Adapt repository creation to latest hf_hub (#21158)

* Adapt repository creation to latest hf_hub

* Update all examples

* Fix other tests, add Flax examples

* Address review comments

examples/flax/image-captioning/run_image_captioning_flax.py

@@ -45,7 +45,7 @@ from flax import jax_utils, traverse_util
 from flax.jax_utils import unreplicate
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from transformers import (
     AutoFeatureExtractor,
     AutoTokenizer,
@@ -430,7 +430,8 @@ def main():
             )
         else:
             repo_name = training_args.hub_model_id
-        repo = Repository(training_args.output_dir, clone_from=repo_name)
+        create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
+        repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
 
     # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/

examples/flax/language-modeling/run_bart_dlm_flax.py

@@ -45,7 +45,7 @@ from flax import jax_utils, traverse_util
 from flax.jax_utils import pad_shard_unpad
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from transformers import (
     CONFIG_MAPPING,
     FLAX_MODEL_FOR_MASKED_LM_MAPPING,
@@ -502,7 +502,8 @@ def main():
             )
         else:
             repo_name = training_args.hub_model_id
-        repo = Repository(training_args.output_dir, clone_from=repo_name)
+        create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
+        repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
 
     # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/

examples/flax/language-modeling/run_clm_flax.py

@@ -46,7 +46,7 @@ from flax import jax_utils, traverse_util
 from flax.jax_utils import pad_shard_unpad, unreplicate
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from transformers import (
     CONFIG_MAPPING,
     FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
@@ -376,7 +376,8 @@ def main():
             )
         else:
             repo_name = training_args.hub_model_id
-        repo = Repository(training_args.output_dir, clone_from=repo_name)
+        create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
+        repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
 
     #  Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/

examples/flax/language-modeling/run_mlm_flax.py

@@ -46,7 +46,7 @@ from flax import jax_utils, traverse_util
 from flax.jax_utils import pad_shard_unpad
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from transformers import (
     CONFIG_MAPPING,
     FLAX_MODEL_FOR_MASKED_LM_MAPPING,
@@ -416,7 +416,8 @@ def main():
             )
         else:
             repo_name = training_args.hub_model_id
-        repo = Repository(training_args.output_dir, clone_from=repo_name)
+        create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
+        repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
 
     # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/

examples/flax/language-modeling/run_t5_mlm_flax.py

@@ -45,7 +45,7 @@ from flax import jax_utils, traverse_util
 from flax.jax_utils import pad_shard_unpad
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from transformers import (
     CONFIG_MAPPING,
     FLAX_MODEL_FOR_MASKED_LM_MAPPING,
@@ -542,7 +542,8 @@ def main():
             )
         else:
             repo_name = training_args.hub_model_id
-        repo = Repository(training_args.output_dir, clone_from=repo_name)
+        create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
+        repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
 
     # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/

examples/flax/question-answering/run_qa.py

@@ -44,7 +44,7 @@ from flax import struct, traverse_util
 from flax.jax_utils import pad_shard_unpad, replicate, unreplicate
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from transformers import (
     AutoConfig,
     AutoTokenizer,
@@ -467,7 +467,8 @@ def main():
             )
         else:
             repo_name = training_args.hub_model_id
-        repo = Repository(training_args.output_dir, clone_from=repo_name)
+        create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
+        repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
 
     # region Load Data
     # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)

examples/flax/summarization/run_summarization_flax.py

@@ -46,7 +46,7 @@ from flax import jax_utils, traverse_util
 from flax.jax_utils import pad_shard_unpad, unreplicate
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from transformers import (
     CONFIG_MAPPING,
     FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
@@ -450,7 +450,8 @@ def main():
             )
         else:
             repo_name = training_args.hub_model_id
-        repo = Repository(training_args.output_dir, clone_from=repo_name)
+        create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
+        repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
 
     # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/

examples/flax/text-classification/run_flax_glue.py

@@ -39,7 +39,7 @@ from flax import struct, traverse_util
 from flax.jax_utils import pad_shard_unpad, replicate, unreplicate
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from transformers import (
     AutoConfig,
     AutoTokenizer,
@@ -350,7 +350,8 @@ def main():
             )
         else:
             repo_name = training_args.hub_model_id
-        repo = Repository(training_args.output_dir, clone_from=repo_name)
+        create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
+        repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
 
     # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
     # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).

examples/flax/token-classification/run_flax_ner.py

@@ -41,7 +41,7 @@ from flax import struct, traverse_util
 from flax.jax_utils import pad_shard_unpad, replicate, unreplicate
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from transformers import (
     AutoConfig,
     AutoTokenizer,
@@ -406,7 +406,8 @@ def main():
             )
         else:
             repo_name = training_args.hub_model_id
-        repo = Repository(training_args.output_dir, clone_from=repo_name)
+        create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
+        repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
 
     # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
     # or just provide the name of one of the public datasets for token classification task available on the hub at https://huggingface.co/datasets/

examples/flax/vision/run_image_classification.py

@@ -43,7 +43,7 @@ from flax import jax_utils
 from flax.jax_utils import pad_shard_unpad, unreplicate
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from transformers import (
     CONFIG_MAPPING,
     FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
@@ -298,7 +298,8 @@ def main():
             )
         else:
             repo_name = training_args.hub_model_id
-        repo = Repository(training_args.output_dir, clone_from=repo_name)
+        create_repo(repo_name, exist_ok=True, token=training_args.hub_token)
+        repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token)
 
     # Initialize datasets and pre-processing transforms
     # We use torchvision here for faster pre-processing

examples/pytorch/image-classification/run_image_classification_no_trainer.py

@@ -40,7 +40,7 @@ import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from transformers import (
     AutoConfig,
     AutoFeatureExtractor,
@@ -246,7 +246,8 @@ def main():
                 repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
             else:
                 repo_name = args.hub_model_id
-            repo = Repository(args.output_dir, clone_from=repo_name)
+            create_repo(repo_name, exist_ok=True, token=args.hub_token)
+            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:

examples/pytorch/language-modeling/run_clm_no_trainer.py

@@ -41,7 +41,7 @@ import transformers
 from accelerate import Accelerator, DistributedType
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from transformers import (
     CONFIG_MAPPING,
     MODEL_MAPPING,
@@ -282,7 +282,8 @@ def main():
                 repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
             else:
                 repo_name = args.hub_model_id
-            repo = Repository(args.output_dir, clone_from=repo_name)
+            create_repo(repo_name, exist_ok=True, token=args.hub_token)
+            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:

examples/pytorch/language-modeling/run_mlm_no_trainer.py

@@ -41,7 +41,7 @@ import transformers
 from accelerate import Accelerator, DistributedType
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from transformers import (
     CONFIG_MAPPING,
     MODEL_MAPPING,
@@ -291,7 +291,8 @@ def main():
                 repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
             else:
                 repo_name = args.hub_model_id
-            repo = Repository(args.output_dir, clone_from=repo_name)
+            create_repo(repo_name, exist_ok=True, token=args.hub_token)
+            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:

examples/pytorch/multiple-choice/run_swag_no_trainer.py

@@ -40,7 +40,7 @@ import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from transformers import (
     CONFIG_MAPPING,
     MODEL_MAPPING,
@@ -317,7 +317,8 @@ def main():
                 repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
             else:
                 repo_name = args.hub_model_id
-            repo = Repository(args.output_dir, clone_from=repo_name)
+            create_repo(repo_name, exist_ok=True, token=args.hub_token)
+            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:

examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py

@@ -38,7 +38,7 @@ import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from transformers import (
     AdamW,
     DataCollatorWithPadding,
@@ -332,7 +332,8 @@ def main():
                 repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
             else:
                 repo_name = args.hub_model_id
-            repo = Repository(args.output_dir, clone_from=repo_name)
+            create_repo(repo_name, exist_ok=True, token=args.hub_token)
+            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:

examples/pytorch/question-answering/run_qa_no_trainer.py

@@ -38,7 +38,7 @@ import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from transformers import (
     CONFIG_MAPPING,
     MODEL_MAPPING,
@@ -370,7 +370,8 @@ def main():
                 repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
             else:
                 repo_name = args.hub_model_id
-            repo = Repository(args.output_dir, clone_from=repo_name)
+            create_repo(repo_name, exist_ok=True, token=args.hub_token)
+            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:

examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py

@@ -36,7 +36,7 @@ import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
-from huggingface_hub import Repository, hf_hub_download
+from huggingface_hub import Repository, create_repo, hf_hub_download
 from transformers import (
     AutoConfig,
     AutoFeatureExtractor,
@@ -354,7 +354,8 @@ def main():
                 repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
             else:
                 repo_name = args.hub_model_id
-            repo = Repository(args.output_dir, clone_from=repo_name)
+            create_repo(repo_name, exist_ok=True, token=args.hub_token)
+            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:

examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py

@@ -31,7 +31,7 @@ from tqdm.auto import tqdm
 import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from transformers import (
     AdamW,
     SchedulerType,
@@ -422,7 +422,8 @@ def main():
                 repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
             else:
                 repo_name = args.hub_model_id
-            repo = Repository(args.output_dir, clone_from=repo_name)
+            create_repo(repo_name, exist_ok=True, token=args.hub_token)
+            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
         elif args.output_dir is not None:
             os.makedirs(args.output_dir, exist_ok=True)
     accelerator.wait_for_everyone()

