import shutil import tempfile import unittest import torch from transformers import GemmaTokenizer from transformers.models.colpali.processing_colpali import ColPaliProcessor from transformers.testing_utils import get_tests_dir, require_torch, require_vision from transformers.utils import is_vision_available from transformers.utils.dummy_vision_objects import SiglipImageProcessor from ...test_processing_common import ProcessorTesterMixin if is_vision_available(): from transformers import ( ColPaliProcessor, PaliGemmaProcessor, SiglipImageProcessor, ) SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model") @require_vision class ColPaliProcessorTest(ProcessorTesterMixin, unittest.TestCase): processor_class = ColPaliProcessor @classmethod def setUpClass(cls): cls.tmpdirname = tempfile.mkdtemp() image_processor = SiglipImageProcessor.from_pretrained("google/siglip-so400m-patch14-384") image_processor.image_seq_length = 0 tokenizer = GemmaTokenizer(SAMPLE_VOCAB, keep_accents=True) processor = PaliGemmaProcessor(image_processor=image_processor, tokenizer=tokenizer) processor.save_pretrained(cls.tmpdirname) @classmethod def tearDownClass(cls): shutil.rmtree(cls.tmpdirname, ignore_errors=True) @require_torch @require_vision def test_process_images(self): # Processor configuration image_input = self.prepare_image_inputs() image_processor = self.get_component("image_processor") tokenizer = self.get_component("tokenizer", max_length=112, padding="max_length") image_processor.image_seq_length = 14 # Get the processor processor = self.processor_class( tokenizer=tokenizer, image_processor=image_processor, ) # Process the image batch_feature = processor.process_images(images=image_input, return_tensors="pt") # Assertions self.assertIn("pixel_values", batch_feature) self.assertEqual(batch_feature["pixel_values"].shape, torch.Size([1, 3, 384, 384])) @require_torch @require_vision def test_process_queries(self): # Inputs queries = [ "Is attention really all you need?", "Are Benjamin, Antoine, Merve, and Jo best friends?", ] # Processor configuration image_processor = self.get_component("image_processor") tokenizer = self.get_component("tokenizer", max_length=112, padding="max_length") image_processor.image_seq_length = 14 # Get the processor processor = self.processor_class( tokenizer=tokenizer, image_processor=image_processor, ) # Process the image batch_feature = processor.process_queries(text=queries, return_tensors="pt") # Assertions self.assertIn("input_ids", batch_feature) self.assertIsInstance(batch_feature["input_ids"], torch.Tensor) self.assertEqual(batch_feature["input_ids"].shape[0], len(queries)) # The following tests are overwritten as ColPaliProcessor can only take one of images or text as input at a time def test_tokenizer_defaults_preserved_by_kwargs(self): if "image_processor" not in self.processor_class.attributes: self.skipTest(f"image_processor attribute not present in {self.processor_class}") processor_components = self.prepare_components() processor_components["tokenizer"] = self.get_component("tokenizer", max_length=117, padding="max_length") processor = self.processor_class(**processor_components) self.skip_processor_without_typed_kwargs(processor) input_str = self.prepare_text_inputs() inputs = processor(text=input_str, return_tensors="pt") self.assertEqual(inputs[self.text_input_name].shape[-1], 117) def test_image_processor_defaults_preserved_by_image_kwargs(self): """ We use do_rescale=True, rescale_factor=-1 to ensure that image_processor kwargs are preserved in the processor. We then check that the mean of the pixel_values is less than or equal to 0 after processing. Since the original pixel_values are in [0, 255], this is a good indicator that the rescale_factor is indeed applied. """ if "image_processor" not in self.processor_class.attributes: self.skipTest(f"image_processor attribute not present in {self.processor_class}") processor_components = self.prepare_components() processor_components["image_processor"] = self.get_component( "image_processor", do_rescale=True, rescale_factor=-1 ) processor_components["tokenizer"] = self.get_component("tokenizer", max_length=117, padding="max_length") processor = self.processor_class(**processor_components) self.skip_processor_without_typed_kwargs(processor) image_input = self.prepare_image_inputs() inputs = processor(images=image_input, return_tensors="pt") self.assertLessEqual(inputs[self.images_input_name][0][0].mean(), 0) def test_kwargs_overrides_default_tokenizer_kwargs(self): if "image_processor" not in