Ti#ddlmZddlZddlZddlmZddlmZddlm Z m Z m Z eje ZddZddZy) ) annotationsN)Path)PretrainedConfig)_save_pretrained_wrapperbackend_should_exportbackend_warn_to_savec 6 ddl}ddlm}m}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$r tdwxYw)a Load and perhaps export an ONNX model using the Optimum library. Args: model_name_or_path (str): The model name on Hugging Face (e.g. 'naver/splade-cocondenser-ensembledistil') or the path to a local model directory. config (PretrainedConfig): The model configuration. task_name (str): The task name for the model (e.g. 'feature-extraction', 'fill-mask', 'sequence-classification'). model_kwargs (dict): Additional keyword arguments for the model loading. rN)ONNX_WEIGHTS_NAMEORTModelForCausalLMORTModelForFeatureExtractionORTModelForMaskedLM!ORTModelForSequenceClassificationzfeature-extractionz fill-maskzsequence-classificationztext-generation, Unsupported task: . Supported tasks: zUsing the ONNX backend requires installing Optimum and ONNX Runtime. You can install them with pip: `pip install sentence-transformers[onnx]` or `pip install sentence-transformers[onnx-gpu]`providerONNXz*.onnx file_nameconfigexportonnx subfolder) onnxruntimeoptimum.onnxruntimer r r r rjoinkeys ValueErrorModuleNotFoundError Exceptionpopget_available_providersrexistsrfrom_pretrainedr_save_pretrainedr)model_name_or_pathr task_name model_kwargsortr r r r rtask_to_model_mappingsupported_tasks model_cls load_pathis_local backend_nametarget_file_globrmodels d/var/www/pod-logistic/pod-ai/venv/lib/python3.12/site-packages/sentence_transformers/backend/load.pyload_onnx_modelr5st !  #?,'H2 !  1 1"ii(=(B(B(DEO1)NP\FL d+ &I % %     E6e6L6LX^_E/<H LQ   ?   s ADDc  ddlm}m}m}m}m}||||d} || vr0dj | j} td|d| | |} t|} | j} d}d }t| | ||||\}}|r|jd d d |vrh|d }t|t sXt|js td t#|d5}t%j&||d <d d d ni|d <| j(|f||d|}t+|j,d|_|r t/|| ||S#t$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_NAMEOVModelForCausalLMOVModelForFeatureExtractionOVModelForMaskedLM OVModelForSequenceClassificationrrrrzUsing the OpenVINO backend requires installing Optimum and OpenVINO. You can install them with pip: `pip install sentence-transformers[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.openvinor7r8r9r:r;rrr r!r"rr%rr# isinstancedictopenjsonloadr&rr'r)r(rr)r*r7r8r9r:r;r,r-r.r/r0r1r2rr=fr3s r4load_openvino_modelrG\s   #>+'G1 !  1 1"ii(=(B(B(DEO1)rSs9" =uu   8 $K\SrR