+
    œ°³j2  ã                   óÒ   € ^ RI t ^ RIt^ RIt^ RIHt ^RIHtHt ]P                  ! ]4      t	] ! R R4      4       t
]! RR7       ! R R	4      4       t ! R
 R4      t ! R R]4      tR# )é    N)Ú	dataclass)Úis_torch_availableÚloggingc                   ó>   a € ] tR t^t o RtRtRtR tV 3R ltRt	V t
R# )ÚInputExamplea  
A single training/test example for simple sequence classification.

Args:
    guid: Unique id for the example.
    text_a: string. The untokenized text of the first sequence. For single
        sequence tasks, only this sequence must be specified.
    text_b: (Optional) string. The untokenized text of the second sequence.
        Only must be specified for sequence pair tasks.
    label: (Optional) string. The label of the example. This should be
        specified for train and dev examples, but not for test examples.
Nc                óh   € \         P                  ! \        P                  ! V 4      ^R7      R,           # )ú*Serializes this instance to a JSON string.)ÚindentÚ
©ÚjsonÚdumpsÚdataclassesÚasdict©Úselfs   &Úc/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/data/processors/utils.pyÚto_json_stringÚInputExample.to_json_string/   s#   € ä�zŠzœ+×,Ò,¨TÓ2¸1Ô=ÀÕDÐDó    c                óf   <€ V ^8„  d   Qh/ S[ ;R&   S[ ;R&   S[ R,          ;R&   S[ R,          ;R&   # )é   ÚguidÚtext_aNÚtext_bÚlabel)Ústr)ÚformatÚ__classdict__s   "€r   Ú__annotate__ÚInputExample.__annotate__   s?   ø‡ ‚ ñ �Iñ ñ  �Kñ! ñ" �$�JÑñ# ñ$ ��:Ñò% r   © )Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__r   r   r   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__©r   s   @r   r   r      s#   ø‡ € ñð €FØ€EòE÷) ƒ r   r   T)Úfrozenc                   óB   a € ] tR t^4t o RtRtRtRtR tV 3R lt	Rt
V tR# )ÚInputFeaturesa“  
A single set of features of data. Property names are the same names as the corresponding inputs to a model.

Args:
    input_ids: Indices of input sequence tokens in the vocabulary.
    attention_mask: Mask to avoid performing attention on padding token indices.
        Mask values selected in `[0, 1]`: Usually `1` for tokens that are NOT MASKED, `0` for MASKED (padded)
        tokens.
    token_type_ids: (Optional) Segment token indices to indicate first and second
        portions of the inputs. Only some models use them.
    label: (Optional) Label corresponding to the input. Int for classification problems,
        float for regression problems.
Nc                ód   € \         P                  ! \        P                  ! V 4      4      R,           # )r	   r   r   r   s   &r   r   ÚInputFeatures.to_json_stringI   s!   € ä�zŠzœ+×,Ò,¨TÓ2Ó3°dÕ:Ð:r   c                ó´   <€ V ^8„  d   Qh/ S[ S[,          ;R&   S[ S[,          R,          ;R&   S[ S[,          R,          ;R&   S[S[,          R,          ;R&   # )r   Ú	input_idsNÚattention_maskÚtoken_type_idsr   )ÚlistÚintÚfloat)r   r   s   "€r   r    ÚInputFeatures.__annotate__4   sX   ø‡ ‚ ñ  ‘C�yÑñ! ñ" ™•I Õ$Ñ+ñ# ñ$ ™•I Õ$Ñ+ñ% ñ& ‘�;˜ÕÑ$ò' r   r"   )r#   r$   r%   r&   r'   r3   r4   r   r   r(   r)   r*   r+   s   @r   r.   r.   4   s'   ø‡ € ñð (,€NØ'+€NØ $€Eò;÷+ ƒ r   r.   c                   ó\   a € ] tR t^Nt o RtR tR tR tR tR t	R t
]RR	 l4       tR
tV tR# )ÚDataProcessorzEBase class for data converters for sequence classification data sets.c                ó   € \        4       h)z�
Gets an example from a dict.

Args:
    tensor_dict: Keys and values should match the corresponding Glue
        tensorflow_dataset examples.
