+
    œ°³jáY  ã                   ó¤  € R t ^ RIt^ RIHt ^ RIHtHtHt ^RIHt	 ^RI
Ht ^RIHtHtHtHtHtHtHt ^RIHt ^RIHt ^RIHtHtHt ^R	IHt ^R
IHtH t H!t!H"t"H#t#H$t$H%t% ^RI&H't' ]PP                  ! ])4      t* ! R R]!4      t+ ! R R]%4      t, ! R R] 4      t- ! R R]"4      t.] ! R R]4      4       t/] ! R R]$4      4       t0 ! R R]#4      t1 ! R R]4      t2]! RR7       ! R R]/]4      4       t3] ! R  R!]/4      4       t4]! R"R7       ! R# R$]/4      4       t5] ! R% R&]/4      4       t6] ! R' R(]/4      4       t7] ! R) R*]/4      4       t8. R+Ot9R# ),zPyTorch Data2VecText model.N)ÚBCEWithLogitsLossÚCrossEntropyLossÚMSELoss)Úinitialization)ÚGenerationMixin)Ú,BaseModelOutputWithPoolingAndCrossAttentionsÚ!CausalLMOutputWithCrossAttentionsÚMaskedLMOutputÚMultipleChoiceModelOutputÚQuestionAnsweringModelOutputÚSequenceClassifierOutputÚTokenClassifierOutput)ÚPreTrainedModel)ÚUnpack)ÚTransformersKwargsÚauto_docstringÚlogging)Úcan_return_tuple)ÚRobertaClassificationHeadÚRobertaCrossAttentionÚRobertaEmbeddingsÚRobertaLayerÚRobertaLMHeadÚRobertaModelÚRobertaSelfAttention)ÚData2VecTextConfigc                   ó   € ] tR t^2tRtR# )ÚData2VecTextEmbeddings© N©Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__static_attributes__r   ó    Ús/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/data2vec/modular_data2vec_text.pyr   r   2   ó   † Ûr%   r   c                   ó   € ] tR t^6tRtR# )ÚData2VecTextSelfAttentionr   Nr   r   r%   r&   r)   r)   6   r'   r%   r)   c                   ó   € ] tR t^:tRtR# )ÚData2VecTextCrossAttentionr   Nr   r   r%   r&   r+   r+   :   r'   r%   r+   c                   ó   € ] tR t^>tRtR# )ÚData2VecTextLayerr   Nr   r   r%   r&   r-   r-   >   r'   r%   r-   c                   óf   a a€ ] tR t^Bt o]tRtRtRR.tRt	Rt
RtRtR]R]R]/tV 3R ltR	tVtV ;t# )
ÚData2VecTextPreTrainedModelÚdata2vec_textTr   r-   Úhidden_statesÚ
attentionsÚcross_attentionsc                óT  <€ \         SV `  V4       \        V\        4      '       d‚   \        P
                  ! VP                  \        P                  ! VP                  P                  R,          4      P                  R4      4       \        P                  ! VP                  4       R# R# )é   Néÿÿÿÿ)r5   r6   )ÚsuperÚ_init_weightsÚ
isinstancer   ÚinitÚcopy_Úposition_idsÚtorchÚarangeÚshapeÚexpandÚzeros_Útoken_type_ids)ÚselfÚmoduleÚ	__class__s   &&€r&   r8   Ú)Data2VecTextPreTrainedModel._init_weightsR   sp   ø€ Ü‰Ñ˜fÔ%Ü�fÔ4×5Ò5Ü�JŠJ�v×*Ñ*¬E¯LªL¸×9LÑ9L×9RÑ9RÐSUÕ9VÓ,W×,^Ñ,^Ð_fÓ,gÔhÜ�KŠK˜×-Ñ-Ö.ñ 6r%   r   )r    r!   r"   r#   r   Úconfig_classÚbase_model_prefixÚsupports_gradient_checkpointingÚ_no_split_modulesÚ_supports_flash_attnÚ_supports_sdpaÚ_supports_flex_attnÚ_supports_attention_backendr-   r)   r+   Ú_can_record_outputsr8   r$   Ú__classdictcell__Ú__classcell__©rE   Ú__classdict__s   @@r&   r/   r/   B   s]   ù‡ € à%€LØ'ÐØ&*Ð#Ø1Ð3FÐGÐØÐØ€NØÐØ"&ÐàÐ*ØÐ/ØÐ6ðÐ÷/õ /r%   r/   c                   ó   € ] tR t^YtRtR# )ÚData2VecTextModelr   Nr   r   r%   r&   rU   rU   Y   s   † ãr%   rU   c                   ó   € ] tR t^^tRtR# )ÚData2VecTextLMHeadr   Nr   r   r%   r&   rW   rW   ^   r'   r%   rW   c                   ó   € ] tR t^btRtR# )ÚData2VecTextClassificationHeadr   Nr   r   r%   r&   rY   rY   b   r'   r%   rY   zX
