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rubbish.py
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import bisect
import sys
import bisect
import numpy as np
# a = np.array([[1,2,3],[4,5,6]])
# print(a.sum(axis=1))
a = np.array([[1,0],[0,1]])
print(np.nanmean(a))
# a = [1,2,3]
# a.pop(0)
# print(a)
# task = {}
# lines = sys.stdin.readlines()
# n, m = map(int, lines[0].strip().split())
# for line in lines[1:-1]:
# if not line.strip().split():
# continue
# a, b = map(int, line.strip().split())
# task[a] = max(task.get(a, 0), b)
# arr = sorted(task.keys())
# for i in range(1, len(arr)):
# if task[arr[i]] < task[arr[i -1]]:
# task[arr[i]] = task[arr[i -1]]
# skills = map(int, lines[-1].strip().split())
# for skill in skills:
# if skill in task:
# print(task[skill])
# else:
# ind = bisect.bisect(arr, skill)
# if ind == 0:
# print(0)
# else:
# print(task[arr[ind -1]])
lines = sys.stdin.readlines()
hashmap = {}
n,m = lines[0].strip().split()
for line in lines[1:-1]:
a,b = map(int,line.strip().split())
hashmap[a] = max(hashmap.get(a,0),b)
arr = sorted(hashmap.keys())
for i in range(1,len(arr)):
if hashmap[arr[i]]<hashmap[arr[i-1]]:
hashmap[arr[i]] = hashmap[arr[i-1]]
print(hashmap)
skills = map(int,lines[-1].strip().split())
for skill in skills:
if skill in hashmap:
print(hashmap[skill])
else:
index = bisect.bisect(arr,skill)
if index ==0:
print(0)
else:
print(hashmap[arr[index-1]])
# import torch
# from torchvision import ops
# import sys
# n = int(sys.stdin.readline().strip())
# point = []
# for i in range(n):
# point.append(list(map(int, sys.stdin.readline().strip().split())))
# point.sort(key=lambda k:k[1],reverse=True)
# res = []
# res.append(point[0])
# for i in range(1,len(point)):
# if point[i][0] > res[-1][0]:
# res.append(point[i])
# else:
# continue
# res.sort(key=lambda k:k[0])
# for i in res:
# print(i[0],i[1])
# if __name__ == "__main__":
# a = torch.randn((3,4))
# print(a)
# print(a.mean())
# import numpy as np
# print(123)
# a = 1+2
# print(a)
# a = np.array([
# [1,2,3,4],
# [2,3,4,5],
# [3,4,5,6]
# ])
# b = np.array([
# [0,0,0,0],
# [0,1,1,0],
# [0,0,0,0]
# ])
# c = a[b==1]
# print(c)
# a = torch.tensor([
# [1,2,3,4],
# [2,3,4,5],
# [3,4,5,6]
# ])
# b = torch.tensor([
# [0,0,0,0],
# [0,1,1,0],
# [0,0,0,0]
# ])
# c = torch.where(b==1,b,a)
# print(c)
#torch.index_select
# torch.masked_select
# def simple_decorator(f):
# def wrapper():
# print("func enter")
# f()
# print("func exit")
# return wrapper
# # @simple_decorator
# # def hello():
# # print("Hello World!")
# @simple_decorator
# def hello1():
# print("Hello World!")
# hello1()