o 0j @sddlmZmZmZmZmZddlZddlm Z ddl m Z m Z ddl mZddlmZd d lmZeje d Gd d d eZdS))AnyDictListOptionalUnionN)pipeline_requires_extra) HPIConfigPaddlePredictorOption) TSAdResult) benchmark) BasePipelinetscseZdZdZdZddddddddedeedeed eeeefd ee d e d ee eeefe fd dffddZ de eeeejeejfd efddZZS)TSAnomalyDetPipelinezTSAnomalyDetPipeline PipelineZts_anomaly_detectionNFdeviceengine engine_config pp_optionuse_hpip hpi_configconfigrrrrrrreturnc s<tjd||||||d||dd} || |_dS)aInitializes the time series anomaly detection 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`. rZ SubModulesZTSAnomalyDetectionN)super__init__Z create_model ts_ad_model) selfrrrrrrrkwargsZts_ad_model_config __class__rz/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddlex/inference/pipelines/ts_anomaly_detection/pipeline.pyr!s zTSAnomalyDetPipeline.__init__inputcks||EdHdS)aPredicts time series anomaly detection 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: TSAdResult: The predicted time series anomaly detection results. N)r)rr$r rrr#predictGs zTSAnomalyDetPipeline.predict)__name__ __module__ __qualname____doc__entitiesrrstrrr boolrr rrpdZ DataFramer r% __classcell__rrr!r#rs>  &r)typingrrrrrZpandasr-Z utils.depsrmodelsr r Z"models.ts_anomaly_detection.resultr Zutils.benchmarkr baserZ time_methodsrrrrr#s