#i"ddlmZddlZddlZddlZddlZddlZddlZddlZddl Z ddl m Z m Z ddl mZddlmZddlmZddlmZmZddlZddlZddlmZddlZddlmZmZdd lm Z dd lm!Z!m"Z"dd lm#Z#m$Z$m%Z%dd l&m'Z'm(Z(dd l)m*Z+ddl,m-Z-ddl.m/Z/ddl0m1Z1m2Z2ddl3m4Z4m5Z5m6Z6ddl7m8Z8m9Z9ddl:m;Z;mm?Z?ddl@mAZAmBZBmCZCmDZDmEZEddlFmGZGe+jeIZJGdde"je?e ZLy)) annotationsN)ABCabstractmethod) OrderedDict)Queue)Path)AnyLiteral)CardDataHfApi)version)Tensornn)PreTrainedModelis_datasets_availableis_torch_npu_available)get_class_from_dynamic_moduleget_relative_import_files)logging) __version__) BaseEvaluator)format_modalityinfer_batch_modality)Modality PairInput SingleInput)BaseModelCardDatagenerate_model_card)ModuleRouter Transformer)PeftAdapterMixin)get_device_nameimport_from_string load_dir_pathload_file_pathsave_to_hub_args_decorator)ORIGINAL_TRANSFORMER_MODELScFeZdZUdZeZdZded<iZded<dZ ded < d?dddddd dd dddddd d  d@fd Z dAdZ dBdZ dCdZ edDdZdEdZdFdZdGdZdHdZdHdZdIdZedJdZedKdZdLdZ d? dMdZdNdZdOdZ dP dQdZdRdZ dP dQd ZdSd!Z dT dUd"Z!e" dV dWd#Z# dX dYd$Z$ dZ d[d%Z%e& dZ d[d&Z' dZ d[d'Z(d\d(Z) d] d^d)Z* d_ d`d*Z+ dad+Z,d?dbd,Z-d?dcd-Z.eddd.Z/ d? ded/Z0edfd0Z1d1Z2e dgd2Z3edhd3Z4e4jjdAd4Z4edhd5Z6edGd6Z7e7jjdAd7Z7edid8Z8eddd9Z9e9jjd?djd:Z9edkd;Z:edFd<Z;edFd=ZZ=xZ>S)m BaseModela Base class for SentenceTransformer, SparseEncoder, and CrossEncoder models. This class provides common functionality for: - Model loading (from Hub, local paths, or creating new models) - Model saving (to disk and Hub) - Device management - Module architecture (sequential composition) - Configuration management - Tokenizer/processor access All models inherit from nn.Sequential and are composed of a sequence of modules that are called sequentially in the forward pass. N str | None default_huggingface_organizationzdict[str, str | None]_default_promptssentence_transformers_model_idstr _model_card_model_id_placeholderFtorch)modulesdevicepromptsdefault_prompt_name cache_foldertrust_remote_coderevisionlocal_files_onlytoken model_kwargsprocessor_kwargs config_kwargsmodel_card_databackendc t|j}|r|j|||_||_||_|xs|j | |_d|_i|_ d|_ |jj|_ ||_|tj d}|"t#}t$j'd||dk(r,t(j*j-d ddlm}||rtj2j5|sqtj6d k(rd |vs|j9d d kDrt;d |d |j<+d |vr'|j?t@vr|j<d |}|r"|jC|| |||| | | |  \}|_|BtE|tFs2tGtI|Dcgc]\}}tK||fc}}}|0tM|dk(r"tOdtQ|jdtR||tW|djYd}|>|jZ}t]|j_d dD]}|ja||ja|d|_1|je|jjg|ycc}}w)a/ Initialize a BaseModel instance. Args: model_name_or_path (str, optional): If a filepath on disk, loads the model from that path. Otherwise, tries to download a pre-trained model. If that fails, tries to construct a model from the Hugging Face Hub with that name. Defaults to None. modules (list[nn.Module], optional): A list of torch modules that are called sequentially. Can be used to create custom models from scratch. Defaults to None. device (str, optional): Device (like ``"cuda"``, ``"cpu"``, ``"mps"``, ``"npu"``) that should be used for computation. If None, checks if a GPU can be used. Defaults to None. prompts (dict[str, str], optional): A dictionary with prompts for the model. The key is the prompt name, the value is the prompt text. The prompt text will be prepended before any text during inference. For example: ``{"query": "query: ", "passage": "passage: "}``. If a model has saved prompts, you can override them by passing your own, or pass ``{"query": "", "document": ""}`` to disable them. Defaults to None. default_prompt_name (str, optional): The name of the prompt that should be used by default. If not set, no prompt will be applied. Defaults to None. cache_folder (str, optional): Path to store models. Can also be set by the ``SENTENCE_TRANSFORMERS_HOME`` environment variable. Defaults to None. trust_remote_code (bool, optional): Whether to allow for custom models defined on the Hub in their own modeling files. Only set to ``True`` for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. Defaults to False. revision (str, optional): The specific model version to use. It can be a branch name, a tag name, or