class_docstr: Creates the SmilesTokenizer class. The tokenizer heavily inherits from the BertTokenizer
  implementation found in Huggingface's transformers library. It runs a WordPiece tokenization
  algorithm over SMILES strings using the tokenisation SMILES regex developed by Schwaller et. al.
  
  Please see https://github.com/huggingface/transformers
  and https://github.com/rxn4chemistry/rxnfp for more details.
  
  Examples
  --------
  >>> from deepchem.feat.smiles_tokenizer import SmilesTokenizer
  >>> current_dir = os.path.dirname(os.path.realpath(__file__))
  >>> vocab_path = os.path.join(current_dir, 'tests/data', 'vocab.txt')
  >>> tokenizer = SmilesTokenizer(vocab_path)
  >>> print(tokenizer.encode("CC(=O)OC1=CC=CC=C1C(=O)O"))
  [12, 16, 16, 17, 22, 19, 18, 19, 16, 20, 22, 16, 16, 22, 16, 16, 22, 16, 20, 16, 17, 22, 19, 18, 19, 13]
  
  
  References
  ----------
  .. [1] Schwaller, Philippe; Probst, Daniel; Vaucher, Alain C.; Nair, Vishnu H; Kreutter, David;
      Laino, Teodoro; et al. (2019): Mapping the Space of Chemical Reactions using Attention-Based Neural
      Networks. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.9897365.v3
  
  Note
  ----
  This class requires huggingface's transformers and tokenizers libraries to be installed.
class_docstr_tok: ["Creates","the","SmilesTokenizer","class",".","The","tokenizer","heavily","inherits","from","the","BertTokenizer","implementation","found","in","Huggingface","'s","transformers","library",".","It","runs","a","WordPiece","tokenization","algorithm","over","SMILES","strings","using","the","tokenisation","SMILES","regex","developed","by","Schwaller","et",".","al",".","Please","see","https",":","\/\/github.com\/huggingface\/transformers","and","https",":","\/\/github.com\/rxn4chemistry\/rxnfp","for","more","details",".","Examples","--","--","--","--",">",">",">","from","deepchem.feat.smiles_tokenizer","import","SmilesTokenizer",">",">",">","current_dir","=","os.path.dirname","(","os.path.realpath","(","__file__",")",")",">",">",">","vocab_path","=","os.path.join","(","current_dir",",","'tests\/data","'",",","'vocab.txt","'",")",">",">",">","tokenizer","=","SmilesTokenizer","(","vocab_path",")",">",">",">","print","(","tokenizer.encode","(","``","CC","(","=O",")","OC1=CC=CC=C1C","(","=O",")","O","''",")",")","[","12",",","16",",","16",",","17",",","22",",","19",",","18",",","19",",","16",",","20",",","22",",","16",",","16",",","22",",","16",",","16",",","22",",","16",",","20",",","16",",","17",",","22",",","19",",","18",",","19",",","13","]","References","--","--","--","--","--","..","[","1","]","Schwaller",",","Philippe",";","Probst",",","Daniel",";","Vaucher",",","Alain","C.",";","Nair",",","Vishnu","H",";","Kreutter",",","David",";","Laino",",","Teodoro",";","et","al",".","(","2019",")",":","Mapping","the","Space","of","Chemical","Reactions","using","Attention-Based","Neural","Networks",".","ChemRxiv",".","Preprint",".","https",":","\/\/doi.org\/10.26434\/chemrxiv.9897365.v3","Note","--","--","This","class","requires","huggingface","'s","transformers","and","tokenizers","libraries","to","be","installed","."]
docstr: Adds special tokens to the a sequence for sequence classification tasks.
  A BERT sequence has the following format: [CLS] X [SEP]
  
  Parameters
  ----------
  tokens: List[str]
      List of tokens for a given string sequence.
docstr_tok: ["Adds","special","tokens","to","the","a","sequence","for","sequence","classification","tasks",".","A","BERT","sequence","has","the","following","format",":","[","CLS","]","X","[","SEP","]","Parameters","--","--","--","--","--","tokens",":","List","[","str","]","List","of","tokens","for","a","given","string","sequence","."]
code: def add_special_tokens_single_sequence(self, tokens: List[str]):
      """Adds special tokens to the a sequence for sequence classification tasks.
      A BERT sequence has the following format: [CLS] X [SEP]
  
      Parameters
      ----------
      tokens: List[str]
          List of tokens for a given string sequence.
      """
      return [self.cls_token] + tokens + [self.sep_token]
  
imports: import collections
  import os
  import re
  import pkg_resources
  from typing import List, Optional
  from transformers import BertTokenizer
  from logging import getLogger
label_desc: Use image node_id 9 for calling the SmilesTokenizer obj's underlying member method code with example usage: obj.add_special_tokens_single_sequence(tokens) and returns: unknown