qi1 UddlZddlZddlZddlZddlZddlmZddlmZm Z m Z m Z ddl m Z mZmZddlmZmZmZmZmZmZddlmZmZmZmZmZmZmZmZm Z ddl!m"Z"dd l#m$Z$erddl%Z%erdd l&m'Z'dd l(m)Z)m*Z*d Z+e jXe-Z.erdd l/m0Z0dZ1GddeZ2GddeZ3GddeZ4 d/de5eezdzde6de5eezfdZ7dddddddddddZ8e9e6e9e6e6ffe:d<dd d!hidd"d#hidZ;e9e6e9e6ed(e"fd)Z?d*e6fd+Z@d,e%jjd-e5e dzfd.ZCy)0N)replace) TYPE_CHECKINGAnyLiteralOptional)_MODEL_TO_CONVERSION_PATTERN!get_checkpoint_conversion_mappingget_model_conversion_mapping) Concatenate ConversionOpsMergeModulelist TransposeWeightConverterWeightRenaming) CONFIG_NAME cached_filecheck_peft_versionextract_commit_hashfind_adapter_config_fileis_accelerate_availableis_peft_availableis_torch_availablelogging)DownloadKwargs)log_state_dict_report)dispatch_model)get_balanced_memoryinfer_auto_device_mapz0.18.0)LoadStateDictConfigcg}tt|dD]3}|jtj|d||d|5tj |d}|S)Nrdim)rangelenappendtorch block_diagstack)tensorslora_b_block_diagiouts P/opt/pipecat/venv/lib/python3.12/site-packages/transformers/integrations/peft.py_block_diag_3dr0Csg 3wqz? #Q  !1!1'!*Q-A!OPQ ++'Q /C JceZdZdZej deeeejfdeedeededeeeejff dZ e de fdZ y ) PeftConcatenatea]Convert per-expert LoRA weights to merged weights. When the base weights are fused, e.g. W01 = [W0, W1], the LoRA weights also need to be fused. To achieve this correctly, concatenate the LoRA A weights along the r (rank) dimension. This doesn't require a new Operation. But for LoRA B, the weights need to be merged in a block diagonal fashion to achieve the correct result. To illustrate: Before W0' = W0 + A0 @ B0 W1' = W1 + A1 @ B1 After W01' = W01 + A01 @ B01_bd where A01 = [A0, A1] B01_bd = [[B0, 0], [0, B1]] This class is responsible for merging LoRA B in this block-diagonal fashion. Assuming that we fuse N weights, it should look like this: 1. LoRA B is 2-dim Normal LoRA weight of shape (out_feat, rank), the output shape should be (N * out_feat, N * rank). 2. LoRA B is 3-dim MoE LoRA weight of shape (experts, out_feat, rank), the output shape should be (experts, N * out_feat, N * rank). After this, the experts x rank dimension are flattened, as PEFT expects 2d tensors for LoRA. input_dictsource_patternstarget_patternsfull_layer_namereturnc |jDcgc]}|j}}t|dhdhfvrtdt|t|dhk(r$|t j |ji}|St |j} t j| d} | jdd} | j} || i}|Scc}w)NrzXTo convert this LoRA adapter, the LoRA weights all need to have either 2 or 3 dims, got rrr"rr") valuesr$set ValueErrorr(r)r0permuteflattenT) selfr4r5r6r7kwargsvdims output_dictr.s r/convertzPeftConcatenate.convertks",!2!2!45A55 t9aS1#J &jknosktjuv  t9 *E,<,O>O>Q,RSK !*"3"3"56C--Y/C++a#C%%C*C0K6sCctd)Nz9Reversing PEFT LoRA MoE conversions is not supported yet.NotImplementedErrorrBs r/ reverse_opzPeftConcatenate.reverse_ops!"]^^r1N)__name__ __module__ __qualname____doc__r(no_graddictstrlistTensorrGpropertyr rLr1r/r3r3Ks> ]]d5<<001cc    c4 %% &0_M__r1r3c eZdZdZdeeedfzfdZejde e e ejfde e de e de e e ejffd Z edefd Zd Zy ) FlattenDimsz8 Flatten the tensors along the given dimensions rE.c8t|tr|f}||_yN) isinstanceintrErBrEs r/__init__zFlattenDims.__init__s dC 7D r1r4r5r6r8c |jDcic]\}}||j|j!