two version of R2R are here HEAD master

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diff --git a/.venv/lib/python3.12/site-packages/huggingface_hub/keras_mixin.py b/.venv/lib/python3.12/site-packages/huggingface_hub/keras_mixin.py
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+import collections.abc as collections
+import json
+import os
+import warnings
+from functools import wraps
+from pathlib import Path
+from shutil import copytree
+from typing import Any, Dict, List, Optional, Union
+
+from huggingface_hub import ModelHubMixin, snapshot_download
+from huggingface_hub.utils import (
+    get_tf_version,
+    is_graphviz_available,
+    is_pydot_available,
+    is_tf_available,
+    yaml_dump,
+)
+
+from . import constants
+from .hf_api import HfApi
+from .utils import SoftTemporaryDirectory, logging, validate_hf_hub_args
+from .utils._typing import CallableT
+
+
+logger = logging.get_logger(__name__)
+
+keras = None
+if is_tf_available():
+    # Depending on which version of TensorFlow is installed, we need to import
+    # keras from the correct location.
+    # See https://github.com/tensorflow/tensorflow/releases/tag/v2.16.1.
+    # Note: saving a keras model only works with Keras<3.0.
+    try:
+        import tf_keras as keras  # type: ignore
+    except ImportError:
+        import tensorflow as tf  # type: ignore
+
+        keras = tf.keras
+
+
+def _requires_keras_2_model(fn: CallableT) -> CallableT:
+    # Wrapper to raise if user tries to save a Keras 3.x model
+    @wraps(fn)
+    def _inner(model, *args, **kwargs):
+        if not hasattr(model, "history"):  # hacky way to check if model is Keras 2.x
+            raise NotImplementedError(
+                f"Cannot use '{fn.__name__}': Keras 3.x is not supported."
+                " Please save models manually and upload them using `upload_folder` or `huggingface-cli upload`."
+            )
+        return fn(model, *args, **kwargs)
+
+    return _inner  # type: ignore [return-value]
+
+
+def _flatten_dict(dictionary, parent_key=""):
+    """Flatten a nested dictionary.
+    Reference: https://stackoverflow.com/a/6027615/10319735
+
+    Args:
+        dictionary (`dict`):
+            The nested dictionary to be flattened.
+        parent_key (`str`):
+            The parent key to be prefixed to the children keys.
+            Necessary for recursing over the nested dictionary.
+
+    Returns:
+        The flattened dictionary.
+    """
+    items = []
+    for key, value in dictionary.items():
+        new_key = f"{parent_key}.{key}" if parent_key else key
+        if isinstance(value, collections.MutableMapping):
+            items.extend(
+                _flatten_dict(
+                    value,
+                    new_key,
+                ).items()
+            )
+        else:
+            items.append((new_key, value))
+    return dict(items)
+
+
+def _create_hyperparameter_table(model):
+    """Parse hyperparameter dictionary into a markdown table."""
+    table = None
+    if model.optimizer is not None:
+        optimizer_params = model.optimizer.get_config()
+        # flatten the configuration
+        optimizer_params = _flatten_dict(optimizer_params)
+        optimizer_params["training_precision"] = keras.mixed_precision.global_policy().name
+        table = "| Hyperparameters | Value |\n| :-- | :-- |\n"
+        for key, value in optimizer_params.items():
+            table += f"| {key} | {value} |\n"
+    return table
+
+
+def _plot_network(model, save_directory):
+    keras.utils.plot_model(
+        model,
+        to_file=f"{save_directory}/model.png",
+        show_shapes=False,
+        show_dtype=False,
+        show_layer_names=True,
+        rankdir="TB",
+        expand_nested=False,
+        dpi=96,
+        layer_range=None,
+    )
+
+
+def _create_model_card(
+    model,
+    repo_dir: Path,
+    plot_model: bool = True,
+    metadata: Optional[dict] = None,
+):
+    """
+    Creates a model card for the repository.
+
+    Do not overwrite an existing README.md file.
