Ë §Ùbi2ãó¼—UddlmZddlmZmZmZddlZddlmZer ddl m Z ddl m Z iZ ded <iZd ed <iZd ed <iZd ed<dd„Z d dd„Zy)é)Ú annotations)Ú TYPE_CHECKINGÚAnyÚOptionalNé)ÚPeftType)Ú PeftConfig)Ú BaseTunerz dict[PeftType, type[PeftConfig]]ÚPEFT_TYPE_TO_CONFIG_MAPPINGzdict[PeftType, type[BaseTuner]]ÚPEFT_TYPE_TO_TUNER_MAPPINGÚ PEFT_TYPE_TO_MIXED_MODEL_MAPPINGzdict[PeftType, str]ÚPEFT_TYPE_TO_PREFIX_MAPPINGcó$—t|ddi|¤ŽS)z  Returns a Peft config object from a dictionary. Args: config_dict (`Dict[str, Any]`): Dictionary containing the configuration parameters. Ú peft_type©)r )Ú config_dicts úG/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/peft/mapping.pyÚget_peft_configr$s€ô ' {°;Ñ'?Ñ @Ñ OÀ;Ñ OÐOócó—|js |jr td«‚|jtj «vrtd|j›d«‚t|j}||||||¬«}|j S)a„ A simple API to create and inject adapter in-place into a model. Currently the API does not support prompt learning methods and adaption prompt. Make sure to have the correct `target_names` set in the `peft_config` object. The API calls `get_peft_model` under the hood but would be restricted only to non-prompt learning methods. Args: peft_config (`PeftConfig`): Configuration object containing the parameters of the Peft model. model (`torch.nn.Module`): The input model where the adapter will be injected. adapter_name (`str`, `optional`, defaults to `"default"`): The name of the adapter to be injected, if not provided, the default adapter name is used ("default"). low_cpu_mem_usage (`bool`, `optional`, defaults to `False`): Create empty adapter weights on meta device. Useful to speed up the loading process. state_dict (`dict`, *optional*, defaults to `None`) If a state_dict is passed here, the adapters will be injected based on the entries of the state_dict. This can be useful when the exact `target_modules` of the PEFT method is unknown, for instance because the checkpoint was created without meta data. Note that the values from the state_dict are not used, only the keys are used to determine the correct layers that should be adapted. zN`create_and_replace` does not support prompt learning and adaption prompt yet.z+`inject_adapter_in_model` does not support z" yet. Please use `get_peft_model`.)Ú adapter_nameÚlow_cpu_mem_usageÚ state_dict)Úis_prompt_learningÚis_adaption_promptÚ ValueErrorrr ÚkeysÚmodel)Ú peft_configrrrrÚ tuner_clsÚ peft_models rÚinject_adapter_in_modelr"/s“€ð6×%Ò%¨×)GÒ)GÜÐiÓjÐjà×ÑÔ$>×$CÑ$CÓ$EÑEÜØ9¸+×:OÑ:OÐ9PÐPrÐ só ð ô+¨;×+@Ñ+@ÑA€IñØ ˆ{¨ÐIZÐgqô€Jð × Ñ Ðr)rzdict[str, Any]Úreturnr )ÚdefaultFN) rr rútorch.nn.ModulerÚstrrÚboolrz!Optional[dict[str, torch.Tensor]]r#r%)Ú __future__rÚtypingrrrÚtorchÚutilsrÚconfigr Útuners.tuners_utilsr r Ú__annotations__r r rrr"rrrúr/s£ðö#ç/Ñ/ã åñÝ"Ý.ðACÐÐ=ÓBØ>@ÐÐ;Ó@ØDFÐ Ð"AÓFØ35ÐÐ0Ó5óPð"Ø#Ø48ð *Øð*à ð*ðð*ðð *ð 2ð *ð ô *r