Accept trust_remote_code and ignore it in `PreTrainedModel.from_pre…

…trained` (#18428)

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

src/transformers/configuration_utils.py

@@ -576,10 +576,17 @@ def _get_config_dict(
         use_auth_token = kwargs.pop("use_auth_token", None)
         local_files_only = kwargs.pop("local_files_only", False)
         revision = kwargs.pop("revision", None)
+        trust_remote_code = kwargs.pop("trust_remote_code", None)
         subfolder = kwargs.pop("subfolder", "")
         from_pipeline = kwargs.pop("_from_pipeline", None)
         from_auto_class = kwargs.pop("_from_auto", False)
 
+        if trust_remote_code is True:
+            logger.warning(
+                "The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is"
+                " ignored."
+            )
+
         user_agent = {"file_type": "config", "from_auto_class": from_auto_class}
         if from_pipeline is not None:
             user_agent["using_pipeline"] = from_pipeline

src/transformers/modeling_flax_utils.py

@@ -594,10 +594,17 @@ def from_pretrained(
         local_files_only = kwargs.pop("local_files_only", False)
         use_auth_token = kwargs.pop("use_auth_token", None)
         revision = kwargs.pop("revision", None)
+        trust_remote_code = kwargs.pop("trust_remote_code", None)
         from_pipeline = kwargs.pop("_from_pipeline", None)
         from_auto_class = kwargs.pop("_from_auto", False)
         _do_init = kwargs.pop("_do_init", True)
 
+        if trust_remote_code is True:
+            logger.warning(
+                "The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is"
+                " ignored."
+            )
+
         user_agent = {"file_type": "model", "framework": "flax", "from_auto_class": from_auto_class}
         if from_pipeline is not None:
             user_agent["using_pipeline"] = from_pipeline

src/transformers/modeling_tf_utils.py

@@ -2159,11 +2159,18 @@ def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
         local_files_only = kwargs.pop("local_files_only", False)
         use_auth_token = kwargs.pop("use_auth_token", None)
         revision = kwargs.pop("revision", None)
+        trust_remote_code = kwargs.pop("trust_remote_code", None)
         mirror = kwargs.pop("mirror", None)
         load_weight_prefix = kwargs.pop("load_weight_prefix", None)
         from_pipeline = kwargs.pop("_from_pipeline", None)
         from_auto_class = kwargs.pop("_from_auto", False)
 
+        if trust_remote_code is True:
+            logger.warning(
+                "The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is"
+                " ignored."
+            )
+
         user_agent = {"file_type": "model", "framework": "tensorflow", "from_auto_class": from_auto_class}
         if from_pipeline is not None:
             user_agent["using_pipeline"] = from_pipeline

src/transformers/modeling_utils.py

@@ -1790,6 +1790,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         local_files_only = kwargs.pop("local_files_only", False)
         use_auth_token = kwargs.pop("use_auth_token", None)
         revision = kwargs.pop("revision", None)
+        trust_remote_code = kwargs.pop("trust_remote_code", None)
         mirror = kwargs.pop("mirror", None)
         from_pipeline = kwargs.pop("_from_pipeline", None)
         from_auto_class = kwargs.pop("_from_auto", False)
@@ -1802,6 +1803,12 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         offload_state_dict = kwargs.pop("offload_state_dict", None)
         subfolder = kwargs.pop("subfolder", "")
 
+        if trust_remote_code is True:
+            logger.warning(
+                "The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is"
+                " ignored."
+            )
+
         if device_map is not None:
             if low_cpu_mem_usage is None:
                 low_cpu_mem_usage = True