o /ѹg,@sddlZddlmZmZmZmZmZddlmZddl m Z ddl m Z ddl mZmZddlmZmZmZmZddlmZdd lmZmZdd lmZmZd d lmZd d lm Z edZ!  d0ddZ"  d0dede#de$fddZ%dddddddedf de#dee#ee#e ee fde#de#de#de#dee#dee#defd d!Z&  "d1de#d#e#d$e#d%e'fd&d'Z(d(d)Z)dee#ee#e ee fd*ee#d+ee#efdee#fd,d-Z*d+ede#fd.d/Z+dS)2N)AnyDictListOptionalUnion)snapshot_downloadDEFAULT_MODEL_FOR_PIPELINE)Model) ConfigDict check_config)DEFAULT_MODEL_REVISIONInvokeTasks ThirdParty) read_config)register_modelhub_reporegister_plugins_repo)Registrybuild_from_cfg)Pipeline)is_official_hub_pathZ pipelinescCst|tr)t||r)tj|s'tjtji}|dur||t j<t ||||d}|St|t rgt|dtrgt t |D]+}t|||rftj||sftjtji}|dur[||t j<t ||||d||<q;|S)z normalize the input model, to ensure that a model str is a valid local path: in other words, for model represented by a model id, the model shall be downloaded locally N)revision user_agentignore_file_patternr)rr) isinstancestrrospathexistsrKEYZPIPELINErrlistrangelen)modelmodel_revision third_partyrridxr)`/Users/admin/.pyenv/versions/3.10.0/lib/python3.10/site-packages/modelscope/pipelines/builder.pynormalize_model_inputs8       r+cfg task_name default_argscCst|t||dS)a# build pipeline given model config dict. Args: cfg (:obj:`ConfigDict`): config dict for model object. task_name (str, optional): task name, refer to :obj:`Tasks` for more details. default_args (dict, optional): Default initialization arguments. )Z group_keyr.N)r PIPELINES)r,r-r.r)r)r*build_pipeline6s r0Zgputaskr% config_file pipeline_name frameworkdevicer&rreturnc Ks*|dur |dur td|durt|ts!t|trt|dtrt||drt|tr2t||dnt|d|d} | rF| didd}|durv| d} |durc|t j t j fvrc| durcd} | rv| d durpd | d <t ||| }|dus~|d kr| t j} | dur| t jt||| |d }t| d t|| dd|rd|i} nAt| | j} n9|durt|tr|dn|}t|dst|j} t| | j|_|j} nt|\}}t||}d|i} nd|i} || d<|| d<t| } t| || r| | |dur|| _t| |dS)a Factory method to build an obj:`Pipeline`. Args: task (str): Task name defining which pipeline will be returned. model (str or List[str] or obj:`Model` or obj:list[`Model`]): (list of) model name or model object. preprocessor: preprocessor object. config_file (str, optional): path to config file. pipeline_name (str, optional): pipeline class name or alias name. framework (str, optional): framework type. model_revision: revision of model(s) if getting from model hub, for multiple models, expecting all models to have the same revision device (str, optional): whether to use gpu or cpu is used to do inference. ignore_file_pattern(`str` or `List`, *optional*, default to `None`): Any file pattern to be ignored in downloading, like exact file names or file extensions. Return: pipeline (obj:`Pipeline`): pipeline object for certain task. Examples: >>> # Using default model for a task >>> p = pipeline('image-classification') >>> # Using pipeline with a model name >>> p = pipeline('text-classification', model='damo/distilbert-base-uncased') >>> # Using pipeline with a model object >>> resnet = Model.from_pretrained('Resnet') >>> p = pipeline('image-classification', model=resnet) >>> # Using pipeline with a list of model names >>> p = pipeline('audio-kws', model=['damo/audio-tts', 'damo/auto-tts2']) Nz!task or pipeline_name is requiredr)rpipelinetypeexternal_engine_for_llmT llm_frameworkswiftllm)r'rZpluginsZ allow_remoteFr%r5)r-) ValueErrorrrr"rrZsafe_getgetlowerrZtext_generationZchatexternal_engine_for_llm_checkerrr!popr+rrr r7hasattr model_dirget_default_pipeline_infor clear_llm_infoupdate preprocessorr0)r1r%rGr2r3r4r5r&rkwargsr,Zprefer_llm_pipeliner'Zpipeline_propsZ first_modelZdefault_model_repor)r)r*r7Es(                 r7F model_name modelhub_name overwritecCs,|s|tvsJd|d||ft|<dS)z Add default model for a task. Args: task (str): task name. model_name (str): model_name. modelhub_name (str): name for default modelhub. overwrite (bool): overwrite default info. ztask z already has default model.Nr)r1rIrJrKr)r)r*add_default_pipeline_infos   rLcCs>|tvrttj|d}d}||fSt|\}}||fS)z Get default info for certain task. Args: task (str): task name. Return: A tuple: first element is pipeline name(model_name), second element is modelhub name. rN)r r"r/moduleskeys)r1r3Z default_modelr)r)r*rDs   rDrrHc Csddlm}m}ddlm}ddlm}t|tr|d}t|t s%|j }| ddkrlt j |r7||}n|}z ||}|dj} Wn#tyg} ztd |d tjd | d} WYd} ~ nd} ~ ww| rld S|j ||d dd d} || r}d SdS)Nr)ModelTypeHelperLLMAdapterRegistry)get_model_id_from_cacher)get_model_info_metar:r;z Cannot using llm_framework with z, ignoring llm_framework=z : r<T-)Z with_adaptersplitZ use_cache)Znlp.llm_pipelinerOrPZhub.check_modelrRZ swift.llmrSrr"rrCr>rrr model_type Exceptionloggerwarningselfr:contains) r%rrHrOrPrRrSZmodel_idinforVer)r)r*r@sB        r@cCs8ddlm}|dd|dkr|dd|dS)Nr)rOr9r<r:)Z"modelscope.utils.model_type_helperrOrA clear_cache)rHr3rOr)r)r*rE s    rE)NN)NF),rtypingrrrrrZ modelscope.hub.snapshot_downloadrZmodelscope.metainfor Zmodelscope.models.baser Zmodelscope.utils.configr r Zmodelscope.utils.constantr rrrZmodelscope.utils.hubrZmodelscope.utils.pluginsrrZmodelscope.utils.registryrrbaserutilrr/r+rdictr0r7boolrLrDr@rEr)r)r)r*s       !  ~   $