Clean push to hub API (#12187)

* Clean push to hub API

* Create working dir if it does not exist

* Different tweak

* New API + all models + test Flax

* Adds the Trainer clean up

* Update src/transformers/file_utils.py

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Address review comments

* (nit) output types

* No need to set clone_from when folder exists

* Update src/transformers/trainer.py

Co-authored-by: Julien Chaumond <julien@huggingface.co>

* Add generated_from_trainer tag

* Update to new version

* Fixes

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Julien Chaumond <julien@huggingface.co>
Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>

setup.py

@@ -100,7 +100,7 @@ _deps = [
     "flake8>=3.8.3",
     "flax>=0.3.4",
     "fugashi>=1.0",
-    "huggingface-hub==0.0.8",
+    "huggingface-hub==0.0.11",
     "importlib_metadata",
     "ipadic>=1.0.0,<2.0",
     "isort>=5.5.4",

src/transformers/configuration_utils.py

@@ -337,12 +337,25 @@ class PretrainedConfig(PushToHubMixin):
                 Directory where the configuration JSON file will be saved (will be created if it does not exist).
             push_to_hub (:obj:`bool`, `optional`, defaults to :obj:`False`):
                 Whether or not to push your model to the Hugging Face model hub after saving it.
+
+                .. warning::
+
+                    Using :obj:`push_to_hub=True` will synchronize the repository you are pushing to with
+                    :obj:`save_directory`, which requires :obj:`save_directory` to be a local clone of the repo you are
+                    pushing to if it's an existing folder. Pass along :obj:`temp_dir=True` to use a temporary directory
+                    instead.
+
             kwargs:
                 Additional key word arguments passed along to the
                 :meth:`~transformers.file_utils.PushToHubMixin.push_to_hub` method.
         """
         if os.path.isfile(save_directory):
             raise AssertionError(f"Provided path ({save_directory}) should be a directory, not a file")
+
+        if push_to_hub:
+            commit_message = kwargs.pop("commit_message", None)
+            repo = self._create_or_get_repo(save_directory, **kwargs)
+
         os.makedirs(save_directory, exist_ok=True)
         # If we save using the predefined names, we can load using `from_pretrained`
         output_config_file = os.path.join(save_directory, CONFIG_NAME)
@@ -351,7 +364,7 @@ class PretrainedConfig(PushToHubMixin):
         logger.info(f"Configuration saved in {output_config_file}")
 
         if push_to_hub:
-            url = self._push_to_hub(save_files=[output_config_file], **kwargs)
+            url = self._push_to_hub(repo, commit_message=commit_message)
             logger.info(f"Configuration pushed to the hub in this commit: {url}")
 
     @classmethod

src/transformers/dependency_versions_table.py

@@ -17,7 +17,7 @@ deps = {
     "flake8": "flake8>=3.8.3",
     "flax": "flax>=0.3.4",
     "fugashi": "fugashi>=1.0",
-    "huggingface-hub": "huggingface-hub==0.0.8",
+    "huggingface-hub": "huggingface-hub==0.0.11",
     "importlib_metadata": "importlib_metadata",
     "ipadic": "ipadic>=1.0.0,<2.0",
     "isort": "isort>=5.5.4",

src/transformers/file_utils.py

@@ -24,6 +24,7 @@ import json
 import os
 import re
 import shutil
+import subprocess
 import sys
 import tarfile
 import tempfile
@@ -31,7 +32,6 @@ import types
 from collections import OrderedDict, UserDict
 from contextlib import contextmanager
 from dataclasses import fields
-from distutils.dir_util import copy_tree
 from enum import Enum
 from functools import partial, wraps
 from hashlib import sha256
@@ -1907,6 +1907,30 @@ def copy_func(f):
     return g
 
 
+def is_local_clone(repo_path, repo_url):
+    """
+    Checks if the folder in `repo_path` is a local clone of `repo_url`.
+    """
+    # First double-check that `repo_path` is a git repo
+    if not os.path.exists(os.path.join(repo_path, ".git")):
+        return False
+    test_git = subprocess.run("git branch".split(), cwd=repo_path)
+    if test_git.returncode != 0:
+        return False
+
+    # Then look at its remotes
+    remotes = subprocess.run(
+        "git remote -v".split(),
+        stderr=subprocess.PIPE,
+        stdout=subprocess.PIPE,
+        check=True,
+        encoding="utf-8",
+        cwd=repo_path,
+    ).stdout
+
+    return repo_url in remotes.split()
+
+
 class PushToHubMixin:
     """
     A Mixin containing the functionality to push a model or tokenizer to the hub.
@@ -1914,24 +1938,31 @@ class PushToHubMixin:
 
     def push_to_hub(
         self,
-        repo_name: Optional[str] = None,
+        repo_path_or_name: Optional[str] = None,
         repo_url: Optional[str] = None,
+        use_temp_dir: bool = False,
         commit_message: Optional[str] = None,
         organization: Optional[str] = None,
-        private: bool = None,
+        private: Optional[bool] = None,
         use_auth_token: Optional[Union[bool, str]] = None,
     ) -> str:
         """
-        Upload model checkpoint or tokenizer files to the 🤗 model hub.
+        Upload model checkpoint or tokenizer files to the 🤗 Model Hub while synchronizing a local clone of the repo in
+        :obj:`repo_path_or_name`.
 