    Data2VecText Model with a `language modeling` head on top for CLM fine-tuning.
    )Úcustom_introc                   ót   a a€ ] tR t^ft oRRRR/tV 3R ltR tR t]]	RV3R lR	 ll4       4       t
R
tVtV ;t# )ÚData2VecTextForCausalLMúlm_head.decoder.weightú/data2vec_text.embeddings.word_embeddings.weightúlm_head.decoder.biasúlm_head.biasc                óØ   <€ \         SV `  V4       VP                  '       g   \        P	                  R 4       \        VRR7      V n        \        V4      V n        V P                  4        R# )zTIf you want to use `Data2VecTextLMHeadModel` as a standalone, add `is_decoder=True.`F©Úadd_pooling_layerN©
r7   Ú__init__Ú
is_decoderÚloggerÚwarningrU   r0   rW   Úlm_headÚ	post_init©rC   ÚconfigrE   s   &&€r&   re   Ú Data2VecTextForCausalLM.__init__q   sP   ø€ Ü‰Ñ˜Ô à× × Ð Ü�N‰NÐqÔrä.¨vÈÔOˆÔÜ)¨&Ó1ˆŒð 	�‰Ör%   c                ó.   € V P                   P                  # ©N©ri   Údecoder©rC   s   &r&   Úget_output_embeddingsÚ-Data2VecTextForCausalLM.get_output_embeddings}   ó   € Ø�|‰|×#Ñ#Ð#r%   c                ó&   € WP                   n        R # ro   rp   ©rC   Únew_embeddingss   &&r&   Úset_output_embeddingsÚ-Data2VecTextForCausalLM.set_output_embeddings€   ó   € Ø-�‰Ör%   c                ó  <€ V ^8„  d   QhRS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          R	S[ P                  R,          R
S[S[S[ P                  ,          ,          R,          RS[R,          RS[S[ P                  ,          RS[S[,          RS[S[	,          /# )é   Ú	input_idsNÚattention_maskrB   r<   Úinputs_embedsÚencoder_hidden_statesÚencoder_attention_maskÚlabelsÚpast_key_valuesÚ	use_cacheÚlogits_to_keepÚkwargsÚreturn)
r=   Ú
LongTensorÚFloatTensorÚtupleÚboolÚintÚTensorr   r   r   )ÚformatrS   s   "€r&   Ú__annotate__Ú$Data2VecTextForCausalLM.__annotate__…   s"  ø€ ÷ F
ñ F
á×#Ñ# dÕ*ðF
ñ ×)Ñ)¨DÕ0ðF
ñ ×(Ñ(¨4Õ/ð	F
ñ
 ×&Ñ&¨Õ-ðF
ñ ×(Ñ(¨4Õ/ðF
ñ  %×0Ñ0°4Õ7ðF
ñ !&× 1Ñ 1°DÕ 8ðF
ñ × Ñ  4Õ'ðF
ñ ™u¡U×%6Ñ%6Õ7Õ8¸4Õ?ðF
ñ ˜$•;ðF
ñ ™eŸl™lÕ*ðF
ñ Ñ+Õ,ðF
ñ 
Ñ2Õ	2ñF
r%   c                óÆ  € Ve   Rp
V P                   ! V3RVRVRVRVRVRVR	V	R
V
RR/	VB pVP                  p\        V\        4      '       d   \	        V) R4      MTpV P                  VRVR3,          4      pRpVe-   V P                  ! RRVRVRV P                  P                  /VB p\        VVVP                  VP                  VP                  VP                  R7      # )aÁ  
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
    Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
    `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
    ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`

