179 lines
8.1 KiB
Python
179 lines
8.1 KiB
Python
# coding=utf-8
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# Copyright 2021 the HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import sys
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import tempfile
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import unittest
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from pathlib import Path
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import transformers
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from transformers import (
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CONFIG_MAPPING,
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FEATURE_EXTRACTOR_MAPPING,
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AutoConfig,
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AutoFeatureExtractor,
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Wav2Vec2Config,
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Wav2Vec2FeatureExtractor,
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)
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from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
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sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
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from test_module.custom_configuration import CustomConfig # noqa E402
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from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
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SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR = get_tests_dir("fixtures")
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SAMPLE_FEATURE_EXTRACTION_CONFIG = get_tests_dir("fixtures/dummy_feature_extractor_config.json")
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SAMPLE_CONFIG = get_tests_dir("fixtures/dummy-config.json")
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class AutoFeatureExtractorTest(unittest.TestCase):
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def setUp(self):
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transformers.dynamic_module_utils.TIME_OUT_REMOTE_CODE = 0
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def test_feature_extractor_from_model_shortcut(self):
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config = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
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self.assertIsInstance(config, Wav2Vec2FeatureExtractor)
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def test_feature_extractor_from_local_directory_from_key(self):
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config = AutoFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
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self.assertIsInstance(config, Wav2Vec2FeatureExtractor)
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def test_feature_extractor_from_local_directory_from_config(self):
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with tempfile.TemporaryDirectory() as tmpdirname:
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model_config = Wav2Vec2Config()
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# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
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config_dict = AutoFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR).to_dict()
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config_dict.pop("feature_extractor_type")
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config = Wav2Vec2FeatureExtractor(**config_dict)
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# save in new folder
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model_config.save_pretrained(tmpdirname)
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config.save_pretrained(tmpdirname)
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config = AutoFeatureExtractor.from_pretrained(tmpdirname)
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# make sure private variable is not incorrectly saved
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dict_as_saved = json.loads(config.to_json_string())
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self.assertTrue("_processor_class" not in dict_as_saved)
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self.assertIsInstance(config, Wav2Vec2FeatureExtractor)
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def test_feature_extractor_from_local_file(self):
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config = AutoFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG)
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self.assertIsInstance(config, Wav2Vec2FeatureExtractor)
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def test_repo_not_found(self):
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with self.assertRaisesRegex(
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EnvironmentError, "bert-base is not a local folder and is not a valid model identifier"
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):
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_ = AutoFeatureExtractor.from_pretrained("bert-base")
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def test_revision_not_found(self):
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with self.assertRaisesRegex(
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EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"
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):
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_ = AutoFeatureExtractor.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, revision="aaaaaa")
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def test_feature_extractor_not_found(self):
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with self.assertRaisesRegex(
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EnvironmentError,
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"hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.",
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):
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_ = AutoFeatureExtractor.from_pretrained("hf-internal-testing/config-no-model")
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def test_from_pretrained_dynamic_feature_extractor(self):
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# If remote code is not set, we will time out when asking whether to load the model.
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with self.assertRaises(ValueError):
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feature_extractor = AutoFeatureExtractor.from_pretrained(
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"hf-internal-testing/test_dynamic_feature_extractor"
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)
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# If remote code is disabled, we can't load this config.
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with self.assertRaises(ValueError):
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feature_extractor = AutoFeatureExtractor.from_pretrained(
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"hf-internal-testing/test_dynamic_feature_extractor", trust_remote_code=False
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)
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feature_extractor = AutoFeatureExtractor.from_pretrained(
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"hf-internal-testing/test_dynamic_feature_extractor", trust_remote_code=True
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)
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self.assertEqual(feature_extractor.__class__.__name__, "NewFeatureExtractor")
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# Test feature extractor can be reloaded.
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with tempfile.TemporaryDirectory() as tmp_dir:
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feature_extractor.save_pretrained(tmp_dir)
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reloaded_feature_extractor = AutoFeatureExtractor.from_pretrained(tmp_dir, trust_remote_code=True)
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self.assertEqual(reloaded_feature_extractor.__class__.__name__, "NewFeatureExtractor")
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def test_new_feature_extractor_registration(self):
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try:
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AutoConfig.register("custom", CustomConfig)
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AutoFeatureExtractor.register(CustomConfig, CustomFeatureExtractor)
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# Trying to register something existing in the Transformers library will raise an error
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with self.assertRaises(ValueError):
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AutoFeatureExtractor.register(Wav2Vec2Config, Wav2Vec2FeatureExtractor)
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# Now that the config is registered, it can be used as any other config with the auto-API
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feature_extractor = CustomFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
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with tempfile.TemporaryDirectory() as tmp_dir:
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feature_extractor.save_pretrained(tmp_dir)
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new_feature_extractor = AutoFeatureExtractor.from_pretrained(tmp_dir)
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self.assertIsInstance(new_feature_extractor, CustomFeatureExtractor)
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finally:
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if "custom" in CONFIG_MAPPING._extra_content:
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del CONFIG_MAPPING._extra_content["custom"]
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if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
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del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
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def test_from_pretrained_dynamic_feature_extractor_conflict(self):
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class NewFeatureExtractor(Wav2Vec2FeatureExtractor):
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is_local = True
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try:
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AutoConfig.register("custom", CustomConfig)
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AutoFeatureExtractor.register(CustomConfig, NewFeatureExtractor)
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# If remote code is not set, the default is to use local
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feature_extractor = AutoFeatureExtractor.from_pretrained(
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"hf-internal-testing/test_dynamic_feature_extractor"
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)
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self.assertEqual(feature_extractor.__class__.__name__, "NewFeatureExtractor")
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self.assertTrue(feature_extractor.is_local)
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# If remote code is disabled, we load the local one.
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feature_extractor = AutoFeatureExtractor.from_pretrained(
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"hf-internal-testing/test_dynamic_feature_extractor", trust_remote_code=False
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)
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self.assertEqual(feature_extractor.__class__.__name__, "NewFeatureExtractor")
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self.assertTrue(feature_extractor.is_local)
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# If remote is enabled, we load from the Hub
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feature_extractor = AutoFeatureExtractor.from_pretrained(
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"hf-internal-testing/test_dynamic_feature_extractor", trust_remote_code=True
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)
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self.assertEqual(feature_extractor.__class__.__name__, "NewFeatureExtractor")
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self.assertTrue(not hasattr(feature_extractor, "is_local"))
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finally:
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if "custom" in CONFIG_MAPPING._extra_content:
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del CONFIG_MAPPING._extra_content["custom"]
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if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
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del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
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