+ j|Rt^RIHt^RIHt^RIt^RIHt^RIHt^RI H t ^RI H t ^RIHt^R IHt^R IHtHtHt^R IHtHt^R IHt^R IHt^RIHtHtH t H!t!H"t"^RI#H$t$^RI%H&t&^RI'H(t(]!PR!]*4t+!RR]PX4t-R5RRllt.!RR]PX4t/!RR]PX4t0!RR]PX4t1!RR]PX4t2!RR]PX4t3!R R!]4t4!R"R#]PX4t5]!R$R%]44t6]!R&R']644t7!R(R)]PX4t8!R*R+]PX4t9]!R,R-]644t:]!R.R/7]!R0R1]444t;]!R2R/7!R3R4]644t<.R6Ot=R#)7zPyTorch Splinter model.)Callable) dataclassN)nn)CrossEntropyLoss)initialization)ACT2FN)create_bidirectional_mask)GradientCheckpointingLayer)BaseModelOutput ModelOutputQuestionAnsweringModelOutput)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)apply_chunking_to_forward)TransformersKwargsauto_docstringcan_return_tupleloggingtorch_compilable_check)merge_with_config_defaults)capture_outputsSplinterConfigcLaa]tRt^(toRtV3RltRV3RlRlltRtVtV;t #)SplinterEmbeddingszGConstruct the embeddings from word, position and token_type embeddings.c<\SV`4\P!VPVP VP R7Vn\P!VPVP 4Vn \P!VPVP 4Vn \P!VP VPR7Vn \P!VP4Vn\P"!\$P&!VP4P)R4RR7VnR#)) padding_idxepsF) persistentN)super__init__r Embedding vocab_size hidden_size pad_token_idword_embeddingsmax_position_embeddingsposition_embeddingstype_vocab_sizetoken_type_embeddings LayerNormlayer_norm_epsDropouthidden_dropout_probdropoutBuffertorcharangeexpand position_idsselfconfig __class__s&&o/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/splinter/modeling_splinter.pyr%SplinterEmbeddings.__init__+s !||F,=,=v?Q?Q_e_r_rs#%<<0N0NPVPbPb#c %'\\&2H2H&J\J\%]"f&8&8f>S>STzz&"<"<= IIell63Q3Q&R&Y&YZa&botuc <V^8dQhRS[PR,RS[PR,RS[PR,RS[PR,RS[/#) input_idsNtoken_type_idsr8 inputs_embedsreturn)r5 LongTensor FloatTensortuple)format __classdict__s"r= __annotate__SplinterEmbeddings.__annotate__7si##d*((4/&&-  ((4/   r?cVeVP4pMVP4RRpV^,pVfVPRRV13,pVf<\P!V\PVPP R7pVfVP V4pVPV4pWG,pVPV4p W, pVPV4pVPV4pV#)N:NNNdtypedevicer#) sizer8r5zeroslongrPr*r.r,r/r3) r:rBrCr8rD input_shape seq_lengthr. embeddingsr,s &&&&& r=forwardSplinterEmbeddings.forward7s  #..*K',,.s3K ^  ,,Q  ^ J": "66|D) ^^J/ \\*- r?)r/r3r,r8r.r*)NNNN __name__ __module__ __qualname____firstlineno____doc__r%rW__static_attributes____classdictcell__ __classcell__r<rJs@@r=rr(sQ vr?rcV^8dQhR\PR\PR\PR\PR\PR,R\R\/#) rAmodulequerykeyvalueattention_maskNscalingr3)rModuler5Tensorfloat)rIs"r=rKrKYsg%% II% <<% % << % LL4' %  %%r?c \P!WP^^44V,pVe W,p\PP VR\P R7PVP4p\PPWVPR7p\P!W4p V P^^4P4p W3#)rA)dimrO)ptrainingr#) r5matmul transposer functionalsoftmaxfloat32torOr3rp contiguous) rdrerfrgrhrir3kwargs attn_weights attn_outputs &&&&&&&, r=eager_attention_forwardr{Ys<<}}Q':;gEL!