code: import math
  from timeit import timeit
  def 	UpperCamelCase			(     snake_case__ :					int			)			->  int:
     UpperCamelCase					:						Tuple					=			0
     UpperCamelCase					:						Union[str, Any]					=			abs(snake_case__			)
     while True:
        UpperCamelCase					:						Dict					=			n // 10
        digits += 1
        if n == 0:
           break
     return digits
  def 	UpperCamelCase			(     snake_case__ :					int			)			->  int:
     return 1 if n == 0 else math.floor(math.log(abs(snake_case__			) ,  10			) + 1			)
  def 	UpperCamelCase			(     snake_case__ :					int			)			->  int:
     return len(str(abs(snake_case__			)			)			)
  def 	UpperCamelCase			(     )			->  None:
     from collections.abc import Callable
  
     def benchmark_a_function(snake_case__ :					Callable ,  snake_case__ :					int			) -> None:
        UpperCamelCase					:						Any					=			F"""{func.__name__}({value})"""
        UpperCamelCase					:						List[str]					=			timeit(F"""__main__.{call}""" ,  setup='import __main__'			)
        print(F"""{call}: {func(snake_case__			)} -- {timing} seconds"""			)
  
     for value in (262144, 1125899906842624, 1267650600228229401496703205376):
        for func in (num_digits, num_digits_fast, num_digits_faster):
           benchmark_a_function(snake_case__ ,  snake_case__			)
        print()
  
  
  if __name__ == "__main__":
      import doctest
  
      doctest.testmod()
      benchmark()
  
  
  
  
style_context: 
  
  
  
  from typing import Optional, Tuple
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_openai import OpenAIGPTTokenizer
  
  
  __UpperCAmelCase      = logging.get_logger(__name__)
  
  __UpperCAmelCase      = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
  
  __UpperCAmelCase      = {
      '''vocab_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/vocab.json'''},
      '''merges_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/merges.txt'''},
      '''tokenizer_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/tokenizer.json'''},
  }
  
  __UpperCAmelCase      = {
      '''openai-gpt''': 512,
  }
  
  
  
  class        lowerCAmelCase_	(						a__							):
         UpperCAmelCase__       :      Union[str, Any]       =						VOCAB_FILES_NAMES
         UpperCAmelCase__       :      int       =						PRETRAINED_VOCAB_FILES_MAP
         UpperCAmelCase__       :      Optional[int]       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
         UpperCAmelCase__       :      Optional[int]       =						["input_ids", "attention_mask"]
         UpperCAmelCase__       :      Tuple       =						OpenAIGPTTokenizer
         def __init__(    self,    SCREAMING_SNAKE_CASE_=None,    SCREAMING_SNAKE_CASE_=None,    SCREAMING_SNAKE_CASE_=None,    SCREAMING_SNAKE_CASE_="<unk>",    **SCREAMING_SNAKE_CASE_ )  ->	List[str]:
            super().__init__(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    tokenizer_file=SCREAMING_SNAKE_CASE_,    unk_token=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_ )
         @property
         def       snake_case_ (    self )  ->	Dict:
            return True
  
  
  
  
  
  
         def       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ = None )  ->	Tuple[str]:
            UpperCamelCase					:						List[Any]					=			self._tokenizer.model.save(SCREAMING_SNAKE_CASE_,    name=SCREAMING_SNAKE_CASE_ )
            return tuple(SCREAMING_SNAKE_CASE_ )
  
  
  
  