From 61d8277fd5ed242073e70932775e72dad84fd254 Mon Sep 17 00:00:00 2001
From: not-lain <hhichri60@gmail.com>
Date: Wed, 21 Feb 2024 14:35:39 +0100
Subject: [PATCH 01/14] add `push_to_hub` to pipeline

---
 src/transformers/pipelines/base.py | 45 ++++++++++++++++++++++++++++--
 1 file changed, 43 insertions(+), 2 deletions(-)

diff --git a/src/transformers/pipelines/base.py b/src/transformers/pipelines/base.py
index 758484107b76f2..e2d3a0f8241c7d 100644
--- a/src/transformers/pipelines/base.py
+++ b/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,
@@ -779,7 +781,7 @@ 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.
@@ -892,7 +894,12 @@ 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: str,
+        safe_serialization: bool = True,
+        **kwargs,
+    ):
         """
         Save the pipeline's model and tokenizer.
 
@@ -902,6 +909,8 @@ def save_pretrained(self, save_directory: str, safe_serialization: bool = True):
             safe_serialization (`str`):
                 Whether to save the model using `safetensors` or the traditional way for PyTorch or Tensorflow.
         """
+        self._set_token_in_kwargs(kwargs)
+
         if os.path.isfile(save_directory):
             logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
             return
@@ -942,6 +951,31 @@ def save_pretrained(self, save_directory: str, safe_serialization: bool = True):
         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__().
@@ -1211,6 +1245,13 @@ 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"
+    )
+
+
 class ChunkPipeline(Pipeline):
     def run_single(self, inputs, preprocess_params, forward_params, postprocess_params):
         all_outputs = []

From 910581d113fec053f2bedaa537123198bb3f593d Mon Sep 17 00:00:00 2001
From: not-lain <hhichri60@gmail.com>
Date: Thu, 22 Feb 2024 11:51:26 +0100
Subject: [PATCH 02/14] fix docs

---
 src/transformers/pipelines/base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/transformers/pipelines/base.py b/src/transformers/pipelines/base.py
index e2d3a0f8241c7d..b8cd283e04aaa3 100644
--- a/src/transformers/pipelines/base.py
+++ b/src/transformers/pipelines/base.py
@@ -1249,7 +1249,7 @@ def iterate(self, inputs, preprocess_params, forward_params, postprocess_params)
 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):

From 4be0c3510f834f696dc55ffd0ede1d340e7bf719 Mon Sep 17 00:00:00 2001
From: not-lain <hhichri60@gmail.com>
Date: Thu, 22 Feb 2024 11:57:16 +0100
Subject: [PATCH 03/14] format with ruff

---
 src/transformers/pipelines/base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/transformers/pipelines/base.py b/src/transformers/pipelines/base.py
index b8cd283e04aaa3..37b19ce0a76afb 100644
--- a/src/transformers/pipelines/base.py
+++ b/src/transformers/pipelines/base.py
@@ -1249,7 +1249,7 @@ def iterate(self, inputs, preprocess_params, forward_params, postprocess_params)
 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","")
+    ).replace(".from_pretrained", "")
 
 
 class ChunkPipeline(Pipeline):

From 8fc4ac54653158cbb09211473272a1c7b2b1ce89 Mon Sep 17 00:00:00 2001
From: not-lain <hhichri60@gmail.com>
Date: Tue, 27 Feb 2024 15:36:47 +0100
Subject: [PATCH 04/14] update save_pretrained

---
 src/transformers/pipelines/base.py | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/src/transformers/pipelines/base.py b/src/transformers/pipelines/base.py
index 37b19ce0a76afb..531c40b2f28891 100644
--- a/src/transformers/pipelines/base.py
+++ b/src/transformers/pipelines/base.py
@@ -896,7 +896,7 @@ def __init__(
 
     def save_pretrained(
         self,
-        save_directory: str,
+        save_directory: Union[str, os.PathLike],
         safe_serialization: bool = True,
         **kwargs,
     ):
@@ -904,7 +904,7 @@ def save_pretrained(
         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.
@@ -937,16 +937,17 @@ def save_pretrained(
             # 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)

From 25bdabedb5975e45501cfec737bff67f3a113015 Mon Sep 17 00:00:00 2001
From: not-lain <hhichri60@gmail.com>
Date: Wed, 28 Feb 2024 17:15:19 +0100
Subject: [PATCH 05/14] update save_pretrained

---
 src/transformers/pipelines/base.py | 17 +++++++++++++----
 1 file changed, 13 insertions(+), 4 deletions(-)

diff --git a/src/transformers/pipelines/base.py b/src/transformers/pipelines/base.py
index 531c40b2f28891..0b12e281d73e25 100644
--- a/src/transformers/pipelines/base.py
+++ b/src/transformers/pipelines/base.py
@@ -898,6 +898,7 @@ def save_pretrained(
         self,
         save_directory: Union[str, os.PathLike],
         safe_serialization: bool = True,
+        token: Optional[Union[str, bool]] = None,
         **kwargs,
     ):
         """
@@ -908,8 +909,13 @@ def save_pretrained(
                 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)
+        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")
@@ -937,17 +943,20 @@ def save_pretrained(
             # Save the pipeline custom code
             custom_object_save(self, save_directory)
 
