add push_to_hub to pipeline

docs/source/de/add_new_pipeline.md

@@ -208,14 +208,10 @@ from transformers import pipeline
 classifier = pipeline("pair-classification", model="sgugger/finetuned-bert-mrpc")
 ```
 
-Dann können wir sie auf dem Hub mit der Methode `save_pretrained` in einem `Repository` freigeben:
+Dann können wir sie auf dem Hub mit der Methode `push_to_hub` freigeben:
 
 ```py
-from huggingface_hub import Repository
-
-repo = Repository("test-dynamic-pipeline", clone_from="{your_username}/test-dynamic-pipeline")
-classifier.save_pretrained("test-dynamic-pipeline")
-repo.push_to_hub()
+classifier.push_to_hub("test-dynamic-pipeline")
 ```
 
 Dadurch wird die Datei, in der Sie `PairClassificationPipeline` definiert haben, in den Ordner `"test-dynamic-pipeline"` kopiert,

docs/source/en/add_new_pipeline.md

@@ -208,14 +208,10 @@ from transformers import pipeline
 classifier = pipeline("pair-classification", model="sgugger/finetuned-bert-mrpc")
 ```
 
-Then we can share it on the Hub by using the `save_pretrained` method in a `Repository`:
+Then we can share it on the Hub by using the `push_to_hub` method:
 
 ```py
-from huggingface_hub import Repository
-
-repo = Repository("test-dynamic-pipeline", clone_from="{your_username}/test-dynamic-pipeline")
-classifier.save_pretrained("test-dynamic-pipeline")
-repo.push_to_hub()
+classifier.push_to_hub("test-dynamic-pipeline")
 ```
 
 This will copy the file where you defined `PairClassificationPipeline` inside the folder `"test-dynamic-pipeline"`,

docs/source/es/add_new_pipeline.md

@@ -212,14 +212,10 @@ from transformers import pipeline
 classifier = pipeline("pair-classification", model="sgugger/finetuned-bert-mrpc")
 ```
 
-Ahora podemos compartirlo en el Hub usando el método `save_pretrained` (guardar pre-entrenado) en un `Repository`:
+Ahora podemos compartirlo en el Hub usando el método `save_pretrained`:
 
 ```py
-from huggingface_hub import Repository
-
-repo = Repository("test-dynamic-pipeline", clone_from="{your_username}/test-dynamic-pipeline")
-classifier.save_pretrained("test-dynamic-pipeline")
-repo.push_to_hub()
+classifier.push_to_hub("test-dynamic-pipeline")
 ```
 
 Esto copiará el archivo donde definiste `PairClassificationPipeline` dentro de la carpeta `"test-dynamic-pipeline"`,

docs/source/it/add_new_pipeline.md

@@ -202,14 +202,10 @@ from transformers import pipeline
 classifier = pipeline("pair-classification", model="sgugger/finetuned-bert-mrpc")
 ```
 
-Successivamente possiamo condividerlo sull'Hub usando il metodo `save_pretrained` in un `Repository`:
+Successivamente possiamo condividerlo sull'Hub usando il metodo `push_to_hub` in un `Repository`:
 
 ```py
-from huggingface_hub import Repository
-
-repo = Repository("test-dynamic-pipeline", clone_from="{your_username}/test-dynamic-pipeline")
-classifier.save_pretrained("test-dynamic-pipeline")
-repo.push_to_hub()
+classifier.push_to_hub("test-dynamic-pipeline")
 ```
 
 Questo codice copierà il file dove è stato definitp `PairClassificationPipeline` all'interno della cartella `"test-dynamic-pipeline"`,

docs/source/ko/add_new_pipeline.md

@@ -203,14 +203,10 @@ from transformers import pipeline
 classifier = pipeline("pair-classification", model="sgugger/finetuned-bert-mrpc")
 ```
 
-그런 다음 `Repository`의 `save_pretrained` 메소드를 사용하여 허브에 공유할 수 있습니다:
+그런 다음 `push_to_hub` 메소드를 사용하여 허브에 공유할 수 있습니다:
 
