o di~@sdZddlZddlZddlZddlmZddlmZddlm Z m Z m Z m Z m Z mZmZmZmZmZddlmZddlmZddlmZdd lmZdd lmZdd lmZm Z m!Z!m"Z"dd l#m$Z$m%Z%m&Z&m'Z'm(Z(d dl)m*Z*m+Z+e rzddl,m-Z-e(.e/Z0e"se!se01de"rddl2Z2ddlm3Z3e!rddlm4Z4e*rddl5m6Z6ddl7m8Z8m9Z9m:Z:e e gdfZ;e edede>> ("multiple-choice", ("onnx",)) ``` The line above means that the multiple-choice task will be supported only by the ONNX backend. exporters (`Dict[str, str]`): The export backend name -> config class name mapping. For instance: ```python >>> exporters = { # doctest: +SKIP ... "onnx": "BertOnnxConfig", ... 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Args: backend (`str`): The name of the backend that the register function will handle. overwrite_existing (`bool`, defaults to `False`): Whether or not the register function is allowed to overwrite an already existing config. Returns: `Callable[[str, Tuple[str, ...]], Callable[[Type], Type]]`: A decorator taking the model type and a the supported tasks. Example: ```python >>> register_for_new_backend = create_register("new-backend") >>> @register_for_new_backend("bert", "text-classification", "token-classification") >>> class BertNewBackendConfig(NewBackendConfig): >>> pass ``` r library_namerOr;rMr0cs$dtdtffdd }|S)Nr.r0cstj}|i}|i}D].}|dd}|vr3d}td|d|ds:||vr:qt||||<q||<||<|S)Nr1r3, z0The TasksManager does not know the task called "z", known tasks: .)r^!_LIBRARY_TO_SUPPORTED_MODEL_TYPESgetr8 get_all_tasksjoinr7r:)r. supported_model_type_for_libraryrBmapping_backendr/normalized_task known_tasks)r)clsrMrOrKr;r+r, decorators$     z@TasksManager.create_register..wrapper..decorator)r )rOrMr;rYr)rXrK)rMrOr;r,wrappers z-TasksManager.create_register..wrapper)rTrr )rXr)rKr[r+rZr,create_registers zTasksManager.create_registerNrOexporter model_namerMc Cs|durtditjtjtjtj}d}ntj|}|r)|d|dn|}d}|tjvr7tj|}||vrY|durB|}nt |d|dt | d|d |d |||vrut |d |d t || d |d |||S)a Retrieves the `task -> exporter backend config constructors` map from the model type. Args: model_type (`str`): The model type to retrieve the supported tasks for. exporter (`str`): The name of the exporter. model_name (`Optional[str]`, defaults to `None`): The name attribute of the model object, only used for the exception message. library_name (`Optional[str]`, defaults to `None`): The library name of the model. Can be any of "transformers", "timm", "diffusers", "sentence_transformers". Returns: `TaskNameToExportConfigDict`: The dictionary mapping each task to a corresponding `ExporterConfig` constructor. NzNot passing the argument `library_name` to `get_supported_tasks_for_model_type` is deprecated and the support will be removed in a future version of Optimum. Please specify a `library_name`. Defaulting to `"transformers`.rz ()z is not supported yet for z. Only z are supported for the library z. If you want to support z) please propose a PR or open up an issue.z is not supported yet with the z backend. Only z' are supported. If you want to support ) loggerwarningr^_DIFFUSERS_SUPPORTED_MODEL_TYPE_TIMM_SUPPORTED_MODEL_TYPE+_SENTENCE_TRANSFORMERS_SUPPORTED_MODEL_TYPE_SUPPORTED_MODEL_TYPErP_MODEL_TYPE_FOR_DEFAULT_CONFIGKeyErrorlistkeys)rOr]r^rMrTmodel_type_and_model_namedefault_model_typer+r+r,"get_supported_tasks_for_model_typesP        z/TasksManager.get_supported_tasks_for_model_typer/csfddtjD}|S)zv Returns the list of supported architectures by the exporter for a given task. 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Please use the `token` argument instead.LYou cannot use both `use_auth_token` and `token` arguments at the same time. user_agentrc 4g|]\}}}|D]}tjtj||q qSr+ospathrelpathrSrndirpath_ filenamesfilefull_model_pathr+r,rpz0TasksManager.get_model_files..model) repo_typerrr3cs*g|]}|r|tddqS)rN) startswithlenrnr)rr+r,rps*)repo_idrrrc rr+rrrr+r,rpr)warningswarn FutureWarningr7rrris_dirrwalkr@rTlist_repo_filesRequestsConnectionErrorrsnapshot_download) rrrrrrrequest_exceptionhf_api all_filese snapshot_pathr+)rrr,get_model_filess`   "    zTasksManager.get_model_filesc s^tj|||||d\}}ttjttjttjttjfdd|D}ttjttjfdd|D}t|rGd} nIt|rNd} nBd|vratfdd |Drad} n/d |vrhd} n(|d urst d |t d ttjdttjdttjdttjd t r| pd} n t r| pd} nt dtd| d| S)a Determines the framework to use for the export. The priority is in the following order: 1. User input via `framework`. 