



'''simple docstring'''

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class        __lowerCAmelCase  (					__magic_name__				):






       """simple docstring"""
       _snake_case			:  Tuple		       =					['image_processor', 'tokenizer']
       _snake_case			:  Union[str, Any]		       =					'AutoImageProcessor'
       _snake_case			:  int		       =					'AutoTokenizer'





       def __init__(							self     :	Optional[int] ,				lowerCAmelCase__     :	int=None ,				lowerCAmelCase__     :	str=None ,				**lowerCAmelCase__     :	Tuple  )					->  Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       					= None
            if "feature_extractor" in kwargs:
                 warnings.warn(
                     '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                     ''' instead.''' ,				lowerCAmelCase__ ,				)
                 _UpperCamelCase       					= kwargs.pop('''feature_extractor'''  )

            _UpperCamelCase       					= image_processor if image_processor is not None else feature_extractor
            if image_processor is None:
                 raise ValueError('''You need to specify an `image_processor`.'''  )
            if tokenizer is None:
                 raise ValueError('''You need to specify a `tokenizer`.'''  )

            super().__init__(lowerCAmelCase__ ,				lowerCAmelCase__  )
            _UpperCamelCase       					= self.image_processor
            _UpperCamelCase       					= False





       def __call__(							self     :	Any ,				*lowerCAmelCase__     :	Optional[Any] ,				**lowerCAmelCase__     :	Optional[int]  )					->  Optional[int]:

            '''simple docstring'''




            if self._in_target_context_manager:
                 return self.current_processor(*lowerCAmelCase__ ,				**lowerCAmelCase__  )

            _UpperCamelCase       					= kwargs.pop('''images''' ,				lowerCAmelCase__  )
            _UpperCamelCase       					= kwargs.pop('''text''' ,				lowerCAmelCase__  )
            if len(lowerCAmelCase__  ) > 0:
                 _UpperCamelCase       					= args[0]
                 _UpperCamelCase       					= args[1:]

            if images is None and text is None:
                 raise ValueError('''You need to specify either an `images` or `text` input to process.'''  )

            if images is not None:
                 _UpperCamelCase       					= self.image_processor(lowerCAmelCase__ ,				*lowerCAmelCase__ ,				**lowerCAmelCase__  )
            if text is not None:
                 _UpperCamelCase       					= self.tokenizer(lowerCAmelCase__ ,				**lowerCAmelCase__  )

            if text is None:
                 return inputs
            elif images is None:
                 return encodings
            else:
                 _UpperCamelCase       					= encodings['''input_ids''']
                 return inputs





       def snake_case__		(							self     :	Any ,				*lowerCAmelCase__     :	Optional[Any] ,				**lowerCAmelCase__     :	int  )					->  List[str]:

            '''simple docstring'''




            return self.tokenizer.batch_decode(*lowerCAmelCase__ ,				**lowerCAmelCase__  )





       def snake_case__		(							self     :	Any ,				*lowerCAmelCase__     :	Tuple ,				**lowerCAmelCase__     :	str  )					->  Dict:

            '''simple docstring'''




            return self.tokenizer.decode(*lowerCAmelCase__ ,				**lowerCAmelCase__  )





       @contextmanager
       def snake_case__		(							self     :	Optional[int]  )					->  Any:

            '''simple docstring'''




            warnings.warn(
                '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
                '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
                '''your images inputs, or in a separate call.'''  )
            _UpperCamelCase       					= True
            _UpperCamelCase       					= self.tokenizer
            yield
            _UpperCamelCase       					= self.image_processor
            _UpperCamelCase       					= False





       @property
       def snake_case__		(							self     :	Optional[int]  )					->  Tuple:

            '''simple docstring'''




            warnings.warn(
                '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,				lowerCAmelCase__ ,				)
            return self.image_processor_class







       @property
       def snake_case__		(							self     :	Optional[int]  )					->  Tuple:

            '''simple docstring'''




            warnings.warn(
                '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,				lowerCAmelCase__ ,				)
            return self.image_processor










'''simple docstring'''

from __future__ import annotations







def       a__       (       lowercase      :  list[int | float]   )	->	int | float:





     """simple docstring"""

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


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)