©ÚNotImplementedError)r   Útensor_dicts   &&r   Úget_example_from_tensor_dictÚ*DataProcessor.get_example_from_tensor_dictQ   s   € ô "Ó#Ð#r   c                ó   € \        4       h)z8Gets a collection of [`InputExample`] for the train set.r<   ©r   Údata_dirs   &&r   Úget_train_examplesÚ DataProcessor.get_train_examples[   ó   € ä!Ó#Ð#r   c                ó   € \        4       h)z6Gets a collection of [`InputExample`] for the dev set.r<   rB   s   &&r   Úget_dev_examplesÚDataProcessor.get_dev_examples_   rF   r   c                ó   € \        4       h)z7Gets a collection of [`InputExample`] for the test set.r<   rB   s   &&r   Úget_test_examplesÚDataProcessor.get_test_examplesc   rF   r   c                ó   € \        4       h)z*Gets the list of labels for this data set.r<   r   s   &r   Ú
get_labelsÚDataProcessor.get_labelsg   rF   r   c                ó    € \        V P                  4       4      ^8”  d0   V P                  4       \        VP                  4      ,          Vn        V# )zŽ
Some tensorflow_datasets datasets are not formatted the same way the GLUE datasets are. This method converts
examples to the correct format.
)ÚlenrN   r6   r   )r   Úexamples   &&r   Útfds_mapÚDataProcessor.tfds_mapk   s9   € ô
 ˆt�‰Ó Ó! AÔ%Ø ŸO™OÓ-¬c°'·-±-Ó.@ÕAˆGŒMØˆr   Nc           
     ó°   € \        VRRR7      ;_uu_ 4       p\        \        P                  ! VRVR7      4      uuRRR4       #   + '       g   i     R# ; i)z!Reads a tab separated value file.Úrz	utf-8-sig)ÚencodingÚ	)Ú	delimiterÚ	quotecharN)Úopenr5   ÚcsvÚreader)ÚclsÚ
input_filerZ   Úfs   &&& r   Ú	_read_tsvÚDataProcessor._read_tsvt   s9   € ô �*˜c¨K×8Õ8¸AÜœŸ
š
 1°À	ÔJÓK÷ 9×8×8Ó8ús   —"AÁA	r"   ©N)r#   r$   r%   r&   r'   r?   rD   rH   rK   rN   rS   Úclassmethodra   r)   r*   r+   s   @r   r:   r:   N   s<   ø‡ € ÙOò$ò$ò$ò$ò$òð óLó öLr   r:   c                   ó€   a € ] tR t^{t o RtRR ltR tR t]RR l4       t	]RR l4       t
RR ltRR	 ltRR
 ltRtV tR# )Ú%SingleSentenceClassificationProcessorz@Generic processor for a single sentence classification data set.Nc                óR   € Vf   . MTV n         Vf   . MTV n        W0n        W@n        R # rc   )ÚlabelsÚexamplesÚmodeÚverbose)r   rh   ri   rj   rk   s   &&&&&r   Ú__init__Ú.SingleSentenceClassificationProcessor.__init__~   s'   € Ø"šN‘b°ˆŒØ&Ò.™°HˆŒØŒ	ØŽr   c                ó,   € \        V P                  4      # rc   )rQ   ri   r   s   &r   Ú__len__Ú-SingleSentenceClassificationProcessor.__len__„   s   € Ü�4—=‘=Ó!Ð!r   c                ó¤   € \        V\        4      '       d)   \        V P                  V P                  V,          R 7      # V P                  V,          # ))rh   ri   )Ú
