
import warnings

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





class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







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



     def __init__(				self							:							Dict     ,						_A							:							List[str]=None     ,						_A							:							List[Any]=None     ,						**_A							:							Any ):







       """simple docstring"""




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

       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		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__(_A     ,						_A )



     def __call__(				self							:							List[str]     ,						_A							:							Union[str, Any]=None     ,						_A							:							Union[str, Any]=None     ,						_A							:							Tuple=None     ,						**_A							:							Optional[int] ):







       """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:
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		self.tokenizer(_A     ,						return_tensors=_A     ,						**_A )

       if images is not None:
         __SCREAMING_SNAKE_CASE      :	str					=		self.image_processor(_A     ,						return_tensors=_A     ,						**_A )

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



     def  UpperCAmelCase__							(				self							:							List[Any]     ,						*_A							:							Tuple     ,						**_A							:							Any ):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							Optional[int]     ,						*_A							:							Optional[Any]     ,						**_A							:							Optional[Any] ):







       """simple docstring"""




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



     @property
     def  UpperCAmelCase__							(				self							:							Optional[Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Dict					=		self.tokenizer.model_input_names
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )



     @property
     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




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




     @property
     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




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







def 		lowerCAmelCase  (	UpperCamelCase__   :   List[Any]       ,			UpperCamelCase__   :   Dict					)      ->    List[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				int           =   [0 for i in range(r + 1					)]
						# nc0 = 1
						__SCREAMING_SNAKE_CASE:				Dict           =   1
						for i in range(1       ,			n + 1					):
												# to compute current row from previous row.
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   min(UpperCamelCase__       ,			UpperCamelCase__					)
												while j > 0:
																		c[j] += c[j - 1]
																		j -= 1
						return c[r]




print(binomial_coefficient(n=10, r=5))



