SUMIN/HuggingFace_transformer
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🚨 🚨 Setup -> setupclass conversion (#37282)

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* More limited setup -> setupclass conversion

* make fixup

* Trigger tests

* Fixup UDOP

* Missed a spot

* tearDown -> tearDownClass where appropriate

* Couple more class fixes

* Fixups for UDOP and VisionTextDualEncoder

* Ignore errors when removing the tmpdir, in case it already got cleaned up somewhere

* CLIP fixes

* More correct classmethods

* Wav2Vec2Bert fixes

* More methods become static

* More class methods

* More class methods

* Revert changes for integration tests / modeling files

* Use a different tempdir for tests that actually write to it

* Remove addClassCleanup and just use teardownclass

* Remove changes in modeling files

* Cleanup get_processor_dict() for got_ocr2

* Fix regression on Wav2Vec2BERT test that was masked by this before

* Rework tests that modify the tmpdir

* make fix-copies

* revert clvp modeling test changes

* Fix CLIP processor test

* make fix-copies
This commit is contained in:
Matt
2025-04-08 17:15:37 +01:00
committed by GitHub
parent c15a7adb28
commit 4d0de5f73a
49 changed files with 740 additions and 574 deletions
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35
tests/models/speecht5/test_processor_speecht5.py
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@@ -36,11 +36,12 @@ SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")
@require_torch
class SpeechT5ProcessorTest(unittest.TestCase):
def setUp(self):
self.tmpdirname = tempfile.mkdtemp()
@classmethod
def setUpClass(cls):
cls.tmpdirname = tempfile.mkdtemp()
tokenizer = SpeechT5Tokenizer(SAMPLE_VOCAB)
tokenizer.save_pretrained(self.tmpdirname)
tokenizer.save_pretrained(cls.tmpdirname)
feature_extractor_map = {
"feature_size": 1,
@@ -58,8 +59,8 @@ class SpeechT5ProcessorTest(unittest.TestCase):
"return_attention_mask": True,
}
self.feature_extraction_file = os.path.join(self.tmpdirname, FEATURE_EXTRACTOR_NAME)
with open(self.feature_extraction_file, "w", encoding="utf-8") as fp:
cls.feature_extraction_file = os.path.join(cls.tmpdirname, FEATURE_EXTRACTOR_NAME)
with open(cls.feature_extraction_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps(feature_extractor_map) + "\n")
def get_tokenizer(self, **kwargs):
@@ -68,8 +69,9 @@ class SpeechT5ProcessorTest(unittest.TestCase):
def get_feature_extractor(self, **kwargs):
return SpeechT5FeatureExtractor.from_pretrained(self.tmpdirname, **kwargs)
def tearDown(self):
shutil.rmtree(self.tmpdirname)
@classmethod
def tearDownClass(cls):
shutil.rmtree(cls.tmpdirname, ignore_errors=True)
def test_save_load_pretrained_default(self):
tokenizer = self.get_tokenizer()
@@ -87,15 +89,20 @@ class SpeechT5ProcessorTest(unittest.TestCase):
self.assertIsInstance(processor.feature_extractor, SpeechT5FeatureExtractor)
def test_save_load_pretrained_additional_features(self):
processor = SpeechT5Processor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
processor.save_pretrained(self.tmpdirname)
with tempfile.TemporaryDirectory() as tmpdir:
processor = SpeechT5Processor(
tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor()
)
processor.save_pretrained(tmpdir)
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0)
tokenizer_add_kwargs = SpeechT5Tokenizer.from_pretrained(tmpdir, bos_token="(BOS)", eos_token="(EOS)")
feature_extractor_add_kwargs = SpeechT5FeatureExtractor.from_pretrained(
tmpdir, do_normalize=False, padding_value=1.0
)
processor = SpeechT5Processor.from_pretrained(
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
)
processor = SpeechT5Processor.from_pretrained(
tmpdir, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
self.assertIsInstance(processor.tokenizer, SpeechT5Tokenizer)