diff --git a/examples/flax/image-captioning/run_image_captioning_flax.py b/examples/flax/image-captioning/run_image_captioning_flax.py
index ef9c515da4..f434752e74 100644
--- a/examples/flax/image-captioning/run_image_captioning_flax.py
+++ b/examples/flax/image-captioning/run_image_captioning_flax.py
@@ -53,7 +53,7 @@ from transformers import (
     HfArgumentParser,
     is_tensorboard_available,
 )
-from transformers.utils import get_full_repo_name, is_offline_mode, send_example_telemetry
+from transformers.utils import is_offline_mode, send_example_telemetry
 
 
 logger = logging.getLogger(__name__)
@@ -424,14 +424,14 @@ def main():
 
     # Handle the repository creation
     if training_args.push_to_hub:
-        if training_args.hub_model_id is None:
-            repo_name = get_full_repo_name(
-                Path(training_args.output_dir).absolute().name, token=training_args.hub_token
-            )
-        else:
-            repo_name = training_args.hub_model_id
-        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)
+        # Retrieve of infer repo_name
+        repo_name = training_args.hub_model_id
+        if repo_name is None:
+            repo_name = Path(training_args.output_dir).absolute().name
+        # Create repo and retrieve repo_id
+        repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
+        # Clone repo locally
+        repo = Repository(training_args.output_dir, clone_from=repo_id, 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/
diff --git a/examples/flax/language-modeling/run_bart_dlm_flax.py b/examples/flax/language-modeling/run_bart_dlm_flax.py
index e863f97e0d..c1df13d08f 100644
--- a/examples/flax/language-modeling/run_bart_dlm_flax.py
+++ b/examples/flax/language-modeling/run_bart_dlm_flax.py
@@ -59,7 +59,7 @@ from transformers import (
     set_seed,
 )
 from transformers.models.bart.modeling_flax_bart import shift_tokens_right
-from transformers.utils import get_full_repo_name, send_example_telemetry
+from transformers.utils import send_example_telemetry
 
 
 MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys())
@@ -496,14 +496,14 @@ def main():
 
     # Handle the repository creation
     if training_args.push_to_hub:
-        if training_args.hub_model_id is None:
-            repo_name = get_full_repo_name(
-                Path(training_args.output_dir).absolute().name, token=training_args.hub_token
-            )
-        else:
-            repo_name = training_args.hub_model_id
-        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)
+        # Retrieve of infer repo_name
+        repo_name = training_args.hub_model_id
+        if repo_name is None:
+            repo_name = Path(training_args.output_dir).absolute().name
+        # Create repo and retrieve repo_id
+        repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
+        # Clone repo locally
+        repo = Repository(training_args.output_dir, clone_from=repo_id, 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/
diff --git a/examples/flax/language-modeling/run_clm_flax.py b/examples/flax/language-modeling/run_clm_flax.py
index 656bfa6807..c028c3e94b 100755
--- a/examples/flax/language-modeling/run_clm_flax.py
+++ b/examples/flax/language-modeling/run_clm_flax.py
@@ -58,7 +58,7 @@ from transformers import (
     set_seed,
 )
 from transformers.testing_utils import CaptureLogger
-from transformers.utils import get_full_repo_name, send_example_telemetry
+from transformers.utils import send_example_telemetry
 
 
 logger = logging.getLogger(__name__)
@@ -372,14 +372,14 @@ def main():
 
     # Handle the repository creation
     if training_args.push_to_hub:
-        if training_args.hub_model_id is None:
-            repo_name = get_full_repo_name(
-                Path(training_args.output_dir).absolute().name, token=training_args.hub_token
-            )
-        else:
-            repo_name = training_args.hub_model_id
-        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)
+        # Retrieve of infer repo_name
+        repo_name = training_args.hub_model_id
+        if repo_name is None:
+            repo_name = Path(training_args.output_dir).absolute().name
+        # Create repo and retrieve repo_id
+        repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
+        # Clone repo locally
+        repo = Repository(training_args.output_dir, clone_from=repo_id, 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/
diff --git a/examples/flax/language-modeling/run_mlm_flax.py b/examples/flax/language-modeling/run_mlm_flax.py
index ae289b8470..0d12de0694 100755
--- a/examples/flax/language-modeling/run_mlm_flax.py
+++ b/examples/flax/language-modeling/run_mlm_flax.py
@@ -59,7 +59,7 @@ from transformers import (
     is_tensorboard_available,
     set_seed,
 )
-from transformers.utils import get_full_repo_name, send_example_telemetry
+from transformers.utils import send_example_telemetry
 
 
 MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys())
@@ -410,14 +410,14 @@ def main():
 
