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It was picked from the main branch on github instead.FrrCrrrmetricsrrrrPrrCr)rrFileNotFoundErrorrwarningrCr@rr5rrPrRrrrUinvalidate_cachesr)r rrrrrPr^rs rQr z$GithubMetricModuleFactory.get_modules^= 55h??J}HH   #!!99(CC MDIMMM j))4#hX]^^^ 0    =A Use the new library 🤗 Evaluate instead: https://huggingface.co/docs/evaluate rNrKrrrPc||_t|j|_|p t |_||_||_dSrrKrstemrCrrrrPr rKrrrPs rQrz!LocalMetricModuleFactory.__init__sC JJO .B.2B2B*$8!!!rfrSc zt|j}t|jt t |jj||j}|jr|jn t}t|j|g|d|j|j \}}tj t||S)Nr"r#r$)r@rKrrCrrrrrPrRrrrUr'r)r rrrPr^rs rQr z#LocalMetricModuleFactory.get_modulesdi((4$ty//011 0    =As~!$)__3f6WW"49oo4{Bdi((4$ty//011 0      % % ' ' b  # #V%FL^H_H_$` a a a  & & ( ( H  # #[2E$F G G G<@r)r rKr>r?rs rQrz/LocalDatasetModuleFactoryWithoutScript.__init__csm  c h// ca[_aabb b JJO $  *rfrSc|jr*tj|j|jn|j}|jt |jnt |}tj||t}d| D}tt| \tfd| Drtd|st!d|j|jt"vrz|t$kro t'|}n#t $rd}YnwxYw|Jt)j||}|D]1}t)|||z||j|jz||<2t,\}} | |tj|jd|jdtjtj|jt4jrt9tj|jt4jd 5} t;j| } dddn #1swxYwY| rbtt| d <t?j tt| d <tjtj|jd rtCj"tG|jd z } tI| %dtLr=| dr5| dd} t?j'| d <d | vr | d d <n^tI| %dtPr6| dr.| d} t?j'| d <d | vr | d d <tS|| S)N)rallowed_extensionsc4i|]\}}|t|Sr)r)rrrs rQ zELocalDatasetModuleFactoryWithoutScript.get_module..}s4   DZE?E.??   rfc3(K|] }f|kV dSrrr split_moduler module_names rQrzDLocalDatasetModuleFactoryWithoutScript.get_module..-hh ^, <hhhhhhrf=Couldn't infer the same data file format for all splits. Got 5No (supported) data files or dataset script found in )rr)rr? config_namerrirjrMinfor6 dataset_infor)*r>rJrKrLr?rrrfrom_local_or_remoteALL_ALLOWED_EXTENSIONSrXnextiterranyrr%r+rrrorigin_metadatar,rrstriprrr8rOrloadr& from_dictr? from_readmer isinstancegetrn_from_yaml_dictrr)r rpatternsr? split_modulesmetadata_patternsmetadata_fileskeyr^rru dataset_infosdataset_metadatadataset_info_dictrrIs @@rQr z1LocalDatasetModuleFactoryWithoutScript.get_moduless>BmZBGLLDM:::QUQZ 26/2M do . . .SlmvSwSw #7 5     ^h^n^n^p^p   '+4 0D0D0F0F+G+G&H&H# ^ hhhhhQ^QeQeQgQghhh h h nl]jllmm m i#$g\`\e$g$ghh h ? "{6O'O'OT\`tTtTt )$A)$L$L!!$ ) ) )$(!!! ) ,!.!CDUaj!k!k!k%C&3"3.8"37.:XX''JsOO7{C T$7++DI,<,>"',,ty&2STT U U cbgll49f.OPP[bccc ?gh26)A,,  ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? c04T-5H5H0I0I}-)4)>tDI]I]I_I_D`D`?a?a)b)bv& 7>>"',,ty+>> ? ? U.:4 ??[;XYY *..~>>EE UJZ[iJj U$4^$DQ$G!)4)DEV)W)Wv& $5554Em4TN=1,00@@$GG UL\]kLl U$4^$D!)4)DEV)W)Wv& $5554Em4TN=1[$???s$E E$#E$J11J58J5r1) rrrr)rr r rrrrrr rrfrQr=r=_s'' #'7;<@ +++3-+U3d?34 +  lC&7 89 ++++ 9@M9@9@9@9@9@9@rfr=ceZdZdZ d dedeedeeeeefdee deee eff dZ d e fd Z dS) PackagedDatasetModuleFactoryz`Get the dataset builder module from the ones that are packaged with the library: csv, json, etc.NrCr>r?rrcn||_||_||_||_||_t |ddSNr_r)rCr?r>rrr~)r rCr>r?rrs rQrz%PackagedDatasetModuleFactory.__init__sB $  .*D ::::::rfrSc|jEtt|jn,tt}|jt |jnt|}tj ||j j |}|j|j tvr|tkrz t|}n#t $rd}YnwxYw|Ut#j ||j j |}|D]1}t#|||z||j|jz||<2t&|j \}}||d} t)||| S)N)rr)rr?)r>rr expanduserresolver?rrrrPrrrCr+rrr%rrUr,r) r rr]r?r_r`rar^rrs rQr z'PackagedDatasetModuleFactory.get_modules?C}?XCT]##..0088:: ; ; ;^abfbhbhbpbpbrbr^s^s 37/2M do . . .SlmvSwSw #7 />   ? "ty4M'M'MRZ^rRrRr )$A)$L$L!!$ ) ) )$(!!! ) ,!.!C%d6J6Yen"""&C&3"3.8"37.:XX''JsOO7tyA T"&jAA[$???s&C66 DDr()rrrr)rr r rrrrrrr rrfrQrfrfsjj #'7;48<@ ; ; ;3- ;U3d?34 ; ".1 ;  lC&7 89 ; ; ; ;@M@@@@@@rfrfceZdZdZ d dedeeeefdeedeeeee fdee deee eff d Z d e fd ZdS) $HubDatasetModuleFactoryWithoutScriptz Get the module of a dataset loaded from data files of a dataset repository. The dataset builder module to use is inferred from the data files extensions. NrCrr>r?rrc||_||_||_||_|p t |_||_t|ddSrh)rCrr?r>rrrr~)r rCrr>r?rrs rQrz-HubDatasetModuleFactoryWithoutScript.__init__sT   $  .B.2B2B*D ::::::rfrSc( ttjjjjjd}jtjnt|j }tj ||j t}fd|D}t!t#|\t'fd|Drt)d|st+djjt,vr|t.kr{ t1|j }n#t*$rd}YnwxYw|Pt3j ||j }|D]1}t3|||z||j|jz||<2t6\}} | |jd d t;jd j jd j} | jd| _ tAt;jtj!j | } tE| d5} tGj$| } dddn #1swxYwY| rbt!t#| d<tKj&t!t#| d<n#t*$rYnwxYwj} | jd| _ tAt;jdj | }tOj(tS|}tU|+dtXr=|dr5|dd}tKj-|d<d|vr |dd<n^tU|+dt\r6|dr.|d}tKj-|d<d|vr |dd<n#t*$rYnwxYwt_|| S)NY@)rtokentimeout)rOrrCcNi|]!