+    """
+    readme_path = repo_dir / "README.md"
+    if readme_path.exists():
+        return
+
+    hyperparameters = _create_hyperparameter_table(model)
+    if plot_model and is_graphviz_available() and is_pydot_available():
+        _plot_network(model, repo_dir)
+    if metadata is None:
+        metadata = {}
+    metadata["library_name"] = "keras"
+    model_card: str = "---\n"
+    model_card += yaml_dump(metadata, default_flow_style=False)
+    model_card += "---\n"
+    model_card += "\n## Model description\n\nMore information needed\n"
+    model_card += "\n## Intended uses & limitations\n\nMore information needed\n"
+    model_card += "\n## Training and evaluation data\n\nMore information needed\n"
+    if hyperparameters is not None:
+        model_card += "\n## Training procedure\n"
+        model_card += "\n### Training hyperparameters\n"
+        model_card += "\nThe following hyperparameters were used during training:\n\n"
+        model_card += hyperparameters
+        model_card += "\n"
+    if plot_model and os.path.exists(f"{repo_dir}/model.png"):
+        model_card += "\n ## Model Plot\n"
+        model_card += "\n<details>"
+        model_card += "\n<summary>View Model Plot</summary>\n"
+        path_to_plot = "./model.png"
+        model_card += f"\n![Model Image]({path_to_plot})\n"
+        model_card += "\n</details>"
+
+    readme_path.write_text(model_card)
+
+
+@_requires_keras_2_model
+def save_pretrained_keras(
+    model,
+    save_directory: Union[str, Path],
+    config: Optional[Dict[str, Any]] = None,
+    include_optimizer: bool = False,
+    plot_model: bool = True,
+    tags: Optional[Union[list, str]] = None,
+    **model_save_kwargs,
+):
+    """
+    Saves a Keras model to save_directory in SavedModel format. Use this if
+    you're using the Functional or Sequential APIs.
+
+    Args:
+        model (`Keras.Model`):
+            The [Keras
+            model](https://www.tensorflow.org/api_docs/python/tf/keras/Model)
+            you'd like to save. The model must be compiled and built.
+        save_directory (`str` or `Path`):
+            Specify directory in which you want to save the Keras model.
+        config (`dict`, *optional*):
+            Configuration object to be saved alongside the model weights.
+        include_optimizer(`bool`, *optional*, defaults to `False`):
+            Whether or not to include optimizer in serialization.
+        plot_model (`bool`, *optional*, defaults to `True`):
+            Setting this to `True` will plot the model and put it in the model
+            card. Requires graphviz and pydot to be installed.
+        tags (Union[`str`,`list`], *optional*):
+            List of tags that are related to model or string of a single tag. See example tags
+            [here](https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1).
+        model_save_kwargs(`dict`, *optional*):
+            model_save_kwargs will be passed to
+            [`tf.keras.models.save_model()`](https://www.tensorflow.org/api_docs/python/tf/keras/models/save_model).
+    """
+    if keras is None:
+        raise ImportError("Called a Tensorflow-specific function but could not import it.")
+
+    if not model.built:
+        raise ValueError("Model should be built before trying to save")
+
+    save_directory = Path(save_directory)
+    save_directory.mkdir(parents=True, exist_ok=True)
+
+    # saving config
+    if config:
+        if not isinstance(config, dict):
+            raise RuntimeError(f"Provided config to save_pretrained_keras should be a dict. Got: '{type(config)}'")
+
+        with (save_directory / constants.CONFIG_NAME).open("w") as f:
+            json.dump(config, f)
+
+    metadata = {}
+    if isinstance(tags, list):
+        metadata["tags"] = tags
+    elif isinstance(tags, str):
+        metadata["tags"] = [tags]
+
+    task_name = model_save_kwargs.pop("task_name", None)
+    if task_name is not None:
+        warnings.warn(
+            "`task_name` input argument is deprecated. Pass `tags` instead.",
+            FutureWarning,
+        )
+        if "tags" in metadata:
+            metadata["tags"].append(task_name)
+        else:
+            metadata["tags"] = [task_name]
+
+    if model.history is not None:
+        if model.history.history != {}:
+            path = save_directory / "history.json"
+            if path.exists():
+                warnings.warn(
+                    "`history.json` file already exists, it will be overwritten by the history of this version.",
+                    UserWarning,
+                )
+            with path.open("w", encoding="utf-8") as f:
+                json.dump(model.history.history, f, indent=2, sort_keys=True)
+
+    _create_model_card(model, save_directory, plot_model, metadata)
+    keras.models.save_model(model, save_directory, include_optimizer=include_optimizer, **model_save_kwargs)
+
+
+def from_pretrained_keras(*args, **kwargs) -> "KerasModelHubMixin":
+    r"""
+    Instantiate a pretrained Keras model from a pre-trained model from the Hub.
+    The model is expected to be in `SavedModel` format.
+
+    Args:
+        pretrained_model_name_or_path (`str` or `os.PathLike`):
+            Can be either:
+                - A string, the `model id` of a pretrained model hosted inside a
+                  model repo on huggingface.co. Valid model ids can be located
+                  at the root-level, like `bert-base-uncased`, or namespaced
+                  under a user or organization name, like
+                  `dbmdz/bert-base-german-cased`.
