





import warnings

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





class  __magic_name__   (							A__						):
		lowercase      :       Union[str, Any]          =['''image_processor''', '''tokenizer''']
		lowercase      :       Dict          ='''ViTImageProcessor'''
		lowercase      :       Optional[Any]          =('''CLIPTokenizer''', '''CLIPTokenizerFast''')






		def __init__(						self      :     Tuple				,		UpperCamelCase__      :     Tuple=None				,		UpperCamelCase__      :     Optional[int]=None				,		**UpperCamelCase__      :     List[str]      )				->					List[str]:
								'''simple docstring'''







								UpperCAmelCase         =     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__				,		)
														UpperCAmelCase         =     kwargs.pop("feature_extractor"      )

								UpperCAmelCase         =     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      :     str				,		UpperCamelCase__      :     int=None				,		UpperCamelCase__      :     Union[str, Any]=None				,		UpperCamelCase__      :     List[str]=None				,		UpperCamelCase__      :     Any=None				,		**UpperCamelCase__      :     List[Any]      )				->					Any:
								'''simple docstring'''







								if text is None and visual_prompt is None and images is None:
														raise ValueError("You have to specify either text, visual prompt or images."      )

								if text is not None and visual_prompt is not None:
														raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."      )

								if text is not None:
														UpperCAmelCase         =     self.tokenizer(UpperCamelCase__				,		return_tensors=UpperCamelCase__				,		**UpperCamelCase__      )

								if visual_prompt is not None:
														UpperCAmelCase         =     self.image_processor(UpperCamelCase__				,		return_tensors=UpperCamelCase__				,		**UpperCamelCase__      )

								if images is not None:
														UpperCAmelCase         =     self.image_processor(UpperCamelCase__				,		return_tensors=UpperCamelCase__				,		**UpperCamelCase__      )

								if visual_prompt is not None and images is not None:
														UpperCAmelCase         =     {
														    "pixel_values": image_features.pixel_values,
														    "conditional_pixel_values": prompt_features.pixel_values,
														}
														return encoding
								elif text is not None and images is not None:
														UpperCAmelCase         =     image_features.pixel_values
														return encoding
								elif text is not None:
														return encoding
								elif visual_prompt is not None:
														UpperCAmelCase         =     {
														    "conditional_pixel_values": prompt_features.pixel_values,
														}
														return encoding
								else:
														return BatchEncoding(data=dict(**UpperCamelCase__      )				,		tensor_type=UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]				,		*UpperCamelCase__      :     Optional[Any]				,		**UpperCamelCase__      :     int      )				->					List[Any]:
								'''simple docstring'''







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






		def SCREAMING_SNAKE_CASE_			(						self      :     str				,		*UpperCamelCase__      :     Optional[Any]				,		**UpperCamelCase__      :     str      )				->					Dict:
								'''simple docstring'''







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






		@property
		def SCREAMING_SNAKE_CASE_			(						self      :     str      )				->					Optional[int]:
								'''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 SCREAMING_SNAKE_CASE_			(						self      :     Tuple      )				->					int:
								'''simple docstring'''







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








def    lowerCamelCase_(lowerCamelCase_    ) ->       list[int]:
						UpperCAmelCase         =     len(lowerCamelCase_    )
						for i in range(lowerCamelCase_    ):
												for j in range(i + 1    ,   lowerCamelCase_    ):
																		if numbers[j] < numbers[i]:
																								UpperCAmelCase       ,		UpperCAmelCase         =     numbers[j], numbers[i]
						return numbers


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



