# Buggy code
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
    r"""Instantiates one of the sequence classification model classes of the library
    from a pre-trained model configuration.

    The `from_pretrained()` method takes care of returning the correct model class instance
    based on the `model_type` property of the config object, or when it's missing,
    falling back to using pattern matching on the `pretrained_model_name_or_path` string.

    The model class to instantiate is selected as the first pattern matching
    in the `pretrained_model_name_or_path` string (in the following order):
        - contains `distilbert`: :class:`~transformers.DistilBertForSequenceClassification` (DistilBERT model)
        - contains `albert`: :class:`~transformers.AlbertForSequenceClassification` (ALBERT model)
        - contains `camembert`: :class:`~transformers.CamembertForSequenceClassification` (CamemBERT model)
        - contains `xlm-roberta`: :class:`~transformers.XLMRobertaForSequenceClassification` (XLM-RoBERTa model)
        - contains `roberta`: :class:`~transformers.RobertaForSequenceClassification` (RoBERTa model)
        - contains `bert`: :class:`~transformers.BertForSequenceClassification` (Bert model)
        - contains `xlnet`: :class:`~transformers.XLNetForSequenceClassification` (XLNet model)
        - contains `flaubert`: :class:`~transformers.FlaubertForSequenceClassification` (Flaubert model)

    The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
    To train the model, you should first set it back in training mode with `model.train()`

    Args:
        pretrained_model_name_or_path: either:

            - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
            - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
            - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
            - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.

        model_args: (`optional`) Sequence of positional arguments:
            All remaining positional arguments will be passed to the underlying model's ``__init__`` method

        config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
            Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:

            - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
            - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
            - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.

        state_dict: (`optional`) dict:
            an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
            This option can be used if you want to create a model from a pretrained configuration but load your own weights.
            In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.

        cache_dir: (`optional`) string:
            Path to a directory in which a downloaded pre-trained model
            configuration should be cached if the standard cache should not be used.

        force_download: (`optional`) boolean, default False:
            Force to (re-)download the model weights and configuration files and override the cached versions if they exists.

        resume_download: (`optional`) boolean, default False:
            Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.

        proxies: (`optional`) dict, default None:
            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.

        output_loading_info: (`optional`) boolean:
            Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.

        kwargs: (`optional`) Remaining dictionary of keyword arguments:
            Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:

            - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
            - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.

    Examples::

        model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased')    # Download model and configuration from S3 and cache.
        model = AutoModelForSequenceClassification.from_pretrained('./test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
        model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', output_attention=True)  # Update configuration during loading
        assert model.config.output_attention == True
        # Loading from a TF checkpoint file instead of a PyTorch model (slower)
        config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
        model = AutoModelForSequenceClassification.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)

    """
    config = kwargs.pop("config", None)
    if not isinstance(config, PretrainedConfig):
        config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)

    for config_class, model_class in MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.items():
        if isinstance(config, config_class):
            return model_class.from_pretrained(
                pretrained_model_name_or_path, *model_args, config=config, **kwargs
            )
    raise ValueError(
        "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
        "Model type should be one of {}.".format(
            config.__class__,
            cls.__name__,
            ", ".join(
                c.__name__ for c in MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys()
            ),
        )
    )


# Traceback (TypeError)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "[...]/transformers/src/transformers/modeling_auto.py", line 384, in from_pretrained
return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
File "[...]/transformers/src/transformers/modeling_utils.py", line 463, in from_pretrained
model = cls(config, *model_args, **model_kwargs)
TypeError: __init__() got an unexpected keyword argument 'output_attention'

# Fixed code
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
    r"""Instantiates one of the sequence classification model classes of the library
    from a pre-trained model configuration.

    The `from_pretrained()` method takes care of returning the correct model class instance
    based on the `model_type` property of the config object, or when it's missing,
    falling back to using pattern matching on the `pretrained_model_name_or_path` string.

    The model class to instantiate is selected as the first pattern matching
    in the `pretrained_model_name_or_path` string (in the following order):
        - contains `distilbert`: :class:`~transformers.DistilBertForSequenceClassification` (DistilBERT model)
        - contains `albert`: :class:`~transformers.AlbertForSequenceClassification` (ALBERT model)
        - contains `camembert`: :class:`~transformers.CamembertForSequenceClassification` (CamemBERT model)
        - contains `xlm-roberta`: :class:`~transformers.XLMRobertaForSequenceClassification` (XLM-RoBERTa model)
        - contains `roberta`: :class:`~transformers.RobertaForSequenceClassification` (RoBERTa model)
        - contains `bert`: :class:`~transformers.BertForSequenceClassification` (Bert model)
        - contains `xlnet`: :class:`~transformers.XLNetForSequenceClassification` (XLNet model)
        - contains `flaubert`: :class:`~transformers.FlaubertForSequenceClassification` (Flaubert model)

    The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
    To train the model, you should first set it back in training mode with `model.train()`

    Args:
        pretrained_model_name_or_path: either:

            - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
            - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
            - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
            - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.

        model_args: (`optional`) Sequence of positional arguments:
            All remaining positional arguments will be passed to the underlying model's ``__init__`` method

        config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
            Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:

            - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
            - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
            - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.

        state_dict: (`optional`) dict:
            an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
            This option can be used if you want to create a model from a pretrained configuration but load your own weights.
            In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.

        cache_dir: (`optional`) string:
            Path to a directory in which a downloaded pre-trained model
            configuration should be cached if the standard cache should not be used.

        force_download: (`optional`) boolean, default False:
            Force to (re-)download the model weights and configuration files and override the cached versions if they exists.

        resume_download: (`optional`) boolean, default False:
            Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.

        proxies: (`optional`) dict, default None:
            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.

        output_loading_info: (`optional`) boolean:
            Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.

        kwargs: (`optional`) Remaining dictionary of keyword arguments:
            These arguments will be passed to the configuration and the model.

    Examples::

        model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased')    # Download model and configuration from S3 and cache.
        model = AutoModelForSequenceClassification.from_pretrained('./test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
        assert model.config.output_attention == True
        # Loading from a TF checkpoint file instead of a PyTorch model (slower)
        config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
        model = AutoModelForSequenceClassification.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)

    """
    config = kwargs.pop("config", None)
    if not isinstance(config, PretrainedConfig):
        config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)

    for config_class, model_class in MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.items():
        if isinstance(config, config_class):
            return model_class.from_pretrained(
                pretrained_model_name_or_path, *model_args, config=config, **kwargs
            )
    raise ValueError(
        "Unrecognized configuration class {} for this kind of AutoModel: {}.\n"
        "Model type should be one of {}.".format(
            config.__class__,
            cls.__name__,
            ", ".join(
                c.__name__ for c in MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING.keys()
            ),
        )
    )