# coding=utf-8 # Copyright 2025 the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import sys import tempfile import unittest from pathlib import Path import transformers from transformers import ( CONFIG_MAPPING, VIDEO_PROCESSOR_MAPPING, AutoConfig, AutoVideoProcessor, LlavaOnevisionConfig, LlavaOnevisionVideoProcessor, ) from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_torch sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils")) from test_module.custom_configuration import CustomConfig # noqa E402 from test_module.custom_video_processing import CustomVideoProcessor # noqa E402 @require_torch class AutoVideoProcessorTest(unittest.TestCase): def setUp(self): transformers.dynamic_module_utils.TIME_OUT_REMOTE_CODE = 0 def test_video_processor_from_model_shortcut(self): config = AutoVideoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-0.5b-ov-hf") self.assertIsInstance(config, LlavaOnevisionVideoProcessor) def test_video_processor_from_local_directory_from_key(self): with tempfile.TemporaryDirectory() as tmpdirname: processor_tmpfile = Path(tmpdirname) / "video_preprocessor_config.json" config_tmpfile = Path(tmpdirname) / "config.json" json.dump( { "video_processor_type": "LlavaOnevisionVideoProcessor", "processor_class": "LlavaOnevisionProcessor", }, open(processor_tmpfile, "w"), ) json.dump({"model_type": "llava_onevision"}, open(config_tmpfile, "w")) config = AutoVideoProcessor.from_pretrained(tmpdirname) self.assertIsInstance(config, LlavaOnevisionVideoProcessor) def test_video_processor_from_local_directory_from_preprocessor_key(self): # Ensure we can load the image processor from the feature extractor config with tempfile.TemporaryDirectory() as tmpdirname: processor_tmpfile = Path(tmpdirname) / "preprocessor_config.json" config_tmpfile = Path(tmpdirname) / "config.json" json.dump( { "video_processor_type": "LlavaOnevisionVideoProcessor", "processor_class": "LlavaOnevisionProcessor", }, open(processor_tmpfile, "w"), ) json.dump({"model_type": "llava_onevision"}, open(config_tmpfile, "w")) config = AutoVideoProcessor.from_pretrained(tmpdirname) self.assertIsInstance(config, LlavaOnevisionVideoProcessor) def test_video_processor_from_local_directory_from_config(self): with tempfile.TemporaryDirectory() as tmpdirname: model_config = LlavaOnevisionConfig() # Create a dummy config file with image_processor_type processor_tmpfile = Path(tmpdirname) / "video_preprocessor_config.json" config_tmpfile = Path(tmpdirname) / "config.json" json.dump( { "video_processor_type": "LlavaOnevisionVideoProcessor", "processor_class": "LlavaOnevisionProcessor", }, open(processor_tmpfile, "w"), ) json.dump({"model_type": "llava_onevision"}, open(config_tmpfile, "w")) # remove video_processor_type to make sure config.json alone is enough to load image processor locally config_dict = AutoVideoProcessor.from_pretrained(tmpdirname).to_dict() config_dict.pop("video_processor_type") config = LlavaOnevisionVideoProcessor(**config_dict) # save in new folder model_config.save_pretrained(tmpdirname) config.save_pretrained(tmpdirname) config = AutoVideoProcessor.from_pretrained(tmpdirname) # make sure private variable is not incorrectly saved dict_as_saved = json.loads(config.to_json_string()) self.assertTrue("_processor_class" not in dict_as_saved) self.assertIsInstance(config, LlavaOnevisionVideoProcessor) def test_video_processor_from_local_file(self): with tempfile.TemporaryDirectory() as tmpdirname: processor_tmpfile = Path(tmpdirname) / "video_preprocessor_config.json" json.dump( { "video_processor_type": "LlavaOnevisionVideoProcessor", "processor_class": "LlavaOnevisionProcessor", }, open(processor_tmpfile, "w"), ) config = AutoVideoProcessor.from_pretrained(processor_tmpfile) self.assertIsInstance(config, LlavaOnevisionVideoProcessor) def test_repo_not_found(self): with self.assertRaisesRegex( EnvironmentError, "llava-hf/llava-doesnt-exist is not a local folder and is not a valid model identifier", ): _ = AutoVideoProcessor.from_pretrained("llava-hf/llava-doesnt-exist") def test_revision_not_found(self): with self.assertRaisesRegex( EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ): _ = AutoVideoProcessor.