





import warnings

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


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			Dict							     =       ['''image_processor''', '''tokenizer''']
			__lowerCamelCase      :			str							     =       '''ChineseCLIPImageProcessor'''
			__lowerCamelCase      :			Union[str, Any]							     =       ('''BertTokenizer''', '''BertTokenizerFast''')






			def __init__(     self  :       Union[str, Any]				,      __lowerCAmelCase  :       Tuple=None				,      __lowerCAmelCase  :       Optional[Any]=None				,      **__lowerCAmelCase  :       Tuple  )      ->      Optional[Any]:







					"""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  )
					A__     =  self.image_processor






			def __call__(     self  :       Dict				,      __lowerCAmelCase  :       List[str]=None				,      __lowerCAmelCase  :       int=None				,      __lowerCAmelCase  :       Any=None				,      **__lowerCAmelCase  :       Optional[int]  )      ->      List[str]:







					"""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   a_			(     self  :       List[Any]				,      *__lowerCAmelCase  :       Optional[Any]				,      **__lowerCAmelCase  :       Tuple  )      ->      Optional[Any]:







					"""simple docstring"""






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






			def   a_			(     self  :       List[Any]				,      *__lowerCAmelCase  :       Optional[Any]				,      **__lowerCAmelCase  :       Any  )      ->      Optional[Any]:







					"""simple docstring"""






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






			@property
			def   a_			(     self  :       Union[str, Any]  )      ->      Dict:







					"""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   a_			(     self  :       str  )      ->      Optional[int]:







					"""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










def   __lowerCamelCase   (				__a  :Optional[Any]       )						->							Tuple:






		"""simple docstring"""

		A__     =  len(__a       )
		while cur > 1:
				# Find the maximum number in arr
				A__     =  arr.index(max(arr[0:cur]       )       )
				# Reverse from 0 to mi
				A__     =  arr[mi::-1] + arr[mi + 1 : len(__a       )]
				# Reverse whole list
				A__     =  arr[cur - 1 :: -1] + arr[cur : len(__a       )]
				cur -= 1
		return arr


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



