bi dZddlZGddejZiddddd d d d d ddddddddddddddddddd d!d"d#d$Zddd d ddddd%Zej eejeiZd&d'iZ d(Z d*d)Z y)+z State dict utilities: utility methods for converting state dicts easily File copied from diffusers to avoid import issues and make TRL compatible with most of diffusers versions. NceZdZdZdZdZy) StateDictTypez6 The mode to use when converting state dicts. diffusers_oldpeftN)__name__ __module__ __qualname____doc__ DIFFUSERS_OLDPEFTN/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/trl/models/sd_utils.pyrrs$M Drrz.q_proj.lora_Bz.q_proj.lora_linear_layer.upz.q_proj.lora_Az.q_proj.lora_linear_layer.downz.k_proj.lora_Bz.k_proj.lora_linear_layer.upz.k_proj.lora_Az.k_proj.lora_linear_layer.downz.v_proj.lora_Bz.v_proj.lora_linear_layer.upz.v_proj.lora_Az.v_proj.lora_linear_layer.downz.out_proj.lora_Bz.out_proj.lora_linear_layer.upz.out_proj.lora_Az .out_proj.lora_linear_layer.downz to_k.lora_Azto_k.lora.downz to_k.lora_Bz to_k.lora.upz to_q.lora_Azto_q.lora.downz to_q.lora_Bz to_q.lora.upz to_v.lora_Azto_v.lora.downz to_v.lora_Bz to_v.lora.upzto_out.0.lora_Azto_out.0.lora.downzto_out.0.lora_Bzto_out.0.lora.up)z .to_q_lora.upz.to_q_lora.downz .to_k_lora.upz.to_k_lora.downz .to_v_lora.upz.to_v_lora.downz.to_out_lora.upz.to_out_lora.downz .processor..ci}|jD]u\}}tjD]"}||vst|}|j||}$|jD]}||vs||}|j||}n|||<w|S)a& Simply iterates over the state dict and replaces the patterns in `mapping` with the corresponding values. Args: state_dict (`dict[str, torch.Tensor]`): The state dict to convert. mapping (`dict[str, str]`): The mapping to use for conversion, the mapping should be a dictionary with the following structure: - key: the pattern to replace - value: the pattern to replace with Returns: converted_state_dict (`dict`) The converted state dict. )itemsKEYS_TO_ALWAYS_REPLACEkeysreplace) state_dictmappingconverted_state_dictkvpattern new_patterns rconvert_state_dictrHs   " $1-224 4G!|4W= IIg{3 4 ||~ G!|%g. IIg{3   #$Q $ rc |jdddznd|td|jDrtj}n`tfd|jDrtj }n-td|jDr|St d|tjvrt d |d t|}t||S) a Converts a state dict to new diffusers format. The state dict can be from previous diffusers format (`OLD_DIFFUSERS`), or PEFT format (`PEFT`) or new diffusers format (`DIFFUSERS`). In the last case the method will return the state dict as is. The method only supports the conversion from diffusers old, PEFT to diffusers new for now. Args: state_dict (`dict[str, torch.Tensor]`): The state dict to convert. original_type (`StateDictType`, *optional*): The original type of the state dict, if not provided, the method will try to infer it automatically. kwargs (`dict`, *args*): Additional arguments to pass to the method. - **adapter_name**: For example, in case of PEFT, some keys will be pre-pended with the adapter name, therefore needs a special handling. By default PEFT also takes care of that in `get_peft_model_state_dict` method: https://github.com/huggingface/peft/blob/ba0477f2985b1ba311b83459d29895c809404e99/src/peft/utils/save_and_load.py#L92 but we add it here in case we don't want to rely on that method. adapter_nameNrc3$K|]}d|v yw) to_out_loraNr .0rs r z2convert_state_dict_to_diffusers..s=a}!=c3.K|] }dd|vyw)z.lora_Az.weightNr )r$rpeft_adapter_names rr%z2convert_state_dict_to_diffusers..s!Vq7,-W5:Vsc3$K|]}d|v yw)lora_linear_layerNr r#s rr%z2convert_state_dict_to_diffusers..sEa$)Er&z-Could not automatically infer state dict typezOriginal type z is not supported) popanyrrr r ValueErrorDIFFUSERS_STATE_DICT_MAPPINGSr)r original_typekwargsrr(s @rconvert_state_dict_to_diffusersr1is, >48$"33 =:??+<= =)77M VJOODUV V)..M E:??3DE E LM M9>>@@>-8IJKK+M:G j' 22r)N) r enumEnumrPEFT_TO_DIFFUSERSDIFFUSERS_OLD_TO_DIFFUSERSr r r.rrr1r rrr6sN  DII4646  4  6 8:#>#>#>+ )!(4737377; !;)! 3  B,3r