# def add():
# ? ? temp = ["姓名", "學(xué)號(hào)", "班級(jí)", "電話(huà)"]
# ? ? dic = {}
# ? ? lst = []
# ? ? for item in temp:
# ? ? ? ? inp = input("請(qǐng)輸入{}:".format(item))
# ? ? ? ? if inp == "exit":
# ? ? ? ? ? ? print("成功退出輸入")
# ? ? ? ? ? ? return False
# ? ? ? ? else:
# ? ? ? ? ? ? dic[item] = inp
# ? ? lst.append(dic)
# ? ? print("添加成功")
# ? ? return lst
#
# def show(lst):
# ? ? print("-"*30)
# ? ? print("姓名\t\t學(xué)號(hào)\t\t班級(jí)\t\t電話(huà)")
# ? ? print("=" * 30)
# ? ? for i in range(len(lst)):
# ? ? ? ? for val in lst[i].values():
# ? ? ? ? ? ? print(val, "\t", end="")
# ? ? ? ? print()
# ? ? print("-" * 30)
#
# def search(total_lst):
# ? ? name = input("請(qǐng)輸入您要查詢(xún)的學(xué)生姓名：")
# ? ? flag = False
# ? ? tmp = []
# ? ? for i in range(len(total_lst)):
# ? ? ? ? if total_lst[i]["姓名"] == name:
# ? ? ? ? ? ? tmp.append(total_lst[i])
# ? ? ? ? ? ? show(tmp)
# ? ? ? ? ? ? flag = True
# ? ? if not flag:
# ? ? ? ? print("抱歉，沒(méi)有找到該學(xué)生")
#
# if __name__ == '__main__':
# ? ? total_lst = []
# ? ? while True:
# ? ? ? ? flag = add()
# ? ? ? ? if flag:
# ? ? ? ? ? ? total_lst = total_lst + flag
# ? ? ? ? else:
# ? ? ? ? ? ? break
# ? ? show(total_lst)
# ? ? search(total_lst)
#
# def show(lst):
# ? ? print("="*30)
# ? ? print("{:^25s}".format("輸出F1賽事車(chē)手積分榜"))
# ? ? print("=" * 30)
# ? ? print("{:<10s}".format("排名"), "{:<10s}".format("車(chē)手"), "{:<10s}".format("積分"))
# ? ? for i in range(len(lst)):
# ? ? ? ? print("{:0>2d}{:<9s}".format(i+1, ""), "{:<10s}".format(lst[i][0]), "{:<10d}".format(lst[i][1]))
#
# if __name__ == '__main__':
# ? ? data = 'lisi 380,jack 256,bob 385,rose 204,alex 212'
# ? ? data = data.split(",")
# ? ? dic = {}
# ? ? da = []
# ? ? for i in range(len(data)):
# ? ? ? ? da.append(data[i].split())
# ? ? for i in range(len(da)):
# ? ? ? ? dic[da[i][0]] = int(da[i][1])
# ? ? data2 = sorted(dic.items(), key=lambda kv: (kv[1], kv[0]), reverse=True)
# ? ? show(data2)


# class Fun:
# ? ? def __init__(self):
# ? ? ? ? print("Fun:__init__()")
# ? ? def test(self):
# ? ? ? ? print("Fun")
#
# class InheritFun(Fun):
# ? ? def __init__(self):
# ? ? ? ? print("InheritedFun.__init__()")
# ? ? ? ? super().__init__()
# ? ? def test(self):
# ? ? ? ? super().test()
# ? ? ? ? print("InheritedFun")
# a = InheritFun()
# a.test()

# from math import *
# class Circle:
# ? ? def __init__(self, radius=1):
# ? ? ? ? self.radius = radius
# ? ? def getPerimeter(self):
# ? ? ? ? return 2 * self.radius * pi
# ? ? def getArea(self):
# ? ? ? ? return self.radius * self.radius * pi
# ? ? def setRadius(self, radius):
# ? ? ? ? self.radius = radius
#
# a=Circle(10)
# print("{:.1f},{:.2f}".format(a.getPerimeter(), a.getArea()))

# from math import *
# class Root:
# ? ? def __init__(self, a, b, c):
# ? ? ? ? self.a = a
# ? ? ? ? self.b = b
# ? ? ? ? self.c = c
# ? ? def getDiscriminant(self):
# ? ? ? ? return pow(self.b, 2)-4*self.a*self.c
# ? ? def getRoot1(self):
# ? ? ? ? return (-self.b+pow(pow(self.b, 2)-4*self.a*self.c, 0.5))/(2*self.a)
# ? ? def getRoot2(self):
# ? ? ? ? return (-self.b - pow(pow(self.b, 2) - 4 * self.a * self.c, 0.5)) / (2 * self.a)
# inp = input("請(qǐng)輸入a，b，c: ").split(" ")
# inp = list(map(int, inp))
# Root = Root(inp[0], inp[1], inp[2])
# print("判別式為：{:.1f}； ?x1：{:.1f}; ?x2：{:.1f}".format(Root.getDiscriminant(), Root.getRoot1(), Root.getRoot2()))

