code: 
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  _lowerCAmelCase       :      List[Any]							=							{
      "configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig", "ImageGPTOnnxConfig"]
  }
  
  try:
   if not is_vision_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _lowerCAmelCase       :      Union[str, Any]							=							["ImageGPTFeatureExtractor"]
   _lowerCAmelCase       :      List[str]							=							["ImageGPTImageProcessor"]
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   _lowerCAmelCase       :      Any							=							[
       "IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "ImageGPTForCausalImageModeling",
       "ImageGPTForImageClassification",
       "ImageGPTModel",
       "ImageGPTPreTrainedModel",
       "load_tf_weights_in_imagegpt",
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig
  
   try:
    if not is_vision_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
    from .image_processing_imagegpt import ImageGPTImageProcessor
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_imagegpt import (
        IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        ImageGPTForCausalImageModeling,
        ImageGPTForImageClassification,
        ImageGPTModel,
        ImageGPTPreTrainedModel,
        load_tf_weights_in_imagegpt,
    )
  
  else:
   import sys
  
   _lowerCAmelCase       :      Optional[int]							=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
style_context: 
  
  
  
  """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 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		str=10       )						->     List[Any]:
  
  
  
  
  
       '''simple docstring'''
  
       for _ in tqdm(range(0		,						args.n_examples		,						SCREAMING_SNAKE_CASE__       )       ):
            yield [next(SCREAMING_SNAKE_CASE__       )[args.text_column] for _ in range(SCREAMING_SNAKE_CASE__       )]
  
  
  # Configuration
  _lowerCAmelCase       :      Optional[Any]							=							HfArgumentParser(TokenizerTrainingArguments)
  _lowerCAmelCase       :      Dict							=							parser.parse_args()
  
  # Base tokenizer
  _lowerCAmelCase       :      int							=							AutoTokenizer.from_pretrained(args.base_tokenizer)
  _lowerCAmelCase       :      List[str]							=							list(bytes_to_unicode().values())
  
  # Load dataset
  _lowerCAmelCase       :      Dict							=							load_dataset(args.dataset_name, split="train", streaming=True)
  _lowerCAmelCase       :      List[Any]							=							iter(dataset)
  
  
  # Training and saving
  _lowerCAmelCase       :      List[str]							=							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)
  