🚨 🚨 Setup -> setupclass conversion (#37282)
* 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
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@@ -72,8 +72,9 @@ _SHIELDGEMMA2_POLICIES: Mapping[str, str] = {
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class ShieldGemma2ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = ShieldGemma2Processor
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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@classmethod
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def setUpClass(cls):
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cls.tmpdirname = tempfile.mkdtemp()
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image_processor = Gemma3ImageProcessor.from_pretrained("google/siglip-so400m-patch14-384")
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extra_special_tokens = {
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@@ -83,14 +84,16 @@ class ShieldGemma2ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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}
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tokenizer = GemmaTokenizer(SAMPLE_VOCAB, keep_accents=True, extra_special_tokens=extra_special_tokens)
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processor_kwargs = self.prepare_processor_dict()
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processor_kwargs = cls.prepare_processor_dict()
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processor = ShieldGemma2Processor(image_processor=image_processor, tokenizer=tokenizer, **processor_kwargs)
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processor.save_pretrained(self.tmpdirname)
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processor.save_pretrained(cls.tmpdirname)
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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@classmethod
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def tearDownClass(cls):
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shutil.rmtree(cls.tmpdirname, ignore_errors=True)
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def prepare_processor_dict(self):
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@classmethod
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def prepare_processor_dict(cls):
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return {
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"chat_template": _CHAT_TEMPLATE,
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"policy_definitions": _SHIELDGEMMA2_POLICIES,
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