
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 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	List[str]:
		SCREAMING_SNAKE_CASE_					      =							[1]
		for i in range(2    ,						lowerCAmelCase ):
				factorials.append(factorials[-1] * i )
		assert 0 <= k < factorials[-1] * n, "k out of bounds"

		SCREAMING_SNAKE_CASE_					      =							[]
		SCREAMING_SNAKE_CASE_					      =							list(range(lowerCAmelCase ) )

		# Find permutation
		while factorials:
				SCREAMING_SNAKE_CASE_					      =							factorials.pop()
				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							divmod(lowerCAmelCase    ,						lowerCAmelCase )
				permutation.append(elements[number] )
				elements.remove(elements[number] )
		permutation.append(elements[0] )

		return permutation


if __name__ == "__main__":
						import doctest

						doctest.testmod()