

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			Dict						      =["image_processor", "tokenizer"]
  lowerCamelCase    :			Tuple						      ="AutoImageProcessor"
  lowerCamelCase    :			Dict						      ="AutoTokenizer"







  def __init__(			self :    Optional[Any]       ,							lowerCAmelCase :    List[str]=None       ,							lowerCAmelCase :    Tuple=None       ,							**lowerCAmelCase :    Union[str, Any]    )							->	List[str]:






     """simple docstring"""







     __lowerCAmelCase  :			Union[str, Any]					    =						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       ,							)
        __lowerCAmelCase  :			List[str]					    =						kwargs.pop("""feature_extractor"""    )

     __lowerCAmelCase  :			List[str]					    =						image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
        raise ValueError("""You have to specify an image_processor."""    )
     if tokenizer is None:
        raise ValueError("""You have to specify a tokenizer."""    )

     super().__init__(lowerCAmelCase       ,							lowerCAmelCase    )
     __lowerCAmelCase  :			Union[str, Any]					    =						self.image_processor







  def __call__(			self :    Optional[Any]       ,							lowerCAmelCase :    List[Any]=None       ,							lowerCAmelCase :    int=None       ,							lowerCAmelCase :    int=None       ,							**lowerCAmelCase :    int    )							->	List[Any]:






     """simple docstring"""







     if text is None and images is None:
        raise ValueError("""You have to specify either text or images. Both cannot be none."""    )

     if text is not None:
        __lowerCAmelCase  :			int					    =						self.tokenizer(lowerCAmelCase       ,							return_tensors=lowerCAmelCase       ,							**lowerCAmelCase    )

     if images is not None:
        __lowerCAmelCase  :			Union[str, Any]					    =						self.image_processor(lowerCAmelCase       ,							return_tensors=lowerCAmelCase       ,							**lowerCAmelCase    )

     if text is not None and images is not None:
        __lowerCAmelCase  :			int					    =						image_features.pixel_values
        return encoding
     elif text is not None:
        return encoding
     else:
        return BatchEncoding(data=dict(**lowerCAmelCase    )       ,							tensor_type=lowerCAmelCase    )







  def        SCREAMING_SNAKE_CASE    (			self :    Dict       ,							*lowerCAmelCase :    str       ,							**lowerCAmelCase :    Any    )							->	str:






     """simple docstring"""







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







  def        SCREAMING_SNAKE_CASE    (			self :    str       ,							*lowerCAmelCase :    Dict       ,							**lowerCAmelCase :    Any    )							->	Tuple:






     """simple docstring"""







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







  @property
  def        SCREAMING_SNAKE_CASE    (			self :    List[str]    )							->	int:






     """simple docstring"""







     __lowerCAmelCase  :			Optional[int]					    =						self.tokenizer.model_input_names
     __lowerCAmelCase  :			str					    =						self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )







  @property
  def        SCREAMING_SNAKE_CASE    (			self :    List[str]    )							->	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        SCREAMING_SNAKE_CASE    (			self :    str    )							->	Optional[int]:






     """simple docstring"""







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





from math import sqrt





def 	snake_case_      (__A     :						int     )			->	list[int]:
   __lowerCAmelCase  :			List[str]					    =						[1]
   for i in range(2					,   int(sqrt(__A     )     ) + 1					,   1     ):
      if number % i == 0:
         values.append(__A     )
         if int(number // i     ) != i:
            values.append(int(number // i     )     )
   return sorted(__A     )





def 	snake_case_      (__A     :						int     )			->	bool:
   return sum(factors(__A     )     ) > n





def 	snake_case_      (__A     :						int     )			->	bool:
   __lowerCAmelCase  :			Any					    =						factors(__A     )
   __lowerCAmelCase  :			Union[str, Any]					    =						len(__A     )
   __lowerCAmelCase  :			str					    =						[[0 for i in range(number + 1     )] for j in range(r + 1     )]
   for i in range(r + 1     ):
      __lowerCAmelCase  :			Tuple					    =						True

   for i in range(1					,   number + 1     ):
      __lowerCAmelCase  :			List[str]					    =						False

   for i in range(1					,   r + 1     ):
      for j in range(1					,   number + 1     ):
         if j < values[i - 1]:
            __lowerCAmelCase  :			Dict					    =						subset[i - 1][j]
         else:
            __lowerCAmelCase  :			Dict					    =						subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

   return subset[r][number] != 0





def 	snake_case_      (__A     :						int     )			->	bool:
   return abundant(__A     ) and not semi_perfect(__A     )


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   for number in (69, 70, 71):
      print(F'{number} is {"" if weird(number) else "not "}weird.')