     # Handle the repository creation
     if training_args.push_to_hub:
-        if training_args.hub_model_id is None:
-            repo_name = get_full_repo_name(
-                Path(training_args.output_dir).absolute().name, token=training_args.hub_token
-            )
-        else:
-            repo_name = training_args.hub_model_id
-        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)
+        # Retrieve of infer repo_name
+        repo_name = training_args.hub_model_id
+        if repo_name is None:
+            repo_name = Path(training_args.output_dir).absolute().name
+        # Create repo and retrieve repo_id
+        repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
+        # Clone repo locally
+        repo = Repository(training_args.output_dir, clone_from=repo_id, 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/
diff --git a/examples/flax/language-modeling/run_t5_mlm_flax.py b/examples/flax/language-modeling/run_t5_mlm_flax.py
index f3cec97b2e..89e0b9e987 100755
--- a/examples/flax/language-modeling/run_t5_mlm_flax.py
+++ b/examples/flax/language-modeling/run_t5_mlm_flax.py
@@ -59,7 +59,7 @@ from transformers import (
     set_seed,
 )
 from transformers.models.t5.modeling_flax_t5 import shift_tokens_right
-from transformers.utils import get_full_repo_name, send_example_telemetry
+from transformers.utils import send_example_telemetry
 
 
 MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys())
@@ -537,14 +537,14 @@ def main():
 
     # Handle the repository creation
     if training_args.push_to_hub:
-        if training_args.hub_model_id is None:
-            repo_name = get_full_repo_name(
-                Path(training_args.output_dir).absolute().name, token=training_args.hub_token
-            )
-        else:
-            repo_name = training_args.hub_model_id
-        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)
+        # Retrieve of infer repo_name
+        repo_name = training_args.hub_model_id
+        if repo_name is None:
+            repo_name = Path(training_args.output_dir).absolute().name
+        # Create repo and retrieve repo_id
+        repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
+        # Clone repo locally
+        repo = Repository(training_args.output_dir, clone_from=repo_id, 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/
diff --git a/examples/flax/question-answering/run_qa.py b/examples/flax/question-answering/run_qa.py
index 48ed6173c7..29e1de744c 100644
--- a/examples/flax/question-answering/run_qa.py
+++ b/examples/flax/question-answering/run_qa.py
@@ -55,7 +55,7 @@ from transformers import (
     PreTrainedTokenizerFast,
     is_tensorboard_available,
 )
-from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
+from transformers.utils import check_min_version, send_example_telemetry
 
 
 logger = logging.getLogger(__name__)
@@ -462,14 +462,14 @@ def main():
 
     # Handle the repository creation
     if training_args.push_to_hub:
-        if training_args.hub_model_id is None:
-            repo_name = get_full_repo_name(
-                Path(training_args.output_dir).absolute().name, token=training_args.hub_token
-            )
-        else:
-            repo_name = training_args.hub_model_id
-        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)
+        # Retrieve of infer repo_name
+        repo_name = training_args.hub_model_id
+        if repo_name is None:
+            repo_name = Path(training_args.output_dir).absolute().name
+        # Create repo and retrieve repo_id
+        repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
+        # Clone repo locally
+        repo = Repository(training_args.output_dir, clone_from=repo_id, 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)
diff --git a/examples/flax/summarization/run_summarization_flax.py b/examples/flax/summarization/run_summarization_flax.py
index 2d7e0acbf5..fc7be6edbf 100644
--- a/examples/flax/summarization/run_summarization_flax.py
+++ b/examples/flax/summarization/run_summarization_flax.py
@@ -56,7 +56,7 @@ from transformers import (
     HfArgumentParser,
     is_tensorboard_available,
 )
-from transformers.utils import get_full_repo_name, is_offline_mode, send_example_telemetry
+from transformers.utils import is_offline_mode, send_example_telemetry
 
 
 logger = logging.getLogger(__name__)
@@ -452,14 +452,14 @@ def main():
 
     # Handle the repository creation
     if training_args.push_to_hub:
-        if training_args.hub_model_id is None:
-            repo_name = get_full_repo_name(
-                Path(training_args.output_dir).absolute().name, token=training_args.hub_token
-            )
-        else:
-            repo_name = training_args.hub_model_id
-        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)
+        # Retrieve of infer repo_name
+        repo_name = training_args.hub_model_id
+        if repo_name is None:
+            repo_name = Path(training_args.output_dir).absolute().name
+        # Create repo and retrieve repo_id
+        repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
+        # Clone repo locally
+        repo = Repository(training_args.output_dir, clone_from=repo_id, 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/
diff --git a/examples/flax/text-classification/run_flax_glue.py b/examples/flax/text-classification/run_flax_glue.py
index 1e6c6c1b0f..8985c3a2f7 100755
--- a/examples/flax/text-classification/run_flax_glue.py
+++ b/examples/flax/text-classification/run_flax_glue.py
@@ -49,7 +49,7 @@ from transformers import (
     TrainingArguments,
     is_tensorboard_available,
 )
-from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
+from transformers.utils import check_min_version, send_example_telemetry
 
 
 logger = logging.getLogger(__name__)
@@ -342,14 +342,14 @@ def main():
 
