9iSrSSKrSSKrSSKrSSKJr SSKJr SSKJr SSK J r SSK J r SSK JrJr SS KJr SS KJr SS KJr SS KJr SS KJr SSKJrJrJr SSKJr \"5r Sr!S*Sjr""SS\RF5r$"SS\RF5r%"SS\RF5r&"SS\RF5r'"SS\RF5r("SS\RF5r)"SS \RF5r*"S!S"\RF5r+"S#S$\RF5r,"S%S&\\ 5r-\R\"\R^\ R`S'9"S(S)\-55r1g)+zPyTorch XLM-RoBERTa model.N)version)nn)ACT2FN)PreTrainedModel)Models)Model TorchModel)MODELS)AttentionBackboneModelOutput)Tasks) get_logger)parse_labels_in_order)apply_chunking_to_forward find_pruneable_heads_and_indicesprune_linear_layer)XLMRobertaConfigrcURU5R5n[R"USS9R U5U-U-nUR 5U-$)z Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: x: torch.Tensor x: Returns: torch.Tensor rdim)neinttorchcumsumtype_aslong) input_ids padding_idxpast_key_values_lengthmaskincremental_indicess j/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/modelscope/models/nlp/xlm_roberta/backbone.py"create_position_ids_from_input_idsr#-s[ << $ ( ( *D <<!4<[TU]5 [R"URUR UR S9Ul[R"URUR 5Ul [R"URUR 5Ul [R"UR URS9Ul [R"UR5Ul[#USS5UlUR'S[(R*"UR5R-S55 UR'S[(R."UR0R35[(R4S9S S 9 UR Ul[R"URUR UR6S9Ul g) N)repsposition_embedding_typeabsolute position_ids)rtoken_type_idsdtypeF) persistent)super__init__r Embedding vocab_size hidden_size pad_token_idword_embeddingsmax_position_embeddingsposition_embeddingstype_vocab_sizetoken_type_embeddings LayerNormlayer_norm_epsDropouthidden_dropout_probdropoutgetattrr+register_bufferrarangeexpandzerosr-sizerrselfconfig __class__s r"r4XLMRobertaEmbeddings.__init__Gsy !||      ++ -$&<<0N0N060B0B$D %'\\&2H2H282D2D&F"    F$9$9;zz&"<"<= '.v/H/9(;$   LL77 8 ? ? H J   KK))..0 C   "..#%<<  * *   (($* r$cUc+Ub[XRU5nOURU5nUbUR5nOUR5SSnUSnUcv[ US5(a-UR SS2SU24nUR USU5n U nO8[R"U[RURRS9nUcURU5nURU5n XJ-n URS:XaURU5n X- n UR!U 5n UR#U 5n U $)Nr.rr/rr1devicer,)r#r&create_position_ids_from_inputs_embedsrHhasattrr/rFrrGrr-rPr9r=r+r;r>rB) rJrr/r- inputs_embedsr input_shape seq_lengthbuffered_token_type_ids buffered_token_type_ids_expandedr= embeddingsr;s r"forwardXLMRobertaEmbeddings.forwardjs]  $A//1G I  $JJ! #   #..*K',,.s3K ^  !t-..*.*=*=a*n*M'3J3Q3QNJ400!A!&**,,33"5   00;M $ : :> J":  ' ': 5"&":":<"H   -J^^J/ \\*- r$cUR5SSnUSn[R"URS-X0R-S-[RUR S9nUR S5RU5$)z We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. Args: inputs_embeds: torch.Tensor Returns: torch.Tensor Nr.rrOr)rHrrErrrP unsqueezerF)rJrSrTsequence_lengthr-s r"rQ;XLMRobertaEmbeddings.create_position_ids_from_inputs_embedss}$((*3B/ %a.||   q .. . 2** '' ) %%a(// <rBrr+r;r=r9)NNNNr) __name__ __module__ __qualname____firstlineno____doc__r4rYrQ__static_attributes__ __classcell__rLs@r"r&r&As/ !*H#!"'