a commit id, for a stored model on Hugging Face. Defaults to None. local_files_only (bool, optional): Whether to only look at local files (i.e., do not try to download the model). Defaults to False. token (bool or str, optional): Hugging Face authentication token to download private models. Defaults to None. model_kwargs (dict[str, Any], optional): Keyword arguments passed to the underlying Hugging Face Transformers model via ``AutoModel.from_pretrained``. Particularly useful options include: - ``torch_dtype``: Override the default ``torch.dtype`` and load the model under a specific dtype. Can be ``torch.float16``, ``torch.bfloat16``, ``torch.float32``, or ``"auto"`` to use the dtype from the model's ``config.json``. - ``attn_implementation``: The attention implementation to use. For example ``"eager"``, ``"sdpa"``, or ``"flash_attention_2"``. If you ``pip install kernels``, then ``"flash_attention_2"`` should work without having to install ``flash_attn``. It is frequently the fastest option. Defaults to ``"sdpa"`` when available (torch>=2.1.1). - ``device_map``: Device map for model parallelism, e.g. ``"auto"``. - ``provider``: For ``backend="onnx"``, the ONNX execution provider (e.g. ``"CUDAExecutionProvider"``). - ``file_name``: For ``backend="onnx"`` or ``"openvino"``, the filename to load (e.g. for optimized or quantized models). - ``export``: For ``backend="onnx"`` or ``"openvino"``, whether to export the model to the backend format. Also set automatically if the exported file doesn't exist. See the `PreTrainedModel.from_pretrained `_ documentation for more details. Defaults to None. processor_kwargs (dict[str, Any], optional): Keyword arguments passed to the Hugging Face Transformers processor/tokenizer via ``AutoProcessor.from_pretrained``. See the `AutoTokenizer.from_pretrained `_ documentation for more details. Defaults to None. config_kwargs (dict[str, Any], optional): Keyword arguments passed to the Hugging Face Transformers config via ``AutoConfig.from_pretrained``. See the `AutoConfig.from_pretrained `_ documentation for more details. Defaults to None. model_card_data (CardData, optional): A model card data object that contains information about the model. Used to generate a model card when saving the model. If not set, a default model card data object is created. Defaults to None. backend (str, optional): The backend to use for inference. Can be ``"torch"`` (default), ``"onnx"``, or ``"openvino"``. Defaults to ``"torch"``. )r9NSENTENCE_TRANSFORMERS_HOMEzNo device provided, using hpuoptimumr)adapt_transformers_to_gaudi\/zPath z not foundr:r6r8r7r9r;r<r=z$An empty modules list was passed to z@. Please provide at least one module, e.g. a Transformer module.F)4dictr-updater4r5r7model_card_data_classr> module_kwargs_model_card_vars_model_card_text __class____name__ model_typer?osgetenvr#loggerinfo importlibutil find_spec*optimum.habana.transformers.modeling_utilsrDpathexistssepcountFileNotFoundErrorr,lowerr( _load_modules isinstancer enumerater/len ValueErrortypesuper__init__next parametersdtypelistchildrentois_hpu_graph_enabled_validate_promptsregister_model)selfmodel_name_or_pathr2r3r4r5r6r7r8r9r:r;r<r=r>r?default_promptsrDidxmodule first_param first_dtyperOs q/var/www/vps2.regionflexible.com/Desarrollo/venv/lib/python3.12/site-packages/sentence_transformers/base/model.pyrgzBaseModel.__init__Jsht445   " "7 +& #6 !2.o$2L2L^n2L2o! " $..11  99%ABL >$&F KK4VH= > U?y~~77 BN ^ ' ) bggnn5G&H$4+=#=BTBZBZ[^B_bcBc'%0B/C:(NOO55A11&,,.6QQ(,(M(M'NaPbOc%d" *.*<*<")!"3!1)!1++= + 'GT'  z';'G!SZI["\+#vCHf#5"\]G  3w<1#46tDz7J7J6KLQQ  ! 47--/6  "%++Kt}}/3 ' +& ' $)!   ++D15#]sK c d|jjD]\}}| d|j|<|jU|j|jvr=td|jdt |jj d|jjDcgc] \}}|dk7s |c}}x}r0t |}tjd|d|dkDrd ndd ||jr$tjd |jd yycc}}w) z9Validate prompt configuration and log prompt information.NzDefault prompt name ';' not found in the configured prompts dictionary with keys .zLoaded z promptrGsz with these keys: zDefault prompt name is set to 'zs'. This prompt will be applied to all inference calls, except if a `prompt` or `prompt_name` parameter is provided.) r4itemsr5rdrkkeysrcrTrU warning_once)rqkeyvaluekvnon_empty_keysns rxrozBaseModel._validate_promptss;,,,,. 