Allow trust_remote_code in example scripts (#25248)

* pytorch examples

* pytorch mim no trainer

* cookiecutter

* flax examples

* missed line in pytorch run_glue

* tensorflow examples

* tensorflow run_clip

* tensorflow run_mlm

* tensorflow run_ner

* tensorflow run_clm

* pytorch example from_configs

* pytorch no trainer examples

* Revert "tensorflow run_clip"

This reverts commit 261f86ac1f1c9e05dd3fd0291e1a1f8e573781d5.

* fix: duplicated argument

examples/pytorch/image-pretraining/run_mim.py

@@ -169,6 +169,16 @@ class ModelArguments:
             "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
         },
     )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
     image_size: Optional[int] = field(
         default=None,
         metadata={
@@ -319,6 +329,7 @@ def main():
         "cache_dir": model_args.cache_dir,
         "revision": model_args.model_revision,
         "token": model_args.token,
+        "trust_remote_code": model_args.trust_remote_code,
     }
     if model_args.config_name_or_path:
         config = AutoConfig.from_pretrained(model_args.config_name_or_path, **config_kwargs)
@@ -371,10 +382,11 @@ def main():
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
             token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
         )
     else:
         logger.info("Training new model from scratch")
-        model = AutoModelForMaskedImageModeling.from_config(config)
+        model = AutoModelForMaskedImageModeling.from_config(config, trust_remote_code=model_args.trust_remote_code)
 
     if training_args.do_train:
         column_names = ds["train"].column_names

examples/pytorch/image-pretraining/run_mim_no_trainer.py

@@ -195,6 +195,16 @@ def parse_args():
             "with private models)."
         ),
     )
+    parser.add_argument(
+        "--trust_remote_code",
+        type=bool,
+        default=False,
+        help=(
+            "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option"
+            "should only be set to `True` for repositories you trust and in which you have read the code, as it will"
+            "execute code present on the Hub on your local machine."
+        ),
+    )
     parser.add_argument(
         "--image_size",
         type=int,
@@ -448,6 +458,7 @@ def main():
         "cache_dir": args.cache_dir,
         "revision": args.model_revision,
         "use_auth_token": True if args.use_auth_token else None,
+        "trust_remote_code": args.trust_remote_code,
     }
     if args.config_name_or_path:
         config = AutoConfig.from_pretrained(args.config_name_or_path, **config_kwargs)
@@ -498,10 +509,14 @@ def main():
             cache_dir=args.cache_dir,
             revision=args.model_revision,
             token=True if args.use_auth_token else None,
+            trust_remote_code=args.trust_remote_code,
         )
     else:
         logger.info("Training new model from scratch")
-        model = AutoModelForMaskedImageModeling.from_config(config)
+        model = AutoModelForMaskedImageModeling.from_config(
+            config,
+            trust_remote_code=args.trust_remote_code,
+        )
 
     column_names = ds["train"].column_names