+                - You can add `revision` by appending `@` at the end of model_id
+                  simply like this: `dbmdz/bert-base-german-cased@main` Revision
+                  is the specific model version to use. It can be a branch name,
+                  a tag name, or a commit id, since we use a git-based system
+                  for storing models and other artifacts on huggingface.co, so
+                  `revision` can be any identifier allowed by git.
+                - A path to a `directory` containing model weights saved using
+                  [`~transformers.PreTrainedModel.save_pretrained`], e.g.,
+                  `./my_model_directory/`.
+                - `None` if you are both providing the configuration and state
+                  dictionary (resp. with keyword arguments `config` and
+                  `state_dict`).
+        force_download (`bool`, *optional*, defaults to `False`):
+            Whether to force the (re-)download of the model weights and
+            configuration files, overriding the cached versions if they exist.
+        proxies (`Dict[str, str]`, *optional*):
+            A dictionary of proxy servers to use by protocol or endpoint, e.g.,
+            `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The
+            proxies are used on each request.
+        token (`str` or `bool`, *optional*):
+            The token to use as HTTP bearer authorization for remote files. If
+            `True`, will use the token generated when running `transformers-cli
+            login` (stored in `~/.huggingface`).
+        cache_dir (`Union[str, os.PathLike]`, *optional*):
+            Path to a directory in which a downloaded pretrained model
+            configuration should be cached if the standard cache should not be
+            used.
+        local_files_only(`bool`, *optional*, defaults to `False`):
+            Whether to only look at local files (i.e., do not try to download
+            the model).
+        model_kwargs (`Dict`, *optional*):
+            model_kwargs will be passed to the model during initialization
+
+    <Tip>
+
+    Passing `token=True` is required when you want to use a private
+    model.
+
+    </Tip>
+    """
+    return KerasModelHubMixin.from_pretrained(*args, **kwargs)
+
+
+@validate_hf_hub_args
+@_requires_keras_2_model
+def push_to_hub_keras(
+    model,
+    repo_id: str,
+    *,
+    config: Optional[dict] = None,
+    commit_message: str = "Push Keras model using huggingface_hub.",
+    private: Optional[bool] = None,
+    api_endpoint: Optional[str] = None,
+    token: Optional[str] = None,
+    branch: Optional[str] = None,
+    create_pr: Optional[bool] = None,
+    allow_patterns: Optional[Union[List[str], str]] = None,
+    ignore_patterns: Optional[Union[List[str], str]] = None,
+    delete_patterns: Optional[Union[List[str], str]] = None,
+    log_dir: Optional[str] = None,
+    include_optimizer: bool = False,
+    tags: Optional[Union[list, str]] = None,
+    plot_model: bool = True,
+    **model_save_kwargs,
+):
+    """
+    Upload model checkpoint to the Hub.
+
+    Use `allow_patterns` and `ignore_patterns` to precisely filter which files should be pushed to the hub. Use
+    `delete_patterns` to delete existing remote files in the same commit. See [`upload_folder`] reference for more
+    details.
+
+    Args:
+        model (`Keras.Model`):
+            The [Keras model](`https://www.tensorflow.org/api_docs/python/tf/keras/Model`) you'd like to push to the
+            Hub. The model must be compiled and built.
+        repo_id (`str`):
+                ID of the repository to push to (example: `"username/my-model"`).
+        commit_message (`str`, *optional*, defaults to "Add Keras model"):
+            Message to commit while pushing.
+        private (`bool`, *optional*):
+            Whether the repository created should be private.
+            If `None` (default), the repo will be public unless the organization's default is private.
+        api_endpoint (`str`, *optional*):
+            The API endpoint to use when pushing the model to the hub.
+        token (`str`, *optional*):
+            The token to use as HTTP bearer authorization for remote files. If
+            not set, will use the token set when logging in with
+            `huggingface-cli login` (stored in `~/.huggingface`).
+        branch (`str`, *optional*):
+            The git branch on which to push the model. This defaults to
+            the default branch as specified in your repository, which
+            defaults to `"main"`.
+        create_pr (`boolean`, *optional*):
+            Whether or not to create a Pull Request from `branch` with that commit.
+            Defaults to `False`.
+        config (`dict`, *optional*):
+            Configuration object to be saved alongside the model weights.
+        allow_patterns (`List[str]` or `str`, *optional*):
+            If provided, only files matching at least one pattern are pushed.
+        ignore_patterns (`List[str]` or `str`, *optional*):
+            If provided, files matching any of the patterns are not pushed.
+        delete_patterns (`List[str]` or `str`, *optional*):
+            If provided, remote files matching any of the patterns will be deleted from the repo.
+        log_dir (`str`, *optional*):
+            TensorBoard logging directory to be pushed. The Hub automatically
+            hosts and displays a TensorBoard instance if log files are included
+            in the repository.