self.processor_class.attributes: self.skipTest(f"image_processor attribute not present in {self.processor_class}") processor_components = self.prepare_components() processor_components["tokenizer"] = self.get_component("tokenizer", padding="longest") processor = self.processor_class(**processor_components) self.skip_processor_without_typed_kwargs(processor) input_str = self.prepare_text_inputs() inputs = processor(text=input_str, return_tensors="pt", max_length=112, padding="max_length") self.assertEqual(inputs[self.text_input_name].shape[-1], 112) def test_kwargs_overrides_default_image_processor_kwargs(self): if "image_processor" not in self.processor_class.attributes: self.skipTest(f"image_processor attribute not present in {self.processor_class}") processor_components = self.prepare_components() processor_components["image_processor"] = self.get_component( "image_processor", do_rescale=True, rescale_factor=1 ) processor_components["tokenizer"] = self.get_component("tokenizer", max_length=117, padding="max_length") processor = self.processor_class(**processor_components) self.skip_processor_without_typed_kwargs(processor) image_input = self.prepare_image_inputs() inputs = processor(images=image_input, do_rescale=True, rescale_factor=-1, return_tensors="pt") self.assertLessEqual(inputs[self.images_input_name][0][0].mean(), 0) def test_unstructured_kwargs(self): if "image_processor" not in self.processor_class.attributes: self.skipTest(f"image_processor attribute not present in {self.processor_class}") processor_components = self.prepare_components() processor = self.processor_class(**processor_components) self.skip_processor_without_typed_kwargs(processor) input_str = self.prepare_text_inputs() inputs = processor( text=input_str, return_tensors="pt", do_rescale=True, rescale_factor=-1, padding="max_length", max_length=76, ) self.assertEqual(inputs[self.text_input_name].shape[-1], 76) def test_unstructured_kwargs_batched(self): if "image_processor" not in self.processor_class.attributes: self.skipTest(f"image_processor attribute not present in {self.processor_class}") processor_components = self.prepare_components() processor = self.processor_class(**processor_components) self.skip_processor_without_typed_kwargs(processor) image_input = self.prepare_image_inputs(batch_size=2) inputs = processor( images=image_input, return_tensors="pt", do_rescale=True, rescale_factor=-1, padding="longest", max_length=76, ) self.assertLessEqual(inputs[self.images_input_name][0][0].mean(), 0) def test_doubly_passed_kwargs(self): if "image_processor" not in self.processor_class.attributes: self.skipTest(f"image_processor attribute not present in {self.processor_class}") processor_components = self.prepare_components() processor = self.processor_class(**processor_components) self.skip_processor_without_typed_kwargs(processor) image_input = self.prepare_image_inputs() with self.assertRaises(ValueError): _ = processor( images=image_input, images_kwargs={"do_rescale": True, "rescale_factor": -1}, do_rescale=True, return_tensors="pt", ) def test_structured_kwargs_nested(self): if "image_processor" not in self.processor_class.attributes: self.skipTest(f"image_processor attribute not present in {self.processor_class}") processor_components = self.prepare_components() processor = self.processor_class(**processor_components) self.skip_processor_without_typed_kwargs(processor) input_str = self.prepare_text_inputs() # Define the kwargs for each modality all_kwargs = { "common_kwargs": {"return_tensors": "pt"}, "images_kwargs": {"do_rescale": True, "rescale_factor": -1}, "text_kwargs": {"padding": "max_length", "max_length": 76}, } inputs = processor(text=input_str, **all_kwargs) self.skip_processor_without_typed_kwargs(processor) self.assertEqual(inputs[self.text_input_name].shape[-1], 76) def test_structured_kwargs_nested_from_dict(self): if "image_processor" not in self.processor_class.attributes: self.skipTest(f"image_processor attribute not present in {self.processor_class}") processor_components = self.prepare_components() processor = self.processor_class(**processor_components) self.skip_processor_without_typed_kwargs(processor) image_input = self.prepare_image_inputs() # Define the kwargs for each modality all_kwargs = { "common_kwargs": {"return_tensors": "pt"}, "images_kwargs": {"do_rescale": True, "rescale_factor": -1}, "text_kwargs": {"padding": "max_length", "max_length": 76}, } inputs = processor(images=image_input, **all_kwargs) self.assertEqual(inputs[self.text_input_name].shape[-1], 76)