+
    œ°³jè4  ã                   ó®  € ^ 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 ^RIHt ^RIHt ^R	IHt ^R
IHtHt ^RIHt ^RIHtHtHtHt ]! RR7      ] ! R R]4      4       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       t%] ! R R]"4      4       t&. ROt'R# ) é    N)Únn)ÚBCEWithLogitsLossÚCrossEntropyLossÚMSELoss)Ústrict)Úcreate_bidirectional_mask)ÚBaseModelOutputÚMaskedLMOutputÚSequenceClassifierOutputÚTokenClassifierOutput)ÚRopeParameters)ÚUnpack)Úauto_docstring)ÚTransformersKwargsÚcan_return_tuple)ÚLlamaConfig)ÚLlamaAttentionÚ
LlamaModelÚLlamaPreTrainedModelÚLlamaRMSNormzEuroBERT/EuroBERT-210m)Ú
checkpointc                   óž   a a€ ] tR t^t oRtRtRtRtRt^t	^t
RtRtRtR	tR
tRtRtRtRt^tRtRtRtRtRtRtRtV 3R ltV3R ltRtVtV ;t # )ÚEuroBertConfigaM  
mask_token_id (`int`, *optional*, defaults to 128002):
    Mask token id.
classifier_pooling (`str`, *optional*, defaults to `"late"`):
    The pooling strategy to use for the classifier. Can be one of ['bos', 'mean', 'late'].

```python
>>> from transformers import EuroBertModel, EuroBertConfig

>>> # Initializing a EuroBert eurobert-base style configuration
>>> configuration = EuroBertConfig()

>>> # Initializing a model from the eurobert-base style configuration
>>> model = EuroBertModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```Úeuroberti õ i   i   NÚsilui    g{®Gáz”?çñhãˆµøä>i ô iô iô Fg        Úlatec                óf   <€ V P                   f   V P                  V n         \        SV `  ! R/ VB  R # )N© )Únum_key_value_headsÚnum_attention_headsÚsuperÚ__post_init__)ÚselfÚkwargsÚ	__class__s   &,€Ún/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/eurobert/modular_eurobert.pyr#   ÚEuroBertConfig.__post_init__N   s-   ø€ Ø×#Ñ#Ò+Ø'+×'?Ñ'?ˆDÔ$Ü‰ÒÑ' Ô'ó    c                ó¶  <€ V ^8„  d   Qh/ S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ ;R&   S[ R,          ;R&   S[;R&   S[ ;R	&   S[;R
&   S[;R&   S[ R,          ;R&   S[ S[S[ ,          ,          R,          ;R&   S[ R,          ;R&   S[ ;R&   S[ ;R&   S[;R&   S[S[,          R,          ;R&   S[;R&   S[ S[,          ;R&   S[;R&   S[ R,          ;R&   S[;R&   # )é   Ú
vocab_sizeÚhidden_sizeÚintermediate_sizeÚnum_hidden_layersr!   Nr    Ú
