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Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. rry)neintrjcumsumtype_aslong)r. input_idsrWmaskincremental_indicess r2"create_position_ids_from_input_ids;LayoutLMv3TextEmbeddings.create_position_ids_from_input_idssP ||K(,,.$||Da8@@F$N"'')K77r4cUR5SSnUSn[R"URS-X0R-S-[RUR S9nUR S5RU5$)zq We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. Nr6rdtypedevicer)r9rjrkrWrr unsqueezerm)r. inputs_embeds input_shapesequence_lengthrZs r2&create_position_ids_from_inputs_embeds?LayoutLMv3TextEmbeddings.create_position_ids_from_inputs_embedss~$((*3B/ %a.||   q /4D4D"Dq"HPUPZPZcpcwcw %%a(// < J": "66|D) &*&P&PQU&V#= ^^J/ \\*- r4) rdrhrtrWrnrcrurarqrrNNNNN) rKrLrMrNrOr!rrrrGrPrQrRs@r2rTrTUs8h.+48 =  ''r4rTcP^\rSrSrU4SjrSSjrSS\\4SjjrSr U=r $) LayoutLMv3SelfAttentionc$>[TU]5 URUR-S:wa7[ US5(d&[ SURSURS35eURUl[ URUR- 5UlURUR-Ul[R"URUR5Ul [R"URUR5Ul [R"URUR5Ul [R"UR5UlUR"UlUR$Ulg)Nrembedding_sizezThe hidden size (z6) is not a multiple of the number of attention heads ())r r!r,num_attention_headshasattr ValueErrorrattention_head_size all_head_sizer)Linearquerykeyvaluerfattention_probs_dropout_probrhhas_relative_attention_biashas_spatial_attention_biasrvs r2r! 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YYz* r4Nrr~rrrZrrBrrc TUb!UR5n U upURn OMUb$UR5SSn U upURn O&Ub[U5n URn O [S5eUcUbUc[R "U W 4U S9nUc$[R "W [RU S9nUc:[R "[[W 5S/-5[RU S9nURUUUUUS9n S=pS=nnUGb*[URSURR- 5[URS URR- 5nnURU5n[R "U URS 4[RU S9nUb[R "UU/S S 9nOUnURR"(dURR$(aURR$(a<UR'U [RU S 9nUb[R "UU/S S 9nOUn[R("S URS [RU S9R+U S 5nUcUbQ[R("S W S U S9R-S 5nUR/U 5n[R "UU/S S 9nOUnUcUb[R "W U/S S 9n OUn UR1U 5n UR3U 5n OURR"(dURR$(amURR$(aUnURR"(a5URR4SS2SW S 24nUR7U5nUnUR9USW R:S9nUR<"U 4UUUUUS.UD6nUR>n[AUS9$)a$ input_ids (`torch.LongTensor` of shape `(batch_size, token_sequence_length)`): Indices of input sequence tokens in the vocabulary. Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS] token. See `pixel_values` for `patch_sequence_length`. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) bbox (`torch.LongTensor` of shape `(batch_size, token_sequence_length, 4)`, *optional*): Bounding boxes of each input sequence tokens. Selected in the range `[0, config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1) format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the position of the lower right corner. Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS] token. See `pixel_values` for `patch_sequence_length`. token_type_ids (`torch.LongTensor` of shape `(batch_size, token_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. Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS] token. See `pixel_values` for `patch_sequence_length`. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `(batch_size, token_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]`. Note that `sequence_length = token_sequence_length + patch_sequence_length + 1` where `1` is for [CLS] token. See `pixel_values` for `patch_sequence_length`. [What are position IDs?](../glossary#position-ids) inputs_embeds (`torch.FloatTensor` of shape `(batch_size, token_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. Examples: ```python >>> from transformers import AutoProcessor, AutoModel >>> from datasets import load_dataset >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False) >>> model = AutoModel.from_pretrained("microsoft/layoutlmv3-base") >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train") >>> example = dataset[0] >>> image = example["image"] >>> words = example["tokens"] >>> boxes = example["bboxes"] >>> encoding = processor(image, words, boxes=boxes, return_tensors="pt") >>> outputs = model(**encoding) >>> last_hidden_states = outputs.last_hidden_state ```Nr6zEYou have to specify either input_ids or inputs_embeds or pixel_values)rrr)rr~rZrrr7rrry)rrr)r)r~rZrrErFr;)!r9rlenrrjonesrrtuplelistrDrr=r/r'rr}rrrrkrrrmrdrhrZ expand_asget_extended_attention_maskrrpr<r )r.rr~rrrZrrBrrr seq_lengthrembedding_output final_bboxfinal_position_idsrErFvisual_embeddingsvisual_attention_maskrVvisual_position_idsextended_attention_maskencoder_outputssequence_outputs r2rGLayoutLMv3Model.forwardmsZ  #..*K%0 "J%%F  &',,.s3K%0 "J"))F  %\*J!((Fde e  M$=%!&j*-Ev!V%!&[ SY!Z|{{5k):aS)@#A\bc##)-+ /  +/. %)) {  #,,,Q/$++2H2HHI,,,Q/$++2H2HHI&L!% 2 2< @ $)JJ.44Q78 SY% !)!&N&#Q#[#[\]#^L#/#6#6{#CL).LBU3V\])^&)<&$ (A#(99.>@Q-RXY#Z #4 #~~.>? #||,<=  [[ 4 4 8^8^{{55! {{66#;;A?OQ?O(>1Q1 ,,  +2%#   *;;-  r4) rdr]r/rhrDrprorlrnr^r9))ri)NNNNNNN)rKrLrMrNr!rvrzr`rrrrrrj LongTensor FloatTensorrrrr rGrPrQrRs@r2rYrYs>/0>. " .2(,3726042615k ##d*k %k ))D0 k ((4/ k &&- k ((4/k ''$.k +,k   k  k r4rYc6^\rSrSrSrSU4SjjrSrSrU=r$)LayoutLMv3ClassificationHeadizT Head for sentence-level classification tasks. Reference: RobertaClassificationHead c>[TU]5 X lU(a4[R"UR S-UR 5UlO0[R"UR UR 5UlURb URO URn[R"U5Ul [R"UR UR5Ul g)Nr) r r! pool_featurer)rr,rclassifier_dropoutrgrfrh num_labelsout_proj)r.r/rrr1s r2r!%LayoutLMv3ClassificationHead.__init__#s ( 6#5#5#96;M;MNDJ6#5#5v7I7IJDJ)/)B)B)NF % %TZTnTn zz"45  &"4"4f6G6GH r4cURU5nURU5n[R"U5nURU5nUR U5nU$rI)rhrrjtanhr)r.xs r2rG$LayoutLMv3ClassificationHead.forward0sI LLO JJqM JJqM LLO MM! r4)rrhrr)FrJrRs@r2rrs Ir4ra LayoutLMv3 Model with a token classification head on top (a linear layer on top of the final hidden states) e.g. for sequence labeling (information extraction) tasks such as [FUNSD](https://guillaumejaume.github.io/FUNSD/), [SROIE](https://rrc.cvc.uab.es/?ch=13), [CORD](https://github.com/clovaai/cord) and [Kleister-NDA](https://github.com/applicaai/kleister-nda). ) custom_introcj^\rSrSrU4SjrSrSr\\SS\ RS-S\ RS-S\ RS-S \ RS-S \ RS-S \ RS-S \ RS-S \ RS-S\ \ S\\-4Sjj55rSrU=r$) LayoutLMv3ForTokenClassificationi9cp>[TU]U5 URUl[U5Ul[ R "UR5UlURS:a1[ R"URUR5Ul O[USS9Ul UR5 g)N Fr)r r!rrYrQr)rfrgrhrr, classifierrrqrvs r2r!)LayoutLMv3ForTokenClassification.