         Parameters:
-            repo_name (:obj:`str`, `optional`):
-                Repository name for your model or tokenizer in the hub. If not specified, the repository name will be
-                the stem of :obj:`save_directory`.
+            repo_path_or_name (:obj:`str`, `optional`):
+                Can either be a repository name for your model or tokenizer in the Hub or a path to a local folder (in
+                which case the repository will have the name of that local folder). If not specified, will default to
+                the name given by :obj:`repo_url` and a local directory with that name will be created.
             repo_url (:obj:`str`, `optional`):
                 Specify this in case you want to push to an existing repository in the hub. If unspecified, a new
                 repository will be created in your namespace (unless you specify an :obj:`organization`) with
                 :obj:`repo_name`.
+            use_temp_dir (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Whether or not to clone the distant repo in a temporary directory or in :obj:`repo_path_or_name` inside
+                the current working directory. This will slow things down if you are making changes in an existing repo
+                since you will need to clone the repo before every push.
             commit_message (:obj:`str`, `optional`):
                 Message to commit while pushing. Will default to :obj:`"add config"`, :obj:`"add tokenizer"` or
                 :obj:`"add model"` depending on the type of the class.
@@ -1948,42 +1979,66 @@ class PushToHubMixin:
 
         Returns:
             The url of the commit of your model in the given repository.
-        """
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            self.save_pretrained(tmp_dir)
-            self._push_to_hub(
-                save_directory=tmp_dir,
-                repo_name=repo_name,
-                repo_url=repo_url,
-                commit_message=commit_message,
-                organization=organization,
-                private=private,
-                use_auth_token=use_auth_token,
-            )
 
-    @classmethod
-    def _push_to_hub(
-        cls,
-        save_directory: Optional[str] = None,
-        save_files: Optional[List[str]] = None,
-        repo_name: Optional[str] = None,
-        repo_url: Optional[str] = None,
-        commit_message: Optional[str] = None,
+        Examples::
+
+            # Upload a model to the Hub:
+            from transformers import AutoModel
+
+            model = BertModel.from_pretrained("bert-base-cased")
+            # Fine-tuning code
+
+            # Push the model to your namespace with the name "my-finetuned-bert" and have a local clone in the
+            # `my-finetuned-bert` folder.
+            model.push_to_hub("my-finetuned-bert")
+
+            # Push the model to your namespace with the name "my-finetuned-bert" with no local clone.
+            model.push_to_hub("my-finetuned-bert", use_temp_dir=True)
+
+            # Push the model to an organization with the name "my-finetuned-bert" and have a local clone in the
+            # `my-finetuned-bert` folder.
+            model.push_to_hub("my-finetuned-bert", organization="huggingface")
+
+            # Make a change to an existing repo that has been cloned locally in `my-finetuned-bert`.
+            model.push_to_hub("my-finetuned-bert", repo_url="https://huggingface.co/sgugger/my-finetuned-bert")
+        """
+        if use_temp_dir:
+            # Make sure we use the right `repo_name` for the `repo_url` before replacing it.
+            if repo_url is None:
+                if use_auth_token is None:
+                    use_auth_token = True
+                repo_name = Path(repo_path_or_name).name
+                repo_url = self._get_repo_url_from_name(
+                    repo_name, organization=organization, private=private, use_auth_token=use_auth_token
+                )
+            repo_path_or_name = tempfile.mkdtemp()
+
+        # Create or clone the repo. If the repo is already cloned, this just retrieves the path to the repo.
+        repo = self._create_or_get_repo(
+            repo_path_or_name=repo_path_or_name,
+            repo_url=repo_url,
+            organization=organization,
+            private=private,
+            use_auth_token=use_auth_token,
+        )
+        # Save the files in the cloned repo
+        self.save_pretrained(repo_path_or_name)
+        # Commit and push!
+        url = self._push_to_hub(repo, commit_message=commit_message)
+
+        # Clean up! Clean up! Everybody everywhere!
+        if use_temp_dir:
+            shutil.rmtree(repo_path_or_name)
+
+        return url
+
+    @staticmethod
+    def _get_repo_url_from_name(
+        repo_name: str,
         organization: Optional[str] = None,
         private: bool = None,
         use_auth_token: Optional[Union[bool, str]] = None,
     ) -> str:
-        # Private version of push_to_hub, that either accepts a folder to push or a list of files.
-        if save_directory is None and save_files is None:
-            raise ValueError("_push_to_hub requires either a `save_directory` or a list of `save_files`.")
-        if repo_name is None and repo_url is None and save_directory is None:
-            raise ValueError("Need either a `repo_name` or `repo_url` to know where to push!")
-
-        if repo_name is None and repo_url is None and save_files is None:
-            repo_name = Path(save_directory).name
-        if use_auth_token is None and repo_url is None:
-            use_auth_token = True
-
         if isinstance(use_auth_token, str):
             token = use_auth_token
         elif use_auth_token:
@@ -1997,33 +2052,56 @@ class PushToHubMixin:
         else:
             token = None
 
