


"""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



"""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))