}}}|Scc}}wr[)itemsr@rE rBr4r5r6configrCkrDrFs r/rGzFlattenDims.convertE=Gr1rYc eZdZdZdeedffdZejde e e ejfde e de e de e e ejffd Z edefd Zd Zy ) PermuteDimsz8 Permute the tensors along the given dimensions rE.c||_yr[rEr^s r/r_zPermuteDims.__init__s  r1r4r5r6r8c |jDcic]\}}||j|j!}}}|Scc}}wr[)rar?rErbs r/rGzPermuteDims.convertrerfctd)Nz2Reversing flatteing operatio is not supported yet.rIrKs r/rLzPermuteDims.reverse_ops!"VWWr1cN|jjd|jdSrirkrKs r/rmzPermuteDims.__repr__rnr1N)rMrNrOrPror]r_r(rQrRrSrTrUrGrVr rLrmrWr1r/rqrqsU38_ ]] d5<<001 c c  c4 %% &  XMXX>r1rqweight_conversions adapter_namer8c |sgStddg}t}ddlm}t |dd}||vr|j ||n|j |jt|D]:}tj|}|jtd|dd| <|D]P} t| tr|j| &t| jd k(rw| jdjd rXd D]P} g} | j D]} t| t"rX| d k(r&| jt%| j&>| j| | jt)dkt| t*s|| j| g} t-| j.D].}|j1dd d}| j|d| d0| jd}|j3d d}|d| d|dg}| j5| || j6| j8| }|j|St| jd k(s| jdjdsd D][} g} | j D]} t| t*s| j| | dk(r| jt)dF| jt;d| jt)d| jt=dd g} t-| j.D].}|j1dd d}| j|d| d0| jd}|j3dd}|d| d|dg}| j5| || j6| j8| }|j|^S|S)Nzbase_model.model.model.zmodel.r)PEFT_TYPE_TO_PREFIX_MAPPING peft_type(z[^\.]*)z\1.)r5r6r" gate_up_proj)lora_Alora_Brr#)rr"rs.z.* base_layerz.weight)r5r6distributed_operationquantization_operation operations down_projr~r;)dim0dim1z .down_proj)rr= peft.mappingrzgetattraddupdater<sortedreescaper'r\r&r6endswithrr r3r$rYrrTr5rsplitrrlrrrqr)rwrx peft_confignew_weight_conversionsprefixesrzr{prefixescaped_prefixorig_conversionlorapeft_weight_operationsopnew_source_patternspatnew_target_patternsnew_conversions r/_build_peft_weight_mappingrs  --FQRuH8 [$7I// 0;<3::<=" 6*%% #$^$4G <#&|n 5   .R> o~ 6 " ) )/ :  .. /1 49X9XYZ9[9d9des9t,$ >)+&)44:B!"k28+299/bff:UV299"=299+6:RS#B8.55b9:')# ? ?@BC**S!,Q/C'..#avR/@A B&55a8kk.,?*-avQ|nG'L&M#"1!:!:$7$7*9*O*O+:+Q+Q5 ";"'--n=I$ >L00 1Q 6?;Z;Z[\;];f;fgr;s,# >)+&)44UB!"o6.55b98+299+6:RS299+9:UV299+6:RS299)QR:STU')# ? ?@BC**S!,Q/C'..#avR/@A B&55a8kk,3*-avQ|nG'L&M#"1!:!:$7$7*9*O*O+:+Q+Q5 ";"'--n=G# >_R>h "!r1z gate.weightr}r)gatew1w3w2)r gate_projup_projr)mixtral qwen2_moe_MOE_TARGET_MODULE_MAPPINGrrrr_MOE_FUSED_TARGETScFddlm}ddl}t|jdd}t |t|j jjjj|fd}||j jjj_ yy)zPEFT currently assumes that expert layers are of shape (expert, in, out) but with Mixtral in transformers v5 this is not true anymore. This will be addressed in PEFT >0.19 until then we need to handle it here for now. r)wrapsN model_typect|ds9|jdvr+|j}|j|_||_d|_|g|i|S)N_swapped_in_out)rr}T)hasattrparameter_name in_features out_featuresr)layerargsrCtmp_in_features update_layers r/new_update_layerz7patch_moe_parameter_targeting..new_update_layer^sZ5"349M9MQn9n"'"3"3$)$6$6!%4"(,%777 7r1) functoolsrpeftrrcr tunersrr ParamWrapperr)modelrrrrrrs @r/patch_moe_parameter_targetingrOs |T:J(4@{{''--::GG |  8  8= 0.18.0), it is possible to: - Load an adapter stored on a local path or in a remote Hub repository, and inject it in the model - Attach new adapters in the model and train them with Trainer or by your own. - Attach multiple adapters and iteratively activate / deactivate them - Activate / deactivate all adapters from the model. - Get the `state_dict` of the active adapter. FN_prepare_peft_hotswap_kwargsauto