( 0d==r$r&c^\rSrSrS U4SjjrS\R S\R 4SjrS S\R 4SjjrSr U=r $) XLMRobertaSelfAttentionc>[TU]5 URUR-S:wa7[ US5(d&[ SURSURS35eURUl[ URUR- 5UlURUR-Ul[R"URUR5Ul [R"URUR5Ul [R"URUR5Ul [R"UR5UlU=(d [#USS5UlUR$S:XdUR$S :XaGUR&Ul[R("S UR&-S - UR5UlUR,Ulg) Nrembedding_sizezThe hidden size (z6) is not a multiple of the number of attention heads ()r+r, relative_keyrelative_key_queryr)r3r4r7num_attention_headsrR ValueErrorrattention_head_size all_head_sizerLinearquerykeyvaluer@attention_probs_dropout_probrBrCr+r:r5distance_embedding is_decoderrJrKr+rLs r"r4 XLMRobertaSelfAttention.__init__s     : : :a ?(I*I*#F$6$6#78 445Q89 9$*#=#= #&v'9'9)/)C)C(D$E !558P8PPYYv1143E3EF 99V//1C1CDYYv1143E3EF zz&"E"EF '>(;' -zC;$  ' '> 9T=Y=Y]q=q+1+I+ID (&(llF222Q6(('*D #!++r$xreturncUR5SSURUR4-nURU5nUR SSSS5$)Nr.rror)rHrprrviewpermute)rJr} new_x_shapes r"transpose_for_scores,XLMRobertaSelfAttention.transpose_for_scoressSffhsmt'?'?'+'?'?'AA FF; yyAq!$$r$ hidden_statescXURU5nUSLn U (aUbUSn USn UnGOU (aCURURU55n URURU55n UnOUbuURURU55n URURU55n [R "USU /SS9n [R "USU /SS9n O@URURU55n URURU55n URU5n USLn UR (aX4n[R"U U RSS55nURS:XdURS:XGaU RSU RSnnU (aB[R"US- [RURS 9RSS5nO>[R"U[RURS 9RSS5n[R"U[RURS 9RSS5nUU- nUR!UUR"-S- 5nUR%U R&S 9nURS:Xa[R("S U U5nUU-nOHURS:Xa8[R("S U U5n[R("S U U5nUU-U-nU[*R,"UR.5- nUbX-n[0R2R5USS9nUR7U5nUbUU-n[R"UU 5nUR9SSSS 5R;5nUR=5SSUR>4-nURU5nU(aUU4OU4nUR (aUU4-nU$)Nrrrorr.rmrnrOr0zbhld,lrd->bhlrzbhrd,lrd->bhlrr) rurrvrwrcatrzmatmul transposer+shapetensorrrPrrEryr:tor1einsummathsqrtrrr functionalsoftmaxrBr contiguousrHrs)rJrattention_mask head_maskencoder_hidden_statesencoder_attention_maskpast_key_valueoutput_attentionsmixed_query_layeris_cross_attention key_layer value_layer query_layer use_cacheattention_scores query_length key_lengthposition_ids_lposition_ids_rdistancepositional_embeddingrelative_position_scoresrelative_position_scores_queryrelative_position_scores_keyattention_probs context_layernew_context_layer_shapeoutputss r"rYXLMRobertaSelfAttention.forwards(!JJ}5 3$> ."<&q)I(+K3N 11./1I33 013K3N  '11$((=2IJI33DJJ}4MNK >!#4i"@aHI))^A%6 $D!LK11$((=2IJI33DJJ}4MNK//0AB "$. ??(5N!<< (1(;(;B(CE  ' '> 9T=Y=Y]q=q'2'8'8';Y__Q=O*L!&N**(//"126b! "' **(//"126b!#\\%**$++--1T!R[ &6H#'#:#:4777!;$= #7#:#:!''$;$) ++~=+0<<$k3G,I(#36N#N --1EE16$k3G2I./4||$i1E0G,#36T#TWs#s +dii  $ $/&&  %/@ --//0@b/I,,7  - 9O _kB %--aAq9DDF "/"4"4"6s";   ?"#"%**+BC (9!"$?L>O  ?? 22Gr$) rsrrryrBrzrvr:rpr+rurwNNNNNNF) r_r`rarbr4rTensorrrYrdrerfs@r"rhrhsP,8%ell%u||%"#t||ttr$rhcz^\rSrSrU4SjrS\R S\R S\R 4SjrSrU=r $)XLMRobertaSelfOutputiMc(>[TU]5 [R"URUR5Ul[R "URURS9Ul[R"UR5Ul gNr)) r3r4rrtr7denser>r?r@rArBrIs r"r4XLMRobertaSelfOutput.