bi ddlmZddlZddlmZddlmZddlmZer GddeZ d ejjd e fd Z y) )OptionalN)is_inc_available)BaseTunerLayer)LinearczeZdZdejj deffd Zd dede e eddfdZ d d Z xZ S) IncLoraLinear base_layer adapter_namec (t|||fi|y)N)super__init__)selfr r kwargs __class__s O/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/peft/tuners/lora/inc.pyrzIncLoraLinear.__init__ s G Z @ @N safe_merge adapter_namesreturnctd)a Merge the active adapter weights into the base weights Args: safe_merge (`bool`, *optional*): If True, the merge operation will be performed in a copy of the original weights and check for NaNs before merging the weights. This is useful if you want to check if the merge operation will produce NaNs. Defaults to `False`. adapter_names (`list[str]`, *optional*): The list of adapter names that should be merged. If None, all active adapters will be merged. Defaults to `None`. z3Merging LoRA with INC layers is not yet implementedNotImplementedError)rrrs rmergezIncLoraLinear.merge(s&&[\ \rctd)z_ This method unmerges all merged adapter layers from the base weights. z5Unmerging LoRA from INC layers is not yet implementedr)rs runmergezIncLoraLinear.unmerge7s&&]^ ^r)FN)rN)__name__ __module__ __qualname__torchnnModulestrrboolrlistrr __classcell__)rs@rr r sS A A A ]D ]$s)AT ]`d ] _rr targetr c d}t|tr|j}n|}trddlm}t||r t ||fi|}|S)Nr) PatchedLinear) isinstancerget_base_layerrIneural_compressor.torch.algorithms.fp8_quant._quant_common.helper_modulesr)r )r'r r new_moduletarget_base_layerr)s r dispatch_incr/>sUJ&.)"113"  ' 7&v|FvFJ r)typingrr peft.import_utilsrpeft.tuners.tuners_utilsrlayerrr r!r"r#r/rrr5sG& .3__>r