a hw @sddlmZddlZddlmZmZddlmZee Z ervddl m Z m Z mZzddlmZWneytYn0ddd d d d d d dddZdS)) annotationsN) TYPE_CHECKINGLiteral)save_or_push_to_hub_model CrossEncoderSentenceTransformer SparseEncoder)OptimizationConfigFz2SentenceTransformer | SparseEncoder | CrossEncoderz4OptimizationConfig | Literal['O1', 'O2', 'O3', 'O4']strboolz str | NoneNone)modeloptimization_configmodel_name_or_path push_to_hub create_pr file_suffixreturnc slddlm}m}m}z ddlm} m} ddlm} Wnt yNt dYn0t ||o~t |o~t |ddo~t |dj | } t ||ot |ot |ddot |dj | } t ||ot |j| }| s|s| std| s| r|dj }n|j}| |t tr4| jvrtdp&t| d urBd tfd d d |||d|d d S)a Export an optimized ONNX model from a SentenceTransformer, SparseEncoder, or CrossEncoder model. The O1-O4 optimization levels are defined by Optimum and are documented here: https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/optimization The optimization levels are: - O1: basic general optimizations. - O2: basic and extended general optimizations, transformers-specific fusions. - O3: same as O2 with GELU approximation. - O4: same as O3 with mixed precision (fp16, GPU-only) See the following pages for more information & benchmarks: - `Sentence Transformer > Usage > Speeding up Inference `_ - `Cross Encoder > Usage > Speeding up Inference `_ Args: model (SentenceTransformer | SparseEncoder | CrossEncoder): The SentenceTransformer, SparseEncoder, or CrossEncoder model to be optimized. Must be loaded with `backend="onnx"`. optimization_config (OptimizationConfig | Literal["O1", "O2", "O3", "O4"]): The optimization configuration or level. model_name_or_path (str): The path or Hugging Face Hub repository name where the optimized model will be saved. push_to_hub (bool, optional): Whether to push the optimized model to the Hugging Face Hub. Defaults to False. create_pr (bool, optional): Whether to create a pull request when pushing to the Hugging Face Hub. Defaults to False. file_suffix (str | None, optional): The suffix to add to the optimized model file name. Defaults to None. Raises: ImportError: If the required packages `optimum` and `onnxruntime` are not installed. ValueError: If the provided model is not a valid SentenceTransformer, SparseEncoder, or CrossEncoder model loaded with `backend="onnx"`. ValueError: If the provided optimization_config is not valid. Returns: None rr)ORTModel ORTOptimizer)AutoOptimizationConfigzPlease install Optimum and ONNX Runtime to use this function. You can install them with pip: `pip install optimum[onnxruntime]` or `pip install optimum[onnxruntime-gpu]` auto_modelz}The model must be a Transformer-based SentenceTransformer, SparseEncoder, or CrossEncoder model loaded with `backend="onnx"`.z\optimization_config must be an OptimizationConfig instance or one of 'O1', 'O2', 'O3', 'O4'.NZ optimizedcsj|dS)N)r)optimize)save_dirrrZ optimizerb/var/www/html/assistant/venv/lib/python3.9/site-packages/sentence_transformers/backend/optimize.pyoz-export_optimized_onnx_model..export_optimized_onnx_modelZonnx) Zexport_functionZexport_function_nameconfigrrrrbackendr)sentence_transformersrrr Zoptimum.onnxruntimerr!optimum.onnxruntime.configurationr ImportError isinstancelenhasattrrr ValueErrorZfrom_pretrainedr Z_LEVELSgetattrr)rrrrrrrrr rrrZviable_st_modelZviable_se_modelZviable_ce_modelZ ort_modelrrrr sf+              r )FFN) __future__rloggingtypingrrZ#sentence_transformers.backend.utilsr getLogger__name__loggerr#rrr r$r r%r rrrrs