How to use the t5.data.SentencePieceVocabulary function in t5

for decoding. Must match the one used during training.
    """
    # TODO(sharannarang) : It would be nice to have a function like
    # load_checkpoint that loads the model once and then call decode_from_file
    # multiple times without having to restore the checkpoint weights again.
    # This would be particularly useful in colab demo.

    if checkpoint_steps == -1:
      checkpoint_steps = _get_latest_checkpoint_from_dir(self._model_dir)

    with gin.unlock_config():
      gin.parse_config_file(_operative_config_path(self._model_dir))
      gin.bind_parameter("Bitransformer.decode.beam_size", beam_size)
      gin.bind_parameter("Bitransformer.decode.temperature", temperature)

    vocabulary = t5.data.SentencePieceVocabulary(sentencepiece_model_path)
    utils.infer_model(self.estimator(vocabulary), vocabulary,
                      self._sequence_length, self.batch_size,
                      self._model_type, self._model_dir, checkpoint_steps,
                      input_file, output_file)

the Mesh TF transformer standalone.

  Args:
    mixture_or_task_name: string, an identifier for a Mixture or Task in the
      appropriate registry. Must be specified via gin.
    sequence_length: dict mapping feature key to the int length for that feature
      the max sequence length.
    vocabulary: a SentencePieceVocabulary.
    dataset_split: string, which split of the dataset to load. In most cases
      this should be "train".
    use_cached: bool, whether to load the cached version of this dataset.

  Returns:
    A tf.data.Dataset of preprocessed, tokenized, and batched examples.
  """
  if not isinstance(vocabulary, t5.data.SentencePieceVocabulary):
    raise ValueError("vocabulary must be a SentencePieceVocabulary")

  mixture_or_task = t5.data.get_mixture_or_task(mixture_or_task_name)

  ds = mixture_or_task.get_dataset(
      sequence_length, split=dataset_split, use_cached=use_cached, shuffle=True)
  ds = transformer_dataset.pack_or_pad(
      ds, sequence_length, pack=True,
      feature_keys=tuple(mixture_or_task.output_features), ensure_eos=True)
  return ds