




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      :					List[Any]     					=    c_float
     @property
     def        lowerCAmelCase__  (				self	):
            return sizeof(self.value	)





def 							lowerCamelCase    (							UpperCAmelCase_	:      int   ,       UpperCAmelCase_	:      int   ,       UpperCAmelCase_	:      int = 0      )->			int:

       """simple docstring"""




       if dimension <= 0:
              a			   =fixed_dimension

       dimension -= num_token_to_add
       return dimension





def 							lowerCamelCase    (							UpperCAmelCase_	:      int   ,       UpperCAmelCase_	:      ParameterFormat      )->			int:

       """simple docstring"""




       return num_parameters * dtype.size






def 							lowerCamelCase    (							UpperCAmelCase_	:      str      )->			Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:

       """simple docstring"""




       from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

       try:
              return AutoProcessor.from_pretrained(UpperCAmelCase_      )
       except (ValueError, OSError, KeyError):
              a   ,      a			   =None, None
              try:
                     a			   =AutoTokenizer.from_pretrained(UpperCAmelCase_      )
              except (OSError, KeyError):
                     pass
              try:
                     a			   =AutoFeatureExtractor.from_pretrained(UpperCAmelCase_      )
              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





def 							lowerCamelCase    (							UpperCAmelCase_	:      list[int | float]      )->			int | float:

       """simple docstring"""




       if len(UpperCAmelCase_      ) == 0:
              raise ValueError("""find_min() arg is an empty sequence"""      )
       a			   =nums[0]
       for num in nums:
              a			   =min(UpperCAmelCase_   ,       UpperCAmelCase_      )
       return min_num


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
