9ib SSKJrJrJrJr SSKrSSKJs Jr SSKJr SSK J r SSK J r SSKJr SSKJr SSKJr SS KJrJrJrJr SS KJr \R6"\R8\R:S 9\R6"\R<\R:S 9\R6"\R>\R:S 9\R6"\R@\R:S 9"S S \55555r!\R6"\RD\RDS 9\R6"\R8\RDS 9\R6"\R<\RDS 9\R6"\R>\RDS 9\R6"\R@\RDS 9"SS\555555r#"SS\RH5r%g))AnyDictListOptionalN)nn)CrossEntropyLoss)ACT2FN)Heads) TorchHead)HEADS)'AttentionTokenClassificationModelOutputModelOutputBase OutputKeysTokenClassificationModelOutput)Tasks) module_namecL^\rSrSrSU4SjjrSS\4SjjrSrSrU=r $) LSTMCRFHead#c >[TU]XS9 Uce[R"US-U5Ul[ USS9Ulg)N hidden_size num_labelsT batch_first)super__init__rLinearffnCRFcrfselfrrkwargs __class__s d/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/modelscope/models/nlp/heads/crf_head.pyrLSTMCRFHead.__init__*sC [H%%%99[1_j9zt4inputsc LURUR5n[SUS9$)N)losslogits)r last_hidden_stater)r$r*attention_masklabel label_maskoffset_mappingr%r-s r'forwardLSTMCRFHead.forward0s+&223-  r)cURS5R5n[R"URSUR S9SSS24USS2S4:nUR RX5RS5nU$N)devicer sumlongtorcharangeshaper9r"decodesqueezer$r-r1seq_lensmaskpredictss r'r@LSTMCRFHead.decode>~>>"%**,||   Q ??$$(!G-/74/@A88??6088;r))r"r )dNNNNN __name__ __module__ __qualname____firstlineno__rrr3r@__static_attributes__ __classcell__r&s@r'rr#s-5 $#  '  r)rcH^\rSrSrU4SjrSS\4SjjrSrSrU=r $)TransformersCRFHeadGc ~>[TU]"SXS.UD6 [R"X5Ul[ USS9Ulg)NrTr)rrrrlinearr!r"r#s r'rTransformersCRFHead.__init__RsA  F# F>D Fii 8 zt4r)r*c URUR5nUbUnURS5R5n [R "U5n [ URS5HEn X{RXRS55RXS5U U SX2SS24'MG U n[SUURURS9$)Nr7r)r,r- hidden_states attentions)rWr.r;r<r= zeros_likeranger? masked_select unsqueezeviewr rZr[) r$r*r/r0r1r2r%r-rDmasked_lengths masked_logitsis r'r3TransformersCRFHead.forwardXsV556  !D!XXb\..0N!,,V4M4::a=)06 0G0G))"-1//3tN4Er/J)))1,.*#F6 ..((   r)cURS5R5n[R"URSUR S9SSS24USS2S4:nUR RX5RS5nU$r6r:rBs r'r@TransformersCRFHead.decodeqrGr))r"rWrIrJrQs@r'rSrSGs-5 $#  ' 2r)rSc,^\rSrSrSrSS\S\SS4U4SjjjrSSjrS\ 4S jr SS \ RS \ RS \\ RS \ S\ R4 SjjrSS \ RS \\ RS\\S\\S\\\\4 SjjrSS \ RS \\ RS \\ RSS4SjjrS \ RS \ RS \ RS\ R4SjrS \ RS \ RS\ R4SjrSS \ R,S \ RS\\S\\\4SjjrSS \ R,S \ RS\S\\S\\\\4 SjjrSrU=r$)r!zu^Conditional random field. This module implements a conditional random field [LMP01]_. The forward computation of this class computes the log likelihood of the given sequence of tags and emission score tensor. This class also has `~CRF.decode` method which finds the best tag sequence given an emission score tensor using `Viterbi algorithm`_. Args: num_tags: Number of tags. batch_first: Whether the first dimension corresponds to the size of a minibatch. Attributes: start_transitions (`~torch.nn.Parameter`): Start transition score tensor of size ``(num_tags,)``. end_transitions (`~torch.nn.Parameter`): End transition score tensor of size ``(num_tags,)``. transitions (`~torch.nn.Parameter`): Transition score tensor of size ``(num_tags, num_tags)``. .. [LMP01] Lafferty, J., McCallum, A., Pereira, F. (2001). "Conditional random fields: Probabilistic models for segmenting and labeling sequence data". *Proc. 18th International Conf. on Machine Learning*. Morgan Kaufmann. pp. 282–289. .. _Viterbi algorithm: https://en.wikipedia.org/wiki/Viterbi_algorithm num_tagsrreturnNc>US::a[SU35e[TU] 5 XlX l[ R "[R"U55Ul [ R "[R"U55Ul [ R "[R"X55Ul UR5 g)Nrzinvalid number of tags: ) ValueErrorrrrirr Parameterr=emptystart_transitionsend_transitions transitionsreset_parameters)r$rirr&s r'r CRF.