code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import torch
  import torch.nn as nn
  from transformers.modeling_utils import ModuleUtilsMixin
  from transformers.models.ta.modeling_ta import (
      TaBlock,
      TaConfig,
      TaLayerNorm,
  )
  
  from ...configuration_utils import ConfigMixin, register_to_config
  from ...models import ModelMixin
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   @register_to_config
   def __init__(	self				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A				,					__A = False				,					)      ->  Tuple:
       super().__init__()
  
       a					=nn.Linear(__A				,					__A				,					bias=__A		)
  
       a					=nn.Embedding(__A				,					__A		)
       a					=False
  
       a					=nn.Dropout(p=__A		)
  
       a					=TaConfig(
           d_model=__A				,					num_heads=__A				,					d_kv=__A				,					d_ff=__A				,					feed_forward_proj=__A				,					dropout_rate=__A				,					is_decoder=__A				,					is_encoder_decoder=__A				,					)
       a					=nn.ModuleList()
       for lyr_num in range(__A		):
           a					=TaBlock(__A		)
           self.encoders.append(__A		)
  
       a					=TaLayerNorm(__A		)
       a					=nn.Dropout(p=__A		)
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A		)      ->  List[Any]:
       a					=self.input_proj(__A		)
  
       # terminal relative positional encodings
       a					=encoder_inputs.shape[1]
       a					=torch.arange(__A				,					device=encoder_inputs.device		)
  
       a					=encoder_inputs_mask.sum(-1		)
       a					=torch.roll(input_positions.unsqueeze(0		)				,					tuple(seq_lens.tolist()		)				,					dims=0		)
       x += self.position_encoding(__A		)
  
       a					=self.dropout_pre(__A		)
  
       # inverted the attention mask
       a					=encoder_inputs.size()
       a					=self.get_extended_attention_mask(__A				,					__A		)
  
       for lyr in self.encoders:
           a					=lyr(__A				,					__A		)[0]
       a					=self.layer_norm(__A		)
  
       return self.dropout_post(__A		), encoder_inputs_mask
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))