent = []
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# 上下左右四个方向
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directions = [(0, -1), (0, 1), (-1, 0), (1, 0)] # 上、下、左、右
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for dx, dy in directions:
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new_x, new_y = x + dx, y + dy
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# 检查边界和可移动性
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if (0 <= new_x < self.cols and
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0 <= new_y < self.rows and
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self.can_move_to((new_x, new_y))):
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adjacent.append((new_x, new_y))
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return adjacent
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def can_move_to(self, pos):
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"""
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检查是否可以移动到指定位置
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Args:
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pos: 目标位置 (x, y)
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Returns:
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bool: 是否可以移动
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"""
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x, y = pos
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cell = self.map_data[y][x]
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# 不能移动到墙壁
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if cell == '1':
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return False
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return True
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def evaluate_resource_value(self, cell):
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"""
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评估资源的价值
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Args:
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cell: 单元格内容
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Returns:
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int: 资源价值，正数表示收益，负数表示损失
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"""
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if cell.startswith('g'):
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# 金币资源，提取数值
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try:
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return int(cell[1:])
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except ValueError:
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return 0
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elif cell.startswith('t'):
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# 陷阱资源，提取数值并取负
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try:
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return -int(cell[1:])
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except ValueError:
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return 0
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else:
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# 其他类型单元格没有资源价值
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return 0
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def find_best_resource_in_3x3_vision(self):
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"""
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在3x3视野内寻找最佳资源
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优先级：金币 > 未走过 > 走过的路(优先很久之前走过的路) > 墙/陷阱
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加入死胡同检测和回溯机制
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"""
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x, y = self.current_pos
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best_pos = None
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best_value = float('-inf')
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best_visited_time = float('inf')
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# 更新当前位置的访问次数
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self.position_visit_count[self.current_pos] = self.position_visit_count.get(self.current_pos, 0) + 1
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# 检查是否处于死胡同中
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if self.is_deadend(self.current_pos):
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self.deadend_positions.add(self.current_pos)
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# 寻找回溯点
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backtrack_point = self.find_backtrack_point()
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if backtrack_point != self.current_pos:
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# 将当前位置到回溯点的路径添加到路径计划中
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self.backtrack_points.append(backtrack_point)
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print(f"检测到死胡同，计划回溯到: {backtrack_point}")
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# 如果回溯点是相邻的，直接返回
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if abs(backtrack_point[0] - x) + abs(backtrack_point[1] - y) == 1:
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return backtrack_point, 0 # 回溯点，价值为0
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# 如果有待回溯的点，优先选择那个方向
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if self.backtrack_points:
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target = self.backtrack_points[-1]
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# 计算到回溯点的方向
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for dx, dy in [(0, 1), (1, 0), (0, -1), (-1, 0)]:
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nx, ny = x + dx, y + dy
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if (nx, ny) == target:
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return (nx, ny), 0 # 回溯点，价值为0
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# 如果相邻点在路径上且朝向回溯点方向，也可以选择
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if (0 <= nx < self.cols and 0 <= ny < self.rows and
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self.map_data[ny][nx] != '1'): # 使用'1'表示墙壁
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if ((nx > x and target[0] > x) or
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(nx < x and target[0] < x) or
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(ny > y and target[1] > y) or
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(ny < y and target[1] < y)):
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return (nx, ny), 0 # 朝向回溯点的方向，价值为0
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# 如果已经到达回溯点或无法向回溯点移动，弹出这个回溯点
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if self.current_pos == self.backtrack_points[-1]:
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self.backtrack_points.pop()
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# 检测是否陷入来回走动的循环
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if len(self.path) >= 2:
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self.oscillation_detection.append(self.current_pos)
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if len(self.oscillation_detection) > self.max_oscillation_length:
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self.oscillation_detection.pop(0)
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if self.detect_oscillation():
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print("检测到来回走动，尝试打破循环")
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# 清空回溯点列表，寻找新的探索方向
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self.backtrack_points = []
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# 尝试找到访问次数最少的相邻位置
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min_visits = float('inf')
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least_visited = None
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for dx, dy in [(0, 1), (1, 0), (0, -1), (-1, 0)]:
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nx, ny = x + dx, y + dy
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if (0 <= nx < self.cols and 0 <= ny < self.rows and
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self.map_data[ny][nx] != '1'): # 使用'1'表示墙壁
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visits = self.position_visit_count.get((nx, ny), 0)
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if visits < min_visits:
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min_visits = visits
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least_visited = (nx, ny)
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if least_visited:
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return least_visited, 0 # 访问次数最少的位置，价值为0
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# 在3x3视野内寻找最佳位置
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for i in range(-1, 2):
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for j in range(-1, 2):
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# 跳过自身和对角线位置
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if (i == 0 and j == 0) or (i != 0 and j != 0):
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continue
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nx, ny = x + i, y + j
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# 检查位置是否在地图范围内
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if 0 <= nx < self.cols and 0 <= ny < self.rows:
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cell = self.map_data[ny][nx]
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pos = (nx, ny)
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# 检查是否是墙，不能走
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if cell == '1':
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continue
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# 计算资源价值
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value = self.evaluate_resource_value(cell)
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# 检查是否已经走过这个位置
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is_visited = pos in self.explored_positions
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visited_time = self.position_visit_count.get(pos, 0)
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# 计算探索潜力
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exploration_potential = self.calculate_exploration_potential(pos)
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# 优先级计算逻辑
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# 1. 金币优先
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if value > 0:
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if (value > best_value or
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(value == best_value and
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((not is_visited and best_visited_time > 0) or
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(is_visited and visited_time < best_visited_time)))):
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best_value = value
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best_pos = pos
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best_visited_time = visited_time if is_visited else 0
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# 2. 没有金币，选择未走过的路
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elif not is_visited:
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if best_value <= 0 and (best_visited_time > 0 or exploration_potential > best_value):
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best_value = exploration_potential
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best_pos = pos
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best_visited_time = 0
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# 3. 如果都走过了，选择走过次数最少的路
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elif is_visited and visited_time < best_visited_time:
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if best_value <= 0:
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best_value = -visited_time # 负值，访问次数越少越好
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best_pos = pos
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best_visited_time = visited_time
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# 如果找不到合适的位置，就选择任意一个可行的相邻位置
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if best_pos is None:
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for dx, dy in [(0, 1), (1, 0), (0, -1), (-1, 0)]:
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nx, ny = x + dx, y + dy
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if (0 <= nx < self.cols and 0 <= ny < self.rows and
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self.map_data[ny][nx] != '1'): # 使用'1'表示墙壁
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best_pos = (nx, ny)
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break
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return best_pos, best_value if best_value > float('-inf') else 0
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def find_exploration_target(self):
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"""
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当视野内没有资源时，寻找探索目标
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严格按照优先级：未走过的路 > 走过的路（很久之前走过的优先）
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"""
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adjacent = self.get_adjacent_cells(self.current_pos)
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# 1. 优先级1：未走过的路
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unexplored = [pos for pos in adjacent if pos not in self.explored_positions]
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if unexplored:
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return unexplored[0] # 选择第一个未探索的位置
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# 2. 优先级2：走过的路，按时间排序（很久之前走过的优先）
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explored = []
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for pos in adjacent:
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if pos in self.explored_positions:
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# 找出这个位置在路径中最早出现的索引
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if pos in self.path:
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earliest_index = self.path.index(pos)
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explored.append((pos, earliest_index))
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else:
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# 如果在explored_positions但不在path中，可能是通过其他方式标记的
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# 给它一个很大的索引，表示是最近才探索的
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explored.append((pos, float('inf')))
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if explored:
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# 按照索引排序，索引越小表示越早走过
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explored.sort(key=lambda x: x[1])
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return explored[0][0]
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return None
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def collect_resource(self, pos):
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"""
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收集指定位置的资源
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Args:
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pos: 资源位置 (x, y)
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"""
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x, y = pos
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cell = self.map_data[y][x]
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value = self.evaluate_resource_value(cell)
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if value != 0:
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self.collected_resources.append({
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'position': pos,
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'type': cell,
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'value': value
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})
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self.total_value += value
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self.visited_resources.add(pos)
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print(f"收集资源: 位置{pos}, 类型{cell}, 价值{value}, 总价值{self.total_value}")
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def run_3x3_greedy_collection(self, max_moves=1000):
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"""
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运行3x3视野贪心资源收集算法
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严格按照优先级：金币 > 未走过的路 > 走过的路 > 墙/陷阱
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对于走过的路，优先走很久之前走过的路
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Args:
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max_moves: 最大移动步数，防止无限循环
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Returns:
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dict: 包含路径、收集的资源等信息
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"""
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print("\\n开始3x3视野贪心资源收集...")