peft_model_idrxradapter_state_dictz torch.Tensorlow_cpu_mem_usage is_trainablehotswaplocal_files_onlyadapter_kwargs load_configr r8c Z !ddlm ddlm} m} |rd| d<| | j ni}|j | |jdd| d&i|} |xs | j}|d k(r8t|d d }t|jxr||jv}|xr|}|r\|jr||jvr td t fd |jjDr td||nd}| xsi} ddlm}m}|jr|s||jvrtd|d|r%|jr||jvr td || | td|`| j$j d&i| t'|fi| j$}|td|d|j(|fi| j$}t+|}t-|||}t/|dr| |_t3|||}t5|||s |||||jsd|_ ||ddg}| j6d ur|j9dx}}d}|D]>} | |dd|j;dr | j6nd dd || j$\}}n||xs t=dgi}}t?| |||} |jA|||| \}}|h!|@t|d!d3!jC|jDjFjId"tJd#tf!fd$ }|jLDchc] }||s |c}|_&tO|| j| jP|tR%y#t<$r }|}Yd}~6d}~wwxYwcc}w)'u Load adapter weights from file or remote Hub folder. If you are not familiar with adapters and PEFT methods, we invite you to read more about them on PEFT official documentation: https://huggingface.co/docs/peft Requires PEFT to be installed as a backend to load the adapter weights. Args: peft_model_id (`str`, *optional*): The identifier of the model to look for on the Hub, or a local path to the saved adapter config file and adapter weights. adapter_name (`str`, *optional*): The adapter name to use. If not set, will use the name "default". load_config (`LoadStateDictConfig`, *optional*): A load configuration to reuse when pulling adapter weights, typically from `from_pretrained`. kwargs (`dict[str, Any]`, *optional*): Additional `LoadStateDictConfig` fields passed as keyword arguments. peft_config (`dict[str, Any]`, *optional*): The configuration of the adapter to add, supported adapters are all non-prompt learning configs (LoRA, IA³, etc). This argument is used in case users directly pass PEFT state dicts. adapter_state_dict (`dict[str, torch.Tensor]`, *optional*): The state dict of the adapter to load. This argument is used in case users directly pass PEFT state dicts. low_cpu_mem_usage (`bool`, *optional*, defaults to `False`): Reduce memory usage while loading the PEFT adapter. This should also speed up the loading process. is_trainable (`bool`, *optional*, defaults to `False`): Whether the adapter should be trainable or not. If `False`, the adapter will be frozen and can only be used for inference. hotswap : (`"auto"` or `bool`, *optional*, defaults to `"auto"`) Whether to substitute an existing (LoRA) adapter with the newly loaded adapter in-place. This means that, instead of loading an additional adapter, this will take the existing adapter weights and replace them with the weights of the new adapter. This can be faster and more memory efficient. However, the main advantage of hotswapping is that when the model is compiled with torch.compile, loading the new adapter does not require recompilation of the model. When using hotswapping, the passed `adapter_name` should be the name of an already loaded adapter. If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need to call an additional method before loading the adapter: ```py model = AutoModel.from_pretrained(...) max_rank = ... # the highest rank among all LoRAs that you want to load # call *before* compiling and loading the LoRA adapter