#4 ==((2U]](SVVW\WbWbcL==((6??([L,,|3K''1-88:K  $$r?cHaa]tRt^ptoV3RltRV3RlRlltRtVtV;t#)SplinterSelfAttentionc\<\SV`4VPVP,^8wd9\ VR4'g'\ RVP RVP R24hWnVPVn\VPVP, 4VnVPVP,Vn \P!VPVP4Vn \P!VPVP4Vn \P!VPVP4Vn\P!VP 4VnVP VnVPR,VnR#)embedding_sizezThe hidden size (z6) is not a multiple of the number of attention heads ()Ng)r$r%r(num_attention_headshasattr ValueErrorr;intattention_head_size all_head_sizerLinearrerfrgr1attention_probs_dropout_probr3attention_dropoutrir9s&&r=r%SplinterSelfAttention.__init__qsD     : : :a ?PVXhHiHi#F$6$6#78 445Q8   #)#=#= #&v'9'9FS>STzz&"<"<= r?ch<V^8dQhRS[PRS[PRS[P/#rAr input_tensorrEr5rk)rIrJs"r=rKSplinterSelfOutput.__annotate__1U\\RWR^R^r?cxVPV4pVPV4pVPW,4pV#rrr3r/r:rrs&&&r=rWSplinterSelfOutput.forward5 =1  ]3 }'CD r?r/rr3rrbs@@r=rr> r?rcHaa]tRt^toV3RltRV3RlRlltRtVtV;t#)SplinterAttentioncd<\SV`4\V4Vn\ V4VnR#r)r$r%r}r:routputr9s&&r=r%SplinterAttention.__init__s& )&1 (0 r?c<V^8dQhRS[PRS[PR,RS[S[,RS[P/#rr5rkrGrr)rIrJs"r=rKSplinterAttention.__annotate__sL  || ))D0 +,   r?c ZTpVP!V3RV/VBwrVPW4pV#rh)r:r)r:rrhrxresidual_s&&&, r=rWSplinterAttention.forwardsE !99  )    M< r?)rr:rrrbs@@r=rrs1   r?rcDaa]tRt^toV3RltV3RlRltRtVtV;t#)SplinterIntermediatec"<\SV`4\P!VPVP 4Vn\VP\4'd\VP,Vn R#VPVn R#r) r$r%rrr(intermediate_sizer isinstance hidden_actstrrintermediate_act_fnr9s&&r=r%SplinterIntermediate.__init__s` YYv1163K3KL f'' - -'-f.?.?'@D $'-'8'8D $r?cN<V^8dQhRS[PRS[P/#)rArrEr)rIrJs"r=rK!SplinterIntermediate.__annotate__s#U\\ellr?cJVPV4pVPV4pV#rrr)r:rs&&r=rWSplinterIntermediate.forwards& =1 00? r?rrrbs@@r=rrs9r?rcDaa]tRt^toV3RltV3RlRltRtVtV;t#)SplinterOutputc0<\SV`4\P!VPVP 4Vn\P!VP VPR7Vn\P!VP4Vn R#r) r$r%rrrr(rr/r0r1r2r3r9s&&r=r%SplinterOutput.__init__s` YYv779K9KL f&8&8f>S>STzz&"<"<= r?ch<V^8dQhRS[PRS[PRS[P/#rr)rIrJs"r=rKSplinterOutput.__annotate__rr?cxVPV4pVPV4pVPW,4pV#rrrs&&&r=rWSplinterOutput.forwardrr?rrrbs@@r=rrrr?rcNaa]tRt^toV3RltRV3RlRlltRtRtVtV;t #) SplinterLayerc<\SV`4VPVn^Vn\ V4Vn\ V4Vn\V4Vn R#)r"N) r$r%chunk_size_feed_forward seq_len_dimr attentionr intermediaterrr9s&&r=r%SplinterLayer.__init__sI '-'E'E$*6208$V, r?c<V^8dQhRS[PRS[PR,RS[S[,RS[P/#rr)rIrJs"r=rKSplinterLayer.__annotate__sL||))D0+,   r?c VP!V3RV/VBp\VPVPVPV4pV#r)rrfeed_forward_chunkrr)r:rrhrxs&&&,r=rWSplinterLayer.forwardsW   )  2  # #T%A%A4CSCSUb r?cJVPV4pVPW!4pV#r)rr)r:attention_outputintermediate_output layer_outputs&& r=r SplinterLayer.feed_forward_chunks)"//0@A{{#6I r?)rrrrrr) rZr[r\r]r%rWrr_r`rarbs@@r=rrs-$r?rcHaa]tRtRtoV3RltRV3RlRlltRtVtV;t#)SplinterEncoderic<\SV`4Wn\P!