hidden_actÚmax_position_embeddingsÚinitializer_rangeÚrms_norm_epsÚbos_token_idÚeos_token_idÚpad_token_idÚmask_token_idÚpretraining_tpÚtie_word_embeddingsÚrope_parametersÚattention_biasÚattention_dropoutÚmlp_biasÚhead_dimÚclassifier_pooling)ÚintÚstrÚfloatÚlistÚboolr   Údict)ÚformatÚ__classdict__s   "€r'   Ú__annotate__ÚEuroBertConfig.__annotate__   sj  ø‡ ‚ ñ0 Ññ1 ñ2 Ññ3 ñ4 Ñ!ñ5 ñ6 Ññ7 ñ8 Ñ!ñ9 ñ: ˜t�Ñ*ñ; ñ< Ññ= ñ> !Ñ'ñ? ñ@ Ñ#ñA ñB ÑñC ñD ˜•*Ñ%ñE ñF ™™S�	•/ DÕ(Ñ1ñG ñH ˜•*Ñ%ñI ñJ ÑñK ñL ÑñM ñN Ñ%ñO ñP $¡dÕ*¨TÕ1Ñ8ñQ ñR Ñ ñS ñT ™U•{Ñ(ñU ñV ÑñW ñX �D�jÑñY ñZ Ñ$ò[ r)   )r    )!Ú__name__Ú
__module__Ú__qualname__Ú__firstlineno__Ú__doc__Ú
model_typer,   r-   r.   r/   r!   r    r0   r1   r2   r3   r4   r5   r6   r7   r8   r9   r:   r;   r<   r=   r>   r?   r#   Ú__annotate_func__Ú__static_attributes__Ú__classdictcell__Ú__classcell__©r&   rG   s   @@r'   r   r      s•   ù‡ € ñð& €Jà€JØ€KØ!ÐØÐØ!ÐØ&*ÐØ€JØ#'ÐØ#ÐØ€LØ%€LØ+1€LØ%€LØ€MØ€NØ %ÐØ48€OØ €NØ%(ÐØ€HØ€HØ$Ðõ(÷_ … r)   r   c                   ó6   a a€ ] tR t^Tt oRV 3R lltRtVtV ;t# )ÚEuroBertRMSNormc                ó&   <€ \         SV `  W4       R # ©N)r"   Ú__init__)r$   r-   Úepsr&   s   &&&€r'   rY   ÚEuroBertRMSNorm.__init__U   s   ø€ Ü‰Ñ˜Ö*r)   r   )r   ©rJ   rK   rL   rM   rY   rQ   rR   rS   rT   s   @@r'   rV   rV   T   s   ù‡ € ÷+÷ +r)   rV   c                   ó>   a a€ ] tR t^Yt oV3R lV 3R lltRtVtV ;t# )ÚEuroBertAttentionc                ó&   <€ V ^8„  d   QhRS[ RS[/# )r+   ÚconfigÚ	layer_idx)r   r@   )rF   rG   s   "€r'   rH   ÚEuroBertAttention.__annotate__Z   s   ø€ ÷ ñ ™~ð ¹#ñ r)   c                ó4   <€ \         SV `  W4       R V n        R# )FN)r"   rY   Ú	is_causal)r$   r`   ra   r&   s   &&&€r'   rY   ÚEuroBertAttention.__init__Z   s   ø€ Ü‰Ñ˜Ô+ØˆŽr)   )rd   r\   rT   s   @@r'   r^   r^   Y   s   ù‡ € ÷÷ ó r)   r^   c                   ó   € ] tR t^_tRtR# )ÚEuroBertPreTrainedModelr   N)rJ   rK   rL   rM   rQ   r   r)   r'   rg   rg   _   s   † Ûr)   rg   c                   ó6   a € ] tR t^ct o RV 3R lR lltRtV tR# )ÚEuroBertModelNc                óØ   <€ V ^8„  d   QhRS[ P                  RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[S[,          RS[S[,          /# )r+   Ú	input_idsÚattention_maskNÚposition_idsÚinputs_embedsr%   Úreturn)ÚtorchÚ
LongTensorÚTensorÚFloatTensorr   r   Útupler	   )rF   rG   s   "€r'   rH   ÚEuroBertModel.__annotate__d   sw   ø€ ÷ &
ñ &
á×#Ñ#ð&
ñ Ÿ™ tÕ+ð&
ñ ×&Ñ&¨Õ-ð	&
ñ
 ×(Ñ(¨4Õ/ð&
ñ Ñ+Õ,ð&
ñ 
‘Õ	 ñ&
r)   c           
     óø  € VR J VR J,          '       d   \        R4      hVf   V P                  V4      pVfC   \        P                  ! VP                  ^,          VP
