Add token arugment in example scripts (#25172)

* fix

* fix

* fix

* fix

* fix

* fix

* fix

---------

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

examples/pytorch/image-pretraining/run_mae.py

@@ -16,6 +16,7 @@
 import logging
 import os
 import sys
+import warnings
 from dataclasses import dataclass, field
 from typing import Optional
 
@@ -133,15 +134,21 @@ class ModelArguments:
         metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
     )
     image_processor_name: str = field(default=None, metadata={"help": "Name or path of preprocessor config."})
-    use_auth_token: bool = field(
-        default=False,
+    token: str = field(
+        default=None,
         metadata={
             "help": (
-                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
-                "with private models)."
+                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
+                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
             )
         },
     )
+    use_auth_token: bool = field(
+        default=None,
+        metadata={
+            "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`."
+        },
+    )
     mask_ratio: float = field(
         default=0.75, metadata={"help": "The ratio of the number of masked tokens in the input sequence."}
     )
@@ -175,6 +182,12 @@ def main():
     else:
         model_args, data_args, training_args = parser.parse_args_into_dataclasses()
 
+    if model_args.use_auth_token is not None:
+        warnings.warn("The `use_auth_token` argument is deprecated and will be removed in v4.34.", FutureWarning)
+        if model_args.token is not None:
+            raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
+        model_args.token = model_args.use_auth_token
+
     # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
     # information sent is the one passed as arguments along with your Python/PyTorch versions.
     send_example_telemetry("run_mae", model_args, data_args)
@@ -224,7 +237,7 @@ def main():
         data_args.dataset_config_name,
         data_files=data_args.data_files,
         cache_dir=model_args.cache_dir,
-        use_auth_token=True if model_args.use_auth_token else None,
+        token=model_args.token,
     )
 
     # If we don't have a validation split, split off a percentage of train as validation.
@@ -242,7 +255,7 @@ def main():
     config_kwargs = {
         "cache_dir": model_args.cache_dir,
         "revision": model_args.model_revision,
-        "use_auth_token": True if model_args.use_auth_token else None,
+        "token": model_args.token,
     }
     if model_args.config_name:
         config = ViTMAEConfig.from_pretrained(model_args.config_name, **config_kwargs)
@@ -280,7 +293,7 @@ def main():
             config=config,
             cache_dir=model_args.cache_dir,
             revision=model_args.model_revision,
-            token=True if model_args.use_auth_token else None,
+            token=model_args.token,
         )
     else:
         logger.info("Training new model from scratch")