


def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				set()
		# edges = list of graph's edges
		lowercase__						=				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:
				lowercase__					,		lowercase__						=				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       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				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_torch_available


lowerCAmelCase	=							{
    'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
    'tokenization_luke': ['LukeTokenizer'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase	=							[
			    'LUKE_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'LukeForEntityClassification',
			    'LukeForEntityPairClassification',
			    'LukeForEntitySpanClassification',
			    'LukeForMultipleChoice',
			    'LukeForQuestionAnswering',
			    'LukeForSequenceClassification',
			    'LukeForTokenClassification',
			    'LukeForMaskedLM',
			    'LukeModel',
			    'LukePreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
			from .tokenization_luke import LukeTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_luke import (
						    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LukeForEntityClassification,
						    LukeForEntityPairClassification,
						    LukeForEntitySpanClassification,
						    LukeForMaskedLM,
						    LukeForMultipleChoice,
						    LukeForQuestionAnswering,
						    LukeForSequenceClassification,
						    LukeForTokenClassification,
						    LukeModel,
						    LukePreTrainedModel,
						)

else:
			import sys

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


