HThvL"SSKJr SSKrSSKrSSKrSSKJr SSKJ r SSK J r SSK J r JrJrJrJr SSKJrJr SSKJrJrJrJrJr SS KJr SS KJr SS KJ r J!r!J"r" SS K#J$r$ \!RJ"\&5r'Sr(\"5(aSSK)r(S \$S\$4Sjr-S/Sjr.Sr/Sr0S0S\ S\1S\\24Sjjr3\-S1S\\4\ 4S\\\4\ 4S\1S\1S\\\5\444 Sjj5r6S2Sjr7\"\-SS SSSSSSSSSSSSS!.S"\4S\\2S#\4S$\\1S%\\4S&\\4S'\\4S(\\1S)\\\\4\44S*\\\\4\44S+\\\\4\44S,\\4S\1S\\\5\44S\14S-jj55r8"S.S\5r9g!\*a SSK+r,\,RPr(Nf=f)3Nwraps)Path)copytree)AnyDictListOptionalUnion) ModelHubMixinsnapshot_download)get_tf_versionis_graphviz_availableis_pydot_availableis_tf_available yaml_dump) constants)HfApi)SoftTemporaryDirectoryloggingvalidate_hf_hub_args) CallableTfnreturnc0^[T5U4Sj5nU$)Ncp>[US5(d[STRS35eT"U/UQ70UD6$)Nhistoryz Cannot use 'z}': Keras 3.x is not supported. Please save models manually and upload them using `upload_folder` or `huggingface-cli upload`.)hasattrNotImplementedError__name__)modelargskwargsrs S/var/www/auris/envauris/lib/python3.13/site-packages/huggingface_hub/keras_mixin.py_inner'_requires_keras_2_model.._inner+sKui((%r{{m,rr %)$)&))r)rr&s` r%_requires_keras_2_modelr))s  2Y** Mr(c&/nUR5Hqup4U(aUSU3OUn[U[R5(a+UR [ UU5R55 M_UR XT45 Ms [U5$)a\Flatten a nested dictionary. Reference: https://stackoverflow.com/a/6027615/10319735 Args: dictionary (`dict`): The nested dictionary to be flattened. parent_key (`str`): The parent key to be prefixed to the children keys. Necessary for recursing over the nested dictionary. Returns: The flattened dictionary. .)items isinstance collectionsMutableMappingextend _flatten_dictappenddict) dictionary parent_keyr,keyvaluenew_keys r%r1r17s E &&( +5ZL#'3 e[77 8 8 LL%'   LL') *) ;r(cSnURbwURR5n[U5n[RR 5R US'SnUR5Hup4USUSUS3- nM U$)z6Parse hyperparameter dictionary into a markdown table.Ntraining_precisionz*| Hyperparameters | Value | | :-- | :-- | z| z | z | ) optimizer get_configr1kerasmixed_precision global_policynamer,)r"tableoptimizer_paramsr6r7s r%_create_hyperparameter_tablerCTs E " ??557()9:161F1F1T1T1V1[1[-.>*002JC r#c%- -E3 Lr(c T[RRUUS3SSSSSSSS9 g)N /model.pngFTTB`)to_file show_shapes show_dtypeshow_layer_namesrankdir expand_nesteddpi layer_range)r=utils plot_model)r"save_directorys r% _plot_networkrSbs< KK !"*-  r(Trepo_dirrQmetadatacUS- nUR5(ag[U5nU(a)[5(a[5(a [ X5 Uc0nSUS'SnU[ USS9- nUS- nUS- nUS - nUS - nUbUS - nUS - nUS - nXe- nUS- nU(aF[ RRUS35(aUS- nUS- nUS- nSnUSUS3- nUS- nURU5 g)zX Creates a model card for the repository. Do not overwrite an existing README.md file. z README.mdNr= library_namez--- F)default_flow_stylez/ ## Model description More information needed z9 ## Intended uses & limitations More information needed z: ## Training and evaluation data More information needed z ## Training procedure z ### Training hyperparameters z; The following hyperparameters were used during training:  rEz ## Model Plot z
z$ View Model Plot z ./model.pngz ![Model Image](z) z
) existsrCrrrSrospath write_text)r"rTrQrU readme_pathhyperparameters model_card path_to_plots r%_create_model_cardrbpsC[(K259O+--2D2F2Fe&&H^J)H??J'JGGJQQJRRJ"11 88 VV % d bggnnz%<==** m# >> $ ),s;; n$ :&r(FrRconfiginclude_optimizertagsc t[c [S5eUR(d [S5e[ U5nUR SSS9 U(an[ U[5(d[S[U5S35eU[R- RS5n[R"X'5 SSS5 0n[ U[5(aXXS 'O[ U[ 5(aU/US 'UR#S S5n U b<[$R&"S [(5 S U;aUS R+U 5 OU /US 'UR,bUR,R,0:wayUS - n U R/5(a[$R&"S [05 U RSSS9n[R"UR,R,USSS9 SSS5 [3XXH5 [R4R6"X4SU0UD6 g!,(df  GNg=f!,(df  NP=f)a Saves a Keras model to save_directory in SavedModel format. Use this if you're using the Functional or Sequential APIs. Args: model (`Keras.Model`): The [Keras model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) you'd like to save. The model must be compiled and built. save_directory (`str` or `Path`): Specify directory in which you want to save the Keras model. config (`dict`, *optional*): Configuration object to be saved alongside the model weights. include_optimizer(`bool`, *optional*, defaults to `False`): Whether or not to include optimizer in serialization. plot_model (`bool`, *optional*, defaults to `True`): Setting this to `True` will plot the model and put it in the model card. Requires graphviz and pydot to be installed. tags (Union[`str`,`list`], *optional*): List of tags that are related to model or string of a single