-        if repo_url is None:
-            # Special provision for the test endpoint (CI)
-            repo_url = HfApi(endpoint=HUGGINGFACE_CO_RESOLVE_ENDPOINT).create_repo(
-                token,
-                repo_name,
-                organization=organization,
-                private=private,
-                repo_type=None,
-                exist_ok=True,
+        # Special provision for the test endpoint (CI)
+        return HfApi(endpoint=HUGGINGFACE_CO_RESOLVE_ENDPOINT).create_repo(
+            token,
+            repo_name,
+            organization=organization,
+            private=private,
+            repo_type=None,
+            exist_ok=True,
+        )
+
+    @classmethod
+    def _create_or_get_repo(
+        cls,
+        repo_path_or_name: Optional[str] = None,
+        repo_url: Optional[str] = None,
+        organization: Optional[str] = None,
+        private: bool = None,
+        use_auth_token: Optional[Union[bool, str]] = None,
+    ) -> Repository:
+        if repo_path_or_name is None and repo_url is None:
+            raise ValueError("You need to specify a `repo_path_or_name` or a `repo_url`.")
+
+        if use_auth_token is None and repo_url is None:
+            use_auth_token = True
+
+        if repo_path_or_name is None:
+            repo_path_or_name = repo_url.split("/")[-1]
+
+        if repo_url is None and not os.path.exists(repo_path_or_name):
+            repo_name = Path(repo_path_or_name).name
+            repo_url = cls._get_repo_url_from_name(
+                repo_name, organization=organization, private=private, use_auth_token=use_auth_token
             )
 
+        # Create a working directory if it does not exist.
+        if not os.path.exists(repo_path_or_name):
+            os.makedirs(repo_path_or_name)
+
+        repo = Repository(repo_path_or_name, clone_from=repo_url, use_auth_token=use_auth_token)
+        repo.git_pull()
+        return repo
+
+    @classmethod
+    def _push_to_hub(cls, repo: Repository, commit_message: Optional[str] = None) -> str:
         if commit_message is None:
             if "Tokenizer" in cls.__name__:
                 commit_message = "add tokenizer"
-            if "Config" in cls.__name__:
+            elif "Config" in cls.__name__:
                 commit_message = "add config"
             else:
                 commit_message = "add model"
 
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            # First create the repo (and clone its content if it's nonempty), then add the files (otherwise there is
-            # no diff so nothing is pushed).
-            repo = Repository(tmp_dir, clone_from=repo_url, use_auth_token=use_auth_token)
-            if save_directory is None:
-                for filename in save_files:
-                    shutil.copy(filename, Path(tmp_dir) / Path(filename).name)
-            else:
-                copy_tree(save_directory, tmp_dir)
-
-            return repo.push_to_hub(commit_message=commit_message)
+        return repo.push_to_hub(commit_message=commit_message)

src/transformers/modelcard.py

@@ -565,6 +565,14 @@ class TrainingSummary:
         if model_name is None:
             model_name = Path(trainer.args.output_dir).name
 
+        # Add `generated_from_trainer` to the tags
+        if tags is None:
+            tags = ["generated_from_trainer"]
+        elif isinstance(tags, str) and tags != "generated_from_trainer":
+            tags = [tags, "generated_from_trainer"]
+        elif "generated_from_trainer" not in tags:
+            tags.append("generated_from_trainer")
+
         _, eval_lines, eval_results = parse_log_history(trainer.state.log_history)
         hyperparameters = extract_hyperparameters_from_trainer(trainer)
 

src/transformers/modeling_flax_utils.py

@@ -28,7 +28,6 @@ from jax.random import PRNGKey
 
 from .configuration_utils import PretrainedConfig
 from .file_utils import (
-    CONFIG_NAME,
     FLAX_WEIGHTS_NAME,
     WEIGHTS_NAME,
     PushToHubMixin,
@@ -409,6 +408,14 @@ class FlaxPreTrainedModel(PushToHubMixin, FlaxGenerationMixin):
                 Directory to which to save. Will be created if it doesn't exist.
             push_to_hub (:obj:`bool`, `optional`, defaults to :obj:`False`):
                 Whether or not to push your model to the Hugging Face model hub after saving it.
+
+                .. warning::
+
+                    Using :obj:`push_to_hub=True` will synchronize the repository you are pushing to with
+                    :obj:`save_directory`, which requires :obj:`save_directory` to be a local clone of the repo you are
+                    pushing to if it's an existing folder. Pass along :obj:`temp_dir=True` to use a temporary directory
+                    instead.
+
             kwargs:
                 Additional key word arguments passed along to the
                 :meth:`~transformers.file_utils.PushToHubMixin.push_to_hub` method.
@@ -416,6 +423,11 @@ class FlaxPreTrainedModel(PushToHubMixin, FlaxGenerationMixin):
         if os.path.isfile(save_directory):
             logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
             return
+
+        if push_to_hub:
+            commit_message = kwargs.pop("commit_message", None)
+            repo = self._create_or_get_repo(save_directory, **kwargs)
+
         os.makedirs(save_directory, exist_ok=True)
 