# class Stock:
# ? ? def __init__(self, num, name, pre_price, now_price):
# ? ? ? ? self.num = num
# ? ? ? ? self.name = name
# ? ? ? ? self.pre_price = pre_price
# ? ? ? ? self.now_price = now_price
# ? ? def getCode(self):
# ? ? ? ? return self.num
# ? ? def getName(self):
# ? ? ? ? return self.name
# ? ? def getPriceYesterday(self):
# ? ? ? ? return self.pre_price
# ? ? def getPriceToday(self):
# ? ? ? ? return self.now_price
# ? ? def getChangePercent(self):
# ? ? ? ? return (self.now_price-self.pre_price)/self.pre_price
#
# sCode = input() #輸入代碼
# sName = input() #輸入名稱(chēng)
# priceYesterday = float(input()) #輸入昨日價(jià)格
# priceToday = float(input()) #輸入今日價(jià)格
# s = Stock(sCode,sName,priceYesterday,priceToday)
# print("代碼:",s.getCode())
# print("名稱(chēng):",s.getName())
# print("昨日價(jià)格:%.2f\n今天價(jià)格:%.2f" % (s.getPriceYesterday(),s.getPriceToday()))
# print("價(jià)格變化百分比:%.2f%%" % (s.getChangePercent()*100))


# from math import pi
#
# class Shape:
# ? ? def __init__(self, name='None', area=None, perimeter=None):
# ? ? ? ? self.name = name
# ? ? ? ? self.area = area
# ? ? ? ? self.perimeter = perimeter
# ? ? def calArea(self):
# ? ? ? ? return self.area
# ? ? def calPerimeter(self):
# ? ? ? ? return self.perimeter
# ? ? def display(self):
# ? ? ? ? print("名稱(chēng)：%s 面積：%.2f 周長(zhǎng)：%.2f" % (self.name, self.area, self.perimeter))
#
# class Rectangle(Shape):
# ? ? def __init__(self, width, height):
# ? ? ? ? super().__init__()
# ? ? ? ? self.width = width
# ? ? ? ? self.height = height
# ? ? def calArea(self):
# ? ? ? ? self.area = self.height*self.width
# ? ? ? ? return self.area
# ? ? def calPerimeter(self):
# ? ? ? ? self.perimeter = (self.height+self.width)*2
# ? ? ? ? return self.perimeter
# ? ? def display(self):
# ? ? ? ? self.name = "Rectangle"
# ? ? ? ? Rectangle.calArea(self)
# ? ? ? ? Rectangle.calPerimeter(self)
# ? ? ? ? super(Rectangle, self).display()
#
# class Triangle(Shape):
# ? ? def __init__(self, bottom, height, edge1, edge2):
# ? ? ? ? super().__init__()
# ? ? ? ? self.bottom = bottom
# ? ? ? ? self.height = height
# ? ? ? ? self.edge1 = edge1
# ? ? ? ? self.edge2 = edge2
# ? ? def calArea(self):
# ? ? ? ? self.area = (self.bottom*self.height) / 2
# ? ? ? ? return self.area
# ? ? def calPerimeter(self):
# ? ? ? ? self.perimeter = self.bottom+self.edge2+self.edge1
# ? ? ? ? return self.perimeter
# ? ? def display(self):
# ? ? ? ? self.name = "Triangle"
# ? ? ? ? Triangle.calArea(self)
# ? ? ? ? Triangle.calPerimeter(self)
# ? ? ? ? super(Triangle, self).display()
#
# class Circle(Shape):
# ? ? def __init__(self, radius):
# ? ? ? ? super(Circle, self).__init__()
# ? ? ? ? self.radius = radius
# ? ? def calArea(self):
# ? ? ? ? self.area = pi*pow(self.radius, 2)
# ? ? ? ? return self.area
# ? ? def calPerimeter(self):
# ? ? ? ? self.perimeter = 2*pi*self.radius
# ? ? ? ? return self.perimeter
# ? ? def display(self):
# ? ? ? ? self.name = "Circle"
# ? ? ? ? Circle.calArea(self)
# ? ? ? ? Circle.calPerimeter(self)
# ? ? ? ? super(Circle, self).display()
#
# rectangle = Rectangle(2, 3)
# rectangle.display()
#
# triangle = Triangle(3,4,4,5)
# triangle.display()
#
# circle = Circle(radius=1)
# circle.display()
#
# lst = list(map(lambda x: int(x), ['1', '2', '3']))
# print(lst)

#
# class ListNode(object):
# ? ? def __init__(self):
# ? ? ? ? self.val = None
# ? ? ? ? self.next = None
#
# #尾插法
# def creatlist_tail(lst):
# ? ? L = ListNode() #頭節(jié)點(diǎn)
# ? ? first_node = L
# ? ? for item in lst:
# ? ? ? ? p = ListNode()
# ? ? ? ? p.val = item
# ? ? ? ? L.next = p
# ? ? ? ? L = p
# ? ? return first_node
# #頭插法
# def creatlist_head(lst):
# ? ? L = ListNode() #頭節(jié)點(diǎn)
# ? ? for item in lst:
# ? ? ? ? p = ListNode()
# ? ? ? ? p.val = item
# ? ? ? ? p.next = L
# ? ? ? ? L = p
# ? ? return L
# #打印linklist
# def print_ll(ll):
# ? ? while True:
# ? ? ? ? if ll.val:
# ? ? ? ? ? ? print(ll.val)
# ? ? ? ? ? ? if ll.next==None: #尾插法停止點(diǎn)
# ? ? ? ? ? ? ? ? break
# ? ? ? ? elif not ll.next: #頭插法停止點(diǎn)
# ? ? ? ? ? ? break
# ? ? ? ? ll = ll.next
# #題解
# class Solution:
# ? ? def printListFromTailToHead(self, listNode):
# ? ? ? ? # write code here
# ? ? ? ? res = []
# ? ? ? ? while(listNode):
# ? ? ? ? ? ? res.append(listNode.val)
# ? ? ? ? ? ? listNode=listNode.next
# ? ? ? ? return res[3:0:-1]
#
# if __name__ == "__main__":
# ? ? lst = [1, 2, 3]
# ? ? linklist = creatlist_tail(lst)
# ? ? solution = Solution()
# ? ? res = solution.printListFromTailToHead(linklist)
# ? ? print(res)