examples/pytorch/summarization/run_summarization_no_trainer.py

@@ -40,7 +40,7 @@ from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
 from filelock import FileLock
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from transformers import (
     CONFIG_MAPPING,
     MODEL_MAPPING,
@@ -373,7 +373,8 @@ def main():
                 repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
             else:
                 repo_name = args.hub_model_id
-            repo = Repository(args.output_dir, clone_from=repo_name)
+            create_repo(repo_name, exist_ok=True, token=args.hub_token)
+            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:

examples/pytorch/text-classification/run_glue_no_trainer.py

@@ -32,7 +32,7 @@ import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from transformers import (
     AutoConfig,
     AutoModelForSequenceClassification,
@@ -244,7 +244,8 @@ def main():
                 repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
             else:
                 repo_name = args.hub_model_id
-            repo = Repository(args.output_dir, clone_from=repo_name)
+            create_repo(repo_name, exist_ok=True, token=args.hub_token)
+            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:

examples/pytorch/token-classification/run_ner_no_trainer.py

@@ -37,7 +37,7 @@ import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from transformers import (
     CONFIG_MAPPING,
     MODEL_MAPPING,
@@ -298,7 +298,8 @@ def main():
                 repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
             else:
                 repo_name = args.hub_model_id
-            repo = Repository(args.output_dir, clone_from=repo_name)
+            create_repo(repo_name, exist_ok=True, token=args.hub_token)
+            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:

examples/pytorch/translation/run_translation_no_trainer.py

@@ -38,7 +38,7 @@ import transformers
 from accelerate import Accelerator
 from accelerate.logging import get_logger
 from accelerate.utils import set_seed
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from transformers import (
     CONFIG_MAPPING,
     MODEL_MAPPING,
@@ -345,7 +345,8 @@ def main():
                 repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
             else:
                 repo_name = args.hub_model_id
-            repo = Repository(args.output_dir, clone_from=repo_name)
+            create_repo(repo_name, exist_ok=True, token=args.hub_token)
+            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:

setup.py

@@ -117,7 +117,7 @@ _deps = [
     "fugashi>=1.0",
     "GitPython<3.1.19",
     "hf-doc-builder>=0.3.0",
-    "huggingface-hub>=0.10.0,<1.0",
+    "huggingface-hub>=0.11.0,<1.0",
     "importlib_metadata",
     "ipadic>=1.0.0,<2.0",
     "isort>=5.5.4",

src/transformers/dependency_versions_table.py

@@ -23,7 +23,7 @@ deps = {
     "fugashi": "fugashi>=1.0",
     "GitPython": "GitPython<3.1.19",
     "hf-doc-builder": "hf-doc-builder>=0.3.0",
-    "huggingface-hub": "huggingface-hub>=0.10.0,<1.0",
+    "huggingface-hub": "huggingface-hub>=0.11.0,<1.0",
     "importlib_metadata": "importlib_metadata",
     "ipadic": "ipadic>=1.0.0,<2.0",
     "isort": "isort>=5.5.4",

src/transformers/keras_callbacks.py

@@ -340,11 +340,7 @@ class PushToHubCallback(Callback):
         self.output_dir = output_dir
         self.hub_model_id = hub_model_id
         create_repo(self.hub_model_id, exist_ok=True)
-        self.repo = Repository(
-            str(self.output_dir),
-            clone_from=self.hub_model_id,
-            use_auth_token=hub_token if hub_token else True,
-        )
+        self.repo = Repository(str(self.output_dir), clone_from=self.hub_model_id, token=hub_token)
 
         self.tokenizer = tokenizer
         self.last_job = None

src/transformers/trainer.py

@@ -60,7 +60,7 @@ from torch import nn
 from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler
 from torch.utils.data.distributed import DistributedSampler
 
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 
 from . import __version__
 from .configuration_utils import PretrainedConfig
@@ -3315,7 +3315,6 @@ class Trainer:
         """
         if not self.is_world_process_zero():
             return
-        use_auth_token = True if self.args.hub_token is None else self.args.hub_token
         if self.args.hub_model_id is None:
             repo_name = Path(self.args.output_dir).absolute().name
         else:
@@ -3323,22 +3322,15 @@ class Trainer:
         if "/" not in repo_name:
             repo_name = get_full_repo_name(repo_name, token=self.args.hub_token)
 
