a h[@szddlmZddlZddlZddlmZddlmZddlm Z m Z m Z e e Zddddd d Zddddd d ZdS) ) annotationsN)Path)PretrainedConfig)_save_pretrained_wrapperbackend_should_exportbackend_warn_to_savestrr)model_name_or_pathconfig task_namecKszbddl}ddlm}m}m}m}|||d} || vrXd| } td|d| | |} Wnt y|t dYn0| d | d|d <t |} | } d }d }t| | ||||\}}|r| d d| j|f||d |}t|jdd|_|rt|| ||S)a Load and perhaps export an ONNX model using the Optimum library. Args: model_name_or_path (str): The model name on Hugging Face (e.g. 'naver/splade-cocondenser-ensembledistil') or the path to a local model directory. config (PretrainedConfig): The model configuration. task_name (str): The task name for the model (e.g. 'feature-extraction', 'fill-mask', 'sequence-classification'). model_kwargs (dict): Additional keyword arguments for the model loading. rN)ONNX_WEIGHTS_NAMEORTModelForFeatureExtractionORTModelForMaskedLM!ORTModelForSequenceClassificationzfeature-extractionz fill-maskzsequence-classification, Unsupported task: . Supported tasks: zUsing the ONNX backend requires installing Optimum and ONNX Runtime. You can install them with pip: `pip install optimum[onnxruntime]` or `pip install optimum[onnxruntime-gpu]`providerZONNXz*.onnx file_namer exportZonnxZ subfolder)Z onnxruntimeZoptimum.onnxruntimer r rrjoinkeys ValueErrorModuleNotFoundError ExceptionpopZget_available_providersrexistsrfrom_pretrainedr_save_pretrainedr)r r r model_kwargsZortr r rrtask_to_model_mappingsupported_tasks model_cls load_pathis_local backend_nametarget_file_globrmodelr+^/var/www/html/assistant/venv/lib/python3.9/site-packages/sentence_transformers/backend/load.pyload_onnx_modelsJ        r-cKsjzZddlm}m}m}m}|||d}||vrPd|} td|d| ||} Wntytt dYn0t |} | } d} d }t | | |||| \}}|r| d d d |vr$|d }t|ts,t | std t|dd}t||d <Wd n1s0Yni|d <| j|f||d|}t|jdd|_|rft|| | |S)a Load and perhaps export an OpenVINO model using the Optimum library. Args: model_name_or_path (str): The model name on Hugging Face (e.g. 'naver/splade-cocondenser-ensembledistil') or the path to a local model directory. config (PretrainedConfig): The model configuration. task_name (str): The task name for the model (e.g. 'feature-extraction', 'fill-mask', 'sequence-classification'). model_kwargs (dict): Additional keyword arguments for the model loading. r)OV_XML_FILE_NAMEOVModelForFeatureExtractionOVModelForMaskedLM OVModelForSequenceClassificationrrrrzUsing the OpenVINO backend requires installing Optimum and OpenVINO. You can install them with pip: `pip install optimum[openvino]`ZOpenVINOz openvino*.xmlrN ov_configzXov_config should be a dictionary or a path to a .json file containing an OpenVINO configzutf-8)encodingrZopenvinor)Zoptimum.intel.openvinor.r/r0r1rrrrrrrrr isinstancedictopenjsonloadr rr!r)r r r r"r.r/r0r1r#r$r%r&r'r(r)rr2fr*r+r+r,load_openvino_modelZsZ          0 r:) __future__rr7loggingpathlibrZ transformers.configuration_utilsrZ#sentence_transformers.backend.utilsrrr getLogger__name__loggerr-r:r+r+r+r,s    L