                  R7      P                  ^ 4      p\        V P                  VVR7      pTpV P                  WsR7      pV P                  R V P                  P                    F  p	V	! V3RVRVRV/VB pK  	  V P                  V4      p\        VR7      # )	Nz:You must specify exactly one of input_ids or inputs_embeds)Údevice)r`   rn   rl   )rm   rl   Úposition_embeddingsrm   )Úlast_hidden_state)Ú
ValueErrorÚembed_tokensrp   ÚarangeÚshaperw   Ú	unsqueezer   r`   Ú
rotary_embÚlayersr/   Únormr	   )
r$   rk   rl   rm   rn   r%   Úbidirectional_maskÚhidden_statesrx   Úencoder_layers
   &&&&&,    r'   ÚforwardÚEuroBertModel.forwardd   s  € ð ˜Ð -°tÐ";×<Ô<ÜÐYÓZÐZàÒ Ø*.×*;Ñ*;¸IÓ*FˆMàÒÜ Ÿ<š<¨×(;Ñ(;¸AÕ(>À}×G[ÑG[Ô\×fÑfÐghÓiˆLä6Ø—;‘;Ø'Ø)ô
Ðð &ˆØ"Ÿo™o¨m˜oÓWÐà!Ÿ[™[Ð)H¨4¯;©;×+HÑ+HÓIˆMÙ)Øñà1ðð %8ðð *ð	ð
 ñŠMñ Jð Ÿ	™	 -Ó0ˆÜØ+ô
ð 	
r)   r   )NNNN)rJ   rK   rL   rM   r…   rQ   rR   )rG   s   @r'   ri   ri   c   s   ø‡ € ÷&
÷ &
ð &
r)   ri   c                   ó�   a a€ ] tR t^�t oRR/tRR/tRR.R.3/tRR/tV3R lV 3R	 llt]	]
RV3R
 lR ll4       4       tRtVtV ;t# )ÚEuroBertForMaskedLMzlm_head.weightzmodel.embed_tokens.weightÚlm_headÚcolwise_gather_outputrƒ   ÚlogitsÚkeep_full_weightc                ó    <€ V ^8„  d   QhRS[ /# ©r+   r`   ©r   )rF   rG   s   "€r'   rH   Ú EuroBertForMaskedLM.__annotate__•   ó   ø€ ÷ ñ ™~ñ r)   c                óÜ   <€ \         SV `  V4       \        V4      V n        \        P
                  ! VP                  VP                  VP                  4      V n	        V P                  4        R # rX   )r"   rY   ri   Úmodelr   ÚLinearr-   r,   r=   r‰   Ú	post_init©r$   r`   r&   s   &&€r'   rY   ÚEuroBertForMaskedLM.__init__•   sL   ø€ Ü‰Ñ˜Ô Ü" 6Ó*ˆŒ
Ü—y’y ×!3Ñ!3°V×5FÑ5FÈÏÉÓXˆŒð 	�‰Ör)   c                ó2  <€ 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[S[,          RS[S[ P                  ,          S[,          /# ©	r+   rk   Nrl   rm   rn   Úlabelsr%   ro   )rp   rq   rr   rs   r   r   rt   r
   )rF   rG   s   "€r'   rH   r�   Ÿ   sš   ø€ ÷ /
ñ /
á×#Ñ# dÕ*ð/
ñ Ÿ™ tÕ+ð/
ñ ×&Ñ&¨Õ-ð	/
ñ
 ×(Ñ(¨4Õ/ð/
ñ × Ñ  4Õ'ð/
ñ Ñ+Õ,ð/
ñ 
‰u�|‰|Õ	™~Õ	-ñ/
r)   c                ó  € V P                   ! R
RVRVRVRV/VB pV P                  VP                  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                  R	7      # )a±  
Example:

```python
>>> from transformers import AutoTokenizer, EuroBertForMaskedLM

>>> model = EuroBertForMaskedLM.from_pretrained("EuroBERT/EuroBERT-210m")
>>> tokenizer = AutoTokenizer.from_pretrained("EuroBERT/EuroBERT-210m")

>>> text = "The capital of France is <|mask|>."