__init__Osc YYv1163E3EF    F$9$9;zz&"<"<= r$r input_tensorr~cpURU5nURU5nURX-5nU$rrrBr>rJrrs r"rYXLMRobertaSelfOutput.forwardV5 =1  ]3 }'CD r$r>rrB r_r`rarbr4rrrYrdrerfs@r"rrM5>U\\#ll/4||r$rcd^\rSrSrSU4SjjrSrSS\R4SjjrSr U=r $) XLMRobertaAttentioni_c|>[TU]5 [XS9Ul[ U5Ul[ 5Ulg)Nr+)r3r4rhrJroutputset pruned_headsr{s r"r4XLMRobertaAttention.__init__as4 + E *62 Er$c6[U5S:Xag[XRRURRUR 5up[ URRU5URl[ URRU5URl[ URRU5URl [ URRUSS9URl URR[U5- URlURRURR-URl UR RU5Ulg)Nrrr)lenrrJrprrrrrurvrwrrrsunion)rJheadsindexs r" prune_headsXLMRobertaAttention.prune_headshs u:? 7 9900 II ) )4+<+<>  -TYY__eD *499==%@ ,TYY__eD .t{{/@/@%QO )- (E(E I) %"&))"?"?$))B_B_"_  --33E:r$rc pURUUUUUUU5nURUSU5n U 4USS-n U $)Nrr)rJr) rJrrrrrrr self_outputsattention_outputrs r"rYXLMRobertaAttention.forward{s\yy    ! "    ;;|A F##AB'(r$)rrrJrr) r_r`rarbr4rrrrYrdrerfs@r"rr_s7";,"#||r$rcb^\rSrSrU4SjrS\R S\R 4SjrSrU=r $)XLMRobertaIntermediateic>[TU]5 [R"URUR 5Ul[UR[5(a[URUl gURUl gr) r3r4rrtr7intermediate_sizer isinstance hidden_actstrrintermediate_act_fnrIs r"r4XLMRobertaIntermediate.__init__s` YYv1163K3KL f'' - -'-f.?.?'@D $'-'8'8D $r$rr~cJURU5nURU5nU$rrr)rJrs r"rYXLMRobertaIntermediate.forwards& =1 00? r$rrrfs@r"rrs(9U\\ellr$rcz^\rSrSrU4SjrS\R S\R S\R 4SjrSrU=r $)XLMRobertaOutputic(>[TU]5 [R"URUR 5Ul[R"UR URS9Ul[R"UR5Ul gr) r3r4rrtrr7rr>r?r@rArBrIs r"r4XLMRobertaOutput.__init__sc YYv779K9KL    F$9$9;zz&"<"<= r$rrr~cpURU5nURU5nURX-5nU$rrrs r"rYXLMRobertaOutput.forwardrr$rrrfs@r"rrrr$rc`^\rSrSrU4SjrSS\R 4SjjrSrSr U=r $)XLMRobertaLayerict>[TU]5 URUlSUl[ U5UlUR UlURUlUR(a.UR (d[US35e[ USS9Ul [U5Ul [U5Ul g)Nrz> should be used as a decoder model if cross attention is addedr,r)r3r4chunk_size_feed_forward seq_len_dimr attentionrzadd_cross_attentionrqcrossattentionr intermediaterrrIs r"r4XLMRobertaLayer.__init__s '-'E'E$,V4 ++#)#=#=  # #?? fZ[#6 #[TU]5 Xl[R"[ UR 5Vs/sHn[U5PM sn5UlSUl gs snf)NF) r3r4rKr ModuleListrangenum_hidden_layersrlayergradient_checkpointing)rJrK_rLs r"r4XLMRobertaEncoder.__init__sV  ]].3F4L4L.M N.M_V $.M NP &+# OsA&rc ,^^U (aSOSn T(aSOSn T(aURR(aSOSn U(aSOSn[UR5HunnU (aX4-n UbX?OSnUbUUOSmUR(akUR (aZU(a[ RS5 SnUU4Sjn[RRRU"U5UUUUU5nOU"UUUUUTT5nUSnU(a UUS4- nT(dMU US4-n URR(dMU US4-n M U (aX4-n U (d[S UUU U U 455$[UUU U U S 9$) NzZ`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...Fc>^UUU4SjnU$)Nc>T"/UQTPTP76$rr )inputsmodulerrs r"custom_forwardPXLMRobertaEncoder.forward..create_custom_forward..custom_forwardEs&% 9v 9~ 9&7 99r$r )r rrrs` r"create_custom_forward8XLMRobertaEncoder.forward..create_custom_forwardCs9*)r$rr.rroc30# UH nUcMUv M g7frr ).0vs r" ,XLMRobertaEncoder.forward..ls" %q  %s )last_hidden_statepast_key_valuesr attentionscross_attentions) rKr enumeraterrtrainingloggerwarningrutils checkpointtupler )rJrrrrrrrroutput_hidden_states return_dictall_hidden_statesall_self_attentionsall_cross_attentionsnext_decoder_cachei layer_modulelayer_head_maskr layer_outputsrs ` @r"rYXLMRobertaEncoder.forward s#7BD$5b4 4;;#B#B HL $-R$(4OA|#$58I$I!.7.CilO%1-7; **t}}NNt!