




'''simple docstring'''



import warnings

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







class 		lowercase_   (			_SCREAMING_SNAKE_CASE       ):


   """simple docstring"""

   lowerCamelCase_      =						['''image_processor''', '''tokenizer''']
   lowerCamelCase_      =						'''CLIPImageProcessor'''
   lowerCamelCase_      =						('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')



   def __init__(   self :     Union[str, Any]  ,				__lowerCamelCase :     List[str]=None  ,				__lowerCamelCase :     Union[str, Any]=None  ,				**__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				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  ,				)
         _SCREAMING_SNAKE_CASE	=				kwargs.pop("feature_extractor"   )

      _SCREAMING_SNAKE_CASE	=				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 :     Optional[Any]  ,				__lowerCamelCase :     List[Any]=None  ,				__lowerCamelCase :     Tuple=None  ,				__lowerCamelCase :     int=None  ,				**__lowerCamelCase :     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	=				self.tokenizer(__lowerCamelCase  ,				return_tensors=__lowerCamelCase  ,				**__lowerCamelCase   )

      if images is not None:
         _SCREAMING_SNAKE_CASE	=				self.image_processor(__lowerCamelCase  ,				return_tensors=__lowerCamelCase  ,				**__lowerCamelCase   )

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



   def 							lowerCAmelCase_      (   self :     int  ,				*__lowerCamelCase :     Optional[Any]  ,				**__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      return self.tokenizer.batch_decode(*__lowerCamelCase  ,				**__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[int]  ,				*__lowerCamelCase :     str  ,				**__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      return self.tokenizer.decode(*__lowerCamelCase  ,				**__lowerCamelCase   )



   @property
   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.tokenizer.model_input_names
      _SCREAMING_SNAKE_CASE	=				self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )










'''simple docstring'''



def 			SCREAMING_SNAKE_CASE_			(							__A    :			list  )			->			list:
   if len(__A  ) <= 1:
      return lst

   _SCREAMING_SNAKE_CASE	=				1

   while i < len(__A  ):
      if lst[i - 1] <= lst[i]:
         i += 1
      else:
         _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				lst[i], lst[i - 1]
         i -= 1
         if i == 0:
            _SCREAMING_SNAKE_CASE	=				1

   return lst


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

