text: # -*- coding: utf-8 -*-
  import sys
  import tensorflow as tf
  from model import *
  
  assert len(sys.argv) == 6
  
  word2vec=sys.argv[1]
  modelsavepath = sys.argv[2]
  filename = sys.argv[3]
  hidden_size=int(sys.argv[4])
  test_file=sys.argv[5]
  
  
  sequences_ = getTextSequences(filename)
  char2vector_, char_dictionary_, char_reverse_dictionary_, label_dictionary_, label_reverse_dictionary_, label_transition_proba_, data_ = build_dataset(word2vec, sequences_)
  
  config = tf.ConfigProto()
  config.gpu_options.allow_growth=True
  sess = tf.Session(config=config)
  
  model = BI_LSTM_CRF(sess=sess,
                      dtype=tf.float32,
                      char2vector=char2vector_,
                      transition_tmatrix=label_transition_proba_,
                      hidden_size=hidden_size,
                      ntags=len(label_dictionary_))
  
  ckpt = tf.train.get_checkpoint_state( modelsavepath ) 
  
  model.restore(  ckpt.model_checkpoint_path )
  
  def viterbi(sent):
    #char_dictionary_, label_reverse_dictionary_
    end_char=''
    if sent[-1]=='\n':
      end_char = '\n'
      sent = sent[:-1]
    if not sent:
      return sent, [], end_char
    sentence = [[char_dictionary_.get(w, 0) for w in sent ]]
    labels = model.test( sentence, label_transition_proba_ )
    labels = [ label_reverse_dictionary_[i] for i in labels ]
    return sent, labels, end_char
  
  with open( test_file , 'rt', encoding='utf8') as f:
    test = f.readlines()
  for sent in test:
    seq, labels, end = viterbi(sent)
    segment = []
    for char, tag in zip(seq, labels):
      if tag == 'B':
        segment.append(char)
      elif tag == 'M':
        if segment:
          segment[-1] += char
        else:
          segment =  [char]
      elif tag == 'E':
        if segment:
          segment[-1] += char
        else:
          segment =  [char]
      elif tag == 'S':
        segment.append(char)
      else:
        raise Exception()
    print('  '.join(segment), sep='', end=end)
    #break