



"""simple docstring"""





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


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



       def __init__(							self    :						List[Any]	,       _UpperCAmelCase    :						Tuple=None	,       _UpperCAmelCase    :						Any=None	,       **_UpperCAmelCase    :						int					)				->		Union[str, Any]:


             '''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    :						List[str]	,       _UpperCAmelCase    :						List[str]=None	,       _UpperCAmelCase    :						Union[str, Any]=None	,       _UpperCAmelCase    :						int=None	,       **_UpperCAmelCase    :						List[Any]					)				->		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:
                   UpperCAmelCase_    				=							self.tokenizer(_UpperCAmelCase	,       return_tensors=_UpperCAmelCase	,       **_UpperCAmelCase					)

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

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



       def   lowercase__       (							self    :						List[Any]	,       *_UpperCAmelCase    :						Tuple	,       **_UpperCAmelCase    :						Union[str, Any]					)				->		Tuple:


             '''simple docstring'''




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



       def   lowercase__       (							self    :						str	,       *_UpperCAmelCase    :						Optional[Any]	,       **_UpperCAmelCase    :						List[str]					)				->		Dict:


             '''simple docstring'''




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







       @property
       def   lowercase__       (							self    :						Optional[Any]					)				->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.tokenizer.model_input_names
             UpperCAmelCase_    				=							self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)









"""simple docstring"""





from __future__ import annotations
def      a__			(   lowerCAmelCase__   ):
      if len(lowerCAmelCase__   ) == 0:
            return []
      UpperCAmelCase_				,					UpperCAmelCase_    				=							min(lowerCAmelCase__   ), max(lowerCAmelCase__   )
      UpperCAmelCase_    				=							int(max_value - min_value   ) + 1
      UpperCAmelCase_    				=							[[] for _ in range(lowerCAmelCase__   )]

      for i in my_list:
            buckets[int(i - min_value   )].append(lowerCAmelCase__   )

      return [v for bucket in buckets for v in sorted(lowerCAmelCase__   )]


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
  assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]




