pyiW SSKrSSKJr SSKJr SSKJr SSKJrJ r \R"\ 5r \"SS55r \"SS 55rS \ \\ -S-S \S-S \\ 4S jr"SS\5r"SS\5r"SS\5rS\\\\4S \\\\ 44SjrSrS \S \\ S \\\\44SjrS\\S-S \\S-4Sjrg)N) defaultdict) dataclass)Any)logging yaml_dumpc\rSrSr%Sr\\S'\\S'\\S'\\S'\\S'Sr\S-\S 'Sr \S-\S 'Sr \S-\S 'Sr \S-\S 'Sr \ \\4S-\S 'Sr\S-\S'Sr\S-\S'Sr\ \\4S-\S'Sr\S-\S'Sr\S-\S'Sr\S-\S'Sr\S-\S'\S\4Sj5rSSS\4SjrSSjrSrg) EvalResult uC Flattened representation of individual evaluation results found in model-index of Model Cards. For more information on the model-index spec, see https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1. Args: task_type (`str`): The task identifier. Example: "image-classification". dataset_type (`str`): The dataset identifier. Example: "common_voice". Use dataset id from https://hf.co/datasets. dataset_name (`str`): A pretty name for the dataset. Example: "Common Voice (French)". metric_type (`str`): The metric identifier. Example: "wer". Use metric id from https://hf.co/metrics. metric_value (`Any`): The metric value. Example: 0.9 or "20.0 ± 1.2". task_name (`str`, *optional*): A pretty name for the task. Example: "Speech Recognition". dataset_config (`str`, *optional*): The name of the dataset configuration used in `load_dataset()`. Example: fr in `load_dataset("common_voice", "fr")`. See the `datasets` docs for more info: https://hf.co/docs/datasets/package_reference/loading_methods#datasets.load_dataset.name dataset_split (`str`, *optional*): The split used in `load_dataset()`. Example: "test". dataset_revision (`str`, *optional*): The revision (AKA Git Sha) of the dataset used in `load_dataset()`. Example: 5503434ddd753f426f4b38109466949a1217c2bb dataset_args (`dict[str, Any]`, *optional*): The arguments passed during `Metric.compute()`. Example for `bleu`: `{"max_order": 4}` metric_name (`str`, *optional*): A pretty name for the metric. Example: "Test WER". metric_config (`str`, *optional*): The name of the metric configuration used in `load_metric()`. Example: bleurt-large-512 in `load_metric("bleurt", "bleurt-large-512")`. See the `datasets` docs for more info: https://huggingface.co/docs/datasets/v2.1.0/en/loading#load-configurations metric_args (`dict[str, Any]`, *optional*): The arguments passed during `Metric.compute()`. Example for `bleu`: max_order: 4 verified (`bool`, *optional*): Indicates whether the metrics originate from Hugging Face's [evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not. Automatically computed by Hugging Face, do not set. verify_token (`str`, *optional*): A JSON Web Token that is used to verify whether the metrics originate from Hugging Face's [evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator) or not. source_name (`str`, *optional*): The name of the source of the evaluation result. Example: "Open LLM Leaderboard". source_url (`str`, *optional*): The URL of the source of the evaluation result. Example: "https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard". task_type dataset_type dataset_name metric_type metric_valueN task_namedataset_config dataset_splitdataset_revision dataset_args metric_name metric_config metric_argsverified verify_token source_name source_urlreturnctURURURURUR4$)z9Returns a tuple that uniquely identifies this evaluation.)r r rrrselfs Z/home/mande/repo/quber/.venv/lib/python3.13/site-packages/huggingface_hub/repocard_data.pyunique_identifierEvalResult.unique_identifiers9 NN           ! !   