






'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			['''image_processor''', '''tokenizer''']
      UpperCamelCase_								=			'''CLIPImageProcessor'''
      UpperCamelCase_								=			('''CLIPTokenizer''', '''CLIPTokenizerFast''')






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


       '''simple docstring'''

       lowercase					:   Optional[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.'''     ,      UpperCAmelCase     ,      )
        lowercase					:   Tuple				  =kwargs.pop('''feature_extractor'''		)

       lowercase					:   List[str]				  =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__(UpperCAmelCase     ,      UpperCAmelCase		)






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


       '''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:
        lowercase					:   int				  =self.tokenizer(UpperCAmelCase     ,      return_tensors=UpperCAmelCase     ,      **UpperCAmelCase		)

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

       if text is not None and images is not None:
        lowercase					:   Optional[Any]				  =image_features.pixel_values
        return encoding
       elif text is not None:
        return encoding
       else:
        return BatchEncoding(data=dict(**UpperCAmelCase		)     ,      tensor_type=UpperCAmelCase		)






      def 				A__      ( self			:		Tuple     ,      *UpperCAmelCase			:		Any     ,      **UpperCAmelCase			:		str		)  ->  Optional[Any]:


       '''simple docstring'''

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






      def 				A__      ( self			:		Optional[int]     ,      *UpperCAmelCase			:		Tuple     ,      **UpperCAmelCase			:		str		)  ->  List[Any]:


       '''simple docstring'''

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






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


       '''simple docstring'''

       lowercase					:   Union[str, Any]				  =self.tokenizer.model_input_names
       lowercase					:   List[str]				  =self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)






      @property
      def 				A__      ( self			:		Dict		)  ->  Tuple:


       '''simple docstring'''

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






      @property
      def 				A__      ( self			:		Any		)  ->  Dict:


       '''simple docstring'''

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











'''simple docstring'''



def 							lowercase_				(    __A :       float					,      __A :       int			)       ->      float:



 """simple docstring"""




 if digit_amount > 0:
  return round(number - int(__A			)					,      __A			)
 return number - int(__A			)


if __name__ == "__main__":
      print(decimal_isolate(1.53, 0))
      print(decimal_isolate(35.345, 1))
      print(decimal_isolate(35.345, 2))
      print(decimal_isolate(35.345, 3))
      print(decimal_isolate(-14.789, 3))
      print(decimal_isolate(0, 2))
      print(decimal_isolate(-14.123, 1))
      print(decimal_isolate(-14.123, 2))
      print(decimal_isolate(-14.123, 3))



