



def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			dict    ):
				"""simple docstring"""







				__a       =		set()
				# edges = list of graph's edges
				__a       =		get_edges(_SCREAMING_SNAKE_CASE    )

				# While there are still elements in edges list, take an arbitrary edge
				# (from_node, to_node) and add his extremity to chosen_vertices and then
				# remove all arcs adjacent to the from_node and to_node
				while edges:
								__a     ,					__a       =		edges.pop()
								chosen_vertices.add(_SCREAMING_SNAKE_CASE    )
								chosen_vertices.add(_SCREAMING_SNAKE_CASE    )
								for edge in edges.copy():
												if from_node in edge or to_node in edge:
																edges.discard(_SCREAMING_SNAKE_CASE    )
				return chosen_vertices

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			dict    ):
				"""simple docstring"""







				__a       =		set()
				for from_node, to_nodes in graph.items():
								for to_node in to_nodes:
												edges.add((from_node, to_node)    )
				return edges


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
						# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase__			= {
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			= [
						    """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """NezhaForNextSentencePrediction""",
						    """NezhaForMaskedLM""",
						    """NezhaForPreTraining""",
						    """NezhaForMultipleChoice""",
						    """NezhaForQuestionAnswering""",
						    """NezhaForSequenceClassification""",
						    """NezhaForTokenClassification""",
						    """NezhaModel""",
						    """NezhaPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nezha import (
												    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NezhaForMaskedLM,
												    NezhaForMultipleChoice,
												    NezhaForNextSentencePrediction,
												    NezhaForPreTraining,
												    NezhaForQuestionAnswering,
												    NezhaForSequenceClassification,
												    NezhaForTokenClassification,
												    NezhaModel,
												    NezhaPreTrainedModel,
												)


else:
						import sys

						lowerCamelCase__			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