othercURR5H/up#US:XaM US:wdM[X5[X5:wdM/ g g)z` Return True if `self` and `other` describe exactly the same metric but with a different value. rrFT)__dict__itemsgetattr)rr$key_s r is_equal_except_value EvalResult.is_equal_except_valuesK mm))+FCn$n$);wu?R)R ,r#cPURbURc [S5egg)NzAIf `source_name` is provided, `source_url` must also be provided.)rr ValueErrorrs r __post_init__EvalResult.__post_init__s,    'DOO,C`a a-D 'r#)rN)__name__ __module__ __qualname____firstlineno____doc__str__annotations__rrrrrrdictrrrrboolrrrpropertytupler!r+r/__static_attributes__r1r#r r r s4-fN !IsTz "&NC$J%!%M3:$$(cDj'+/L$sCx.4'.#Kt" !%M3:$*.Kc3h$&-!HdTk  $L#*##Kt""Jd !  5   < D br#r c\rSrSrSrSS\4SjjrSrSrSS\ \ S-S \ 4S jjr S r S r SS \ S\S \4SjjrSS \ S\S \4SjjrS \ S \4SjrS \ S\S S4SjrS \ S \4SjrS \4SjrSrg)CardDataaStructure containing metadata from a RepoCard. [`CardData`] is the parent class of [`ModelCardData`] and [`DatasetCardData`]. Metadata can be exported as a dictionary or YAML. Export can be customized to alter the representation of the data (example: flatten evaluation results). `CardData` behaves as a dictionary (can get, pop, set values) but do not inherit from `dict` to allow this export step. ignore_metadata_errorsc :URRU5 gN)r&update)rrAkwargss r __init__CardData.__init__s V$r#c[R"UR5nURU5 UR 5VVs0sH up#UcM X#_M snn$s snnf)zConverts CardData to a dict. Returns: `dict`: CardData represented as a dictionary ready to be dumped to a YAML block for inclusion in a README.md file. )copydeepcopyr&_to_dictr')r data_dictr)values r to_dictCardData.to_dictsKMM$--0  i -6__->T->zs%  ->TTTs  AAcg)zUse this method in child classes to alter the dict representation of the data. Alter the dict in-place. Args: data_dict (`dict`): The raw dict representation of the card data. Nr1rrLs r rKCardData._to_dicts r#Noriginal_orderrcFU(aoU[[URR55[U5- 5-Vs0sH#nX0R;dMX0RU_M% snUl[ UR 5SUS9R 5$s snf)zDumps CardData to a YAML block for inclusion in a README.md file. Args: line_break (str, *optional*): The line break to use when dumping to yaml. Returns: `str`: CardData represented as a YAML block. F) sort_keys line_break)listsetr&keysrrNstrip)rrVrSks r to_yamlCardData.to_yamls ($s4==3E3E3G/H3~K^/^*___A %$==##_DM 5ZPVVXX s BBc,[UR5$rC)reprr&rs r __repr__CardData.__repr__sDMM""r#c"UR5$rC)r\rs r __str__CardData.__str__s||~r#r)defaultcFURRU5nUcU$U$z#Get value for a given metadata key.)r&get)rr)rerMs r rh CardData.gets% !!#&-w2U2r#c8URRX5$)z#Pop value for a given metadata key.)r&pop)rr)res r rk CardData.pops}}  ..r#c URU$rgr&rr)s r __getitem__CardData.__getitem__s}}S!!r#rMc X RU'g)z#Set value for a given metadata key.Nrn)rr)rMs r __setitem__CardData.__setitem__s" cr#cXR;$)z%Check if a given metadata key is set.rnros r __contains__CardData.__contains__smm##r#c,[UR5$)z'Return the number of metadata keys set.)lenr&rs r __len__CardData.