code: 
  
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  from arguments import TokenizerTrainingArguments
  from datasets import load_dataset
  from tqdm import tqdm
  
  from transformers import AutoTokenizer, HfArgumentParser
  from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase=10					)   -> Tuple:
  
       '''simple docstring'''
       for _ in tqdm(range(0   ,       args.n_examples   ,       __lowerCamelCase					)					):
            yield [next(__lowerCamelCase					)[args.text_column] for _ in range(__lowerCamelCase					)]
  
  
  # Configuration
  SCREAMING_SNAKE_CASE__		:       Tuple   							=							HfArgumentParser(TokenizerTrainingArguments)
  SCREAMING_SNAKE_CASE__		:       Optional[Any]   							=							parser.parse_args()
  
  # Base tokenizer
  SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							AutoTokenizer.from_pretrained(args.base_tokenizer)
  SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							list(bytes_to_unicode().values())
  
  # Load dataset
  SCREAMING_SNAKE_CASE__		:       Dict   							=							load_dataset(args.dataset_name, split="""train""", streaming=True)
  SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							iter(dataset)
  
  
  # Training and saving
  SCREAMING_SNAKE_CASE__		:       List[Any]   							=							tokenizer.train_new_from_iterator(
      batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
  )
  
  
  
  
  
  
  
  new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import warnings
  
  from ...utils import logging
  from .image_processing_vilt import ViltImageProcessor
  
  
  SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							logging.get_logger(__name__)
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		__lowerCamelCase		):
  
  
  
  
       def __init__(							self  ,	*_lowerCAmelCase  ,	**_lowerCAmelCase		):
            warnings.warn(
                """The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
                """ use ViltImageProcessor instead."""  ,	_lowerCAmelCase  ,	)
            super().__init__(*_lowerCAmelCase  ,	**_lowerCAmelCase		)
  
  
  
  
  
  
  