


"""simple docstring"""


def 	snake_case		(  UpperCamelCase__							:    Any     ) ->   list:

      if len(__snake_case     ) < 2:
            return collection

      def circle_sort_util(UpperCamelCase__							:    List[Any]      ,					UpperCamelCase__							:    str      ,					UpperCamelCase__							:    Optional[Any]     ) -> bool:
            lowerCamelCase       :       List[Any]   =						False

            if low == high:
                  return swapped

            lowerCamelCase       :       Union[str, Any]   =						low
            lowerCamelCase       :       Optional[int]   =						high

            while left < right:
                  if collection[left] > collection[right]:
                        lowerCamelCase       :       int   =						(
                            collection[right],
                            collection[left],
                        )
                        lowerCamelCase       :       List[str]   =						True

                  left += 1
                  right -= 1

            if left == right and collection[left] > collection[right + 1]:
                  lowerCamelCase       :       List[Any]   =						(
                      collection[right + 1],
                      collection[left],
                  )

                  lowerCamelCase       :       Tuple   =						True

            lowerCamelCase       :       Union[str, Any]   =						low + int((high - low) / 2     )
            lowerCamelCase       :       List[str]   =						circle_sort_util(__snake_case      ,					__snake_case      ,					__snake_case     )
            lowerCamelCase       :       Dict   =						circle_sort_util(__snake_case      ,					mid + 1      ,					__snake_case     )

            return swapped or left_swap or right_swap

      lowerCamelCase       :       Dict   =						True

      while is_not_sorted is True:
            lowerCamelCase       :       Optional[Any]   =						circle_sort_util(__snake_case      ,					0      ,					len(__snake_case     ) - 1     )

      return collection


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(circle_sort(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      A__  (    __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	@register_to_config
	def __init__(	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						float			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						bool = False			,     )							->			Any:

				"""simple docstring"""

				super().__init__()

				__lowerCAmelCase :    str				=			nn.Embedding(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			nn.Embedding(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    str				=			False

				__lowerCAmelCase :    Optional[Any]				=			nn.Dropout(p=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			TaConfig(
				    vocab_size=_SCREAMING_SNAKE_CASE			,     d_model=_SCREAMING_SNAKE_CASE			,     num_heads=_SCREAMING_SNAKE_CASE			,     d_kv=_SCREAMING_SNAKE_CASE			,     d_ff=_SCREAMING_SNAKE_CASE			,     dropout_rate=_SCREAMING_SNAKE_CASE			,     feed_forward_proj=_SCREAMING_SNAKE_CASE			,     is_decoder=_SCREAMING_SNAKE_CASE			,     is_encoder_decoder=_SCREAMING_SNAKE_CASE			,     )

				__lowerCAmelCase :    str				=			nn.ModuleList()
				for lyr_num in range(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    int				=			TaBlock(_SCREAMING_SNAKE_CASE)
							self.encoders.append(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			TaLayerNorm(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Any				=			nn.Dropout(p=_SCREAMING_SNAKE_CASE)







	def 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						str)							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			self.token_embedder(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Optional[Any]				=			encoder_input_tokens.shape[1]
				__lowerCAmelCase :    List[Any]				=			torch.arange(_SCREAMING_SNAKE_CASE			,     device=encoder_input_tokens.device)
				x += self.position_encoding(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    List[Any]				=			self.dropout_pre(_SCREAMING_SNAKE_CASE)

				# inverted the attention mask
				__lowerCAmelCase :    List[Any]				=			encoder_input_tokens.size()
				__lowerCAmelCase :    Any				=			self.get_extended_attention_mask(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				for lyr in self.encoders:
							__lowerCAmelCase :    Union[str, Any]				=			lyr(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)[0]
				__lowerCAmelCase :    int				=			self.layer_norm(_SCREAMING_SNAKE_CASE)

				return self.dropout_post(_SCREAMING_SNAKE_CASE), encoder_inputs_mask