__init__s q=7zBC C  &!#ekk(.C!D!||EKK,AB<< H(GH r)c[RRURSS5 [RRURSS5 [RRUR SS5 g)zInitialize the transition parameters. The parameters will be initialized randomly from a uniform distribution between -0.1 and 0.1. gg?N)rinituniform_rorprqr$s r'rrCRF.reset_parameterssX //s; --tS9 ))45r)cNURRSURS3$)Nz (num_tags=))r&rKrirws r'__repr__ CRF.__repr__s$..))**T]]O1EEr) emissionstagsrD reductioncUS;a[SU35eUc.[R"U[RURS9nUR [R:waUR 5nURXUS9 UR(a6URSS5nURSS5nURSS5nURXU5nURX5nXV- nUS:XaU$US:XaUR5$US :XaUR5$UR5UR5R5- $) a?Compute the conditional log likelihood of a sequence of tags given emission scores. Args: emissions (`~torch.Tensor`): Emission score tensor of size ``(seq_length, batch_size, num_tags)`` if ``batch_first`` is ``False``, ``(batch_size, seq_length, num_tags)`` otherwise. tags (`~torch.LongTensor`): Sequence of tags tensor of size ``(seq_length, batch_size)`` if ``batch_first`` is ``False``, ``(batch_size, seq_length)`` otherwise. mask (`~torch.ByteTensor`): Mask tensor of size ``(seq_length, batch_size)`` if ``batch_first`` is ``False``, ``(batch_size, seq_length)`` otherwise. reduction: Specifies the reduction to apply to the output: ``none|sum|mean|token_mean``. ``none``: no reduction will be applied. ``sum``: the output will be summed over batches. ``mean``: the output will be averaged over batches. ``token_mean``: the output will be averaged over tokens. Returns: `~torch.Tensor`: The log likelihood. This will have size ``(batch_size,)`` if reduction is ``none``, ``()`` otherwise. )noner;mean token_meanzinvalid reduction: dtyper9)r~rDrr8rr;r)rlr= ones_likeuint8r9rbyte _validater transpose_compute_score_compute_normalizerr;rfloat)r$r}r~rDr numerator denominatorllhs r'r3 CRF.forwards&. A A29+>? ? <??4u{{4;;OD :: $99;D y$7   !++Aq1I>>!Q'D>>!Q'D'' > ..y? %  J  779   88: wwy4::<++---r)nbestpad_tagcUcSnUc;[R"URSS[RURS9nUR [R:waUR 5nURXS9 UR(a$URSS5nURSS5nUS:Xa!URXU5RS5$URXX45$)aPFind the most likely tag sequence using Viterbi algorithm. Args: emissions (`~torch.Tensor`): Emission score tensor of size ``(seq_length, batch_size, num_tags)`` if ``batch_first`` is ``False``, ``(batch_size, seq_length, num_tags)`` otherwise. mask (`~torch.ByteTensor`): Mask tensor of size ``(seq_length, batch_size)`` if ``batch_first`` is ``False``, ``(batch_size, seq_length)`` otherwise. nbest (`int`): Number of most probable paths for each sequence pad_tag (`int`): Tag at padded positions. Often input varies in length and the length will be padded to the maximum length in the batch. Tags at the padded positions will be assigned with a padding tag, i.e. `pad_tag` Returns: A PyTorch tensor of the best tag sequence for each batch of shape (nbest, batch_size, seq_length) Nr8rr)rDr) r=onesr?rr9rrrrr_viterbi_decoder__viterbi_decode_nbest)r$r}rDrrs r'r@ CRF.decodes( =E <::#kk '')D :: $99;D y,   !