'JC}$& S! '  # # /D4L4LTXT`T`4`'(@(@'AB((,T\\->->-@(A'DAG  -1LL,>,>,@LDAqAGaL L> LN#A KK'!G1q5Cb+AASTbScd e  # #   1$2J2J1KLEF  $Ms 3 D,D,c H|H| |j|}|S|j &|jj |j d}|S|tjd|S#t$r4td|dt|jj dwxYw)a&Resolve a prompt from a prompt name or the default prompt name. Args: prompt: An explicit prompt string, or None. prompt_name: A key into ``self.prompts``, or None. Returns: The resolved prompt string, or None if no prompt applies. Nz Prompt name 'r{r|zvProvide either a `prompt`, a `prompt_name`, or neither, but not both. Ignoring the `prompt_name` in favor of `prompt`.) r4KeyErrorrdrkrr5getrTwarning)rqprompt prompt_names rx_resolve_promptzBaseModel._resolve_prompts >&!\\+6F ))5))$*B*BDI   $ NNC   $' }4optuyvBvBvGvGvIqJpMMNOs A$$=B!c|jS)zReturn the backend used for inference, which can be one of "torch", "onnx", or "openvino". Returns: str: The backend used for inference. )r?rqs rx get_backendzBaseModel.get_backend s ||c$t|dddgS)zmReturn the list of modalities supported by this model, e.g. ``["text"]`` or ``["text", "image", "message"]``.r modalitiestextgetattrrs rxrzBaseModel.modalities(stAw vh77rcx|j|vryt|trdvrtfd|DSy)aCheck if the model supports the given modality. A modality is supported if: 1. It is directly listed in :attr:`modalities` (including tuple modalities that are explicitly listed), or 2. It is a tuple of modalities (e.g. ``("image", "text")``) where each part is individually supported and the model also supports ``"message"`` format, which is used to combine multiple modalities into a single input. Args: modality: A single modality string (e.g. ``"text"``, ``"image"``) or a tuple of modality strings (e.g. ``("image", "text")``). Returns: bool: Whether the model supports the given modality. Example:: >>> from sentence_transformers import SentenceTransformer >>> model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2") >>> model.supports("text") True >>> model.supports("image") False Tmessagec3&K|]}|v ywN).0part supporteds rx z%BaseModel.supports..Ls>Tty(>sF)rratupleall)rqmodalityrs @rxsupportszBaseModel.supports-s>6OO y  h &9 +A>X>> >rct|j}t}|r|j\}}t |t rG|j jD]*}|jt|j,|jr,||jvr|j|j|t|dr|j|j|rt|S)a Get the keyword arguments specific to this model for inference methods like `encode` or `predict`. Example: >>> from sentence_transformers import SentenceTransformer, SparseEncoder >>> SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2").get_model_kwargs() [] >>> SentenceTransformer("jinaai/jina-embeddings-v4", trust_remote_code=True).get_model_kwargs() ['task', 'truncate_dim'] >>> SparseEncoder("opensearch-project/opensearch-neural-sparse-encoding-doc-v3-distill").get_model_kwargs() ['task'] Returns: list[str]: A list of keyword arguments for the forward pass. forward_kwargs) rknamed_childrensetpoprar sub_modulesvaluesextendrLrJhasattrr)rqr2r module_nameru route_moduless rxget_model_kwargszBaseModel.get_model_kwargsOs"t**,-")++- K&&)%+%7%7%>%>%@IMNN4 (D(D(F#GHI!!kT5G5G&G%%d&8&8&EFv/0%%f&;&;<N##rc|jS)a_ .. deprecated:: Use the :attr:`max_seq_length` property instead. Returns the maximal sequence length that the first module of the model accepts. Longer inputs will be truncated. Returns: Optional[int]: The maximal sequence length that the model accepts, or None if it is not defined. max_seq_lengthrs rxget_max_seq_lengthzBaseModel.get_max_seq_lengthms"""rcX|jtt|jS)z4Returns the first module of this sequential embedder)_modulesrhiterrs rx _first_modulezBaseModel._first_modulezs}}T$t}}"5677rcX|jtt|jS)z3Returns the last module of this sequential embedder)rrhreversedrs rx _last_modulezBaseModel._last_module~s}}T(4=="9:;;rcP|d}t|tr |jSt|tru|jj D]C}t t|jd}t|tr |jrCyt|jSy)aCheck if the first module (Transformer or Router containing Transformers) supports flattened text-only inputs. For Router models, this returns True only if ALL routes support flattening, since we cannot know at this point which route will be used. rNF) rar!can_flatten_inputsr rrrhrrlbool)rq input_moduleroutefirst_in_routes rx_can_flatten_inputszBaseModel._can_flatten_inputss Aw lK 022 2 lF +%1188: !!