\}}|t|jj"S)r)rrr)rrrr s rQrEzCHubDatasetModuleFactoryWithoutScript.get_module.. sC   & .tOcOrsss   rfc3(K|] }f|kV dSrrrGs rQrzBHubDatasetModuleFactoryWithoutScript.get_module..rJrfrKrL)rOrrrr!r)rr?rMrrepo_idDownloading metadatarrirjrMrNDownloading readmer6rOr)0rr HF_ENDPOINTrOrCrrrr?rrr>r from_hf_reporQrXrRrSrrTrr%r+rrrrUr,rr;rrr3r8rOrrWr&rXr?rYrrZr[rnr\rr)r hfh_dataset_infor]r?r^r_r`rar^rrr:rurbr;rcrdrrIs` @@rQr z/HubDatasetModuleFactoryWithoutScript.get_modulese !344AA I]&5 B  * do . . .89I4=YY  #/ )m5        *4*:*:*<*<   '+4 0D0D0F0F+G+G&H&H# ^ hhhhhQ^QeQeQgQghhh h h nl]jllmm m i#$g\`\e$g$ghh h ? "{6O'O'OT\`tTtTt )$OP`bfbo$p$p!!$ ) ) )$(!!! ) ,!.!;%4DPTP]"""&C&3"3.8"37.:XX''JsOO7{C T$9,,S$77#DIrDMJJJy     .3355  ( 0,BO ) !,49f&GRVR_``` /""" (7;;; ?q26)A,,  ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? c04T-5H5H0I0I}-)4)>tDI]I]I_I_D`D`?a?a)b)bv&     D .3355  ( 0,@O ) "-49kDMJJJ /###  /:4@S;T;TUU *..~>>EE UJZ[iJj U$4^$DQ$G!)4)DEV)W)Wv& $5554Em4TN=1,00@@$GG UL\]kLl U$4^$D!)4)DEV)W)Wv& $5554Em4TN=1     D [$???s] E66 FFAL+J;/ L+;J??L+J?A'L++ L87L8#DQ33 R?R)NNNNN)rrrr)rr r rArrrrrrr rrfrQrmrms37"&7;48<@;;;5g./;3- ; U3d?34 ; ".1 ; lC&7 89;;;;"X@MX@X@X@X@X@X@rfrmc eZdZdZ ddedeeeefdeedeee efdeef dZ d efd Z d efd Z d efd Z d efd ZdS)!HubDatasetModuleFactoryWithScriptz~Get the module of a dataset from a dataset repository. The dataset script comes from the script inside the dataset repository.NrCrrrrPc||_||_|p t|_||_||_t |ddSrh)rCrrrrrPr~rs rQrz*HubDatasetModuleFactoryWithScript.__init__UsN   .B.2B2B*$8!D ::::::rfrSct|j|jdddz|j}|j}|jd|_t||S)NrrryrvrKrrr)r;rCrrrrrr3)r rsrs rQrz9HubDatasetModuleFactoryWithScript.download_loading_scriptdsmtytys7K7KB7ORW7Wbfboppp .3355  ( 0,HO )9oFFFFrfct|jtj|j}|j}|jd|_ t||S#ttf$rYdSwxYw)Nrrwr) r;rCrr8rrrrr3r%ConnectionError)r rbrs rQdownload_dataset_infos_filez=HubDatasetModuleFactoryWithScript.download_dataset_infos_fileks"496;\gkgtuuu .3355  ( 0,BO )  / "?3   44 sA A54A5ct|jd|j}|j}|jd|_ t ||S#ttf$rYdSwxYw)Nr6rrxr) r;rCrrrrr3r%r)r  readme_urlrs rQdownload_dataset_readme_filez>HubDatasetModuleFactoryWithScript.download_dataset_readme_fileys dm\\\ .3355  ( 0,@O )  / "?3   44 sAA+*A+c |}|}|}t|}t |jt |jd|j||j}g}|r!| tj |f|r| d|f|j r|j n t}t||||d|j|j\}} t!j| t |jd|j|jd} t%|| | S) Nr!rr"r6r7r$ru)rrrv)rrrr@rrCr;rrrprr8rPrRrrrUr'r) r rr:r;rrrrPr^rrs rQr z,HubDatasetModuleFactoryWithScript.get_modulesl1133 !==??"??AAj))4 dmTTT 0      ]  # #V%FHZ$[ \ \ \  H  # #[2E$F G G G<@eZdZdZ ddedeefdZdefdZdS) CachedDatasetModuleFactoryz Get the module of a dataset that has been loaded once already and cached. The script that is loaded from the cache is the most recent one with a matching name. NrCrPcb||_||_|jddksJdS)NrrrCrPrr rCrPs rQrz#CachedDatasetModuleFactory.__init__s8  $8!ys##q((((((rfrSc jrjn t}tj|djddtjrdtjDnd}|stdjd|fd}t||d }d tj|d tj ||d jd }tjs|dz }t|d tj|djdd|jdd g}t'j|jd}t+|||S)Nr7rrc8g|]}t|dk|S@rrhs rQrz9CachedDatasetModuleFactory.get_module..# N N N1Q2QrfzDataset  is not cached in ct|z jdddzz jS)Nrrry)rrCrstatst_mtime module_hashrr s rQ_get_modification_timezECachedDatasetModuleFactory.get_module.._get_modification_timesF233kATY__UXEYEYZ\E]`eEefllnnw wrfrar3Using the latest cached version of the module from  (last modified on () since it couldn't be found locally at r&, or remotely on the Hugging Face Hub.)rrv)rPrRrJrKrLrCrrllistdirr%sortedtimectimerr{rr&rrrUr'r) r rPhashesrr warning_msgr^rrs ` @rQr z%CachedDatasetModuleFactory.get_modules<@ Use the new library 🤗 Evaluate instead: https://huggingface.co/docs/evaluate rNrCrPcb||_||_|jddksJdS)Nrrrrs rQrz"CachedMetricModuleFactory.__init__s8  $8!ys##q((((((rfrSc jrjn t}tj|djtjrdtjDnd}|stdjd|fd}t||d}t dtj|d tj ||d jd d tj|dj|jg}tjt#||S) Nr#c8g|]}t|dk|Srrrs rQrz8CachedMetricModuleFactory.get_module..rrfzMetric rclt|z jdzz jS)Nry)rrCrrrs rQrzDCachedMetricModuleFactory.get_module.._get_modification_times3233kATYQVEVW]]__h hrfrrrrrrr)rPrRrJrKrLrCrlrr%rrr&rrrrUr'r)r rPrrrr^rs` @rQr z$CachedMetricModuleFactory.get_modules<@r?c n | td"i|}t|p tj}d|_d|_|tjk|_ttd| tj d dd}| ds|dz}tj||} |t vr&t#|||||S| |rbtj|r$t)|||St+d t-|tj| r$t)| ||Stj|r%t1|||| St3|r~|dd krd t7t9t:j} | |||j d } n#tB$r} tE| tFtHj%j&tHj%j'fr(tOd|dtQ| j)ddtU| vr d|d} t+|r | d|dzn| dtU| vr%d|d} |r | d|dzn| } t+| dz| d} ~ wwxYw|d| j+Dvr&tY|||||St[||||||S#tB$r} t]||cYd}~S#tB$r}tE|tFrtOd|d|dtE|t^r|dtE|t*r;t+d t-| d|dtQ|j)d |d|dd}~wwxYwd}~wwxYwt+d t-| d!)