tag. See example tags [here](https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1). model_save_kwargs(`dict`, *optional*): model_save_kwargs will be passed to [`tf.keras.models.save_model()`](https://www.tensorflow.org/api_docs/python/tf/keras/models/save_model). Nz>Called a Tensorflow-specific function but could not import it.z+Model should be built before trying to saveT)parentsexist_okzAProvided config to save_pretrained_keras should be a dict. Got: ''wre task_namez>`task_name` input argument is deprecated. Pass `tags` instead.z history.jsonzZ`history.json` file already exists, it will be overwritten by the history of this version.zutf-8)encoding)indent sort_keysrd)r= ImportErrorbuilt ValueErrorrmkdirr-r3 RuntimeErrortyper CONFIG_NAMEopenjsondumpliststrpopwarningswarn FutureWarningr2rrZ UserWarningrbmodels save_model) r"rRrcrdrQremodel_save_kwargsfrUrkr\s r%save_pretrained_kerasrsF }Z[[ ;;FGG.)N5&$''!bcghncobppqrs sy444 : :3 ?1 IIf @H$ D#   6!%%k48I L   X  V  # #I . ){HV  }} == B &!N2D{{}} p31Q %--//1M2ujC LLElEVlZklA@ ?821s H3,H) H&) H7KerasModelHubMixinc.[R"U0UD6$)a Instantiate a pretrained Keras model from a pre-trained model from the Hub. The model is expected to be in `SavedModel` format. Args: pretrained_model_name_or_path (`str` or `os.PathLike`): Can be either: - A string, the `model id` of a pretrained model hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a user or organization name, like `dbmdz/bert-base-german-cased`. - You can add `revision` by appending `@` at the end of model_id simply like this: `dbmdz/bert-base-german-cased@main` Revision is the specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier allowed by git. - A path to a `directory` containing model weights saved using [`~transformers.PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. - `None` if you are both providing the configuration and state dictionary (resp. with keyword arguments `config` and `state_dict`). force_download (`bool`, *optional*, defaults to `False`): Whether to force the (re-)download of the model weights and configuration files, overriding the cached versions if they exist. proxies (`Dict[str, str]`, *optional*): A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. token (`str` or `bool`, *optional*): The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated when running `transformers-cli login` (stored in `~/.huggingface`). cache_dir (`Union[str, os.PathLike]`, *optional*): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. local_files_only(`bool`, *optional*, defaults to `False`): Whether to only look at local files (i.e., do not try to download the model). model_kwargs (`Dict`, *optional*): model_kwargs will be passed to the model during initialization Passing `token=True` is required when you want to use a private model. )rfrom_pretrained)r#r$s r%from_pretrained_kerasrsj  - -t >v >>r(z'Push Keras model using huggingface_hub.)rccommit_messageprivate api_endpointtokenbranch create_prallow_patternsignore_patternsdelete_patternslog_dirrdrerQrepo_idrrrrrrrrrrc [US9nURXUSS9Rn[5n[ U5U- n[ UU4UU UUS.UD6 U b?U c/O[ U [5(aU /OU n U RS5 [U US- 5 URSUUUUUUU U U S 9 sSSS5 $!,(df  g=f) a Upload model checkpoint to the Hub. Use `allow_patterns` and `ignore_patterns` to precisely filter which files should be pushed to the hub. Use `delete_patterns` to delete existing remote files in the same commit. See [`upload_folder`] reference for more details. Args: model (`Keras.Model`): The [Keras model](`https://www.tensorflow.org/api_docs/python/tf/keras/Model`) you'd like to push to the Hub. The model must be compiled and built. repo_id (`str`): ID of the repository to push to (example: `"username/my-model"`). commit_message (`str`, *optional*, defaults to "Add Keras model"): Message to commit while pushing. private (`bool`, *optional*): Whether the repository created should be private. If `None` (default), the repo will be public unless the organization's default is private. api_endpoint (`str`, *optional*): The API endpoint to use when pushing the model to the hub. token (`str`, *optional*): The token to use as HTTP bearer authorization for remote files. If not set, will use the token set