>>> inputs = tokenizer(text, return_tensors="pt")
>>> outputs = model(**inputs)

>>> # To get predictions for the mask:
>>> masked_index = inputs["input_ids"][0].tolist().index(tokenizer.mask_token_id)
>>> predicted_token_id = outputs.logits[0, masked_index].argmax(axis=-1)
>>> predicted_token = tokenizer.decode(predicted_token_id)
>>> print("Predicted token:", predicted_token)
Predicted token:  Paris
```rk   rl   rm   rn   Nr‹   rš   r,   ©Úlossr‹   rƒ   Ú
attentionsr   )	r“   r‰   ry   Úloss_functionr`   r,   r
   rƒ   rž   )
r$   rk   rl   rm   rn   rš   r%   Úoutputsr‹   r�   s
   &&&&&&,   r'   r…   ÚEuroBertForMaskedLM.forward�   s³   € ð> $(§:¢:ñ $
Øð$
à)ð$
ð &ð$
ð (ð	$
ð
 ñ$
ˆð —‘˜g×7Ñ7Ó8ˆØˆØÒØ×%Ò%Ñp¨VÐp¸FÐpÈtÏ{É{×OeÑOeÐpÐioÑpˆDäØØØ!×/Ñ/Ø×)Ñ)ô	
ð 	
r)   )r‰   r“   ©NNNNN)rJ   rK   rL   rM   Ú_tied_weights_keysÚ_tp_planÚ_pp_planÚ
_fsdp_planrY   r   r   r…   rQ   rR   rS   rT   s   @@r'   rˆ   rˆ   �   sk   ù‡ € à*Ð,GÐHÐØÐ2Ð3€HØ˜_Ð-°¨zÐ:Ð;€HàÐ/Ð0€J÷ó ð Ø÷/
ñ /
ó ó ÷/
ð /
r)   rˆ   c                   óh   a a€ ] tR t^Ñt oV3R lV 3R llt]]RV3R lR ll4       4       tRtVt	V ;t
# )Ú!EuroBertForSequenceClassificationc                ó    <€ V ^8„  d   QhRS[ /# rŽ   r�   )rF   rG   s   "€r'   rH   Ú.EuroBertForSequenceClassification.__annotate__Ó   s   ø€ ÷ 	ñ 	™~ñ 	r)   c                óž  <€ \         SV `  V4       VP                  V n        VP                  V n        \	        V4      V n        \        P                  ! VP                  VP                  4      V n	        \        P                  ! 4       V n        \        P                  ! VP                  V P                  4      V n        V P                  4        R # rX   )r"   rY   Ú
num_labelsr?   ri   r“   r   r”   r-   ÚdenseÚGELUÚ
activationÚ
classifierr•   r–   s   &&€r'   rY   Ú*EuroBertForSequenceClassification.__init__Ó   sˆ   ø€ Ü‰Ñ˜Ô Ø ×+Ñ+ˆŒØ"(×";Ñ";ˆÔä" 6Ó*ˆŒ
Ü—Y’Y˜v×1Ñ1°6×3EÑ3EÓFˆŒ
ÜŸ'š'›)ˆŒÜŸ)š) F×$6Ñ$6¸¿¹ÓHˆŒØ�‰Ör)   c                ó2  <€ 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[S[,          RS[S[ P                  ,          S[,          /# r™   )rp   rq   rr   rs   r   r   rt   r   )rF   rG   s   "€r'   rH   rª   à   s¤   ø€ ÷ J
ñ J
á×#Ñ# dÕ*ðJ
ñ Ÿ™ tÕ+ðJ
ñ ×&Ñ&¨Õ-ð	J
ñ
 ×(Ñ(¨4Õ/ðJ
ñ × Ñ  4Õ'ðJ
ñ Ñ+Õ,ðJ
ñ 
‰u�|‰|Õ	Ñ7Õ	7ñJ
