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[tests] migrate to unittest.assertEqual #10670

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Merged
merged 1 commit into from
Jul 4, 2025
Merged

[tests] migrate to unittest.assertEqual #10670

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12 changes: 6 additions & 6 deletions tests/transformers/speecht5/test_feature_extraction.py
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Original file line number Diff line number Diff line change
Expand Up @@ -333,7 +333,7 @@ def test_attention_mask_target(self):
feat_dict["return_attention_mask"] = True
feat_extract = self.feature_extraction_class(**feat_dict)
speech_inputs = self.feat_extract_tester.prepare_inputs_for_target()
input_lenghts = [len(x) for x in speech_inputs]
input_lengths = [len(x) for x in speech_inputs]
input_name = feat_extract.model_input_names[0]

processed = BatchFeature({input_name: speech_inputs})
Expand All @@ -343,18 +343,18 @@ def test_attention_mask_target(self):
processed = feat_extract.pad(processed, padding="longest", return_tensors="np")
self.assertIn("attention_mask", processed)
self.assertListEqual(list(processed.attention_mask.shape), list(processed[input_name].shape[:2]))
self.assertListEqual(processed.attention_mask.sum(-1).tolist(), input_lenghts)
self.assertListEqual(processed.attention_mask.sum(-1).tolist(), input_lengths)

def test_attention_mask_with_truncation_target(self):
feat_dict = self.feat_extract_dict
feat_dict["return_attention_mask"] = True
feat_extract = self.feature_extraction_class(**feat_dict)
speech_inputs = self.feat_extract_tester.prepare_inputs_for_target()
input_lenghts = [len(x) for x in speech_inputs]
input_lengths = [len(x) for x in speech_inputs]
input_name = feat_extract.model_input_names[0]

processed = BatchFeature({input_name: speech_inputs})
max_length = min(input_lenghts)
max_length = min(input_lengths)

feat_extract.feature_size = feat_extract.num_mel_bins # hack!

Expand Down Expand Up @@ -393,7 +393,7 @@ def test_integration(self):
input_speech = self._load_datasamples(1)
feature_extractor = SpeechT5FeatureExtractor()
input_values = feature_extractor(input_speech, return_tensors="pd").input_values
self.assertEquals(input_values.shape, [1, 93680])
self.assertEqual(input_values.shape, [1, 93680])
self.assertTrue(paddle.allclose(input_values[0, :30], EXPECTED_INPUT_VALUES, atol=1e-6))

def test_integration_target(self):
Expand All @@ -409,5 +409,5 @@ def test_integration_target(self):
input_speech = self._load_datasamples(1)
feature_extractor = SpeechT5FeatureExtractor()
input_values = feature_extractor(audio_target=input_speech, return_tensors="pd").input_values
self.assertEquals(input_values.shape, [1, 366, 80])
self.assertEqual(input_values.shape, [1, 366, 80])
self.assertTrue(paddle.allclose(input_values[0, 0, :30], EXPECTED_INPUT_VALUES, atol=1e-4))
4 changes: 2 additions & 2 deletions tests/transformers/yuan/test_tokenizer.py
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Expand Up @@ -45,5 +45,5 @@ def test_extract_non_learnable_parts(self):
context_data=context_data,
)
for idx, round in enumerate(conversation_result["conversations"]):
self.assertEquals(tokenizer.decode(round[0]), decode_outputs[idx][0])
self.assertEquals(tokenizer.decode(round[1]), decode_outputs[idx][1])
self.assertEqual(tokenizer.decode(round[0]), decode_outputs[idx][0])
self.assertEqual(tokenizer.decode(round[1]), decode_outputs[idx][1])
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