isinstanceÚslicerf   rh   ri   )r   Úidxs   &&r   Ú__getitem__Ú1SingleSentenceClassificationProcessor.__getitem__‡   s<   € Ü�cœ5×!Ò!Ü8ÀÇÁÐVZ×VcÑVcÐdgÕVhÔiÐiØ�}‰}˜SÕ!Ð!r   c                óH   € V ! R/ VB pVP                  VVVVVVR R R7       V# )T)Ú
split_nameÚcolumn_labelÚcolumn_textÚ	column_idÚskip_first_rowÚoverwrite_labelsÚoverwrite_examplesr"   )Úadd_examples_from_csv)	r^   Ú	file_namerx   ry   rz   r{   r|   ÚkwargsÚ	processors	   &&&&&&&, r   Úcreate_from_csvÚ5SingleSentenceClassificationProcessor.create_from_csvŒ   sB   € ñ ‘M˜&‘Mˆ	Ø×'Ñ'ØØ!Ø%Ø#ØØ)Ø!Ø#ð 	(ô 		
ð Ðr   c                ó:   € V ! R/ VB pVP                  WR 7       V# ))rh   r"   )Úadd_examples)r^   Útexts_or_text_and_labelsrh   r�   r‚   s   &&&, r   Úcreate_from_examplesÚ:SingleSentenceClassificationProcessor.create_from_examples�   s%   € á‘M˜&‘Mˆ	Ø×ÑÐ7ÐÔGØÐr   c	                óŽ  € V P                  V4      p	V'       d
   V	R ,          p	. p
. p. p\        V	4       F{  w  rÞV
P                  Wä,          4       VP                  Wã,          4       Ve   VP                  Wå,          4       KP  V'       d   V RV 2M
\        V4      pVP                  V4       K}  	  V P	                  W«WÇVR7      # ):é   NNÚ-)r}   r~   )ra   Ú	enumerateÚappendr   r†   )r   r€   rx   ry   rz   r{   r|   r}   r~   ÚlinesÚtextsrh   ÚidsÚiÚliner   s   &&&&&&&&&       r   r   Ú;SingleSentenceClassificationProcessor.add_examples_from_csv£   s¶   € ð —‘˜yÓ)ˆßØ˜"•IˆEØˆØˆØˆÜ  Ö'‰GˆAØ�L‰L˜Õ*Ô+Ø�M‰M˜$Õ,Ô-ØÒ$Ø—
‘
˜4�?Ö+ç.8˜*˜ Q q cÑ*¼cÀ!»f�Ø—
‘
˜4Ö ñ (ð × Ñ Ø˜3ÐVhð !ó 
ð 	
r   c           
     ód  € Ve=   \        V4      \        V4      8w  d$   \        R\        V4       R\        V4       24      hVe=   \        V4      \        V4      8w  d$   \        R\        V4       R\        V4       24      hVf   R .\        V4      ,          pVf   R .\        V4      ,          p. p\        4       p\        WV4       F[  w  r‰p
\	        V\
        \        34      '       d
   V	f   Vw  r¹MTpVP                  V	4       VP                  \        W«R V	R7      4       K]  	  V'       d   W`n
        MV P                  P                  V4       V'       d   \        V4      V n        V P                  # \        \        V P                  4      P                  V4      4      V n        V P                  # )Nz(Text and labels have mismatched lengths z and z%Text and ids have mismatched lengths )r   r   r   r   )rQ   Ú