# -*- coding:utf-8 -*-
# class Solution:
# ? ? def __init__(self):
# ? ? ? ? self.stack1 = []
# ? ? ? ? self.stack2 = []
# ? ? def push(self, node):
# ? ? ? ? # write code here
# ? ? ? ? self.stack1.append(node)
# ? ? def pop(self):
# ? ? ? ? # return xx
# ? ? ? ? if self.stack2:
# ? ? ? ? ? ? return self.stack2.pop()
# ? ? ? ? else:
# ? ? ? ? ? ? for i in range(len(self.stack1)):
# ? ? ? ? ? ? ? ? self.stack2.append(self.stack1.pop())
# ? ? ? ? ? ? return self.stack2.pop()
#
# if __name__ == '__main__':
# ? ? solution = Solution()
# ? ? solution.push(1)
# ? ? solution.push(2)
# ? ? print(solution.pop())
# ? ? print(solution.pop())


# # binary search
# def binary_search(lst, x):
# ? ? lst.sort()
# ? ? if len(lst) > 0:
# ? ? ? ? pivot = len(lst) // 2
# ? ? ? ? if lst[pivot] == x:
# ? ? ? ? ? ? return True
# ? ? ? ? elif lst[pivot] > x:
# ? ? ? ? ? ? return binary_search(lst[:pivot], x)
# ? ? ? ? elif lst[pivot] < x:
# ? ? ? ? ? ? return binary_search(lst[pivot+1:], x)
# ? ? return False
#
# def binary_search2(lst, x):
# ? ? lst.sort()
# ? ? head = 0
# ? ? tail = len(lst)
# ? ? pivot = len(lst) // 2
# ? ? while head <= tail:
# ? ? ? ? if lst[pivot]>x:
# ? ? ? ? ? ? tail = pivot
# ? ? ? ? ? ? pivot = (head+tail) // 2
# ? ? ? ? elif lst[pivot]<x:
# ? ? ? ? ? ? head = pivot
# ? ? ? ? ? ? pivot = (head+tail) // 2
# ? ? ? ? elif lst[pivot] == x:
# ? ? ? ? ? ? return True
# ? ? return False
# if __name__ == '__main__':
# ? ? lst = [5, 3, 1, 8, 9]
# ? ? print(binary_search(lst, 3))
# ? ? print(binary_search(lst, 100))
#
# ? ? print(binary_search(lst, 8))
# ? ? print(binary_search(lst, 100))


# 括號(hào)匹配
# def bracket_matching(ans):
# ? ? stack = []
# ? ? flag = True
# ? ? left = ['(', '{', '[']
# ? ? right = [')', '}', ']']
# ? ? for i in range(len(ans)):
# ? ? ? ? if ans[i] in left:
# ? ? ? ? ? ? stack.append(ans[i])
# ? ? ? ? else:
# ? ? ? ? ? ? tmp = stack.pop()
# ? ? ? ? ? ? if left.index(tmp) != right.index(ans[i]):
# ? ? ? ? ? ? ? ? flag = False
# ? ? if stack:
# ? ? ? ? flag = False
# ? ? return flag
#
# print(bracket_matching('({})()[[][]'))
# print(bracket_matching('({})()[[]]'))


# def longestValidParentheses(s):
# ? ? maxlen = 0
# ? ? stack = []
# ? ? for i in range(len(s)):
# ? ? ? ? if s[i] == '(':
# ? ? ? ? ? ? stack.append(s[i])
# ? ? ? ? if s[i] == ')' and len(stack) != 0:
# ? ? ? ? ? ? stack.pop()
# ? ? ? ? ? ? maxlen += 2
# ? ? return maxlen
# print(longestValidParentheses('()(()'))


# def GetLongestParentheses(s):
# ? ? maxlen = 0
# ? ? start = -1
# ? ? stack = []
# ? ? for i in range(len(s)):
# ? ? ? ? if s[i]=='(':
# ? ? ? ? ? ? stack.append(i)
# ? ? ? ? else:
# ? ? ? ? ? ? if not stack:
# ? ? ? ? ? ? ? ? start = i
# ? ? ? ? ? ? else:
# ? ? ? ? ? ? ? ? stack.pop()
# ? ? ? ? ? ? ? ? if not stack:
# ? ? ? ? ? ? ? ? ? ? maxlen = max(maxlen, i-start)
# ? ? ? ? ? ? ? ? else:
# ? ? ? ? ? ? ? ? ? ? maxlen = max(maxlen, i-stack[-1])
# ? ? return maxlen
# print(GetLongestParentheses('()(()'))
# print(GetLongestParentheses('()(()))'))
# print(GetLongestParentheses(')()())'))

# import torch
# a = torch.tensor([[[1,0,3],
# ? ? ? ? ? ? ? ? ? [4,6,5]]])
# print(a.size())
# b = torch.squeeze(a)
# print(b, b.size())
# b = torch.squeeze(a,-1)
# print(b, b.size())
# b = torch.unsqueeze(a,2)
# print(b, b.size())
#
# print('-----------------')
# x = torch.zeros(2, 1, 2, 1, 2)
# print(x.size())
# y = torch.squeeze(x)
# print(y.size())
# y = torch.squeeze(x, 0)
# print(y.size())
# y = torch.squeeze(x, 1)
# print(y.size())


