old_contents: from os import sep
  from deepchem.feat import Featurizer
  from typing import List
  import numpy as np
  try:
      from transformers import RobertaTokenizerFast
  except ModuleNotFoundError:
    raise ImportError(
        'Transformers must be installed for RobertaFeaturizer to be used!')
    pass
  
  class RxnFeaturizer(Featurizer):
    def __init__(self, tokenizer: RobertaTokenizerFast, sep_reagent: bool):
      if not isinstance(tokenizer, RobertaTokenizerFast):
        raise TypeError(f"""`tokenizer` must be a constructed `RobertaTokenizerFast`
                         object, not {type(tokenizer)}""")
      else:
        self.tokenizer = tokenizer
      self.sep_reagent = sep_reagent
  
    def _featurize(self, datapoint: str, **kwargs) -> List[List[int]]:
      #if dont want to tokenize, return raw reaction SMILES.
      #sep_reagent then tokenize, source and target separately.
      reactant = list(map(lambda x: x.split('>')[0], datapoint))
      reagent = list(map(lambda x: x.split('>')[1], datapoint))
      product = list(map(lambda x: x.split('>')[2], datapoint))
  
      if self.sep_reagent:
        source = [x + '>' + y for x, y in zip(reactant, reagent)]
      else:
        source = [
            x + '.' + y + '>' if y else x + '>' + y
            for x, y in zip(reactant, reagent)
        ]
  
      target = product
  
  
  
  
      return self.tokenizer(source), self.tokenizer(target)
new_contents: from os import sep
  from deepchem.feat import Featurizer
  from typing import List
  import numpy as np
  try:
      from transformers import RobertaTokenizerFast
  except ModuleNotFoundError:
    raise ImportError(
        'Transformers must be installed for RobertaFeaturizer to be used!')
    pass
  
  class RxnFeaturizer(Featurizer):
    def __init__(self, tokenizer: RobertaTokenizerFast, sep_reagent: bool):
      if not isinstance(tokenizer, RobertaTokenizerFast):
        raise TypeError(f"""`tokenizer` must be a constructed `RobertaTokenizerFast`
                         object, not {type(tokenizer)}""")
      else:
        self.tokenizer = tokenizer
      self.sep_reagent = sep_reagent
  
    def _featurize(self, datapoint: str, **kwargs) -> List[List[List[int]], List[List[int]]]:
      #if dont want to tokenize, return raw reaction SMILES.
      #sep_reagent then tokenize, source and target separately.
      reactant = list(map(lambda x: x.split('>')[0], datapoint))
      reagent = list(map(lambda x: x.split('>')[1], datapoint))
      product = list(map(lambda x: x.split('>')[2], datapoint))
  
      if self.sep_reagent:
        source = [x + '>' + y for x, y in zip(reactant, reagent)]
      else:
        source = [
            x + '.' + y + '>' if y else x + '>' + y
            for x, y in zip(reactant, reagent)
        ]
      target = product
  
      source_encoding = list(self.tokenizer(source, **kwargs).values())
      target_encoding = list(self.tokenizer(target, **kwargs).values())
  
      return [source_encoding, target_encoding]
subject: change ret type