j*zSSKrSSKrSSKJr SSKJs Jr SSKJ r SSK J r J r SSK Jr SrSrS!SjrSrSS jrS rS rS r"S S\5r\R2"5S"Sj5rSrSrSrSr"SS\R>5r S#S\!S\"S\RF4Sjjr$"SS5r%S\&4Sjr'S\(4S jr)g)$N)Image) BasicBlockconv1x1)box_areacjSn[U5HnUS:wa O US- nM US:XaUS4$USU*nX14$)z Remove the trailing zeros from the provided input Parameters ---------- list: List of integers Predicted sequence Returns ------- list: List of integers The part of the input before the zero padding rN)reversed)seqpad_lenx un_paddeds g/home/mande/repo/quber/.venv/lib/python3.13/site-packages/docling_ibm_models/tableformer/utils/utils.pyremove_paddingr sRG c] 6 1 !|Av IgXI  c0[R"USS9nU$)a  Convert probabilities to predictions Parameters ---------- probabilities : Tensor[batch_size, vocab_size, seq_len] All log probabilities coming out at the last stage of the decoder Returns ------- predictions : tensor [batch_size, output_sequence_length] The prediceted trags rdim)torchargmax) probabilitiesmax_idxs rprobabilities_to_predictionsr%s ll=a0G NrcXR5nXR5nSnSnUbX#n[[U5[U55n[SR UR US5UR 555 [SR UR US5UR 555 g)a For the Tags, print the target and predicted tensors for the specified batch index We expect to have the batch size as the first dimension. Only the specified batch is extractred and the remaining dimenions are flattened. The results are printed as 2 lists with the target on top and the predictions below underlined Parameters --------- target : tensor [batch_size, output_sequence_length] The ground truth tags predictions : tensor [batch_size, output_sequence_length] The prediceted trags filenames : list of string The actual filename that provides the data batch_idx : int Which index in the batch dimension will be printed targetpredictN{}: {} )flattenmaxlenprintformatljusttolist) r predictions filenames batch_idx target_flatpredictions_flat target_label predict_label label_lens rprint_target_predictr-9s,#++-K"-557LM + C %s='9:I (//,,,YP>P>R ST  ++Is;=M=T=T=VWrc[R"U5n[R"U5nUR SSS5nSSS5 U$!,(df  W$=f)z Load an image from the disk as a numpy array Parameters ---------- full_fn : string The full path filename of the image Results ------- img : numpy array: (channels, width, height) The loaded image as a numpy array rrN)ropennpasarray transpose)full_fnfimgs r load_imager7\sM G jjmmmAq!$  J   Js *A  Ac/n[R"[SSU5[R"S55nUR [ SSX55 UR [ SSS55 [R"U6$)Nir)nn Sequentialr BatchNorm2dappendr)stridelayers downsamples r resnet_blockrApsh FS&! sJ MM*S#v:; MM*S#q)* ==& !!rc[U[R5(aUR5$[ SU55$)zH Wraps hidden states in new Tensors, to detach them from their history. c38# UHn[U5v M g7fN)repackage_hidden).0vs r #repackage_hidden..s4!Q%a((!s) isinstancerTensordetachtuple)hs rrErE{s2!U\\""xxz4!444rc6URS5nURUSSS5upEURURSS5R U55nURS5R 5R 5nUR5SU- -$)z Computes top-k accuracy, from predicted and true labels. :param scores: scores from the model :param targets: true labels :param k: k in top-k accuracy :return: top-k accuracy rrTY@)sizetopkeqview expand_asfloatsumitem)scorestargetsk batch_size_indcorrect correct_totals raccuracyrbsaJ [[AtT *FAffW\\"a(22378GLL$**,002M    5:#5 66rcURHEnUSH9nURcMURRRU*U5 M; MG g)z Clips gradients computed during backpropagation to avoid explosion of gradients. :param optimizer: optimizer with the gradients to be clipped :param grad_clip: clip value paramsN) param_groupsgraddataclamp_) optimizer grad_clipgroupparams r clip_gradientrmsF''8_Ezz% && z9=%(rc.\rSrSrSrSrSrSSjrSrg) AverageMeterzB Keeps track of most recent, average, sum, and count of a metric. c$UR5 grD)resetselfs r__init__AverageMeter.