# from typing import List
# class Solution:
# ? ? def duplicate(self, numbers: List[int]) -> int:
# ? ? ? ? # write code here
# ? ? ? ? dic = dict()
# ? ? ? ? for i in range(len(numbers)):
# ? ? ? ? ? ? if numbers[i] not in dic.keys():
# ? ? ? ? ? ? ? ? dic[numbers[i]] = 1
# ? ? ? ? ? ? else:
# ? ? ? ? ? ? ? ? dic[numbers[i]] += 1
# ? ? ? ? for key, value in dic.items():
# ? ? ? ? ? ? if value > 1:
# ? ? ? ? ? ? ? ? return key
# ? ? ? ? return -1
# if __name__ == '__main__':
# ? ? solution = Solution()
# ? ? print(solution.duplicate([2,3,1,0,2,5,3]))

# class TreeNode:
# ? ? def __init__(self, data=0):
# ? ? ? ? self.val = data
# ? ? ? ? self.left = None
# ? ? ? ? self.right = None
#
#
# class Solution:
# ? ? def TreeDepth(self , pRoot: TreeNode) -> int:
# ? ? ? ? # write code here
# ? ? ? ? if pRoot is None:
# ? ? ? ? ? ? return 0
# ? ? ? ? count = 0
# ? ? ? ? now_layer =[pRoot]
# ? ? ? ? next_layer = []
# ? ? ? ? while now_layer:
# ? ? ? ? ? ? for i in now_layer:
# ? ? ? ? ? ? ? ? if i.left:
# ? ? ? ? ? ? ? ? ? ? next_layer.append(i.left)
# ? ? ? ? ? ? ? ? if i.right:
# ? ? ? ? ? ? ? ? ? ? next_layer.append(i.right)
# ? ? ? ? ? ? count +=1
# ? ? ? ? ? ? now_layer, next_layer = next_layer,[]
# ? ? ? ? return count
#
# if __name__ == '__main__':
# ? ? inp = [1,2,3,4,5,'#',6,'#','#',7]
# ? ? bt = TreeNode(1)
#
# ? ? bt.left = TreeNode(2)
# ? ? bt.right = TreeNode(3)
#
# ? ? bt.left.left = TreeNode(4)
# ? ? bt.left.right = TreeNode(5)
# ? ? bt.right.left = None
# ? ? bt.right.right = TreeNode(6)
#
# ? ? bt.left.left.left = None
# ? ? bt.left.left.right = None
# ? ? bt.left.right.left = TreeNode(7)
#
# ? ? solution = Solution()
# ? ? print('深度：', solution.TreeDepth(bt))

# class ListNode:
# ? ? def __init__(self):
# ? ? ? ? self.val = None
# ? ? ? ? self.next = None
#
# def creatlist_tail(lst):
# ? ? L = ListNode()
# ? ? first_node = L
# ? ? for item in lst:
# ? ? ? ? p = ListNode()
# ? ? ? ? p.val = item
# ? ? ? ? L.next = p
# ? ? ? ? L = p
# ? ? return first_node
#
# def show(node:ListNode):
# ? ? print(node.val,end=' ')
# ? ? if node.next is not None:
# ? ? ? ? node = show(node.next)
#
# class Solution:
# ? ? def ReverseList(self, head: ListNode) -> ListNode:
# ? ? ? ? # write code here
# ? ? ? ? res = None
# ? ? ? ? while head:
# ? ? ? ? ? ? nextnode = head.next
# ? ? ? ? ? ? head.next = res
# ? ? ? ? ? ? res = head
# ? ? ? ? ? ? head = nextnode
# ? ? ? ? return res
#
# if __name__ == '__main__':
# ? ? lst = [1,2,3]
# ? ? linklist = creatlist_tail(lst)
# ? ? show(linklist)
# ? ? print()
# ? ? solution = Solution()
# ? ? show(solution.ReverseList(linklist))


# 字典推導(dǎo)式

# a = ['a', 'b', 'c']
# b = [4, 5, 6]
# dic = {k:v for k,v in zip(a,b)}
# print(dic)

#列表推導(dǎo)式

# l = [i for i in range(10)]
# print(l)
#
#
#
# # 生成器推導(dǎo)式
# l1 = (i for i in range(10))
# print(type(l1)) ?# 輸出結(jié)果：<class 'generator'>
# for i in l1:
# ? ? print(i)

# print('{pi:0>10.1f}'.format(pi=3.14159855))
# print("'","center".center(40),"'")
# print("center".center(40,'-'))
# print("center".zfill(40))
# print("center".ljust(40,'-'))
# print("center".rjust(40,'-'))

