code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import copy
  from typing import Dict
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto.configuration_auto import CONFIG_MAPPING
  from ..bit import BitConfig
  
  
  lowerCamelCase_     :     Union[str, Any]    		=						logging.get_logger(__name__)
  
  lowerCamelCase_     :     List[str]    		=						{
      """google/vit-hybrid-base-bit-384""": """https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json""",
      # See all ViT hybrid models at https://huggingface.co/models?filter=vit
  }
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"vit-hybrid"
  
  
   def __init__(	self				,					__A=None				,					__A=768				,					__A=12				,					__A=12				,					__A=3072				,					__A="gelu"				,					__A=0.0				,					__A=0.0				,					__A=0.02				,					__A=1E-1_2				,					__A=224				,					__A=1				,					__A=3				,					__A=[1, 1024, 24, 24]				,					__A=True				,					**__A				,					)      ->  Optional[int]:
       super().__init__(**__A		)
  
       if backbone_config is None:
           logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'''		)
           a					={
               '''global_padding''': '''same''',
               '''layer_type''': '''bottleneck''',
               '''depths''': [3, 4, 9],
               '''out_features''': ['''stage3'''],
               '''embedding_dynamic_padding''': True,
           }
  
       if isinstance(__A				,					__A		):
           if "model_type" in backbone_config:
               a					=CONFIG_MAPPING[backbone_config['''model_type''']]
           else:
               logger.info(
                   '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'''		)
               a					=BitConfig
           a					=backbone_config_class(**__A		)
  
       a					=backbone_featmap_shape
       a					=backbone_config
       a					=hidden_size
       a					=num_hidden_layers
       a					=num_attention_heads
       a					=intermediate_size
       a					=hidden_act
       a					=hidden_dropout_prob
       a					=attention_probs_dropout_prob
       a					=initializer_range
       a					=layer_norm_eps
       a					=image_size
       a					=patch_size
       a					=num_channels
       a					=qkv_bias
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict[str, any]:
       a					=copy.deepcopy(self.__dict__		)
       a					=self.backbone_config.to_dict()
       a					=self.__class__.model_type
       return output
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from __future__ import annotations
  
  from random import randrange
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      if len(lowercase	) < 2:
          return collection
      a					=randrange(len(lowercase	)	)  # Use random element as pivot
      a					=collection[pivot_index]
      a					=[]  # All elements greater than pivot
      a					=[]  # 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_     :     Union[str, Any]    		=						input("""Enter numbers separated by a comma:\n""").strip()
         lowerCamelCase_     :     Union[str, Any]    		=						[int(item) for item in user_input.split(""",""")]
         print(quick_sort(unsorted))