&I*!& 6 6 A A),7!"#)* ! !-!"#)*"%! *!,M"}R'8&;;"  &9!!$=('(#;;222+?%a(C,,,(e 5j 14E E  "!#$ %   ,+.+*1   r$)rKrr) NNNNNNFFTrrfs@r"rrs>,"#"Y ||Y Y r$rcb^\rSrSrU4SjrS\R S\R 4SjrSrU=r $)XLMRobertaPooleri}c>[TU]5 [R"URUR5Ul[R "5Ulgr)r3r4rrtr7rTanh activationrIs r"r4XLMRobertaPooler.__init__s9 YYv1163E3EF '')r$rr~c\USS2S4nURU5nURU5nU$)Nr)rr1)rJrfirst_token_tensor pooled_outputs r"rYXLMRobertaPooler.forwards6+1a40 #56  6 r$)r1rrrfs@r"r.r.}s($ U\\ellr$r.c\^\rSrSrSr\rSrSr/r Sr S Sjr Sr \ U4Sj5rS rU=r$) XLMRobertaPreTrainedModelizz An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. robertaTc[U[R5(akURRR SUR RS9 URb%URRR5 gg[U[R5(axURRR SUR RS9 URb2URRURR5 gg[U[R5(aJURRR5 URRRS5 gg)zInitialize the weightsg)meanstdNg?)rrrtweightdatanormal_rKinitializer_rangebiaszero_r5rr>fill_)rJr s r" _init_weights'XLMRobertaPreTrainedModel._init_weightss fbii ( ( MM   & &dkk;; ' ={{&   &&('  - - MM   & &dkk;; ' =!!- ""6#5#56<<>.  - - KK   " " $ MM   $ $S ).r$c<[U[5(aX!lggr)rrr)rJr rws r"_set_gradient_checkpointing5XLMRobertaPreTrainedModel._set_gradient_checkpointings f/ 0 0,1 ) 1r$cUR(dSURVs/sH nX2;dM UPM snUlURVs/sH nX2;dM UPM snUlggs snfs snf)z!Remove some keys from ignore listN)tie_word_embeddings_keys_to_ignore_on_save_keys_to_ignore_on_load_missing)rJrKdel_keys_to_ignoreks r"update_keys_to_ignore/XLMRobertaPreTrainedModel.update_keys_to_ignoresv)) 77,7a.7,D (  ??4?a.?4D 0 *,4s A&A& A+A+c >URSS5nURSS5n[X#40UD6nUc[S0UD6nU"U5nO[[U]"SSU0UD6nX&lU$)a Instantiate the model. Args: kwargs: Input args. model_dir: The model dir used to load the checkpoint and the label information. num_labels: An optional arg to tell the model how many classes to initialize. Method will call utils.parse_label_mapping if num_labels not supplied. If num_labels is not found, the model will use the default setting (2 classes). Returns: The loaded model, which is initialized by transformers.PreTrainedModel.from_pretrained model_dirNcfgpretrained_model_name_or_pathr )poprrr3rfrom_pretrainedrR)clskwargsrRrS model_argsrKmodelrLs r" _instantiate&XLMRobertaPreTrainedModel._instantiates~JJ{D1 jj%*9DVD  %3 3FKE%5G.7G;EGE# r$)rLrK)F)r_r`rarbrcr config_classbase_model_prefixsupports_gradient_checkpointing_no_split_modulesrDrGrO classmethodr[rdrerfs@r"r8r8sD $L!&*#*$2 r$r8) group_key module_namecl^\rSrSrSrS/rS U4SjjrSrSrSr S Sjr S r U=r $) XLMRobertaModelia The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in *Attention is all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. .. _*Attention is all you need*: https://arxiv.org/abs/1706.03762 r-c>[TU]U5 Xl[U5Ul[ U5UlU(a [U5OSUlUR5 gr) r3r4rKr&rXrencoderr.pooler post_init)rJrKadd_pooling_layerrLs r"r4XLMRobertaModel.