__init__Bs   ++)&1zz&"<"<=   r ! ii(:(:F>> from transformers import AutoProcessor, AutoModelForTokenClassification >>> from datasets import load_dataset >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False) >>> model = AutoModelForTokenClassification.from_pretrained("microsoft/layoutlmv3-base", num_labels=7) >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train") >>> example = dataset[0] >>> image = example["image"] >>> words = example["tokens"] >>> boxes = example["bboxes"] >>> word_labels = example["ner_tags"] >>> encoding = processor(image, words, boxes=boxes, word_labels=word_labels, return_tensors="pt") >>> outputs = model(**encoding) >>> loss = outputs.loss >>> logits = outputs.logits ```)r~rrrZrrBNr6rrlosslogitsrrT) rQr9rhrrr;rr rrT)r.rr~rrrZrrrBroutputsrrrrrloss_fcts r2rG(LayoutLMv3ForTokenClassification.forwardUsZ//   ))%'%      #..*K',,.s3K ^ !!*Q  ^4,,71  ')HFKKDOO[TU]U5 URUl[U5Ul[ USS9UlUR5 gNFr)r r!rrYrQr qa_outputsrqrvs r2r!'LayoutLMv3ForQuestionAnswering.__init__sA   ++)&16vER r4c6URR5$rIrrus r2rv3LayoutLMv3ForQuestionAnswering.get_input_embeddingsrr4c:URRU5 grIrrys r2rz3LayoutLMv3ForQuestionAnswering.set_input_embeddingsrr4NrrrrZrstart_positions end_positionsr~rBrrc UR"U4UUUUUU S.U D6n U Sn URU 5n U RSSS9upURS5R 5nURS5R 5nSnUbUb[ UR 55S:aURS5n[ UR 55S:aURS5nUR S5nURSU5nURSU5n[US9nU"X5nU"X5nUU-S- n[UUUU RU RS 9$) aG bbox (`torch.LongTensor` of shape `(batch_size, sequence_length, 4)`, *optional*): Bounding boxes of each input sequence tokens. Selected in the range `[0, config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1) format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the position of the lower right corner. Examples: ```python >>> from transformers import AutoProcessor, AutoModelForQuestionAnswering >>> from datasets import load_dataset >>> import torch >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False) >>> model = AutoModelForQuestionAnswering.from_pretrained("microsoft/layoutlmv3-base") >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train") >>> example = dataset[0] >>> image = example["image"] >>> question = "what's his name?" >>> words = example["tokens"] >>> boxes = example["bboxes"] >>> encoding = processor(image, question, words, boxes=boxes, return_tensors="pt") >>> start_positions = torch.tensor([1]) >>> end_positions = torch.tensor([3]) >>> outputs = model(**encoding, start_positions=start_positions, end_positions=end_positions) >>> loss = outputs.loss >>> start_scores = outputs.start_logits >>> end_scores = outputs.end_logits ```rrrZrr~rBrrr6ryN) ignore_indexr7)r start_logits end_logitsrrT) rQrsplitsqueezerrr9clamprr rrT)r.rrrrZrrrr~rBrrrrrr total_loss ignored_indexr start_lossend_losss r2rG&LayoutLMv3ForQuestionAnswering.forwards|`$(??  $ ))%'% $  $ "!*1#)<<r<#: #++B/::< ''+668   &=+D?'')*Q."1"9"9""==%%'(1, - 5 5b 9 (--a0M-33A}EO)//=AM']CH!,@J :H$x/14J+%!!