%d5>>+;&nFgFg  !  001 1rct|}tj||j}|d|dzdz|ddd<||dz |dz dzd|ddd<|S)aJInterleave a largest-to-smallest sorted index array so that each consecutive batch contains a mix of long and short inputs. When text-only inputs are flattened via flash attention, there is no padding. Total tokens per batch equals the sum of actual lengths. Grouping all long inputs together creates peak-memory batches, while grouping all short inputs together under-utilises the GPU. Interleaving (largest, smallest, 2nd largest, 2nd smallest, ...) balances the total token count across batches for more uniform memory usage. )rjNrGr)rcnpemptyrj) sorted_idxr interleaveds rx_interleave_sorted_indicesz$BaseModel._interleave_sorted_indicessp  Ohhq (8(89 &~!a%A6 ADqD&q1uA!|b'@A ADqDrct|tr t|St|ttfr.|r,t|dt rd|dvst d|DSt|t rd|vrtj|dSd|vrC|d}t|tr t|St|trt d|DSyt d|jDSt|tr'|r%t|dt rt d |DSt|tjtjfr6t|tjr|jS|jS dd lm}t||r|jd|jdzS y#t$$rYywxYw) zEstimate the "size" of an input sample for length-based batch sorting. The exact value doesn't matter, it's only used to group similarly sized inputs together so that padding waste is minimised within each batch. rrolec3FK|]}tj|ywrr* _input_length)rr}s rxrz*BaseModel._input_length..sF!92215F!arraycontentc 3K|]9}t|tstt|j dd;yw)rrzN)rarIrcr/r)ritems rxrz*BaseModel._input_length..s1l$U_`dfjUks3txx';#<=ls A)ArGc3FK|]}tj|ywrr)rrs rxrz*BaseModel._input_length..sKay..q1Krc3FK|]}tj|ywrr)rmsgs rxrz*BaseModel._input_length..sFy..s3Fr)Image)rar/rcrrkrIsumr*rrrndarrayr1rnelementsize PIL.Imager ImportError)samplerPILImages rxrzBaseModel._input_lengths fc "v;  fudm ,z&)T:vPQ?RFvFFF fd #&  ..vg??F" +gs+w<'gt,l'lllK6==?KK K fd #:fQi3NFvFF F frzz5<<8 9(265<<(H6??$ Yfkk Y  3&(+{{1~ A66,    s 0F>> G  G c T|jD]\}}i}t|tr|}nkg}|j|jj |g}|j Dcic]&\}}||vst |dr||jvr||(}}}||fi|}|Scc}}w)z.Forward pass through all modules in the model.r)rrar rLrr~rr) rqinputkwargsrrurLmodule_kwarg_keysrrs rxforwardzBaseModel.forwards#'#6#6#8 3 KM&&) & $&!%%1(,(:(:(>(>{B(O%'-lln!"U//GFDT4UZ]agavavZvJ! ! 52M2E 3 !s)+B$c |siSd} t|j}|j |sdj djD}dt |dtjd|}t|tr,tfd|Dr|d d j |d z }t| d j|fd |i|}|S#ttf$rYwxYw#t$rX|r<|dk(r7|Dcgc]&}t|tr||d zf|ddzn||z(ncc}w}}d j|fi|}Y|St$r~|r<|dk(r7|Dcgc]&}t|tr||d zf|ddzn||z(ncc}w}} d j|fi|}Y|S#t$rd j|}YY|SwxYwwxYw)a Preprocesses the inputs for the model. Args: inputs (list[SingleInput | PairInput]): A list of inputs to be preprocessed. Each input can be a string, dict, tuple, PIL Image, numpy array, torch Tensor, or other supported modality. If a single input is provided, it must be wrapped in a list. prompt (str, optional): A prompt string to prepend to text inputs. Defaults to None. If the model supports the ``message`` modality, the prompt will be added as a system message to the input messages instead of being prepended to text. Returns: dict[str, Tensor | Any]: A dictionary of tensors with the preprocessed inputs. N)supported_modalities, c32K|]}t|ywr)r)rms rxrz'BaseModel.preprocess.. s!N/!"4!Nsz Modality 'z' is not supported by this z model. Supported modalities: c3:K|]}|jvywr)r)rrrqs rxrz'BaseModel.preprocess..s2`t44??3J2`sz This model supports z and zR individually, but not in the same input. Please process each modality separately.rrrrG)rrrd TypeErrorrjoinrrerPrarr preprocessAttributeErrortokenize) rqinputsrrrrr preprocessedinps ` rxrzBaseModel.preprocesssJ(I  +FYH   h(? !Ndoo!NNI_X677RSWX\S]SfSfRgh))2 5 (E*s2`W_2`/`,W\\(-C,DEZZW% % 8-47--fNVNvNLCI&   * @(f,ntugj*S%:P6CF?,s12w6V\_bVbbuuu-47--f??L 8(f,ntugj*S%:P6CF?,s12w6V\_bVbbuuu 8/tAw//A&A  8#Aw//7  8  8sYCC3C0/C03G+D32GG&+FGF33GGGGc Ttjd|jdd|i|S)za .. deprecated:: `tokenize` is deprecated. Use `preprocess` instead. zEThe `tokenize` method is deprecated, please use `preprocess` instead.rr)rTrr)rqtextsrs rxrzBaseModel.tokenize)s, cdt6e6v66rcttf}tr ddlm}||fz }t ||ryt |tjr(|jdk\r|jjdvryy#t $rY[wxYw)z Check if the input represents a single example or a batch of examples. Args: inputs: The input to check. Returns: bool: True if the input is a single example, False if it is a batch. r)ColumnFrG)UOT) rkrrdatasetsrrrarrndimrjkind)rqr list_typesrs rxis_singular_inputzBaseModel.is_singular_input2s{E] " +vi'  fj ) fbjj )fkkQ.