model.enable_peft_hotswap(target_rank=max_rank) model.load_adapter(file_name_1, adapter_name="default") # optionally compile the model now model = torch.compile(model, ...) output_1 = model(...) # now you can hotswap the 2nd adapter, use the same name as for the 1st # hotswap is activated by default since enable_peft_hotswap was called model.load_adapter(file_name_2, adapter_name="default") output_2 = model(...) ``` By default, hotswap is disabled and requires passing `hotswap=True`. If you called `enable_peft_hotswap` first, it is enabled. You can still manually disable it in that case by passing `hotswap=False`. Note that hotswapping comes with a couple of limitations documented here: https://huggingface.co/docs/peft/main/en/package_reference/hotswap adapter_kwargs (`dict[str, Any]`, *optional*): Additional keyword arguments passed along to the `from_pretrained` method of the adapter config and `find_adapter_config_file` method. rPeftTyper)r _get_resolved_checkpoint_filesTrNpretrained_model_name_or_pathr_hotswap_enabledFz\To hotswap an adapter, there must already be an existing adapter with the same adapter name.c3PK|]}|jjk7ywr[)r{LORA).0confrs r/ z0PeftAdapterMixin.load_adapter..sYt4>>X]]2Ys#&zMHotswapping is currently only supported for LoRA, please set `hotswap=False`.default PeftConfiginject_adapter_in_modelAdapter with name - already exists. Please use a different name.zhYou should either pass a `peft_model_id` or a `peft_config` and `adapter_state_dict` to load an adapter.z adapter model file not found in zB. Make sure you are passing the correct path to the adapter model.)r conversionsinference_mode)r)rrzadapter_model.safetensorszadapter_model.binz .safetensors)rvariant gguf_fileuse_safetensors user_agentis_remote_codetransformers_explicit_filenamedownload_kwargsz@Could not download either a .bin or a .safetensors adapter file.)rsharded_metadataweight_mapping)r state_dictcheckpoint_filesrr{keyr8c.tfdDS)Nc3&K|]}|v ywr[rW)rmarkerrs r/rzHPeftAdapterMixin.load_adapter..is_adapter_key..KsGv}Gs)any)radapter_key_markerss`r/is_adapter_keyz5PeftAdapterMixin.load_adapter..is_adapter_keyJsG3FGG Gr1)rrignore_mismatched_sizes loading_infologgerrW)*rrmodeling_utilsr r__dict__r setdefaultrrbool_hf_peft_config_loadedrr>rr<rrrrfrom_pretrainedr $convert_peft_config_for_transformersrrrrrreverserOSErrorr_load_pretrained_modelrr{valuelowerrS missing_keysrrr)"rBrrxrrrrrrrrrCr rbase_load_confighotswap_enablednot_first_adapterrradapter_config_filerwpeft_weight_conversionsadapter_filenamesrr last_erroradapter_filenameerrorr_rrdrrs" @@r/ load_adapterzPeftAdapterMixin.load_adaptersDZ "X )-F% &3>3J;//PR'##$CTJ)=,<= %R)R)R f %d,>FO $T%@%@%glVZVfVfFf h %;*;G //\IYIY5Y rYt?O?O?V?V?XYY !pqq'3'?|Y '-2<  & &ldN^N^>^1,?lmn n t:: TXTdTd@dn   &8&@[EXz    .K ' ' . . @ @":#--#  #* 6}oF%% 5*44--K :$?:;d`rs ; 0 1-9)9K &"<=OQ]kv"w%DkJ #K| D***.D '  %!<>Q R **e3!))+26 6 /J$5 ' '9W6C $"&;K;T;TUc;dK77jo#'',7G(3(C(C :6$&6 '$ ' oG,n$oo13R.  *7-2  55)-# 6  a ,n  "w{K'N'Z  # #K$9$9$?$?$E$E$G H H H H1=0I0I$_1^\]M^Q$_ !*5*S*S$/$G$G%  ='!&J'8%`s$.:N N(N( N%N  N% target_rankcheck_compiled)rwarnignorect|dir?