Example:

```python
>>> from transformers import AutoTokenizer, Data2VecTextForCausalLM, Data2VecTextConfig
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("facebook/data2vec-text-base")
>>> config = Data2VecTextConfig.from_pretrained("facebook/data2vec-text-base")
>>> config.is_decoder = True
>>> model = Data2VecTextForCausalLM.from_pretrained("facebook/data2vec-text-base", config=config)

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)

>>> prediction_logits = outputs.logits
```NFr   rB   r<   r€   r�   r‚   r„   r…   Úreturn_dictT:NNNÚlogitsrƒ   Ú
vocab_size)Úlossr”   r„   r1   r2   r3   r   )r0   Úlast_hidden_stater9   r�   Úsliceri   Úloss_functionrl   r•   r   r„   r1   r2   r3   )rC   r~   r   rB   r<   r€   r�   r‚   rƒ   r„   r…   r†   r‡   Úoutputsr1   Úslice_indicesr”   r–   s   &&&&&&&&&&&&,     r&   ÚforwardÚData2VecTextForCausalLM.forwardƒ   sD  € ðN ÒØˆIà@D×@RÒ@RØñA
à)ðA
ð *ðA
ð &ð	A
ð
 (ðA
ð #8ðA
ð $:ðA
ð ,ðA
ð  ðA
ð ðA
ð ñA
ˆð  ×1Ñ1ˆä8BÀ>ÔSV×8WÒ8Wœ˜~˜o¨tÔ4Ð]kˆØ—‘˜m¨A¨}¸aÐ,?Õ@ÓAˆàˆØÒØ×%Ò%Ñp¨VÐp¸FÐpÈtÏ{É{×OeÑOeÐpÐioÑpˆDä0ØØØ#×3Ñ3Ø!×/Ñ/Ø×)Ñ)Ø$×5Ñ5ô
ð 	
r%   ©r0   ri   )NNNNNNNNNNé    ©r    r!   r"   r#   Ú_tied_weights_keysre   rs   ry   r   r   rœ   r$   rP   rQ   rR   s   @@r&   r\   r\   f   sS   ù‡ € ð 	!Ð"SØ ðÐõ

