text: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import operator
  import collections
  import math
  import time
  import os
  import random
  import zipfile
  import time
  import numpy as np
  import sys
  from six.moves import urllib
  from six.moves import xrange  # pylint: disable=redefined-builtin
  import tensorflow as tf
  sys.path.append( '../util/')
  from generators import *
  from loader import *
  from print_tweets import *
  from similar_tokens import * 
  from training import *
  from similar_tokens import *
  from expand_query import *
  from argument_loader import *
  from setup import *
  
  dataset, query_type, filename, num_steps, num_steps_roll, num_steps_train, expand_flag,lr_, matchname, counter = import_arguments(sys.argv)
  
  # Read the data into a list of strings.
  # import data
  char_batch_dict, word_batch_dict,data, count, dictionary, reverse_dictionary, word_max_len, char_max_len, vocabulary_size, char_dictionary, reverse_char_dictionary, data_index, char_data_index, buffer_index, batch_list, char_batch_list, word_batch_list, char_data = build_everything(dataset)
  
  
  data_index, batch, labels = generate_batch(data, data_index, batch_size=8, num_skips=2, skip_window=1,)
  for i in range(8):
    print(batch[i], reverse_dictionary[batch[i]],
          '->', labels[i, 0], reverse_dictionary[labels[i, 0]])
  char_data_index, batch, labels = generate_batch_char(char_data, char_data_index, batch_size=8, num_skips=2, skip_window=1)
  for i in range(8):
    print(batch[i], reverse_char_dictionary[batch[i]],
          '->', labels[i, 0], reverse_char_dictionary[labels[i, 0]])
  
  lambda_1, tweet_batch_size, expand_start_count, query_name, query_tokens, query_tokens_alternate, char_batch_size, num_sampled, valid_examples, valid_window, valid_size, skip_window, num_skips, embedding_size, char_vocabulary_size, batch_size, num_char_skips, skip_char_window = setup(char_dictionary, dictionary, query_type)
  
  learning_rate = lr_
  
  expand_count = 3
  need_tweet_list , avail_tweet_list = na_loader(dataset, query_name)
  saver = tf.train.import_meta_graph('../results/%s/%s/%s_model.ckpt.meta'%(dataset, query_name, filename))
  print("Loaded graph")
  graph = tf.get_default_graph()
  
  expand_count = 3
  with tf.Session(graph=graph) as session:
    count = 0
    saver.restore(session, '../results/%s/%s/%s_model.ckpt'%(dataset, query_name, filename))
    query_tweet_holder = [graph.get_tensor_by_name('tweet_query_word_holder:0'), graph.get_tensor_by_name('tweet_query_char_holder:0')]
    tweet_similarity = graph.get_tensor_by_name('match_similarity:0')
    tweet_word_holder = graph.get_tensor_by_name('tweet_word_holder:0')
    tweet_char_holder = graph.get_tensor_by_name('tweet_char_holder:0')
    for query_tweet in need_tweet_list:
      count = print_tweets(dataset, tweet_similarity, query_tweet, query_tweet_holder, query_name, session, avail_tweet_list, char_batch_list, tweet_word_holder, tweet_char_holder, count ,tweet_batch_size, "%s_%s"%(filename, matchname), True, counter)
      if count % 25 == 0: print("Completed for %d need tweets, saved as avail list"%(count))
  
  