[AutoFeatureExtractor] Fix loading of local folders if config.json exists (#13166)

* up

* up

src/transformers/models/auto/configuration_auto.py

@@ -20,6 +20,7 @@ from collections import OrderedDict
 from typing import List, Union
 
 from ...configuration_utils import PretrainedConfig
+from ...file_utils import CONFIG_NAME
 
 
 CONFIG_MAPPING_NAMES = OrderedDict(
@@ -520,6 +521,6 @@ class AutoConfig:
 
         raise ValueError(
             f"Unrecognized model in {pretrained_model_name_or_path}. "
-            "Should have a `model_type` key in its config.json, or contain one of the following strings "
+            f"Should have a `model_type` key in its {CONFIG_NAME}, or contain one of the following strings "
             f"in its name: {', '.join(CONFIG_MAPPING.keys())}"
         )

src/transformers/models/auto/feature_extraction_auto.py

@@ -20,7 +20,7 @@ from collections import OrderedDict
 # Build the list of all feature extractors
 from ...configuration_utils import PretrainedConfig
 from ...feature_extraction_utils import FeatureExtractionMixin
-from ...file_utils import FEATURE_EXTRACTOR_NAME
+from ...file_utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME
 from .auto_factory import _LazyAutoMapping
 from .configuration_auto import (
     CONFIG_MAPPING_NAMES,
@@ -142,7 +142,12 @@ class AutoFeatureExtractor:
             os.path.join(pretrained_model_name_or_path, FEATURE_EXTRACTOR_NAME)
         )
 
-        if not is_feature_extraction_file and not is_directory:
+        has_local_config = (
+            os.path.exists(os.path.join(pretrained_model_name_or_path, CONFIG_NAME)) if is_directory else False
+        )
+
+        # load config, if it can be loaded
+        if not is_feature_extraction_file and (has_local_config or not is_directory):
             if not isinstance(config, PretrainedConfig):
                 config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
 
@@ -150,6 +155,7 @@ class AutoFeatureExtractor:
         config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs)
 
         model_type = config_class_to_model_type(type(config).__name__)
+
         if model_type is not None:
             return FEATURE_EXTRACTOR_MAPPING[type(config)].from_dict(config_dict, **kwargs)
         elif "feature_extractor_type" in config_dict:
@@ -157,7 +163,7 @@ class AutoFeatureExtractor:
             return feature_extractor_class.from_dict(config_dict, **kwargs)
 
         raise ValueError(
-            f"Unrecognized model in {pretrained_model_name_or_path}. Should have a `feature_extractor_type` key in "
-            f"its {FEATURE_EXTRACTOR_NAME}, or contain one of the following strings "
-            f"in its name: {', '.join(FEATURE_EXTRACTOR_MAPPING.keys())}"
+            f"Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a `feature_extractor_type` key in "
+            f"its {FEATURE_EXTRACTOR_NAME}, or one of the following `model_type` keys in its {CONFIG_NAME}: "
+            f"{', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys())}"
         )

tests/test_feature_extraction_auto.py

@@ -14,15 +14,17 @@
 # limitations under the License.
 
 import os
+import tempfile
 import unittest
 
-from transformers import AutoFeatureExtractor, Wav2Vec2FeatureExtractor
+from transformers import AutoFeatureExtractor, Wav2Vec2Config, Wav2Vec2FeatureExtractor
 
 
 SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures")
 SAMPLE_FEATURE_EXTRACTION_CONFIG = os.path.join(
     os.path.dirname(os.path.abspath(__file__)), "fixtures/dummy_feature_extractor_config.json"
 )
+SAMPLE_CONFIG = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/dummy-config.json")
 
 
 class AutoFeatureExtractorTest(unittest.TestCase):
@@ -30,10 +32,27 @@ class AutoFeatureExtractorTest(unittest.TestCase):
         config = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
         self.assertIsInstance(config, Wav2Vec2FeatureExtractor)
 
-    def test_feature_extractor_from_local_directory(self):
+    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)
+
+        self.assertIsInstance(config, Wav2Vec2FeatureExtractor)
+
     def test_feature_extractor_from_local_file(self):
         config = AutoFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG)
         self.assertIsInstance(config, Wav2Vec2FeatureExtractor)