qidZddlZddlmZddlZddlmZddlmZddlm Z ddl m Z dd l mZdd lmZmZmZdd lmZdd lmZdd lmZmZmZmZddlmZej<eZ eedGddeZ!GddejDZ#GddejDZ$GddejDZ%GddejDZ&GddejDZ'GddejDZ(Gd d!ejDZ)Gd"d#ejDZ*Gd$d%ejDZ+Gd&d'eZ,Gd(d)ejDZ-Gd*d+ejDZ.Gd,d-ejDZ/eGd.d/eZ0eGd0d1e0Z1eGd2d3e0Z2ed4Gd5d6e0Z3ed7Gd8d9e0Z4gd:Z5y);zPyTorch Bros model.N) dataclass)nn)CrossEntropyLoss)initialization)ACT2FN)GradientCheckpointingLayer)"BaseModelOutputWithCrossAttentions,BaseModelOutputWithPoolingAndCrossAttentionsTokenClassifierOutput)PreTrainedModel)apply_chunking_to_forward) ModelOutputauto_docstringcan_return_tuplelogging) BrosConfigz@ Base class for outputs of token classification models. ) custom_introceZdZUdZdZej dzed<dZej dzed<dZ ej dzed<dZ e ej dzed<dZ e ej dzed<y)BrosSpadeOutputa loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification loss. initial_token_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`): Classification scores for entity initial tokens (before SoftMax). subsequent_token_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, sequence_length+1)`): Classification scores for entity sequence tokens (before SoftMax). Nlossinitial_token_logitssubsequent_token_logits hidden_states attentions) __name__ __module__ __qualname____doc__rtorch FloatTensor__annotations__rrrtuplerX/opt/pipecat/venv/lib/python3.12/site-packages/transformers/models/bros/modeling_bros.pyrr(s~&*D%  d ")59%++d298<U..5<59M5**+d2926Je''(4/6r&rcVeZdZfdZdej dej fdZxZS)BrosPositionalEmbedding1Dct||j|_ddtjd|jd|jz zz }|j d|y)Nr'@inv_freq)super__init__dim_bbox_sinusoid_emb_1dr!arangeregister_buffer)selfconfigr. __class__s r'r0z"BrosPositionalEmbedding1D.__init__Bs^ (.(G(G% ell3(E(EsKdNkNkk l  Z2r&pos_seqreturnc|j}|\}}}|j|||d|jjddd|jdzz}t j |j |jgd}|S)Nrdim)sizeviewr.r1r!catsincos)r4r7seq_sizeb1b2b3 sinusoid_inppos_embs r'forwardz!BrosPositionalEmbedding1D.forwardLs<<> B||BB2T]]5G5G1aQUQnQnrsQs5tt ))\--/1A1A1CD"Mr&rrrr0r!TensorrI __classcell__r6s@r'r)r)?s#3u|| r&r)cVeZdZfdZdej dej fdZxZS)BrosPositionalEmbedding2Dct||j|_t||_t||_yN)r/r0dim_bboxr) x_pos_emb y_pos_embr4r5r6s r'r0z"BrosPositionalEmbedding2D.__init__Us1  26:26:r&bboxr8cg}t|jD]U}|dzdk(r&|j|j|d|f1|j|j |d|fWt j |d}|S)Nr:r.r;r<)rangerRappendrSrTr!r@)r4rVstacki bbox_pos_embs r'rIz!BrosPositionalEmbedding2D.forward\s|t}}% ;A1uz T^^DaL9: T^^DaL9:  ; yyB/ r&rJrMs@r'rOrOTs#;ELLU\\r&rOc>eZdZfdZdej fdZxZS)BrosBboxEmbeddingsct|t||_t j |j |jd|_y)NF)bias) r/r0rObbox_sinusoid_embrLineardim_bbox_sinusoid_emb_2ddim_bbox_projectionbbox_projectionrUs r'r0zBrosBboxEmbeddings.