+        # Make sure the repo exists.
+        create_repo(repo_name, token=self.args.hub_token, private=self.args.hub_private_repo, exist_ok=True)
         try:
-            self.repo = Repository(
-                self.args.output_dir,
-                clone_from=repo_name,
-                use_auth_token=use_auth_token,
-                private=self.args.hub_private_repo,
-            )
+            self.repo = Repository(self.args.output_dir, clone_from=repo_name, token=self.args.hub_token)
         except EnvironmentError:
             if self.args.overwrite_output_dir and at_init:
                 # Try again after wiping output_dir
                 shutil.rmtree(self.args.output_dir)
-                self.repo = Repository(
-                    self.args.output_dir,
-                    clone_from=repo_name,
-                    use_auth_token=use_auth_token,
-                )
+                self.repo = Repository(self.args.output_dir, clone_from=repo_name, token=self.args.hub_token)
             else:
                 raise
 

tests/models/auto/test_processor_auto.py

@@ -21,7 +21,7 @@ import unittest
 from pathlib import Path
 from shutil import copyfile
 
-from huggingface_hub import HfFolder, Repository, delete_repo, set_access_token
+from huggingface_hub import HfFolder, Repository, create_repo, delete_repo, set_access_token
 from requests.exceptions import HTTPError
 from transformers import (
     CONFIG_MAPPING,
@@ -282,7 +282,8 @@ class ProcessorPushToHubTester(unittest.TestCase):
         processor = CustomProcessor(feature_extractor, tokenizer)
 
         with tempfile.TemporaryDirectory() as tmp_dir:
-            repo = Repository(tmp_dir, clone_from=f"{USER}/test-dynamic-processor", use_auth_token=self._token)
+            create_repo(f"{USER}/test-dynamic-processor", token=self._token)
+            repo = Repository(tmp_dir, clone_from=f"{USER}/test-dynamic-processor", token=self._token)
             processor.save_pretrained(tmp_dir)
 
             # This has added the proper auto_map field to the feature extractor config

tests/pipelines/test_pipelines_common.py

@@ -29,7 +29,7 @@ from unittest import skipIf
 import datasets
 import numpy as np
 
-from huggingface_hub import HfFolder, Repository, delete_repo, set_access_token
+from huggingface_hub import HfFolder, Repository, create_repo, delete_repo, set_access_token
 from requests.exceptions import HTTPError
 from transformers import (
     FEATURE_EXTRACTOR_MAPPING,
@@ -1023,7 +1023,8 @@ class DynamicPipelineTester(unittest.TestCase):
         model = BertForSequenceClassification(config).eval()
 
         with tempfile.TemporaryDirectory() as tmp_dir:
-            repo = Repository(tmp_dir, clone_from=f"{USER}/test-dynamic-pipeline", use_auth_token=self._token)
+            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:

tests/trainer/test_trainer.py

@@ -2079,7 +2079,7 @@ class TrainerIntegrationWithHubTester(unittest.TestCase):
                 time.sleep(0.5)
 
         with tempfile.TemporaryDirectory() as tmp_dir:
-            _ = Repository(tmp_dir, clone_from=f"{USER}/test-trainer-epoch", use_auth_token=self._token)
+            _ = Repository(tmp_dir, clone_from=f"{USER}/test-trainer-epoch", token=self._token)
             commits = self.get_commit_history(tmp_dir)
             self.assertIn("initial commit", commits)
             # We can't test that epoch 2 and 3 are in the commits without being flaky as those might be skipped if
@@ -2106,7 +2106,7 @@ class TrainerIntegrationWithHubTester(unittest.TestCase):
                 time.sleep(0.5)
 
         with tempfile.TemporaryDirectory() as tmp_dir:
-            _ = Repository(tmp_dir, clone_from=f"{USER}/test-trainer-step", use_auth_token=self._token)
+            _ = Repository(tmp_dir, clone_from=f"{USER}/test-trainer-step", token=self._token)
             commits = self.get_commit_history(tmp_dir)
             self.assertIn("initial commit", commits)
             # We can't test that epoch 2 and 3 are in the commits without being flaky as those might be skipped if

utils/update_metadata.py

@@ -214,9 +214,7 @@ def update_metadata(token, commit_sha):
     Update the metadata for the Transformers repo.
     """
     with tempfile.TemporaryDirectory() as tmp_dir:
-        repo = Repository(
-            tmp_dir, clone_from="huggingface/transformers-metadata", repo_type="dataset", use_auth_token=token
-        )
+        repo = Repository(tmp_dir, clone_from="huggingface/transformers-metadata", repo_type="dataset", token=token)
 
         frameworks_table = get_frameworks_table()
         frameworks_dataset = Dataset.from_pandas(frameworks_table)