r)   c           	     ó0  € V P                   ! V3R VRVRV/VB pV^ ,          pV P                  R9   dÓ   V P                  R8X  d   VR,          p	MƒV P                  R8X  ds   Vf   VP                  ^R7      p	M[VP                  VP                  4      pW‚P                  R4      ,          P                  ^R7      p	W’P                  ^RR7      ,          p	V P                  X	4      p	V P                  V	4      p	V P                  V	4      p
M¶V P                  R	8X  d¦   V P                  V4      pV P                  V4      pV P                  V4      p
Vf   V
P                  ^R7      p
M[VP                  V
P                  4      pW¢P                  R4      ,          P                  ^R7      p
W¢P                  ^RR7      ,          p
RpVEe³   VP                  X
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X
VP.                  VP0                  R7      # )rl   rm   rn   ÚbosÚmeanN)ÚdimT)r¶   Úkeepdimr   Ú
regressionÚsingle_label_classificationÚmulti_label_classificationrœ   )r´   rµ   ):NNNr   éÿÿÿÿ)r“   r?   rµ   Útorw   r~   Úsumr­   r¯   r°   r`   Úproblem_typer¬   Údtyperp   Úlongr@   r   Úsqueezer   Úviewr   r   rƒ   rž   )r$   rk   rl   rm   rn   rš   r%   Úencoder_outputry   Úpooled_outputr‹   Úxr�   Úloss_fcts   &&&&&&,       r'   r…   Ú)EuroBertForSequenceClassification.forwardÞ   s  € ð ŸšØñ
à)ð
ð &ð
ð (ð	
ð
 ñ
ˆð +¨1Õ-Ðà×"Ñ" oÔ5Ø×&Ñ&¨%Ô/Ø 1°$Õ 7‘à×(Ñ(¨FÔ2Ø!Ò)Ø$5×$:Ñ$:¸qÐ$:Ó$A‘Mà%3×%6Ñ%6Ð7H×7OÑ7OÓ%P�NØ%6×9QÑ9QÐRTÓ9UÕ%U×$ZÑ$ZÐ_`Ð$ZÓ$a�MØ!×%7Ñ%7¸AÀtÐ%7Ó%LÕL�Mà ŸJ™J }Ó5ˆMØ ŸO™O¨MÓ:ˆMØ—_‘_ ]Ó3‰Fà×$Ñ$¨Ô.Ø—
‘
Ð,Ó-ˆAØ—‘ Ó"ˆAØ—_‘_ QÓ'ˆFØÒ%ØŸ™¨˜Ó+‘à!/×!2Ñ!2°6·=±=Ó!A�Ø ×#;Ñ#;¸BÓ#?Õ?×DÑDÈÐDÓK�Ø×,Ñ,°¸DÐ,ÓAÕA�àˆØÓØ—Y‘Y˜vŸ}™}Ó-ˆFØ�{‰{×'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(×6Ñ6Ø%×0Ñ0ô	
ð 	
r)   )r¯   r°   r?   r­   r“   r¬   r¢   )rJ   rK   rL   rM   rY   r   r   r…   rQ   rR   rS   rT   s   @@r'   r¨   r¨   Ñ   s9   ù‡ € ÷	ó 	ð Ø÷J
ñ J
ó ó ÷J
ð J
r)   r¨   c                   ót   a a€ ] tR tRt oV3R lV 3R lltR tR t]]R	V3R lR ll4       4       t	Rt
VtV ;t# )
ÚEuroBertForTokenClassificationi-  c                ó    <€ V ^8„  d   QhRS[ /# rŽ   r�   )rF   rG   s   "€r'   rH   Ú+EuroBertForTokenClassification.__annotate__/  r‘   r)   c                óè   <€ \         SV `  V4       VP                  V n        \        V4      V n        \
        P                  ! VP                  VP                  4      V n        V P                  4        R # rX   )