__init__hs= !:6!B!yy)H)H&JdJdkpqr&rVc|jdd}|dddddddf|dddddddfz }|j|}|j|}|S)Nrr) transposerare)r4rVbbox_tbbox_posr\s r'rIzBrosBboxEmbeddings.forwardms\1%$1a-(6!T1a-+@@--h7 ++L9 r&rJrMs@r'r^r^gsr ELLr&r^c eZdZdZfdZ d dej dzdej dzdej dzdej dzdej f d ZxZS) BrosTextEmbeddingszGConstruct the embeddings from word, position and token_type embeddings.c@t|tj|j|j |j |_tj|j|j |_ tj|j|j |_ tj|j |j|_ tj|j|_|j#dt%j&|jj)d|j#dt%j*|j,j/t$j0|j,j2dy) N) padding_idxeps position_idsrr;token_type_idsdtypedeviceF) persistent)r/r0r Embedding vocab_size hidden_size pad_token_idword_embeddingsmax_position_embeddingsposition_embeddingstype_vocab_sizetoken_type_embeddings LayerNormlayer_norm_epsDropouthidden_dropout_probdropoutr3r!r2expandzerosrpr>longrurUs r'r0zBrosTextEmbeddings.__init__ys# !||F,=,=v?Q?Q_e_r_rs#%<<0N0NPVPbPb#c %'\\&2H2H&J\J\%]"f&8&8f>S>STzz&"<"<=  ^U\\&:X:X-Y-`-`ah-ij   KK!!&&(jj((//    r&N input_idsrrrp inputs_embedsr8c6||j}n|jdd}|d}||jddd|f}|st|dr-|jddd|f}|j |d|}|}n:t j |t j|jj}||j|}|j|} || z} |j|} | | z } |j| } |j| } | S)Nr;rrrrrs)r>rphasattrrrrr!rrrur{rr}rr) r4rrrrpr input_shape seq_lengthbuffered_token_type_ids buffered_token_type_ids_expandedr embeddingsr}s r'rIzBrosTextEmbeddings.forwards/  #..*K',,.s3K ^  ,,Q  ^ J"%:: "66|D)) ^^J/ \\*- r&)NNNN) rrrr r0r!rKrIrLrMs@r'rkrkvsvQ .*..2,0-1 #<<$&# t+#llT) # ||d* #  #r&rkceZdZfdZ d dej dej dej dzdej dzdej dzdej dzd eej fd ZxZS) BrosSelfAttentionct||j|jzdk7r2t |ds&t d|jd|jd|j|_t |j|jz |_|j|jz|_tj|j|j|_ tj|j|j|_ tj|j|j|_ tj|j|_|j"|_y)Nrembedding_sizezThe hidden size (z6) is not a multiple of the number of attention heads ())r/r0rynum_attention_headsr ValueErrorintattention_head_size all_head_sizerrbquerykeyvaluerattention_probs_dropout_probr is_decoderrUs r'r0zBrosSelfAttention.__init__s)     : : :a ?PVXhHi#F$6$6#78 445Q8  $*#=#= #&v'9'9Fbnijr<rrQ)shaperrrr?rgrrr!matmulpermuteeinsummathsqrtrSoftmaxr contiguousr>rr)r4rr\rrrr hidden_shape query_layeris_cross_attention key_layer value_layerattention_scores batch_sizen_headrd_headbbox_pos_scoresattention_probs context_layernew_context_layer_shapeoutputss r'rIzBrosSelfAttention.forwardsc&++A.D4L4LdNfNfg jj/44\BLLQPQR 3$> !67<<\JTTUVXYZI**%:;@@NXXYZ\]^K3N/44\BLLQPQRI**]388FPPQRTUVK!<< Y5H5HR5PQ2=1B1B. FJ#((ZVT #++L9 ,,'8; :UV+o=+dii8P8P.QQ  %/.@ -"**,-=>,,7 _kB %--aAq9DDF "/"4"4"6s";t?Q?Q>S"S* **,CD 6G=/2mM] ??'Gr&NNNF) rrrr0r!rKr$rIrLrMs@r'rrs,0/3596:16:||:ll: t+ : %||d2 : !& t 3 :!<<$.: u|| :r&rcneZdZfdZdej dej dej fdZxZS)BrosSelfOutputc(t|tj|j|j|_tj |j|j|_tj|j|_ yNrn) r/r0rrbrydenserrrrrrUs r'r0zBrosSelfOutput.