code: 
  
  
  
  """simple docstring"""
  
  
  
  
  import math
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int       )						->     int:
  
  
  
  
  
       '''simple docstring'''
  
       return sum(int(SCREAMING_SNAKE_CASE__       ) for digit in str(SCREAMING_SNAKE_CASE__       )       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int = 30       )						->     int:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Any              =					[]
       for digit in range(2		,						100       ):
            for power in range(2		,						100       ):
                 _UpperCAmelCase    :			Union[str, Any]              =					int(math.pow(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )       )
                 if digit == digit_sum(SCREAMING_SNAKE_CASE__       ):
                      digit_to_powers.append(SCREAMING_SNAKE_CASE__       )
  
       digit_to_powers.sort()
       return digit_to_powers[n - 1]
  
  
  if __name__ == "__main__":
   print(solution())
  
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)
  