


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_   :				Optional[Any]						      =		logging.get_logger(__name__)

UpperCAmelCase_   :				Dict						      =		{
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  int       =		'''megatron-bert'''
      def __init__( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : List[Any]=2_9_0_5_6  ,							SCREAMING_SNAKE_CASE__       : Dict=1_0_2_4  ,							SCREAMING_SNAKE_CASE__       : int=2_4  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=1_6  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=4_0_9_6  ,							SCREAMING_SNAKE_CASE__       : List[Any]="gelu"  ,							SCREAMING_SNAKE_CASE__       : Tuple=0.1  ,							SCREAMING_SNAKE_CASE__       : Tuple=0.1  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=5_1_2  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=2  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=0.02  ,							SCREAMING_SNAKE_CASE__       : Dict=1E-12  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=0  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]="absolute"  ,							SCREAMING_SNAKE_CASE__       : Dict=True  ,							**SCREAMING_SNAKE_CASE__       : Tuple  ,							)	->    List[str]:
             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )

             a_      :	Tuple							=						vocab_size
             a_      :	Optional[int]							=						hidden_size
             a_      :	Dict							=						num_hidden_layers
             a_      :	List[Any]							=						num_attention_heads
             a_      :	List[str]							=						hidden_act
             a_      :	Optional[Any]							=						intermediate_size
             a_      :	Optional[int]							=						hidden_dropout_prob
             a_      :	List[Any]							=						attention_probs_dropout_prob
             a_      :	str							=						max_position_embeddings
             a_      :	Union[str, Any]							=						type_vocab_size
             a_      :	List[Any]							=						initializer_range
             a_      :	Union[str, Any]							=						layer_norm_eps
             a_      :	Union[str, Any]							=						position_embedding_type
             a_      :	Optional[int]							=						use_cache






def  SCREAMING_SNAKE_CASE_		(    __A				:    list       )			->	list:

       """simple docstring"""

       if len(__A       ) <= 1:
              return [tuple(__A       )]

       a_      :	Optional[Any]							=						[]

       def generate(__A				:    int						,					__A				:    list       ):
              a_      :	str							=						[0] * n
              res.append(tuple(__A       )       )

              a_      :	Optional[int]							=						0
              while i < n:
                     if c[i] < i:
                            if i % 2 == 0:
                                   a_							,	a_      :	Dict							=						arr[i], arr[0]
                            else:
                                   a_							,	a_      :	Union[str, Any]							=						arr[i], arr[c[i]]
                            res.append(tuple(__A       )       )
                            c[i] += 1
                            a_      :	Dict							=						0
                     else:
                            a_      :	Optional[Any]							=						0
                            i += 1

       generate(len(__A       )						,					__A       )
       return res


if __name__ == "__main__":
   UpperCAmelCase_   :				Union[str, Any]						      = input('Enter numbers separated by a comma:\n').strip()
   UpperCAmelCase_   :				Optional[Any]						      = [int(item) for item in user_input.split(',')]
   print(heaps(arr))


