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  Examples

  --------

  >>> from deepchem.feat import BertFeaturizer

  >>> featurizer = BertFeaturizer.from_pretrained("Rostlab/prot_bert", do_lower_case=False)

  >>> inputs = featurizer.featurize('D L I P [MASK] L V T', return_tensors="pt")

  >>> from transformers import BertTokenizerFast

  >>> tokenizer = BertTokenizerFast.from_pretrained("Rostlab/prot_bert", do_lower_case=False)

  >>> featurizer = BertFeaturizer(tokenizer)

  >>> feats = featurizer.featurize('D L I P [MASK] L V T', return_tensors="pt")

  Notes

  -----

    # the encoding is natively a dictionary with keys 'input_ids', 'token_type_ids', and 'attention_mask'

    encoding = list(self.tokenizer(datapoint, **kwargs).values())

    return encoding

  def __call__(self, *args, **kwargs) -> np.ndarray:

    return self.featurize(*args, **kwargs)