\ VP 4Uu.uFp\V4NK up4VnRVn R#uupi)FN) r$r%r;r ModuleListrangenum_hidden_layersrlayergradient_checkpointing)r:r;ir<s&& r=r%SplinterEncoder.__init__sR  ]]5IaIaCb#cCbaM&$9Cb#cd &+#$dsA&cx<V^8dQhRS[PRS[PR,RS[S[,RS[/#r)r5rkrGrrr )rIrJs"r=rKSplinterEncoder.__annotate__sH  || ))D0 +,    r?c TVPF pV!VV3/VBpK \VR7#)last_hidden_state)rr )r:rrhrx layer_modules&&&, r=rWSplinterEncoder.forwards> !JJL(M'+  r?)r;rrrrrbs@@r=rrs,    r?rcRaa]tRtRtoRtRtR]R]/tV3Rlt V3Rlt Rt Vt V;t #) SplinterPreTrainedModeli'splinterTr attentionsc<\SV`V4\V\4'db\P !VP \P!VP PR,4PR44R#R#)r"Nr#r!) r$ _init_weightsrrinitcopy_r8r5r6rr7)r:rdr<s&&r=r%SplinterPreTrainedModel._init_weights1s^ f% f0 1 1 JJv**ELL9L9L9R9RSU9V,W,^,^_f,g h 2r?c&<V^8dQh/S[;R&#)rAr;r)rIrJs"r=rK$SplinterPreTrainedModel.__annotate__'s r?)rZr[r\r]base_model_prefixsupports_gradient_checkpointingrr}_can_record_outputsr__annotate_func__r_r`rarbs@@r=rr's3#&*#+ ir?rcvaa]tRtRtoRtV3RltRtRt]] ] R V3RlRll444t Rt Vt V;t#) SplinterModeli7a" The model is an encoder (with only self-attention) following the architecture described in [Attention is all you need](https://huggingface.co/papers/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. c<\SV`V4Wn\V4Vn\ V4VnVP4R#r)r$r%r;rrVrencoder post_initr9s&&r=r%SplinterModel.__init__?s9   ,V4&v.  r?c.VPP#rrVr*)r:s&r=get_input_embeddings"SplinterModel.get_input_embeddingsIs...r?c&WPnR#rr)r:rgs&&r=set_input_embeddings"SplinterModel.set_input_embeddingsLs */'r?c<V^8dQhRS[PR,RS[PR,RS[PR,RS[PR,RS[PR,RS[S[,RS[S[,/#) rArBNrhrCr8rDrxrE)r5rkrrrHr )rIrJs"r=rKSplinterModel.__annotate__Rs@ @ <<$&@  t+@  t+ @ llT) @ ||d* @ +,@   @ r?c 8VeVe \R4hVe#VPW4VP4pM#VeVP4RR pM \R4hVwrVe VPM VPp Vf\P !W3V R7pVf(\P !V\PV R7pVPVVVVR7p \VPV VR7pVP!V 3RV/VBp V ^,p \V R 7#) a token_type_ids (`torch.LongTensor` of shape `batch_size, sequence_length`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `batch_size, sequence_length`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) NzDYou cannot specify both input_ids and inputs_embeds at the same timez5You have to specify either input_ids or inputs_embedsrPrN)rBr8rCrD)r;rDrhrhrr#) r%warn_if_padding_and_no_attention_maskrQrPr5onesrRrSrVrr;r r )r:rBrhrCr8rDrxrT batch_sizerUrPembedding_outputencoder_outputssequence_outputs&&&&&&, r=rWSplinterModel.forwardOs16  ]%>cd d  "  6 6y Q#..*K  &',,.s3KTU U!, %.%:!! @T@T  !"ZZ*)A6RN  !"[[EJJvVN??