o pi @sddlZddlZddlmZddlmZmZddlZddl m Z ddl m Z ddl mZmZmZmZer9ddlZeeZejejejejejejejejejej ej!ej"d Z#Gd d d Z$dS) N)Path)OptionalUnion)hf_hub_download)validate_hf_hub_args)ONNX_EXTERNAL_WEIGHTS_NAMEONNX_WEIGHTS_NAMEis_onnx_availablelogging) z tensor(bool)z tensor(int8)z tensor(uint8)z tensor(int16)ztensor(uint16)z tensor(int32)ztensor(uint32)z tensor(int64)ztensor(uint64)ztensor(float16)z tensor(float)ztensor(double)c@s eZdZdddZddZeddeeeffddZ dd eeefd e efd d Z d eee j ffd dZee       ddeeefde eeedfde eedfdede ed e ede ede dfddZee   d deeefdede ede efddZdS)!OnnxRuntimeModelNcKs0td||_|dd|_|dt|_dS)NzL`diffusers.OnnxRuntimeModel` is experimental and might change in the future.model_save_dirlatest_model_name)loggerinfomodelgetr r r)selfrkwargsr\/home/ubuntu/SoloSpeech/.venv/lib/python3.10/site-packages/diffusers/pipelines/onnx_utils.py__init__5s zOnnxRuntimeModel.__init__cKs dd|D}|jd|S)NcSsi|] \}}|t|qSr)nparray).0kvrrr <sz-OnnxRuntimeModel.__call__..)itemsrrun)rrinputsrrr__call__;szOnnxRuntimeModel.__call__pathcCs(|dur tdd}tj||g|dS)au Loads an ONNX Inference session with an ExecutionProvider. Default provider is `CPUExecutionProvider` Arguments: path (`str` or `Path`): Directory from which to load provider(`str`, *optional*): Onnxruntime execution provider to use for loading the model, defaults to `CPUExecutionProvider` Nz=No onnxruntime provider specified, using CPUExecutionProviderCPUExecutionProvider) providers sess_options)rrortInferenceSession)r"providerr%rrr load_model?s zOnnxRuntimeModel.load_modelsave_directory file_namecKs|dur|nt}|j|j}t||}zt||Wn tjy(Ynw|jt}| rOt|t}z t||WdStjyNYdSwdS)a? Save a model and its configuration file to a directory, so that it can be re-loaded using the [`~optimum.onnxruntime.modeling_ort.ORTModel.from_pretrained`] class method. It will always save the latest_model_name. Arguments: save_directory (`str` or `Path`): Directory where to save the model file. file_name(`str`, *optional*): Overwrites the default model file name from `"model.onnx"` to `file_name`. This allows you to save the model with a different name. N) r r joinpathrrshutilcopyfile SameFileErrorrexists)rr*r+rmodel_file_namesrc_pathdst_pathrrr_save_pretrainedPs"  z!OnnxRuntimeModel._save_pretrainedcKsFtj|rtd|ddStj|dd|j|fi|dS)a$ Save a model to a directory, so that it can be re-loaded using the [`~OnnxModel.from_pretrained`] class method.: Arguments: save_directory (`str` or `os.PathLike`): Directory to which to save. Will be created if it doesn't exist. zProvided path (z#) should be a directory, not a fileNT)exist_ok)osr"isfilererrormakedirsr4)rr*rrrrsave_pretrainedos z OnnxRuntimeModel.save_pretrainedFmodel_idtokenrevisionforce_download cache_dirr(r%zort.SessionOptionsc Ks|dur|nt} tj|r"tjt|| ||d} t|| d<n t|| ||||d} t| j | d<t| j | d<tj| ||d} |dd| i| S)aA Load a model from a directory or the HF Hub. Arguments: model_id (`str` or `Path`): Directory from which to load token (`str` or `bool`): Is needed to load models from a private or gated repository revision (`str`): Revision is the specific model version to use. It can be a branch name, a tag name, or a commit id cache_dir (`Union[str, Path]`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (`bool`, *optional*, defaults to `False`): Whether or not to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. file_name(`str`): Overwrites the default model file name from `"model.onnx"` to `file_name`. This allows you to load different model files from the same repository or directory. provider(`str`): The ONNX runtime provider, e.g. `CPUExecutionProvider` or `CUDAExecutionProvider`. kwargs (`Dict`, *optional*): kwargs will be passed to the model during initialization N)r(r%r )repo_idfilenamer<r=r?r>rrr) r r6r"isdirr r)ras_posixrparentname) clsr;r<r=r>r?r+r(r%rr1rmodel_cache_pathrrr_from_pretraineds$& z!OnnxRuntimeModel._from_pretrainedTcKsDd}tt|ddkr|d\}}|jd|||||d|S)N@r)r;r=r?r>r<r)lenstrsplitrH)rFr;r>r<r? model_kwargsr=rrrfrom_pretraineds z OnnxRuntimeModel.from_pretrained)N)NN)NNFNNNN)TNN)__name__ __module__ __qualname__rr! staticmethodrrKrr)rr4r6PathLiker: classmethodrboolrHrNrrrrr 4sb      ; r )%r6r-pathlibrtypingrrnumpyrhuggingface_hubrhuggingface_hub.utilsrutilsrr r r onnxruntimer& get_loggerrOrbool_int8uint8int16uint16int32uint32int64uint64float16float32float64ORT_TO_NP_TYPEr rrrrs2