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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42
tests/models/trocr/test_processor_trocr.py
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@@ -27,21 +27,23 @@ class TrOCRProcessorTest(ProcessorTesterMixin, unittest.TestCase):
text_input_name = "labels"
processor_class = TrOCRProcessor
def setUp(self):
self.tmpdirname = tempfile.mkdtemp()
@classmethod
def setUpClass(cls):
cls.tmpdirname = tempfile.mkdtemp()
vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"] # fmt: skip
self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
cls.vocab_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
with open(cls.vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
image_processor = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
tokenizer = XLMRobertaTokenizerFast.from_pretrained("FacebookAI/xlm-roberta-base")
processor = TrOCRProcessor(image_processor=image_processor, tokenizer=tokenizer)
processor.save_pretrained(self.tmpdirname)
processor.save_pretrained(cls.tmpdirname)
def tearDown(self):
shutil.rmtree(self.tmpdirname)
@classmethod
def tearDownClass(cls):
shutil.rmtree(cls.tmpdirname, ignore_errors=True)
def get_tokenizer(self, **kwargs):
return XLMRobertaTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
@@ -50,12 +52,13 @@ class TrOCRProcessorTest(ProcessorTesterMixin, unittest.TestCase):
return ViTImageProcessor.from_pretrained(self.tmpdirname, **kwargs)
def test_save_load_pretrained_default(self):
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = TrOCRProcessor(image_processor=image_processor, tokenizer=tokenizer)
with tempfile.TemporaryDirectory() as tmpdir:
image_processor = self.get_image_processor()
tokenizer = self.get_tokenizer()
processor = TrOCRProcessor(image_processor=image_processor, tokenizer=tokenizer)
processor.save_pretrained(self.tmpdirname)
processor = TrOCRProcessor.from_pretrained(self.tmpdirname)
processor.save_pretrained(tmpdir)
processor = TrOCRProcessor.from_pretrained(tmpdir)
self.assertIsInstance(processor.tokenizer, XLMRobertaTokenizerFast)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
@@ -63,14 +66,15 @@ class TrOCRProcessorTest(ProcessorTesterMixin, unittest.TestCase):
self.assertEqual(processor.image_processor.to_json_string(), image_processor.to_json_string())
def test_save_load_pretrained_additional_features(self):
processor = TrOCRProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor())
processor.save_pretrained(self.tmpdirname)
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)
with tempfile.TemporaryDirectory() as tmpdir:
processor = TrOCRProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor())
processor.save_pretrained(tmpdir)
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)
processor = TrOCRProcessor.from_pretrained(
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
)
processor = TrOCRProcessor.from_pretrained(
tmpdir, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
)
self.assertIsInstance(processor.tokenizer, XLMRobertaTokenizerFast)
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())