# s = "python is easy to learn, easy to use."
# print(s.find('to',0,len(s)))
# print(s.find('es'))

# num = [1,2,3]
# print("+".join(str(i) for i in num),"=",sum(num))
# print(''.center(40,'-'))

#
# import torch
# from torch import nn
# import numpy as np
#
# # 一維BN
# d1 = torch.rand([2,3,4]) #BCW
# bn1 = nn.BatchNorm1d(3, momentum=1)
# res = bn1(d1)
# print(res.shape)
#
# #二維BN（常用）
# d2 = torch.rand([2,3,4,5]) ?#BCHW
# bn2 = nn.BatchNorm2d(3, momentum=1)
# res = bn2(d2)
# print(res.shape)
# print(bn2.running_mean) #3個(gè)chanel均值
# print(bn2.running_var) #3個(gè)chanel方差
#
#
# a = np.array(d2.tolist())
# mean = np.mean(a,axis=(0,2,3))
# print(mean)
#
#
# def batchnorm_forward(x, gamma, beta, bn_param):
# ? ? """
# ? ? Forward pass for batch normalization
#
# ? ? Input:
# ? ? - x: Data of shape (N, D)
# ? ? - gamma: Scale parameter of shape (D,)
# ? ? - beta: Shift parameter of shape (D,)
# ? ? - bn_param: Dictionary with the following keys:
# ? ? ? - mode: 'train' or 'test'
# ? ? ? - eps: Constant for numeric stability
# ? ? ? - momentum: Constant for running mean / variance
# ? ? ? - running_mean: Array of shape(D,) giving running mean of features
# ? ? ? - running_var Array of shape(D,) giving running variance of features
# ? ? Returns a tuple of:
# ? ? - out: of shape (N, D)
# ? ? - cache: A tuple of values needed in the backward pass
# ? ? """
# ? ? mode = bn_param['mode']
# ? ? eps = bn_param.get('eps', 1e-5)
# ? ? momentum = bn_param.get('momentum', 0.9)
#
# ? ? N, D = x.shape
# ? ? running_mean = bn_param.get('running_mean', np.zeros(D, dtype=x.dtype))
# ? ? running_var = bn_param.get('running_var', np.zeros(D, dtype=x.dtype))
#
# ? ? out, cache = None, None
#
# ? ? if mode == 'train':
# ? ? ? ? sample_mean = np.mean(x, axis=0) ?# np.mean([[1,2],[3,4]])->[2,3]
# ? ? ? ? sample_var = np.var(x, axis=0)
# ? ? ? ? out_ = (x - sample_mean) / np.sqrt(sample_var + eps)
#
# ? ? ? ? running_mean = momentum * running_mean + (1 - momentum) * sample_mean
# ? ? ? ? running_var = momentum * running_var + (1 - momentum) * sample_var
#
# ? ? ? ? out = gamma * out_ + beta
# ? ? ? ? cache = (out_, x, sample_var, sample_mean, eps, gamma, beta)
# ? ? elif mode == 'test':
# ? ? ? ? # scale = gamma / np.sqrt(running_var + eps)
# ? ? ? ? # out = x * scale + (beta - running_mean * scale)
# ? ? ? ? x_hat = (x - running_mean) / (np.sqrt(running_var + eps))
# ? ? ? ? out = gamma * x_hat + beta
# ? ? else:
# ? ? ? ? raise ValueError('Invalid forward batchnorm mode "%s"' % mode)
#
# ? ? # Store the updated running means back into bn_param
# ? ? bn_param['running_mean'] = running_mean
# ? ? bn_param['running_var'] = running_var
#
# ? ? return out, cache
#


# import numpy as np
# import matplotlib.pyplot as plt
#
#
# def py_cpu_nms(dets, thresh):
#
# ? ?x1 = dets[:, 0]
# ? ?y1 = dets[:, 1]
# ? ?x2 = dets[:, 2]
# ? ?y2 = dets[:, 3]
# ? ?scores = dets[:, 4]
# ? ?areas = (x2-x1+1)*(y2-y1+1)
# ? ?res = []
# ? ?index = scores.argsort()[::-1]
# ? ?while index.size>0:
# ? ? ? ?i = index[0]
# ? ? ? ?res.append(i)
# ? ? ? ?x11 = np.maximum(x1[i],x1[index[1:]])
# ? ? ? ?y11 = np.maximum(y1[i], y1[index[1:]])
# ? ? ? ?x22 = np.minimum(x2[i],x2[index[1:]])
# ? ? ? ?y22 = np.minimum(y2[i],y2[index[1:]])
#
# ? ? ? ?w = np.maximum(0,x22-x11+1)
# ? ? ? ?h = np.maximum(0,y22-y11+1)
#
# ? ? ? ?overlaps = w * h
# ? ? ? ?iou = overlaps/(areas[i]+areas[index[1:]]-overlaps)
#
# ? ? ? ?idx = np.where(iou<=thresh)[0]
# ? ? ? ?index = index[idx+1]
# ? ?print(res)
# ? ?return res
#
# def plot_boxs(box,c):
# ? ? x1 = box[:, 0]
# ? ? y1 = box[:, 1]
# ? ? x2 = box[:, 2]
# ? ? y2 = box[:, 3]
#
# ? ? plt.plot([x1,x2],[y1,y1],c)
# ? ? plt.plot([x1,x2],[y2,y2],c)
# ? ? plt.plot([x1,x1],[y1,y2],c)
# ? ? plt.plot([x2,x2],[y1,y2],c)
#
# if __name__ == '__main__':
# ? ? boxes = np.array([[100, 100, 210, 210, 0.72],
# ? ? ? ? ? ? ? ? ? ? ? [250, 250, 420, 420, 0.8],
# ? ? ? ? ? ? ? ? ? ? ? [220, 220, 320, 330, 0.92],
# ? ? ? ? ? ? ? ? ? ? ? [230, 240, 325, 330, 0.81],
# ? ? ? ? ? ? ? ? ? ? ? [220, 230, 315, 340, 0.9]])
# ? ? plt.figure()
# ? ? ax1 = plt.subplot(121)
# ? ? ax2 = plt.subplot(122)
# ? ? plt.sca(ax1)
# ? ? plot_boxs(boxes,'k')
#
# ? ? res = py_cpu_nms(boxes,0.7)
# ? ? plt.sca(ax2)
# ? ? plot_boxs(boxes[res],'r')
# ? ? plt.show()