     # Handle the repository creation
     if training_args.push_to_hub:
-        if training_args.hub_model_id is None:
-            repo_name = get_full_repo_name(
-                Path(training_args.output_dir).absolute().name, token=training_args.hub_token
-            )
-        else:
-            repo_name = training_args.hub_model_id
-        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)
+        # Retrieve of infer repo_name
+        repo_name = training_args.hub_model_id
+        if repo_name is None:
+            repo_name = Path(training_args.output_dir).absolute().name
+        # Create repo and retrieve repo_id
+        repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
+        # Clone repo locally
+        repo = Repository(training_args.output_dir, clone_from=repo_id, 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).
diff --git a/examples/flax/token-classification/run_flax_ner.py b/examples/flax/token-classification/run_flax_ner.py
index 6f773e2f8c..8d7f263f5a 100644
--- a/examples/flax/token-classification/run_flax_ner.py
+++ b/examples/flax/token-classification/run_flax_ner.py
@@ -49,7 +49,7 @@ from transformers import (
     HfArgumentParser,
     is_tensorboard_available,
 )
-from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
+from transformers.utils import check_min_version, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
@@ -398,14 +398,14 @@ def main():
 
     # Handle the repository creation
     if training_args.push_to_hub:
-        if training_args.hub_model_id is None:
-            repo_name = get_full_repo_name(
-                Path(training_args.output_dir).absolute().name, token=training_args.hub_token
-            )
-        else:
-            repo_name = training_args.hub_model_id
-        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)
+        # Retrieve of infer repo_name
+        repo_name = training_args.hub_model_id
+        if repo_name is None:
+            repo_name = Path(training_args.output_dir).absolute().name
+        # Create repo and retrieve repo_id
+        repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
+        # Clone repo locally
+        repo = Repository(training_args.output_dir, clone_from=repo_id, 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/
diff --git a/examples/flax/vision/run_image_classification.py b/examples/flax/vision/run_image_classification.py
index 6a88f0f8d6..49e147e310 100644
--- a/examples/flax/vision/run_image_classification.py
+++ b/examples/flax/vision/run_image_classification.py
@@ -54,7 +54,7 @@ from transformers import (
     is_tensorboard_available,
     set_seed,
 )
-from transformers.utils import get_full_repo_name, send_example_telemetry
+from transformers.utils import send_example_telemetry
 
 
 logger = logging.getLogger(__name__)
@@ -293,14 +293,14 @@ def main():
 
     # Handle the repository creation
     if training_args.push_to_hub:
-        if training_args.hub_model_id is None:
-            repo_name = get_full_repo_name(
-                Path(training_args.output_dir).absolute().name, token=training_args.hub_token
-            )
-        else:
-            repo_name = training_args.hub_model_id
-        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)
+        # Retrieve of infer repo_name
+        repo_name = training_args.hub_model_id
+        if repo_name is None:
+            repo_name = Path(training_args.output_dir).absolute().name
+        # Create repo and retrieve repo_id
+        repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
+        # Clone repo locally
+        repo = Repository(training_args.output_dir, clone_from=repo_id, token=training_args.hub_token)
 
     # Initialize datasets and pre-processing transforms
     # We use torchvision here for faster pre-processing
diff --git a/examples/pytorch/image-classification/run_image_classification_no_trainer.py b/examples/pytorch/image-classification/run_image_classification_no_trainer.py
index 27b4bee144..c416273132 100644
--- a/examples/pytorch/image-classification/run_image_classification_no_trainer.py
+++ b/examples/pytorch/image-classification/run_image_classification_no_trainer.py
@@ -42,7 +42,7 @@ from tqdm.auto import tqdm
 