>6<r$| |j>vr|j>| x}r||d<|jA|0t tj j|ddd5}tj||d ddd|r|jC|||yy#1swYxYw#t$$r| j#| YwxYw#1swYNxYw) Saves a model and its configuration files to a directory, so that it can be loaded again. Args: path (str): Path on disk where the model will be saved. model_name (str, optional): Optional model name. create_model_card (bool, optional): If True, create a README.md with basic information about this model. train_datasets (List[str], optional): Optional list with the names of the datasets used to train the model. safe_serialization (bool, optional): If True, save the model using safetensors. If False, save the model the traditional (but unsafe) PyTorch way. NTexist_okzSaving model to !config_sentence_transformers.jsonwutf8encodingr)indent sort_keysr save_in_rootrz_)safe_serializationztransformers_modules.r|r)rtnamerZrer modules.json)r)"rRmakedirsrTrU_get_model_configopenrZrjsondumprbrrr r/rerPsaver __module__ startswithsysr2__file__rr shutilcopyrsplitbasenamerLappend_create_model_card)rqrZ model_namecreate_model_cardtrain_datasetsr modules_configconfigfOutrtr ru model_path class_ref class_file dest_file needed_file module_configrLs rxrzBaseModel.saveJs& <  D4( &tf-.'') "'',,t%HI3Y_ ` >dh IIfd1 = >#4==1' 1IC!]]40FqWV^<ATATWW\\$3 WW\\$C3fAVAV0VW KK T 2 ( J;M NV //I##$;< [[3<< !,Z0@0E0EF  J 2$=Z#H8K $Z 0D4E4J4J KIKK Y78 )s3B78$v,:O:O9PQ (k4<+@+@*AB $'rww?O?OPZ?[enoM!!dd.@.@&@W[WiWijnWoFomFo*7 h'  ! !- 0O' 1R"'',,t^4cF K 6t IInd1 5 6   # #D*n E a > >" ( J' (8 6 6s*1L=M M+=M M('M(+M4c|jttjtjd|j|j dS)N)sentence_transformers transformerspytorch)rQrr4r5)rQrr,r1r4r5rs rxrzBaseModel._get_model_configs?//)4 , 8 8 ,, ||#'#;#;  rc0|j|||||y)rrrr r N)r)rqrZrrr r s rxsave_pretrainedzBaseModel.save_pretraineds$& !/)1  rc|jjrf|jd|jjd|j dd|jjd|jjd}|S)z.Update the default model ID in the model card.zmodel = z("")r>model_idreplacerOrPr0)rq model_cards rx_update_default_model_idz"BaseModel._update_default_model_idsy    ( (#++4>>2232d6[6[5\\]^4>>2232d6J6J6S6S5TTUVJrcb|rBt|}|js'|jjs||j_||j_|j r6d|jj vr|j }|j|}n t|}ttj j#|ddd5}|j%|dddy#t$r.tjdtjdYywxYw#1swYyxYw) a Create an automatic model card and store it in the specified path. Args: path (str): The path where the model card will be stored. model_name (Optional[str], optional): The name of the model. Defaults to None. train_datasets (Optional[List[str]], optional): Deprecated argument, ignored. Returns: None generated_from_trainerz#Error while generating model card: zConsider opening an issue on https://github.com/huggingface/sentence-transformers/issues with this traceback. Skipping model card creation.N README.mdrrr)rr[r>r3save_dirrNtagsr6r ExceptionrTerror traceback format_excrrRrZrwrite)rqrZrr r$r5r#s rxrzBaseModel._create_model_cards j)J$$&t/C/C/L/L0:$$-)-%  %=TEYEYE^E^%^..J66zBJ 06 "'',,t[13 H #D JJz " # #  :9;O;O;Q:R44    # #s C+D%+4D"!D"%D.c *tjd|rcd|vr$tjd|d|d|d|}n;|jdd|k7r tdtjd|d|j |||||||| |  S)aY DEPRECATED, use `push_to_hub` instead. Uploads all elements of this model to a new HuggingFace Hub repository. Args: repo_id (str): Repository name for your model in the Hub, including the user or organization. token (str, optional): An authentication token (See https://huggingface.co/settings/token) private (bool, optional): Set to true, for hosting a private model safe_serialization (bool, optional): If true, save the model using safetensors. If false, save the model the