How to use test_unhashable method in avocado

Best Python code snippet using avocado_python

test_mathstuff.py

Source:test_mathstuff.py Github

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...16 (['a', 'b', 'c'], ['d', 'c', 'e'], (['c'], sorted(['a', 'b']), sorted(['a', 'b', 'd', 'e']), sorted(['a', 'b', 'c', 'e', 'd']))),17 )18@pytest.mark.parametrize('data, expected', UNIQUE_DATA)19class TestUnique:20 def test_unhashable(self, data, expected):21 assert sorted(ms.unique(list(data))) == expected22 def test_hashable(self, data, expected):23 assert sorted(ms.unique(tuple(data))) == expected24@pytest.mark.parametrize('dataset1, dataset2, expected', TWO_SETS_DATA)25class TestIntersect:26 def test_unhashable(self, dataset1, dataset2, expected):27 assert sorted(ms.intersect(list(dataset1), list(dataset2))) == expected[0]28 def test_hashable(self, dataset1, dataset2, expected):29 assert sorted(ms.intersect(tuple(dataset1), tuple(dataset2))) == expected[0]30@pytest.mark.parametrize('dataset1, dataset2, expected', TWO_SETS_DATA)31class TestDifference:32 def test_unhashable(self, dataset1, dataset2, expected):33 assert sorted(ms.difference(list(dataset1), list(dataset2))) == expected[1]34 def test_hashable(self, dataset1, dataset2, expected):35 assert sorted(ms.difference(tuple(dataset1), tuple(dataset2))) == expected[1]36@pytest.mark.parametrize('dataset1, dataset2, expected', TWO_SETS_DATA)37class TestSymmetricDifference:38 def test_unhashable(self, dataset1, dataset2, expected):39 assert sorted(ms.symmetric_difference(list(dataset1), list(dataset2))) == expected[2]40 def test_hashable(self, dataset1, dataset2, expected):41 assert sorted(ms.symmetric_difference(tuple(dataset1), tuple(dataset2))) == expected[2]42class TestMin:43 def test_min(self):44 assert ms.min((1, 2)) == 145 assert ms.min((2, 1)) == 146 assert ms.min(('p', 'a', 'w', 'b', 'p')) == 'a'47class TestMax:48 def test_max(self):49 assert ms.max((1, 2)) == 250 assert ms.max((2, 1)) == 251 assert ms.max(('p', 'a', 'w', 'b', 'p')) == 'w'52class TestLogarithm:...

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