 import transformers
 from transformers import AutoConfig, AutoImageProcessor, AutoModelForImageClassification, SchedulerType, get_scheduler
-from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
+from transformers.utils import check_min_version, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
@@ -236,12 +236,14 @@ def main():
     # Handle the repository creation
     if accelerator.is_main_process:
         if args.push_to_hub:
-            if args.hub_model_id is None:
-                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
-            else:
-                repo_name = args.hub_model_id
-            create_repo(repo_name, exist_ok=True, token=args.hub_token)
-            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
+            # Retrieve of infer repo_name
+            repo_name = args.hub_model_id
+            if repo_name is None:
+                repo_name = Path(args.output_dir).absolute().name
+            # Create repo and retrieve repo_id
+            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
+            # Clone repo locally
+            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:
diff --git a/examples/pytorch/image-pretraining/run_mim_no_trainer.py b/examples/pytorch/image-pretraining/run_mim_no_trainer.py
index 5f83fd2da8..f8c2cdbd3c 100644
--- a/examples/pytorch/image-pretraining/run_mim_no_trainer.py
+++ b/examples/pytorch/image-pretraining/run_mim_no_trainer.py
@@ -25,7 +25,7 @@ import torch
 from accelerate import Accelerator, DistributedType
 from accelerate.utils import set_seed
 from datasets import load_dataset
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from torch.utils.data import DataLoader
 from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
 from tqdm.auto import tqdm
@@ -41,7 +41,7 @@ from transformers import (
     SchedulerType,
     get_scheduler,
 )
-from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
+from transformers.utils import check_min_version, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
@@ -406,11 +406,14 @@ def main():
     # Handle the repository creation
     if accelerator.is_main_process:
         if args.push_to_hub:
-            if args.hub_model_id is None:
-                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)
+            # Retrieve of infer repo_name
+            repo_name = args.hub_model_id
+            if repo_name is None:
+                repo_name = Path(args.output_dir).absolute().name
+            # Create repo and retrieve repo_id
+            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
+            # Clone repo locally
+            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:
diff --git a/examples/pytorch/language-modeling/run_clm_no_trainer.py b/examples/pytorch/language-modeling/run_clm_no_trainer.py
index dc619acb9a..58954bd853 100755
--- a/examples/pytorch/language-modeling/run_clm_no_trainer.py
+++ b/examples/pytorch/language-modeling/run_clm_no_trainer.py
@@ -52,7 +52,7 @@ from transformers import (
     default_data_collator,
     get_scheduler,
 )
-from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
+from transformers.utils import check_min_version, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
@@ -286,12 +286,14 @@ def main():
     # Handle the repository creation
     if accelerator.is_main_process:
         if args.push_to_hub:
-            if args.hub_model_id is None:
-                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
-            else:
-                repo_name = args.hub_model_id
-            create_repo(repo_name, exist_ok=True, token=args.hub_token)
-            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
+            # Retrieve of infer repo_name
+            repo_name = args.hub_model_id
+            if repo_name is None:
+                repo_name = Path(args.output_dir).absolute().name
+            # Create repo and retrieve repo_id
+            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
+            # Clone repo locally
+            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:
diff --git a/examples/pytorch/language-modeling/run_mlm_no_trainer.py b/examples/pytorch/language-modeling/run_mlm_no_trainer.py
index b57a7d331c..af1e8d5209 100755
--- a/examples/pytorch/language-modeling/run_mlm_no_trainer.py
+++ b/examples/pytorch/language-modeling/run_mlm_no_trainer.py
@@ -52,7 +52,7 @@ from transformers import (
     SchedulerType,
     get_scheduler,
 )
-from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
+from transformers.utils import check_min_version, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
@@ -295,12 +295,14 @@ def main():
     # Handle the repository creation
     if accelerator.is_main_process:
         if args.push_to_hub:
-            if args.hub_model_id is None:
-                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
-            else:
-                repo_name = args.hub_model_id
-            create_repo(repo_name, exist_ok=True, token=args.hub_token)
-            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
+            # Retrieve of infer repo_name
+            repo_name = args.hub_model_id
+            if repo_name is None:
+                repo_name = Path(args.output_dir).absolute().name
+            # Create repo and retrieve repo_id
+            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
+            # Clone repo locally
+            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:
diff --git a/examples/pytorch/multiple-choice/run_swag_no_trainer.py b/examples/pytorch/multiple-choice/run_swag_no_trainer.py
index 7f5969934e..26ae2d4944 100755
--- a/examples/pytorch/multiple-choice/run_swag_no_trainer.py
+++ b/examples/pytorch/multiple-choice/run_swag_no_trainer.py
@@ -52,7 +52,7 @@ from transformers import (
     default_data_collator,
     get_scheduler,
 )
-from transformers.utils import PaddingStrategy, check_min_version, get_full_repo_name, send_example_telemetry
+from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
@@ -313,12 +313,14 @@ def main():
     # Handle the repository creation
     if accelerator.is_main_process:
         if args.push_to_hub:
-            if args.hub_model_id is None:
-                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
-            else:
-                repo_name = args.hub_model_id
-            create_repo(repo_name, exist_ok=True, token=args.hub_token)
-            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
+            # Retrieve of infer repo_name
+            repo_name = args.hub_model_id
+            if repo_name is None:
+                repo_name = Path(args.output_dir).absolute().name
+            # Create repo and retrieve repo_id
+            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
+            # Clone repo locally
+            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:
diff --git a/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py b/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py
index 79aaa22cdb..6d00455059 100644
--- a/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py
+++ b/examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py
@@ -51,7 +51,7 @@ from transformers import (
     default_data_collator,
     get_scheduler,
 )
-from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
+from transformers.utils import check_min_version, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
@@ -328,12 +328,14 @@ def main():
     # Handle the repository creation
     if accelerator.is_main_process:
         if args.push_to_hub:
-            if args.hub_model_id is None:
-                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
-            else:
-                repo_name = args.hub_model_id
-            create_repo(repo_name, exist_ok=True, token=args.hub_token)
-            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
+            # Retrieve of infer repo_name
+            repo_name = args.hub_model_id
+            if repo_name is None:
+                repo_name = Path(args.output_dir).absolute().name
+            # Create repo and retrieve repo_id
+            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
+            # Clone repo locally
+            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:
diff --git a/examples/pytorch/question-answering/run_qa_no_trainer.py b/examples/pytorch/question-answering/run_qa_no_trainer.py
index 7f13c250e7..2a7cdc7273 100755
--- a/examples/pytorch/question-answering/run_qa_no_trainer.py
+++ b/examples/pytorch/question-answering/run_qa_no_trainer.py
@@ -52,7 +52,7 @@ from transformers import (
     default_data_collator,
     get_scheduler,
 )
-from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
+from transformers.utils import check_min_version, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
@@ -366,12 +366,14 @@ def main():
     # Handle the repository creation
     if accelerator.is_main_process:
         if args.push_to_hub:
-            if args.hub_model_id is None:
-                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
-            else:
-                repo_name = args.hub_model_id
-            create_repo(repo_name, exist_ok=True, token=args.hub_token)
-            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
+            # Retrieve of infer repo_name
+            repo_name = args.hub_model_id
+            if repo_name is None:
+                repo_name = Path(args.output_dir).absolute().name
+            # Create repo and retrieve repo_id
+            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
+            # Clone repo locally
+            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:
diff --git a/examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py b/examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
index bcad655727..af9880901c 100644
--- a/examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
+++ b/examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
@@ -45,7 +45,7 @@ from transformers import (
     default_data_collator,
     get_scheduler,
 )
-from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
+from transformers.utils import check_min_version, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
@@ -350,12 +350,14 @@ def main():
     # Handle the repository creation
     if accelerator.is_main_process:
         if args.push_to_hub:
-            if args.hub_model_id is None:
-                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
-            else:
-                repo_name = args.hub_model_id
-            create_repo(repo_name, exist_ok=True, token=args.hub_token)
-            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
+            # Retrieve of infer repo_name
+            repo_name = args.hub_model_id
+            if repo_name is None:
+                repo_name = Path(args.output_dir).absolute().name
+            # Create repo and retrieve repo_id
+            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
+            # Clone repo locally
+            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:
diff --git a/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py b/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py
index 603202e696..6bde6d2b7d 100755
--- a/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py
+++ b/examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py
@@ -43,7 +43,7 @@ from transformers import (
     set_seed,
 )
 from transformers.models.wav2vec2.modeling_wav2vec2 import _compute_mask_indices, _sample_negative_indices
-from transformers.utils import get_full_repo_name, send_example_telemetry
+from transformers.utils import send_example_telemetry
 