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moves = 0
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stuck_count = 0 # 连续无法找到资源的次数
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max_stuck = 20 # 最大连续无资源次数
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while moves < max_moves and stuck_count < max_stuck:
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moves += 1
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# 在3x3视野内寻找最佳位置（按照严格优先级）
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best_pos, best_value = self.find_best_resource_in_3x3_vision()
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if best_pos is not None:
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# 移动到选定位置
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self.current_pos = best_pos
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self.path.append(best_pos)
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self.explored_positions.add(best_pos)
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# 如果是资源位置，进行收集
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if best_value != 0:
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print(f"第{moves}步: 发现视野内金币 位置{best_pos}, 价值{best_value}")
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self.collect_resource(best_pos)
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stuck_count = 0 # 收集到资源后重置无资源计数
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else:
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# 是普通路径
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if best_pos not in self.explored_positions:
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print(f"第{moves}步: 移动到未走过的路 位置{best_pos}")
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else:
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print(f"第{moves}步: 移动到走过的路 位置{best_pos}")
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stuck_count += 1
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else:
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# 没有可移动位置，结束收集
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print(f"第{moves}步: 无法进行任何移动，结束收集")
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break
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# 检查是否达到终点
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if self.current_pos == self.end:
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print(f"第{moves}步: 到达终点！")
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break
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if moves >= max_moves:
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print(f"达到最大移动步数 {max_moves}，结束收集")
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elif stuck_count >= max_stuck:
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print(f"连续 {max_stuck} 步未找到资源，结束收集")
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print("3x3视野资源收集完成！")
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print(f"总步数: {len(self.path)-1}, 收集资源数: {len(self.collected_resources)}, 资源总价值: {self.total_value}")
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return self.get_collection_result()
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def get_collection_result(self):
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"""获取收集结果"""
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return {
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'path': self.path.copy(),
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'collected_resources': self.collected_resources.copy(),
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'total_value': self.total_value,
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'total_moves': len(self.path) - 1,
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'resources_count': len(self.collected_resources),
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'start_pos': self.start,
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'end_pos': self.end,
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'final_pos': self.current_pos,
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'explored_positions': len(self.explored_positions)
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}
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def reset(self):
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"""重置收集器状态"""
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self.map_data = copy.deepcopy(self.original_map)
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self.current_pos = self.start
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self.path = [self.start]
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self.collected_resources = []
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self.total_value = 0
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self.visited_resources = set()
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self.explored_positions = set([self.start])
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self.position_visit_count = {self.start: 1}
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self.deadend_positions = set()
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self.backtrack_points = []
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self.oscillation_detection = []
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def get_path(self):
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"""
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||
获取完整的资源收集路径
|
||
返回：路径列表，格式为 [(x1, y1), (x2, y2), ...]