# 2 3 3 4
# 1 2 3
# 4 5 6
# 1 2 3 4
# 5 6 7 8
# 9 10 11 12
# lst1, lst2 = [], []
# n1,m1,n2,m2 = map(int,input().split())
# for i in range(n1):
# ? ? nums = list(map(int,input().split())) #輸入一行數(shù)據(jù)
# ? ? lst1.append(nums)
# for i in range(n2):
# ? ? nums = list(map(int,input().split()))
# ? ? lst2.append(nums)
# res = []
# for i in range(n1):
# ? ? res.append([])
# ? ? for j in range(m2):
# ? ? ? ? lst4 = []
# ? ? ? ? lst3 = lst1[i]
# ? ? ? ? for k in range(n2):
# ? ? ? ? ? ? lst4.append(lst2[k][j])
# ? ? ? ? res_num = sum(map(lambda x,y:x*y,lst3,lst4))
# ? ? ? ? res[i].append(res_num)
# print(res)
#
# import numpy as np
# print('numpy:',np.dot(lst1,lst2))


#定義殘差塊
# import torch
# import torch.nn as nn
# import torch.nn.functional as F
#
# class ResBlock(nn.Module):
# ? ? def __init__(self,inchanel,outchanel,stride=1):
# ? ? ? ? super(ResBlock,self).__init__()
# ? ? ? ? self.left = nn.Sequential(
# ? ? ? ? ? ? nn.Conv2d(inchanel,outchanel,kernel_size=3,stride=stride,padding=1,bias=False),
# ? ? ? ? ? ? nn.BatchNorm2d(outchanel),
# ? ? ? ? ? ? nn.ReLU(inplace=True),
# ? ? ? ? ? ? nn.Conv2d(outchanel,outchanel,kernel_size=3,stride=1,padding=1,bias=False),
# ? ? ? ? ? ? nn.BatchNorm2d(outchanel)
# ? ? ? ? )
# ? ? ? ? self.shortcut = nn.Sequential()
# ? ? ? ? if stride!=1 or inchanel!=outchanel:
# ? ? ? ? ? ? self.shortcut = nn.Sequential(
# ? ? ? ? ? ? ? ? nn.Conv2d(inchanel,outchanel,kernel_size=1,stride=stride,padding=1,bias=False),
# ? ? ? ? ? ? ? ? nn.BatchNorm2d(outchanel)
# ? ? ? ? ? ? )
# ? ? def forward(self,x):
# ? ? ? ? out = self.left(x)
# ? ? ? ? out = out + self.shortcut(x)
# ? ? ? ? out = F.relu(out)
#
# ? ? ? ? return out
#
# class ResNet(nn.Module):
# ? ? def __init__(self,Resblock,num_classes=10):
# ? ? ? ? super(ResNet,self).__init__()
# ? ? ? ? self.inchanel = 64
# ? ? ? ? self.conv1 = nn.Sequential(
# ? ? ? ? ? ? nn.Conv2d(3,64,kernel_size=3,stride=1,padding=1,bias=False),
# ? ? ? ? ? ? nn.BatchNorm2d(64),
# ? ? ? ? ? ? nn.ReLU()
# ? ? ? ? )
# ? ? ? ? self.layer1 = self.make_layer(ResBlock,64,2,1)
# ? ? ? ? self.layer2 = self.make_layer(ResBlock, 128, 2, 2)
# ? ? ? ? self.layer3 = self.make_layer(ResBlock, 256, 2, 2)
# ? ? ? ? self.layer4 = self.make_layer(ResBlock, 512, 2, 2)
# ? ? ? ? self.fc = nn.Linear(512,num_classes)
#
# ? ? def make_layer(self,ResBlock,channels,num_blocks,stride):
# ? ? ? ? strides = [stride] + [1] * (num_blocks-1)
# ? ? ? ? layers = []
# ? ? ? ? for stride in strides:
# ? ? ? ? ? ? layers.append(ResBlock(self.inchanel,channels,stride))
# ? ? ? ? ? ? self.inchanel=channels
# ? ? ? ? return nn.Sequential(*layers)
# ? ? def forward(self,x):
# ? ? ? ? out = self.conv1(x)
# ? ? ? ? out = self.layer1(out)
# ? ? ? ? out = self.layer2(out)
# ? ? ? ? out = self.layer3(out)
# ? ? ? ? out = self.layer4(out)
# ? ? ? ? out = F.avg_pool2d(out,4)
# ? ? ? ? out = out.view(out.size(0),-1)
# ? ? ? ? out = self.fc(out)
# ? ? ? ? return out


