






'''simple docstring'''







import math
from timeit import timeit






def  lowerCAmelCase__		(					a_  :     int			)   -> int:
       UpperCAmelCase__	:   Any												=	0
       UpperCAmelCase__	:   Optional[Any]												=	abs(a_			)
       while True:
              UpperCAmelCase__	:   int												=	n // 1_0
              digits += 1
              if n == 0:
                     break
       return digits






def  lowerCAmelCase__		(					a_  :     int			)   -> int:
       return 1 if n == 0 else math.floor(math.log(abs(a_			)			,						1_0			) + 1			)






def  lowerCAmelCase__		(					a_  :     int			)   -> int:
       return len(str(abs(a_			)			)			)






def  lowerCAmelCase__		(					)   -> None:
       from collections.abc import Callable

       def benchmark_a_function(a_  :     Callable			,						a_  :     int			) -> None:
              UpperCAmelCase__	:   Optional[int]												=	f"""{func.__name__}({value})"""
              UpperCAmelCase__	:   str												=	timeit(f"""__main__.{call}"""			,						setup='''import __main__'''			)
              print(f"""{call}: {func(a_			)} -- {timing} seconds"""			)

       for value in (2_6_2_1_4_4, 1_1_2_5_8_9_9_9_0_6_8_4_2_6_2_4, 1_2_6_7_6_5_0_6_0_0_2_2_8_2_2_9_4_0_1_4_9_6_7_0_3_2_0_5_3_7_6):
              for func in (num_digits, num_digits_fast, num_digits_faster):
                     benchmark_a_function(a_			,						a_			)
              print()


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  benchmark()







'''simple docstring'''







from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
  from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    Dict				       =					c_float




      @property
      def 							lowerCamelCase     (					self						):
             return sizeof(self.value						)






def  lowerCAmelCase__		(					a_  :     int			,						a_  :     int			,						a_  :     int = 0			)   -> int:
       # < 0 is possible if using a dynamic axis
       if dimension <= 0:
              UpperCAmelCase__	:   List[Any]												=	fixed_dimension

       dimension -= num_token_to_add
       return dimension






def  lowerCAmelCase__		(					a_  :     int			,						a_  :     ParameterFormat			)   -> int:
       return num_parameters * dtype.size






def  lowerCAmelCase__		(					a_  :     str			)   -> Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
       from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

       try:
              return AutoProcessor.from_pretrained(a_			)
       except (ValueError, OSError, KeyError):
              UpperCAmelCase__							,							UpperCAmelCase__	:   Dict												=	None, None
              try:
                     UpperCAmelCase__	:   Union[str, Any]												=	AutoTokenizer.from_pretrained(a_			)
              except (OSError, KeyError):
                     pass
              try:
                     UpperCAmelCase__	:   List[str]												=	AutoFeatureExtractor.from_pretrained(a_			)
              except (OSError, KeyError):
                     pass

              if tokenizer is not None and feature_extractor is not None:
                     raise ValueError(
                         f"""Couldn't auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."""			)
              elif tokenizer is None and feature_extractor is None:
                     return None
              elif tokenizer is not None:
                     return tokenizer
              else:
                     return feature_extractor