ò$ò.ð Ø÷F
ñ F
ó ó ÷F
ð F
r%   r\   c                   ót   a a€ ] tR t^Ît oRRRR/tV 3R ltR tR t]]	RV3R lR	 ll4       4       t
R
tVtV ;t# )ÚData2VecTextForMaskedLMr]   r^   r_   r`   c                óØ   <€ \         SV `  V4       VP                  '       d   \        P	                  R 4       \        VRR7      V n        \        V4      V n        V P                  4        R# )zsIf you want to use `Data2VecTextForMaskedLM` make sure `config.is_decoder=False` for bi-directional self-attention.Frb   Nrd   rk   s   &&€r&   re   Ú Data2VecTextForMaskedLM.__init__Õ   sV   ø€ Ü‰Ñ˜Ô à××ÐÜ�N‰Nð1ôô
 /¨vÈÔOˆÔÜ)¨&Ó1ˆŒð 	�‰Ör%   c                ó.   € V P                   P                  # ro   rp   rr   s   &r&   rs   Ú-Data2VecTextForMaskedLM.get_output_embeddingsä   ru   r%   c                ó&   € WP                   n        R # ro   rp   rw   s   &&r&   ry   Ú-Data2VecTextForMaskedLM.set_output_embeddingsç   r{   r%   c                ó†  <€ V ^8„  d   QhRS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          R	S[ P                  R,          R
S[S[,          RS[S[,          /
# )r}   r~   Nr   rB   r<   r€   r�   r‚   rƒ   r‡   rˆ   )r=   r‰   rŠ   r   r   r‹   r	   )r�   rS   s   "€r&   r�   Ú$Data2VecTextForMaskedLM.__annotate__ì   sÒ   ø€ ÷ ,
ñ ,
á×#Ñ# dÕ*ð,
ñ ×)Ñ)¨DÕ0ð,
ñ ×(Ñ(¨4Õ/ð	,
ñ
 ×&Ñ&¨Õ-ð,
ñ ×(Ñ(¨4Õ/ð,
ñ  %×0Ñ0°4Õ7ð,
ñ !&× 1Ñ 1°DÕ 8ð,
ñ × Ñ  4Õ'ð,
ñ Ñ+Õ,ð,
ñ 
‘Õ	ñ,
r%   c	                óŒ  € V P                   ! V3RVRVRVRVRVRVRR/V	B p
V
^ ,          pV P                  V4      pR	pVeb   \        4       pVP                  VP                  4      pV! VP                  RV P                  P                  4      VP                  R4      4      p\        VVV
P                  V
P                  R
7      # )az  
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
    Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
    config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
    loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
r   rB   r<   r€   r�   r‚   r“   TN©r–   r”   r1   r2   r6   )r0   ri   r   ÚtoÚdeviceÚviewrl   r•   r	   r1   r2   )rC   r~   r   rB   r<   r€   r�   r‚   rƒ   r‡   rš   Úsequence_outputÚprediction_scoresÚmasked_lm_lossÚloss_fcts   &&&&&&&&&,     r&   rœ   ÚData2VecTextForMaskedLM.forwardê   sý   € ð( ×$Ò$Øñ

