move to general feat dir

from deepchem.feat import MolecularFeaturizer

from deepchem.utils.typing import RDKitMol

from deepchem.feat import Featurizer

from typing import Dict, List

try:

  from transformers import RobertaTokenizerFast

  pass

class RobertaFeaturizer(RobertaTokenizerFast, MolecularFeaturizer):

class RobertaFeaturizer(RobertaTokenizerFast, Featurizer):

  """Roberta Featurizer.

  The Roberta Featurizer is a wrapper class of the Roberta Tokenizer,

    self.attention_mask = attention_mask

    return

  def _featurize(self, smiles_string: str) -> List[List[int]]:

  def _featurize(self, sequence: str) -> List[List[int]]:

    """Calculate encoding using HuggingFace's RobertaTokenizerFast

    Parameters

    ----------

    smiles_string: str

      String containing SMILES sequence.

    sequence: str

      Arbitrary string sequence to be tokenized.

    Returns

    -------

    # -> make this a list of two lists to allow np to handle it

    # encoding = list(self(smiles_string, **kwargs).values())

    encoding = list(

        self(smiles_string, self.input_ids, self.attention_mask).values())

        self(sequence, self.input_ids, self.attention_mask).values())

    return encoding

    - torchvision==0.10.0+cpu

    - torch-scatter

    - torch-sparse

    - transformers==4.6.*