bi):ddlZddlZddlZddlZddlmZmZmZddlm Z m Z ddl m Z ddl mZmZddlmZmZmZdhZd ZeGd d eZeGd d eZeGddeZy)N)asdict dataclassfield)OptionalUnion)hf_hub_download)PushToHubMixinhttp_user_agent) CONFIG_NAMEPeftTypeTaskType peft_typectj|jj}t |j t |j z }|D]}||=||fS)aMake PEFT configs forward-compatible by removing unused kwargs that were added in later PEFT versions. This assumes that removing the unused kwargs will not affect the default behavior. Returns the filtered kwargs and the set of removed keys. )inspect signature__init__ parameterssetkeys)clskwargssignature_parametersunexpected_kwargskeys F/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/peft/config.py_check_and_remove_unused_kwargsrsf#,,S\\:EEFKKM*S1E1J1J1L-MM  3K $ $$cVeZdZUdZedddiZeeed<edddiZ ee ed<eddd iZ ee ed <d Z d e fd Zded dfdZedZeddedeefdZedefdZedZedefdZedZed efdZed efdZy)PeftConfigMixinaC This is the base configuration class for PEFT adapter models. It contains all the methods that are common to all PEFT adapter models. This class inherits from [`~transformers.utils.PushToHubMixin`] which contains the methods to push your model to the Hub. The method `save_pretrained` will save the configuration of your adapter model in a directory. The method `from_pretrained` will load the configuration of your adapter model from a directory. Args: peft_type (Union[[`~peft.utils.config.PeftType`], `str`]): The type of Peft method to use. NhelpzThe type of task.defaultmetadata task_typezThe type of PEFT model.rzEAn auto mapping dict to help retrieve the base model class if needed. auto_mappingc|jK|jttvr/td|jddj tdyy)NzInvalid task type: 'z,'. Must be one of the following task types: z, .)r%listr ValueErrorjoinselfs r __post_init__zPeftConfigMixin.__post_init__@sX NN &T^^4>-Q&t~~&66bcgclclmucvbwwxy .R &rreturnct|S)zS Returns the configuration for your adapter model as a dictionary. )rr,s rto_dictzPeftConfigMixin.to_dictGsd|rsave_directoryc &tjj|rtd|dtj|d|j dd}|j }|jD]$\}}t|tst|||<&tjj|t}|||d<t|d5}|jtj |d d dddy#1swYyxYw) a This method saves the configuration of your adapter model in a directory. Args: save_directory (`str`): The directory where the configuration will be saved. kwargs (additional keyword arguments, *optional*): Additional keyword arguments passed along to the [`~transformers.utils.PushToHubMixin.push_to_hub`] method. zProvided path (z#) should be a directory, not a fileT)exist_okauto_mapping_dictNr&w)indent sort_keys)ospathisfileAssertionErrormakedirspopr1items isinstancerr)r+r openwritejsondumps) r-r2rr5 output_dictrvalue output_pathwriters rsave_pretrainedzPeftConfigMixin.save_pretrainedMs 77>>. ) ?>2BBe!fg g NT2"JJ':DAlln %++- /JC%%#'; C  /ggll>;?   (*;K '+s # Lv LLKTJ K L L Ls (DDc ddlm}d|vr |d}||}n|} |d i|}|S#t$r}dt|vr|t ||\}}t j t|js td|jdt dtjdt|d |jd |jd i|}Yd }~|Sd }~wwxYw) a This method loads the configuration of your adapter model from a set of kwargs. The appropriate configuration type is determined by the `peft_type` argument. If `peft_type` is not provided, the calling class type is instantiated. Args: kwargs (configuration keyword arguments): Keyword arguments passed along to the configuration initialization. r)PEFT_TYPE_TO_CONFIG_MAPPINGrz"got an unexpected keyword argumentzThe z> config that is trying to be loaded is missing required keys: r(zUnexpected keyword arguments z for class a,, these are ignored. This probably means that you're loading a configuration file that was saved using a higher version of the library and additional parameters have been introduced since. 