code: 
  
  
  
  
  
  
  from __future__ import annotations
  
  from random import randrange
  def        UpperCAmelCase      (   _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      if len(_lowercase      ) < 2:
          return collection
      _lowercase     =	randrange(len(_lowercase      )      )  # Use random element as pivot
      _lowercase     =	collection[pivot_index]
      _lowercase     =	[]  # All elements greater than pivot
      _lowercase     =	[]  # All elements less than or equal to pivot
  
      for element in collection[:pivot_index]:
          (greater if element > pivot else lesser).append(_lowercase      )
  
      for element in collection[pivot_index + 1 :]:
          (greater if element > pivot else lesser).append(_lowercase      )
  
      return [*quick_sort(_lowercase      ), pivot, *quick_sort(_lowercase      )]
  
  
  if __name__ == "__main__":
     lowerCamelCase										=  input('''Enter numbers separated by a comma:\n''').strip()
     lowerCamelCase										=  [int(item) for item in user_input.split(''',''')]
     print(quick_sort(unsorted))
  
  
  
  
style_context: 
  
  
  
  
  
  
  from typing import List, Union
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCamelCase										=  logging.get_logger(__name__)
  
  lowerCamelCase										=  {
      '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/config.json''',
      # See all LED models at https://huggingface.co/models?filter=led
  }
  
  class 					snake_case_							(       lowercase_      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						"""led"""
     __lowerCAmelCase      =						{
         """num_attention_heads""": """encoder_attention_heads""",
         """hidden_size""": """d_model""",
         """attention_probs_dropout_prob""": """attention_dropout""",
         """initializer_range""": """init_std""",
     }
  
     def __init__(    self:	int   ,       snake_case__:	str=50_265   ,       snake_case__:	Dict=16_384   ,       snake_case__:	str=1_024   ,       snake_case__:	Tuple=12   ,       snake_case__:	int=4_096   ,       snake_case__:	Union[str, Any]=16   ,       snake_case__:	int=12   ,       snake_case__:	Union[str, Any]=4_096   ,       snake_case__:	Optional[Any]=16   ,       snake_case__:	str=0.0   ,       snake_case__:	Optional[Any]=0.0   ,       snake_case__:	List[Any]=True   ,       snake_case__:	List[Any]=True   ,       snake_case__:	List[Any]="gelu"   ,       snake_case__:	Optional[int]=1_024   ,       snake_case__:	List[Any]=0.1   ,       snake_case__:	int=0.0   ,       snake_case__:	List[str]=0.0   ,       snake_case__:	List[str]=0.02   ,       snake_case__:	Dict=2   ,       snake_case__:	int=0.0   ,       snake_case__:	Optional[int]=1   ,       snake_case__:	Tuple=0   ,       snake_case__:	Union[str, Any]=2   ,       snake_case__:	Union[List[int], int] = 512   ,       **snake_case__:	int   ,       )  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	vocab_size
         _lowercase     =	max_encoder_position_embeddings
         _lowercase     =	max_decoder_position_embeddings
         _lowercase     =	d_model
         _lowercase     =	encoder_ffn_dim
         _lowercase     =	encoder_layers
         _lowercase     =	encoder_attention_heads
         _lowercase     =	decoder_ffn_dim
         _lowercase     =	decoder_layers
         _lowercase     =	decoder_attention_heads
         _lowercase     =	dropout
         _lowercase     =	attention_dropout
         _lowercase     =	activation_dropout
         _lowercase     =	activation_function
         _lowercase     =	init_std
         _lowercase     =	encoder_layerdrop
         _lowercase     =	decoder_layerdrop
         _lowercase     =	classifier_dropout
         _lowercase     =	use_cache
         _lowercase     =	encoder_layers
         _lowercase     =	attention_window
  
         super().__init__(
             pad_token_id=snake_case__   ,       bos_token_id=snake_case__   ,       eos_token_id=snake_case__   ,       is_encoder_decoder=snake_case__   ,       decoder_start_token_id=snake_case__   ,       **snake_case__   ,       )
  