madrugado

#!/usr/bin/python
# -*- coding: utf-8 -*-
__author__ = 'madrugado'
import sys
import pymystem3
import itertools
from collections import Counter
import math

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#include <iostream>
#import <vector>
#import <string>
#import <set>
#import <algorithm>
#import <map>

using namespace std;

int main() {

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import random
import matplotlib.pyplot as plt

from IPython import display

"""
IPython Display rc0

Try:
dsp = IDisplay()

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def sample_gumbel(shape, eps=1e-20): 
  """Sample from Gumbel(0, 1)"""
  U = tf.random_uniform(shape,minval=0,maxval=1)
  return -tf.log(-tf.log(U + eps) + eps)

def gumbel_softmax_sample(logits, temperature): 
  """ Draw a sample from the Gumbel-Softmax distribution"""
  y = logits + sample_gumbel(tf.shape(logits))
  return tf.nn.softmax( y / temperature)

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#!/bin/sh

# this sample script translates a test set, including
# preprocessing (tokenization, truecasing, and subword segmentation),
# and postprocessing (merging subword units, detruecasing, detokenization).

# instructions: set paths to mosesdecoder, subword_nmt, and nematus,
# then run "./translate.sh < input_file > output_file"

# suffix of source language

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# Copyright 2017 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

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from random import random, choice

def noise_generator(string, noise_level, chars):
    noised = ""
    for c in string:
        if random() > noise_level:
            noised += c
        if random() < noise_level:
            noised += choice(chars)
    return noised

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import tensorflow as tf
from tensorflow.contrib.rnn import RNNCell
from tensorflow.contrib.rnn import LSTMStateTuple
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import array_ops
from tensorflow.python.ops.rnn_cell_impl import _linear
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_ops