codingutf8 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 orglicenseslicense2 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 remove featureextractortype to make sure config json alone is enough to load feature processor locally save in new folder make sure private variable is not incorrectly saved if remote code is not set we will time out when asking whether to load the model if remote code is disabled we can t load this config test feature extractor can be reloaded trying to register something existing in the transformers library will raise an error now that the config is registered it can be used as any other config with the autoapi if remote code is not set the default is to use local if remote code is disabled we load the local one if remote is enabled we load from the hub coding utf 8 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 noqa e402 noqa e402 remove feature_extractor_type to make sure config json alone is enough to load feature processor locally save in new folder make sure private variable is not incorrectly saved if remote code is not set we will time out when asking whether to load the model if remote code is disabled we can t load this config test feature extractor can be reloaded trying to register something existing in the transformers library will raise an error now that the config is registered it can be used as any other config with the auto api if remote code is not set the default is to use local if remote code is disabled we load the local one if remote is enabled we load from the hub
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  
from test_module.custom_feature_extraction import CustomFeatureExtractor  


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()

            
            config_dict = AutoFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR).to_dict()

            config_dict.pop("feature_extractor_type")
            config = Wav2Vec2FeatureExtractor(**config_dict)

            
            model_config.save_pretrained(tmpdirname)
            config.save_pretrained(tmpdirname)

            config = AutoFeatureExtractor.from_pretrained(tmpdirname)

            
            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):
        
        with self.assertRaises(ValueError):
            feature_extractor = AutoFeatureExtractor.from_pretrained(
                "hf-internal-testing/test_dynamic_feature_extractor"
            )
        
        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")

        
        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)
            
            with self.assertRaises(ValueError):
                AutoFeatureExtractor.register(Wav2Vec2Config, Wav2Vec2FeatureExtractor)

            
            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)
            
            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)

            
            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)

            
            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]