o 8wi @sUddlmZddlmZmZddlZddlmZer'ddlm Z ddl m Z iZ de d <iZd e d <iZd e d <iZd e d<d ddZ d!d"ddZdS)#) annotations) TYPE_CHECKINGAnyN)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_MAPPING config_dictdict[str, Any]returnrcCst|ddi|S)z Returns a Peft config object from a dictionary. Args: config_dict (`Dict[str, Any]`): Dictionary containing the configuration parameters. peft_typeN)r )r rrI/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/peft/mapping.pyget_peft_config$srdefaultF peft_configmodeltorch.nn.Module adapter_namestrlow_cpu_mem_usageboolcCsT|js|jr td|jtvrtd|jdt|j}|||||d}|jS)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. 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`.)rr)is_prompt_learningis_adaption_prompt ValueErrorrr keysr)rrrr tuner_cls peft_modelrrrinject_adapter_in_model/s   r")r rrr)rF) rrrrrrrrrr) __future__rtypingrrtorchutilsrconfigrtuners.tuners_utilsrr __annotations__r r r rr"rrrrs