text: import nltk
  import pickle
  import argparse
  from collections import Counter
  from pycocotools.coco import COCO
  
  from utils.vocabulary import Vocabulary
  
  '''
  class Vocabulary(object):
      """Simple vocabulary wrapper."""
      def __init__(self):
          self.word2idx = {}
          self.idx2word = {}
          self.idx = 0
  
      def add_word(self, word):
          if not word in self.word2idx:
              self.word2idx[word] = self.idx
              self.idx2word[self.idx] = word
              self.idx += 1
  
      def __call__(self, word):
          if not word in self.word2idx:
              return self.word2idx['<unk>']
          return self.word2idx[word]
  
      def __len__(self):
          return len(self.word2idx)
  '''
  
  def build_vocab(json, threshold):
      """Build a simple vocabulary wrapper."""
      coco = COCO(json)
      counter = Counter()
      ids = coco.anns.keys()
      for i, id in enumerate(ids):
          caption = str(coco.anns[id]['caption'])
          tokens = nltk.tokenize.word_tokenize(caption.lower())
          counter.update(tokens)
  
          if i % 1000 == 0:
              print("[%d/%d] Tokenized the captions." %(i, len(ids)))
  
      # If the word frequency is less than 'threshold', then the word is discarded.
      words = [word for word, cnt in counter.items() if cnt >= threshold]
  
      # Creates a vocab wrapper and add some special tokens.
      vocab = Vocabulary()
      vocab.add_word('<pad>')
      vocab.add_word('<start>')
      vocab.add_word('<end>')
      vocab.add_word('<unk>')
  
      # Adds the words to the vocabulary.
      for i, word in enumerate(words):
          vocab.add_word(word)
      return vocab
  
  
  def main(args):
      vocab = build_vocab(json=args.caption_path,
                          threshold=args.threshold)
      vocab_path = args.vocab_path
      with open(vocab_path, 'wb') as f:
          pickle.dump(vocab, f)
      print("Total vocabulary size: %d" %len(vocab))
      print("Saved the vocabulary wrapper to '%s'" %vocab_path)
      print(Vocabulary.__module__)
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser()
      parser.add_argument('--caption_path', type=str,
                          default='./data/annotations/captions_train2014.json',
                          help='path for train annotation file')
      parser.add_argument('--vocab_path', type=str, default='./data/vocab.pkl',
                          help='path for saving vocabulary wrapper')
      parser.add_argument('--threshold', type=int, default=4,
                          help='minimum word count threshold')
      args = parser.parse_args()
      main(args)
  