importHuggingFaceHubModel.py

from huggingface_hub import ModelHubMixin, snapshot_download
import os
from huggingface_hub.utils import (
    get_tf_version,
    is_graphviz_available,
    is_pydot_available,
    is_tf_available,
    yaml_dump,
)

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.
        resume_download (`bool`, *optional*, defaults to `False`):
            Whether to delete incompletely received files. Will attempt to
            resume the download if such a file exists.
        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)


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")
    ```
    """

    @classmethod
    def _from_pretrained(
        cls,
        model_id,
        revision,
        cache_dir,
        force_download,
        proxies,
        resume_download,
        local_files_only,
        token,
        **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 is_tf_available():
            import tensorflow as tf
        else:
            raise ImportError("Called a TensorFlow-specific function but could not import it.")

        # TODO - Figure out what to do about these config values. Config is not going to be needed to load model
        cfg = model_kwargs.pop("config", None)

        # 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

        # Search the first file which ends with .keras
        files = os.listdir(storage_folder)
        modelFileName = None
        for file in files :
          if file.endswith(".keras"):
            modelFileName = file
            break
        
        if modelFileName==None:
          raise ValueError("Repesitory does not have model ends with .keras")

        modelPath = storage_folder + '/' + modelFileName

        model = tf.keras.models.load_model(modelPath, **model_kwargs)

        # For now, we add a new attribute, config, to store the config loaded from the hub/a local dir.
        model.config = cfg

        return model