





"""simple docstring"""




def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     Tuple     =		set()
	# edges = list of graph's edges
	snake_case_						:     List[str]     =		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:
		snake_case_  ,snake_case_						:     Tuple     =		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 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     Optional[int]     =		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


_SCREAMING_SNAKE_CASE 		=						{
    """configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
    """feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
    """processing_mctct""": ["""MCTCTProcessor"""],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """MCTCTForCTC""",
						    """MCTCTModel""",
						    """MCTCTPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
						from .feature_extraction_mctct import MCTCTFeatureExtractor
						from .processing_mctct import MCTCTProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
						import sys

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

