
'''simple docstring'''







def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: dict							):



						"""simple docstring"""





						lowercase_	:     List[Any]													=       set()
						# edges = list of graph's edges
						lowercase_	:     Union[str, Any]													=       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_	:     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 					snake_case_		(				__SCREAMING_SNAKE_CASE					: dict							):



						"""simple docstring"""





						lowercase_	:     Any													=       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'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
							from transformers import AutoModelForSeqaSeqLM, AutoTokenizer






@require_torch
@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase__   (  unittest.TestCase     ):






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''				,							return_dict=__SCREAMING_SNAKE_CASE		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       AutoTokenizer.from_pretrained('''google/mt5-small'''		)

											lowercase_	:     int													=       tokenizer('''Hello there'''				,							return_tensors='''pt'''		).input_ids
											lowercase_	:     Union[str, Any]													=       tokenizer('''Hi I am'''				,							return_tensors='''pt'''		).input_ids

											lowercase_	:     Union[str, Any]													=       model(input_ids.to(__SCREAMING_SNAKE_CASE		)				,							labels=labels.to(__SCREAMING_SNAKE_CASE		)		).loss
											lowercase_	:     int													=       -(labels.shape[-1] * loss.item())

											lowercase_	:     Any													=       -84.9_127
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)