#a# Download/extract/cache a dataset module. Dataset codes are cached inside the dynamic modules cache to allow easy import (avoid ugly sys.path tweaks). Args: path (str): Path or name of the dataset. Depending on ``path``, the dataset builder that is used comes from a generic dataset script (JSON, CSV, Parquet, text etc.) or from the dataset script (a python file) inside the dataset directory. For local datasets: - if ``path`` is a local directory (containing data files only) -> load a generic dataset builder (csv, json, text etc.) based on the content of the directory e.g. ``'./path/to/directory/with/my/csv/data'``. - if ``path`` is a local dataset script or a directory containing a local dataset script (if the script has the same name as the directory): -> load the dataset builder from the dataset script e.g. ``'./dataset/squad'`` or ``'./dataset/squad/squad.py'``. For datasets on the Hugging Face Hub (list all available datasets and ids with ``datasets.list_datasets()``) - if ``path`` is a dataset repository on the HF hub (containing data files only) -> load a generic dataset builder (csv, text etc.) based on the content of the repository e.g. ``'username/dataset_name'``, a dataset repository on the HF hub containing your data files. - if ``path`` is a dataset repository on the HF hub with a dataset script (if the script has the same name as the directory) -> load the dataset builder from the dataset script in the dataset repository e.g. ``glue``, ``squad``, ``'username/dataset_name'``, a dataset repository on the HF hub containing a dataset script `'dataset_name.py'`. revision (:class:`~utils.Version` or :obj:`str`, optional): Version of the dataset script to load. As datasets have their own git repository on the Datasets Hub, the default version "main" corresponds to their "main" branch. You can specify a different version than the default "main" by using a commit SHA or a git tag of the dataset repository. download_config (:class:`DownloadConfig`, optional): Specific download configuration parameters. download_mode (:class:`DownloadMode` or :obj:`str`, default ``REUSE_DATASET_IF_EXISTS``): Download/generate mode. dynamic_modules_path (Optional str, defaults to HF_MODULES_CACHE / "datasets_modules", i.e. ~/.cache/huggingface/modules/datasets_modules): Optional path to the directory in which the dynamic modules are saved. It must have been initialized with :obj:`init_dynamic_modules`. By default, the datasets and metrics are stored inside the `datasets_modules` module. data_dir (:obj:`str`, optional): Directory with the data files. Used only if `data_files` is not specified, in which case it's equal to pass `os.path.join(data_dir, "**")` as `data_files`. data_files (:obj:`Union[Dict, List, str]`, optional): Defining the data_files of the dataset configuration. **download_kwargs (additional keyword arguments): optional attributes for DownloadConfig() which will override the attributes in download_config if supplied. Returns: DatasetModule NTc|Srrxs rQz(dataset_module_factory..UsQrfrrry)r>r?rrrrPz"Couldn't find a dataset script at )r>r?rrrp)rvrrrrzCouldn't reach 'z' on the Hub ()404z Dataset 'z' doesn't exist on the Hubz at revision ''401zT. If the repo is private or gated, make sure to log in with `huggingface-cli login`.cg|] }|j Sr) rfilename)rsiblings rQrz*dataset_module_factory..sSSS'G-SSSrfrrrrP)rr>r?rrrPz1Couldn't reach the Hugging Face Hub for dataset 'z': z8 or any data file in the same directory. Couldn't find 'z"' on the Hugging Face Hub either: rz( or any data file in the same directory.r)0rrREUSE_DATASET_IF_EXISTSextract_compressed_file force_extractrforce_downloadrnfilterrrJseprrrKrLr,rfr rr3r%r8rlr=r7rr2rrryrOrr}rZr1requests exceptionsConnectTimeoutrtyperrsiblingsr}rmrr)rKrrrrPr>r?download_kwargsrz combined_pathhf_apirOemsge1e2s rQdataset_module_factoryrsn(;;?;; !V,2VWWM.2O+$(O!%2l6S%SO"F;; RVS(A(A(G(G(L(LMMNNrRH   U # #$e#GLLx00M$ )))+ !