 
 logger = get_logger(__name__)
@@ -418,12 +418,14 @@ def main():
     # Handle the repository creation
     if accelerator.is_main_process:
         if args.push_to_hub and not args.preprocessing_only:
-            if args.hub_model_id is None:
-                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
-            else:
-                repo_name = args.hub_model_id
-            create_repo(repo_name, exist_ok=True, token=args.hub_token)
-            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
+            # Retrieve of infer repo_name
+            repo_name = args.hub_model_id
+            if repo_name is None:
+                repo_name = Path(args.output_dir).absolute().name
+            # Create repo and retrieve repo_id
+            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
+            # Clone repo locally
+            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
         elif args.output_dir is not None:
             os.makedirs(args.output_dir, exist_ok=True)
     accelerator.wait_for_everyone()
diff --git a/examples/pytorch/summarization/run_summarization_no_trainer.py b/examples/pytorch/summarization/run_summarization_no_trainer.py
index bfefbee250..543b6bfaa2 100644
--- a/examples/pytorch/summarization/run_summarization_no_trainer.py
+++ b/examples/pytorch/summarization/run_summarization_no_trainer.py
@@ -51,7 +51,7 @@ from transformers import (
     SchedulerType,
     get_scheduler,
 )
-from transformers.utils import check_min_version, get_full_repo_name, is_offline_mode, send_example_telemetry
+from transformers.utils import check_min_version, is_offline_mode, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
@@ -360,12 +360,14 @@ def main():
     # Handle the repository creation
     if accelerator.is_main_process:
         if args.push_to_hub:
-            if args.hub_model_id is None:
-                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
-            else:
-                repo_name = args.hub_model_id
-            create_repo(repo_name, exist_ok=True, token=args.hub_token)
-            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
+            # Retrieve of infer repo_name
+            repo_name = args.hub_model_id
+            if repo_name is None:
+                repo_name = Path(args.output_dir).absolute().name
+            # Create repo and retrieve repo_id
+            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
+            # Clone repo locally
+            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:
diff --git a/examples/pytorch/text-classification/run_glue_no_trainer.py b/examples/pytorch/text-classification/run_glue_no_trainer.py
index a21a698ec9..e137f87638 100644
--- a/examples/pytorch/text-classification/run_glue_no_trainer.py
+++ b/examples/pytorch/text-classification/run_glue_no_trainer.py
@@ -43,7 +43,7 @@ from transformers import (
     default_data_collator,
     get_scheduler,
 )
-from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
+from transformers.utils import check_min_version, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
@@ -240,12 +240,14 @@ def main():
     # Handle the repository creation
     if accelerator.is_main_process:
         if args.push_to_hub:
-            if args.hub_model_id is None:
-                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
-            else:
-                repo_name = args.hub_model_id
-            create_repo(repo_name, exist_ok=True, token=args.hub_token)
-            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
+            # Retrieve of infer repo_name
+            repo_name = args.hub_model_id
+            if repo_name is None:
+                repo_name = Path(args.output_dir).absolute().name
+            # Create repo and retrieve repo_id
+            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
+            # Clone repo locally
+            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:
diff --git a/examples/pytorch/token-classification/run_ner_no_trainer.py b/examples/pytorch/token-classification/run_ner_no_trainer.py
index 8ad0b6d7e4..dc57b514e7 100755
--- a/examples/pytorch/token-classification/run_ner_no_trainer.py
+++ b/examples/pytorch/token-classification/run_ner_no_trainer.py
@@ -51,7 +51,7 @@ from transformers import (
     default_data_collator,
     get_scheduler,
 )
-from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
+from transformers.utils import check_min_version, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
@@ -295,12 +295,14 @@ def main():
     # Handle the repository creation
     if accelerator.is_main_process:
         if args.push_to_hub:
-            if args.hub_model_id is None:
-                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
-            else:
-                repo_name = args.hub_model_id
-            create_repo(repo_name, exist_ok=True, token=args.hub_token)
-            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
+            # Retrieve of infer repo_name
+            repo_name = args.hub_model_id
+            if repo_name is None:
+                repo_name = Path(args.output_dir).absolute().name
+            # Create repo and retrieve repo_id
+            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
+            # Clone repo locally
+            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:
diff --git a/examples/pytorch/translation/run_translation_no_trainer.py b/examples/pytorch/translation/run_translation_no_trainer.py
index 70f831620d..77efa14f4f 100644
--- a/examples/pytorch/translation/run_translation_no_trainer.py
+++ b/examples/pytorch/translation/run_translation_no_trainer.py
@@ -52,7 +52,7 @@ from transformers import (
     default_data_collator,
     get_scheduler,
 )
-from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
+from transformers.utils import check_min_version, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
@@ -340,12 +340,14 @@ def main():
     # Handle the repository creation
     if accelerator.is_main_process:
         if args.push_to_hub:
-            if args.hub_model_id is None:
-                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
-            else:
-                repo_name = args.hub_model_id
-            create_repo(repo_name, exist_ok=True, token=args.hub_token)
-            repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
+            # Retrieve of infer repo_name
+            repo_name = args.hub_model_id
+            if repo_name is None:
+                repo_name = Path(args.output_dir).absolute().name
+            # Create repo and retrieve repo_id
+            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
+            # Clone repo locally
+            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
 