         # get abs dir
@@ -434,8 +446,7 @@ class FlaxPreTrainedModel(PushToHubMixin, FlaxGenerationMixin):
         logger.info(f"Model weights saved in {output_model_file}")
 
         if push_to_hub:
-            saved_files = [os.path.join(save_directory, CONFIG_NAME), output_model_file]
-            url = self._push_to_hub(save_files=saved_files, **kwargs)
+            url = self._push_to_hub(repo, commit_message=commit_message)
             logger.info(f"Model pushed to the hub in this commit: {url}")
 
 

src/transformers/modeling_tf_utils.py

@@ -30,7 +30,6 @@ from tensorflow.python.keras.saving import hdf5_format
 
 from .configuration_utils import PretrainedConfig
 from .file_utils import (
-    CONFIG_NAME,
     DUMMY_INPUTS,
     TF2_WEIGHTS_NAME,
     WEIGHTS_NAME,
@@ -1029,6 +1028,14 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
                 https://www.tensorflow.org/tfx/serving/serving_basic
             push_to_hub (:obj:`bool`, `optional`, defaults to :obj:`False`):
                 Whether or not to push your model to the Hugging Face model hub after saving it.
+
+                .. warning::
+
+                    Using :obj:`push_to_hub=True` will synchronize the repository you are pushing to with
+                    :obj:`save_directory`, which requires :obj:`save_directory` to be a local clone of the repo you are
+                    pushing to if it's an existing folder. Pass along :obj:`temp_dir=True` to use a temporary directory
+                    instead.
+
             kwargs:
                 Additional key word arguments passed along to the
                 :meth:`~transformers.file_utils.PushToHubMixin.push_to_hub` method.
@@ -1036,6 +1043,11 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
         if os.path.isfile(save_directory):
             logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
             return
+
+        if push_to_hub:
+            commit_message = kwargs.pop("commit_message", None)
+            repo = self._create_or_get_repo(save_directory, **kwargs)
+
         os.makedirs(save_directory, exist_ok=True)
 
         if saved_model:
@@ -1053,8 +1065,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
         logger.info(f"Model weights saved in {output_model_file}")
 
         if push_to_hub:
-            saved_files = [os.path.join(save_directory, CONFIG_NAME), output_model_file]
-            url = self._push_to_hub(save_files=saved_files, **kwargs)
+            url = self._push_to_hub(repo, commit_message=commit_message)
             logger.info(f"Model pushed to the hub in this commit: {url}")
 
     @classmethod

src/transformers/modeling_utils.py

@@ -30,7 +30,6 @@ from .activations import get_activation
 from .configuration_utils import PretrainedConfig
 from .deepspeed import deepspeed_config, is_deepspeed_zero3_enabled
 from .file_utils import (
-    CONFIG_NAME,
     DUMMY_INPUTS,
     FLAX_WEIGHTS_NAME,
     TF2_WEIGHTS_NAME,
@@ -852,6 +851,14 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                 need to replace :obj:`torch.save` by another method.
             push_to_hub (:obj:`bool`, `optional`, defaults to :obj:`False`):
                 Whether or not to push your model to the Hugging Face model hub after saving it.
+
+                .. warning::
+
+                    Using :obj:`push_to_hub=True` will synchronize the repository you are pushing to with
+                    :obj:`save_directory`, which requires :obj:`save_directory` to be a local clone of the repo you are
+                    pushing to if it's an existing folder. Pass along :obj:`temp_dir=True` to use a temporary directory
+                    instead.
+
             kwargs:
                 Additional key word arguments passed along to the
                 :meth:`~transformers.file_utils.PushToHubMixin.push_to_hub` method.
@@ -859,6 +866,11 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
         if os.path.isfile(save_directory):
             logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
             return
+
+        if push_to_hub:
+            commit_message = kwargs.pop("commit_message", None)
+            repo = self._create_or_get_repo(save_directory, **kwargs)
+
         os.makedirs(save_directory, exist_ok=True)
 
         # Only save the model itself if we are using distributed training
@@ -886,10 +898,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
         logger.info(f"Model weights saved in {output_model_file}")
 
         if push_to_hub:
-            saved_files = [output_model_file]
-            if save_config:
-                saved_files.append(os.path.join(save_directory, CONFIG_NAME))
-            url = self._push_to_hub(save_files=saved_files, **kwargs)
+            url = self._push_to_hub(repo, commit_message=commit_message)
             logger.info(f"Model pushed to the hub in this commit: {url}")
 
