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mZ eG dd de	ZdS )	    )annotations)	dataclassfield)OptionalUnion)
PeftConfig)PeftTypec                      sj   e Zd ZU dZedddidZded< edddidZded	< eddd
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S )LNTuningConfiga  
    This is the configuration class to store the configuration of a :class:`~peft.tuners.LNTuningModel`.

    Args:
        target_modules (`Optional[Union[List[str], str]]`):
            List of module names or regex expression of the module names to replace with LNTuning. For example,
            '.*decoder.*' or '.*encoder.*'. If this is not specified, modules will be chosen according to the model
            architecture. If the architecture is not known, an error will be raised -- in this case, you should specify
            the target modules manually.
        exclude_modules (`Optional[Union[List[str], str]]`):
            The names of the modules to not apply the adapter. When passing a string, a regex match will be performed.
            When passing a list of strings, either an exact match will be performed or it is checked if the name of the
            module ends with any of the passed strings.
        modules_to_save (`Optional[Union[List[str], str]]`):
            List of modules to be set as trainable and saved in the final checkpoint. For example, in Sequence
            Classification or Token Classification tasks, the final layer `classifier/score` are randomly initialized
            and as such need to be trainable and saved.
    NhelpaJ  List of module names or regex expression of the module names to replace with LNTuning.For example, '.*decoder.*' or '.*encoder.*'. If not specified, modules will be chosen according to the model architecture, If the architecture is not known, an error will be raised -- in this case, you shoud specify the target modules manually.)defaultmetadatazOptional[Union[list[str], str]]target_moduleszVList of module names or regex expression of the module names to exclude from LNTuning.exclude_moduleszList of modules to be set as trainable and saved in the final checkpoint. For example, in Sequence Classification or Token Classification tasks, the final layer `classifier/score` are randomly initialized and as such need to be trainable and saved.modules_to_savec                   s   t    tj| _d S )N)super__post_init__r   	LN_TUNING	peft_type)self	__class__ P/home/ubuntu/.local/lib/python3.10/site-packages/peft/tuners/ln_tuning/config.pyr   D   s   
zLNTuningConfig.__post_init__)__name__
__module____qualname____doc__r   r   __annotations__r   r   r   __classcell__r   r   r   r   r	      s$   
 	r	   N)
__future__r   dataclassesr   r   typingr   r   peft.configr   
peft.utilsr   r	   r   r   r   r   <module>   s   