r"   rY   r¬   ri   r“   r   r”   r-   r°   r•   r–   s   &&€r'   rY   Ú'EuroBertForTokenClassification.__init__/  sQ   ø€ Ü‰Ñ˜Ô Ø ×+Ñ+ˆŒÜ" 6Ó*ˆŒ
äŸ)š) F×$6Ñ$6¸×8IÑ8IÓJˆŒØ�‰Ör)   c                ó.   € V P                   P                  # rX   ©r“   r{   )r$   s   &r'   Úget_input_embeddingsÚ3EuroBertForTokenClassification.get_input_embeddings7  s   € Ø�z‰z×&Ñ&Ð&r)   c                ó&   € WP                   n        R # rX   rÏ   )r$   Úvalues   &&r'   Úset_input_embeddingsÚ3EuroBertForTokenClassification.set_input_embeddings:  s   € Ø"'�
‰
Ör)   c                ó  <€ V ^8„  d   QhRS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[ P                  R,          RS[S[,          RS[S[,          /# r™   )rp   rq   rr   rs   r   r   rt   r   )rF   rG   s   "€r'   rH   rË   ?  s’   ø€ ÷ #
ñ #
á×#Ñ# dÕ*ð#
ñ Ÿ™ tÕ+ð#
ñ ×&Ñ&¨Õ-ð	#
ñ
 ×(Ñ(¨4Õ/ð#
ñ × Ñ  4Õ'ð#
ñ Ñ+Õ,ð#
ñ 
Ñ&Õ	&ñ#
r)   c           	     ó2  € V P                   ! V3RVRVRV/VB pV^ ,          pV P                  V4      p	Rp
Ve=   \        4       pV! V	P                  RV P                  4      VP                  R4      4      p
\        V
V	VP                  VP                  R7      # )ae  
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
    Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
    config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
    `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
rl   rm   rn   Nrœ   r»   )r“   r°   r   rÂ   r¬   r   rƒ   rž   )r$   rk   rl   rm   rn   rš   r%   r    Úsequence_outputr‹   r�   rÆ   s   &&&&&&,     r'   r…   Ú&EuroBertForTokenClassification.forward=  s¯   € ð" —*’*Øñ
à)ð
ð &ð
ð (ð	
ð
 ñ
ˆð " !�*ˆØ—‘ Ó1ˆàˆØÒÜ'Ó)ˆHÙ˜FŸK™K¨¨D¯O©OÓ<¸f¿k¹kÈ"»oÓNˆDä$ØØØ!×/Ñ/Ø×)Ñ)ô	
ð 	
r)   )r°   r“   r¬   r¢   )rJ   rK   rL   rM   rY   rÐ   rÔ   r   r   r…   rQ   rR   rS   rT   s   @@r'   rÉ   rÉ   -  s?   ù‡ € ÷ó ò'ò(ð Ø÷#
ñ #
ó ó ÷#
ð #
r)   rÉ   )r   rg   ri   rˆ   r¨   rÉ   )(rp   r   Útorch.nnr   r   r   Úconfiguration_utilsr   Úmasking_utilsr   Úmodeling_outputsr	   r
   r   r   Úmodeling_rope_utilsr   Úprocessing_utilsr   Úutilsr   Úutils.genericr   r   Úllama.configuration_llamar   Úllama.modeling_llamar   r   r   r   r   rV   r^   rg   ri   rˆ   r¨   rÉ   Ú__all__r   r)   r'   Ú<module>rå      sô   ðó  Ý ß AÑ Aå )Ý 6ß pÓ pÝ 1Ý &Ý #ß AÝ 3ß aÓ añ Ð3Ô4Øô0(�[ó 0(ó ó 5ð0(ôf+�lô +ô
˜ô ô	Ð2ô 	ô'
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