             with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:
diff --git a/examples/research_projects/luke/run_luke_ner_no_trainer.py b/examples/research_projects/luke/run_luke_ner_no_trainer.py
index 6b59643cf7..f12a7d76d8 100644
--- a/examples/research_projects/luke/run_luke_ner_no_trainer.py
+++ b/examples/research_projects/luke/run_luke_ner_no_trainer.py
@@ -29,7 +29,7 @@ import datasets
 import torch
 from accelerate import Accelerator, DistributedDataParallelKwargs
 from datasets import ClassLabel, load_dataset, load_metric
-from huggingface_hub import Repository
+from huggingface_hub import Repository, create_repo
 from luke_utils import DataCollatorForLukeTokenClassification, is_punctuation, padding_tensor
 from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
@@ -45,7 +45,6 @@ from transformers import (
     get_scheduler,
     set_seed,
 )
-from transformers.file_utils import get_full_repo_name
 from transformers.utils.versions import require_version
 
 
@@ -258,11 +257,14 @@ def main():
     # Handle the repository creation
     if accelerator.is_main_process:
         if args.push_to_hub:
-            if args.hub_model_id is None:
-                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)
+            # Retrieve of infer repo_name
+            repo_name = args.hub_model_id
+            if repo_name is None:
+                repo_name = Path(args.output_dir).absolute().name
+            # Create repo and retrieve repo_id
+            repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
+            # Clone repo locally
+            repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
         elif args.output_dir is not None:
             os.makedirs(args.output_dir, exist_ok=True)
     accelerator.wait_for_everyone()
diff --git a/src/transformers/file_utils.py b/src/transformers/file_utils.py
index 921974634d..d710296fc0 100644
--- a/src/transformers/file_utils.py
+++ b/src/transformers/file_utils.py
@@ -17,6 +17,8 @@ File utilities: utilities related to download and cache models
 This module should not be update anymore and is only left for backward compatibility.
 """
 
+from huggingface_hub import get_full_repo_name  # for backward compatibility
+
 from . import __version__
 