ValueErrorÚsetÚziprr   Útupler5   ÚaddrŽ   r   ri   Úextendrh   Úunion)r   r‡   rh   r‘   r}   r~   ri   Úadded_labelsÚtext_or_text_and_labelr   r   Útexts   &&&&&&      r   r†   Ú2SingleSentenceClassificationProcessor.add_examplesÁ   sŠ  € ð Ò¤#Ð&>Ó"?Ä3ÀvÃ;Ô"NÜØ:¼3Ð?WÓ;XÐ:YÐY^Ô_bÐciÓ_jÐ^kÐlóð ð Š?œsÐ#;Ó<ÄÀCÃÔHÜÐDÄSÐIaÓEbÐDcÐchÔilÐmpÓiqÐhrÐsÓtÐtØŠ;Ø�&œ3Ð7Ó8Õ8ˆCØŠ>Ø�VœcÐ":Ó;Õ;ˆFØˆÜ“uˆÜ36Ð7OÐY\Ö3]Ñ/Ð"¨4ÜÐ0´5¼$°-×@Ò@ÀUÂ]Ø4‘��eà-�Ø×Ñ˜UÔ#Ø�O‰OœL¨dÈÐTYÔZÖ[ñ 4^÷ Ø$�Mà�M‰M× Ñ  Ô*÷ Ü˜|Ó,ˆDŒKð �}‰}Ðô œs 4§;¡;Ó/×5Ñ5°lÓCÓDˆDŒKà�}‰}Ðr   c                ó 
  € Vf   VP                   p\        V P                  4       UUu/ uF  w  rxW‡bK	  	  p	pp. p
\        V P                  4       Fm  w  r¼VR,          ^ 8X  d   \        P                  RV 24       VP                  VP                  R\        W!P                   4      R7      pV
P                  V4       Ko  	  \        R V
 4       4      p. p\        \        W P                  4      4       EF›  w  pw  rÜVR,          ^ 8X  d/   \        P                  RV R\        V P                  4       24       V'       d   ^M^ .\        V4      ,          pV\        V4      ,
          pV'       d.   V.V,          V,           pV'       d   ^ M^.V,          V,           pM+WÔ.V,          ,           pTV'       d   ^ M^.V,          ,           p\        V4      V8w  d   \        R	\        V4       R
V 24      h\        V4      V8w  d   \        R	\        V4       R
V 24      hV P                  R8X  d   WœP                  ,          pM<V P                  R8X  d   \!        VP                  4      pM\        V P                  4      hV^8  dð   V P"                  '       dÞ   \        P                  R4       \        P                  RVP$                   24       \        P                  RRP'                  V Uu. uF  p\)        V4      NK  	  up4       24       \        P                  RRP'                  V Uu. uF  p\)        V4      NK  	  up4       24       \        P                  RVP                   RV R24       VP                  \+        VVVR7      4       EKž  	  Vf   V# VR8X  Ed1   \-        4       '       g   \/        R4      h^ RIp^ RIHp TP7                  V Uu. uF  pVP8                  NK  	  upVP:                  R7      p
TP7                  V Uu. uF  pVP<                  NK  	  upVP:                  R7      pV P                  R8X  d9   TP7                  V Uu. uF  pVP                  NK  	  upVP:                  R7      pMHV P                  R8X  d8   TP7                  V Uu. uF  pVP                  NK  	  upVP                   R7      pV! V
VX4      pV# \        R4      hu uppi u upi u upi u upi u upi u upi u upi )au  
Convert examples in a list of `InputFeatures`

Args:
    tokenizer: Instance of a tokenizer that will tokenize the examples
    max_length: Maximum example length
    pad_on_left: If set to `True`, the examples will be padded on the left rather than on the right (default)
