



import warnings

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



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							["image_processor", "tokenizer"]
	__A							=							"CLIPImageProcessor"
	__A							=							("CLIPTokenizer", "CLIPTokenizerFast")




	def __init__(self		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   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_		,       )
											a					     =   kwargs.pop("feature_extractor"		)

						a					     =   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_		)




	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:
											a					     =   self.tokenizer(lowerCamelCase_		,       return_tensors=lowerCamelCase_		,       **lowerCamelCase_		)

						if images is not None:
											a					     =   self.image_processor(lowerCamelCase_		,       return_tensors=lowerCamelCase_		,       **lowerCamelCase_		)

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




	def      UpperCamelCase_       (self		,       *lowerCamelCase_		,       **lowerCamelCase_		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		,       *lowerCamelCase_		,       **lowerCamelCase_		):
						"""simple docstring"""

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




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.tokenizer.model_input_names
						a					     =   self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)




	@property
	def      UpperCamelCase_       (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




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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





_lowercase: Dict      			=		[
    (1000, "M"),
    (900, "CM"),
    (500, "D"),
    (400, "CD"),
    (100, "C"),
    (90, "XC"),
    (50, "L"),
    (40, "XL"),
    (10, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]






def     a(      A       :    str       )		->       int:
					"""simple docstring"""





					a					     =   {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
					a					     =   0
					a					     =   0
					while place < len(A       ):
										if (place + 1 < len(A       )) and (vals[roman[place]] < vals[roman[place + 1]]):
															total += vals[roman[place + 1]] - vals[roman[place]]
															place += 2
										else:
															total += vals[roman[place]]
															place += 1
					return total






def     a(      A       :    int       )		->       str:
					"""simple docstring"""





					a					     =   []
					for arabic, roman in ROMAN:
										((a)     ,				(a))					     =   divmod(A ,     A       )
										result.append(roman * factor       )
										if number == 0:
															break
					return "".join(A       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