__len__s4==!!r#rn)F)NNrC)r2r3r4r5r6r:rFrNrKrWr7r\r`rcrrhrkrprsrvintrzr=r1r#r r?r?s%t% U YtCy47GYSVY$#3s3S3C3 /s/S/C/"s"s"#s#3#4#$$$""r#r? eval_results model_namercUc/$[U[5(aU/n[U[5(a[SU55(d[ S[ U5S35eUc [ S5eU$)Nc3B# UHn[U[5v M g7frC) isinstancer ).0rs r )_validate_eval_results..s4eXdSTZ:5N5NXdszM`eval_results` should be of type `EvalResult` or a list of `EvalResult`, got .z7Passing `eval_results` requires `model_name` to be set.)rr rWallr.type)r}r~s r _validate_eval_resultsrs} , ++$~ lD ) )4eXd4e1e1e[\`am\n[oop q  RSS r#c^\rSrSrSrSSSSSSSSSSSSSS. S\\\-S-S\\\-S-S\\S-S \\\-S-S \S-S \S-S \S-S \S-S\\S-S\S-S\S-S\\S-S\4U4Sjjjr Sr Sr U=r $) ModelCardDatai aiModel Card Metadata that is used by Hugging Face Hub when included at the top of your README.md Args: base_model (`str` or `list[str]`, *optional*): The identifier of the base model from which the model derives. This is applicable for example if your model is a fine-tune or adapter of an existing model. The value must be the ID of a model on the Hub (or a list of IDs if your model derives from multiple models). Defaults to None. datasets (`Union[str, list[str]]`, *optional*): Dataset or list of datasets that were used to train this model. Should be a dataset ID found on https://hf.co/datasets. Defaults to None. eval_results (`Union[list[EvalResult], EvalResult]`, *optional*): List of `huggingface_hub.EvalResult` that define evaluation results of the model. If provided, `model_name` is used to as a name on PapersWithCode's leaderboards. Defaults to `None`. language (`Union[str, list[str]]`, *optional*): Language of model's training data or metadata. It must be an ISO 639-1, 639-2 or 639-3 code (two/three letters), or a special value like "code", "multilingual". Defaults to `None`. library_name (`str`, *optional*): Name of library used by this model. Example: keras or any library from https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/src/model-libraries.ts. Defaults to None. license (`str`, *optional*): License of this model. Example: apache-2.0 or any license from https://huggingface.co/docs/hub/repositories-licenses. Defaults to None. license_name (`str`, *optional*): Name of the license of this model. Defaults to None. To be used in conjunction with `license_link`. Common licenses (Apache-2.0, MIT, CC-BY-SA-4.0) do not need a name. In that case, use `license` instead. license_link (`str`, *optional*): Link to the license of this model. Defaults to None. To be used in conjunction with `license_name`. Common licenses (Apache-2.0, MIT, CC-BY-SA-4.0) do not need a link. In that case, use `license` instead. metrics (`list[str]`, *optional*): List of metrics used to evaluate this model. Should be a metric name that can be found at https://hf.co/metrics. Example: 'accuracy'. Defaults to None. model_name (`str`, *optional*): A name for this model. It is used along with `eval_results` to construct the `model-index` within the card's metadata. The name you supply here is what will be used on PapersWithCode's leaderboards. If None is provided then the repo name is used as a default. Defaults to None. pipeline_tag (`str`, *optional*): The pipeline tag associated with the model. Example: "text-classification". tags (`list[str]`, *optional*): List of tags to add to your model that can be used when filtering on the Hugging Face Hub. Defaults to None. ignore_metadata_errors (`str`): If True, errors while parsing the metadata section will be ignored. Some information might be lost during the process. Use it at your own risk. kwargs (`dict`, *optional*): Additional metadata that will be added to the model card. Defaults to None. Example: ```python >>> from