|dk(r td|dk(rtjdn|dk7rt d|dd |_||d |_y ) aEnables the possibility to hotswap PEFT adapters with different ranks, or, if the model is compiled, without triggering recompilation. Right now, hotswapping is only supported for LoRA. Calling this method is only required when hotswapping adapters and if the model is compiled or if the ranks of the loaded adapters differ. If the ranks are all identical and the model is not compiled, hotswapping works without calling this method first. Args: target_rank (`int`, *optional*, defaults to `128`): The highest rank among all the adapters that will be loaded. check_compiled (`str`, *optional*, defaults to `"error"`): How to handle the case when the model is already compiled, which should generally be avoided. The options are: - "error" (default): raise an error - "warn": issue a warning - "ignore": do nothing rrzrr)rBr r s r/enable_peft_hotswapz$PeftAdapterMixin.enable_peft_hotswapWsy, 4 +("#abb6)~ 8+ YZhYiist!%r\ TypeErrortypergetbase_model_name_or_path set_adapter)rBadapter_configrxrrs r/ add_adapterzPeftAdapterMixin.add_adapter|s$ '78<#0y ***.D ' T-- -1,?lmn n.*5VW[\jWkVlluvw w261B1B>SW1X.lC &r1c |tt|js tdt |t rpt |t |jz }t|dkDrtddj|dt |jj||jvr2td|dt |jjdd l m }dd l m}d }|jD]'\}}t |||fs|j!|d })|s td y)a If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: https://huggingface.co/docs/peft Sets a specific adapter by forcing the model to use a that adapter and disable the other adapters. Args: adapter_name (`Union[list[str], str]`): The name of the adapter to set. Can be also a list of strings to set multiple adapters. r0No adapter loaded. Please load an adapter first.rz)Following adapter(s) could not be found: , zV. Make sure you are passing the correct adapter name(s). current loaded adapters are: rz7 not found. Please pass the correct adapter name among BaseTunerLayerModulesToSaveWrapperFTzhDid not succeeded in setting the adapter. Please make sure you are using a model that supports adapters.N)rrrr>r\rTr=rr&joinkeyspeft.tuners.tuners_utilsr" peft.utilsr$ named_modulesr)rBrxmissingr"r$_adapters_has_been_setrmodules r/rzPeftAdapterMixin.set_adaptersZ '78**OP P  d +,'#d.>.>*??G7|a ? '@R?ST559$:J:J:O:O:Q5R4SU!1!1 1$\N2ijnosoopEpEpGkHjIJ  <3!&++- .IAv&>3G"HI""<0)-& . &z &r1ctt|js tdddlm}ddlm}|jD]&\}}t|||fs|jd(y) a, If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: https://huggingface.co/docs/peft Disable all adapters that are attached to the model. This leads to inferring with the base model only. rrrr!r#FenabledN) rrrr>r'r"r(r$r)r\enable_adapters)rBr"r$rr,s r/disable_adaptersz!PeftAdapterMixin.disable_adaptersse '78**OP P;3++- 6IAv&>3G"HI&&u&5 6r1ctt|js tdddlm}|j D]$\}}t||s|jd&y)z If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: https://huggingface.co/docs/peft Enable adapters that are attached to the model. rrrr!Tr.N) rrrr>r'r"r)r\r0)rBr"rr,s r/r0z PeftAdapterMixin.enable_adapterss\ '78**OP P;++- 5IAv&.1&&t&4 5r1ctt|js tdddlm}|j D]\}}t||s|j}nttr|g}|S)a3 If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: https://huggingface.co/docs/peft