# import torch
# import torch.nn as nn
# import torch.nn.functional as F
#
# class ASPP(nn.Module):
# ? ? def __init__(self,in_channel=512,depth=256):
# ? ? ? ? super(ASPP,self).__init__()
# ? ? ? ? self.mean = nn.AdaptiveAvgPool2d((1,1))
# ? ? ? ? self.conv = nn.Conv2d(in_channel,depth,1,1)
# ? ? ? ? self.atrous_block1 = nn.Conv2d(in_channel,depth,1,1)
# ? ? ? ? self.atrous_block6 = nn.Conv2d(in_channel,depth,3,1,padding=6,dilation=6)
# ? ? ? ? self.atrous_block12 = nn.Conv2d(in_channel,depth,3,1,padding=12,dilation=12)
# ? ? ? ? self.atrous_block18 = nn.Conv2d(in_channel,depth,3,1,padding=18,dilation=18)
# ? ? ? ? self.conv1x1_output = nn.Conv2d(depth*5,depth,1,1)
# ? ? def forward(self,x):
# ? ? ? ? size = x[2:]
# ? ? ? ? pool_feat = self.mean(x)
# ? ? ? ? pool_feat = self.conv(pool_feat)
# ? ? ? ? pool_feat = F.upsample(pool_feat,size=size,mode='bilinear')
#
# ? ? ? ? atrous_block1 = self.atrous_block1(x)
# ? ? ? ? atrous_block6 = self.atrous_block6(x)
# ? ? ? ? atrous_block12 = self.atrous_block12(x)
# ? ? ? ? atrous_block18 = self.atrous_block18(x)
#
# ? ? ? ? out = self.conv1x1_output(torch.cat([pool_feat,atrous_block1,atrous_block6,
# ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?atrous_block12,atrous_block18],dim=1))
# ? ? ? ? return out

#牛頓法求三次根
# def sqrt(n):
# ? ? k = n
# ? ? while abs(k*k-n)>1e-6:
# ? ? ? ? k = (k + n/k)/2
# ? ? print(k)
#
# def cube_root(n):
# ? ? k = n
# ? ? while abs(k*k*k-n)>1e-6:
# ? ? ? ? k = k + (k*k*k-n)/3*k*k
# ? ? print(k)
# sqrt(2)
# cube_root(8)

# -*- coding:utf-8 -*-
# import random
#
# import numpy as np
# from matplotlib import pyplot
#
#
# class K_Means(object):
# ? ? # k是分組數(shù)；tolerance‘中心點(diǎn)誤差'；max_iter是迭代次數(shù)
# ? ? def __init__(self, k=2, tolerance=0.0001, max_iter=300):
# ? ? ? ? self.k_ = k
# ? ? ? ? self.tolerance_ = tolerance
# ? ? ? ? self.max_iter_ = max_iter
#
# ? ? def fit(self, data):
# ? ? ? ? self.centers_ = {}
# ? ? ? ? for i in range(self.k_):
# ? ? ? ? ? ? self.centers_[i] = data[random.randint(0,len(data))]
# ? ? ? ? # print('center', self.centers_)
# ? ? ? ? for i in range(self.max_iter_):
# ? ? ? ? ? ? self.clf_ = {} #用于裝歸屬到每個(gè)類(lèi)中的點(diǎn)[k,len(data)]
# ? ? ? ? ? ? for i in range(self.k_):
# ? ? ? ? ? ? ? ? self.clf_[i] = []
# ? ? ? ? ? ? # print("質(zhì)點(diǎn):",self.centers_)
# ? ? ? ? ? ? for feature in data:
# ? ? ? ? ? ? ? ? distances = [] #裝中心點(diǎn)到每個(gè)點(diǎn)的距離[k]
# ? ? ? ? ? ? ? ? for center in self.centers_:
# ? ? ? ? ? ? ? ? ? ? # 歐拉距離
# ? ? ? ? ? ? ? ? ? ? distances.append(np.linalg.norm(feature - self.centers_[center]))
# ? ? ? ? ? ? ? ? classification = distances.index(min(distances))
# ? ? ? ? ? ? ? ? self.clf_[classification].append(feature)
#
# ? ? ? ? ? ? # print("分組情況:",self.clf_)
# ? ? ? ? ? ? prev_centers = dict(self.centers_)
#
# ? ? ? ? ? ? for c in self.clf_:
# ? ? ? ? ? ? ? ? self.centers_[c] = np.average(self.clf_[c], axis=0)
#
# ? ? ? ? ? ? # '中心點(diǎn)'是否在誤差范圍
# ? ? ? ? ? ? optimized = True
# ? ? ? ? ? ? for center in self.centers_:
# ? ? ? ? ? ? ? ? org_centers = prev_centers[center]
# ? ? ? ? ? ? ? ? cur_centers = self.centers_[center]
# ? ? ? ? ? ? ? ? if np.sum((cur_centers - org_centers) / org_centers * 100.0) > self.tolerance_:
# ? ? ? ? ? ? ? ? ? ? optimized = False
# ? ? ? ? ? ? if optimized:
# ? ? ? ? ? ? ? ? break
#
# ? ? def predict(self, p_data):
# ? ? ? ? distances = [np.linalg.norm(p_data - self.centers_[center]) for center in self.centers_]
# ? ? ? ? index = distances.index(min(distances))
# ? ? ? ? return index
#
#
# if __name__ == '__main__':
# ? ? x = np.array([[1, 2], [1.5, 1.8], [5, 8], [8, 8], [1, 0.6], [9, 11]])
# ? ? k_means = K_Means(k=2)
# ? ? k_means.fit(x)
# ? ? for center in k_means.centers_:
# ? ? ? ? pyplot.scatter(k_means.centers_[center][0], k_means.centers_[center][1], marker='*', s=150)
#
# ? ? for cat in k_means.clf_:
# ? ? ? ? for point in k_means.clf_[cat]:
# ? ? ? ? ? ? pyplot.scatter(point[0], point[1], c=('r' if cat == 0 else 'b'))
#
# ? ? predict = [[2, 1], [6, 9]]
# ? ? for feature in predict:
# ? ? ? ? cat = k_means.predict(feature)
# ? ? ? ? pyplot.scatter(feature[0], feature[1], c=('r' if cat == 0 else 'b'), marker='x')
#
# ? ? pyplot.show()

