>:ja4.SSKJr SSKrSSKrSSKrSSKrSSKJr SSKJr SSK J r SSK J r J r SSKrSSKJr \ (a SSKJrJrJr \R("\5rS S jrSS jrSS jrSSS jjrg)) annotationsN)Callable)fnmatch)Path) TYPE_CHECKINGAny)list_repo_files CrossEncoderSentenceTransformer SparseEncoderc^^SUU4SjjnU$)z Wraps the save_pretrained method of a model to save to a subfolder. Args: _save_pretrained_fn: The original save_pretrained function subfolder: The subfolder to save to Returns: A wrapped function that saves to the specified subfolder cp>[R"[U5T- SS9 T"[U5T- 40UD6$)NT)exist_ok)osmakedirsr)save_directorykwargs_save_pretrained_fn subfolders `/home/mande/repo/quber/.venv/lib/python3.13/site-packages/sentence_transformers/backend/utils.pywrapper)_save_pretrained_wrapper..wrapper!s5 D(94tD"4#7)#CNvNN)rz str | PathreturnNone)rrrs`` r_save_pretrained_wrapperrsOO Nrc URSS5nU(aXb4$UR5nURSU5nURSS5n U (a[X5R 5O[U5R 5n U (a[XU5R 5O[Xx5R 5n U (aU SU3OSU3n U(a>UR U 5V s/sH!oR U5R 5PM# nn O_[UR 5SURSS5URS S5S 9nUVs/sHn[UU 5(dMUPM nnX;nU(d{X;nU(ap[U5S :a8SU;a2[RS US UR 5<SUSU <S3 5 U (a[X5R 5OUUS'XS'UcU(+n[U5Rn[U5S :a8USUS'[URSS5/USSQ76R 5US'U(aX[RSU<SUR 5<SUS35 U(a"[RSUSUSUSS35 Xb4$s sn fs snf)a Determines whether the model should be exported to the backend, or if it can be loaded directly. Also update the `file_name` and `subfolder` model_kwargs if necessary. These are the cases: 1. If export is set in model_kwargs, just return export 2. If `/` exists; set export to False 3. If `/` exists; set export to False and set subfolder to the backend (e.g. "onnx") 4. If `` contains a folder, add those folders to the subfolder and set the file_name to the last part We will warn if: 1. The expected file does not exist in the model directory given the optional file_name and subfolder. If there are valid files for this backend, but they're don't align with file_name, then we give a useful warning. 2. Multiple files are found in the model directory that match the target file name and the user did not specify the desired file name via `model_kwargs={"file_name": ""}` Args: load_path: The model repository or directory, as a Path instance is_local: Whether the model is local or remote, i.e. whether load_path is a local directory model_kwargs: The model_kwargs dictionary. Notable keys are "export", "file_name", and "subfolder" target_file_name: The expected file name in the model directory, e.g. "model.onnx" or "openvino_model.xml" target_file_glob: The glob pattern to match the target file name, e.g. "*.onnx" or "openvino*.xml" backend_name: The human-readable name of the backend for use in warnings, e.g. "ONNX" or "OpenVINO" Returns: Tuple[bool, dict[str, Any]]: A tuple of the export boolean and the updated model_kwargs dictionary. Notable keys in model_kwargs are "export", "file_name", and "subfolder". exportN file_namerz/**/z**/modelrevisiontoken) repo_typer#r$z Multiple z files found in z: z, defaulting to zW. Please specify the desired file name via `model_kwargs={"file_name": ""}`.zNo z found in z. Exporting the model to .z#If you intended to load one of the  zN files, please specify the desired file name via `model_kwargs={"file_name": "rz"}`.) poplowergetras_posixglob relative_tor rlenloggerwarningparts) load_pathis_local model_kwargstarget_file_nametarget_file_glob backend_namer backendr!rprimary_full_pathsecondary_full_path glob_patternpathmodel_file_names all_filesfname model_foundfile_name_partss rbackend_should_exportrE(sL  h -F ##  "G  .>?I  d3IAJY2;;=PTU^P_PhPhPj:CY+446gIaIjIjIlP>P>R=UUWXhWiiy{NzQQnoPYY(@(I(I(K^eL %(1 % ~ 9o++O ?a$3B$7 [!