à)ð

ð *ð

ð &ð	

ð
 (ð

ð #8ð

ð $:ð

ð ð

ð ñ

ˆð " !�*ˆØ ŸL™L¨Ó9ÐàˆØÒÜ'Ó)ˆHà—Y‘YÐ0×7Ñ7Ó8ˆFÙ%Ð&7×&<Ñ&<¸RÀÇÁ×AWÑAWÓ&XÐZ`×ZeÑZeÐfhÓZiÓjˆNäØØ$Ø!×/Ñ/Ø×)Ñ)ô	
ð 	
r%   rž   )NNNNNNNNr    rR   s   @@r&   r£   r£   Î   sO   ù‡ € ð 	!Ð"SØ ðÐõ
ò$ò.ð Ø÷,
ñ ,
ó ó ÷,
ð ,
r%   r£   z¤
    Data2VecText Model transformer with a sequence classification/regression head on top (a linear layer on top of the
    pooled output) e.g. for GLUE tasks.
    c                   ó\   a a€ ] tR tRt oV 3R lt]]RV3R lR ll4       4       tRtVt	V ;t
# )Ú%Data2VecTextForSequenceClassificationi  c                ó¸   <€ \         SV `  V4       VP                  V n        Wn        \	        VR R7      V n        \        V4      V n        V P                  4        R# ©Frb   N)	r7   re   Ú
num_labelsrl   rU   r0   rY   Ú
classifierrj   rk   s   &&€r&   re   Ú.Data2VecTextForSequenceClassification.__init__"  sI   ø€ Ü‰Ñ˜Ô Ø ×+Ñ+ˆŒØŒä.¨vÈÔOˆÔÜ8¸Ó@ˆŒð 	�‰Ör%   c                ó6  <€ V ^8„  d   QhRS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[S[,          R	S[S[,          /# ©
r}   r~   Nr   rB   r<   r€   rƒ   r‡   rˆ   )r=   r‰   rŠ   r   r   r‹   r   )r�   rS   s   "€r&   r�   Ú2Data2VecTextForSequenceClassification.__annotate__/  s©   ø€ ÷ :
ñ :
á×#Ñ# dÕ*ð:
ñ ×)Ñ)¨DÕ0ð:
ñ ×(Ñ(¨4Õ/ð	:
ñ
 ×&Ñ&¨Õ-ð:
ñ ×(Ñ(¨4Õ/ð:
ñ × Ñ  4Õ'ð:
ñ Ñ+Õ,ð:
ñ 
Ñ)Õ	)ñ:
r%   c                ó(  € V P                   ! V3RVRVRVRVRR/VB pV^ ,          p	V P                  V	4      p
RpVEe³   VP                  V
P                  4      pV P                  P
                  f–   V P                  ^8X  d   RV P                  n        MrV P                  ^8”  dQ   VP                  \        P                  8X  g    VP                  \        P                  8X  d   R	V P                  n        MR
V P                  n        V P                  P
                  R8X  dK   \        4       pV P                  ^8X  d'   V! V
P                  4       VP                  4       4      pMŽV! W¦4      pM…V P                  P
                  R	8X  d>   \        4       pV! V
P                  RV P                  4      VP                  R4      4      pM-V P                  P
                  R
8X  d   \        4       pV! W¦4      p\!        VV
VP"                  VP$                  R7      # )ae  
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
    Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
    config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
    `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
r   rB   r<   r€   r“   TNÚ