+        # TODO:
+        # depricate the safe_serialization parameter and use kwargs instead
+        # or update the save_pretrained to get all the parameters such as max_shard_size, ...
         kwargs["safe_serialization"] = safe_serialization
         self.model.save_pretrained(save_directory, **kwargs)
 
         if self.tokenizer is not None:
-            self.tokenizer.save_pretrained(save_directory,**kwargs)
+            self.tokenizer.save_pretrained(save_directory, **kwargs)
 
         if self.feature_extractor is not None:
-            self.feature_extractor.save_pretrained(save_directory,**kwargs)
+            self.feature_extractor.save_pretrained(save_directory, **kwargs)
 
         if self.image_processor is not None:
-            self.image_processor.save_pretrained(save_directory,**kwargs)
+            self.image_processor.save_pretrained(save_directory, **kwargs)
 
         if self.modelcard is not None:
             self.modelcard.save_pretrained(save_directory)

From 547edb60b217578393a41c3c1ce8b65955f9a1cd Mon Sep 17 00:00:00 2001
From: not-lain <hhichri60@gmail.com>
Date: Fri, 1 Mar 2024 15:38:07 +0100
Subject: [PATCH 06/14] remove unnecessary comment

---
 src/transformers/pipelines/base.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/src/transformers/pipelines/base.py b/src/transformers/pipelines/base.py
index 0b12e281d73e25..d672d65fa8c037 100644
--- a/src/transformers/pipelines/base.py
+++ b/src/transformers/pipelines/base.py
@@ -943,9 +943,6 @@ def save_pretrained(
             # Save the pipeline custom code
             custom_object_save(self, save_directory)
 
-        # TODO:
-        # depricate the safe_serialization parameter and use kwargs instead
-        # or update the save_pretrained to get all the parameters such as max_shard_size, ...
         kwargs["safe_serialization"] = safe_serialization
         self.model.save_pretrained(save_directory, **kwargs)
 

From e69e24170b9c25792359f9dec8556e4fe6300846 Mon Sep 17 00:00:00 2001
From: not-lain <hhichri60@gmail.com>
Date: Wed, 6 Mar 2024 21:56:26 +0100
Subject: [PATCH 07/14] switch to push_to_hub method in DynamicPipelineTester

---
 tests/pipelines/test_pipelines_common.py | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/tests/pipelines/test_pipelines_common.py b/tests/pipelines/test_pipelines_common.py
index 5e3e15f39c10ea..82af40fbc0b712 100644
--- a/tests/pipelines/test_pipelines_common.py
+++ b/tests/pipelines/test_pipelines_common.py
@@ -823,9 +823,6 @@ 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]))
@@ -837,7 +834,7 @@ 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,
                 {
@@ -848,8 +845,8 @@ 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):

From 6b108db8d09cc3a80bae9e5df2e5af1dc4a9c417 Mon Sep 17 00:00:00 2001
From: not-lain <hhichri60@gmail.com>
Date: Wed, 6 Mar 2024 22:55:45 +0100
Subject: [PATCH 08/14] remove unused imports

---
 tests/pipelines/test_pipelines_common.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/pipelines/test_pipelines_common.py b/tests/pipelines/test_pipelines_common.py
index 82af40fbc0b712..0c936c3850270f 100644
--- a/tests/pipelines/test_pipelines_common.py
+++ b/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 (

From e777027e967a435cf273b07f05056d554d8db2b4 Mon Sep 17 00:00:00 2001
From: Lain <hhichri60@gmail.com>
Date: Fri, 29 Mar 2024 04:17:16 +0100
Subject: [PATCH 09/14] update docs for add_new_pipeline

---
 docs/source/de/add_new_pipeline.md | 8 ++------
 docs/source/en/add_new_pipeline.md | 8 ++------
 2 files changed, 4 insertions(+), 12 deletions(-)

diff --git a/docs/source/de/add_new_pipeline.md b/docs/source/de/add_new_pipeline.md
index f5e64be7db310f..47d93e90ac1494 100644
--- a/docs/source/de/add_new_pipeline.md
+++ b/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,
diff --git a/docs/source/en/add_new_pipeline.md b/docs/source/en/add_new_pipeline.md
index 9e10c310f07f39..1e5b95e9b48cfc 100644
--- a/docs/source/en/add_new_pipeline.md
+++ b/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"`,

From b465ac52068a0bcfc1dff8ce8ccf6b7c9362f089 Mon Sep 17 00:00:00 2001
From: Lain <hhichri60@gmail.com>
Date: Fri, 29 Mar 2024 04:27:57 +0100
Subject: [PATCH 10/14] fix docs for add_new_pipeline

