
"""simple docstring"""
def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			None:


					'''simple docstring'''
					if right <= left:
										return
					__a:						Optional[int]		    =					left
					__a:						Dict		    =					right
					__a:						List[str]		    =					sorting[left]
					while i <= b:
										if sorting[i] < pivot:
															__a    ,				__a:						Optional[int]		    =					sorting[i], sorting[a]
															a += 1
															i += 1
										elif sorting[i] > pivot:
															__a    ,				__a:						Tuple		    =					sorting[i], sorting[b]
															b -= 1
										else:
															i += 1
					quick_sort_apartition(lowerCamelCase_	,				lowerCamelCase_	,				a - 1 )
					quick_sort_apartition(lowerCamelCase_	,				b + 1	,				lowerCamelCase_ )


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			None:


					'''simple docstring'''
					if left < right:
										__a:						Tuple		    =					lomuto_partition(lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )
										quick_sort_lomuto_partition(lowerCamelCase_	,				lowerCamelCase_	,				pivot_index - 1 )
										quick_sort_lomuto_partition(lowerCamelCase_	,				pivot_index + 1	,				lowerCamelCase_ )


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			int:


					'''simple docstring'''
					__a:						List[str]		    =					sorting[right]
					__a:						Optional[Any]		    =					left
					for i in range(lowerCamelCase_	,				lowerCamelCase_ ):
										if sorting[i] < pivot:
															__a    ,				__a:						List[Any]		    =					sorting[i], sorting[store_index]
															store_index += 1
					__a    ,				__a:						Optional[Any]		    =					sorting[store_index], sorting[right]
					return store_index


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			list:


					'''simple docstring'''
					if len(lowerCamelCase_ ) <= 1:
										return sorting
					return (
					    three_way_radix_quicksort([i for i in sorting if i < sorting[0]] )
					    + [i for i in sorting if i == sorting[0]]
					    + three_way_radix_quicksort([i for i in sorting if i > sorting[0]] )
					)


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)

			lowercase		:						Optional[Any] =					input('Enter numbers separated by a comma:\n').strip()
			lowercase		:						str =					[int(item) for item in user_input.split(',')]
			quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
			print(unsorted)


"""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    _snake_case  (						_lowercase ,				_lowercase ,				_lowercase       ):






							@register_to_config
							def __init__(					self						:			str						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			float						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			int						, snake_case__						:			str						, snake_case__						:			bool = False						, ):






												"""simple docstring"""



												super().__init__()

												__a:						Optional[Any]		    =					nn.Linear(snake_case__						, snake_case__						, bias=snake_case__    )

												__a:						Optional[Any]		    =					nn.Embedding(snake_case__						, snake_case__    )
												__a:						Any		    =					False

												__a:						Any		    =					nn.Dropout(p=snake_case__    )

												__a:						List[str]		    =					TaConfig(
												    d_model=snake_case__						, num_heads=snake_case__						, d_kv=snake_case__						, d_ff=snake_case__						, feed_forward_proj=snake_case__						, dropout_rate=snake_case__						, is_decoder=snake_case__						, is_encoder_decoder=snake_case__						, )
												__a:						Optional[int]		    =					nn.ModuleList()
												for lyr_num in range(snake_case__    ):
																	__a:						Union[str, Any]		    =					TaBlock(snake_case__    )
																	self.encoders.append(snake_case__    )

												__a:						List[str]		    =					TaLayerNorm(snake_case__    )
												__a:						Optional[int]		    =					nn.Dropout(p=snake_case__    )






							def 							__lowercase  (					self						:			str						, snake_case__						:			Optional[Any]						, snake_case__						:			int    ):






												"""simple docstring"""



												__a:						str		    =					self.input_proj(snake_case__    )

												# terminal relative positional encodings
												__a:						Optional[int]		    =					encoder_inputs.shape[1]
												__a:						List[Any]		    =					torch.arange(snake_case__						, device=encoder_inputs.device    )

												__a:						Union[str, Any]		    =					encoder_inputs_mask.sum(-1    )
												__a:						str		    =					torch.roll(input_positions.unsqueeze(0    )						, tuple(seq_lens.tolist()    )						, dims=0    )
												x += self.position_encoding(snake_case__    )

												__a:						Any		    =					self.dropout_pre(snake_case__    )

												# inverted the attention mask
												__a:						Any		    =					encoder_inputs.size()
												__a:						str		    =					self.get_extended_attention_mask(snake_case__						, snake_case__    )

												for lyr in self.encoders:
																	__a:						Any		    =					lyr(snake_case__						, snake_case__    )[0]
												__a:						int		    =					self.layer_norm(snake_case__    )

												return self.dropout_post(snake_case__    ), encoder_inputs_mask
