


"""simple docstring"""







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


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}






class 						_A  (  _SCREAMING_SNAKE_CASE  ):







    """simple docstring"""




    UpperCAmelCase	:							Any     =	"""lilt"""






    def __init__( self    :   str    ,			__UpperCAmelCase    :   str=30522    ,			__UpperCAmelCase    :   List[Any]=768    ,			__UpperCAmelCase    :   str=12    ,			__UpperCAmelCase    :   Any=12    ,			__UpperCAmelCase    :   Tuple=3072    ,			__UpperCAmelCase    :   str="gelu"    ,			__UpperCAmelCase    :   Tuple=0.1    ,			__UpperCAmelCase    :   int=0.1    ,			__UpperCAmelCase    :   Dict=512    ,			__UpperCAmelCase    :   List[Any]=2    ,			__UpperCAmelCase    :   List[Any]=0.02    ,			__UpperCAmelCase    :   Any=1e-12    ,			__UpperCAmelCase    :   List[str]=0    ,			__UpperCAmelCase    :   Tuple="absolute"    ,			__UpperCAmelCase    :   List[Any]=None    ,			__UpperCAmelCase    :   Union[str, Any]=4    ,			__UpperCAmelCase    :   Tuple=1024    ,			**__UpperCAmelCase    :   str    ,			):
      super().__init__(pad_token_id=__UpperCAmelCase    ,			**__UpperCAmelCase)

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










"""simple docstring"""







from __future__ import annotations






class 						_A  :







    """simple docstring"""




    def __init__( self    :   Tuple    ,			__UpperCAmelCase    :   List[Any]=None):
      a						:		int											=      data
      a						:		Dict											=      None






    def __repr__( self    :   Dict):
      a						:		List[Any]											=      []
      a						:		str											=      self
      while temp:
        string_rep.append(f'''{temp.data}''')
        a						:		Tuple											=      temp.next
      return "->".join(__UpperCAmelCase)






def 					lowercase      (    A_	)->				Any:
  '''simple docstring'''







  if not elements_list:
    raise Exception("The Elements List is empty"	)

  a						:		Any											=      Node(elements_list[0]	)
  for i in range(1  ,       len(A_	)	):
    a						:		int											=      Node(elements_list[i]	)
    a						:		Optional[Any]											=      current.next
  return head


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if head_node is not None and isinstance(A_  ,       A_	):
    print_reverse(head_node.next	)
    print(head_node.data	)


def 					lowercase      (    )->				List[Any]:
  '''simple docstring'''







  from doctest import testmod

  testmod()

  a						:		Union[str, Any]											=      make_linked_list([14, 52, 14, 12, 43]	)
  print("Linked List:"	)
  print(A_	)
  print("Elements in Reverse:"	)
  print_reverse(A_	)


if __name__ == "__main__":
 main()