%)' + 3;;*)  ,,  )   *!,-  r?)r;rVr )NNNNN)rZr[r\r]r^r%rrrrrrWr_r`rarbs@@r=r r 7sK /0 @ @  @ @ r?r cHaa]tRtRtoRV3RlltV3RlRltRtVtV;t#)SplinterFullyConnectedLayeric <\SV`4WnW n\P !VPVP4Vn\V,Vn\P!VP4Vn R#r) r$r% input_dim output_dimrrrract_fnr/)r:r#r$rr<s&&&&r=r%$SplinterFullyConnectedLayer.__init__sR "$YYt~~t? Z( doo6r?cN<V^8dQhRS[PRS[P/#)rAinputsrEr)rIrJs"r=rK(SplinterFullyConnectedLayer.__annotate__s#ellu||r?clVPV4pVPV4pVPV4pV#r)rr%r/)r:r(rs&& r=rW#SplinterFullyConnectedLayer.forwards2 6*  M2 }5 r?)r/r%rr#r$)gelurrbs@@r=r!r!s7r?r!c<aa]tRtRtoRtV3RltRtRtVtV;t #)QuestionAwareSpanSelectionHeadiz^ Implementation of Question-Aware Span Selection (QASS) head, described in Splinter's paper: c<\SV`4\VPVP4Vn\VPVP4Vn\VPVP4Vn\VPVP4Vn\P!VPVPRR7Vn \P!VPVPRR7Vn R#)F)biasN) r$r%r!r(query_start_transformquery_end_transformstart_transform end_transformrrstart_classifierend_classifierr9s&&r=r%'QuestionAwareSpanSelectionHead.__init__s %@ASASU[UgUg%h"#>v?Q?QSYSeSe#f :6;M;MvOaOab89K9KVM_M_` " &*<*P>PW\ ] ii(:(:F? ''1a0 ||M> ++N; ##Aq!,\\-: ''r?)r6r4r2r1r5r3rYrbs@@r=r.r.s \((r?r.c\aa]tRtRtoV3Rlt]]RV3RlRll44tRtVt V;t #)SplinterForQuestionAnsweringic<\SV`V4\V4Vn\ V4VnVP VnVP4R#rr$r%r rr. splinter_qassquestion_token_idr r9s&&r=r%%SplinterForQuestionAnswering.__init__C  %f- ;FC!'!9!9 r?c<V^8dQhRS[PR,RS[PR,RS[PR,RS[PR,RS[PR,RS[PR,RS[PR,R S[PR,R S[S[,R S[S[,/ # rArBNrhrCr8rDstart_positions end_positionsquestion_positionsrxrE)r5rkrFrrrHr )rIrJs"r=rK)SplinterForQuestionAnswering.__annotate__sW W <<$&W  t+W  t+ W llT) W ||d* W ))D0W ''$.W ",,t3W +,W  - -W r?c Rp VfVeF\P!\P!WP4P 4R R7p MK\P !VP ^4\PVPVPR7p V PR 4pRp VP!V3RVRVRVR V/V Bp V ^,p VPW4wrV 'd"VP^4VP^4rVeV^V, \P!VP4P ,,pV^V, \P!VP4P ,,pRpVeVe\#VP 44^8dVPR 4p\#VP 44^8dVPR 4pVP ^4pVP%^V4VP%^V4\'VR 7pV!W4pV!W4pVV,^, p\)VVVV P*V P,R 7#) aI token_type_ids (`torch.LongTensor` of shape `batch_size, 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) position_ids (`torch.LongTensor` of shape `batch_size, 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) question_positions (`torch.LongTensor` of shape `(batch_size, num_questions)`, *optional*): The positions of all question tokens. If given, start_logits and end_logits will be of shape `(batch_size, num_questions, sequence_length)`. If None, the first question token in each sequence in the batch will be the only one for which start_logits and end_logits are calculated and they will be of shape `(batch_size, sequence_length)`. FN)rn)rOlayoutrPTrhrCr8rD ignore_indexlossrDrErrr#)r5argmaxeqrLrrRrQrSrVrPr:rrKsqueezefinforOminlenclamp_rr rr)r:rBrhrCr8rDrQrRrSrxquestion_positions_were_none"question_position_for_each_exampleoutputsrrDrE total_loss ignored_indexloss_fct start_lossend_losss&&&&&&&&&, r=rW$SplinterForQuestionAnswering.forwardsLD(-$  %$5:\\XXi)?)