//))   r4)rQrr) NNNNNNNNN)rKrLrMrNr!rvrzrrrjrrrrrr rGrPrQrRs@r2rrs364.2372604263715(,04W ##d*W ))D0W ((4/ W &&- W ((4/ W ))D0W ''$.W %W &&-W +,W  - -W W r4ra  LayoutLMv3 Model with a sequence classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for document image classification tasks such as the [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset. cj^\rSrSrU4SjrSrSr\\SS\ RS-S\ RS-S\ RS-S \ RS-S \ RS-S \ RS-S \ RS-S \ RS-S\ \ S\\-4Sjj55rSrU=r$)#LayoutLMv3ForSequenceClassificationic>[TU]U5 URUlXl[ U5Ul[ USS9UlUR5 gr) r r!rr/rYrQrrrqrvs r2r!,LayoutLMv3ForSequenceClassification.__init__sF   ++ )&16vER r4c6URR5$rIrrus r2rv8LayoutLMv3ForSequenceClassification.get_input_embeddings"rr4c:URRU5 grIrrys r2rz8LayoutLMv3ForSequenceClassification.set_input_embeddings%rr4NrrrrZrrr~rBrrc UR"U4UUUUUUS.U D6n U SSS2SSS24n URU 5n Sn UGbURRcURS:XaSURlOoURS:aNUR [ R:XdUR [ R:XaSURlOSURlURRS:XaI[5nURS:Xa&U"U R5UR55n OU"X5n OURRS:Xa=[5nU"U RSUR5URS55n O,URRS:Xa[5nU"X5n [U U U RU R S 9$) a bbox (`torch.LongTensor` of shape `(batch_size, sequence_length, 4)`, *optional*): Bounding boxes of each input sequence tokens. Selected in the range `[0, config.max_2d_position_embeddings-1]`. Each bounding box should be a normalized version in (x0, y0, x1, y1) format, where (x0, y0) corresponds to the position of the upper left corner in the bounding box, and (x1, y1) represents the position of the lower right corner. Examples: ```python >>> from transformers import AutoProcessor, AutoModelForSequenceClassification >>> from datasets import load_dataset >>> import torch >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False) >>> model = AutoModelForSequenceClassification.from_pretrained("microsoft/layoutlmv3-base") >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train") >>> example = dataset[0] >>> image = example["image"] >>> words = example["tokens"] >>> boxes = example["bboxes"] >>> encoding = processor(image, words, boxes=boxes, return_tensors="pt") >>> sequence_label = torch.tensor([1]) >>> outputs = model(**encoding, labels=sequence_label) >>> loss = outputs.loss >>> logits = outputs.logits ```rrNr regressionsingle_label_classificationmulti_label_classificationr6r)rQrr/ problem_typerrrjrrrrrr;rr rrT)r.rrrrZrrr~rBrrrrrrs r2rG+LayoutLMv3ForSequenceClassification.forward(sX$(??  $ ))%'% $  $ "!*Q1W-1  {{''/??a'/;DKK,__q(fllejj.HFLL\a\e\eLe/LDKK,/KDKK,{{''<7"9??a'#FNN$4fnn6FGD#F3D))-JJ+- B @&++b/R))-II,./'!//))   r4)rr/rQrr)rKrLrMrNr!rvrzrrrjrrrrrr rGrPrQrRs@r2rrs64.237260426*.(,04T ##d*T ))D0T ((4/ T &&- T ((4/ T   4'T %T &&-T +,T  ) )T T r4r)rrrrYrP)>rOr$rrjtorch.nnr)torch.nn.functional functionalr>rrrrr[ activationsrmodeling_layersr modeling_outputsr r r r modeling_utilsrprocessing_utilsr pytorch_utilsrutilsrrrr utils.genericrrutils.output_capturingrconfiguration_layoutlmv3r get_loggerrKloggerModulerrTrrrrrrrrPrYrrrr__all__rcr4r2rs  AA&!9 .&6 L56   H %  FsryyslV.biiV.t 299  ")) 6"0"Jn@ n@d RYY  ryy iii(A /A A H2996` '@` ` Fi %>i i Xf *Cf f R r4