     @classmethod

src/transformers/tokenization_utils_base.py

@@ -1884,6 +1884,15 @@ class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin):
                 value error is raised.
             filename_prefix: (:obj:`str`, `optional`):
                 A prefix to add to the names of the files saved by the tokenizer.
+            push_to_hub (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Whether or not to push your model to the Hugging Face model hub after saving it.
+
+                .. warning::
+
+                    Using :obj:`push_to_hub=True` will synchronize the repository you are pushing to with
+                    :obj:`save_directory`, which requires :obj:`save_directory` to be a local clone of the repo you are
+                    pushing to if it's an existing folder. Pass along :obj:`temp_dir=True` to use a temporary directory
+                    instead.
 
         Returns:
             A tuple of :obj:`str`: The files saved.
@@ -1891,6 +1900,11 @@ class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin):
         if os.path.isfile(save_directory):
             logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
             return
+
+        if push_to_hub:
+            commit_message = kwargs.pop("commit_message", None)
+            repo = self._create_or_get_repo(save_directory, **kwargs)
+
         os.makedirs(save_directory, exist_ok=True)
 
         special_tokens_map_file = os.path.join(
@@ -1949,9 +1963,7 @@ class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin):
         )
 
         if push_to_hub:
-            # Annoyingly, the return contains files that don't exist.
-            existing_files = [f for f in save_files if os.path.isfile(f)]
-            url = self._push_to_hub(save_files=existing_files, **kwargs)
+            url = self._push_to_hub(repo, commit_message=commit_message)
             logger.info(f"Tokenizer pushed to the hub in this commit: {url}")
 
         return save_files

src/transformers/trainer.py

@@ -24,7 +24,6 @@ import random
 import re
 import shutil
 import sys
-import tempfile
 import time
 import warnings
 from logging import StreamHandler
@@ -391,9 +390,12 @@ class Trainer:
         # Will be set to True by `self._setup_loggers()` on first call to `self.log()`.
         self._loggers_initialized = False
 
-        # Create output directory if needed
+        # Create clone of distant repo and output directory if needed
+        if self.args.push_to_hub:
+            self.init_git_repo()
         if self.is_world_process_zero():
             os.makedirs(self.args.output_dir, exist_ok=True)
+
         if not callable(self.data_collator) and callable(getattr(self.data_collator, "collate_batch", None)):
             raise ValueError("The `data_collator` should be a simple callable (function, class with `__call__`).")
 
@@ -2430,6 +2432,27 @@ class Trainer:
         else:
             return 0
 
+    def init_git_repo(self):
+        """
+        Initializes a git repo in :obj:`self.args.push_to_hub_model_id`.
+        """
+        if not self.is_world_process_zero():
+            return
+        use_auth_token = True if self.args.push_to_hub_token is None else self.args.push_to_hub_token
+        repo_url = PushToHubMixin._get_repo_url_from_name(
+            self.args.push_to_hub_model_id,
+            organization=self.args.push_to_hub_organization,
+            use_auth_token=use_auth_token,
+        )
+        self.repo = PushToHubMixin._create_or_get_repo(
+            self.args.output_dir, repo_url=repo_url, use_auth_token=use_auth_token
+        )
+
+        # By default, ignore the checkpoint folders
+        if not os.path.exists(os.path.join(self.args.output_dir, ".gitignore")):
+            with open(os.path.join(self.args.output_dir, ".gitignore"), "w", encoding="utf-8") as writer:
+                writer.writelines(["checkpoint-*/"])
+
     def create_model_card(
         self,
         language: Optional[str] = None,
@@ -2458,38 +2481,13 @@ class Trainer:
         with open(os.path.join(self.args.output_dir, "README.md"), "w") as f:
             f.write(model_card)
 
-    def push_to_hub(
-        self,
-        repo_name: Optional[str] = None,
-        repo_url: Optional[str] = None,
-        commit_message: Optional[str] = "add model",
-        organization: Optional[str] = None,
-        private: bool = None,
-        use_auth_token: Optional[Union[bool, str]] = None,
-        **kwargs,
-    ):
+    def push_to_hub(self, commit_message: Optional[str] = "add model", **kwargs) -> str:
         """
-        Upload `self.model` to the 🤗 model hub.
+        Upload `self.model` and `self.tokenizer` to the 🤗 model hub on the repo `self.args.push_to_hub_model_id`.
 
