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import numpy as np 
import cv2
import time
from datetime import datetime
import pathlib
  
save_gap = 1800
done = False

while not done:

wwiiiii

import time
from torch.multiprocessing import Process, Queue as mpQueue, set_start_method
from line_profiler import LineProfiler

def test(x):
	for i in range(3):
		if x == 1:
			print(x)
			time.sleep(0.1)
		else:

wwiiiii

import gc
import time
import numpy as np

def foo(repeat):
    a = []
    for _ in range(repeat):
        a.append(np.zeros((84, 84, 1)))
    s = time.time()
    return s

wwiiiii

from io import BytesIO
from PIL import Image, ImageDraw, ImageFont

class schedule:
    def __init__(self, fname):
        self.talks = []
        with open(fname, 'r', encoding='utf-8') as f:
            for line in f:
                line = line.split()
                time, line = line[0], ' '.join(line[1:])

wwiiiii

import gc
import time
from io import BytesIO
import multiprocessing
from multiprocessing.pool import ThreadPool 

import torch
import requests
from PIL import Image
from torchvision import transforms

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import random
import gym
import numpy as np
import tensorflow as tf

L2_REG = 1e-2
LR = 1e-3

class pg:
    def __init__(self):

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import random
import gym
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.distributions import Categorical

L2_REG = 1e-2

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def _batch_normalization(inp, is_train, name=None, is_conv=True):
    now_mean, now_var = tf.nn.moments(inp, axes=[0, 1, 2] if is_conv else [0])
    if name is None:
        name = str(time.time())
    gamma = tf.get_variable('gamma_%s' % name, shape=[inp.shape[-1]])
    beta = tf.get_variable('beta_%s' % name, shape=[inp.shape[-1]])
    ema = tf.train.ExponentialMovingAverage(decay=0.99)

    def update():
        with tf.control_dependencies([ema.apply([now_mean, now_var])]):

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#include <cstdio>
#include <queue>
#include <cmath>
#include <algorithm>
#include <vector>
using namespace std;

int depth(int a)
{
	int now = 3, res = 0, sum = 0;

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#include <cstdio>
#include <vector>
#include <algorithm>
using namespace std;
 
int n, m, k;
vector<long long int> container; //raw data 저장 배열, 범위 [1...n]
vector<long long int> tree; // 세그먼트 트리, 루트는 1, 왼쪽 자식은 2i, 오른쪽 자식은 2i+1
vector<long long int> lazy; // 현재 구간에 할당된 lazy값. 하위 구간을 참조해야 되면 0으로 바꾸고 밑으로 내려보냄
/*