__init__sJ   .v6(0 2C&v.  r$c.URR$rrXr9)rJs r"get_input_embeddings$XLMRobertaModel.get_input_embeddingss...r$c$XRlgrrm)rJrws r"set_input_embeddings$XLMRobertaModel.set_input_embeddingss */'r$cUR5H7up#URRURR U5 M9 g)z Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel N)itemsrgrrr)rJheads_to_prunerrs r" _prune_headsXLMRobertaModel._prune_headss< +002LE LL  u % / / ; ;E B3r$c U bU OURRn U bU OURRn U bU OURRn URR(aU bU OURR n OSn UbUb [ S5eUbUR5nO"UbUR5SSnO [ S5eUunnUb URO URnU bU SSRSOSnUc[R"UUU-4US9nUcs[URS 5(a4URRSS2SU24nURUU5nUnO$[R "U[R"US 9nUR%X.5nURR(aEUbBUR5unnnUU4nUc[R"UUS9nUR'U5nOSnUR)UURR*5nURUUUUUS 9nUR-UUUUUU U U U U S 9 nUSnUR.bUR/U5OSnU (d UU4US S-$[1UUUR2UR4UR6UR8S9$)a3 Args: input_ids (`torch.LongTensor` of shape `((batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`BertTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.FloatTensor` of shape `((batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) 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) head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `((batch_size, 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. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). NFzDYou cannot specify both input_ids and inputs_embeds at the same timer.z5You have to specify either input_ids or inputs_embedsrro)rPr/rO)rr-r/rSr) rrrrrrrr"r#r)r pooler_outputrrrr)rKrr"use_return_dictrzrrqrHrPrronesrRrXr/rFrGrget_extended_attention_maskinvert_attention_mask get_head_maskrrgrhr rrrr)rJrrr/r-rrSrrrrrr"r#rT batch_sizerUrPrrVrWextended_attention_maskencoder_batch_sizeencoder_sequence_lengthrencoder_hidden_shapeencoder_extended_attention_maskembedding_outputencoder_outputssequence_outputr5s r"rYXLMRobertaModel.forward sSv2C1N-TXT_T_TqTq$8$D KK , , &1%V  "#..*K  &',,.s3KGI I"- J%.%:!! @T@T"-"1!3A!6! :  7$6$;$= %-).(*9&.2.H.H&/( +/3 +&&y'+{{'D'DF  ??%)'#9 + ,, 2"7#B+/!5#'  *!, $ 7 =A #]3oab6II I+-'+;;)77&11,==   r$)rKrXrgrh)T) NNNNNNNNNNNNN) r_r`rarbrcrLr4rnrqrvrYrdrerfs@r"reres] (7&7# /0C"#!F F r$re)r)2rcrrtorch.utils.checkpoint packagingrrtransformers.activationsrtransformers.modeling_utilsrmodelscope.metainformodelscope.modelsrr modelscope.models.builderr modelscope.outputsr modelscope.utils.constantr modelscope.utils.loggerr modelscope.utils.nlp.utilsrmodelscope.utils.torch_utilsrrr configurationrr_CONFIG_FOR_DOCr#Moduler&rhrrrrrrr.r8register_modulebackbone xlm_robertarer r$r"rsA ! +7&/,;+.<>>, $ ?@4(l=299l=`XbiiXx299$2"))2l RYY "ryy$[bii[~b  b L ryy "H OHVnn&*<*<>t /t >t r$