regressionÚsingle_label_classificationÚmulti_label_classificationr­   r6   )r0   r»   r®   r¯   rl   Úproblem_typerº   Údtyper=   Úlongr�   r   Úsqueezer   r°   r   r   r1   r2   ©rC   r~   r   rB   r<   r€   rƒ   r‡   rš   r±   r”   r–   r´   s   &&&&&&&,     r&   rœ   Ú-Data2VecTextForSequenceClassification.forward-  sÅ  € ð$ ×$Ò$Øñ
à)ð
ð *ð
ð &ð	
ð
 (ð
ð ð
ð ñ
ˆð " !�*ˆØ—‘ Ó1ˆàˆØÓØ—Y‘Y˜vŸ}™}Ó-ˆFà�{‰{×'Ñ'Ò/Ø—?‘? aÔ'Ø/;�D—K‘KÕ,Ø—_‘_ qÔ(¨f¯l©l¼e¿j¹jÔ.HÈFÏLÉLÔ\a×\eÑ\eÔLeØ/L�D—K‘KÕ,à/K�D—K‘KÔ,à�{‰{×'Ñ'¨<Ô7Ü"›9�Ø—?‘? aÔ'Ù# F§N¡NÓ$4°f·n±nÓ6FÓG‘Dá# FÓ3‘DØ—‘×)Ñ)Ð-JÔJÜ+Ó-�Ù §¡¨B°·±Ó @À&Ç+Á+ÈbÃ/ÓR‘Ø—‘×)Ñ)Ð-IÔIÜ,Ó.�Ù Ó/�ä'ØØØ!×/Ñ/Ø×)Ñ)ô	
ð 	
r%   )r»   rl   r0   rº   ©NNNNNN©r    r!   r"   r#   re   r   r   rœ   r$   rP   rQ   rR   s   @@r&   r·   r·     s0   ù‡ € õ	ð Ø÷:
ñ :
ó ó ÷:
ð :
r%   r·   c                   ó\   a a€ ] tR tRt oV 3R lt]]RV3R lR ll4       4       tRtVt	V ;t
# )ÚData2VecTextForMultipleChoiceil  c                óü   <€ \         SV `  V4       \        V4      V n        \        P
                  ! VP                  4      V n        \        P                  ! VP                  ^4      V n
        V P                  4        R# )r5   N)r7   re   rU   r0   ÚnnÚDropoutÚhidden_dropout_probÚdropoutÚLinearÚhidden_sizer»   rj   rk   s   &&€r&   re   Ú&Data2VecTextForMultipleChoice.__init__n  sW   ø€ Ü‰Ñ˜Ô ä.¨vÓ6ˆÔÜ—z’z &×"<Ñ"<Ó=ˆŒÜŸ)š) F×$6Ñ$6¸Ó:ˆŒð 	�‰Ör%   c                ó6  <€ V ^8„  d   QhRS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[S[,          R	S[S[,          /# )
r}   r~   NrB   r   rƒ   r<   r€   r‡   rˆ   )r=   r‰   rŠ   r   r   r‹   r
   )r�   rS   s   "€r&   r�   Ú*Data2VecTextForMultipleChoice.__annotate__z  s³   ø€ ÷ O
ñ O
á×#Ñ# dÕ*ðO
ñ ×(Ñ(¨4Õ/ðO
ñ ×)Ñ)¨DÕ0ð	O
ñ
 × Ñ  4Õ'ðO
ñ ×&Ñ&¨Õ-ðO
ñ ×(Ñ(¨4Õ/ðO
ñ Ñ+Õ,ðO
ñ 
Ñ*Õ	*ñO
r%   c                ó^  € Ve   VP                   ^,          MVP                   ^,          pVe"   VP                  R	VP                  R	4      4      MRp	Ve"   VP                  R	VP                  R	4      4      MRp
Ve"   VP                  R	VP                  R	4      4      MRpVe"   VP                  R	VP                  R	4      4      MRpVe2   VP                  R	VP                  R
4      VP                  R	4      4      MRpV P                  ! V	3RV
RVRVRVRR/VB pV^,          pV P	                  V4      pV P                  V4      pVP                  R	V4      pRpVe/   \        4       pVP                  VP                  4      pV! VV4      p\        VVVP                  VP                  R7      # )a›  
input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`):
    Indices of input sequence tokens in the vocabulary.

    Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
    [`PreTrainedTokenizer.__call__`] for details.

    [What are input IDs?](../glossary#input-ids)
token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
    Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
    1]`:

    - 0 corresponds to a *sentence A* token,
    - 1 corresponds to a *sentence B* token.

    [What are token type IDs?](../glossary#token-type-ids)
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
    Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
    num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
    `input_ids` above)
position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*):
    Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
    config.max_position_embeddings - 1]`.

    [What are position IDs?](../glossary#position-ids)
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*):
    Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
    is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
    model's internal embedding lookup matrix.
Nr<   rB   r   r€   r“   Tr­   r6   éþÿÿÿ)r?   r°   Úsizer0   rÒ   r»   r   r®   r¯   r
   r1   r2   )rC   r~   rB   r   rƒ   r<   r€   r‡   Únum_choicesÚflat_input_idsÚflat_position_idsÚflat_token_type_idsÚflat_attention_maskÚflat_inputs_embedsrš   Úpooled_outputr”   Úreshaped_logitsr–   r´   s   &&&&&&&,            r&   rœ   Ú%Data2VecTextForMultipleChoice.forwardx  sÑ  € ðT -6Ò,A�i—o‘o aÖ(À}×GZÑGZÐ[\ÕG]ˆàCLÒCX˜Ÿ™¨¨I¯N©N¸2Ó,>Ô?Ð^bˆØLXÒLd˜L×-Ñ-¨b°,×2CÑ2CÀBÓ2GÔHÐjnÐØR`ÒRl˜n×1Ñ1°"°n×6IÑ6IÈ"Ó6MÔNÐrvÐØR`ÒRl˜n×1Ñ1°"°n×6IÑ6IÈ"Ó6MÔNÐrvÐð Ò(ð ×Ñ˜r =×#5Ñ#5°bÓ#9¸=×;MÑ;MÈbÓ;QÔRàð 	ð ×$Ò$Øñ
à*ð
ð /ð
ð /ð	
ð
 -ð
ð ð
ð ñ
ˆð   �
ˆàŸ™ ]Ó3ˆØ—‘ Ó/ˆØ Ÿ+™+ b¨+Ó6ˆàˆØÒÜ'Ó)ˆHà—Y‘Y˜×5Ñ5Ó6ˆFÙ˜O¨VÓ4ˆDä(ØØ"Ø!×/Ñ/Ø×)Ñ)ô	
ð 	
r%   )r»   r0   rÒ   rÊ   rË   rR   s   @@r&   rÍ   rÍ   l  s4   ù‡ € õð Ø÷O
ñ O
ó ó ÷O
ð O
r%   rÍ   c                   ó\   a a€ ] tR tRt oV 3R lt]]RV3R lR ll4       4       tRtVt	V ;t
# )Ú"Data2VecTextForTokenClassificationiÌ  c                ón  <€ \         SV `  V4       VP                  V n        \        VR R7      V n        VP
                  e   VP
                  MVP                  p\        P                  ! V4      V n	        \        P                  ! VP                  VP                  4      V n        V P                  4        R# r¹   )r7   re   rº   rU   r0   Úclassifier_dropoutrÑ   rÏ   rÐ   rÒ   rÓ   rÔ   r»   rj   )rC   rl   rç   rE   s   && €r&   re   Ú+Data2VecTextForTokenClassification.__init__Î  s‹   ø€ Ü‰Ñ˜Ô Ø ×+Ñ+ˆŒä.¨vÈÔOˆÔà)/×)BÑ)BÒ)NˆF×%Ò%ÐTZ×TnÑTnð 	ô —z’zÐ"4Ó5ˆŒÜŸ)š) F×$6Ñ$6¸×8IÑ8IÓJˆŒð 	�‰Ör%   c                ó6  <€ V ^8„  d   QhRS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[S[,          R	S[S[,          /# r¾   )r=   r‰   rŠ   r   r   r‹   r   )r�   rS   s   "€r&   r�   Ú/Data2VecTextForTokenClassification.__annotate__Þ  s©   ø€ ÷ )
ñ )
á×#Ñ# dÕ*ð)
ñ ×)Ñ)¨DÕ0ð)
ñ ×(Ñ(¨4Õ/ð	)
ñ
 ×&Ñ&¨Õ-ð)
ñ ×(Ñ(¨4Õ/ð)
ñ × Ñ  4Õ'ð)
ñ Ñ+Õ,ð)
ñ 
Ñ&Õ	&ñ)
r%   c                ó’  € V P                   ! V3RVRVRVRVRR/VB pV^ ,          p	V P                  V	4      p	V P                  V	4      p
RpVeX   \        4       pVP	                  V
P
                  4      pV! V
P                  R	V P                  4      VP                  R	4      4      p\        VV
VP                  VP                  R7      # )
zÃ
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
    Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
