





def 			_a		(       UpperCAmelCase     ,    UpperCAmelCase	)							->							Tuple:







   """simple docstring"""
   _validate_point(UpperCAmelCase	)
   _validate_point(UpperCAmelCase	)
   if len(UpperCAmelCase	) != len(UpperCAmelCase	):
      raise ValueError('''Both points must be in the same n-dimensional space'''	)

   return float(sum(abs(a - b	) for a, b in zip(UpperCAmelCase     ,    UpperCAmelCase	)	)	)



def 			_a		(       UpperCAmelCase	)							->							Any:







   """simple docstring"""






   if point:
      if isinstance(UpperCAmelCase     ,    UpperCAmelCase	):
         for item in point:
            if not isinstance(UpperCAmelCase     ,    (int, float)	):
               lowerCamelCase__  :		str     =				(
                   '''Expected a list of numbers as input, found '''
                   f"{type(UpperCAmelCase	).__name__}"
               )
               raise TypeError(UpperCAmelCase	)
      else:
         lowerCamelCase__  :		List[Any]     =				f"Expected a list of numbers as input, found {type(UpperCAmelCase	).__name__}"
         raise TypeError(UpperCAmelCase	)
   else:
      raise ValueError('''Missing an input'''	)



def 			_a		(       UpperCAmelCase     ,    UpperCAmelCase	)							->							List[Any]:







   """simple docstring"""
   _validate_point(UpperCAmelCase	)
   _validate_point(UpperCAmelCase	)
   if len(UpperCAmelCase	) != len(UpperCAmelCase	):
      raise ValueError('''Both points must be in the same n-dimensional space'''	)

   return float(sum(abs(x - y	) for x, y in zip(UpperCAmelCase     ,    UpperCAmelCase	)	)	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()











import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	['''image_processor''', '''tokenizer''']
				a__       							=	'''ChineseCLIPImageProcessor'''
				a__       							=	('''BertTokenizer''', '''BertTokenizerFast''')







				def __init__(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   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							,       )
												__magic_name__		:Optional[Any]   =   kwargs.pop('''feature_extractor'''  )

								__magic_name__		:Tuple   =   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  )
								__magic_name__		:List[Any]   =   self.image_processor







				def __call__(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""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:
												__magic_name__		:int   =   self.tokenizer(__lowerCAmelCase							,       return_tensors=__lowerCAmelCase							,       **__lowerCAmelCase  )

								if images is not None:
												__magic_name__		:Dict   =   self.image_processor(__lowerCAmelCase							,       return_tensors=__lowerCAmelCase							,       **__lowerCAmelCase  )

								if text is not None and images is not None:
												__magic_name__		:Union[str, Any]   =   image_features.pixel_values
												return encoding
								elif text is not None:
												return encoding
								else:
												return BatchEncoding(data=dict(**__lowerCAmelCase  )							,       tensor_type=__lowerCAmelCase  )







				def       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.batch_decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				def       A						(    self							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return self.tokenizer.decode(*__lowerCAmelCase							,       **__lowerCAmelCase  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.tokenizer.model_input_names
								__magic_name__		:Any   =   self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )







				@property
				def       A						(    self  ):
								"""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






