(data_dir, en_vocabulary_size, fr_vocabulary_size,
        load_embeddings=False, tokenizer=None)
return (en_train_ids_path, fr_train_ids_path,
          en_dev_ids_path, fr_dev_ids_path,
          en_vocab_path, fr_vocab_path)
Get WMT data into data_dir, create vocabularies and tokenize data.

  Args:
    data_dir: directory in which the data sets will be stored.
    en_vocabulary_size: size of the English vocabulary to create and use.
    fr_vocabulary_size: size of the French vocabulary to create and use.
    tokenizer: a function to use to tokenize each data sentence;
      if None, basic_tokenizer will be used.

  Returns:
    A tuple of 6 elements:
      (1) path to the token-ids for English training data-set,
      (2) path to the token-ids for French training data-set,
      (3) path to the token-ids for English development data-set,
      (4) path to the token-ids for French development data-set,
      (5) path to the English vocabulary file,
      (6) path to the French vocabulary file.
Get WMT data into data_dir, create vocabularies and tokenize data.
Get WMT data into data_dir create vocabularies and tokenize data .
def prepare_wmt_data(data_dir, en_vocabulary_size, fr_vocabulary_size,
        load_embeddings=False, tokenizer=None):
  """Get WMT data into data_dir, create vocabularies and tokenize data.

  Args:
    data_dir: directory in which the data sets will be stored.
    en_vocabulary_size: size of the English vocabulary to create and use.
    fr_vocabulary_size: size of the French vocabulary to create and use.
    tokenizer: a function to use to tokenize each data sentence;
      if None, basic_tokenizer will be used.

  Returns:
    A tuple of 6 elements:
      (1) path to the token-ids for English training data-set,
      (2) path to the token-ids for French training data-set,
      (3) path to the token-ids for English development data-set,
      (4) path to the token-ids for French development data-set,
      (5) path to the English vocabulary file,
      (6) path to the French vocabulary file.
  """
  # Get wmt data to the specified directory.
  train_path = os.path.join(data_dir, "train.txt")
  dev_path = os.path.join(data_dir, "dev.txt")

  # Create vocabularies of the appropriate sizes.
  fr_vocab_path = os.path.join(data_dir, "vocab%d.out" % fr_vocabulary_size)
  en_vocab_path = os.path.join(data_dir, "vocab%d.in" % en_vocabulary_size)
  create_vocabulary(fr_vocab_path, train_path + ".out", fr_vocabulary_size,
          os.path.join(data_dir, "dec_embedding{0}.tsv".format(fr_vocabulary_size)),
          tokenizer)
  create_vocabulary(en_vocab_path, train_path + ".in", en_vocabulary_size,
          os.path.join(data_dir, "enc_embedding{0}.tsv".format(en_vocabulary_size)),
          tokenizer)
  #if load_embeddings:
  #  embed_utils.save_embeddings(fr_vocab_path, "embed5000.txt")
  #  embed_utils.save_embeddings(en_vocab_path, "embed5000.txt")
    

  # Create token ids for the training data.
  fr_train_ids_path = train_path + (".ids%d.out" % fr_vocabulary_size)
  en_train_ids_path = train_path + (".ids%d.in" % en_vocabulary_size)
  data_to_token_ids(train_path + ".out", fr_train_ids_path, fr_vocab_path, tokenizer)
  data_to_token_ids(train_path + ".in", en_train_ids_path, en_vocab_path, tokenizer)

  # Create token ids for the development data.
  fr_dev_ids_path = dev_path + (".ids%d.out" % fr_vocabulary_size)
  en_dev_ids_path = dev_path + (".ids%d.in" % en_vocabulary_size)
  data_to_token_ids(dev_path + ".out", fr_dev_ids_path, fr_vocab_path, tokenizer)
  data_to_token_ids(dev_path + ".in", en_dev_ids_path, en_vocab_path, tokenizer)

  return (en_train_ids_path, fr_train_ids_path,
          en_dev_ids_path, fr_dev_ids_path,
          en_vocab_path, fr_vocab_path)
def prepare_wmt_data ( data_dir , en_vocabulary_size , fr_vocabulary_size , load_embeddings = False , tokenizer = None ) : # Get wmt data to the specified directory. train_path = os . path . join ( data_dir , "train.txt" ) dev_path = os . path . join ( data_dir , "dev.txt" ) # Create vocabularies of the appropriate sizes. fr_vocab_path = os . path . join ( data_dir , "vocab%d.out" % fr_vocabulary_size ) en_vocab_path = os . path . join ( data_dir , "vocab%d.in" % en_vocabulary_size ) create_vocabulary ( fr_vocab_path , train_path + ".out" , fr_vocabulary_size , os . path . join ( data_dir , "dec_embedding{0}.tsv" . format ( fr_vocabulary_size ) ) , tokenizer ) create_vocabulary ( en_vocab_path , train_path + ".in" , en_vocabulary_size , os . path . join ( data_dir , "enc_embedding{0}.tsv" . format ( en_vocabulary_size ) ) , tokenizer ) #if load_embeddings: #  embed_utils.save_embeddings(fr_vocab_path, "embed5000.txt") #  embed_utils.save_embeddings(en_vocab_path, "embed5000.txt") # Create token ids for the training data. fr_train_ids_path = train_path + ( ".ids%d.out" % fr_vocabulary_size ) en_train_ids_path = train_path + ( ".ids%d.in" % en_vocabulary_size ) data_to_token_ids ( train_path + ".out" , fr_train_ids_path , fr_vocab_path , tokenizer ) data_to_token_ids ( train_path + ".in" , en_train_ids_path , en_vocab_path , tokenizer ) # Create token ids for the development data. fr_dev_ids_path = dev_path + ( ".ids%d.out" % fr_vocabulary_size ) en_dev_ids_path = dev_path + ( ".ids%d.in" % en_vocabulary_size ) data_to_token_ids ( dev_path + ".out" , fr_dev_ids_path , fr_vocab_path , tokenizer ) data_to_token_ids ( dev_path + ".in" , en_dev_ids_path , en_vocab_path , tokenizer ) return ( en_train_ids_path , fr_train_ids_path , en_dev_ids_path , fr_dev_ids_path , en_vocab_path , fr_vocab_path )