    pad_token: Padding token
    mask_padding_with_zero: If set to `True`, the attention mask will be filled by `1` for actual values
        and by `0` for padded values. If set to `False`, inverts it (`1` for padded values, `0` for actual
        values)

Returns:
    Will return a list of task-specific `InputFeatures` which can be fed to the model.

Ni'  zTokenizing example T)Úadd_special_tokensÚ
max_lengthc              3   ó8   "  € T F  p\        V4      x € K  	  R # 5irc   )rQ   )Ú.0r2   s   & r   Ú	<genexpr>ÚESingleSentenceClassificationProcessor.get_features.<locals>.<genexpr>  s   é € ÐI¹=¨iœ3˜yŸ>˜>»=ùs   ‚zWriting example Ú/zError with input length z vs ÚclassificationÚ
regressionz*** Example ***zguid: zinput_ids: Ú zattention_mask: zlabel: z (id = Ú))r2   r3   r   Úptz8return_tensors set to 'pt' but PyTorch can't be imported)ÚTensorDataset)Údtypez)return_tensors should be `'pt'` or `None`)Úmax_lenr�   rh   ri   ÚloggerÚinfoÚencoder   ÚminrŽ   Úmaxr˜   rQ   r–   rj   r   r7   rk   r   Újoinr   r.   r   ÚRuntimeErrorÚtorchÚtorch.utils.datar®   Útensorr2   Úlongr3   )r   Ú	tokenizerr£   Úpad_on_leftÚ	pad_tokenÚmask_padding_with_zeroÚreturn_tensorsr’   r   Ú	label_mapÚall_input_idsÚex_indexrR   r2   Úbatch_lengthÚfeaturesr3   Úpadding_lengthÚxr¸   r®   r`   Úall_attention_maskÚ
all_labelsÚdatasets   &&&&&&&                  r   Úget_featuresÚ2SingleSentenceClassificationProcessor.get_featuresæ   s1  € ð2 ÒØ"×*Ñ*ˆJä.7¸¿¹Ô.DÔEÑ.D¡( !�U’XÑ.Dˆ	ÑEàˆÜ!*¨4¯=©=Ö!9ÑˆHØ˜%Õ 1Ô$Ü—‘Ð1°(°Ð<Ô=à!×(Ñ(Ø—‘Ø#'Ü˜z×+<Ñ+<Ó=ð )ó ˆIð
 × Ñ  Ö+ñ ":ô ÑI¹=ÓIÓIˆàˆÜ.7¼¸MÏ=É=Ó8Y×.ZÑ*ˆHÑ*�yØ˜%Õ 1Ô$Ü—‘Ð.¨x¨j¸¼#¸d¿m¹mÓ:LÐ9MÐNÔO÷ $:™a¸qÐAÄCÈ	ÃNÕRˆNð *¬C°	«NÕ:ˆNßØ'˜[¨>Õ9¸YÕF�	ß(>¡1ÀAÐ"FÈÕ"WÐ[iÕ!i‘à%¨°~Õ)EÕF�	Ø!/×9O±AÐUVÐ3WÐZhÕ3hÕ!i�ä�9‹~ Ô-Ü Ð#;¼CÀ	»NÐ;KÈ4ÐP\È~Ð!^Ó_Ð_Ü�>Ó" lÔ2Ü Ð#;¼CÀÓ<OÐ;PÐPTÐUaÐTbÐ!cÓdÐdà�y‰yÐ,Ô,Ø!§-¡-Õ0‘Ø—‘˜lÔ*Ü˜gŸm™mÓ,‘ä  §¡Ó+Ð+à˜!Œ| §§ Ü—‘Ð-Ô.Ü—‘˜f W§\¡\ NÐ3Ô4Ü—‘˜k¨#¯(©(ÁIÓ3NÁI¸q´C¸¶FÁIÑ3NÓ*OÐ)PÐQÔRÜ—‘Ð.¨s¯x©xÉÓ8XÉÀA¼¸Q¾ÉÑ8XÓ/YÐ.ZÐ[Ô\Ü—‘˜g g§m¡m _°G¸E¸7À!ÐDÔEà�O‰OœM°IÈnÐdiÔj×kñG /[ðJ Ò!ØˆOØ˜tÕ#Ü%×'Ò'Ü"Ð#]Ó^Ð^ÛÝ6à!ŸL™L¹xÓ)H¹x¸!¨!¯+¬+¹xÑ)HÐPU×PZÑPZ˜LÓ[ˆMØ!&§¡ÉÓ.RÉÀA¨q×/?Ô/?ÉÑ.RÐZ_×ZdÑZd Ó!eÐØ�y‰yÐ,Ô,Ø"Ÿ\™\¹HÓ*E¹H°q¨1¯7¬7¹HÑ*EÈUÏZÉZ˜\ÓX‘
Ø—‘˜lÔ*Ø"Ÿ\™\¹HÓ*E¹H°q¨1¯7¬7¹HÑ*EÈUÏ[É[˜\ÓY�
á# MÐ3EÀzÓRˆGØˆNäÐHÓIÐIùóQ Fùò` 4OùÚ8Xùò *IùÚ.Rùâ*Eùâ*Es)   ©SË*S"Ì*S'ÏS,ÐS1ÑS6ÒS;)ri   rh   rj   rk   )NNr©   F)Ú r   r‹   NFrc   )rÍ   r   r‹   NFFF)NNFF)NFr   TN)r#   r$   r%   r&   r'   rl   ro   ru   rd   rƒ   rˆ   r   r†   rË   r)   r*   r+   s   @r   rf   rf   {   sX   ø‡ € ÙJôò"ò"ð
 óó ðð  óó ðô

ô<#÷JdJò dJr   rf   )r\   r   r   r   Úutilsr   r   Ú
get_loggerr#   r±   r   r.   r:   rf   r"   r   r   Ú<module>rÐ      s   ðó  Û Û Ý !ç 0ð 
×	Ò	˜HÓ	%€ð ÷Eð Eó ðEñ0 �$Ô÷;ð ;ó ð;÷2*Lñ *LôZOJ¨Mö OJr   