text: # coding: utf-8
  
  from __future__ import division
  
  import models, data, main
  
  import sys
  import tensorflow as tf
  import numpy as np
  
  def to_array(arr, dtype=np.int32):
      # minibatch of 1 sequence as column
      return np.array([arr], dtype=dtype).T
  
  def convert_punctuation_to_readable(punct_token):
      if punct_token == data.SPACE:
          return " "
      else:
          return punct_token[0]
  
  def punctuate(word_vocabulary, punctuation_vocabulary, reverse_punctuation_vocabulary, text, model):
  
      if len(text) == 0:
          sys.exit("Input text from stdin missing.")
  
      text = [w for w in text.split() if w not in punctuation_vocabulary] + [data.END]
  
      i = 0
  
      while True:
  
          subsequence = text[i:i+data.MAX_SEQUENCE_LEN]
  
          if len(subsequence) == 0:
              break
  
          converted_subsequence = [word_vocabulary.get(w, word_vocabulary[data.UNK]) for w in subsequence]
  
          y = predict(to_array(converted_subsequence), model)
  
          print(subsequence[0], end="")
  
          last_eos_idx = 0
          punctuations = []
          for y_t in y:
  
              p_i = np.argmax(tf.reshape(y_t, [-1]))
              punctuation = reverse_punctuation_vocabulary[p_i]
  
              punctuations.append(punctuation)
  
              if punctuation in data.EOS_TOKENS:
                  last_eos_idx = len(punctuations) # we intentionally want the index of next element
  
          if subsequence[-1] == data.END:
              step = len(subsequence) - 1
          elif last_eos_idx != 0:
              step = last_eos_idx
          else:
              step = len(subsequence) - 1
  
          for j in range(step):
              print(" " + punctuations[j] + " " if punctuations[j] != data.SPACE else " ", end="")
              if j < step - 1:
                  print(subsequence[1+j], end="")
  
          if subsequence[-1] == data.END:
              break
  
          i += step
  
  def predict(x, model):
      return tf.nn.softmax(net(x))
  
  if __name__ == "__main__":
  
      if len(sys.argv) > 1:
          model_file = sys.argv[1]
      else:
          sys.exit("Model file path argument missing")
  
      vocab_len = len(data.read_vocabulary(data.WORD_VOCAB_FILE))
      x_len = vocab_len if vocab_len < data.MAX_WORD_VOCABULARY_SIZE else data.MAX_WORD_VOCABULARY_SIZE + data.MIN_WORD_COUNT_IN_VOCAB
      x = np.ones((x_len, main.MINIBATCH_SIZE)).astype(int)
  
      print("Loading model parameters...")
      net, _ = models.load(model_file, x)
  
      print("Building model...")
          
      word_vocabulary = net.x_vocabulary
      punctuation_vocabulary = net.y_vocabulary
      reverse_punctuation_vocabulary = {v:k for k,v in net.y_vocabulary.items()}
  
      while True:
          text = input("\nTEXT: ")
          punctuate(word_vocabulary, punctuation_vocabulary, reverse_punctuation_vocabulary, text, net)
  