




"""simple docstring"""

class        lowercase      :







				def __init__(							self   :					Tuple       ):



					"""simple docstring"""

					A_  :				List[str]	   =		{}







				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					print(self.vertex       )
					for i in self.vertex:
						print(lowercase_      ,     ''' -> '''      ,     ''' -> '''.join([str(lowercase_       ) for j in self.vertex[i]]       )       )







				def 					a_  (							self   :					Any      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					int       ):



					"""simple docstring"""

					if from_vertex in self.vertex:
						self.vertex[from_vertex].append(lowercase_       )
					else:
						# else make a new vertex
						A_  :				Dict	   =		[to_vertex]







				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		[False] * len(self.vertex       )

					# call the recursive helper function
					for i in range(len(self.vertex       )       ):
						if not visited[i]:
							self.dfs_recursive(lowercase_      ,     lowercase_       )







				def 					a_  (							self   :					List[str]      ,     _lowerCamelCase   :					int      ,     _lowerCamelCase   :					list       ):



					"""simple docstring"""

					A_  :				Any	   =		True

					print(lowercase_      ,     end=''' '''       )

					# Recur for all the vertices that are adjacent to this node
					for i in self.vertex:
						if not visited[i]:
							self.dfs_recursive(lowercase_      ,     lowercase_       )


if __name__ == "__main__":
		_lowerCamelCase  :			Optional[int]        =       Graph()
		g.add_edge(0, 1)
		g.add_edge(0, 2)
		g.add_edge(1, 2)
		g.add_edge(2, 0)
		g.add_edge(2, 3)
		g.add_edge(3, 3)

		g.print_graph()
		print('DFS:')
		g.dfs()

		# OUTPUT:
		# 0  ->  1 -> 2
		# 1  ->  2
		# 2  ->  0 -> 3
		# 3  ->  3
		# DFS:
		#  0 1 2 3










"""simple docstring"""

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


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

_lowerCamelCase  :			Any        =       {
    'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
    'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    Any         =   """luke"""







				def __init__(							self   :					List[str]      ,     _lowerCamelCase   :					Union[str, Any]=5_02_67      ,     _lowerCamelCase   :					Tuple=50_00_00      ,     _lowerCamelCase   :					int=7_68      ,     _lowerCamelCase   :					Union[str, Any]=2_56      ,     _lowerCamelCase   :					Dict=12      ,     _lowerCamelCase   :					str=12      ,     _lowerCamelCase   :					Dict=30_72      ,     _lowerCamelCase   :					Union[str, Any]="gelu"      ,     _lowerCamelCase   :					Optional[int]=0.1      ,     _lowerCamelCase   :					str=0.1      ,     _lowerCamelCase   :					Union[str, Any]=5_12      ,     _lowerCamelCase   :					int=2      ,     _lowerCamelCase   :					Optional[int]=0.02      ,     _lowerCamelCase   :					int=1E-12      ,     _lowerCamelCase   :					List[str]=True      ,     _lowerCamelCase   :					str=None      ,     _lowerCamelCase   :					Union[str, Any]=1      ,     _lowerCamelCase   :					str=0      ,     _lowerCamelCase   :					Dict=2      ,     **_lowerCamelCase   :					Optional[int]      ,     ):



					"""simple docstring"""

					super().__init__(pad_token_id=_lowerCamelCase      ,     bos_token_id=_lowerCamelCase      ,     eos_token_id=_lowerCamelCase      ,     **_lowerCamelCase       )

					A_  :				str	   =		vocab_size
					A_  :				Optional[Any]	   =		entity_vocab_size
					A_  :				Optional[Any]	   =		hidden_size
					A_  :				List[str]	   =		entity_emb_size
					A_  :				Union[str, Any]	   =		num_hidden_layers
					A_  :				Dict	   =		num_attention_heads
					A_  :				Union[str, Any]	   =		hidden_act
					A_  :				Dict	   =		intermediate_size
					A_  :				int	   =		hidden_dropout_prob
					A_  :				str	   =		attention_probs_dropout_prob
					A_  :				List[Any]	   =		max_position_embeddings
					A_  :				Optional[Any]	   =		type_vocab_size
					A_  :				Optional[int]	   =		initializer_range
					A_  :				str	   =		layer_norm_eps
					A_  :				List[str]	   =		use_entity_aware_attention
					A_  :				Optional[int]	   =		classifier_dropout





