# coding=utf-8 # Copyright 2021 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, FEATURE_EXTRACTOR_MAPPING, AutoConfig, AutoFeatureExtractor, Wav2Vec2Config, Wav2Vec2FeatureExtractor, ) from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils")) from test_module.custom_configuration import CustomConfig # noqa E402 from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402 SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR = get_tests_dir("fixtures") SAMPLE_FEATURE_EXTRACTION_CONFIG = get_tests_dir("fixtures/dummy_feature_extractor_config.json") SAMPLE_CONFIG = get_tests_dir("fixtures/dummy-config.json") class AutoFeatureExtractorTest(unittest.TestCase): def setUp(self): transformers.dynamic_module_utils.TIME_OUT_REMOTE_CODE = 0 def test_feature_extractor_from_model_shortcut(self): config = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h") self.assertIsInstance(config, Wav2Vec2FeatureExtractor) def test_feature_extractor_from_local_directory_from_key(self): config = AutoFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR) self.assertIsInstance(config, Wav2Vec2FeatureExtractor) def test_feature_extractor_from_local_directory_from_config(self): with tempfile.TemporaryDirectory() as tmpdirname: model_config = Wav2Vec2Config() # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally config_dict = AutoFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR).to_dict() config_dict.pop("feature_extractor_type") config = Wav2Vec2FeatureExtractor(**config_dict) # save in new folder model_config.save_pretrained(tmpdirname) config.save_pretrained(tmpdirname) config = AutoFeatureExtractor.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, Wav2Vec2FeatureExtractor) def test_feature_extractor_from_local_file(self): config = AutoFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG) self.assertIsInstance(config, Wav2Vec2FeatureExtractor) def test_repo_not_found(self): with self.assertRaisesRegex( EnvironmentError, "bert-base is not a local folder and is not a valid model identifier" ): _ = AutoFeatureExtractor.from_pretrained("bert-base") def test_revision_not_found(self): with self.assertRaisesRegex( EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ): _ = AutoFeatureExtractor.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, revision="aaaaaa") def test_feature_extractor_not_found(self): with self.assertRaisesRegex( EnvironmentError, "hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.", ): _ = AutoFeatureExtractor.from_pretrained("hf-internal-testing/config-no-model") def test_from_pretrained_dynamic_feature_extractor(self): # If remote code is not set, we will time out when asking whether to load the model. with self.assertRaises(ValueError): feature_extractor = AutoFeatureExtractor.from_pretrained( "hf-internal-testing/test_dynamic_feature_extractor" ) # If remote code is disabled, we can't load this config. with self.assertRaises(ValueError): feature_extractor = AutoFeatureExtractor.from_pretrained( "hf-internal-testing/test_dynamic_feature_extractor", trust_remote_code=False ) feature_extractor = AutoFeatureExtractor.from_pretrained( "hf-internal-testing/test_dynamic_feature_extractor", trust_remote_code=True ) self.assertEqual(feature_extractor.__class__.__name__, "NewFeatureExtractor") # Test feature extractor can be reloaded. with tempfile.TemporaryDirectory() as tmp_dir: feature_extractor.save_pretrained(tmp_dir) reloaded_feature_extractor = AutoFeatureExtractor.from_pretrained(tmp_dir, trust_remote_code=True) self.assertEqual(reloaded_feature_extractor.__class__.__name__, "NewFeatureExtractor") def test_new_feature_extractor_registration(self): try: AutoConfig.register("custom", CustomConfig) AutoFeatureExtractor.register(CustomConfig, CustomFeatureExtractor) # Trying to register something existing in the Transformers library will raise an error with self.assertRaises(ValueError): AutoFeatureExtractor.register(Wav2Vec2Config, Wav2Vec2FeatureExtractor) # Now that the config is registered, it can be used as any other config with the auto-API feature_extractor = CustomFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR) with tempfile.TemporaryDirectory() as tmp_dir: feature_extractor.save_pretrained(tmp_dir) new_feature_extractor = AutoFeatureExtractor.from_pretrained(tmp_dir) self.assertIsInstance(new_feature_extractor, CustomFeatureExtractor) finally: if "custom" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["custom"] if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content: del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig] def test_from_pretrained_dynamic_feature_extractor_conflict(self): class NewFeatureExtractor(Wav2Vec2FeatureExtractor): is_local = True try: AutoConfig.register("custom", CustomConfig) AutoFeatureExtractor.register(CustomConfig, NewFeatureExtractor) # If remote code is not set, the default is to use local feature_extractor = AutoFeatureExtractor.from_pretrained( "hf-internal-testing/test_dynamic_feature_extractor" ) self.assertEqual(feature_extractor.__class__.__name__, "NewFeatureExtractor") self.assertTrue(feature_extractor.is_local) # If remote code is disabled, we load the local one. feature_extractor = AutoFeatureExtractor.from_pretrained( "hf-internal-testing/test_dynamic_feature_extractor", trust_remote_code=False ) self.assertEqual(feature_extractor.__class__.__name__, "NewFeatureExtractor") self.assertTrue(feature_extractor.is_local) # If remote is enabled, we load from the Hub feature_extractor = AutoFeatureExtractor.from_pretrained( "hf-internal-testing/test_dynamic_feature_extractor", trust_remote_code=True ) self.assertEqual(feature_extractor.__class__.__name__, "NewFeatureExtractor") self.assertTrue(not hasattr(feature_extractor, "is_local")) finally: if "custom" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["custom"] if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content: del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]