+'    *,,   x T 7>>$   l6MH\jll $$jIbcgIhIh$j$jkk k  & &M 2 Ma   *,,  t  I 5 8 -   *,,  $  D DJJsOOq$8$8? # - / / /6-..F %22 %#2#A! 3      , +: +;**dT*d*dQUVWQXQXQa*d*d*deeec!ff__FdFFFC+RZ,cC2N82N2N2N,N,N`cdddc!ff__FdFFFC@HQ# < < < << Use the new library 🤗 Evaluate instead: https://huggingface.co/docs/evaluate Metrics codes are cached inside the dynamic modules cache to allow easy import (avoid ugly sys.path tweaks). Args: path (str): Path or name of the metric script. - if ``path`` is a local metric script or a directory containing a local metric script (if the script has the same name as the directory): -> load the module from the metric script e.g. ``'./metrics/accuracy'`` or ``'./metrics/accuracy/accuracy.py'``. - if ``path`` is a metric on the Hugging Face Hub (ex: `glue`, `squad`) -> load the module from the metric script in the GitHub repository at huggingface/datasets e.g. ``'accuracy'`` or ``'rouge'``. revision (Optional ``Union[str, datasets.Version]``): If specified, the module will be loaded from the datasets repository at this version. By default: - it is set to the local version of the lib. - it will also try to load it from the main branch if it's not available at the local version of the lib. Specifying a version that is different from your local version of the lib might cause compatibility issues. download_config (:class:`DownloadConfig`, optional): Specific download configuration parameters. download_mode (:class:`DownloadMode` or :obj:`str`, default ``REUSE_DATASET_IF_EXISTS``): Download/generate mode. dynamic_modules_path (Optional str, defaults to HF_MODULES_CACHE / "datasets_modules", i.e. ~/.cache/huggingface/modules/datasets_modules): Optional path to the directory in which the dynamic modules are saved. It must have been initialized with :obj:`init_dynamic_modules`. By default, the datasets and metrics are stored inside the `datasets_modules` module. **download_kwargs (additional keyword arguments): optional attributes for DownloadConfig() which will override the attributes in download_config if supplied. Returns: MetricModule ignore'.*https://huggingface.co/docs/evaluate$messagecategoryNTc|Srrrs rQrz'metric_module_factory..srfrrryrz!Couldn't find a metric script at rrrz . Metric 'z/' doesn't exist on the Hugging Face Hub either.rr)warningscatch_warningsfilterwarnings FutureWarningrrrrrrnrrrJrrrrKrLrr+r r%r8r7rrr}rrZ) rKrrrrPrrzrrrs rQmetric_module_factoryrs`  " "0u0u2[fstttt  ",????O$]%Zl6Z[[ 26/(,%{{DLL,E,E,K,KC,P,PQQRRSUV  '' (%'H T844 == " "! uw~~d## o/ L`*,,'0u0u0u0u0u0u0u0u*((mLefjLkLk(m(mnnn W^^M * * u+]Qejll30u0u0u0u0u0u0u0u4d # # u 31(<(< 0%$3"/)=  *,, 90u0u0u0u0u0u0u0uH  4TPdeeepprrrrrrrO0u0u0u0u0u0u0u0uP!   %b*;<<+ d*+Y Use `evaluate.load` instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate Args: path (``str``): path to the metric processing script with the metric builder. Can be either: - a local path to processing script or the directory containing the script (if the script has the same name as the directory), e.g. ``'./metrics/rouge'`` or ``'./metrics/rogue/rouge.py'`` - a metric identifier on the HuggingFace datasets repo (list all available metrics with ``datasets.list_metrics()``) e.g. ``'rouge'`` or ``'bleu'`` config_name (:obj:`str`, optional): selecting a configuration for the metric (e.g. the GLUE metric has a configuration for each subset) process_id (:obj:`int`, optional): for distributed evaluation: id of the process num_process (:obj:`int`, optional): for distributed evaluation: total number of processes cache_dir (Optional str): path to store the temporary predictions and references (default to `~/.cache/huggingface/metrics/`) experiment_id (``str``): A specific experiment id. This is used if several distributed evaluations share the same file system. This is useful to compute metrics in distributed setups (in particular non-additive metrics like F1). keep_in_memory (bool): Whether to store the temporary results in memory (defaults to False) download_config (Optional ``datasets.DownloadConfig``: specific download configuration parameters. download_mode (:class:`DownloadMode` or :obj:`str`, default ``REUSE_DATASET_IF_EXISTS``): Download/generate mode. revision (Optional ``Union[str, datasets.Version]``): if specified, the module will be loaded from the datasets repository at this version. By default, it is set to the local version of the lib. Specifying a version that is different from your local version of the lib might cause compatibility issues. Returns: `datasets.Metric` Example: ```py >>> from datasets import load_metric >>> accuracy = load_metric('accuracy') >>> accuracy.compute(references=[1, 0], predictions=[1, 1]) {'accuracy': 0.5} ``` rrr)rrrF)r_)rMrrrrrrNr) rrrrrrrr^redownload_and_prepare)rKrMrrrrrrrrmetric_init_kwargs metric_module metric_clsrs rQ load_metricr-s2n  " "2[fstttt$]%Zl6Z[[ - 8_Ta    '}eDDD  #!#)'   !   ##O#DDD-sBB((B,/B,featuresstorage_optionsc (t|p tj}| +|r|n t}| |_t |||||} t | j} | j}| d|}| d|}| d}tvr;|9dd}fdtD}|r|d |d d z }t|| d ||||||| | d || }|S)aLoad a dataset builder from the Hugging Face Hub, or a local dataset. A dataset builder can be used to inspect general information that is required to build a dataset (cache directory, config, dataset info, etc.) without downloading the dataset itself. You can find the list of datasets on the [Hub](https://huggingface.co/datasets) or with [`datasets.list_datasets`]. A dataset is a directory that contains: - some data files in generic formats (JSON, CSV, Parquet, text, etc.) - and optionally a dataset script, if it requires some code to read the data files. This is used to load any kind of formats or structures. Note that dataset scripts can also download and read data files from anywhere - in case your data files already exist online. Args: path (`str`): Path or name of the dataset. Depending on `path`, the dataset builder that is used comes from a generic dataset script (JSON, CSV, Parquet, text etc.) or from the dataset script (a python file) inside the dataset directory. For local datasets: - if `path` is a local directory (containing data files only) -> load a generic dataset builder (csv, json, text etc.) based on the content of the directory e.g. `'./path/to/directory/with/my/csv/data'`. - if `path` is a local dataset script or a directory containing a local dataset script (if the script has the same name as the directory) -> load the dataset builder from the dataset script e.g. `'./dataset/squad'` or `'./dataset/squad/squad.py'`. For datasets on the Hugging Face Hub (list all available datasets and ids with [`datasets.list_datasets`]) - if `path` is a dataset repository on the HF hub (containing data files only) -> load a generic dataset builder (csv, text etc.) based on the content of the repository e.g. `'username/dataset_name'`, a dataset repository on the HF hub containing your data files. - if `path` is a dataset repository on the HF hub with a dataset script (if the script has the same name as the directory) -> load the dataset builder from the dataset script in the dataset repository e.g. `glue`, `squad`, `'username/dataset_name'`, a dataset repository on the HF hub containing a dataset script `'dataset_name.py'`. name (`str`, *optional*): Defining the name of the dataset configuration. data_dir (`str`, *optional*): Defining the `data_dir` of the dataset configuration. If specified for the generic builders (csv, text etc.) or the Hub datasets and `data_files` is `None`, the behavior is equal to passing `os.path.join(data_dir, **)` as `data_files` to reference all the files in a directory. data_files (`str` or `Sequence` or `Mapping`, *optional*): Path(s) to source data file(s). cache_dir (`str`, *optional*): Directory to read/write data. Defaults to `"~/.cache/huggingface/datasets"`. features ([`Features`], *optional*): Set the features type to use for this dataset. download_config ([`DownloadConfig`], *optional*): Specific download configuration parameters. download_mode ([`DownloadMode`] or `str`, defaults to `REUSE_DATASET_IF_EXISTS`): Download/generate mode. revision ([`Version`] or `str`, *optional*): Version of the dataset script to load. As datasets have their own git repository on the Datasets Hub, the default version "main" corresponds to their "main" branch. You can specify a different version than the default "main" by using a commit SHA or a git tag of the dataset repository. use_auth_token (`str` or `bool`, *optional*): Optional string or boolean to use as Bearer token for remote files on the Datasets Hub. If `True`, or not specified, will get token from `"~/.huggingface"`. storage_options (`dict`, *optional*, defaults to `None`): **Experimental**. Key/value pairs to be passed on to the dataset file-system backend, if any. **config_kwargs (additional keyword arguments): Keyword arguments to be passed to the [`BuilderConfig`] and used in the [`DatasetBuilder`]. Returns: [`DatasetBuilder`] Example: ```py >>> from datasets import load_dataset_builder >>> ds_builder = load_dataset_builder('rotten_tomatoes') >>> ds_builder.info.features {'label': ClassLabel(num_classes=2, names=['neg', 'pos'], id=None), 'text': Value(dtype='string', id=None)} ``` N)rrrr>r?r?rMrz.Please specify the data files to load for the z dataset builder.c6g|]}t|k|Sr)r*)r extensionrKs rQrz(load_dataset_builder..s2   #?ST]?