huggingface_hub import ModelCardData >>> card_data = ModelCardData( ... language="en", ... license="mit", ... library_name="timm", ... tags=['image-classification', 'resnet'], ... ) >>> card_data.to_dict() {'language': 'en', 'license': 'mit', 'library_name': 'timm', 'tags': ['image-classification', 'resnet']} ``` NF) base_modeldatasetsr}language library_namelicense license_name license_linkmetricsr~ pipeline_tagtagsrArrr}rrrrrrr~rrrAc  >XlX lX0lX@lXPlX`lXplXlXlXl Xl [U 5Ul URSS5nU(a[U5upXl X0l[*TU]X"S0UD6 UR(a'[/URUR5Ulgg![[ 4aBnU (a["R%S5 SnANz['SUR(SUS35eSnAff=f![0a9nU (a["R%SUS35 SnAg['SU35UeSnAff=f) N model-indexz    ('B4??TXTeTe'fIm $.)9\+B )r#) rrr}rrrrrrr~rr) r2r3r4r5r6r7rWr r:rFrKr= __classcell__rs@r rr s5=D.2+/04+/#'"#'#'$(!%#'!%',8U$s)Od*8US /D( 8U :&- 8U S /D( 8UDj8Ut8UDj8UDj8UcT!8U$J8UDj8U3i$8U!%8U8UtCCr#rcP^\rSrSrSrSSSSSSSSSSSSSSS.S\\\-S-S\\\-S-S\\\-S-S \\\-S-S \\\-S-S \\\-S-S \\S-S \\\-S-S\\\-S-S\S-S\S-S\S-S\\\-S-S\4U4Sjjjr Sr Sr U=r $)DatasetCardDataiaG Dataset Card Metadata that is used by Hugging Face Hub when included at the top of your README.md Args: language (`list[str]`, *optional*): Language of dataset's data or metadata. It must be an ISO 639-1, 639-2 or 639-3 code (two/three letters), or a special value like "code", "multilingual". license (`Union[str, list[str]]`, *optional*): License(s) of this dataset. Example: apache-2.0 or any license from https://huggingface.co/docs/hub/repositories-licenses. annotations_creators (`Union[str, list[str]]`, *optional*): How the annotations for the dataset were created. Options are: 'found', 'crowdsourced', 'expert-generated', 'machine-generated', 'no-annotation', 'other'. language_creators (`Union[str, list[str]]`, *optional*): How the text-based data in the dataset was created. Options are: 'found', 'crowdsourced', 'expert-generated', 'machine-generated', 'other' multilinguality (`Union[str, list[str]]`, *optional*): Whether the dataset is multilingual. Options are: 'monolingual', 'multilingual', 'translation', 'other'. size_categories (`Union[str, list[str]]`, *optional*): The number of examples in the dataset. Options are: 'n<1K', '1K1T', and 'other'. source_datasets (`list[str]]`, *optional*): Indicates whether the dataset is an original dataset or extended from another existing dataset. Options are: 'original' and 'extended'. task_categories (`Union[str, list[str]]`, *optional*): What categories of task does the dataset support? task_ids (`Union[str, list[str]]`, *optional*): What specific tasks does the dataset support? paperswithcode_id (`str`, *optional*): ID of the dataset on PapersWithCode. pretty_name (`str`, *optional*): A more human-readable name for the dataset. (ex. "Cats vs. Dogs") train_eval_index (`dict`, *optional*): A dictionary that describes the necessary spec for doing evaluation on the Hub. If not provided, it will be gathered from the 'train-eval-index' key of the kwargs. config_names (`Union[str, list[str]]`, *optional*): A list of the available dataset configs for the dataset. NF)rrannotations_creatorslanguage_creatorsmultilingualitysize_categoriessource_datasetstask_categoriestask_idspaperswithcode_id pretty_nametrain_eval_index config_namesrArrrrrrrrrrrrrrAc >X0lX@lXlX lXPlX`lXplXlXlXl Xl Xl U =(d URSS5Ul [TU]<"S0UD6 g)Ntrain-eval-indexr1)rrrrrrrrrrrrrkrrrF)rrrrrrrrrrrrrrrArErs r rFDatasetCardData.