r   rB   r<   r€   r“   TNr­   r6   )r0   rÒ   r»   r   r®   r¯   r°   rº   r   r1   r2   rÈ   s   &&&&&&&,     r&   rœ   Ú*Data2VecTextForTokenClassification.forwardÜ  så   € ð  ×$Ò$Øñ
à)ð
ð *ð
ð &ð	
ð
 (ð
ð ð
ð ñ
ˆð " !�*ˆàŸ,™, Ó7ˆØ—‘ Ó1ˆàˆØÒÜ'Ó)ˆHà—Y‘Y˜vŸ}™}Ó-ˆFÙ˜FŸK™K¨¨D¯O©OÓ<¸f¿k¹kÈ"»oÓNˆDä$ØØØ!×/Ñ/Ø×)Ñ)ô	
ð 	
r%   )r»   r0   rÒ   rº   rÊ   rË   rR   s   @@r&   rå   rå   Ì  s0   ù‡ € õð Ø÷)
ñ )
ó ó ÷)
ð )
r%   rå   c                   ó\   a a€ ] tR tRt oV 3R lt]]RV3R lR ll4       4       tRtVt	V ;t
# )Ú Data2VecTextForQuestionAnsweringi
  c                óì   <€ \         SV `  V4       VP                  V n        \        VR R7      V n        \
        P                  ! VP                  VP                  4      V n        V P                  4        R# r¹   )
r7   re   rº   rU   r0   rÏ   rÓ   rÔ   Ú
qa_outputsrj   rk   s   &&€r&   re   Ú)Data2VecTextForQuestionAnswering.__init__  sV   ø€ Ü‰Ñ˜Ô Ø ×+Ñ+ˆŒä.¨vÈÔOˆÔÜŸ)š) F×$6Ñ$6¸×8IÑ8IÓJˆŒð 	�‰Ör%   c                ó^  <€ V ^8„  d   QhRS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          R	S[S[,          R
S[S[,          /	# )r}   r~   Nr   rB   r<   r€   Ústart_positionsÚend_positionsr‡   rˆ   )r=   r‰   rŠ   r   r   r‹   r   )r�   rS   s   "€r&   r�   Ú-Data2VecTextForQuestionAnswering.__annotate__  s¾   ø€ ÷ 3
ñ 3
á×#Ñ# dÕ*ð3
ñ ×)Ñ)¨DÕ0ð3
ñ ×(Ñ(¨4Õ/ð	3
ñ
 ×&Ñ&¨Õ-ð3
ñ ×(Ñ(¨4Õ/ð3
ñ ×)Ñ)¨DÕ0ð3
ñ ×'Ñ'¨$Õ.ð3
ñ Ñ+Õ,ð3
ñ 
Ñ-Õ	-ñ3
r%   c                óð  € V P                   ! V3R VRVRVRVRR/VB p	V	^ ,          p
V P                  V
4      pVP                  ^R
R7      w  rÍVP                  R
4      P	                  4       pVP                  R
4      P	                  4       pRpVeÄ   VeÀ   \        VP                  4       4      ^8”  d   VP                  R
4      p\        VP                  4       4      ^8”  d   VP                  R
4      pVP                  ^4      pVP                  ^ V4      pVP                  ^ V4      p\        VR7      pV! WÆ4      pV! W×4      pVV,           ^,          p\        VVVV	P                  V	P                  R	7      # )r   rB   r<   r€   r“   T)ÚdimN)Úignore_index)r–   Ústart_logitsÚ
end_logitsr1   r2   r6   )r0   rð   ÚsplitrÇ   Ú
contiguousÚlenrÚ   Úclampr   r   r1   r2   )rC   r~   r   rB   r<   r€   ró   rô   r‡   rš   r±   r”   rù   rú   Ú
total_lossÚignored_indexr´   Ú
start_lossÚend_losss   &&&&&&&&,          r&   rœ   Ú(Data2VecTextForQuestionAnswering.forward  s–  € ð ×$Ò$Øñ
à)ð
ð *ð
ð &ð	
ð
 (ð
ð ð
ð ñ
ˆð " !�*ˆà—‘ Ó1ˆØ#)§<¡<°°r <Ó#:Ñ ˆØ#×+Ñ+¨BÓ/×:Ñ:Ó<ˆØ×'Ñ'¨Ó+×6Ñ6Ó8ˆ
àˆ
ØÒ&¨=Ò+Dä�?×'Ñ'Ó)Ó*¨QÔ.Ø"1×"9Ñ"9¸"Ó"=�Ü�=×%Ñ%Ó'Ó(¨1Ô,Ø -× 5Ñ 5°bÓ 9�à(×-Ñ-¨aÓ0ˆMØ-×3Ñ3°A°}ÓEˆOØ)×/Ñ/°°=ÓAˆMä'°]ÔCˆHÙ! ,Ó@ˆJÙ 
Ó:ˆHØ$ xÕ/°1Õ4ˆJä+ØØ%Ø!Ø!×/Ñ/Ø×)Ñ)ô
ð 	
r%   )r0   rº   rð   )NNNNNNNrË   rR   s   @@r&   rî   rî   
  s0   ù‡ € õð Ø÷3
ñ 3
ó ó ÷3
ð 3
r%   rî   )r\   r£   rÍ   rî   r·   rå   rU   r/   ):Ú__doc__r=   Útorch.nnrÏ   r   r   r   Ú r   r:   Ú
generationr   Úmodeling_outputsr   r   r	   r
   r   r   r   Úmodeling_utilsr   Úprocessing_utilsr   Úutilsr   r   r   Úutils.genericr   Úroberta.modeling_robertar   r   r   r   r   r   r   Úconfiguration_data2vec_textr   Ú
get_loggerr    rg   r   r)   r+   r-   r/   rU   rW   rY   r\   r£   r·   rÍ   rå   rî   Ú__all__r   r%   r&   Ú<module>r     s¾  ðñ "ã Ý ß AÑ Aå &Ý )÷÷ ñ õ .Ý &ß @Ñ @Ý -÷÷ ñ õ <ð 
×	Ò	˜HÓ	%€ô	Ð.ô 	ô	Ð 4ô 	ô	Ð!6ô 	ô	˜ô 	ð ô/ /ó /ó ð/ð, ô	˜ó 	ó ð	ô	˜ô 	ô	Ð%>ô 	ñ ðôô
`
Ð9¸?ó `
óð
`
ðF ôI
Ð9ó I
ó ðI
ñX ðôôH
Ð,Gó H
óðH
ðV ô\
Ð$?ó \
ó ð\
ð~ ô:
Ð)Dó :
ó ð:
ðz ô@
Ð'Bó @
ó ð@
òF	‚r%   