^bf?f?fI?f?f?frfz9 For example `data_files={"train": "path/to/data/train/*.rz"}`)rrMr>r?rrrrr) rrrrrrrer^rpopr,r*r)rKrCr>r?rrrrrrr config_kwargsdataset_module builder_clsrrMr error_msgexample_extensionsbuilder_instances` rQload_dataset_builderr}sz!!V,2VWWM!4CY/..000IYIY)7&+ '# N$N$>??K#2N##L*==J $$]D99K   f % %D )))j.@\T\\\     ';     s rWijkWlrrr rI###(3{ ( %' ( (  (  ( ( rfr0rverification_mode save_infostask streamingnum_procc J| dkr=| r tjn tj} tjd| jdt ||std|dt|tj  rtd|r|tdt|p tj}t| s| p tjn tj} t!d ||||||||| ||d |}|r|| S|t$v}|||| ||| | | nt)|jj} ||| | }|||}| r||S)a'Load a dataset from the Hugging Face Hub, or a local dataset. You can find the list of datasets on the [Hub](https://huggingface.co/datasets) or with [`datasets.list_datasets`]. A dataset is a directory that contains: - some data files in generic formats (JSON, CSV, Parquet, text, etc.). - and optionally a dataset script, if it requires some code to read the data files. This is used to load any kind of formats or structures. Note that dataset scripts can also download and read data files from anywhere - in case your data files already exist online. This function does the following under the hood: 1. Download and import in the library the dataset script from `path` if it's not already cached inside the library. If the dataset has no dataset script, then a generic dataset script is imported instead (JSON, CSV, Parquet, text, etc.) Dataset scripts are small python scripts that define dataset builders. They define the citation, info and format of the dataset, contain the path or URL to the original data files and the code to load examples from the original data files. You can find the complete list of datasets in the Datasets [Hub](https://huggingface.co/datasets). 2. Run the dataset script which will: * Download the dataset file from the original URL (see the script) if it's not already available locally or cached. * Process and cache the dataset in typed Arrow tables for caching. Arrow table are arbitrarily long, typed tables which can store nested objects and be mapped to numpy/pandas/python generic types. They can be directly accessed from disk, loaded in RAM or even streamed over the web. 3. Return a dataset built from the requested splits in `split` (default: all). It also allows to load a dataset from a local directory or a dataset repository on the Hugging Face Hub without dataset script. In this case, it automatically loads all the data files from the directory or the dataset repository. Args: path (`str`): Path or name of the dataset. Depending on `path`, the dataset builder that is used comes from a generic dataset script (JSON, CSV, Parquet, text etc.) or from the dataset script (a python file) inside the dataset directory. For local datasets: - if `path` is a local directory (containing data files only) -> load a generic dataset builder (csv, json, text etc.) based on the content of the directory e.g. `'./path/to/directory/with/my/csv/data'`. - if `path` is a local dataset script or a directory containing a local dataset script (if the script has the same name as the directory) -> load the dataset builder from the dataset script e.g. `'./dataset/squad'` or `'./dataset/squad/squad.py'`. For datasets on the Hugging Face Hub (list all available datasets and ids with [`datasets.list_datasets`]) - if `path` is a dataset repository on the HF hub (containing data files only) -> load a generic dataset builder (csv, text etc.) based on the content of the repository e.g. `'username/dataset_name'`, a dataset repository on the HF hub containing your data files. - if `path` is a dataset repository on the HF hub with a dataset script (if the script has the same name as the directory) -> load the dataset builder from the dataset script in the dataset repository e.g. `glue`, `squad`, `'username/dataset_name'`, a dataset repository on the HF hub containing a dataset script `'dataset_name.py'`. name (`str`, *optional*): Defining the name of the dataset configuration. data_dir (`str`, *optional*): Defining the `data_dir` of the dataset configuration. If specified for the generic builders (csv, text etc.) or the Hub datasets and `data_files` is `None`, the behavior is equal to passing `os.path.join(data_dir, **)` as `data_files` to reference all the files in a directory. data_files (`str` or `Sequence` or `Mapping`, *optional*): Path(s) to