#
Normalized Configurations

Model configuration classes in 🤗 Transformers are not standardized. Although Transformers implements an `attribute_map` attribute that mitigates the issue to some extent, it does not make it easy to reason on common configuration attributes in the code. NormalizedConfig classes try to fix that by allowing access to the configuration attribute they wrap in a standardized way.

##
Base class

> While it is possible to create `NormalizedConfig` subclasses for common use-cases, it is also possible to overwrite the `original attribute name -> normalized attribute name` mapping directly using the `with_args()` class method.

###
class optimum.utils.NormalizedConfig

< source >

( config: typing.Union[ForwardRef('PretrainedConfig'), typing.Dict] allow_new: bool = False **kwargs )

Parameters

- config (`PretrainedConfig`) — The config to normalize.

Handles the normalization of `PretrainedConfig` attribute names, allowing to access attributes in a general way.

##
Existing normalized configurations

###
class optimum.utils.NormalizedTextConfig

< source >

( config: typing.Union[ForwardRef('PretrainedConfig'), typing.Dict] allow_new: bool = False **kwargs )

###
class optimum.utils.NormalizedSeq2SeqConfig

< source >

( config: typing.Union[ForwardRef('PretrainedConfig'), typing.Dict] allow_new: bool = False **kwargs )

###
class optimum.utils.NormalizedVisionConfig

< source >

( config: typing.Union[ForwardRef('PretrainedConfig'), typing.Dict] allow_new: bool = False **kwargs )

###
class optimum.utils.NormalizedTextAndVisionConfig

< source >

( config: typing.Union[ForwardRef('PretrainedConfig'), typing.Dict] allow_new: bool = False **kwargs )