__init__sv&%9!!2  .... !2&(!1 XFJJ?QSW4X "6"r#c,URS5US'g)Nrr)rkrQs r rKDatasetCardData._to_dicts(1 6H(I $%r#) rrrrrrrrrrrrr) r2r3r4r5r6r7rWr9r:rFrKr=rrs@r rrsb%T,0*.7;482626,026+/(,"&(,/3',!"#S /D("#tCy4' "# "DIo4 "# c?T1 "#tCy4/"#tCy4/"#cT)"#tCy4/"#S /D("#:"#4Z"#+"#DIo,"# !%!"#"#HJJr#rc^\rSrSrSrSSSSSSSSSSSSS. S\S-S\S-S\S-S \S-S \S-S \S-S \S-S \S-S\\S-S\\S-S\\S-S\4U4Sjjjr Sr U=r $) SpaceCardDataia8 Space Card Metadata that is used by Hugging Face Hub when included at the top of your README.md To get an exhaustive reference of Spaces configuration, please visit https://huggingface.co/docs/hub/spaces-config-reference#spaces-configuration-reference. Args: title (`str`, *optional*) Title of the Space. sdk (`str`, *optional*) SDK of the Space (one of `gradio`, `streamlit`, `docker`, or `static`). sdk_version (`str`, *optional*) Version of the used SDK (if Gradio/Streamlit sdk). python_version (`str`, *optional*) Python version used in the Space (if Gradio/Streamlit sdk). app_file (`str`, *optional*) Path to your main application file (which contains either gradio or streamlit Python code, or static html code). Path is relative to the root of the repository. app_port (`str`, *optional*) Port on which your application is running. Used only if sdk is `docker`. license (`str`, *optional*) License of this model. Example: apache-2.0 or any license from https://huggingface.co/docs/hub/repositories-licenses. duplicated_from (`str`, *optional*) ID of the original Space if this is a duplicated Space. models (list[`str`], *optional*) List of models related to this Space. Should be a dataset ID found on https://hf.co/models. datasets (`list[str]`, *optional*) List of datasets related to this Space. Should be a dataset ID found on https://hf.co/datasets. tags (`list[str]`, *optional*) List of tags to add to your Space that can be used when filtering on the Hub. ignore_metadata_errors (`str`): If True, errors while parsing the metadata section will be ignored. Some information might be lost during the process. Use it at your own risk. kwargs (`dict`, *optional*): Additional metadata that will be added to the space card. Example: ```python >>> from huggingface_hub import SpaceCardData >>> card_data = SpaceCardData( ... title="Dreambooth Training", ... license="mit", ... sdk="gradio", ... duplicated_from="multimodalart/dreambooth-training" ... ) >>> card_data.to_dict() {'title': 'Dreambooth Training', 'sdk': 'gradio', 'license': 'mit', 'duplicated_from': 'multimodalart/dreambooth-training'} ``` NF) titlesdk sdk_versionpython_versionapp_fileapp_portrduplicated_frommodelsrrrArrrrrrrrrrrrAc  >XlX lX0lX@lXPlX`lXplXlXlXl [U 5Ul [TU]4"S0U D6 g)Nr1)rrrrrrrrrrrrrrF)rrrrrrrrrrrrrArErs r rFSpaceCardData.__init__ sV" &,   .  #D)  "6"r#) rrrrrrrrrrr) r2r3r4r5r6r7r|rWr:rFr=rrs@r rrs/h!"&%)##"&*#'%)!%',#Tz#4Z # 4Z # d #*#*#t#t#S D #s)d"#3i$#!