Gets the current active adapters of the model. In case of multi-adapter inference (combining multiple adapters for inference) returns the list of all active adapters so that users can deal with them accordingly. For previous PEFT versions (that does not support multi-adapter inference), `module.active_adapter` will return a single string. rrrr!) rrrr>r'r"r)r\active_adapterrS)rBr"rr,active_adapterss r/r5z PeftAdapterMixin.active_adaptersss '78**OP P;++- IAv&.1"("7"7  os +./Or1rctt|js tdddlm}||j d}||||}|S)ax If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: https://huggingface.co/docs/peft Gets the adapter state dict that should only contain the weights tensors of the specified adapter_name adapter. If no adapter_name is passed, the active adapter is used. Args: adapter_name (`str`, *optional*): The name of the adapter to get the state dict from. If no name is passed, the active adapter is used. state_dict (nested dictionary of `torch.Tensor`, *optional*) The state dictionary of the model. Will default to `self.state_dict()`, but can be used if special precautions need to be taken when recovering the state dictionary of a model (like when using model parallelism). rrr)get_peft_model_state_dict)rrx)rrrr>rr7r5)rBrxrr7rs r/get_adapter_state_dictz'PeftAdapterMixin.get_adapter_state_dictsT '78**OP P2  //1!4L6t amn!!r1 device_map max_memoryoffload_folder offload_indexci}dtjtjvr||d<|j}|dk7rt ||||dk(}t |trt|||}t|f||d|y)a Optional re-dispatch the model and attach new hooks to the model in case the model has been loaded with accelerate (i.e. with `device_map=xxx`) Args: device_map (`str` or `dict[str, Union[int, str, torch.device]]` or `int` or `torch.device`, *optional*): A map that specifies where each submodule should go. It doesn't need to be refined to each parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the same device. If we only pass the device (*e.g.*, `"cpu"`, `"cuda:1"`, `"mps"`, or a GPU ordinal rank like `1`) on which the model will be allocated, the device map will map the entire model to this device. Passing `device_map = 0` means put the whole model on GPU 0. To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For more information about each option see [designing a device map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map). max_memory (`Dict`, *optional*): A dictionary device identifier to maximum memory. Will default to the maximum memory available for each GPU and the available CPU RAM if unset. offload_folder (`str` or `os.PathLike`, *optional*): If the `device_map` contains any value `"disk"`, the folder where we will offload weights. offload_index (`int`, *optional*): The offload_index argument to be passed to `accelerate.dispatch_model` method. r< sequentialbalanced_low_0)r:no_split_module_classeslow_zero)r:r@)r9 offload_dirN) inspect signaturer parameters_no_split_modulesrr\rSr)rBr9r:r;r<dispatch_model_kwargsr@s r/_dispatch_accelerate_modelz+PeftAdapterMixin._dispatch_accelerate_model,s<!# g//?JJ J5B !/ 2"&"8"8  %,%(?$(88 J j# &.E\J   !