


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 A						(     UpperCamelCase_  ):
      UpperCamelCase__							:						int   =c_float






      @property
      def  lowerCamelCase (      self		:  Union[str, Any]				)      ->		int:
             """simple docstring"""




             return sizeof(self.value				)




def 			a_   (					SCREAMING_SNAKE_CASE__      :   int	,    SCREAMING_SNAKE_CASE__      :   int	,    SCREAMING_SNAKE_CASE__      :   int = 0  ):
       '''simple docstring'''





       if dimension <= 0:
              _lowerCamelCase :	List[str]						=fixed_dimension

       dimension -= num_token_to_add
       return dimension




def 			a_   (					SCREAMING_SNAKE_CASE__      :   int	,    SCREAMING_SNAKE_CASE__      :   ParameterFormat  ):
       '''simple docstring'''





       return num_parameters * dtype.size




def 			a_   (					SCREAMING_SNAKE_CASE__      :   str  ):
       '''simple docstring'''





       from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

       try:
              return AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE__  )
       except (ValueError, OSError, KeyError):
              _lowerCamelCase			,  _lowerCamelCase :	List[Any]						=None, None
              try:
                     _lowerCamelCase :	Optional[Any]						=AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__  )
              except (OSError, KeyError):
                     pass
              try:
                     _lowerCamelCase :	Any						=AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__  )
              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






from __future__ import annotations

from collections.abc import Generator




def 			a_   (					SCREAMING_SNAKE_CASE__      :   int  ):
       '''simple docstring'''





       if not isinstance(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ) or n < 1:
              raise Exception('Sequence only defined for positive integers'  )

       yield n
       while n != 1:
              if n % 2 == 0:
                     n //= 2
              else:
                     _lowerCamelCase :	Optional[int]						=3 * n + 1
              yield n




def 			a_   (					):
       '''simple docstring'''





       _lowerCamelCase :	Dict						=43
       _lowerCamelCase :	Dict						=tuple(collatz_sequence(SCREAMING_SNAKE_CASE__  )  )
       print(SCREAMING_SNAKE_CASE__  )
       print(F'''Collatz sequence from {n} took {len(SCREAMING_SNAKE_CASE__  )} steps.'''  )


if __name__ == "__main__":
 main()