?@EEGR626;[[!&&q)MDXDXanauau62"D!M!Mb!Q +/ (--  ) * &  (    "!*#'#5#5o#Z ''3';';A'> @R@RST@U*  %'1~+=\M_M_A`AdAd*ddL#q>'9U[[IYIY=Z=^=^&^^J  &=+D?'')*Q."1"9"9""==%%'(1, - 5 5b 9 (--a0M  " "1m 4  M 2']CH!,@J :H$x/14J+%!!//))   r?rLrrKNNNNNNNN) rZr[r\r]r%rrrWr_r`rarbs@@r=rHrHs4W W W W r?rHzB Class for outputs of Splinter as a span selection model. ) custom_introcDa]tRtRtoRtRtRtRtRtRt V3Rlt Rt Vt R#)SplinterForPreTrainingOutputi5a loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when start and end positions are provided): Total span extraction loss is the sum of a Cross-Entropy for the start and end positions. start_logits (`torch.FloatTensor` of shape `(batch_size, num_questions, sequence_length)`): Span-start scores (before SoftMax). end_logits (`torch.FloatTensor` of shape `(batch_size, num_questions, sequence_length)`): Span-end scores (before SoftMax). Nc <V^8dQh/S[PR,;R&S[PR,;R&S[PR,;R&S[S[P,R,;R&S[S[P,R,;R&#)rANrZrDrErr)r5rGrH)rIrJs"r=rK)SplinterForPreTrainingOutput.__annotate__5s   d ")!"##d*1#$!!D(/%&**+d29'(e''(4/6)r?r) rZr[r\r]r^rZrDrErrrr_r`)rJs@r=roro5s,&*D-1L+/J59M26J)r?roz Splinter Model for the recurring span selection task as done during the pretraining. The difference to the QA task is that we do not have a question, but multiple question tokens that replace the occurrences of recurring spans instead. cnaa]tRtRtoV3Rlt]]RV3RlRll44tV3RlRltRt Vt V;t #) SplinterForPreTrainingiLc<\SV`V4\V4Vn\ V4VnVP VnVP4R#rrJr9s&&r=r%SplinterForPreTraining.__init__TrNr?c<V^8dQhRS[PR,RS[PR,RS[PR,RS[PR,RS[PR,RS[PR,RS[PR,R S[PR,R S[S[,R S[S[,/ #rP)r5rkrFrrrHro)rIrJs"r=rK#SplinterForPreTraining.__annotate__`sn n <<$&n  t+n  t+ n llT) n ||d* n ))D0n ''$.n ",,t3n +,n  - -n r?c VfVeVe \R4hVfVf \R4hVfVPV4pVP!V3RVRVRVRV/V Bp V ^,p V P4wrpVP W4wppVP^4pVeVP ^4P V VV 4pV^V, \P!VP4P,,pV^V, \P!VP4P,,pRpVeVeVP^\^V ^, 44VP^\^V ^, 44\VPPR7pV!VP!V V,V 4VP!V V,44pV!VP!V V,V 4VP!V V,44pVV,^, p\#VVVV P$V P&R 7#) a input_ids (`torch.LongTensor` of shape `(batch_size, num_questions, 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_questions, 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) position_ids (`torch.LongTensor` of shape `batch_size, num_questions, 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_questions, 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. start_positions (`torch.LongTensor` of shape `(batch_size, num_questions)`, *optional*): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. end_positions (`torch.LongTensor` of shape `(batch_size, num_questions)`, *optional*): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence are not taken into account for computing the loss. question_positions (`torch.LongTensor` of shape `(batch_size, num_questions)`, *optional*): The positions of all question tokens. If given, start_logits and end_logits will be of shape `(batch_size, num_questions, sequence_length)`. If None, the first question token in each sequence in the batch will be the only one for which start_logits and end_logits are calculated and they will be of shape `(batch_size, sequence_length)`. NzCquestion_positions must be specified in order to calculate the lossz?question_positions must be specified when inputs_embeds is usedrhrCr8rDrWrY) TypeError_prepare_question_positionsrrQrKr:r7r5r^rOr_ramaxrr;r)rrorr)r:rBrhrCr8rDrQrRrSrxrdrrsequence_lengthrnrDrE num_questions attention_mask_for_each_questionrergrhris&&&&&&&&&, r=rWSplinterForPreTraining.forward^sRj  %/*E-Jcab b  'I,=]^ ^  '!%!A!A)!L --  ) * &  (    "!*+:+?+?+A( S#'#5#5o#Z j*//2  %/=/G/G/J/Q/QM?0 ,(1/O+OSXS^S^_k_q_qSrSvSv*vvL#q+K'Ku{{[e[k[kOlOpOp&ppJ  &=+D  " "1c!_q-@&A B  C?Q+>$? @ (T[[5M5MNH!!!*}"H%x/14J+%!!//))   r?cN<V^8dQhRS[PRS[P/#)rArBrEr)rIrJs"r=rKrws#U\\ellr?c\\P!WPP8H4wr#\P!V4p\ VP ^4VP ^48HR4\P!W"RR7p\P!VP ^4VPR7V, p\P!VP ^4VP43VPP\PVPR7pW7W&3&V#)rz?All samples in the batch must have at least one question token.left)siderrN)r5wherer;rLbincountrrQ searchsortedr6rPfullr{r)rS)r:rBrowsflat_positionsr} first_idxcolsr>s&& r=rz2SplinterForPreTraining._prepare_question_positionss${{9 8U8U+UVt,    q !Y^^A%6 6 M &&t? ||DIIaL= IJJ ^^A  1 1 3 4 KK $ $**##  !/$*r?rkrl) rZr[r\r]r%rrrWrzr_r`rarbs@@r=rsrsLs>n n n `r?rs)r)rHrsrr r)>r^collections.abcr dataclassesrr5rtorch.nnrrr activationsr masking_utilsrmodeling_layersr modeling_outputsr r r modeling_utilsr rprocessing_utilsr pytorch_utilsrutilsrrrrr utils.genericrutils.output_capturingrconfiguration_splinterr get_loggerrZloggerrjrr{r}rrrrrrrr r!r.rHrors__all__rr?r=rs$! %&!69ZZF&6jj752   H %--b%.3)BII3)n   . 299  RYY .D bii 2 io i iZ +Z Z z"))$#(RYY#(Ld #:d d N  7;7  7"N4NNb r?