source data file(s). split (`Split` or `str`): Which split of the data to load. If `None`, will return a `dict` with all splits (typically `datasets.Split.TRAIN` and `datasets.Split.TEST`). If given, will return a single Dataset. Splits can be combined and specified like in tensorflow-datasets. cache_dir (`str`, *optional*): Directory to read/write data. Defaults to `"~/.cache/huggingface/datasets"`. features (`Features`, *optional*): Set the features type to use for this dataset. download_config ([`DownloadConfig`], *optional*): Specific download configuration parameters. download_mode ([`DownloadMode`] or `str`, defaults to `REUSE_DATASET_IF_EXISTS`): Download/generate mode. verification_mode ([`VerificationMode`] or `str`, defaults to `BASIC_CHECKS`): Verification mode determining the checks to run on the downloaded/processed dataset information (checksums/size/splits/...). ignore_verifications (`bool`, defaults to `False`): Ignore the verifications of the downloaded/processed dataset information (checksums/size/splits/...). `ignore_verifications` was deprecated in version 2.9.1 and will be removed in 3.0.0. Please use `verification_mode` instead. keep_in_memory (`bool`, defaults to `None`): Whether to copy the dataset in-memory. If `None`, the dataset will not be copied in-memory unless explicitly enabled by setting `datasets.config.IN_MEMORY_MAX_SIZE` to nonzero. See more details in the [improve performance](../cache#improve-performance) section. save_infos (`bool`, defaults to `False`): Save the dataset information (checksums/size/splits/...). revision ([`Version`] or `str`, *optional*): Version of the dataset script to load. As datasets have their own git repository on the Datasets Hub, the default version "main" corresponds to their "main" branch. You can specify a different version than the default "main" by using a commit SHA or a git tag of the dataset repository. use_auth_token (`str` or `bool`, *optional*): Optional string or boolean to use as Bearer token for remote files on the Datasets Hub. If `True`, or not specified, will get token from `"~/.huggingface"`. task (`str`): The task to prepare the dataset for during training and evaluation. Casts the dataset's [`Features`] to standardized column names and types as detailed in `datasets.tasks`. streaming (`bool`, defaults to `False`): If set to `True`, don't download the data files. Instead, it streams the data progressively while iterating on the dataset. An [`IterableDataset`] or [`IterableDatasetDict`] is returned instead in this case. Note that streaming works for datasets that use data formats that support being iterated over like txt, csv, jsonl for example. Json files may be downloaded completely. Also streaming from remote zip or gzip files is supported but other compressed formats like rar and xz are not yet supported. The tgz format doesn't allow streaming. num_proc (`int`, *optional*, defaults to `None`): Number of processes when downloading and generating the dataset locally. Multiprocessing is disabled by default. storage_options (`dict`, *optional*, defaults to `None`): **Experimental**. Key/value pairs to be passed on to the dataset file-system backend, if any. **config_kwargs (additional keyword arguments): Keyword arguments to be passed to the `BuilderConfig` and used in the [`DatasetBuilder`]. Returns: [`Dataset`] or [`DatasetDict`]: - if `split` is not `None`: the dataset requested, - if `split` is `None`, a [`~datasets.DatasetDict`] with each split. or [`IterableDataset`] or [`IterableDatasetDict`]: if `streaming=True` - if `split` is not `None`, the dataset is requested - if `split` is `None`, a [`~datasets.streaming.IterableDatasetDict`] with each split. Example: Load a dataset from the Hugging Face Hub: ```py >>> from datasets import load_dataset >>> ds = load_dataset('rotten_tomatoes', split='train') # Map data files to splits >>> data_files = {'train': 'train.csv', 'test': 'test.csv'} >>> ds = load_dataset('namespace/your_dataset_name', data_files=data_files) ``` Load a local dataset: ```py # Load a CSV file >>> from datasets import load_dataset >>> ds = load_dataset('csv', data_files='path/to/local/my_dataset.csv') # Load a JSON file >>> from datasets import load_dataset >>> ds = load_dataset('json', data_files='path/to/local/my_dataset.json') # Load from a local loading