Auto processor fix (#14623)

* Add AutoProcessor class
Init and tests
Add doc
Fix init
Update src/transformers/models/auto/processing_auto.py

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Reverts to tokenizer or feature extractor when available
Adapt test

* Revert "Adapt test"

This reverts commit bbdde5fab02465f24b54b227390073082cb32093.

* Revert "Reverts to tokenizer or feature extractor when available"

This reverts commit 77659ff5d21b6cc0baf6f443017e35e056a525bb.

* Don't revert everything Lysandre!

Co-authored-by: Sylvain Gugger <sylvain.gugger@gmail.com>

src/transformers/models/auto/processing_auto.py

@@ -28,8 +28,6 @@ from .configuration_auto import (
     model_type_to_module_name,
     replace_list_option_in_docstrings,
 )
-from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING_NAMES, AutoFeatureExtractor
-from .tokenization_auto import TOKENIZER_MAPPING_NAMES, AutoTokenizer
 
 
 PROCESSOR_MAPPING_NAMES = OrderedDict(
@@ -85,9 +83,6 @@ class AutoProcessor:
 
         List options
 
-        For other types of models, this class will return the appropriate tokenizer (if available) or feature
-        extractor.
-
         Params:
             pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
                 This can be either:
@@ -167,24 +162,11 @@ class AutoProcessor:
             return processor_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
 
         model_type = config_class_to_model_type(type(config).__name__)
-        if model_type is not None and model_type in PROCESSOR_MAPPING_NAMES:
+        if model_type is not None:
             return PROCESSOR_MAPPING[type(config)].from_pretrained(pretrained_model_name_or_path, **kwargs)
 
-        # At this stage there doesn't seem to be a `Processor` class available for this model, so let's try a tokenizer
-        if model_type in TOKENIZER_MAPPING_NAMES:
-            return AutoTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs)
-
-        # At this stage there doesn't seem to be a `Processor` class available for this model, so let's try a tokenizer
-        if model_type in FEATURE_EXTRACTOR_MAPPING_NAMES:
-            return AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path, **kwargs)
-
-        all_model_types = set(
-            PROCESSOR_MAPPING_NAMES.keys() + TOKENIZER_MAPPING_NAMES.keys() + FEATURE_EXTRACTOR_MAPPING_NAMES.keys()
-        )
-        all_model_types = list(all_model_types)
-        all_model_types.sort()
         raise ValueError(
             f"Unrecognized processor in {pretrained_model_name_or_path}. Should have a `processor_type` key in "
             f"its {FEATURE_EXTRACTOR_NAME}, or one of the following `model_type` keys in its {CONFIG_NAME}: "
-            f"{', '.join(all_model_types)}"
+            f"{', '.join(c for c in PROCESSOR_MAPPING_NAMES.keys())}"
         )

tests/test_processor_auto.py

@@ -17,8 +17,7 @@ import os
 import tempfile
 import unittest
 
-from transformers import AutoProcessor, BeitFeatureExtractor, BertTokenizerFast, Wav2Vec2Config, Wav2Vec2Processor
+from transformers import AutoProcessor, Wav2Vec2Config, Wav2Vec2Processor
-from transformers.testing_utils import require_torch
 
 
 SAMPLE_PROCESSOR_CONFIG_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures")
@@ -45,12 +44,3 @@ class AutoFeatureExtractorTest(unittest.TestCase):
             processor = AutoProcessor.from_pretrained(tmpdirname)
 
         self.assertIsInstance(processor, Wav2Vec2Processor)
-
-    def test_auto_processor_reverts_to_tokenizer(self):
-        processor = AutoProcessor.from_pretrained("bert-base-cased")
-        self.assertIsInstance(processor, BertTokenizerFast)
-
-    @require_torch
-    def test_auto_processor_reverts_to_feature_extractor(self):
-        processor = AutoProcessor.from_pretrained("microsoft/beit-base-patch16-224")
-        self.assertIsInstance(processor, BeitFeatureExtractor)