 # Backward compatibility imports, to make sure all those objects can be found in file_utils
@@ -71,7 +73,6 @@ from .utils import (
     define_sagemaker_information,
     get_cached_models,
     get_file_from_repo,
-    get_full_repo_name,
     get_torch_version,
     has_file,
     http_user_agent,
diff --git a/src/transformers/keras_callbacks.py b/src/transformers/keras_callbacks.py
index 15b4edf07d..3bb4e859b1 100644
--- a/src/transformers/keras_callbacks.py
+++ b/src/transformers/keras_callbacks.py
@@ -12,7 +12,6 @@ from tensorflow.keras.callbacks import Callback
 
 from . import IntervalStrategy, PreTrainedTokenizerBase
 from .modelcard import TrainingSummary
-from .utils import get_full_repo_name
 
 
 logger = logging.getLogger(__name__)
@@ -334,14 +333,13 @@ class PushToHubCallback(Callback):
             raise ValueError("Please supply a positive integer argument for save_steps when save_strategy == 'steps'!")
         self.save_steps = save_steps
         output_dir = Path(output_dir)
+
+        # Create repo and retrieve repo_id
         if hub_model_id is None:
             hub_model_id = output_dir.absolute().name
-        if "/" not in hub_model_id:
-            hub_model_id = get_full_repo_name(hub_model_id, token=hub_token)
+        self.hub_model_id = create_repo(repo_id=hub_model_id, exist_ok=True, token=hub_token).repo_id
 
         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, token=hub_token)
 
         self.tokenizer = tokenizer
diff --git a/src/transformers/modeling_tf_utils.py b/src/transformers/modeling_tf_utils.py
index 9548b1d67f..ebdafdbe87 100644
--- a/src/transformers/modeling_tf_utils.py
+++ b/src/transformers/modeling_tf_utils.py
@@ -1357,21 +1357,16 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
                 "Checkpoint loading failed as no optimizer is attached to the model. "
                 "This is most likely caused by the model not being compiled."
             )
-        if not os.path.isdir(repo_path_or_name):
+        if os.path.isdir(repo_path_or_name):
+            local_dir = repo_path_or_name
+        else:
             # If this isn't a local path, check that the remote repo exists and has a checkpoint in it
             repo_files = list_repo_files(repo_path_or_name)
             for file in ("checkpoint/weights.h5", "checkpoint/extra_data.pickle"):
                 if file not in repo_files:
                     raise FileNotFoundError(f"Repo {repo_path_or_name} does not contain checkpoint file {file}!")
-            if "/" not in repo_path_or_name:
-                model_id = repo_path_or_name
-                repo_path_or_name = self.get_full_repo_name(repo_path_or_name)
-            else:
-                model_id = repo_path_or_name.split("/")[-1]
-            repo = Repository(model_id, clone_from=f"https://huggingface.co/{repo_path_or_name}")
+            repo = Repository(repo_path_or_name.split("/")[-1], clone_from=repo_path_or_name)
             local_dir = repo.local_dir
-        else:
-            local_dir = repo_path_or_name
 
         # Now make sure the repo actually has a checkpoint in it.
         checkpoint_dir = os.path.join(local_dir, "checkpoint")
diff --git a/src/transformers/trainer.py b/src/transformers/trainer.py
index c4a64997b4..319c36e7c8 100755
--- a/src/transformers/trainer.py
+++ b/src/transformers/trainer.py
@@ -129,7 +129,6 @@ from .utils import (
     WEIGHTS_NAME,
     can_return_loss,
     find_labels,
-    get_full_repo_name,
     is_accelerate_available,
     is_apex_available,
     is_datasets_available,
@@ -3396,22 +3395,22 @@ class Trainer:
         """
         if not self.is_world_process_zero():
             return
-        if self.args.hub_model_id is None:
-            repo_name = Path(self.args.output_dir).absolute().name
-        else:
-            repo_name = self.args.hub_model_id
-        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)
+        # Make sure the repo exists + retrieve "real" repo_id
+        repo_name = self.args.hub_model_id
+        if repo_name is None:
+            repo_name = Path(self.args.output_dir).absolute().name
+        repo_id = create_repo(
+            repo_id=repo_name, token=self.args.hub_token, private=self.args.hub_private_repo, exist_ok=True
+        ).repo_id
+
         try:
-            self.repo = Repository(self.args.output_dir, clone_from=repo_name, token=self.args.hub_token)
+            self.repo = Repository(self.args.output_dir, clone_from=repo_id, 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, token=self.args.hub_token)
+                self.repo = Repository(self.args.output_dir, clone_from=repo_id, token=self.args.hub_token)
             else:
                 raise
 
diff --git a/src/transformers/training_args.py b/src/transformers/training_args.py
index b6e35b73b9..8189c22fe5 100644
--- a/src/transformers/training_args.py
+++ b/src/transformers/training_args.py
@@ -24,6 +24,7 @@ from enum import Enum
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
 