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, revision="aaaaaa") def test_video_processor_not_found(self): with self.assertRaisesRegex( EnvironmentError, "Can't load video processor for 'hf-internal-testing/config-no-model'.", ): _ = AutoVideoProcessor.from_pretrained("hf-internal-testing/config-no-model") def test_from_pretrained_dynamic_video_processor(self): # If remote code is not set, we will time out when asking whether to load the model. with self.assertRaises(ValueError): video_processor = AutoVideoProcessor.from_pretrained("hf-internal-testing/test_dynamic_video_processor") # If remote code is disabled, we can't load this config. with self.assertRaises(ValueError): video_processor = AutoVideoProcessor.from_pretrained( "hf-internal-testing/test_dynamic_video_processor", trust_remote_code=False ) video_processor = AutoVideoProcessor.from_pretrained( "hf-internal-testing/test_dynamic_video_processor", trust_remote_code=True ) self.assertEqual(video_processor.__class__.__name__, "NewVideoProcessor") # Test the dynamic module is loaded only once. reloaded_video_processor = AutoVideoProcessor.from_pretrained( "hf-internal-testing/test_dynamic_video_processor", trust_remote_code=True ) self.assertIs(video_processor.__class__, reloaded_video_processor.__class__) # Test image processor can be reloaded. with tempfile.TemporaryDirectory() as tmp_dir: video_processor.save_pretrained(tmp_dir) reloaded_video_processor = AutoVideoProcessor.from_pretrained(tmp_dir, trust_remote_code=True) self.assertEqual(reloaded_video_processor.__class__.__name__, "NewVideoProcessor") def test_new_video_processor_registration(self): try: AutoConfig.register("custom", CustomConfig) AutoVideoProcessor.register(CustomConfig, CustomVideoProcessor) # Trying to register something existing in the Transformers library will raise an error with self.assertRaises(ValueError): AutoVideoProcessor.register(LlavaOnevisionConfig, LlavaOnevisionVideoProcessor) with tempfile.TemporaryDirectory() as tmpdirname: processor_tmpfile = Path(tmpdirname) / "video_preprocessor_config.json" config_tmpfile = Path(tmpdirname) / "config.json" json.dump( { "video_processor_type": "LlavaOnevisionVideoProcessor", "processor_class": "LlavaOnevisionProcessor", }, open(processor_tmpfile, "w"), ) json.dump({"model_type": "llava_onevision"}, open(config_tmpfile, "w")) video_processor = CustomVideoProcessor.from_pretrained(tmpdirname) # Now that the config is registered, it can be used as any other config with the auto-API with tempfile.TemporaryDirectory() as tmp_dir: video_processor.save_pretrained(tmp_dir) new_video_processor = AutoVideoProcessor.from_pretrained(tmp_dir) self.assertIsInstance(new_video_processor, CustomVideoProcessor) finally: if "custom" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["custom"] if CustomConfig in VIDEO_PROCESSOR_MAPPING._extra_content: del VIDEO_PROCESSOR_MAPPING._extra_content[CustomConfig] def test_from_pretrained_dynamic_video_processor_conflict(self): class NewVideoProcessor(LlavaOnevisionVideoProcessor): is_local = True try: AutoConfig.register("custom", CustomConfig) AutoVideoProcessor.register(CustomConfig, NewVideoProcessor) # If remote code is not set, the default is to use local video_processor = AutoVideoProcessor.from_pretrained("hf-internal-testing/test_dynamic_video_processor") self.assertEqual(video_processor.__class__.__name__, "NewVideoProcessor") self.assertTrue(video_processor.is_local) # If remote code is disabled, we load the local one. video_processor = AutoVideoProcessor.from_pretrained( "hf-internal-testing/test_dynamic_video_processor", trust_remote_code=False ) self.assertEqual(video_processor.__class__.__name__, "NewVideoProcessor") self.assertTrue(video_processor.is_local) # If remote is enabled, we load from the Hub video_processor = AutoVideoProcessor.from_pretrained( "hf-internal-testing/test_dynamic_video_processor", trust_remote_code=True ) self.assertEqual(video_processor.__class__.__name__, "NewVideoProcessor") self.assertTrue(not hasattr(video_processor, "is_local")) finally: if "custom" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["custom"] if CustomConfig in VIDEO_PROCESSOR_MAPPING._extra_content: del VIDEO_PROCESSOR_MAPPING._extra_content[CustomConfig]