Download corpora if necessary and unzip them.

  Args:
    tmp_dir: directory containing dataset.
    dataset_split: whether we're in train/dev/test mode.

  Returns:
    List of all files generated and path to file containing
      train/dev/test split info.

def _maybe_download_corpora(tmp_dir, dataset_split):
  """Download corpora if necessary and unzip them.

  Args:
    tmp_dir: directory containing dataset.
    dataset_split: whether we're in train/dev/test mode.

  Returns:
    List of all files generated and path to file containing
      train/dev/test split info.
  """
  cnn_filename = "cnn_stories.tgz"
  cnn_finalpath = os.path.join(tmp_dir, "cnn/stories/")
  dailymail_filename = "dailymail_stories.tgz"
  dailymail_finalpath = os.path.join(tmp_dir, "dailymail/stories/")
  if not tf.gfile.Exists(cnn_finalpath):
    cnn_file = generator_utils.maybe_download_from_drive(
        tmp_dir, cnn_filename, _CNN_STORIES_DRIVE_URL)
    with tarfile.open(cnn_file, "r:gz") as cnn_tar:
      cnn_tar.extractall(tmp_dir)
  if not tf.gfile.Exists(dailymail_finalpath):
    dailymail_file = generator_utils.maybe_download_from_drive(
        tmp_dir, dailymail_filename, _DAILYMAIL_STORIES_DRIVE_URL)
    with tarfile.open(dailymail_file, "r:gz") as dailymail_tar:
      dailymail_tar.extractall(tmp_dir)

  cnn_files = tf.gfile.Glob(cnn_finalpath + "*")
  dailymail_files = tf.gfile.Glob(dailymail_finalpath + "*")
  all_files = cnn_files + dailymail_files

  if dataset_split == problem.DatasetSplit.TRAIN:
    urls_path = generator_utils.maybe_download(tmp_dir, "all_train.txt",
                                               _TRAIN_URLS)
  elif dataset_split == problem.DatasetSplit.EVAL:
    urls_path = generator_utils.maybe_download(tmp_dir, "all_val.txt",
                                               _DEV_URLS)
  else:
    urls_path = generator_utils.maybe_download(tmp_dir, "all_test.txt",
                                               _TEST_URLS)

  return all_files, urls_path