|
||
"""
|
||
# 先重置状态
|
||
self.reset()
|
||
|
||
max_steps = self.rows * self.cols * 3 # 设置最大步数限制，避免无限循环
|
||
steps = 0
|
||
reached_goal = False
|
||
|
||
while steps < max_steps and not reached_goal:
|
||
_, _, reached_goal = self.next_step()
|
||
steps += 1
|
||
|
||
# 如果路径长度已经很长但还没到达目标，可能是在循环
|
||
if steps > self.rows * self.cols * 2:
|
||
print(f"警告：路径过长 ({steps} 步)，可能存在循环。提前结束。")
|
||
break
|
||
|
||
if reached_goal:
|
||
print(f"找到路径！总步数: {steps}, 总收集价值: {self.total_value}")
|
||
else:
|
||
print(f"未能找到到达目标的路径，已走 {steps} 步，总收集价值: {self.total_value}")
|
||
|
||
print(f"发现的死胡同数量: {len(self.deadend_positions)}")
|
||
|
||
return self.path
|
||
|
||
def next_step(self):
|
||
"""
|
||
执行下一步移动
|
||
返回：(新位置, 收集的资源价值, 是否到达目标)
|
||
"""
|
||
if self.current_pos == self.end:
|
||
return self.current_pos, 0, True
|
||
|
||
next_pos, value = self.find_best_resource_in_3x3_vision()
|
||
if next_pos is None:
|
||
# 如果找不到下一步，说明卡住了，可能是迷宫设计问题
|
||
print("找不到下一步移动，可能被卡住了")
|
||
return self.current_pos, 0, False
|
||
|
||
# 记录新位置和路径
|
||
self.current_pos = next_pos
|
||
self.path.append(next_pos)
|
||
self.explored_positions.add(next_pos)
|
||
|
||
# 更新位置访问计数
|
||
self.position_visit_count[next_pos] = self.position_visit_count.get(next_pos, 0) + 1
|
||
|
||
# 如果当前位置是回溯点且有多个回溯点，移除当前回溯点
|
||
if self.backtrack_points and next_pos == self.backtrack_points[-1]:
|
||
self.backtrack_points.pop()
|
||
|
||
# 收集资源
|
||
x, y = next_pos
|
||
cell = self.map_data[y][x]
|
||
value = self.evaluate_resource_value(cell)
|
||
|
||
if value > 0 and next_pos not in self.visited_resources:
|
||
self.collected_resources.append((next_pos, value))
|
||
self.visited_resources.add(next_pos)
|
||
self.total_value += value
|
||
# 标记资源已被收集，避免重复计算
|
||
if cell.startswith('g') or cell.startswith('c'):
|
||
try:
|
||
self.map_data[y][x] = 'v' # 将收集过的资源标记为已访问
|
||
except:
|
||
pass
|
||
|
||
# 检查是否到达目标
|
||
reached_goal = (next_pos == self.end)
|
||
|
||
# 调试信息
|
||
if len(self.path) % 10 == 0:
|
||
print(f"当前路径长度: {len(self.path)}, 总收集价值: {self.total_value}")
|
||
print(f"已发现的死胡同数量: {len(self.deadend_positions)}")
|
||
|
||
return next_pos, value, reached_goal
|
||
|
||
def is_deadend(self, pos):
|
||
"""
|
||
判断当前位置是否是死胡同
|
||
死胡同的定义：除了来路外，周围全是墙/陷阱/已走过的路
|
||
"""
|
||
x, y = pos
|
||
valid_directions = 0
|
||
|
||
for dx, dy in [(0, 1), (1, 0), (0, -1), (-1, 0)]:
|
||
nx, ny = x + dx, y + dy
|
||
if (0 <= nx < self.cols and 0 <= ny < self.rows and
|
||
self.map_data[ny][nx] != '1' and # 使用'1'表示墙壁
|
||
(nx, ny) not in self.explored_positions):
|
||
valid_directions += 1
|
||
|
||
