9i|SSKJrJr SSKJrJrJrJr SSKJ r SSK J r SSK J r SSKJr SS KJr "S S \\ S 9rg ))ABCabstractmethod)AnyDictOptionalUnion)logging)AutoRegisterABCMetaClass) BasePredictor) HPIConfig)PaddlePredictorOptionc ^\rSrSrSrSrSS\S\S\S\ \ \ \\ 4\ 4S S4 U4S jjjr\S 5rS \ S \4S jrS \ 4SjrSrSrSrU=r$) BasePipelinezBase class for all pipelines. This class serves as a foundation for creating various pipelines. It includes common attributes and methods that are shared among all pipeline implementations. TNdevice pp_optionuse_hpip hpi_configreturncR>[TU]5 XlX lX0lX@lg)a Initializes the class with specified parameters. Args: device (str, optional): The device to use for prediction. Defaults to None. pp_option (PaddlePredictorOption, optional): The options for PaddlePredictor. Defaults to None. use_hpip (bool, optional): Whether to use the high-performance inference plugin (HPIP) by default. Defaults to False. hpi_config (Optional[Union[Dict[str, Any], HPIConfig]], optional): The default high-performance inference configuration dictionary. Defaults to None. N)super__init__rrrr)selfrrrrargskwargs __class__s `/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/paddlex/inference/pipelines/base.pyrBasePipeline.__init__#s$*  " $c [S5e)z Declaration of an abstract method. Subclasses are expected to provide a concrete implementation of predict. Args: input: The input data to predict. **kwargs: Additional keyword arguments. z2The method `predict` has not been implemented yet.)NotImplementedErrorrinputrs rpredictBasePipeline.predict>s""VWWr!configc SU;a[US5eURSS5nURSUR5nURSS5nURbU=(d 0n0UREUEnURSS5nSSKJn [ R"S US U45 URbURR5nOSnU"SUS UURURS S 5UUUUS .UD6n U $)z Create a model instance based on the given configuration. Args: config (Dict): A dictionary containing configuration settings. **kwargs: The model arguments that needed to be pass. Returns: BasePredictor: An instance of the model. model_config_error model_dirNrr genai_configr )create_predictorzCreating model: %s model_name batch_size)r.r+rr/rrrr,) ValueErrorgetrrr-r inforcopyr) rr(rr+rrr,r-rmodels r create_modelBasePipeline.create_modelIs 6 )V$89: :JJ{D1 ::j$--8ZZ d3 ?? &#)rJ:DOO:z:Jzz.$7 ' )F<,@)+LM >> %++-II  l+;;zz,2!%     r!cpSU;a[US5eSSKJn URSUR5nURSS5nUR bU=(d 0n0UR EUEnU"UUR URbURR5O URUUS9nU$)z Creates a pipeline based on the provided configuration. Args: config (Dict): A dictionary containing the pipeline configuration. Returns: BasePipeline: An instance of the created pipeline. pipeline_config_errorr0)create_pipelinerrN)r(rrrr) r2r4r<r3rrrrr6)rr(r<rrpipelines rr<BasePipeline.create_pipelinews #f ,V$;<= =%::j$--8ZZ d3 ?? &#)rJ:DOO:z:J";;)-)C##%! r!cg)Nr1)rs rcloseBasePipeline.closes r!c (UR"U40UD6$)a Calls the predict method with the given input and keyword arguments. Args: input: The input data to be predicted. **kwargs: Additional keyword arguments to be passed to the predict method. Returns: The prediction result from the predict method. )r&r$s r__call__BasePipeline.__call__s||E,V,,r!)rrrr)NNFN)__name__ __module__ __qualname____firstlineno____doc___BasePipeline__is_basestrrboolrrrrrrrr&r r8r<r@rC__static_attributes__ __classcell__)rs@rrrsI+/AE %%)% % U4S>9#<=> % %%6XX,4,m,\d@  - -r!r) metaclassN)abcrrtypingrrrrutilsr utils.subclass_registerr modelsr utils.hpirutils.pp_optionrrr1r!rrWs0$--?"!3L-3":L-r!