=pj SSKJrJrJrJrJr SSKrSSKJ r SSK J r J r SSK Jr SSKJr S S KJr \R&\ "S 5"S S \555rg))AnyDictListOptionalUnionN)pipeline_requires_extra) HPIConfigPaddlePredictorOption) TSClsResult) benchmark) BasePipelinetsc^\rSrSrSrSrSSSSSSS.S\S\\S \\S \\\\ 4S \\ S \ S \\ \\\ 4\ 4SS4U4SjjjrS\ \\\\R"\\R"4S\4SjrSrU=r$) TSClsPipelinezTSClsPipeline Pipelinets_classificationNFdeviceengine engine_config pp_optionuse_hpip hpi_configconfigrrrrrrreturnc p>[T U]"SUUUUUUS.UD6 USSn URU 5Ulg)aInitializes the time series classification pipeline. Args: config (Dict): Configuration dictionary containing various settings. device (Optional[str], optional): The device to use for prediction. Defaults to `None`. engine (Optional[str], optional): Inference engine. Defaults to `None`. engine_config (Optional[Dict[str, Any]], optional): Engine-specific config. Defaults to `None`. pp_option (Optional[PaddlePredictorOption], optional): Paddle predictor options. Defaults to `None`. use_hpip (bool, optional): Whether to use HPIP. Defaults to `False`. hpi_config (Optional[Union[Dict[str, Any], HPIConfig]], optional): HPIP configuration. Defaults to `None`. r SubModulesTSClassificationN)super__init__ create_modelts_classification_model) selfrrrrrrrkwargsts_classification_model_config __class__s r/var/www/html/pdf-tiff/venv/lib/python3.13/site-packages/paddlex/inference/pipelines/ts_classification/pipeline.pyr$TSClsPipeline.__init__!sZ2  '!   *0 )=>P)Q&'+'8'89W'X$inputc+B# URU5ShvN gN7f)aUPredicts time series classification results for the given input. Args: input (Union[str, list[str], pd.DataFrame, list[pd.DataFrame]]): The input image(s) or path(s) to the images. **kwargs: Additional keyword arguments that can be passed to the function. Returns: TSFcResult: The predicted time series classification results. Nr&)r'r.r(s r+predictTSClsPipeline.predictGs//666s r0)__name__ __module__ __qualname____firstlineno____doc__entitiesrrstrrr boolrr r$rpd DataFramer r1__static_attributes__ __classcell__)r*s@r+rrs!"H !% $2659AE$Y$Y $Y  $Y  S#X/ $Y12$Y$YU4S>9#<=>$Y $Y$YL 73S 2<<bll9KKL 7  7 7r-r)typingrrrrrpandasr; utils.depsr modelsr r models.ts_classification.resultr utils.benchmarkrbaser time_methodsrr"r-r+rGsM4326:( 77L7777r-