2. If local checkpoint is provided, use the same framework as the checkpoint. 3. If model repo, try to infer the framework from the cache if available, else from the Hub. 4. If could not infer, use available framework in environment, with priority given to PyTorch. Args: model_name_or_path (`Union[str, Path]`): Can be either the model id of a model repo on the Hugging Face Hub, or a path to a local directory containing a model. subfolder (`str`, *optional*, defaults to `""`): In case the model files are located inside a subfolder of the model directory / repo on the Hugging Face Hub, you can specify the subfolder name here. revision (`Optional[str]`, defaults to `None`): Revision is the specific model version to use. It can be a branch name, a tag name, or a commit id. cache_dir (`Optional[str]`, *optional*): Path to a directory in which a downloaded pretrained model weights have been cached if the standard cache should not be used. token (`Optional[Union[bool,str]]`, defaults to `None`): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `huggingface_hub.constants.HF_TOKEN_PATH`). Returns: `str`: The framework to use for the export. )rrrrcs4g|]}|r|p|o|qSr+rendswithr)pt_weight_extensionpt_weight_namesafe_weight_extensionsafe_weight_namer+r,rp>s z4TasksManager.determine_framework..cs g|] }|o |qSr+rr)weight_extension weight_namer+r,rpFrrRrSmodel_index.jsonc3s|] }|fVqdSr|)rr)rrr+r, Ls z3TasksManager.determine_framework..!config_sentence_transformers.jsonNzThe framework could not be automatically inferred. If using the command-line, please provide the argument --framework (pt,tf) Detailed error: zMCannot determine framework from given checkpoint location. There should be a *z for PyTorch or z for TensorFlow.zINeither PyTorch nor TensorFlow found in environment. Cannot export model.zFramework not specified. Using z to export the model.)r^rrrstemsuffixrranyrFileNotFoundErrorrrEnvironmentErrorr`info) rrrrrrris_pt_weight_fileis_tf_weight_filerXr+)rrrrrrr,determine_frameworksZ%         z TasksManager.determine_frameworkr)r!r"r#rPc Cs\|dur |dur td|dur|durtd|dur |jjn|j}|dur+|jjn|j}d}|dr9|j}n|drB|j}n|drJ|j}|durn|D]\}}t |t r^|f}|D] }||krl|Sq`qRd} |dr|dr~|j } n |j } n|dr|j } | dur| D]\}} | D]\} } || kr|Sqqtd ) NKEither a model or a model class must be provided, but both were given here.KEither a model or a model class must be provided, but none were given here.rgr AutoPipelinerTFrThe task name could not be automatically inferred. If using the command-line, please provide the argument --task task-name. Example: `--task text-classification`.)r7 __class__rK __module__r$_TRANSFORMERS_TASKS_TO_MODEL_LOADERS'_TRANSFORMERS_TASKS_TO_TF_MODEL_LOADERS!_DIFFUSERS_TASKS_TO_MODEL_LOADERSr=r@rT(_TRANSFORMERS_TASKS_TO_TF_MODEL_MAPPINGS%_TRANSFORMERS_TASKS_TO_MODEL_MAPPINGS"_DIFFUSERS_TASKS_TO_MODEL_MAPPINGS) rXrrPtarget_class_nametarget_class_moduletasks_to_model_loadersrMrZmodel_loader_class_namerLrNrOrr+r+r,%_infer_task_from_model_or_model_classksN         z2TasksManager._infer_task_from_model_or_model_classc CsXd}tjtj||}|rtddtd|dkr$tdztt|dj |||d} Wnt t fyItddtdw|durX|j |||||d }|d kr_d }n|d kr| j } } | j} | duru|| }n| dur| dur| d ddur| d d } |jD]\} }|D] \}}| |kr| }nq|durnqnp|dkrd}ni|dkr| j } | j}| dur|| j }nS|dur|dd}|dd}|dur|d} || }n4|dur|dd}|jD]#\} }t|tr|f}|D] }||kr| }nq|durnq|dur*td|d|S)NzTCannot infer the task from a local directory yet, please specify the task manually (rNz).r3z_Cannot infer the task from a model repo with a subfolder yet, please specify the task manually.r)rrzHugging Face Hub is not reachable and