         Parameters:
-            repo_name (:obj:`str`, `optional`):
-                Repository name for your model or tokenizer in the hub. If not specified and :obj:`repo_url` is not
-                specified either, will default to the stem of :obj:`self.args.output_dir`.
-            repo_url (:obj:`str`, `optional`):
-                Specify this in case you want to push to an existing repository in the hub. If unspecified, a new
-                repository will be created in your namespace (unless you specify an :obj:`organization`) with
-                :obj:`repo_name`.
             commit_message (:obj:`str`, `optional`, defaults to :obj:`"add model"`):
                 Message to commit while pushing.
-            organization (:obj:`str`, `optional`):
-                Organization in which you want to push your model or tokenizer (you must be a member of this
-                organization).
-            private (:obj:`bool`, `optional`):
-                Whether or not the repository created should be private (requires a paying subscription).
-            use_auth_token (:obj:`bool` or :obj:`str`, `optional`):
-                The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token
-                generated when running :obj:`transformers-cli login` (stored in :obj:`~/.huggingface`). Will default to
-                :obj:`True` if :obj:`repo_url` is not specified.
             kwargs:
                 Additional keyword arguments passed along to :meth:`~transformers.Trainer.create_model_card`.
 
@@ -2499,37 +2497,9 @@ class Trainer:
         if not self.is_world_process_zero():
             return
 
-        if not isinstance(unwrap_model(self.model), PushToHubMixin):
-            raise ValueError(
-                "The `upload_model_to_hub` method only works for models that inherit from `PushToHubMixin` models."
-            )
-
-        if repo_url is None and repo_name is None:
-            repo_name = Path(self.args.output_dir).name
-
-        if repo_name is not None:
-            model_name = repo_name
-        elif repo_url is not None:
-            model_name = repo_url.split("/")[-1]
-        else:
-            model_name = None
-        self.create_model_card(model_name=model_name, **kwargs)
-
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            shutil.copy(os.path.join(self.args.output_dir, "README.md"), os.path.join(tmp_dir, "README.md"))
-            unwrap_model(self.model).save_pretrained(tmp_dir)
-            if self.tokenizer is not None:
-                self.tokenizer.save_pretrained(tmp_dir)
-
-            return unwrap_model(self.model)._push_to_hub(
-                save_directory=tmp_dir,
-                repo_name=repo_name,
-                repo_url=repo_url,
-                commit_message=commit_message,
-                organization=organization,
-                private=private,
-                use_auth_token=use_auth_token,
-            )
+        self.create_model_card(model_name=self.args.push_to_hub_model_id, **kwargs)
+        self.save_model()
+        return self.repo.push_to_hub(commit_message=commit_message)
 
     #
     # Deprecated code

src/transformers/training_args.py

@@ -17,6 +17,7 @@ import os
 import warnings
 from dataclasses import asdict, dataclass, field
 from enum import Enum
+from pathlib import Path
 from typing import Any, Dict, List, Optional
 
 from .debug_utils import DebugOption
@@ -157,7 +158,7 @@ class TrainingArguments:
             node.
         logging_dir (:obj:`str`, `optional`):
             `TensorBoard <https://www.tensorflow.org/tensorboard>`__ log directory. Will default to
-            `runs/**CURRENT_DATETIME_HOSTNAME**`.
+            `output_dir/runs/**CURRENT_DATETIME_HOSTNAME**`.
         logging_strategy (:obj:`str` or :class:`~transformers.trainer_utils.IntervalStrategy`, `optional`, defaults to :obj:`"steps"`):
             The logging strategy to adopt during training. Possible values are:
 
@@ -318,15 +319,22 @@ class TrainingArguments:
             Whether to skip adding of memory profiler reports to metrics. This is skipped by default because it slows
             down the training and evaluation speed.
         push_to_hub (:obj:`bool`, `optional`, defaults to :obj:`False`):
-            Whether or not to upload the trained model to the hub after training. This argument is not directly used by
-            :class:`~transformers.Trainer`, it's intended to be used by your training/evaluation scripts instead. See
-            the `example scripts <https://github.com/huggingface/transformers/tree/master/examples>`__ for more
-            details.
+            Whether or not to upload the trained model to the hub after training. If this is activated, and
+            :obj:`output_dir` exists, it needs to be a local clone of the repository to which the
+            :class:`~transformers.Trainer` will be pushed.
         resume_from_checkpoint (:obj:`str`, `optional`):
             The path to a folder with a valid checkpoint for your model. This argument is not directly used by
             :class:`~transformers.Trainer`, it's intended to be used by your training/evaluation scripts instead. See
             the `example scripts <https://github.com/huggingface/transformers/tree/master/examples>`__ for more
             details.
+        push_to_hub_model_id (:obj:`str`, `optional`):
+            The name of the repository to which push the :class:`~transformers.Trainer` when :obj:`push_to_hub=True`.
+            Will default to the name of :obj:`output_dir`.
+        push_to_hub_organization (:obj:`str`, `optional`):
+            The name of the organization in with to which push the :class:`~transformers.Trainer`.
+        push_to_hub_token (:obj:`str`, `optional`):
+            The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with
+            :obj:`huggingface-cli login`.
     """
 
