



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





class 		UpperCAmelCase    (    snake_case_					):
   SCREAMING_SNAKE_CASE__					      =  ['''image_processor''', '''tokenizer''']
   SCREAMING_SNAKE_CASE__					      =  '''EfficientNetImageProcessor'''
   SCREAMING_SNAKE_CASE__					      =  ('''BertTokenizer''', '''BertTokenizerFast''')





   def __init__(						self							,  _lowerCAmelCase							,  _lowerCAmelCase      ):
         super().__init__(_lowerCAmelCase							,  _lowerCAmelCase      )





   def __call__(						self							,  _lowerCAmelCase=None							,  _lowerCAmelCase=None							,  _lowerCAmelCase="max_length"							,  _lowerCAmelCase=64							,  _lowerCAmelCase=None							,  **_lowerCAmelCase      ):
         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:
               _lowerCAmelCase			      =	self.tokenizer(
                   _lowerCAmelCase							,  padding=_lowerCAmelCase							,  max_length=_lowerCAmelCase							,  return_tensors=_lowerCAmelCase							,  **_lowerCAmelCase      )

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

         if text is not None and images is not None:
               _lowerCAmelCase			      =	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							,  *_lowerCAmelCase							,  **_lowerCAmelCase      ):
         return self.tokenizer.batch_decode(*_lowerCAmelCase							,  **_lowerCAmelCase      )





   def 						__lowerCAmelCase				(						self							,  *_lowerCAmelCase							,  **_lowerCAmelCase      ):
         return self.tokenizer.decode(*_lowerCAmelCase							,  **_lowerCAmelCase      )





   @property
   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	self.tokenizer.model_input_names
         _lowerCAmelCase			      =	self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )




from __future__ import annotations

from random import randrange
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						list					)->list:
      if len(_SCREAMING_SNAKE_CASE					) < 2:
            return collection
      _lowerCAmelCase			      =	randrange(len(_SCREAMING_SNAKE_CASE					)					)  # Use random element as pivot
      _lowerCAmelCase			      =	collection[pivot_index]
      _lowerCAmelCase			      =	[]  # All elements greater than pivot
      _lowerCAmelCase			      =	[]  # All elements less than or equal to pivot

      for element in collection[:pivot_index]:
            (greater if element > pivot else lesser).append(_SCREAMING_SNAKE_CASE					)

      for element in collection[pivot_index + 1 :]:
            (greater if element > pivot else lesser).append(_SCREAMING_SNAKE_CASE					)

      return [*quick_sort(_SCREAMING_SNAKE_CASE					), pivot, *quick_sort(_SCREAMING_SNAKE_CASE					)]


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