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import contextlib
import time

@contextlib.contextmanager
def stopwatch(message):
    """Context manager to print how long a block of code took."""
    t0 = time.time()
    try:
        yield
    finally:

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client = paramiko.SSHClient()
        client._policy = paramiko.WarningPolicy()
        client.set_missing_host_key_policy(paramiko.AutoAddPolicy())

        ssh_config = paramiko.SSHConfig()
        user_config_file = os.path.expanduser("~/.ssh/config")
        if os.path.exists(user_config_file):
            with open(user_config_file) as f:
                ssh_config.parse(f)

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import socket

def pick_unused():
  s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
  s.bind(('localhost', 0))
  addr, port = s.getsockname()
  s.close()
  return port

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#include <stdio.h>
#include <termios.h>
#include <unistd.h>
#include <fcntl.h>

int kbhit(void) {
  struct termios oldt, newt;
  int ch;
  int oldf;
  

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#!/bin/bash

echo ""
read -e -p "Target folder name: " folder

echo "Duplicate folder structure of $folder.."
if [ -d "Compressed-${folder}" ]; then
	echo ""
	read -r -p "\"Compressed-${folder}\" exist, overwrite? [y/n] " response
	if [[ $response =~ ^([yY][eE][sS]|[yY])$ ]] 

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'''
A fork of this gist https://gist.github.com/BigglesZX/4016539 .
Ported to Python3 and verify it with Pillow.
'''

import argparse
import os
from PIL import Image

def find_dir(path):

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#!/usr/bin/env python3

import os, argparse, logging
# using doc2vec model
from gensim.models import Doc2Vec
# generate compressed pickle file
import pickle, numpy, gzip

logging.basicConfig(format='%(asctime)s [%(levelname)s] %(message)s')

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#!/usr/bin/env python3

import os, sys, argparse, logging

baseline = '/tmp2/b03902036/train-punc.pro'
result = '/tmp2/b03902036/result.cmb'

ground_truth = dict()
with open(baseline, 'r') as in_file :
    for line in in_file :

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from gensim.models.doc2vec import Doc2Vec
from gensim.test.test_doc2vec import ConcatenatedDoc2Vec
from sklearn.linear_model import LogisticRegression, SGDClassifier, LogisticRegressionCV
from sklearn import svm
from sklearn.externals import joblib
import jieba
import numpy as np
import logging
import os
import sys

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#!/usr/bin/env python3

import requests, json, sys, random
from html.parser import HTMLParser
from time import sleep
from tqdm import tqdm

import cProfile

url = 'http://words.sinica.edu.tw/ftms-bin/scripts/words_scripts/zizi1.pl'