+from huggingface_hub import get_full_repo_name
 from packaging import version
 
 from .debug_utils import DebugOption
@@ -38,7 +39,6 @@ from .trainer_utils import (
 from .utils import (
     ExplicitEnum,
     cached_property,
-    get_full_repo_name,
     is_accelerate_available,
     is_safetensors_available,
     is_sagemaker_dp_enabled,
diff --git a/src/transformers/utils/__init__.py b/src/transformers/utils/__init__.py
index bca5440f8e..3a5dea4616 100644
--- a/src/transformers/utils/__init__.py
+++ b/src/transformers/utils/__init__.py
@@ -15,6 +15,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from huggingface_hub import get_full_repo_name  # for backward compatibility
 from packaging import version
 
 from .. import __version__
@@ -79,7 +80,6 @@ from .hub import (
     extract_commit_hash,
     get_cached_models,
     get_file_from_repo,
-    get_full_repo_name,
     has_file,
     http_user_agent,
     is_offline_mode,
diff --git a/src/transformers/utils/hub.py b/src/transformers/utils/hub.py
index 1634abe6ea..9fba97f098 100644
--- a/src/transformers/utils/hub.py
+++ b/src/transformers/utils/hub.py
@@ -36,7 +36,6 @@ from huggingface_hub import (
     get_hf_file_metadata,
     hf_hub_download,
     hf_hub_url,
-    whoami,
 )
 from huggingface_hub.file_download import REGEX_COMMIT_HASH, http_get
 from huggingface_hub.utils import (
@@ -690,6 +689,10 @@ class PushToHubMixin:
                 "The `repo_url` argument is deprecated and will be removed in v5 of Transformers. Use `repo_id` "
                 "instead."
             )
+            if repo_id is not None:
+                raise ValueError(
+                    "`repo_id` and `repo_url` are both specified. Please set only the argument `repo_id`."
+                )
             repo_id = repo_url.replace(f"{HUGGINGFACE_CO_RESOLVE_ENDPOINT}/", "")
         if organization is not None:
             warnings.warn(
@@ -702,11 +705,7 @@ class PushToHubMixin:
                 repo_id = f"{organization}/{repo_id}"
 
         url = create_repo(repo_id=repo_id, token=token, private=private, exist_ok=True)
-
-        # If the namespace is not there, add it or `upload_file` will complain
-        if "/" not in repo_id and url != f"{HUGGINGFACE_CO_RESOLVE_ENDPOINT}/{repo_id}":
-            repo_id = get_full_repo_name(repo_id, token=token)
-        return repo_id
+        return url.repo_id
 
     def _get_files_timestamps(self, working_dir: Union[str, os.PathLike]):
         """
@@ -786,8 +785,7 @@ class PushToHubMixin:
         **deprecated_kwargs,
     ) -> str:
         """
-        Upload the {object_files} to the 🤗 Model Hub while synchronizing a local clone of the repo in
-        `repo_path_or_name`.
+        Upload the {object_files} to the 🤗 Model Hub.
 
         Parameters:
             repo_id (`str`):
@@ -838,22 +836,35 @@ class PushToHubMixin:
                 )
             token = use_auth_token
 
-        if "repo_path_or_name" in deprecated_kwargs:
+        repo_path_or_name = deprecated_kwargs.pop("repo_path_or_name", None)
+        if repo_path_or_name is not None:
+            # Should use `repo_id` instead of `repo_path_or_name`. When using `repo_path_or_name`, we try to infer
+            # repo_id from the folder path, if it exists.
             warnings.warn(
                 "The `repo_path_or_name` argument is deprecated and will be removed in v5 of Transformers. Use "
-                "`repo_id` instead."
+                "`repo_id` instead.",
+                FutureWarning,
             )
-            repo_id = deprecated_kwargs.pop("repo_path_or_name")
+            if repo_id is not None:
+                raise ValueError(
+                    "`repo_id` and `repo_path_or_name` are both specified. Please set only the argument `repo_id`."
+                )
+            if os.path.isdir(repo_path_or_name):
+                # repo_path: infer repo_id from the path
+                repo_id = repo_id.split(os.path.sep)[-1]
+                working_dir = repo_id
+            else:
+                # repo_name: use it as repo_id
+                repo_id = repo_path_or_name
+                working_dir = repo_id.split("/")[-1]
+        else:
+            # Repo_id is passed correctly: infer working_dir from it
+            working_dir = repo_id.split("/")[-1]
+
         # Deprecation warning will be sent after for repo_url and organization
         repo_url = deprecated_kwargs.pop("repo_url", None)
         organization = deprecated_kwargs.pop("organization", None)
 
-        if os.path.isdir(repo_id):
-            working_dir = repo_id
-            repo_id = repo_id.split(os.path.sep)[-1]
-        else:
-            working_dir = repo_id.split("/")[-1]
-
         repo_id = self._create_repo(
             repo_id, private=private, token=token, repo_url=repo_url, organization=organization
         )
@@ -877,14 +888,6 @@ class PushToHubMixin:
             )
 
 
-def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
-    if organization is None:
-        username = whoami(token)["name"]
-        return f"{username}/{model_id}"
-    else:
-        return f"{organization}/{model_id}"
-
-
 def send_example_telemetry(example_name, *example_args, framework="pytorch"):
     """
     Sends telemetry that helps tracking the examples use.