# 如果没有未探索的方向，则是死胡同
|
||
return valid_directions == 0
|
||
|
||
def find_backtrack_point(self):
|
||
"""
|
||
寻找回溯点，即从路径中找到最近的有未探索方向的点
|
||
"""
|
||
# 从最近访问到最早访问的路径点遍历
|
||
for pos in reversed(self.path):
|
||
x, y = pos
|
||
|
||
# 检查这个点的四个方向是否有未探索的路
|
||
for dx, dy in [(0, 1), (1, 0), (0, -1), (-1, 0)]:
|
||
nx, ny = x + dx, y + dy
|
||
if (0 <= nx < self.cols and 0 <= ny < self.rows and
|
||
self.map_data[ny][nx] != '1' and # 使用'1'表示墙壁
|
||
(nx, ny) not in self.explored_positions):
|
||
return pos
|
||
|
||
# 如果找不到回溯点，则返回起始点
|
||
return self.start
|
||
|
||
def detect_oscillation(self):
|
||
"""
|
||
检测路径中是否有来回走动的情况
|
||
"""
|
||
if len(self.oscillation_detection) < self.max_oscillation_length:
|
||
return False
|
||
|
||
# 检查最近的移动是否形成循环
|
||
recent_moves = self.oscillation_detection[-self.max_oscillation_length:]
|
||
|
||
# 打印调试信息
|
||
print(f"检查振荡: {recent_moves[-6:]}")
|
||
|
||
# 检查是否有重复位置模式 (例如A-B-A-B或A-B-C-A-B-C)
|
||
for pattern_length in range(2, self.max_oscillation_length // 2 + 1):
|
||
if recent_moves[-pattern_length:] == recent_moves[-2*pattern_length:-pattern_length]:
|
||
print(f"检测到振荡！模式长度: {pattern_length}")
|
||
return True
|
||
|
||
# 更简单的检测：检查是否在有限步数内多次访问同一位置
|
||
position_counts = {}
|
||
for pos in recent_moves:
|
||
if pos in position_counts:
|
||
position_counts[pos] += 1
|
||
if position_counts[pos] >= 3: # 在短时间内访问同一位置3次以上
|
||
print(f"检测到位置 {pos} 被频繁访问 {position_counts[pos]} 次")
|
||
return True
|
||
else:
|
||
position_counts[pos] = 1
|
||
|
||
return False
|
||
|
||
def calculate_exploration_potential(self, pos):
|
||
"""
|
||
计算位置的探索潜力值
|
||
潜力值基于：
|
||
1. 周围未探索的方向数
|
||
2. 到达过这个位置的次数（次数越多潜力越低）
|
||
3. 是否含有资源
|
||
"""
|
||
x, y = pos
|
||
potential = 0
|
||
|
||
# 检查周围四个方向是否有未探索的路
|
||
for dx, dy in [(0, 1), (1, 0), (0, -1), (-1, 0)]:
|
||
nx, ny = x + dx, y + dy
|
||
if (0 <= nx < self.cols and 0 <= ny < self.rows):
|
||
# 未探索的路增加潜力
|
||
if (nx, ny) not in self.explored_positions and self.map_data[ny][nx] != '1':
|
||
potential += 10
|
||
|
||
# 有资源的路增加更多潜力
|
||
cell = self.map_data[ny][nx]
|
||
if cell.startswith('g'):
|
||
try:
|
||
value = int(cell[1:])
|
||
potential += value * 2
|
||
except ValueError:
|
||
potential += 5 # 如果无法解析值，则默认增加5点潜力
|
||
|
||
# 访问次数越多，潜力越低
|
||
visit_penalty = self.position_visit_count.get(pos, 0) * 5
|
||
potential = max(0, potential - visit_penalty)
|
||
|
||
return potential
|
||
|
||
|
||
# 使用示例
|
||
def main():
|
||
obj = MazeGenerator(20,'demo.csv',name="龙脊峡谷迷宫")
|
||
obj.generate(
|
||
seed=123,
|
||
boss_count=2,
|
||
traps_range=(5, 10),
|
||
mechanisms_range=(3, 7),