     output_dir: str = field(
@@ -590,6 +598,13 @@ class TrainingArguments:
         default=None,
         metadata={"help": "The path to a folder with a valid checkpoint for your model."},
     )
+    push_to_hub_model_id: str = field(
+        default=None, metadata={"help": "The name of the repository to which push the `Trainer`."}
+    )
+    push_to_hub_organization: str = field(
+        default=None, metadata={"help": "The name of the organization in with to which push the `Trainer`."}
+    )
+    push_to_hub_token: str = field(default=None, metadata={"help": "The token to use to push to the Model Hub."})
     _n_gpu: int = field(init=False, repr=False, default=-1)
     mp_parameters: str = field(
         default="",
@@ -612,6 +627,8 @@ class TrainingArguments:
         #  see https://github.com/huggingface/transformers/issues/10628
         if self.output_dir is not None:
             self.output_dir = os.path.expanduser(self.output_dir)
+        if self.logging_dir is None and self.output_dir is not None:
+            self.logging_dir = os.path.join(self.output_dir, default_logdir())
         if self.logging_dir is not None:
             self.logging_dir = os.path.expanduser(self.logging_dir)
 
@@ -705,6 +722,9 @@ class TrainingArguments:
             self.hf_deepspeed_config = HfTrainerDeepSpeedConfig(self.deepspeed)
             self.hf_deepspeed_config.trainer_config_process(self)
 
+        if self.push_to_hub_model_id is None:
+            self.push_to_hub_model_id = Path(self.output_dir).name
+
     def __str__(self):
         self_as_dict = asdict(self)
 

tests/test_configuration_common.py

@@ -113,7 +113,7 @@ class ConfigPushToHubTester(unittest.TestCase):
             vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
         )
         with tempfile.TemporaryDirectory() as tmp_dir:
-            config.save_pretrained(tmp_dir, push_to_hub=True, repo_name="test-config", use_auth_token=self._token)
+            config.save_pretrained(os.path.join(tmp_dir, "test-config"), push_to_hub=True, use_auth_token=self._token)
 
             new_config = BertConfig.from_pretrained(f"{USER}/test-config")
             for k, v in config.__dict__.items():
@@ -127,9 +127,8 @@ class ConfigPushToHubTester(unittest.TestCase):
 
         with tempfile.TemporaryDirectory() as tmp_dir:
             config.save_pretrained(
-                tmp_dir,
+                os.path.join(tmp_dir, "test-config-org"),
                 push_to_hub=True,
-                repo_name="test-config-org",
                 use_auth_token=self._token,
                 organization="valid_org",
             )

tests/test_modeling_common.py

@@ -1613,7 +1613,7 @@ class ModelPushToHubTester(unittest.TestCase):
         )
         model = BertModel(config)
         with tempfile.TemporaryDirectory() as tmp_dir:
-            model.save_pretrained(tmp_dir, push_to_hub=True, repo_name="test-model", use_auth_token=self._token)
+            model.save_pretrained(os.path.join(tmp_dir, "test-model"), push_to_hub=True, use_auth_token=self._token)
 
             new_model = BertModel.from_pretrained(f"{USER}/test-model")
             for p1, p2 in zip(model.parameters(), new_model.parameters()):
@@ -1626,9 +1626,8 @@ class ModelPushToHubTester(unittest.TestCase):
         model = BertModel(config)
         with tempfile.TemporaryDirectory() as tmp_dir:
             model.save_pretrained(
-                tmp_dir,
+                os.path.join(tmp_dir, "test-model-org"),
                 push_to_hub=True,
-                repo_name="test-model-org",
                 use_auth_token=self._token,
                 organization="valid_org",
             )

tests/test_modeling_flax_common.py

@@ -16,14 +16,25 @@ import copy
 import inspect
 import random
 import tempfile
+import unittest
 from typing import List, Tuple
 
 import numpy as np
 
 import transformers
-from transformers import is_flax_available, is_torch_available
+from huggingface_hub import HfApi
+from requests.exceptions import HTTPError
+from transformers import BertConfig, FlaxBertModel, is_flax_available, is_torch_available
 from transformers.models.auto import get_values
-from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow
+from transformers.testing_utils import (
+    ENDPOINT_STAGING,
+    PASS,
+    USER,
+    is_pt_flax_cross_test,
+    is_staging_test,
+    require_flax,
+    slow,
+)
 