traditional PyTorch way commit_message (str, optional): Message to commit while pushing. local_model_path (str, optional): Path of the model locally. If set, this file path will be uploaded. Otherwise, the current model will be uploaded exist_ok (bool, optional): If true, saving to an existing repository is OK. If false, saving only to a new repository is possible replace_model_card (bool, optional): If true, replace an existing model card in the hub with the automatically created model card train_datasets (List[str], optional): Datasets used to train the model. If set, the datasets will be added to the model card in the Hub. Returns: str: The url of the commit of your model in the repository on the Hugging Face Hub. zThe `save_to_hub` method is deprecated and will be removed in a future version of SentenceTransformers. Please use `push_to_hub` instead for future model uploads.rFzQProviding an `organization` to `save_to_hub` is deprecated. Please use `repo_id="z "` instead.rzYProviding an `organization` to `save_to_hub` is deprecated. Please use `repo_id` instead.) repo_idr:privater commit_messagelocal_model_pathrreplace_model_cardr )rTrrrd push_to_hub) rqrB organizationr:rCr rDrErrFr s rx save_to_hubzBaseModel.save_to_hubsB  J '!ghtguuvw~v@KL*N!G95s#A&,6 oghogpp{|1)-1)  rc bt|} | j||d|xs| } | j}|jj || | j || d|M|j }|dk(rd|jjd}nd|jjd |d }d }| r.d |jjd |d|j|}|r| j|||| r|nd| | }ngtj5}|rd}nt|d|| d}|du}|j|||| || j|||| r|nd| | }ddd| r.tj!dj"|j"Sj$S#1swYExYw)az Uploads all elements of this model to a HuggingFace Hub repository, creating it if it doesn't exist. Args: repo_id (str): Repository name for your model in the Hub, including the user or organization. token (str, optional): An authentication token (See https://huggingface.co/settings/token) private (bool, optional): Set to true, for hosting a private model safe_serialization (bool, optional): If true, save the model using safetensors. If false, save the model the traditional PyTorch way commit_message (str, optional): Message to commit while pushing. local_model_path (str, optional): Path of the model locally. If set, this file path will be uploaded. Otherwise, the current model will be uploaded exist_ok (bool, optional): If true, saving to an existing repository is OK. If false, saving only to a new repository is possible replace_model_card (bool, optional): If true, replace an existing model card in the hub with the automatically created model card. If false (default), keep the existing model card if one exists in the repository. train_datasets (List[str], optional): Datasets used to train the model. If set, the datasets will be added to the model card in the Hub. revision (str, optional): Branch to push the uploaded files to create_pr (bool, optional): If True, create a pull request instead of pushing directly to the main branch Returns: str: The url of the commit of your model in the repository on the Hugging Face Hub. )r:N)rBrC repo_typerT)rBbranchrr1zAdd new z modelz model with an z backendrzzBHello! This pull request has been automatically generated to add z1 compatibility. ## Full Model Architecture: ``` z ``` )rB folder_pathrDcommit_description create_prr8r9F)r:r8r9r/z#A pull request has been created at )r create_reporBr> set_model_id create_branchrrOrP_push_to_hub_usage_tip upload_foldertempfileTemporaryDirectoryr&rrTrUpr_url commit_url)rqrBr:rCr rDrErrFr r8rOapirepo_urlr?rN folder_urltmp_dircreate_model_card_for_pathexisting_readmes rxrGzBaseModel.push_to_hub4sF% ??* #  "" ))'2     gh  N  !&&(G'!#+DNN,C,C+DF!K#+DNN,C,C+DOT[S\\d!e &;<@>>;R;R:STW%& "*  **,-9B#5#! +J,,. '%15.'5EH_d'O2AD1H. &&@#1'9 !..# '#1=F'9D'% / ! 2  KK=j>O>O=PQ R$$ $$$$;  s A F%%F.c |||||||| d} t|d||||} | 9tjd|d|jd|j|fi| S|j |||||} | |jk(r9tjd|jd|d |j |fi| Stjd | d |d |jd |j|fi| d | iS)NrHr r:r6r8r9zNo modules.json found for z, initializing a new z model.zLoading z model from r|z Converting z model z to rQ)r&rTrUrQ_load_default_modules_get_model_type_load_config_modules_load_converted_modules) rqrrr:r6r8r7r9r;r<r= load_kwargsmodules_json_pathmodel_type_being_loadeds rxr`zBaseModel._load_modulessH( !2 0( 0*  +  %-    $ KK45G4HH]^b^m^m]nnuv w-4--.