we cannot infer the task from a cached model. Make sure you are not offline, or otherwise please specify the `task` (or `--task` in command-line) argument (rrrrrrir _class_namerrfr pipeline_tag auto_modelrz2Could not find the proper task name for the model rO)rrisdirrSr}r^rRrr model_inforrinfer_library_from_modelrconfigrvrQrr=transformersInfor8rr@rTrg)rXrrrrrrMinferred_task_nameis_localrr model_configdiffusers_class_namerMrNrOrtransformers_infotransformers_pipeline_tagtransformers_auto_modelrZrr+r+r,#_infer_task_from_model_name_or_paths                z0TasksManager._infer_task_from_model_name_or_pathr!r"r#cCs`d}t|tr|j||||||d}nt|tur |j|d}n|j|d}|dur.td|S)a Infers the task from the model repo, model instance, or model class. Args: model (`Union[str, PreTrainedModel, TFPreTrainedModel, DiffusionPipeline, Type]`): The model to infer the task from. This can either be the name of a repo on the HuggingFace Hub, an instance of a model, or a model class. subfolder (`str`, *optional*, defaults to `""`): In case the model files are located inside a subfolder of the model directory / repo on the Hugging Face Hub, you can specify the subfolder name here. revision (`Optional[str]`, defaults to `None`): Revision is the specific model version to use. It can be a branch name, a tag name, or a commit id. cache_dir (`Optional[str]`, *optional*): Path to a directory in which a downloaded pretrained model weights have been cached if the standard cache should not be used. token (`Optional[Union[bool,str]]`, defaults to `None`): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `huggingface_hub.constants.HF_TOKEN_PATH`). library_name (`Optional[str]`, defaults to `None`): The library name of the model. Can be any of "transformers", "timm", "diffusers", "sentence_transformers". See `TasksManager.infer_library_from_model` for the priority should none be provided. Returns: `str`: The task name automatically detected from the HF hub repo, model instance, or model class. N)rrrrrrMrPrr)r@rTrtyperr7)rXrrrrrrMrr+r+r,infer_task_from_models$!   z"TasksManager.infer_task_from_modelcCsd}|dur|durtd|dur|durtd|dur"|jjn|j}|dr-d}n|dr5d}n|dr=d}n|drDd}|durLtd|S)NrrrrrrThe library name could not be automatically inferred. If using the command-line, please provide the argument --library {transformers,diffusers,timm,sentence_transformers}. Example: `--library diffusers`.)r7rrr)rXrrPinferred_library_namerr+r+r,(_infer_library_from_model_or_model_class0s&    z5TasksManager._infer_library_from_model_or_model_classc Csd}tj|||||d\}}d|vrd}nFtdd|Ds"d|vr%d}n6d |vr[||||d } tj|fi| \} } tj| fi| } t| d sNt| d rQd }n t| drYd}nd}|durctd|S)a Infers the library from the model name or path. Args: model_name_or_path (`str`): The model to infer the task from. This can either be the name of a repo on the HuggingFace Hub, or a path to a local directory containing the model. subfolder (`str`, defaults to `""`): In case the model files are located inside a subfolder of the model directory / repo on the Hugging Face Hub, you can specify the subfolder name here. revision (`Optional[str]`, *optional*, defaults to `None`): Revision is the specific model version to use. It can be a branch name, a tag name, or a commit id. cache_dir (`Optional[str]`, *optional*): Path to a directory in which a downloaded pretrained model weights have been cached if the standard cache should not be used. token (`Optional[Union[bool,str]]`, defaults to `None`): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `huggingface_hub.constants.HF_TOKEN_PATH`). Returns: `str`: The library name automatically detected from the model repo. N)rrrrrrcss|]}|dVqdS) sentence_N)r)rn file_pathr+r+r,rzszFTasksManager._infer_library_from_model_name_or_path..rrz config.jsonrpretrained_cfg architecturer_diffusers_versionrr)r^rrrget_config_dict from_dicthasattrr7) rXrrrrrrrrkwargs config_dictrr+r+r,&_infer_library_from_model_name_or_pathNs@  