Copy code when using local trust remote code (#24785)

* Copy code when using local trust remote code

* Remote upgrade strategy

* Revert "Remote upgrade strategy"

This reverts commit 4f0392f5d747bcbbcf7211ef9f9b555a86778297.

src/transformers/models/auto/auto_factory.py

@@ -15,6 +15,7 @@
 """Factory function to build auto-model classes."""
 import copy
 import importlib
+import os
 from collections import OrderedDict
 
 from ...configuration_utils import PretrainedConfig
@@ -418,6 +419,9 @@ class _BaseAutoModelClass:
             else:
                 repo_id = config.name_or_path
             model_class = get_class_from_dynamic_module(class_ref, repo_id, **kwargs)
+            if os.path.isdir(config._name_or_path):
+                model_class.register_for_auto_class(cls.__name__)
+            else:
                 cls.register(config.__class__, model_class, exist_ok=True)
             _ = kwargs.pop("code_revision", None)
             return model_class._from_config(config, **kwargs)
@@ -477,6 +481,9 @@ class _BaseAutoModelClass:
                 class_ref, pretrained_model_name_or_path, **hub_kwargs, **kwargs
             )
             _ = hub_kwargs.pop("code_revision", None)
+            if os.path.isdir(pretrained_model_name_or_path):
+                model_class.register_for_auto_class(cls.__name__)
+            else:
                 cls.register(config.__class__, model_class, exist_ok=True)
             return model_class.from_pretrained(
                 pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs

src/transformers/models/auto/configuration_auto.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 """ Auto Config class."""
 import importlib
+import os
 import re
 import warnings
 from collections import OrderedDict
@@ -984,6 +985,8 @@ class AutoConfig:
         if has_remote_code and trust_remote_code:
             class_ref = config_dict["auto_map"]["AutoConfig"]
             config_class = get_class_from_dynamic_module(class_ref, pretrained_model_name_or_path, **kwargs)
+            if os.path.isdir(pretrained_model_name_or_path):
+                config_class.register_for_auto_class()
             _ = kwargs.pop("code_revision", None)
             return config_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
         elif "model_type" in config_dict:

src/transformers/models/auto/feature_extraction_auto.py

@@ -340,6 +340,8 @@ class AutoFeatureExtractor:
                 feature_extractor_auto_map, pretrained_model_name_or_path, **kwargs
             )
             _ = kwargs.pop("code_revision", None)
+            if os.path.isdir(pretrained_model_name_or_path):
+                feature_extractor_class.register_for_auto_class()
             return feature_extractor_class.from_dict(config_dict, **kwargs)
         elif feature_extractor_class is not None:
             return feature_extractor_class.from_dict(config_dict, **kwargs)

src/transformers/models/auto/image_processing_auto.py

@@ -364,6 +364,8 @@ class AutoImageProcessor:
                 image_processor_auto_map, pretrained_model_name_or_path, **kwargs
             )
             _ = kwargs.pop("code_revision", None)
+            if os.path.isdir(pretrained_model_name_or_path):
+                image_processor_class.register_for_auto_class()
             return image_processor_class.from_dict(config_dict, **kwargs)
         elif image_processor_class is not None:
             return image_processor_class.from_dict(config_dict, **kwargs)

src/transformers/models/auto/processing_auto.py

@@ -16,6 +16,7 @@
 import importlib
 import inspect
 import json
+import os
 from collections import OrderedDict
 
 # Build the list of all feature extractors
@@ -262,6 +263,8 @@ class AutoProcessor:
                 processor_auto_map, pretrained_model_name_or_path, **kwargs
             )
             _ = kwargs.pop("code_revision", None)
+            if os.path.isdir(pretrained_model_name_or_path):
+                processor_class.register_for_auto_class()
             return processor_class.from_pretrained(
                 pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs
             )

src/transformers/models/auto/tokenization_auto.py

@@ -684,6 +684,8 @@ class AutoTokenizer:
                 class_ref = tokenizer_auto_map[0]
             tokenizer_class = get_class_from_dynamic_module(class_ref, pretrained_model_name_or_path, **kwargs)
             _ = kwargs.pop("code_revision", None)
+            if os.path.isdir(pretrained_model_name_or_path):
+                tokenizer_class.register_for_auto_class()
             return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
         elif config_tokenizer_class is not None:
             tokenizer_class = None