(ti " ddlmZddlZddlZddlmZddlZddlm Z ddl m Z ddl m Z mZmZmZerddlZej&eZej,ej.ej0ej2ej4ej6ej8ej:ej<ej>ej@ejBd Z"Gd d Z#y) ) annotationsN)Path)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)ceZdZd dZdZed d dZd d dZ d dZe e d ddZ e e d ddZ y)OnnxRuntimeModelNc tjd||_|jdd|_|jdt |_y)NzL`diffusers.OnnxRuntimeModel` is experimental and might change in the future.model_save_dirlatest_model_name)loggerinfomodelgetrr r)selfrkwargss m/Volumes/fast/ai/experiments/MLX_z-image/.venv/lib/python3.12/site-packages/diffusers/pipelines/onnx_utils.py__init__zOnnxRuntimeModel.__init__5s? bc $jj)94@!',?AR!Sc |jDcic]\}}|tj|}}}|jj d|Scc}}wN)itemsnparrayrrun)rrkvinputss r__call__zOnnxRuntimeModel.__call__;sF-3\\^<^TQ!RXXa[.^<zz~~dF++=s Ac|tjdd}|g}nt|ts|g}t j ||g||S)au Loads an ONNX Inference session with an ExecutionProvider. Default provider is `CPUExecutionProvider` Arguments: path (`str` or `Path`): Directory from which to load provider(`str`, *optional*): Onnxruntime execution provider to use for loading the model, defaults to `CPUExecutionProvider` z=No onnxruntime provider specified, using CPUExecutionProviderCPUExecutionProvider) providers sess_optionsprovider_options)rr isinstancelistortInferenceSession)pathproviderr'r(s r load_modelzOnnxRuntimeModel.load_model?s[   KKW X-H  #! ,d3 01 ## XJ\Td  rc ||nt}|jj|j}t |j|} t j |||jjt}|jr6t |jt} t j ||yy#t j$rY{wxYw#t j$rYywxYw)a? Save a model and its configuration file to a directory, so that it can be re-loaded using the [`~optimum.onnxruntime.modeling_ort.ORTModel.from_pretrained`] class method. It will always save the latest_model_name. Arguments: save_directory (`str` or `Path`): Directory where to save the model file. file_name(`str`, *optional*): Overwrites the default model file name from `"model.onnx"` to `file_name`. This allows you to save the model with a different name. N) r rjoinpathrrshutilcopyfile SameFileErrorrexists)rsave_directory file_namermodel_file_namesrc_pathdst_paths r_save_pretrainedz!OnnxRuntimeModel._save_pretrainedWs(1'<)BS&&//0F0FG'00A  OOHh / &&//0JK ?? N+445OPH (3  ##   ''  s$ C/C CC C65C6c tjj|rtj d|dytj |d|j |fi|y)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-isfilererrormakedirsr;)rr6rs rsave_pretrainedz OnnxRuntimeModel.save_pretrainedvsS 77>>. ) LL?>*::]^ _  NT2 n77rc  ||nt} tjj|rOtj t || j||| jd} t || d<nit|| ||||} t | j| d<t | j| d<tj | ||| jd} |dd| i| S)a; 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 (`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 r()r.r'r(r)repo_idfilenametokenrevision cache_dirforce_downloadrr) r r>r-isdirr r/ras_posixpoprparentname) clsmodel_idrFrGrIrHr7r.r'rr8rmodel_cache_paths r_from_pretrainedz!OnnxRuntimeModel._from_pretrainedsL(1'<)BS 77== "$//X/88:!)!',>!? 0E (,H~F# $ / (!#-   (,,<'='D'DF# $*./?*@*E*EF& '$// !)!',>!? 0E ))&))rc d}tt|jddk(r|jd\}}|jd|||||d|S)N@r)rQrGrHrIrFrJ)lenstrsplitrS)rPrQrIrFrH model_kwargsrGs rfrom_pretrainedz OnnxRuntimeModel.from_pretrainedsk s8}""3' (A -!)!4 Hh#s## )     rr)NNN)r- str | Path)r6r[r7 str | None)r6zstr | os.PathLike)NNFNNNN)rQr[rFzbool | str | None | NonerGzstr | None | NonerIboolrHr\r7r\r.r\r'z'ort.SessionOptions' | None)TNN)rQr[rIr]rFr\rHr\) __name__ __module__ __qualname__rr# staticmethodr/r;rB classmethodrrSrZrJrrr r 4s T ,  .>8)8,+/&*$ $ $#48A*A*(A*$ A*  A*  A*A*A*2A*A*F $ $         rr )$ __future__rr>r2pathlibrnumpyrhuggingface_hubrhuggingface_hub.utilsrutilsrr r r onnxruntimer+ get_loggerr^rbool_int8uint8int16uint16int32uint32int64uint64float16float32float64ORT_TO_NP_TYPEr rJrrrxs"# +6]]   H %HHGGXXXXiiXXiiXXiizzZZjj  r r r