


import warnings

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

class 						__a (							__A     ):
		_a      :    int           =      ['image_processor', 'tokenizer']
		_a      :    List[Any]           =      'ViTImageProcessor'
		_a      :    List[str]           =      ('CLIPTokenizer', 'CLIPTokenizerFast')
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=None     ,       **_SCREAMING_SNAKE_CASE		)					->			int:




									"""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.'     ,       __A     ,       )
																_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__(__A     ,       __A		)
		def __call__(     self     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=None     ,       **_SCREAMING_SNAKE_CASE		)					->			Optional[int]:




									"""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(__A     ,       return_tensors=__A     ,       **__A		)

									if visual_prompt is not None:
																_UpperCAmelCase											=     self.image_processor(__A     ,       return_tensors=__A     ,       **__A		)

									if images is not None:
																_UpperCAmelCase											=     self.image_processor(__A     ,       return_tensors=__A     ,       **__A		)

									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(**__A		)     ,       tensor_type=__A		)
		def       UpperCAmelCase__							(     self     ,       *_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)					->			List[str]:




									"""simple docstring"""





									return self.tokenizer.batch_decode(*__A     ,       **__A		)
		def       UpperCAmelCase__							(     self     ,       *_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)					->			Optional[int]:




									"""simple docstring"""





									return self.tokenizer.decode(*__A     ,       **__A		)
		@property
		def       UpperCAmelCase__							(     self		)					->			Optional[Any]:




									"""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		)					->			Optional[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__   ):
   __a					=  False
   while is_sorted is False:  # Until all the indices are traversed keep looping
      __a					=  True
      for i in range(0		,				len(lowerCAmelCase__   ) - 1		,				2   ):  # iterating over all even indices
         if input_list[i] > input_list[i + 1]:
            __a	,    __a					=  input_list[i + 1], input_list[i]
            # swapping if elements not in order
            __a					=  False

      for i in range(1		,				len(lowerCAmelCase__   ) - 1		,				2   ):  # iterating over all odd indices
         if input_list[i] > input_list[i + 1]:
            __a	,    __a					=  input_list[i + 1], input_list[i]
            # swapping if elements not in order
            __a					=  False
   return input_list


if __name__ == "__main__":
  print('Enter list to be sorted')
  SCREAMING_SNAKE_CASE							    =							[int(x) for x in input().split()]
  # inputing elements of the list in one line
  SCREAMING_SNAKE_CASE							    =							odd_even_sort(input_list)
  print('The sorted list is')
  print(sorted_list)



