[examples/flax] use Repository API for push_to_hub (#13672)

* use Repository for push_to_hub

* update readme

* update other flax scripts

* update readme

* update qa example

* fix push_to_hub call

* fix typo

* fix more typos

* update readme

* use abosolute path to get repo name

* fix glue script

examples/flax/language-modeling/run_mlm_flax.py

@@ -41,6 +41,7 @@ import optax
 from flax import jax_utils, traverse_util
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
+from huggingface_hub import Repository
 from transformers import (
     CONFIG_MAPPING,
     FLAX_MODEL_FOR_MASKED_LM_MAPPING,
@@ -54,6 +55,7 @@ from transformers import (
     is_tensorboard_available,
     set_seed,
 )
+from transformers.file_utils import get_full_repo_name
 
 
 MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys())
@@ -308,6 +310,16 @@ if __name__ == "__main__":
     # Set seed before initializing model.
     set_seed(training_args.seed)
 
+    # 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
+        repo = Repository(training_args.output_dir, clone_from=repo_name)
+
     # 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/
     # (the dataset will be downloaded automatically from the datasets Hub).
@@ -683,9 +695,7 @@ if __name__ == "__main__":
                 # save checkpoint after each epoch and push checkpoint to the hub
                 if jax.process_index() == 0:
                     params = jax.device_get(jax.tree_map(lambda x: x[0], state.params))
-                    model.save_pretrained(
-                        training_args.output_dir,
-                        params=params,
-                        push_to_hub=training_args.push_to_hub,
-                        commit_message=f"Saving weights and logs of step {cur_step}",
-                    )
+                    model.save_pretrained(training_args.output_dir, params=params)
+                    tokenizer.save_pretrained(training_args.output_dir)
+                    if training_args.push_to_hub:
+                        repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False)