
"""simple docstring"""





def       lowerCamelCase__		(			__snake_case	)							->		set:


					"""simple docstring"""





					_UpperCamelCase							 =  set()
					# edges = list of graph's edges
					_UpperCamelCase							 =  get_edges(__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:
										_UpperCamelCase ,  _UpperCamelCase							 =  edges.pop()
										chosen_vertices.add(__snake_case	)
										chosen_vertices.add(__snake_case	)
										for edge in edges.copy():
															if from_node in edge or to_node in edge:
																				edges.discard(__snake_case	)
					return chosen_vertices



def       lowerCamelCase__		(			__snake_case	)							->		set:


					"""simple docstring"""





					_UpperCamelCase							 =  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)}")








"""simple docstring"""





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a														=		{
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		[
				    """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """AutoformerForPrediction""",
				    """AutoformerModel""",
				    """AutoformerPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_autoformer import (
				    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    AutoformerConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_autoformer import (
								    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    AutoformerForPrediction,
								    AutoformerModel,
								    AutoformerPreTrainedModel,
								)

else:
				import sys

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