when logging in with `huggingface-cli login` (stored in `~/.huggingface`). branch (`str`, *optional*): The git branch on which to push the model. This defaults to the default branch as specified in your repository, which defaults to `"main"`. create_pr (`boolean`, *optional*): Whether or not to create a Pull Request from `branch` with that commit. Defaults to `False`. config (`dict`, *optional*): Configuration object to be saved alongside the model weights. allow_patterns (`List[str]` or `str`, *optional*): If provided, only files matching at least one pattern are pushed. ignore_patterns (`List[str]` or `str`, *optional*): If provided, files matching any of the patterns are not pushed. delete_patterns (`List[str]` or `str`, *optional*): If provided, remote files matching any of the patterns will be deleted from the repo. log_dir (`str`, *optional*): TensorBoard logging directory to be pushed. The Hub automatically hosts and displays a TensorBoard instance if log files are included in the repository. include_optimizer (`bool`, *optional*, defaults to `False`): Whether or not to include optimizer during serialization. tags (Union[`list`, `str`], *optional*): List of tags that are related to model or string of a single tag. See example tags [here](https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1). plot_model (`bool`, *optional*, defaults to `True`): Setting this to `True` will plot the model and put it in the model card. Requires graphviz and pydot to be installed. model_save_kwargs(`dict`, *optional*): model_save_kwargs will be passed to [`tf.keras.models.save_model()`](https://www.tensorflow.org/api_docs/python/tf/keras/models/save_model). Returns: The url of the commit of your model in the given repository. )endpointT)rrrrh)rcrdrerQNzlogs/*logsr") repo_typer folder_pathrrrevisionrrrr) r create_reporrrrr-r{r2r upload_folder)r"rrcrrrrrrrrrrrdrerQrapitmp saved_paths r%push_to_hub_kerasr(s`  &CoogGVZo[ccG !S#Y(    /!      #*"/377%%(   " "8 , Wj61 2  "))++!  5 " ! !s A:B33 CcL\rSrSrSrSr\SS\\\ \ 44Sjj5r Sr g) ria Implementation of [`ModelHubMixin`] to provide model Hub upload/download capabilities to Keras models. ```python >>> import tensorflow as tf >>> from huggingface_hub import KerasModelHubMixin >>> class MyModel(tf.keras.Model, KerasModelHubMixin): ... def __init__(self, **kwargs): ... super().__init__() ... self.config = kwargs.pop("config", None) ... self.dummy_inputs = ... ... self.layer = ... ... def call(self, *args): ... return ... >>> # Initialize and compile the model as you normally would >>> model = MyModel() >>> model.compile(...) >>> # Build the graph by training it or passing dummy inputs >>> _ = model(model.dummy_inputs) >>> # Save model weights to local directory >>> model.save_pretrained("my-awesome-model") >>> # Push model weights to the Hub >>> model.push_to_hub("my-awesome-model") >>> # Download and initialize weights from the Hub >>> model = MyModel.from_pretrained("username/super-cool-model") ``` c[X5 gN)r)selfrRs r%_save_pretrained#KerasModelHubMixin._save_pretraineds d3r(Nrcc  [c [S5e[RR U5(d[ UUUS[ 5S9n OUn [RRU 5n Xl U $)zHere we just call [`from_pretrained_keras`] function so both the mixin and functional APIs stay in sync. TODO - Some args above aren't used since we are calling snapshot_download instead of hf_hub_download. z>Called a TensorFlow-specific function but could not import it.r=)rr cache_dirrWlibrary_version) r=rpr[r\isdirr rr load_modelrc) clsmodel_idrrforce_downloadproxiesresume_downloadlocal_files_onlyrrc model_kwargsstorage_folderr"s r%_from_pretrained#KerasModelHubMixin._from_pretrainedso( =^_ _ww}}X&&. !#$ . 0 N&N ''7  r(r) r! __module__ __qualname____firstlineno____doc__r classmethodr rr{rr__static_attributes__rr(r%rrs=!F4,0(c3h(((r())TN)NFTN)rr):collections.abcabcr.rxr[r} functoolsrpathlibrshutilrtypingrrr r r huggingface_hubr r huggingface_hub.utilsrrrrrrrhf_apirrPrrr utils._typingr get_loggerr!loggerr=tf_kerasrp tensorflowtfr)r1rCrSboolr3rbr{rzrrrrrr(r%rs% 33<HH$   H %     i :  "# )')')'tn )'X(,#'+ Pm#t)$Pm T#s(^ $Pm Pm  Pm 5s# $ PmPmf5?p "C""& $6:7;7;!#'+#w w  TN w  w d^ w 3-w  C=w  SMw ~w U49c>23w eDIsN34w eDIsN34w c]w w 5s# $!w "#w w tPPC sE,,FF