@PKP P"&"6"6 %- #7#  #doo 5 KK(4??"3<@R?SSTU V,4,,-?O;O O k"9!:'BTAUUYZ^ZiZiYjjklm+t++,>r+rZqrrrc y)a Args: model_name_or_path (str): The name or path of the pre-trained model. token (Optional[Union[bool, str]]): The token to use for the model. cache_folder (Optional[str]): The folder to cache the model. revision (Optional[str], optional): The revision of the model. Defaults to None. trust_remote_code (bool, optional): Whether to trust remote code. Defaults to False. local_files_only (bool, optional): Whether to use only local files. Defaults to False. model_kwargs (Optional[Dict[str, Any]], optional): Additional keyword arguments for the model. Defaults to None. processor_kwargs (Optional[Dict[str, Any]], optional): Additional keyword arguments for the processor/tokenizer. Defaults to None. config_kwargs (Optional[Dict[str, Any]], optional): Additional keyword arguments for the config. Defaults to None. Returns: List[nn.Module]: A list containing the transformer model and the pooling model. Nr) rqrrr:r6r8r7r9r;r<r=s rxrazBaseModel._load_default_modulessrc Lt|d||||} | t| d5} tj| } dddd vrbd| dvr[t j | ddt j t kDr&tjd| ddd t d |j| t|d ||||} | , t| d5} | j|_ dddt|d ||||}t|d5} tj| }dddt}t}D]}|d }|j|||||}tj |j}t#|j$dk(rtj |j&}t)|j$}hd|z}ddh|k}|s|rt+j,||d|||||||| |j. }dddd}|r3|s1|j1D]\}}||vs |j3|||< ddh}|D]}||vs||vs |j3|||fi|}nOt5||d||||} |j| }n)|j||d|||||||| |j. }|||d<|j7dg||d<|]t9|}!t#|!j:dk\r:t9|j:d}"t#|"dk(rt=d |"Dr|"}|s|j>jA||!||fS#1swY:xYw#1swYxYw#t$rYwxYw#1swYrxYw)"a Loads a full model using the modules.json file. Args: model_name_or_path (str): The name or path of the pre-trained model. token (Optional[Union[bool, str]]): The token to use for the model. cache_folder (Optional[str]): The folder to cache the model. revision (Optional[str], optional): The revision of the model. Defaults to None. trust_remote_code (bool, optional): Whether to trust remote code. Defaults to False. local_files_only (bool, optional): Whether to use only local files. Defaults to False. model_kwargs (Optional[Dict[str, Any]], optional): Additional keyword arguments for the model. Defaults to None. processor_kwargs (Optional[Dict[str, Any]], optional): Additional keyword arguments for the processor/tokenizer. Defaults to None. config_kwargs (Optional[Dict[str, Any]], optional): Additional keyword arguments for the config. Defaults to None. Returns: Tuple[OrderedDict[str, nn.Module], OrderedDict[str, Any]]: An ordered dictionary containing the modules of the model and their kwargs. rr`Nrrrr+z:This model was created with Sentence Transformers version z, but you're using version zD. Consider updating to the latest version to avoid potential issues.r9r rerG> model_args config_argstokenizer_argsr;r=rZ) subfolderr:r6r8r9r7r;r<r=r?rjrlrk)r;r<r=modality_configmodule_output_name)rrrmr:r6r8r9r rr(c3$K|]}|dv yw)0123456789abcdefNr)rcs rxrz1BaseModel._load_config_modules..s8mUV>P9P8ms)r8)!r&rrloadr parserrTr_parse_model_configreadrNr<r_load_module_class_from_refinspect signaturercrirgrr!_load_init_kwargsr?r~rr%rrpartsrr>set_base_model)#rqrrr:r6r8r7r9r;r<r=&config_sentence_transformers_json_pathfIn model_configmodel_card_pathrfr!r2rLr)r% module_classload_signaturer{ init_paramsuses_old_namesuses_new_names init_kwargsnew_to_old_name_mappingnew_nameold_nameoptional_paramsru local_path path_partsrevision_path_parts# rxrczBaseModel._load_config_modulessj<2@  /%- 2 . 2 =U"VWZaZgZghsZttPQ]^kQlnERFQGG00;}=AB  $ $\ 2)  %-    & /F;7s,/HHJD)7 +  %-  #f 5 ,!YYs^N ,-# +Y SM%f-I#'#C#C-/@(L$L %..|/@/@AN>,,-2#--l.C.CD ")"6"67 !PS^!^"0/!Bk!Q!^"-"?"?*"/"7#%1!))9*;%1)9&3 $ #K$)5,<)6/+ &n2I2O2O2QR.Hh';68C8Q H 5R (9:N&OO$36#{2x{7R'OOH56**<L LF "/+="/"7#%1!))9 "J*..z:F &**&+F3!-%%5&7!