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 			__lowercase	(						_UpperCamelCase=10     )    ->Optional[int]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        for _ in tqdm(range(0,    args.n_examples,    _UpperCamelCase     )     ):
              yield [next(_UpperCamelCase     )[args.text_column] for _ in range(_UpperCamelCase     )]
  
  
  # Configuration
  __a   				=      HfArgumentParser(TokenizerTrainingArguments)
  __a   				=      parser.parse_args()
  
  # Base tokenizer
  __a   				=      AutoTokenizer.from_pretrained(args.base_tokenizer)
  __a   				=      list(bytes_to_unicode().values())
  
  # Load dataset
  __a   				=      load_dataset(args.dataset_name, split='''train''', streaming=True)
  __a   				=      iter(dataset)
  
  
  # Training and saving
  __a   				=      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: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  __a   				=      {'''configuration_yoso''': ['''YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YosoConfig''']}
  
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __a   				=      [
        '''YOSO_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''YosoForMaskedLM''',
        '''YosoForMultipleChoice''',
        '''YosoForQuestionAnswering''',
        '''YosoForSequenceClassification''',
        '''YosoForTokenClassification''',
        '''YosoLayer''',
        '''YosoModel''',
        '''YosoPreTrainedModel''',
    ]
  
  
  if TYPE_CHECKING:
    from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig
  
    try:
      if not is_torch_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_yoso import (
          YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
          YosoForMaskedLM,
          YosoForMultipleChoice,
          YosoForQuestionAnswering,
          YosoForSequenceClassification,
          YosoForTokenClassification,
          YosoLayer,
          YosoModel,
          YosoPreTrainedModel,
      )
  
  
  else:
    import sys
  
    __a   				=      _LazyModule(__name__, globals()['''__file__'''], _import_structure)
  
  