rh[ddlmZddlZddlZddlmZddlmZddlm Z m Z m Z eje ZddZddZy) ) annotationsN)Path)PretrainedConfig)_save_pretrained_wrapperbackend_should_exportbackend_warn_to_savec 0 ddl}ddlm}m}m}m}|||d} || vr0dj | j} td|d| | |} |jd |jd|d <t|} | j} d }d }t| | ||||\}}|r|jd d| j |f||d |}t#|j$d|_|r t'|| ||S#t$r tdwxYw)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]`providerONNXz*.onnx file_nameconfigexportonnx subfolder) onnxruntimeoptimum.onnxruntimer r r r joinkeys ValueErrorModuleNotFoundError Exceptionpopget_available_providersrexistsrfrom_pretrainedr_save_pretrainedr)model_name_or_pathr task_name model_kwargsortr r r r task_to_model_mappingsupported_tasks model_cls load_pathis_local backend_nametarget_file_globrmodels u/var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/sentence_transformers/backend/load.pyload_onnx_modelr4sq !  #?,'H!  1 1"ii(=(B(B(DEO1)NP\FL d+ &I % %     E6e6L6LX^_E/<H LQ   8   s ADDc  ddlm}m}m}m}|||d}||vr0dj |j } td|d| ||} t|} | j} d} d }t| | |||| \}}|r|jd d d |vrh|d }t|tsXt|js td t!|d5}t#j$||d <d d d ni|d <| j&|f||d|}t)|j*d|_|r t-|| | |S#t$r tdwxYw#1swYdxYw)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]`OpenVINOz openvino*.xmlrN ov_configzXov_config should be a dictionary or a path to a .json file containing an OpenVINO configzutf-8)encodingropenvinor)optimum.intel.openvinor6r7r8r9rrrr r!rr$rr" isinstancedictopenjsonloadr%rr&r)r'rr(r)r6r7r8r9r+r,r-r.r/r0r1rr;fr2s r3load_openvino_modelrEZs   #>+'G!  1 1"ii(=(B(B(DEO1)rQs9" =uu   8 $IXQrP