---
 docs/source/es/add_new_pipeline.md | 8 ++------
 docs/source/it/add_new_pipeline.md | 8 ++------
 docs/source/ko/add_new_pipeline.md | 8 ++------
 3 files changed, 6 insertions(+), 18 deletions(-)

diff --git a/docs/source/es/add_new_pipeline.md b/docs/source/es/add_new_pipeline.md
index 289444350dfa35..4ccacbd18a1853 100644
--- a/docs/source/es/add_new_pipeline.md
+++ b/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"`,
diff --git a/docs/source/it/add_new_pipeline.md b/docs/source/it/add_new_pipeline.md
index cd42e5cc2cd3d9..b62140e1741b1c 100644
--- a/docs/source/it/add_new_pipeline.md
+++ b/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"`,
diff --git a/docs/source/ko/add_new_pipeline.md b/docs/source/ko/add_new_pipeline.md
index 9ddd4981154a37..42c9b57c9d7be6 100644
--- a/docs/source/ko/add_new_pipeline.md
+++ b/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.

From 8b524fc4a461f369b01ed2752ee7de2f0576af98 Mon Sep 17 00:00:00 2001
From: Lain <hhichri60@gmail.com>
Date: Thu, 4 Apr 2024 15:51:52 +0100
Subject: [PATCH 11/14] add comment

---
 src/transformers/pipelines/base.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/transformers/pipelines/base.py b/src/transformers/pipelines/base.py
index d672d65fa8c037..730d0e0901a7a8 100644
--- a/src/transformers/pipelines/base.py
+++ b/src/transformers/pipelines/base.py
@@ -958,6 +958,7 @@ def save_pretrained(
         if self.modelcard is not None:
             self.modelcard.save_pretrained(save_directory)
 
+    # Copied from src/transformers/configuration_utils.py
     @staticmethod
     def _set_token_in_kwargs(kwargs, token=None):
         """Temporary method to deal with `token` and `use_auth_token`.

From 4872b570441b3294dd6c9680678e195a2fe17787 Mon Sep 17 00:00:00 2001
From: Lain <hhichri60@gmail.com>
Date: Fri, 5 Apr 2024 13:06:07 +0100
Subject: [PATCH 12/14] fix italien docs

---
 docs/source/it/add_new_pipeline.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/it/add_new_pipeline.md b/docs/source/it/add_new_pipeline.md
index b62140e1741b1c..fcc4da1899a2b1 100644
--- a/docs/source/it/add_new_pipeline.md
+++ b/docs/source/it/add_new_pipeline.md
@@ -202,7 +202,7 @@ from transformers import pipeline
 classifier = pipeline("pair-classification", model="sgugger/finetuned-bert-mrpc")
 ```
 
-Successivamente possiamo condividerlo sull'Hub usando il metodo `push_to_hub` in un `Repository`:
+Successivamente possiamo condividerlo sull'Hub usando il metodo `push_to_hub`
 
 ```py
 classifier.push_to_hub("test-dynamic-pipeline")

From 5912999886e398eec5e78966ca4385d86c267335 Mon Sep 17 00:00:00 2001
From: Lain <hhichri60@gmail.com>
Date: Mon, 15 Apr 2024 15:31:55 +0100
Subject: [PATCH 13/14] changes to token retrieval for pipelines

---
 src/transformers/pipelines/base.py | 42 +++++++++---------------------
 1 file changed, 12 insertions(+), 30 deletions(-)

diff --git a/src/transformers/pipelines/base.py b/src/transformers/pipelines/base.py
index 92146d0bb8a22f..182af4011bb7f3 100644
--- a/src/transformers/pipelines/base.py
+++ b/src/transformers/pipelines/base.py
@@ -922,13 +922,21 @@ def save_pretrained(
                 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)
+        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 kwargs.get("token", None) is not None:
+                raise ValueError(
+                    "`token` and `use_auth_token` are both specified. Please set only the argument `token`."
+                )
+            kwargs["token"] = use_auth_token
 
         if os.path.isfile(save_directory):
             logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
@@ -971,32 +979,6 @@ def save_pretrained(
         if self.modelcard is not None:
             self.modelcard.save_pretrained(save_directory)
 
-    # Copied from src/transformers/configuration_utils.py
-    @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__().

From 33cc7f568fe03c5a2c621c879d344a38206feb56 Mon Sep 17 00:00:00 2001
From: Hafedh <70411813+not-lain@users.noreply.github.com>
Date: Mon, 15 Apr 2024 20:28:13 +0100
Subject: [PATCH 14/14] Update src/transformers/pipelines/base.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
---
 src/transformers/pipelines/base.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/transformers/pipelines/base.py b/src/transformers/pipelines/base.py
index 356b29e705f9ac..1e84c86425870a 100644
--- a/src/transformers/pipelines/base.py
+++ b/src/transformers/pipelines/base.py
@@ -914,7 +914,6 @@ def save_pretrained(
         self,
         save_directory: Union[str, os.PathLike],
         safe_serialization: bool = True,
-        token: Optional[Union[str, bool]] = None,
         **kwargs,
     ):
         """