++Aq1I>>!Q'D A:'' AKKAN N)))5JJr)c>UR5S:wa[SUR535eURS5UR:wa*[SURSURS535eUbWURSSUR:wa:[S[ URSS5S[ UR535eUbURSSUR:wa:[S[ URSS5S[ UR535eUR (+=(a US R5nUR =(a USS2S 4R5nU(dU(d [S 5eggg) Nz(emissions must have dimension of 3, got rz(expected last dimension of emissions is z, got z?the first two dimensions of emissions and tags must match, got z and z?the first two dimensions of emissions and mask must match, got rz)mask of the first timestep must all be on)dimrlsizerir?tuplerall)r$r}r~rD no_empty_seqno_empty_seq_bfs r'r CRF._validates ==?a :9==?:KLN N >>!  -:4==/J ~~a()+, ,  r"djj0  !!456eE$**>3CO !LMM)8< r)cURupEUR5nURUSnXaS[R"U5US4- n[ SU5HGnX`R X'S- X'4X7-- nXaU[R"U5X'4X7-- nMI UR5RSS9S- nX([R"U54n X`RU - nU$)Nrr8r) r?rror=r>r]rqr<r;rp) r$r}r~rD seq_length batch_sizescorercseq_ends last_tagss r'rCRF._compute_score"s "& zz|&&tAw/ 1ell:6Q?@@q*%A %%dq5k47&:;dgE EE q%,,z":DGCDtwN NE&99;??q?)A-5<< #;;<  %%i00 r)cURS5nURUS-n[SU5HunURS5nXRS5nX`R-U-n[ R "USS9n[ R"X%RS5X5nMw X@R- n[ R "USS9$)Nrr8rr) rror]r_rqr= logsumexpwhererp) r$r}rDrrrcbroadcast_scorebroadcast_emissions next_scores r'rCRF._compute_normalizerBs^^A& &&15q*%A$ooa0O#,,"8"8"; )+;+;;>QQJ ;JKK 1 1! 4jHE1&8 %%%u!,,r)c UcSnURnURupVURUS-n[R"XVUR 4[R US9n[R"X`R 4[R US9n [R"XV4U[R US9n [SU5Hn URS5n XRS5n XR-U -nURSS9up[R"X+RS5R5UU5n[R"X+RS5R5UU 5nXU S- 'M XpR-nURSS9unnUR 5RSS9S- nUR!SS5R#5nUR%SUR'SSS5R)SSUR 5UR'SSS5R)SSUR 55 UR!SS5R#5n[R"XV4[R US9n[R"US[R US9n[US- SS5H<n[R*"UUSU5nUR,R'U5UU'M> [R"UR5UU 5R!SS5$)Nrrr8rrr7)r9r?ror=zerosrir<fullr]r_rqmaxrboolrpr;r contiguousscatter_r`expandgatherdata)r$r}rDrr9rrr history_idxoor_idxoor_tagrcrbroadcast_emissionrindices end_score_end_tagr best_tags_arr best_tagsidxs r'rCRF._viterbi_decodeps ?G!!!% &&15kk:4=="I(- )/1 ++z==9$)JJ%+-**j5$#(::$*,q*%A$ooa0O"+!7!7!:  )+;+;;>PPJ#-..Q."7 J KK 1 1" 5 : : ")+G!(A 5&<000 ]]q]) 799;??q?)A-"++Aq1<<>  MM"a # * *2q$-- @ LLQ " ) )"a ? A"++Aq1<<>  Z$<*/**+13 KK AUZZO aR0C [%5q)DI!*!4!4Z!@M# 1{{499; w?II!QOOr)c  UcSnURnURupgURUS-n[R"XgUR U4[R US9n [R"XpR U4[R US9n [R"XgU4U[R US9n [SU5GHn U S:Xa6URS5n XRS5nXR-U-nOSURS5n XRS5RS5nXRRS5-U-nURUSUR 5RUSS9unnU S:Xa'URS5RSSU5nUU-nURSS5nURSS5n[R"X,RS5R!5RS5X5n[R"X,RS5RS5R!5UU 5nUXS- 'GM XR"RS5-nURUS5RUSS9unnUR 5R%SS9S- nU RSS5R'5n U R)SURSSSS5RSSUR U5URSSSU5RSSUR U55 U RSS5R'5n [R"XgU4[R US9n[R*"U[R US9RSS5RUS5n[US- SS5HPn[R,"U URUS5SU5nUR.RUS5U-UU'MR [R"URS5UU 5R1SSS5$)Nrrr8r7rr)r9r?ror=rrir<rr]r_rqr`topkrrrrrpr;rrr>rrpermute)r$r}rDrrr9rrrrrrrcrrrrrrrrrrrs r'rCRF._viterbi_decode_nbests, ?G!!!% &&15kk T]]E :** ++z==%@$)JJ%+-**je<$#(::$*,q*%AAv"'//""5%.\%;%;A%>",/?/??BTT "'//""5%.\%;%;A%>%H%H%K",/?/?/I/I0+, #-//*b26--#AAE6;BFBD JAv+222r5A!E/$--a3J''1-G KK 1 1" 5 : : < F Fr J *3Ekk$'"3"3B"7"A"A""E"J"J"L")74G!(KA E&J00::2>> ^^J388A8F 799;??q?)A-"++Aq1<<>  MM"aA & - -b!T]]E J LLQ5 ) 0 0Q u M O"++Aq1<<>  ZU$C*/**+13 LLejjH$q"+ffZ&< aR0C [%5%:%::r%JA%.0I!*!4!4Z!D!MM# 1 {{4>>"-}"$$+GAq!$4 5r))rrprirorq)F)rjN)Nr)NNN)NN)N)rKrLrMrN__doc__intrrrrstrr{r=Tensor LongTensorr ByteTensorr3rr@rrr FloatTensorrrrOrPrQs@r'r!r!zs`.    4  D   6F#F 48!' 1. <<1.&&1.u//01. 1.-2LL 1.j37&*(, %K,,%Ke../%Ksm%K! %K26d49o1F %KR6:59N"\\N !1!12N!!1!12N?CN< Ers$-, %+%,+@@,u11u~~N ""@u..ENNKu++H)IL@O > u'<'<> E,A,AC ""0E0EG )>)>@u++9N9NO')'P@GC>'Ty5"))y5r)