oi&%SSKJr SSKJrJrJr SSKrSSKJr \(a SSK J r SSK J r 0r S\S '0rS \S '0rS \S '0rS \S'SSjrSSSjjrg)) annotations) TYPE_CHECKINGAnyOptionalN)PeftType) PeftConfig) BaseTunerz dict[PeftType, type[PeftConfig]]PEFT_TYPE_TO_CONFIG_MAPPINGzdict[PeftType, type[BaseTuner]]PEFT_TYPE_TO_TUNER_MAPPING PEFT_TYPE_TO_MIXED_MODEL_MAPPINGzdict[PeftType, str]PEFT_TYPE_TO_PREFIX_MAPPINGc&[US"S0UD6$)z Returns a Peft config object from a dictionary. Args: config_dict (`Dict[str, Any]`): Dictionary containing the configuration parameters. peft_type)r ) config_dicts F/home/james-whalen/.local/lib/python3.13/site-packages/peft/mapping.pyget_peft_configr$s '{;'? @ O; OOc UR(dUR(a [S5eUR[R 5;a[SURS35e[URnU"XX#US9nUR $)a, Create PEFT layers and inject them into the model in-place. Currently the API does not support prompt learning methods and adaption prompt. This function is similar to [`get_peft_model`] but it does not return a [`PeftModel`] instance. Instead, it returns the original, mutated instance of the passed model. Args: peft_config (`PeftConfig`): Configuration object containing the parameters of the PEFT model. model (`torch.nn.Module`): The input model where the adapter will be injected. adapter_name (`str`, `optional`, defaults to `"default"`): The name of the adapter to be injected, if not provided, the default adapter name is used ("default"). low_cpu_mem_usage (`bool`, `optional`, defaults to `False`): Create empty adapter weights on meta device. Useful to speed up the loading process. state_dict (`dict`, *optional*, defaults to `None`) If a `state_dict` is passed here, the adapters will be injected based on the entries of the state_dict. This can be useful when the exact `target_modules` of the PEFT method is unknown, for instance because the checkpoint was created without meta data. Note that the values from the `state_dict` are not used, only the keys are used to determine the correct layers that should be adapted. zN`create_and_replace` does not support prompt learning and adaption prompt yet.z+`inject_adapter_in_model` does not support z" yet. Please use `get_peft_model`.) adapter_namelow_cpu_mem_usage state_dict)is_prompt_learningis_adaption_prompt ValueErrorrr keysmodel) peft_configrrrr tuner_cls peft_models rinject_adapter_in_modelr"/s<%%)G)Gijj$>$C$C$EE9+:O:O9PPr s  +;+@+@AI gqJ   r)rzdict[str, Any]returnr )defaultFN) rr rtorch.nn.Modulerstrrboolrz!Optional[dict[str, torch.Tensor]]r#r%) __future__rtypingrrrtorchutilsrconfigr tuners.tuners_utilsr r __annotations__r r rrr"rrrr/s#// ".AC=B>@;@DF "AF3505P"#48 -- -- - 2 -  -r