# def pred(key, value):
# ? ? if key == 'math':
# ? ? ? ? return value>=40
# ? ? else:
# ? ? ? ? return value>=60
# def func(dic,pred):
# ? ? # temp = []
# ? ? # for item in dic:
# ? ? # ? ? if not pred(item,dic[item]):
# ? ? # ? ? ? ? temp.append(item)
# ? ? # for item in temp:
# ? ? # ? ? del dic[item]
# ? ? # return dic
#
# ? ? for k in list(dic.keys()):
# ? ? ? ? if dic[k]<60:
# ? ? ? ? ? ? del dic[k]
# ? ? return dic
#
# if __name__ == '__main__':
# ? ? dic={'math':66,'c':78,'c++':59,'python':55}
# ? ? dic = func(dic,pred)
# ? ? print(dic)

#
# class TreeNode:
# ? ? def __init__(self):
# ? ? ? ? self.left = None
# ? ? ? ? self.right = None
# ? ? ? ? self.data = None
#
# def insert(tree,x):
# ? ? temp = TreeNode()
# ? ? temp.data = x
# ? ? if tree.data>x:
# ? ? ? ? if tree.left == None:
# ? ? ? ? ? ? tree.left = temp
# ? ? ? ? else:
# ? ? ? ? ? ? insert(tree.left,x)
# ? ? else:
# ? ? ? ? if tree.right == None:
# ? ? ? ? ? ? tree.right = temp
# ? ? ? ? else:
# ? ? ? ? ? ? insert(tree.right,x)
#
# def print_tree(node):
# ? ? if node is None:
# ? ? ? ? return 0
# ? ? print_tree(node.left)
# ? ? print(node.data)
# ? ? print_tree(node.right)
#
#
# def sort(lst):
# ? ? tree = TreeNode()
# ? ? tree.data = lst[0]
# ? ? for i in range(1, len(lst)):
# ? ? ? ? insert(tree,lst[i])
# ? ? print_tree(tree)
#
# sort([5,2,4])


# from collections import Iterable, Iterator
#
#
# class Person(object):
# ? ? """定義一個(gè)人類(lèi)"""
#
# ? ? def __init__(self):
# ? ? ? ? self.name = list()
# ? ? ? ? self.name_num = 0
#
# ? ? def add(self, name):
# ? ? ? ? self.name.append(name)
#
# ? ? def __iter__(self):
# ? ? ? ? return self
# ? ? def __next__(self):
# ? ? ? ? # 記憶性返回?cái)?shù)據(jù)
# ? ? ? ? if self.name_num < len(self.name):
# ? ? ? ? ? ? ret = self.name[self.name_num]
# ? ? ? ? ? ? self.name_num += 1
# ? ? ? ? ? ? return ret
# ? ? ? ? else:
# ? ? ? ? ? ? raise StopIteration
#
# person1 = Person()
# person1.add("張三")
# person1.add("李四")
# person1.add("王五")
#
# print("判斷是否是可迭代的對(duì)象：", isinstance(person1, Iterable))
# print("判斷是否是迭代器：", isinstance(person1,Iterator))
# for name in person1:
# ? ? print(name)

# nums = []
# a = 0
# b = 1
# i = 0
# while i < 10:
# ? ? nums.append(a)
# ? ? a,b = b,a+b
# ? ? i += 1
# for i in nums:
# ? ? print(i)
#
# class Fb():
# ? ? def __init__(self):
# ? ? ? ? self.a = 0
# ? ? ? ? self.b = 1
# ? ? ? ? self.i = 0
# ? ? def __iter__(self):
# ? ? ? ? return self
# ? ? def __next__(self):
# ? ? ? ? res = self.a
# ? ? ? ? if self.i<10:
# ? ? ? ? ? ? self.a,self.b = self.b,self.a+self.b
# ? ? ? ? ? ? self.i += 1
# ? ? ? ? ? ? return res
# ? ? ? ? else:
# ? ? ? ? ? ? raise StopIteration
#
# fb = Fb()
# for i in fb:
# ? ? print(i)


import time

def get_time(func):
? ? def wraper(*args, **kwargs):
? ? ? ? start_time = time.time()
? ? ? ? result = func(*args, **kwargs)
? ? ? ? end_time = time.time()
? ? ? ? print("Spend:", end_time - start_time)
? ? ? ? return result
? ? return wraper

@get_time
def _list(n):
? ? l = [i*i*i for i in range(n)]


@get_time
def _generator(n):
? ? ge = (i*i*i for i in range(n))

@get_time
def _list_print(l1):
? ? for i in l1:
? ? ? ? print(end='')

@get_time
def _ge_print(ge):
? ? for i in ge:
? ? ? ? print(end='')

n = 100000
print('list 生成耗時(shí)：')
_list(n)
print('生成器 生成耗時(shí)：')
_generator(n)


l1 = [i*i*i for i in range(n)]
ge = (i*i*i for i in range(n))
# print(l1)
# print(ge)
print('list遍歷耗時(shí)：')
_list_print(l1)
print('生成器遍歷耗時(shí)：')
_ge_print(ge)