z3TasksManager._infer_library_from_model_name_or_pathcCsNt|tr|j|||||d}|St|tur|j|d}|S|j|d}|S)a Infers the library from the model repo, model instance, or model class. Args: model (`Union[str, PreTrainedModel, TFPreTrainedModel, DiffusionPipeline, Type]`): The model to infer the task from. This can either be the name of a repo on the HuggingFace Hub, an instance of a model, or a model class. subfolder (`str`, defaults to `""`): In case the model files are located inside a subfolder of the model directory / repo on the Hugging Face Hub, you can specify the subfolder name here. revision (`Optional[str]`, *optional*, defaults to `None`): Revision is the specific model version to use. It can be a branch name, a tag name, or a commit id. cache_dir (`Optional[str]`, *optional*): Path to a directory in which a downloaded pretrained model weights have been cached if the standard cache should not be used. token (`Optional[Union[bool,str]]`, defaults to `None`): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `huggingface_hub.constants.HF_TOKEN_PATH`). Returns: `str`: The library name automatically detected from the model repo, model instance, or model class. )rrrrrrr)r@rTrrr)rXrrrrrrMr+r+r,rs   z%TasksManager.infer_library_from_modelcCs|dur t|}|dkr8d}|jD]\}}|D]\}}|jj|kr*|}nq|dur1nq||j_dS|dkrLt |j |_|j d|j_dS|dkrd|djjvre|dj j|_d|j_dSd |djjvrz|dj j|_d |j_dSt d |djjd dS) a> Updates the model for export. This function is suitable to make required changes to the models from different libraries to follow transformers style. Args: model (`Union[PreTrainedModel, TFPreTrainedModel, DiffusionPipeline]`): The instance of the model. Nrrrr TransformerrrCLIPrzHThe export of a sentence_transformers model with the first module being z_ is currently not supported in Optimum. Please open an issue or submit a PR to add the support.)r^rrr=rrKrexport_model_typerrrrrr7)rXrrMinferred_model_typerMrNrOrr+r+r,standardize_model_attributess8     z)TasksManager.standardize_model_attributescCsxg}tr d}tj}nd}tj}g}|D] }|t|7}qtjD]}|d|kr3||dq$tt |}|S)zq Retrieves all the possible tasks. Returns: `List`: all the possible tasks. rRrSrr) rr^rrvaluesrhrirappendrx)tasksrXrBd custom_classr+r+r,rRs    zTasksManager.get_all_tasks torch_dtypez torch.dtypedevicez torch.devicec  Ks(|durtj|||||d}| durtj|||||d} |} |dkr-tj|||||| d}d} d} |||d| }| dkrrtj|fi|}|j} | dksQ|dkr`| dkr_|jdur_|jd} n| d ksh|d krr|jdurr|jd} | d krtj |fi|}| d d}t | }t ||}n tj||| | | d }| d kr|d|ddd}|||}n| dkr| dd}| dd}| dd}| dd}|| d<|durtdt|durtd|}|||||||| d}nze|dkrL||d<t|trt|}n |durtd}ttjtdkr?| d kr?||j|fi|}Wdn 1s9wYn|j|fi||}n |j|fi|}Wn4ty|dkrvtdd|d<|j|fi|}ntd d|d!<|j|fi|}Ynwtj|| d"|S)#a Retrieves a model from its name and the task to be enabled. Args: task (`str`): The task required. model_name_or_path (`Union[str, Path]`): Can be either the model id of a model repo on the Hugging Face Hub, or a path to a local directory containing a model. subfolder (`str`, defaults to `""`): In case the model files are located inside a subfolder of the model directory / repo on the Hugging Face Hub, you can specify the subfolder name here. revision (`Optional[str]`, *optional*): Revision is the specific model version to use. It can be a branch name, a tag name, or a commit id. cache_dir (`Optional[str]`, *optional*): Path to a directory in which a downloaded pretrained model weights have been cached if the standard cache should not be used. token (`Optional[Union[bool,str]]`, defaults to `None`): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `huggingface-cli login` (stored in `huggingface_hub.constants.HF_TOKEN_PATH`). framework (`Optional[str]`, *optional*): The framework to use for the export. See `TasksManager.determine_framework` for the priority should none be provided. torch_dtype (`Optional[torch.dtype]`, defaults to `None`): Data type to load the model on. PyTorch-only argument. device (`Optional[torch.device]`, defaults to `None`): Device