
'''simple docstring'''







import warnings

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







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				Dict   =					['''image_processor''', '''tokenizer''']
  __UpperCAmelCase				:				Optional[int]   =					'''ViTImageProcessor'''
  __UpperCAmelCase				:				List[str]   =					('''CLIPTokenizer''', '''CLIPTokenizerFast''')


  def __init__(		self      :     Dict							,_a      :     List[str]=None							,_a      :     List[Any]=None							,**_a      :     int       ):


     '''simple docstring'''






     _a				:	Dict			=   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							,)
        _a				:	Tuple			=   kwargs.pop('feature_extractor'       )

     _a				:	Any			=   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      :     str							,_a      :     Tuple=None							,_a      :     str=None							,_a      :     Tuple=None							,_a      :     int=None							,**_a      :     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:
        _a				:	str			=   self.tokenizer(_a							,return_tensors=_a							,**_a       )

     if visual_prompt is not None:
        _a				:	Optional[int]			=   self.image_processor(_a							,return_tensors=_a							,**_a       )

     if images is not None:
        _a				:	Optional[Any]			=   self.image_processor(_a							,return_tensors=_a							,**_a       )

     if visual_prompt is not None and images is not None:
        _a				:	List[str]			=   {
            '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:
        _a				:	List[Any]			=   image_features.pixel_values
        return encoding
     elif text is not None:
        return encoding
     elif visual_prompt is not None:
        _a				:	List[str]			=   {
            'conditional_pixel_values': prompt_features.pixel_values,
        }
        return encoding
     else:
        return BatchEncoding(data=dict(**_a       )							,tensor_type=_a       )


  def   __lowercase		(		self      :     List[Any]							,*_a      :     Dict							,**_a      :     Any       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     str							,*_a      :     Optional[int]							,**_a      :     List[Any]       ):


     '''simple docstring'''






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


  @property
  def   __lowercase		(		self      :     Optional[int]       ):


     '''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   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






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





'''simple docstring'''







from __future__ import annotations

from random import choice





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   return choice(__a )





def 					UpperCAmelCase_		(__a      :    list[int] ,	__a      :    int ):






   """simple docstring"""

   _a				:	Dict			=   random_pivot(__a )

   # partition based on pivot
   # linear time
   _a				:	Optional[int]			=   [e for e in lst if e < pivot]
   _a				:	List[str]			=   [e for e in lst if e > pivot]

   # if we get lucky, pivot might be the element we want.
   # we can easily see this:
   # small (elements smaller than k)
   # + pivot (kth element)
   # + big (elements larger than k)
   if len(__a ) == k - 1:
      return pivot
   # pivot is in elements bigger than k
   elif len(__a ) < k - 1:
      return kth_number(__a ,	k - len(__a ) - 1 )
   # pivot is in elements smaller than k
   else:
      return kth_number(__a ,	__a )


if __name__ == "__main__":
  import doctest

  doctest.testmod()



