How to use test_list_handling method in gabbi

Best Python code snippet using gabbi_python

test_preprocessors.py

Source:test_preprocessors.py Github

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...53 ]54 processor = Tokenizer(regex)55 for datum, expected in fixtures:56 self.assertEqual(expected, processor(datum))57 def test_list_handling(self):58 regex = '\\w+'59 fixtures = [60 (61 [62 'Some first string,',63 '-- and a second -- are handled!'64 ],65 [66 'some', 'first', 'string', 'and', 'a', 'second',67 'are', 'handled'68 ],69 ),70 ]71 processor = Tokenizer(regex)...

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test_jsonpath.py

Source:test_jsonpath.py Github

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...34 def test_basic_match(self):35 data = ['hi']36 match = extract(data, '$[0]')37 self.assertEqual('hi', match)38 def test_list_handling(self):39 data = ['hi', 'bye']40 match = extract(data, '$')41 self.assertEqual(data, match)42 def test_embedded_list_handling(self):43 match = extract(nested_data, '$.objects..name')44 self.assertEqual(['one', 'two'], match)45 def test_sorted_object_list(self):46 match = extract(nested_data, r'$.objects[\name][0].value')47 self.assertEqual('beta', match)48 def test_filtered_list(self):49 match = extract(nested_data, r'$.objects[?name = "one"].value')50 self.assertEqual('alpha', match)51 def test_sorted_simple_list(self):52 match = extract(simple_list, r'$.objects.`sorted`[-1]')...

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test_transformer_base.py

Source:test_transformer_base.py Github

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...3import warnings4from textwiser import TextWiser, Embedding, Transformation, WordOptions, device5from tests.test_base import BaseTest, docs6class BaseTransformerTest(BaseTest):7 def test_list_handling(self):8 with warnings.catch_warnings():9 warnings.simplefilter("ignore")10 tw = TextWiser(Embedding.Word(word_option=WordOptions.word2vec, pretrained='en-turian'), Transformation.SVD(n_components=2), dtype=torch.float32)11 predicted = tw.fit_transform(docs)12 expected = [13 torch.tensor([[-0.9719871283, 0.0947150663],14 [-0.3805825114, -1.0427029133],15 [-0.6929296255, 0.1793890595],16 [0.0000000000, 0.0000000000]], dtype=torch.float32),17 torch.tensor([[-0.9719871283, 0.0947150663],18 [-0.3805825114, -1.0427029133],19 [-0.7170552015, 0.0105144158],20 [-0.9385635853, 0.6596723199],21 [0.0000000000, 0.0000000000]], dtype=torch.float32),...

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