old_contents: # Requirements - transformers, tokenizers
  import os
  from unittest import TestCase
  from deepchem.feat.smiles_tokenizer import SmilesTokenizer
  from transformers import RobertaForMaskedLM
  
  
  class TestSmilesTokenizer(TestCase):
    """Tests the SmilesTokenizer to load the USPTO vocab file and a ChemBERTa Masked LM model with pre-trained weights.."""
  
  
    def test_tokenize(self):
        current_dir = os.path.dirname(os.path.realpath(__file__))
        vocab_path = os.path.join(current_dir, 'data', 'vocab.txt')
        tokenized_smiles = [12, 16, 16, 16, 17, 16, 16, 18, 16, 19, 16, 17, 22, 19, 18, 33, 17, 16, 18, 23, 181, 17, 22, 19, 18, 17, 19, 16, 33, 20, 19, 55, 17, 16, 38, 23, 18, 17, 33, 17, 19, 18, 35, 20, 19, 18, 16, 20, 22, 16, 16, 22, 16, 21, 23, 20, 23, 22, 16, 23, 22, 16, 21, 23, 18, 19, 16, 20, 22, 16, 16, 22, 16, 16, 22, 16, 20, 13]
  
        model = RobertaForMaskedLM.from_pretrained('seyonec/SMILES_tokenized_PubChem_shard00_50k')
        model.num_parameters()
  
        tokenizer = SmilesTokenizer(vocab_path, max_len=model.config.max_position_embeddings)
        print(tokenizer.encode("CCC(CC)COC(=O)[C@H](C)N[P@](=O)(OC[C@H]1O[C@](C#N)([C@H](O)[C@@H]1O)C1=CC=C2N1N=CN=C2N)OC1=CC=CC=C1"))
  
        assert tokenized_smiles == tokenizer.encode("CCC(CC)COC(=O)[C@H](C)N[P@](=O)(OC[C@H]1O[C@](C#N)([C@H](O)[C@@H]1O)C1=CC=C2N1N=CN=C2N)OC1=CC=CC=C1")
  
new_contents: # Requirements - transformers, tokenizers
  import os
  from unittest import TestCase
  from deepchem.feat.smiles_tokenizer import SmilesTokenizer
  from transformers import RobertaForMaskedLM
  
  
  class TestSmilesTokenizer(TestCase):
    """Tests the SmilesTokenizer to load the USPTO vocab file and a ChemBERTa Masked LM model with pre-trained weights.."""
  
  
    def test_tokenize(self):
        current_dir = os.path.dirname(os.path.realpath(__file__))
        vocab_path = os.path.join(current_dir, 'data', 'vocab.txt')
        tokenized_smiles = [12, 16, 16, 16, 17, 16, 16, 18, 16, 19, 16, 17, 22, 19, 18, 33, 17, 16, 18, 23, 181, 17, 22, 19, 18, 17, 19, 16, 33, 20, 19, 55, 17, 16, 38, 23, 18, 17, 33, 17, 19, 18, 35, 20, 19, 18, 16, 20, 22, 16, 16, 22, 16, 21, 23, 20, 23, 22, 16, 23, 22, 16, 21, 23, 18, 19, 16, 20, 22, 16, 16, 22, 16, 16, 22, 16, 20, 13]
  
        model = RobertaForMaskedLM.from_pretrained('seyonec/SMILES_tokenized_PubChem_shard00_50k')
        model.num_parameters()
  
        tokenizer = SmilesTokenizer(vocab_path, max_len=model.config.max_position_embeddings)
  
        assert tokenized_smiles == tokenizer.encode("CCC(CC)COC(=O)[C@H](C)N[P@](=O)(OC[C@H]1O[C@](C#N)([C@H](O)[C@@H]1O)C1=CC=C2N1N=CN=C2N)OC1=CC=CC=C1")
  
subject: remove print with assert