 ```py
-from huggingface_hub import Repository
-
-repo = Repository("test-dynamic-pipeline", clone_from="{your_username}/test-dynamic-pipeline")
-classifier.save_pretrained("test-dynamic-pipeline")
-repo.push_to_hub()
+classifier.push_to_hub("test-dynamic-pipeline")
 ```
 
 이렇게 하면 "test-dynamic-pipeline" 폴더 내에 `PairClassificationPipeline`을 정의한 파일이 복사되며, 파이프라인의 모델과 토크나이저도 저장한 후, `{your_username}/test-dynamic-pipeline` 저장소에 있는 모든 것을 푸시합니다.

src/transformers/pipelines/base.py

@@ -36,7 +36,9 @@
 from ..tokenization_utils import PreTrainedTokenizer
 from ..utils import (
     ModelOutput,
+    PushToHubMixin,
     add_end_docstrings,
+    copy_func,
     infer_framework,
     is_tf_available,
     is_torch_available,
@@ -780,7 +782,7 @@ def build_pipeline_init_args(
 
 
 @add_end_docstrings(build_pipeline_init_args(has_tokenizer=True, has_feature_extractor=True, has_image_processor=True))
-class Pipeline(_ScikitCompat):
+class Pipeline(_ScikitCompat, PushToHubMixin):
     """
     The Pipeline class is the class from which all pipelines inherit. Refer to this class for methods shared across
     different pipelines.
@@ -905,16 +907,29 @@ def __init__(
                 # then we should keep working
                 self.image_processor = self.feature_extractor
 
-    def save_pretrained(self, save_directory: str, safe_serialization: bool = True):
+    def save_pretrained(
+        self,
+        save_directory: Union[str, os.PathLike],
+        safe_serialization: bool = True,
+        token: Optional[Union[str, bool]] = None,
+        **kwargs,
+    ):
         """
         Save the pipeline's model and tokenizer.
 
         Args:
-            save_directory (`str`):
+            save_directory (`str` or `os.PathLike`):
                 A path to the directory where to saved. It will be created if it doesn't exist.
             safe_serialization (`str`):
                 Whether to save the model using `safetensors` or the traditional way for PyTorch or Tensorflow.
+            token (`str` or `bool`, *optional*):
+                The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use
+                the token generated when running `huggingface-cli login` (stored in `~/.huggingface`).
+            kwargs (`Dict[str, Any]`, *optional*):
+                Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
         """
+        self._set_token_in_kwargs(kwargs, token=token)
+
         if os.path.isfile(save_directory):
             logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
             return
@@ -941,20 +956,46 @@ def save_pretrained(self, save_directory: str, safe_serialization: bool = True):
             # Save the pipeline custom code
             custom_object_save(self, save_directory)
 
-        self.model.save_pretrained(save_directory, safe_serialization=safe_serialization)
+        kwargs["safe_serialization"] = safe_serialization
+        self.model.save_pretrained(save_directory, **kwargs)
 
         if self.tokenizer is not None:
-            self.tokenizer.save_pretrained(save_directory)
+            self.tokenizer.save_pretrained(save_directory, **kwargs)
 
         if self.feature_extractor is not None:
-            self.feature_extractor.save_pretrained(save_directory)
+            self.feature_extractor.save_pretrained(save_directory, **kwargs)
 
         if self.image_processor is not None:
-            self.image_processor.save_pretrained(save_directory)
+            self.image_processor.save_pretrained(save_directory, **kwargs)
 
         if self.modelcard is not None:
             self.modelcard.save_pretrained(save_directory)
 
+    @staticmethod
+    def _set_token_in_kwargs(kwargs, token=None):
+        """Temporary method to deal with `token` and `use_auth_token`.
+        This method is to avoid apply the same changes in all model config classes that overwrite `from_pretrained`.
+        Need to clean up `use_auth_token` in a follow PR.
+        """
+        # Some model config classes like CLIP define their own `from_pretrained` without the new argument `token` yet.
+        if token is None:
+            token = kwargs.pop("token", None)
+        use_auth_token = kwargs.pop("use_auth_token", None)
+
+        if use_auth_token is not None:
+            warnings.warn(
+                "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
+                FutureWarning,
+            )
+            if token is not None:
+                raise ValueError(
+                    "`token` and `use_auth_token` are both specified. Please set only the argument `token`."
+                )
+            token = use_auth_token
+
+        if token is not None:
+            kwargs["token"] = token
+
     def transform(self, X):
         """
         Scikit / Keras interface to transformers' pipelines. This method will forward to __call__().
@@ -1232,6 +1273,13 @@ def iterate(self, inputs, preprocess_params, forward_params, postprocess_params)
             yield self.run_single(input_, preprocess_params, forward_params, postprocess_params)
 
 
+Pipeline.push_to_hub = copy_func(Pipeline.push_to_hub)
+if Pipeline.push_to_hub.__doc__ is not None:
+    Pipeline.push_to_hub.__doc__ = Pipeline.push_to_hub.__doc__.format(
+        object="pipe", object_class="pipeline", object_files="pipeline file"
+    ).replace(".from_pretrained", "")
+
+
 class ChunkPipeline(Pipeline):
     def run_single(self, inputs, preprocess_params, forward_params, postprocess_params):
         all_outputs = []

tests/pipelines/test_pipelines_common.py

@@ -22,7 +22,7 @@
 
 import datasets
 import numpy as np
-from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
+from huggingface_hub import HfFolder, delete_repo
 from requests.exceptions import HTTPError
 
 from transformers import (
@@ -846,9 +846,6 @@ def test_push_to_hub_dynamic_pipeline(self):
         model = BertForSequenceClassification(config).eval()
 
         with tempfile.TemporaryDirectory() as tmp_dir:
-            create_repo(f"{USER}/test-dynamic-pipeline", token=self._token)
-            repo = Repository(tmp_dir, clone_from=f"{USER}/test-dynamic-pipeline", token=self._token)
-
             vocab_file = os.path.join(tmp_dir, "vocab.txt")
             with open(vocab_file, "w", encoding="utf-8") as vocab_writer:
                 vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
@@ -860,7 +857,7 @@ def test_push_to_hub_dynamic_pipeline(self):
             del PIPELINE_REGISTRY.supported_tasks["pair-classification"]
 
             classifier.save_pretrained(tmp_dir)
-            # checks
+            # checks if the configuration has been added after calling the save_pretrained method
             self.assertDictEqual(
                 classifier.model.config.custom_pipelines,
                 {
@@ -871,8 +868,8 @@ def test_push_to_hub_dynamic_pipeline(self):
                     }
                 },
             )
-
-            repo.push_to_hub()
+            # use push_to_hub method to push the pipeline
+            classifier.push_to_hub(f"{USER}/test-dynamic-pipeline", token=self._token)
 
         # Fails if the user forget to pass along `trust_remote_code=True`
         with self.assertRaises(ValueError):