__init__s  rc<SUlSUlSUlSUlgNr)valavgrXcountrss rrrAverageMeter.resets rcXlU=RX-- slU=RU- slURUR- UlgrD)ryrXr{rz)rtryns rupdateAverageMeter.updates8 CG a 88djj(r)rzr{rXryNr) __name__ __module__ __qualname____firstlineno____doc__rurrr__static_attributes__rrroros )rrorcUR5S:Xa [R"/URS9/$[ U5nUR S5nUR USSS5upVUR5nURURSS5RU55n/nUHWn USU RS5R5RS5n URU RSU- 55 MY U$)z6Computes the precision@k for the specified values of krdevicerTrPNrQ)numelrzerosrrrRrStrTrUrVrWrXr=mul_) outputrrSmaxkr]r^predr`resr\ correct_ks r bip_accuracyrs||~ Bv}}566 t9DQJkk$4.GA 668Dggfkk!R(22489G C BQK$$R(..044Q7  9>>%*"456 JrcURS5upp4USU-- USU-- USU--USU--/n[R"USS9$)NrPg?runbindrstack)r x_cy_cwrNbs rbox_cxcywh_to_xyxyrsRXXb\NCa a-3q=C#'MS37]LA ;;qb !!rc|URS5upp4X-S- X$-S- X1- XB- /n[R"USS9$)NrPr/rr)r x0y0x1y1rs rbox_xyxy_to_cxcywhrsBXXb\NBB 'QA 27[TU]5 X@lU/US- -n[R"S[ U/U-XS/-555Ulg)Nrc3R# UHup[R"X5v M g7frD)r:Linear)rFr~r\s rrHMLP.__init__..s $ (NBIIaOO(Ns%')superru num_layersr: ModuleListzipr?)rt input_dim hidden_dim output_dimrrN __class__s rru MLP.__init__sR $ LJN +mm$ (+YK!OQ=M(N$   rc[UR5H;up#X RS- :a[R"U"U55OU"U5nM= U$)Nr) enumerater?rFrelu)rtr ilayers rforward MLP.forward sB!$++.HA$%!(;$;uQx qA/r)r?r) rrrrrrurr __classcell__)rs@rrrs> rrszrreturnc*[R"[R"X55S:HRSS5nUR 5R US:H[ S55R US:H[ S55R US9nU$)z/Generate the attention mask for causal decodingrrz-infgr)rtriuonesr3rW masked_fillto)rrmasks rgenerate_square_subsequent_maskrst JJuzz") *a / : :1a @D TQYf . TQYc +bb  Krc0\rSrSrSrSSS\4SjrSrSrg ) EarlyStoppingizEarly stops the training if validation loss doesn't improve after a given patience. Source from: https://github.com/Bjarten/early-stopping-pytorch r/FrcXlX lSUlSUlSUl[ R UlX0lX@l g)a Args: patience (int): How long to wait after last time validation loss improved. Default: 7 verbose (bool): If True, prints a message for each validation loss improvement. Default: False delta (float): Minimum change in the monitored quantity to qualify as an improvement. Default: 0 path (str): Path for the checkpoint to be saved to. Default: 'checkpoint.pt' trace_func (function): trace print function. Default: print rNF) _patience_verbose_counter _best_score _early_stopr1Inf _val_loss_min_delta _trace_func)rtpatienceverbosedelta trace_funcs rruEarlyStopping.__init__!s<"   VV %rcRU*nSnURcGX lSnUR(a&SURSSUSS3nURU5 XlU$X RUR-:aeU=R S- slURSUR SUR 35 UR UR :a SUlS nU$X lSnS UlUR(a&SURSSUSS3nURU5 XlU$) NTzValidation loss decreased (z.6fz --> z).rzEarlyStopping counter: z out of Fr)rrrrrrrr)rtval_lossscoresave_checkpointverbs r__call__EarlyStopping.__call__8s6     #$ "O}}4T5G5G4LERZ[^Q__ab  &!) "!%% 3 3 MMQ M   )$--@PQ }}.#' "' % "ODM}}4T5G5G4LERZ[^Q__ab  &!) r)rrrrrrrrN) rrrrrr!rurrrrrrrs!"5e&.rrmc[U5S:Xag[[U55n[U[5=(a UR 5nU(a4[ UR5Vs/sHn[U5PM sn5nO)[ UR5Vs/sHo3PM sn5nUH7nU(aU[ U5nOXn[SRX555 M9 gs snfs snf)z6 Print dict elements in separate lines sorted by keys rNr) r nextiterrJstr isnumericsortedkeysintr!r")r first_key is_numericr\rrGs r print_dictrUs 1v{T!W IIs+E 0C0C0EJqvvx0x!s1vx01!&&(+(Qq(+,  #a& AA hooa#$  1+s *C. C3lstc [U5H[up[U[5(a'[SR U[ U5U55 MA[SR X55 M] g)z' Print list elements in separate lines z {}: ({}) - {}rN)rrJlistr!r"r )rrelms r print_listrlsMC. c4 /((CHc: ; (//!) * !rrx)r)cpu)*numpyr1rtorch.nnr:torch.nn.functional functionalrPILrtorchvision.models.resnetrrtorchvision.ops.boxesrrrr-r7rArErbrmobjectrono_gradrrrrrModulerrrrKrrdictrrrrrrrs 9*6( F("57" >)6)*$" "  '0"))"SU\\66r%$%.+D+r