+        include_optimizer (`bool`, *optional*, defaults to `False`):
+            Whether or not to include optimizer during serialization.
+        tags (Union[`list`, `str`], *optional*):
+            List of tags that are related to model or string of a single tag. See example tags
+            [here](https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1).
+        plot_model (`bool`, *optional*, defaults to `True`):
+            Setting this to `True` will plot the model and put it in the model
+            card. Requires graphviz and pydot to be installed.
+        model_save_kwargs(`dict`, *optional*):
+            model_save_kwargs will be passed to
+            [`tf.keras.models.save_model()`](https://www.tensorflow.org/api_docs/python/tf/keras/models/save_model).
+
+    Returns:
+        The url of the commit of your model in the given repository.
+    """
+    api = HfApi(endpoint=api_endpoint)
+    repo_id = api.create_repo(repo_id=repo_id, token=token, private=private, exist_ok=True).repo_id
+
+    # Push the files to the repo in a single commit
+    with SoftTemporaryDirectory() as tmp:
+        saved_path = Path(tmp) / repo_id
+        save_pretrained_keras(
+            model,
+            saved_path,
+            config=config,
+            include_optimizer=include_optimizer,
+            tags=tags,
+            plot_model=plot_model,
+            **model_save_kwargs,
+        )
+
+        # If `log_dir` provided, delete remote logs and upload new ones
+        if log_dir is not None:
+            delete_patterns = (
+                []
+                if delete_patterns is None
+                else (
+                    [delete_patterns]  # convert `delete_patterns` to a list
+                    if isinstance(delete_patterns, str)
+                    else delete_patterns
+                )
+            )
+            delete_patterns.append("logs/*")
+            copytree(log_dir, saved_path / "logs")
+
+        return api.upload_folder(
+            repo_type="model",
+            repo_id=repo_id,
+            folder_path=saved_path,
+            commit_message=commit_message,
+            token=token,
+            revision=branch,
+            create_pr=create_pr,
+            allow_patterns=allow_patterns,
+            ignore_patterns=ignore_patterns,
+            delete_patterns=delete_patterns,
+        )
+
+
+class KerasModelHubMixin(ModelHubMixin):
+    """
+    Implementation of [`ModelHubMixin`] to provide model Hub upload/download
+    capabilities to Keras models.
+
+
+    ```python
+    >>> import tensorflow as tf
+    >>> from huggingface_hub import KerasModelHubMixin
+
+
+    >>> class MyModel(tf.keras.Model, KerasModelHubMixin):
+    ...     def __init__(self, **kwargs):
+    ...         super().__init__()
+    ...         self.config = kwargs.pop("config", None)
+    ...         self.dummy_inputs = ...
+    ...         self.layer = ...
+
+    ...     def call(self, *args):
+    ...         return ...
+
+
+    >>> # Initialize and compile the model as you normally would
+    >>> model = MyModel()
+    >>> model.compile(...)
+    >>> # Build the graph by training it or passing dummy inputs
+    >>> _ = model(model.dummy_inputs)
+    >>> # Save model weights to local directory
+    >>> model.save_pretrained("my-awesome-model")
+    >>> # Push model weights to the Hub
+    >>> model.push_to_hub("my-awesome-model")
+    >>> # Download and initialize weights from the Hub
+    >>> model = MyModel.from_pretrained("username/super-cool-model")
+    ```
+    """
+
+    def _save_pretrained(self, save_directory):
+        save_pretrained_keras(self, save_directory)
+
+    @classmethod
+    def _from_pretrained(
+        cls,
+        model_id,
+        revision,
+        cache_dir,
+        force_download,
+        proxies,
+        resume_download,
+        local_files_only,
+        token,
+        config: Optional[Dict[str, Any]] = None,
+        **model_kwargs,
+    ):
+        """Here we just call [`from_pretrained_keras`] function so both the mixin and
+        functional APIs stay in sync.
+
+                TODO - Some args above aren't used since we are calling
+                snapshot_download instead of hf_hub_download.
+        """
+        if keras is None:
+            raise ImportError("Called a TensorFlow-specific function but could not import it.")
+
+        # Root is either a local filepath matching model_id or a cached snapshot
+        if not os.path.isdir(model_id):
+            storage_folder = snapshot_download(
+                repo_id=model_id,
+                revision=revision,
+                cache_dir=cache_dir,
+                library_name="keras",
+                library_version=get_tf_version(),
+            )
+        else:
+            storage_folder = model_id
+
+        # TODO: change this in a future PR. We are not returning a KerasModelHubMixin instance here...
+        model = keras.models.load_model(storage_folder)
+
+        # For now, we add a new attribute, config, to store the config loaded from the hub/a local dir.
+        model.config = config
+
+        return model