& $  r1 adapter_namesctt|js tdddlm}t |tr|g}|Dcgc]}||jvs|}}|rtddj||Dcgc]6}|j|jjjd8}}t||D]G\}}||||t|d d st|d s,|jj!|d It#|jdk(r |`d |_y y cc}wcc}w) z Delete a PEFT adapter from the underlying model. Args: adapter_names (`Union[list[str], str]`): The name(s) of the adapter(s) to delete. rrr)delete_adapterz@The following adapter(s) are not present and cannot be deleted: r r)rxrrFrN)rrrr>peft.functionalrKr\rSrr%r{rrziprrpopr&)rBrIrKnamemissing_adaptersrxrrs r/rKzPeftAdapterMixin.delete_adapterdsI '78**OP P2 mS )*OM.;d$JZJZ>ZD[[ RSWS\S\]mSnRop fssUat'' 5??EEKKMNaPss$' x$@ 9 L& 4l6 Jt5u='$P]B^  $$\48  9 t A % */D ' &\ tsD8D8;D=) NNNNFFrFNN)rr[)r8N)NN)NNN)rMrNrOrPrrrR__annotations__rSrrrrr r]rrrTrr1r0r5r8rHrKrWr1r/rrjs$#04 $+4%)#'-1?C"'"*0!&047;U TzU DjU #s(^d* U !n!45< U  U U 'U U S#X-U 34U  U p\c#k#k6=>W6X#k #kJ&'d &'d&'P'S C'D'R6&5$c:"3:"RVY]R]"im"@"&%)$( 6 6 $J6 d 6 Tz 6  6 p%0DIO%0%0r1rrc | tsd||fS|jd}|jdt|t|jdt |jdd|jdt |jdd||jd|jd ddd }t |d|d<|j d d}|j dd}||j}d D]}||vs||vs ||||<d|vr|d|d <t |jdd|d<t |jdd|d<|xs||d<t|fi|}|Ptjj|r1t|dd5} |}tj| d}ddd|||fS#1swYxYw)Ntoken commit_hash cache_dirforce_downloadFproxiesrrevision subfolder) rVrWrXrrTrYrZ _raise_exceptions_for_gated_repo%_raise_exceptions_for_missing_entries'_raise_exceptions_for_connection_errors_adapter_model_path)rVrXrZ _commit_hashrzutf-8)encodingr)rrrrrrrNcopyrospathisfileopenjsonload) rrrrTresolved_config_filer^token_from_adapter_kwargs peft_kwargsarg_namefs r/maybe_load_adaptersrns& %,4E4G2NBB    (E=)1* ) %))+6 3 34De LM#'' 2!/"5"56H%"PQ$((4%))+6-22749 *==QSW)X &(,,-BDI . 2 27D A"$))+ = BH +(o2M(7(A H% B O +*9-*HK '(,_-@-@AQSX-Y(Z $%*./B/BCUW\/]*^ &'$A(A G6 )   &277>>:M+N %sW = T"? ,0IIaL9R,S ) T  =~ MM  T Ts 'GGrc tj|d}||St|}tj|i}t |j xsg|_t |j xsg|_t|dr |ji|_|j j}t }|Dcic] }|t }}|j D]} d} d} |jD]$\} }| | k(s| jd| s |} | } n| |j| R|j| | |vsh| k|j| t j| |jD]\}} |j|t }dt|cxkrt| krOnnLdjt!| |z }djt!|}t#d|d|d|dt|t| k(st| dkDs|j$t| z|jd t'j(|<||_||_|Scc}w) N rank_patternrrr zCannot convert PEFT target(s) z without also targeting z because they are fused into r"z.*\.)r rrrr=target_parameterstarget_modulesrrprbrarrrr&r%rr>r`rr)rrbase_model_typetarget_module_mapping fused_targetsnew_target_parametersremaining_target_modulesnew_namematched_targetstargetmapped_new_namemapped_old_nameold_namerequired_old_targetspresent_targetsr*presents r/_convert_peft_config_moers266z4HO6G&**?B?M$' (E(E(K$LK!!$[%?%?%E2!FK ; /;3K3K3S#%  '99>>@"uLY+ZHceO+ZO+Z,,T"7"="="?  Hh("v8*~'F"*"*    " $ ( ( 0 !!/2 m +0K  & & > B B? ST"+8*=*=*? p&&)--h> s?# ?c*>&? ?ii';o'M NOGii 78G0 9QRYQZZwyAxBBCD   3';#< <EYAZ]^A^FQmmVYZnVoFoK $ $RYYx-@,A%B C p%:K!!9K E,[s=Jrrcddlm}|j|jk7r|St |ds|St |j ds|St j|}t|j dd}t| t||}|S)Nrrrcr) rrr{rrrcrbdeepcopyrr r)rrrrrs r/rrs - 5( # 5<< .-- ,K|T:J(4@.{JG r1r[)DrbrCrgrcr dataclassesrtypingrrrrconversion_mappingr r r core_model_loadingr r rrrrutilsrrrrrrrrr utils.hubrutils.loading_reportrr( accelerateraccelerate.utilsrrr get_loggerrMrrr r0r3rYrqrTrSrrrRrRrr=rrrnrnnModulerrWr1r/rs  88    '8)K   H %4;_k;_|>-><>->:gkq"_~=>Eq"UXq" /N *+q"r #!  9Dd38n!45  t - ; ":;6Dd3C=112 L6_0_0D3N#3ND1c1hUXX__[_`c[dgk[kr1