o 1Ai= @sXUddlmZddlmZmZddlZddlmZddlm Z ddl m Z m Z m Z mZmZmZmZddlmZmZmZmZmZmZmZmZmZmZmZmZmZm Z m!Z!m"Z"m#Z#m$Z$dd l%m&Z&dd l'm(Z(ermdd l)m*Z*eee eee d Z+d e,d<ee$e"e#eeeeeeee d Z-de,d<e&eeeeee!dZ.d(ddZ/ d)d*d#d$Z0 d+d,d&d'Z1dS)-) annotations) TYPE_CHECKINGAnyN) PeftConfig)PeftMixedModel) PeftModelPeftModelForCausalLMPeftModelForFeatureExtractionPeftModelForQuestionAnsweringPeftModelForSeq2SeqLM"PeftModelForSequenceClassificationPeftModelForTokenClassification) AdaLoraConfig AdaLoraModelAdaptionPromptConfig IA3ConfigIA3Model LoHaConfig LoHaModel LoKrConfig LoKrModel LoraConfigMultitaskPromptTuningConfig OFTConfigOFTModel PolyConfig PolyModelPrefixTuningConfigPromptEncoderConfigPromptTuningConfig) LoraModel)_prepare_prompt_learning_config)PreTrainedModel)SEQ_CLS SEQ_2_SEQ_LM CAUSAL_LM TOKEN_CLS QUESTION_ANSFEATURE_EXTRACTIONzdict[str, PeftModel] MODEL_TYPE_TO_PEFT_MODEL_MAPPING) ADAPTION_PROMPT PROMPT_TUNING PREFIX_TUNINGP_TUNINGLORALOHALOKRADALORAIA3MULTITASK_PROMPT_TUNINGOFTPOLYzdict[str, PeftConfig]PEFT_TYPE_TO_CONFIG_MAPPING)r/r0r1r2r3r5r6 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)r7)r8r<r<@/data/cameron/vidgen/svd_motion_lora/Motion-LoRA/LoRA/mapping.pyget_peft_config`sr>defaultFmodelr# peft_config adapter_namestrmixedboolPeftModel | PeftMixedModelcCst|dddi}t|dr|}|jdd|_|r"t|||dS|jt vr3|j s3t |||dS|j r;t ||}t|j|||dS)aK Returns a Peft model object from a model and a config. Args: model ([`transformers.PreTrainedModel`]): Model to be wrapped. peft_config ([`PeftConfig`]): Configuration object containing the parameters of the Peft model. 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"). mixed (`bool`, `optional`, defaults to `False`): Whether to allow mixing different (compatible) adapter types. config model_typecustomto_dict name_or_pathNrB) getattrhasattrrJ__dict__getbase_model_name_or_pathr task_typer*keysis_prompt_learningrr")r@rArBrD model_configr<r<r=get_peft_modelks  rVtorch.nn.ModulecCsR|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"). 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`.rL)rTis_adaption_prompt ValueErrorr;PEFT_TYPE_TO_TUNER_MAPPINGrSr@)rAr@rB tuner_cls peft_modelr<r<r=inject_adapter_in_models   r])r8r9r:r)r?F) r@r#rArrBrCrDrEr:rF)r?)rArr@rWrBrCr:rW)2 __future__rtypingrrtorchZ peft.configrZpeft.mixed_modelrZpeft.peft_modelrrr r r r r Z peft.tunersrrrrrrrrrrrrrrrrrrr@r! peft.utilsr" transformersr#r*__annotations__r7rZr>rVr]r<r<r<r=sX  $P      "