




'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__         =     {'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''QDQBertForMaskedLM''',
		    '''QDQBertForMultipleChoice''',
		    '''QDQBertForNextSentencePrediction''',
		    '''QDQBertForQuestionAnswering''',
		    '''QDQBertForSequenceClassification''',
		    '''QDQBertForTokenClassification''',
		    '''QDQBertLayer''',
		    '''QDQBertLMHeadModel''',
		    '''QDQBertModel''',
		    '''QDQBertPreTrainedModel''',
		    '''load_tf_weights_in_qdqbert''',
		]


if TYPE_CHECKING:
		from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_qdqbert import (
				    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    QDQBertForMaskedLM,
				    QDQBertForMultipleChoice,
				    QDQBertForNextSentencePrediction,
				    QDQBertForQuestionAnswering,
				    QDQBertForSequenceClassification,
				    QDQBertForTokenClassification,
				    QDQBertLayer,
				    QDQBertLMHeadModel,
				    QDQBertModel,
				    QDQBertPreTrainedModel,
				    load_tf_weights_in_qdqbert,
				)


else:
		import sys

		a__         =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




_lowercase:					Optional[Any]      =						[[0, 1, 1], [0, 0, 1], [1, 0, 0]]



class       _lowercase				:





	"""simple docstring"""




	def __init__(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  name
						a   	=  []
						a   	=  []




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						self.inbound.append(lowerCamelCase_						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						self.outbound.append(lowerCamelCase_						)




	def __repr__(self						):
						"""simple docstring"""

						return F'''<node={self.name} inbound={self.inbound} outbound={self.outbound}>'''






def        a( A			:       Optional[Any]					,	A			:       Optional[int]=3					,	A			:       str=0.85			)						->			Optional[Any]:
					"""simple docstring"""





					a   	=  {}
					for node in nodes:
										a   	=  1

					a   	=  {}
					for node in nodes:
										a   	=  len(node.outbound			)

					for i in range(A			):
										print(f'''======= Iteration {i + 1} ======='''			)
										for _, node in enumerate(A			):
															a   	=  (1 - d) + d * sum(
															    ranks[ib] / outbounds[ib] for ib in node.inbound			)
										print(A			)






def        a( )						->			List[Any]:
					"""simple docstring"""





					a   	=  list(input("Enter Names of the Nodes: "			).split()			)

					a   	=  [Node(A			) for name in names]

					for ri, row in enumerate(A			):
										for ci, col in enumerate(A			):
															if col == 1:
																				nodes[ci].add_inbound(names[ri]			)
																				nodes[ri].add_outbound(names[ci]			)

					print("======= Nodes ======="			)
					for node in nodes:
										print(A			)

					page_rank(A			)


if __name__ == "__main__":
				main()
