src/transformers/feature_extraction_utils.py/FeatureExtractionMixin/from_pretrained
class FeatureExtractionMixin:    def from_pretrained(
        cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
    ) -> PreTrainedFeatureExtractor:
        r"""
        Instantiate a type of :class:`~transformers.feature_extraction_utils.FeatureExtractionMixin` from a feature
        extractor, *e.g.* a derived class of :class:`~transformers.SequenceFeatureExtractor`.

        Args:
            pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
                This can be either:

                - a string, the `model id` of a pretrained feature_extractor 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``.
                - a path to a `directory` containing a feature extractor file saved using the
                  :func:`~transformers.feature_extraction_utils.FeatureExtractionMixin.save_pretrained` method, e.g.,
                  ``./my_model_directory/``.
                - a path or url to a saved feature extractor JSON `file`, e.g.,
                  ``./my_model_directory/preprocessor_config.json``.
            cache_dir (:obj:`str` or :obj:`os.PathLike`, `optional`):
                Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
                standard cache should not be used.
            force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to force to (re-)download the feature extractor files and override the cached versions
                if they exist.
            resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to delete incompletely received file. Attempts to resume the download if such a file
                exists.
            proxies (:obj:`Dict[str, str]`, `optional`):
                A dictionary of proxy servers to use by protocol or endpoint, e.g., :obj:`{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
            use_auth_token (:obj:`str` or `bool`, `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`).
            revision(:obj:`str`, `optional`, defaults to :obj:`"main"`):
                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.
            return_unused_kwargs (:obj:`bool`, `optional`, defaults to :obj:`False`):
                If :obj:`False`, then this function returns just the final feature extractor object. If :obj:`True`,
                then this functions returns a :obj:`Tuple(feature_extractor, unused_kwargs)` where `unused_kwargs` is a
                dictionary consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the
                part of ``kwargs`` which has not been used to update ``feature_extractor`` and is otherwise ignored.
            kwargs (:obj:`Dict[str, Any]`, `optional`):
                The values in kwargs of any keys which are feature extractor attributes will be used to override the
                loaded values. Behavior concerning key/value pairs whose keys are *not* feature extractor attributes is
                controlled by the ``return_unused_kwargs`` keyword parameter.

        .. note::

            Passing :obj:`use_auth_token=True` is required when you want to use a private model.


        Returns:
            A feature extractor of type :class:`~transformers.feature_extraction_utils.FeatureExtractionMixin`.

        Examples::

            # We can't instantiate directly the base class `FeatureExtractionMixin` nor `SequenceFeatureExtractor` so let's show the examples on a
            # derived class: `Wav2Vec2FeatureExtractor`
            feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h')    # Download feature_extraction_config from huggingface.co and cache.
            feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained('./test/saved_model/')  # E.g. feature_extractor (or model) was saved using `save_pretrained('./test/saved_model/')`
            feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained('./test/saved_model/preprocessor_config.json')
            feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h', return_attention_mask=False, foo=False)
            assert feature_extractor.return_attention_mask is False
            feature_extractor, unused_kwargs = Wav2Vec2FeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h', return_attention_mask=False,
                                                               foo=False, return_unused_kwargs=True)
            assert feature_extractor.return_attention_mask is False
            assert unused_kwargs == {'foo': False}
        """
        feature_extractor_dict, kwargs = cls.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs)

        return cls.from_dict(feature_extractor_dict, **kwargs)