import warnings

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






class 		a__						(					A__					):
					A	       =							['image_processor', 'tokenizer']
					A	       =							'CLIPImageProcessor'
					A	       =							('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, 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.",_A,)
									SCREAMING_SNAKE_CASE_  :		Any				      =    kwargs.pop("feature_extractor"    )

							SCREAMING_SNAKE_CASE_  :		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      :       Optional[Any],_A      :       int=None,_A      :       str=None,_A      :       List[Any]=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_  :		Dict				      =    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_  :		str				      =    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      :       Dict,*_A      :       int,**_A      :       Union[str, Any]    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       Union[str, Any],*_A      :       str,**_A      :       Any    ):







							"""simple docstring"""






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






					@property
					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.tokenizer.model_input_names
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )

def       _snake_case	(   lowerCAmelCase							:   list	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Dict				      =    len(lowerCAmelCase	)
		for i in range(1   , lowerCAmelCase	):
				SCREAMING_SNAKE_CASE_  :		int				      =    collection[i]
				SCREAMING_SNAKE_CASE_  :		Any				      =    0
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    i - 1

				while low <= high:
						SCREAMING_SNAKE_CASE_  :		int				      =    (low + high) // 2
						if val < collection[mid]:
								SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    mid - 1
						else:
								SCREAMING_SNAKE_CASE_  :		Tuple				      =    mid + 1
				for j in range(lowerCAmelCase   , lowerCAmelCase   , -1	):
						SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    collection[j - 1]
				SCREAMING_SNAKE_CASE_  :		int				      =    val
		return collection


if __name__ == "__main__":
		__lowerCamelCase					:	Dict       =  input('''Enter numbers separated by a comma:\n''').strip()
		__lowerCamelCase					:	List[str]       =  [int(item) for item in user_input.split(''',''')]
		print(binary_insertion_sort(unsorted))
