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import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

seqs = ['gigantic_string','tiny_str','medium_str']

# make <pad> idx 0
vocab = ['<pad>'] + sorted(set(''.join(seqs)))

# make model

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#!/usr/bin/python
import os, sys, gzip
from StringIO import StringIO
from datetime import datetime

def readByte(f):
	return ord(f.read(1))

def readInt(f):
	l = 0

vgoklani

from datetime import datetime

import string

from nltk.stem.lancaster import LancasterStemmer
from nltk.corpus import stopwords

#Gets the tweet time.
def get_time(tweet):
    return datetime.strptime(tweet['created_at'], "%a %b %d %H:%M:%S +0000 %Y")

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"""
The most simple DNS client written for Python with asyncio:

* Only A record is support (no CNAME, no AAAA, no MX, etc.)
* Almost no error handling
* Doesn't  support fragmented UDP packets (is it possible?)

"""
import asyncio
import logging

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# this script has been tested and worked in a freshly installed Ubuntu 16.04 and 16.10
# it assumes that you are running airflow in a private netowrk and no need to be worry about outside access
# if that's not the case, the lines for PostgreSQL and Redis in this script need to be updated accordingly
# run as root
sudo su

# initial system updates and installs
apt-get update && apt-get upgrade -y && apt-get autoremove && apt-get autoclean

apt-get -y install build-essential binutils gcc make git htop nethogs tmux

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from random import random

# function to optimize: takes in a list of decision variables, returns an objective value
# this is the Rosenbrock function: http://en.wikipedia.org/wiki/Rosenbrock_function
# the global minimum is located at x = (1,1) where f(x) = 0
def my_function(x):
  return (1-x[0])**2 + 100*(x[1] - x[0]**2)**2

# function to perform (a very crude, stupid) optimization
# bounds = lower and upper bounds for each decision variable (2D list)

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from __future__ import print_function

from itertools import starmap

import tensorflow as tf
import random
from tensorflow.python.ops import rnn
import math

flags = tf.flags

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#!/usr/bin/env python
"""
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
"""

from __future__ import print_function, division

import numpy as np
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
from keras.models import Sequential