src/transformers/configuration_utils.py/PretrainedConfig/from_pretrained
class PretrainedConfig:    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
        r"""
        Instantiate a [`PretrainedConfig`] (or a derived class) from a pretrained model configuration.

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

                - a string, the *model id* of a pretrained model configuration 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 configuration file saved using the
                  [`~PretrainedConfig.save_pretrained`] method, e.g., `./my_model_directory/`.
                - a path or url to a saved configuration JSON *file*, e.g., `./my_model_directory/configuration.json`.
            cache_dir (`str` or `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.
            force_download (`bool`, *optional*, defaults to `False`):
                Whether or not to force to (re-)download the configuration files and override the cached versions if
                they exist.
            resume_download (`bool`, *optional*, defaults to `False`):
                Whether or not to delete incompletely received file. Attempts 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.
            use_auth_token (`str` or `bool`, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use
                the token generated when running `huggingface-cli login` (stored in `~/.huggingface`).
            revision (`str`, *optional*, defaults to `"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.

                <Tip>

                To test a pull request you made on the Hub, you can pass `revision="refs/pr/<pr_number>".

                </Tip>

            return_unused_kwargs (`bool`, *optional*, defaults to `False`):
                If `False`, then this function returns just the final configuration object.

                If `True`, then this functions returns a `Tuple(config, unused_kwargs)` where *unused_kwargs* is a
                dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
                part of `kwargs` which has not been used to update `config` and is otherwise ignored.
            subfolder (`str`, *optional*, defaults to `""`):
                In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can
                specify the folder name here.
            kwargs (`Dict[str, Any]`, *optional*):
                The values in kwargs of any keys which are configuration attributes will be used to override the loaded
                values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled
                by the `return_unused_kwargs` keyword parameter.

        Returns:
            [`PretrainedConfig`]: The configuration object instantiated from this pretrained model.

        Examples:

        ```python
        # We can't instantiate directly the base class *PretrainedConfig* so let's show the examples on a
        # derived class: BertConfig
        config = BertConfig.from_pretrained(
            "bert-base-uncased"
        )  # Download configuration from huggingface.co and cache.
        config = BertConfig.from_pretrained(
            "./test/saved_model/"
        )  # E.g. config (or model) was saved using *save_pretrained('./test/saved_model/')*
        config = BertConfig.from_pretrained("./test/saved_model/my_configuration.json")
        config = BertConfig.from_pretrained("bert-base-uncased", output_attentions=True, foo=False)
        assert config.output_attentions == True
        config, unused_kwargs = BertConfig.from_pretrained(
            "bert-base-uncased", output_attentions=True, foo=False, return_unused_kwargs=True
        )
        assert config.output_attentions == True
        assert unused_kwargs == {"foo": False}
        ```"""
        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
            )

        return cls.from_dict(config_dict, **kwargs)