$()9)9+r)J$b_]`^`Ma$b$k$k$m [! YMI4F4F4H3KKdeqdrrstu  NN56F5GqWZZjklZmYnnsu   UmZs1(K K3KcrSUS3nU(a USU<S3- nO USU<S3- n[RU5 g)z Warns the user to save the model if they just exported it. Args: model_name_or_path: The model name or path is_local: Whether the model is local backend_name: The name of the backend (ONNX or OpenVINO) zSaving the exported zA model is heavily recommended to avoid having to export it again.z# Do so with `model.save_pretrained(z)`.z Do so with `model.push_to_hub(z, create_pr=True)`.N)r2r3)model_name_or_pathr6r:to_logs rbackend_warn_to_saverIsO$L>1r sF78J7MSQQ34F3II\]] NN6rc lSSKJn Jn Jn US:XaSUS3n O US:XaSUS3n [R "5n U"U 5 US:Xaf[ U 5U- n [R"U S - U W - 5 [R"U S - X- RS 55 U R5n US:XaW[ U 5U- nURS S S 9 [ U 5W - nX- n[R"UU5 UR5n U(aSnU(a[U5RSS5RSS5RSS5n[X5(aSUSW SUSUSUSU SUSUS3nOZUb[X5(aSUSW SUSUSUSU SUSUS3nO+[X5(aSUSW SUSUSUSU S USUS3n[R "U UUS!S"US#W <3UUS$9 O[ U5U- nURS S S 9 [ U 5W - nX- n[R""UU5 US:XaQ[ U 5U - RS 5n[ U5U - RS 5n[R""UU5 SSS5 g!,(df  g=f)%Nrr onnxmodel_z.onnxopenvinoopenvino_model_z.xmlzopenvino_model.xmlzopenvino_model.binz.binT)parentsrr((z( z, z, )z )z+Hello! This pull request adds an exported z model (`z`). ## Config ```python a- ``` ## Testing this pull request You can test this pull request before merging by loading the model from this PR with the `revision` argument: ```python from sentence_transformers import SparseEncoder # NOTE: Update this to the number of your pull request pr_number = 2 model = SparseEncoder( "z5", revision=f"refs/pr/{pr_number}", backend="z#", model_kwargs={"file_name": "a5"}, ) # Verify that everything works as expected embeddings = model.encode(["The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium."]) print(embeddings.shape) similarities = model.similarity(embeddings, embeddings) print(similarities) ``` --- *This PR was auto-generated with [`zT`](https://sbert.net/docs/package_reference/util.html#sentence_transformers.backend.z).* a9 ``` ## Testing this pull request You can test this pull request before merging by loading the model from this PR with the `revision` argument: ```python from sentence_transformers import SentenceTransformer # NOTE: Update this to the number of your pull request pr_number = 2 model = SentenceTransformer( "a+ ``` ## Testing this pull request You can test this pull request before merging by loading the model from this PR with the `revision` argument: ```python from sentence_transformers import CrossEncoder # NOTE: Update this to the number of your pull request pr_number = 2 model = CrossEncoder( "a2"}, ) # Verify that everything works as expected query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) ``` --- *This PR was auto-generated with [`r"z Add exported z model ) folder_path path_in_reporepo_idr%commit_messagecommit_description create_pr)sentence_transformersr r r tempfileTemporaryDirectoryrshutilmove with_suffixr.mkdirreprreplace isinstancehuggingface_hub upload_foldercopy)export_functionexport_function_nameconfigrG push_to_hubrW file_suffixr;r"r r r r!save_dirdst_dirsource destinationrVopt_config_string bin_sourcebin_destinations rsave_or_push_to_hub_modelrqsWV&[M/ J %k]$7  $ $ &(! j H~/H KK#77I9M N KK#77(:N9[9[\b9c d((*H f 8nw.G MM$M 6(^i/F!-K KK ,'')H !# $(L$8$8g$F$N$NtU\$]$e$efikp$q!e33.$%,9Ii[A Y""+ -$%9#9:NOcNddC"*&F]j&L&L.$%,9Ii[A Y""+ -$%9#9:NOcNddC"*&F 44.$%,9Ii[A Y""+-$"%9#9:NOcNddM'*&R  ) )$$*!!.wiwymL#5# -.8G MM$M 6(^i/F!-K KK ,*$"8ny8EEfM #'=9#<"I"I&"Q J8E ' & &s I#J%% J3)rrrstrrzCallable[..., None])r5rr6boolr7zdict[str, Any]r8rrr9rrr:rrrztuple[bool, dict[str, Any]])rGrrr6rsr:rrrr)FFNrKN)rerrfrrrGrrrhrsrWrsriz str | Noner;rrr"z9SentenceTransformer | SparseEncoder | CrossEncoder | None) __future__rloggingrr[rYcollections.abcrrpathlibrtypingrrrbr rXr r r getLogger__name__r2rrErIrqrrrr{s" $%+VV   8 $&_ _ _ !_  _  _  _ !_ D,"GKt9t9t9 t9  t9  t9t9t9 Et9r