script >>> from datasets import load_dataset >>> ds = load_dataset('path/to/local/loading_script/loading_script.py', split='train') ``` Load an [`~datasets.IterableDataset`]: ```py >>> from datasets import load_dataset >>> ds = load_dataset('rotten_tomatoes', split='train', streaming=True) ``` Load an image dataset with the `ImageFolder` dataset builder: ```py >>> from datasets import load_dataset >>> ds = load_dataset('imagefolder', data_dir='/path/to/images', split='train') ``` r0z'ignore_verifications' was deprecated in favor of 'verification_mode' in version 2.9.1 and will be removed in 3.0.0. You can remove this warning by passing 'verification_mode=z ' instead.NzEmpty 'data_files': 'z3'. It should be either non-empty or None (default).zjYou are trying to load a dataset that was saved using `save_to_disk`. Please use `load_from_disk` instead.zLoading a streaming dataset in parallel with `num_proc` is not implemented. To parallelize streaming, you can wrap the dataset with a PyTorch DataLoader using `num_workers` > 1 instead.) rKrCr>r?rrrrrrrr)rrrtry_from_hf_gcsrr)rr in_memoryr)r< NO_CHECKS ALL_CHECKSrwarnvaluerrrrDATASET_STATE_JSON_FILENAMErNrrr BASIC_CHECKSras_streaming_datasetr,rr=rN dataset_size as_datasetprepare_for_task _save_infos)rKrCr>r?rrrrrrignore_verificationsrrrrrrrrrrrdss rQ load_datasetrsCT|++:No,66TdTo  mIZI` m m m    jppppqqq D&455<<>>  3    X)! |   !!V,2VWWM(DNo  ;.;TdTo ,   '#%'      B4454AAA"< `fs` was deprecated in version 2.9.0 and will be removed in 3.0.0. Please use `storage_options` instead, e.g. `storage_options=fs.storage_options`. keep_in_memory (`bool`, defaults to `None`): Whether to copy the dataset in-memory. If `None`, the dataset will not be copied in-memory unless explicitly enabled by setting `datasets.config.IN_MEMORY_MAX_SIZE` to nonzero. See more details in the [improve performance](../cache#improve-performance) section. storage_options (`dict`, *optional*): Key/value pairs to be passed on to the file-system backend, if any. Returns: [`Dataset`] or [`DatasetDict`]: - If `dataset_path` is a path of a dataset directory: the dataset requested. - If `dataset_path` is a path of a dataset dict directory, a [`DatasetDict`] with each split. Example: ```py >>> from datasets import load_from_disk >>> ds = load_from_disk('path/to/dataset/directory') ``` r0z'fs' was deprecated in favor of 'storage_options' in version 2.9.0 and will be removed in 3.0.0. You can remove this warning by passing 'storage_options=fs.storage_options' instead.)rrfilez Directory z not found)rrz@ is neither a `Dataset` directory nor a `DatasetDict` directory.)rrrrfsspecget_fs_token_pathsr%r$ posixpathrL filesystemrJrKrNr%rrDATASET_INFO_FILENAMErrload_from_diskDATASETDICT_JSON_FILENAMEr)r fsrrfs_token_pathsdest_dataset_path path_joins rQrrsZ \  c    ,.|_]]]N$21$5BB!1,??N  v & &(GL 99& ' 'G E\ E E EFFF yy,f.JKKLL  QSQZQZ #V%GHHRR  %l>crssss 99.0PQQ R R ),~gvwwww g g g g   rf)Tr)NNNNNNr() NrrNNFNNN) NNNNNNNNNN)NNNNNNNNNr0NFNNNFNN)r0NN)r)rrUrYrrJrrrr collectionsr dataclassesrpathlibrtypingrrrr r r r r r rhuggingface_hubrr!r arrow_datasetrbuilderrr?rrrrrrrrr dataset_dictrrdownload.download_configrdownload.download_managerr#download.streaming_download_managerr r!r"rr# filesystemsr$r%rNr&r'iterable_datasetr(rr)packaged_modulesr*r+r,r-splitsr.tasksr/utils.deprecation_utilsr0utils.file_utilsr1r2r3r4r5r6r7r8r9utils.filelockr: utils.hubr;utils.info_utilsr<r= utils.loggingr>utils.metadatar?utils.py_utilsr@ utils.versionrArrrnrrQMODULE_NAME_FOR_DYNAMIC_MODULESrrRrervr~rrrboolrrrrrr rr+r3r=rfrmr}rrrrintrrrrrrrfrQr3sR   !!!!!!NNNNNNNNNNNNNNNNNNNN !!!!!!""""""######                      ;:::::::444444333333WWWWWWWWWWDDDDDDDD////////------ //////                      %$$$$$!!!!!!@@@@@@@@%%%%%%++++++''''''"""""" H  72799::eWD6gk   JRSXY]_bYbScJd    $HU4CWY]^dYeCe=f4g4%d3i%C%%%%(c#> >>(-c3S.@(A>T\]kTl> %S/>>>>BS% S%"S%S% S% c3h( S% 5c?+ S%E,"345S% S%S%S%S%lc3h(5c?+       38_<RV"4?p p p p p f  ]^^/3048<*. _u_u _uuS'\*+_un-_uE,"345 _u #3- _u_u_u_u_^_uD  stt"&##' 048<.2LL L#LL L } L C= LLn-LE,"345LuS'\*+L LLLutLb"_c##'048<.215&*HH H 3-HsmHsHSM73c8TW=FX@Y;Y3ZZ[\ H } H x Hn-HE,"345HuS'\*+HU49-.Hd^HHHHHZ"_c)-##'048<@D%%).215/3"&*'QQ Q 3-QsmQsHSM73c8TW=FX@Y;Y3ZZ[\ Q E#u*% & Q } Qx Qn-QE,"345Q &6&; <=QTNQQuS'\*+QU49-.Q 5l*+ ,!Q"#Q$sm%Q&d^'Q* ;!4o EF+QQQQjrvL L L 8@L `him`nL  7K  L L L L L L rf