 
 if is_flax_available():
@@ -504,3 +515,65 @@ class FlaxModelTesterMixin:
                 list(self_attentions[0].shape[-3:]),
                 [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
             )
+
+
+@require_flax
+@is_staging_test
+class FlaxModelPushToHubTester(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls._api = HfApi(endpoint=ENDPOINT_STAGING)
+        cls._token = cls._api.login(username=USER, password=PASS)
+
+    @classmethod
+    def tearDownClass(cls):
+        try:
+            cls._api.delete_repo(token=cls._token, name="test-model-flax")
+        except HTTPError:
+            pass
+
+        try:
+            cls._api.delete_repo(token=cls._token, name="test-model-flax-org", organization="valid_org")
+        except HTTPError:
+            pass
+
+    def test_push_to_hub(self):
+        config = BertConfig(
+            vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
+        )
+        model = FlaxBertModel(config)
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model.save_pretrained(
+                os.path.join(tmp_dir, "test-model-flax"), push_to_hub=True, use_auth_token=self._token
+            )
+
+            new_model = FlaxBertModel.from_pretrained(f"{USER}/test-model-flax")
+
+            base_params = flatten_dict(unfreeze(model.params))
+            new_params = flatten_dict(unfreeze(new_model.params))
+
+            for key in base_params.keys():
+                max_diff = (base_params[key] - new_params[key]).sum().item()
+                self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
+
+    def test_push_to_hub_in_organization(self):
+        config = BertConfig(
+            vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
+        )
+        model = FlaxBertModel(config)
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model.save_pretrained(
+                os.path.join(tmp_dir, "test-model-flax-org"),
+                push_to_hub=True,
+                use_auth_token=self._token,
+                organization="valid_org",
+            )
+
+            new_model = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org")
+
+            base_params = flatten_dict(unfreeze(model.params))
+            new_params = flatten_dict(unfreeze(new_model.params))
+
+            for key in base_params.keys():
+                max_diff = (base_params[key] - new_params[key]).sum().item()
+                self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")

tests/test_modeling_tf_common.py

@@ -1487,7 +1487,7 @@ class TFModelPushToHubTester(unittest.TestCase):
         # Make sure model is properly initialized
         _ = model(model.dummy_inputs)
         with tempfile.TemporaryDirectory() as tmp_dir:
-            model.save_pretrained(tmp_dir, push_to_hub=True, repo_name="test-model-tf", use_auth_token=self._token)
+            model.save_pretrained(os.path.join(tmp_dir, "test-model-tf"), push_to_hub=True, use_auth_token=self._token)
 
             new_model = TFBertModel.from_pretrained(f"{USER}/test-model-tf")
             models_equal = True
@@ -1503,9 +1503,8 @@ class TFModelPushToHubTester(unittest.TestCase):
         model = TFBertModel(config)
         with tempfile.TemporaryDirectory() as tmp_dir:
             model.save_pretrained(
-                tmp_dir,
+                os.path.join(tmp_dir, "test-model-tf-org"),
                 push_to_hub=True,
-                repo_name="test-model-tf-org",
                 use_auth_token=self._token,
                 organization="valid_org",
             )

tests/test_tokenization_common.py

@@ -3173,7 +3173,7 @@ class TokenizerPushToHubTester(unittest.TestCase):
                 vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
             tokenizer = BertTokenizer(vocab_file)
             tokenizer.save_pretrained(
-                tmp_dir, push_to_hub=True, repo_name="test-tokenizer", use_auth_token=self._token
+                os.path.join(tmp_dir, "test-tokenizer"), push_to_hub=True, use_auth_token=self._token
             )
 
             new_tokenizer = BertTokenizer.from_pretrained(f"{USER}/test-tokenizer")
@@ -3186,9 +3186,8 @@ class TokenizerPushToHubTester(unittest.TestCase):
                 vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]))
             tokenizer = BertTokenizer(vocab_file)
             tokenizer.save_pretrained(
-                tmp_dir,
+                os.path.join(tmp_dir, "test-tokenizer-org"),
                 push_to_hub=True,
-                repo_name="test-tokenizer-org",
                 use_auth_token=self._token,
                 organization="valid_org",
             )

tests/test_trainer.py

@@ -1274,8 +1274,12 @@ class TrainerIntegrationWithHubTester(unittest.TestCase):
 
     def test_push_to_hub(self):
         with tempfile.TemporaryDirectory() as tmp_dir:
-            trainer = get_regression_trainer(output_dir=tmp_dir)
-            url = trainer.push_to_hub(repo_name="test-trainer", use_auth_token=self._token)
+            trainer = get_regression_trainer(
+                output_dir=os.path.join(tmp_dir, "test-trainer"),
+                push_to_hub=True,
+                push_to_hub_token=self._token,
+            )
+            url = trainer.push_to_hub()
 
             # Extract repo_name from the url
             re_search = re.search(ENDPOINT_STAGING + r"/([^/]+/[^/]+)/", url)
@@ -1292,9 +1296,13 @@ class TrainerIntegrationWithHubTester(unittest.TestCase):
         with tempfile.TemporaryDirectory() as tmp_dir:
             trainer = get_regression_trainer(output_dir=tmp_dir)
             trainer.save_model()
-            url = trainer.push_to_hub(
-                repo_name="test-trainer-org", organization="valid_org", use_auth_token=self._token
+            trainer = get_regression_trainer(
+                output_dir=os.path.join(tmp_dir, "test-trainer-org"),
+                push_to_hub=True,
+                push_to_hub_organization="valid_org",
+                push_to_hub_token=self._token,
             )
+            url = trainer.push_to_hub()
 
             # Extract repo_name from the url
             re_search = re.search(ENDPOINT_STAGING + r"/([^/]+/[^/]+)/", url)