%##r#rrc :/nUGHnUSnUSnUGHznUSSnUSRS5nUSSnUSSn USRS5n USRS5n USRS5n USRS 5n URS 05RS5nURS 05RS 5nUS HnUSnUS nURS5nURS 5nURS5nURS5nURS5n[S 0SU_SU_SU _SU_SU_SU_SU _SU _SU _SU _SU_SU_SU_SU_SU_SU_SU_6nURU5 M GM} GM WU4$)!aTakes in a model index and returns the model name and a list of `huggingface_hub.EvalResult` objects. A detailed spec of the model index can be found here: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 Args: model_index (`list[dict[str, Any]]`): A model index data structure, likely coming from a README.md file on the Hugging Face Hub. Returns: model_name (`str`): The name of the model as found in the model index. This is used as the identifier for the model on leaderboards like PapersWithCode. eval_results (`list[EvalResult]`): A list of `huggingface_hub.EvalResult` objects containing the metrics reported in the provided model_index. Example: ```python >>> from huggingface_hub.repocard_data import model_index_to_eval_results >>> # Define a minimal model index >>> model_index = [ ... { ... "name": "my-cool-model", ... "results": [ ... { ... "task": { ... "type": "image-classification" ... }, ... "dataset": { ... "type": "beans", ... "name": "Beans" ... }, ... "metrics": [ ... { ... "type": "accuracy", ... "value": 0.9 ... } ... ] ... } ... ] ... } ... ] >>> model_name, eval_results = model_index_to_eval_results(model_index) >>> model_name 'my-cool-model' >>> eval_results[0].task_type 'image-classification' >>> eval_results[0].metric_type 'accuracy' ``` nameresultstaskrdatasetconfigsplitrevisionargssourceurlrrMr verifyTokenr r r rrrrrrrrrrrrrr1)rhr append)rr}elemrrresultr rr r rrrrrrmetricrrrrrrr eval_results r rr+s0pLF|y/Fvv.Iv**62I!),V4L!),V4L#I.228KHb155e.sG#Qa#sc3f# UH'upUcM UcM[U5[U54v M) g7frCr)rr[vs r rrs+w WX;ij;,q/<?; s 111)rrWr<rXrr9r')objs r rrsZ#eS)**CyG#GGG C  Cyw www r#c[[5nUH nX#RRU5 M" /nUR 5GHnUSnUR UR S.URURURURURURS.UVs/sHSnURURURUR UR"UR$UR&S.PMU snS.nUR(b.SUR(0n UR*bUR*U S'XS'URU5 GM UUS ./n [-U 5$s snf) a@Takes in given model name and list of `huggingface_hub.EvalResult` and returns a valid model-index that will be compatible with the format expected by the Hugging Face Hub. Args: model_name (`str`): Name of the model (ex. "my-cool-model"). This is used as the identifier for the model on leaderboards like PapersWithCode. eval_results (`list[EvalResult]`): List of `huggingface_hub.EvalResult` objects containing the metrics to be reported in the model-index. Returns: model_index (`list[dict[str, Any]]`): The eval_results converted to a model-index. Example: ```python >>> from huggingface_hub.repocard_data import eval_results_to_model_index, EvalResult >>> # Define minimal eval_results >>> eval_results = [ ... EvalResult( ... task_type="image-classification", # Required ... dataset_type="beans", # Required ... dataset_name="Beans", # Required ... metric_type="accuracy", # Required ... metric_value=0.9, # Required ... ) ... ] >>> eval_results_to_model_index("my-cool-model", eval_results) [{'name': 'my-cool-model', 'results': [{'task': {'type': 'image-classification'}, 'dataset': {'name': 'Beans', 'type': 'beans'}, 'metrics': [{'type': 'accuracy', 'value': 0.9}]}]}] ``` r)rr)rrrrrr)rrMrrrrr)rrrrrr)rr)rrWr!rvaluesr rr r rrrrrrrrrrrrrr) r~r}task_and_ds_types_maprmodel_index_datar sample_resultrdatarrs r rrsJ:ET9J# ;;<CCKP$.0(//1 &//%// &22%22'66&44)::%22 $& &F#..#00"..$22".. &#)#6#6&  4  # # /}//&F((4!.!:!:v#N%I2T' K  $$= s1AE2 rcVUcU$/nUHnX!;dM URU5 M U$rC)r)r unique_tagstags r rrs7 | K  !   s # r#)rI collectionsr dataclassesrtypingrhuggingface_hub.utilsrr get_loggerr2rr r?rWr7rrrrr9r<rrrrr1r#r rsa #!4   H % TbTb Tbn P"P" P"ftJ//$6d  *"~CH~CBMJhMJ`N#HN#beT$sCx.-AeeCQUV`QaLaFbeP Y%CY%tJ?OY%TXY]^acf^fYgThY%x$s)d*tCy4/?r#