Adds use_auth_token with pipelines (#11123)

* added model_kwargs to infer_framework_from_model

* added model_kwargs to tokenizer

* added use_auth_token as named parameter

* added dynamic get for use_auth_token

src/transformers/pipelines/__init__.py

@@ -246,6 +246,7 @@ def pipeline(
     framework: Optional[str] = None,
     revision: Optional[str] = None,
     use_fast: bool = True,
+    use_auth_token: Optional[Union[str, bool]] = None,
     model_kwargs: Dict[str, Any] = {},
     **kwargs
 ) -> Pipeline:
@@ -308,6 +309,10 @@ def pipeline(
             artifacts on huggingface.co, so ``revision`` can be any identifier allowed by git.
         use_fast (:obj:`bool`, `optional`, defaults to :obj:`True`):
             Whether or not to use a Fast tokenizer if possible (a :class:`~transformers.PreTrainedTokenizerFast`).
+        use_auth_token (:obj:`str` or `bool`, `optional`):
+            The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token
+            generated when running :obj:`transformers-cli login` (stored in :obj:`~/.huggingface`).
+            revision(:obj:`str`, `optional`, defaults to :obj:`"main"`):
         model_kwargs:
             Additional dictionary of keyword arguments passed along to the model's :obj:`from_pretrained(...,
             **model_kwargs)` function.
@@ -367,6 +372,9 @@ def pipeline(
 
     task_class, model_class = targeted_task["impl"], targeted_task[framework]
 
+    # Retrieve use_auth_token and add it to model_kwargs to be used in .from_pretrained
+    model_kwargs["use_auth_token"] = model_kwargs.get("use_auth_token", use_auth_token)
+
     # Instantiate tokenizer if needed
     if isinstance(tokenizer, (str, tuple)):
         if isinstance(tokenizer, tuple):
@@ -377,12 +385,12 @@ def pipeline(
             )
         else:
             tokenizer = AutoTokenizer.from_pretrained(
-                tokenizer, revision=revision, use_fast=use_fast, _from_pipeline=task
+                tokenizer, revision=revision, use_fast=use_fast, _from_pipeline=task, **model_kwargs
             )
 
     # Instantiate config if needed
     if isinstance(config, str):
-        config = AutoConfig.from_pretrained(config, revision=revision, _from_pipeline=task)
+        config = AutoConfig.from_pretrained(config, revision=revision, _from_pipeline=task, **model_kwargs)
 
     # Instantiate modelcard if needed
     if isinstance(modelcard, str):

src/transformers/pipelines/base.py

@@ -48,7 +48,7 @@ logger = logging.get_logger(__name__)
 
 
 def infer_framework_from_model(
-    model, model_classes: Optional[Dict[str, type]] = None, revision: Optional[str] = None, task: Optional[str] = None
+    model, model_classes: Optional[Dict[str, type]] = None, task: Optional[str] = None, **model_kwargs
 ):
     """
     Select framework (TensorFlow or PyTorch) to use from the :obj:`model` passed. Returns a tuple (framework, model).
@@ -65,10 +65,11 @@ def infer_framework_from_model(
             from.
         model_classes (dictionary :obj:`str` to :obj:`type`, `optional`):
             A mapping framework to class.
-        revision (:obj:`str`, `optional`):
-            The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
-            git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any
-            identifier allowed by git.
+        task (:obj:`str`):
+            The task defining which pipeline will be returned.
+        model_kwargs:
+            Additional dictionary of keyword arguments passed along to the model's :obj:`from_pretrained(...,
+            **model_kwargs)` function.
 
     Returns:
         :obj:`Tuple`: A tuple framework, model.
@@ -80,19 +81,20 @@ def infer_framework_from_model(
             "To install PyTorch, read the instructions at https://pytorch.org/."
         )
     if isinstance(model, str):
+        model_kwargs["_from_pipeline"] = task
         if is_torch_available() and not is_tf_available():
             model_class = model_classes.get("pt", AutoModel)
-            model = model_class.from_pretrained(model, revision=revision, _from_pipeline=task)
+            model = model_class.from_pretrained(model, **model_kwargs)
         elif is_tf_available() and not is_torch_available():
             model_class = model_classes.get("tf", TFAutoModel)
-            model = model_class.from_pretrained(model, revision=revision, _from_pipeline=task)
+            model = model_class.from_pretrained(model, **model_kwargs)
         else:
             try:
                 model_class = model_classes.get("pt", AutoModel)
-                model = model_class.from_pretrained(model, revision=revision, _from_pipeline=task)
+                model = model_class.from_pretrained(model, **model_kwargs)
             except OSError:
                 model_class = model_classes.get("tf", TFAutoModel)
-                model = model_class.from_pretrained(model, revision=revision, _from_pipeline=task)
+                model = model_class.from_pretrained(model, **model_kwargs)
 
     framework = "tf" if model.__class__.__name__.startswith("TF") else "pt"
     return framework, model