to initialize the model on. PyTorch-only argument. For PyTorch, defaults to "cpu". library_name (`Optional[str]`, defaults to `None`): The library name of the model. Can be any of "transformers", "timm", "diffusers", "sentence_transformers". See `TasksManager.infer_library_from_model` for the priority should none be provided. model_kwargs (`Dict[str, Any]`, *optional*): Keyword arguments to pass to the model `.from_pretrained()` method. library_name (`Optional[str]`, defaults to `None`): The library name of the model. Can be any of "transformers", "timm", "diffusers", "sentence_transformers". See `TasksManager.infer_library_from_model` for the priority should none be provided. Returns: The instance of the model. Nrauto)rrrrrM)rrrrrbrrwrr)rOrrrzhf_hub:T) pretrained exportabler cache_folderrrtrust_remote_codeFr rr)rrrrr model_kwargsrRcpuz2.0z5Loading TensorFlow model in PyTorch before exporting.from_tfz5Loading PyTorch model in TensorFlow before exporting.from_ptrL) r^rrrrfrom_pretrainedrO architecturesr# load_configrQr>r?r6rtopoprrrr7r@rTtorchrrparse __version__OSErrorr`rr)r/rrrrrrXr rrMr original_taskrOrrrrrrPrrrrr+r+r,get_model_from_tasks6                   "    z TasksManager.get_model_from_task)r!r"exporter_config_kwargsc CsL|durtditjtjtjtj}d}ntj|}|dur+|dur+td|durOt |j dr:|j j }nt |j dd}|durItdt |d|}tj ||||d }||vrt|} | D] } | |vrm| }nqc||vrt|d |d |d d |d||vrtj|}||||} |durt| fi|} | S)a Gets the `ExportConfigConstructor` for a model (or alternatively for a model type) and task combination. Args: exporter (`str`): The exporter to use. model (`Optional[Union[PreTrainedModel, TFPreTrainedModel]]`, defaults to `None`): The instance of the model. task (`str`, defaults to `"feature-extraction"`): The task to retrieve the config for. model_type (`Optional[str]`, defaults to `None`): The model type to retrieve the config for. model_name (`Optional[str]`, defaults to `None`): The name attribute of the model object, only used for the exception message. exporter_config_kwargs (`Optional[Dict[str, Any]]`, defaults to `None`): Arguments that will be passed to the exporter config class when building the config constructor. library_name (`Optional[str]`, defaults to `None`): The library name of the model. Can be any of "transformers", "timm", "diffusers", "sentence_transformers". Returns: `ExportConfigConstructor`: The `ExporterConfig` constructor for the requested backend. NzPassing the argument `library_name` to `get_supported_tasks_for_model_type` is required, but got library_name=None. Defaulting to `transformers`. An error will be raised in a future version of Optimum if `library_name` is not provided.rzNEither a model_type or model should be provided to retrieve the export config.rrOzKModel type cannot be inferred. Please provide the model_type for the model!name)r^rMz doesn't support task z for the z backend. Supported tasks are: rNrO)r`rar^rbrcrdrerPr7rrrr6rlr{rSrirfr) r]rr/rOr^r#rMrT model_tasksrzsynonymexporter_config_constructorr+r+r,get_exporter_config_constructors\        z,TasksManager.get_exporter_config_constructor)F)NN)rRNNrr|)NrfNNNN)ErKr __qualname____doc__rr_TIMM_TASKS_TO_MODEL_LOADERSrrrrrrr]-_SENTENCE_TRANSFORMERS_TASKS_TO_MODEL_LOADERSrrQrrrwr_ENCODER_DECODER_TASKSrfrFrbrcrdrerP_UNSUPPORTED_CLI_MODEL_TYPErxri_SUPPORTED_CLI_MODEL_TYPE classmethodrTboolrr r r\ staticmethodr TaskNameToExportConfigDictrlrrrr r{rvr~rrr rrrrrrrrrrrRr"rrExportConfigConstructorr(r+r+r+r,r^s "             %-5?GOWbfjmsv $,48=BFOUX^bmqw $,3:=AFNRV\bhlrz  "&/7<@DJPY^dgmuy} %,28>FKRY_cgkp{   $'*-47;CHMQW[afmsx{ "-  >     "6<&   T ; Y 4W7  H-2    ! r^)rR)Dr*r>rr functoolsrpathlibrtypingrrrrrr r r r r huggingface_hubrhuggingface_hub.constantsrhuggingface_hub.errorsr packagingrrequests.exceptionsrrrrrrrtransformers.utilsrrrrrutils.import_utilsrrbaser get_loggerrKr`rarr!r"rr#!diffusers.pipelines.auto_pipeliner$r%r&r4rTr3r-r:rFrQr]r^r+r+r+r,sX  0               /