-%5"/ LL+ .4GM&) *3@3D3DXr3RM-/ 0sY Sv  /0J:##$)%)*;%<%B%B2%F")*b0S8mZl8m5m1H  / /0BX / V %%a . .477   , ,sAM/ N M<-N N/M9<NN NNN#c|jdijD]2\}}||jvs|j|$||j|<4|j|jdd|_yy)aParse model configuration and merge saved prompts/defaults with user-provided values. User-provided prompts and default_prompt_name take precedence over saved config values. Saved prompts are only used for keys not already present in ``self.prompts``, or where the current value is ``None`` (i.e. a default placeholder, not yet filled by the user or config). Empty string ``""`` is treated as an intentional user-provided value and will not be overwritten. r4Nr5)rr~r4r5)rqrr prompt_texts rxrwzBaseModel._parse_model_configs)5(8(8B(G(M(M(O 8 $K$,,.$,,{2K2S,7 [) 8  # # +'3'7'78Mt'TD $ ,rc 6|j|||||||||  S)NrH)ra) rqrrr:r6r8r7r9r;r<r=rQs rxrdz!BaseModel._load_converted_moduless7)) %/-%-'*  rct|d||||}|yt|d5}tj|}|j ddcdddS#1swYyxYw)at Retrieves the model_type from the config_sentence_transformers.json file. This is used to determine whether the model being loaded matches the current class (e.g., a SentenceTransformer model loaded with SentenceTransformer, or a SparseEncoder model loaded with SparseEncoder). When the model type doesn't match, we switch to a converted loading method to ensure compatibility. Defaults to "SentenceTransformer" if the config file is missing or has no "model_type" key, for backwards compatibility with older models. Args: model_name_or_path (str): The name or path of the pre-trained model. token (Optional[Union[bool, str]]): The token to use for the model. cache_folder (Optional[str]): The folder to cache the model. revision (Optional[str], optional): The revision of the model. Defaults to None. local_files_only (bool, optional): Whether to use only local files. Defaults to False. Returns: str: The model type, e.g. "SentenceTransformer", "SparseEncoder", or "CrossEncoder". rr`NSentenceTransformerrrrQ)r&rrrur) rqrrr:r6r8r9rrr"s rxrbzBaseModel._get_model_typeso:2@  /%- 2 . 2 9( 86 J CcYYs^F::l,AB C C Cs 'AAc|jdr t|S|stjj |r&|r|j ddnd} t ||||St|S#ttf$r Yt|SwxYw)a Load a module class from a class reference string. Args: class_ref: The class reference string (e.g., "sentence_transformers.sentence_transformer.modules.Pooling") model_name_or_path: The model name or path trust_remote_code: Whether to trust remote code revision: The model revision model_kwargs: Additional model kwargs Returns: The module class zsentence_transformers. code_revisionN)r8r) rr$rRrZr[rrOSErrorrd)rqr%rrr7r8r;rs rxryz%BaseModel._load_module_class_from_refs.    8 9%i0 0 /A BGSL,,_dCY]M 4&%"/ "),, Z( !),,  sA//B  B cF|tj|d|||S)a? Evaluate the model based on an evaluator Args: evaluator (BaseEvaluator): The evaluator used to evaluate the model. output_path (str, optional): The path where the evaluator can write the results. Defaults to None. Returns: The evaluation results. 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Starting 4 CPU workersrz(Starting multi-process pool on devices: rspawnT)targetargsdaemon)routput processes)r1cuda is_availablerange device_countrnpurTrUrmapr/rm share_memorymp get_contextrProcessrO_multi_process_workerstartr) rqtarget_devicesictx input_queue output_queuer device_idps rxstart_multi_process_poolz"BaseModel.start_multi_process_pool^se&  !zz&&(7tyySR`Ia?b>cde  nnW%iik yy{  ' I ~~;;{LAA GGI   Q  % 9UU7"Y!Vs F F"c|dD]}|j|dD]"}|j|j$|dj|djy)z Stops all processes started with start_multi_process_pool. Args: pool (Dict[str, object]): A dictionary containing the input queue, output queue, and process list. Returns: None rrrN) terminaterclose)poolrs rxstop_multi_process_poolz!BaseModel.stop_multi_process_poolskk" A KKM k" A FFH GGI  W  Xrctd)N0This method should be implemented in subclasses.NotImplementedError)rqrrs rx_multi_processzBaseModel._multi_processs!"TUUrctd)afWorker function for multi-process inference. Must be overridden by subclasses. 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