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N user_agentr] Can't find '' at ''rM) r:r;r+ _split_kwargsr r<r r Exceptionr*from_json_file check_kwargsrW) rr\r]rr;hf_hub_download_kwargs class_kwargs_ config_filerZloaded_attributess rfrom_pretrainedzPeftConfigMixin.from_pretraineds2$ GGLL6 B. 362C2CF2K/ a 5 53B3D "< 0 77>>"'',,t[9 :'',,t[9K n-1;JSWm  ..{;6L6$56!!!+F+!s!!+F++  n < }FC`Baab!cdjmm ns2D D% D  D%path_json_filec t|5}tj|}ddddvrtjd|d=|S#1swY'xYw)z Loads a configuration file from a json file. Args: path_json_file (`str`): The path to the json file. Nruntime_configzzThe configuration file contains a `runtime_config` key. This is ignored. Runtime configurations are only valid at runtime.)rBrDloadrTrU)rrmrfile json_objects rrezPeftConfigMixin.from_json_files]. ! *T))D/K * { * MMM ,- * *s AAci}i}i}|jD]R\}}|tjtjvr|||<1|t |j vr|||<N|||<T|||fSN)r@rrrrr)__annotations__)rrrgrh other_kwargsrrGs rrczPeftConfigMixin._split_kwargss!#   ,,. *JCg''8CCC.3&s+S0011$) S!$) S!  *&|\AArmodel_idc |jdd}| tjj||n|}tjj tjj|t r%tjj|t }n t |t fi|}|j|}|dS#t$rtdt d|dwxYw)Nr]r`rarbr) getr:r;r+r<r rrdr*re)rrwrgr]r;rjrks r_get_peft_typezPeftConfigMixin._get_peft_types +..{DA 4=4Irww||Hi0x 77>>"'',,t[9 :'',,t[9K P--  ..{; --  P < }F8*A!NOO Ps C C%c |S)zCheck kwargs before initializing the config instance. Subclasses can override this method to add specific checks. rM)rrs rrfzPeftConfigMixin.check_kwargss  rcy)V Utility method to check if the configuration is for prompt learning. FrMr,s ris_prompt_learningz"PeftConfigMixin.is_prompt_learnings rcy)z1Return True if this is an adaption prompt config.FrMr,s ris_adaption_promptz"PeftConfigMixin.is_adaption_prompt!srrt)rS __module__ __qualname____doc__rr%rrrurr r&dictr.r1rPrJ classmethodrWrlrercrzrfpropertyboolr~rrMrrr r .s]%*$&J]A^$_Ix!_$)$&JcAd$eIx!e#((op$L(4. LcLLB>>@!,C!,HUXM!,!,FC( B B...0D Drr ceZdZUdZedddiZeeed<edddiZ eeed<eddd iZ ee ee fed <eddd iZ ee eefed <ed ddiZeed<y) PeftConfiga This is the base configuration class to store the configuration of a [`PeftModel`]. Args: peft_type (Union[[`~peft.utils.config.PeftType`], `str`]): The type of Peft method to use. task_type (Union[[`~peft.utils.config.TaskType`], `str`]): The type of task to perform. inference_mode (`bool`, defaults to `False`): Whether to use the Peft model in inference mode. Nr!z"The name of the base model to use.r"base_model_name_or_pathz'The specific base model version to use.revisionz Peft typerz Task typer%FzWhether to use inference modeinference_mode)rSrrrrrrrPrurrrr r%rrrrMrrrr's.3(LM.Xc]$DFDm;noHhsmo05dfVaMb0cIxc8m,-c05dfVaMb0cIxc8m,-c &Ba9bcNDcrrceZdZUdZedddiZeed<edddiZeed<eddd iZ e eed <eddd iZ e eed <eddd iZ e eed<edddiZ e eeed<edefdZy)PromptLearningConfigaY This is the base configuration class to store the configuration of [`PrefixTuning`], [`PromptEncoder`], or [`PromptTuning`]. Args: num_virtual_tokens (`int`): The number of virtual tokens to use. token_dim (`int`): The hidden embedding dimension of the base transformer model. num_transformer_submodules (`int`): The number of transformer submodules in the base transformer model. num_attention_heads (`int`): The number of attention heads in the base transformer model. num_layers (`int`): The number of layers in the base transformer model. Nr!zNumber of virtual tokensr"num_virtual_tokensz