|
||
skill_traps=8
|
||
)
|
||
obj.export_to_csv()
|
||
|
||
map_data = obj.read_csv()
|
||
see = MazeGenerator(1,filename ='demo.csv')
|
||
see.read_from_csv()
|
||
print("=== 原始迷宫 ===")
|
||
see.print_maze()
|
||
|
||
print("\\n" + "="*60)
|
||
print("使用传统贪心算法:")
|
||
print("="*60)
|
||
player = GreedyPlayer(map_data)
|
||
player.find_path()
|
||
see.maze = player.marked_map
|
||
see.print_maze()
|
||
print(f"传统贪心算法总收益: {player.get_total_reward()}")
|
||
|
||
print("\\n" + "="*60)
|
||
print("使用3x3视野贪心算法:")
|
||
print("="*60)
|
||
# 使用新的3x3视野算法
|
||
greedy_3x3 = Greedy3x3ResourceCollector(map_data)
|
||
result = greedy_3x3.run_3x3_greedy_collection()
|
||
|
||
# 显示结果
|
||
see.maze = greedy_3x3.add_path_to_map()
|
||
see.print_maze()
|
||
|
||
print(f"\\n3x3视野算法结果:")
|
||
print(f" 总移动步数: {result['total_moves']}")
|
||
print(f" 收集资源数量: {result['resources_count']}")
|
||
print(f" 资源总价值: {result['total_value']}")
|
||
print(f" 探索位置数: {result['explored_positions']}")
|
||
|
||
print("\\n收集的资源详情:")
|
||
for i, resource in enumerate(result['collected_resources'], 1):
|
||
print(f" {i}. 位置{resource['position']}: {resource['type']} (价值: {resource['value']})")
|
||
|
||
|
||
def demo_3x3_greedy():
|
||
"""演示3x3视野贪心算法"""
|
||
|
||
# 创建一个示例迷宫
|
||
demo_maze = [
|
||
['s', '0', 'g5', '1', 't3'],
|
||
['0', '1', '0', '0', 'g2'],
|
||
['g3', '0', '1', 't2', '0'],
|
||
['0', 't1', '0', '0', 'g4'],
|
||
['1', '0', 'g1', '0', 'e']
|
||
]
|
||
|
||
print("=== 3x3视野贪心算法演示 ===")
|
||
print("迷宫说明:")
|
||
print(" s: 起点, e: 终点")
|
||
print(" g数字: 金币资源 (正收益)")
|
||
print(" t数字: 陷阱资源 (负收益)")
|
||
print(" 0: 可通行路径, 1: 墙壁")
|
||
print("\\n原始迷宫:")
|
||
for row in demo_maze:
|
||
print(' '.join(f"{cell:>2}" for cell in row))
|
||
|
||
# 使用3x3视野贪心算法
|
||
collector = Greedy3x3ResourceCollector(demo_maze)
|
||
result = collector.run_3x3_greedy_collection()
|
||
|
||
# 显示标记后的迷宫
|
||
marked_maze = collector.add_path_to_map()
|
||
print("\\n标记路径后的迷宫:")
|
||
print("S: 起点, E: 终点, *: 已收集资源, .: 路径")
|
||
for row in marked_maze:
|
||
print(' '.join(f"{cell:>2}" for cell in row))
|
||
|
||
print(f"\\n算法结果:")
|
||
print(f" 总移动步数: {result['total_moves']}")
|
||
print(f" 收集资源数量: {result['resources_count']}")
|
||
print(f" 资源总价值: {result['total_value']}")
|
||
|
||
return collector, result
|
||
|
||
|
||
|
||
|
||
if __name__ == "__main__":
|
||
# 运行演示
|
||
print("选择运行模式:")
|
||
print("1. 完整迷宫生成和算法比较")
|
||
print("2. 简单3x3视野算法演示")
|
||
|
||
choice = input("请输入选择 (1 或 2，默认为 2): ").strip()
|
||
|
||
if choice == "1":
|
||
main()
|
||
else:
|
||
demo_3x3_greedy()
|
||
|
||
|
||
|
||
|