__init__s` YYv1163E3EF f&8&8f>S>STzz&"<"<= r&r input_tensorr8cr|j|}|j|}|j||z}|SrQrrrr4rrs r'rIzBrosSelfOutput.forward7 =1  ]3 }|'CD r&rJrMs@r'rr1> U\\RWR^R^r&rceZdZfdZ d dej dej dej dzdej dzdej dzdedzd eej fd ZxZ S) BrosAttentioncbt|t||_t ||_yrQ)r/r0rr4routputrUs r'r0zBrosAttention.__init__s& %f- $V, r&Nrr\rrrrr8cp|j||||||}|j|d|}|f|ddz} | S)Nrr\rrrrrr)r4r) r4rr\rrrr self_outputsattention_outputrs r'rIzBrosAttention.forwardsYyy'%)"7#9/ !  ;;|A F#% QR(88r&r) rrrr0r!rKboolr$rIrLrMs@r'rrs-/3596:).||ll t+  %||d2  !& t 3  $; u|| r&rcVeZdZfdZdej dej fdZxZS)BrosIntermediatect|tj|j|j |_t|jtrt|j|_ y|j|_ yrQ) r/r0rrbryintermediate_sizer isinstance hidden_actstrrintermediate_act_fnrUs r'r0zBrosIntermediate.__init__3s] YYv1163K3KL f'' -'-f.?.?'@D $'-'8'8D $r&rr8cJ|j|}|j|}|SrQ)rr)r4rs r'rIzBrosIntermediate.forward;s& =1 00? r&rJrMs@r'rr2s#9U\\ellr&rcneZdZfdZdej dej dej fdZxZS) BrosOutputc(t|tj|j|j |_tj|j |j|_tj|j|_ yr) r/r0rrbrryrrrrrrrUs r'r0zBrosOutput.__init__Bs` YYv779K9KL f&8&8f>S>STzz&"<"<= r&rrr8cr|j|}|j|}|j||z}|SrQrrs r'rIzBrosOutput.forwardHrr&rJrMs@r'rrArr&rceZdZfdZ d dej dej dej dzdej dzdej dzdedzd eej fd Z d Z xZ S) BrosLayercbt||j|_d|_t ||_|j |_|j|_|jr*|j st|dt ||_ t||_ t||_ y)Nrz> should be used as a decoder model if cross attention is added)r/r0chunk_size_feed_forward seq_len_dimr attentionradd_cross_attention Exceptioncrossattentionr intermediaterrrUs r'r0zBrosLayer.__init__Ps '-'E'E$&v. ++#)#=#=  # #??4&(f ghh"/"7D ,V4 ( r&Nrr\rrrrr8c|j||||}|d}|jr|dd} n|dd} |jr@|>t|drtd|d|j |||||} | d}| | ddz} t |j |j|j|} | f| z} |jr| d z} | S) N)r\rrrrr;rz'If `encoder_hidden_states` are passed, z` has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`)rrrrrQ) rrrrrrfeed_forward_chunkrr) r4rr\rrrrself_attention_outputsrrcross_attention_outputs layer_outputs r'rIzBrosLayer.forward^s"& %)/ "0"  2!4 ??,Qr2G,QR0G ??4@t-.=dVDde'+&9&9 -&;'="3 ':' # 7q9  7" ==G0  # #  ( (       /G+ ??'Gr&cL|j|}|j||}|SrQ)rr)r4rintermediate_outputrs r'rzBrosLayer.feed_forward_chunks,"//0@A{{#68HI r&r) rrrr0r!rKr"rr$rIrrLrMs@r'rrOs )$48:>;?).3||3ll3))D0 3 %0047 3 !& 1 1D 8 3 $;3 u|| 3jr&rc eZdZfdZe d dej dej dejdzdejdzdejdzdedzd edzd edzd e ej e zfd Z xZ S) BrosEncoderct|||_tjt |j Dcgc] }t|c}|_ycc}wrQ) r/r0r5r ModuleListrXnum_hidden_layersrlayer)r4r5_r6s r'r0zBrosEncoder.__init__sC  ]]uVE]E]?^#_!If$5#_` #_sANrr\rrrroutput_hidden_states return_dictr8c H|rdnd} |rdnd} |r|jjrdnd} t|jD]L\} } |r| |fz} | ||||||}|d}|s$| |dfz} |jjsD| |dfz} N|r| |fz} t || | | S)Nr%rrrr:)last_hidden_staterrcross_attentions)r5r enumeraterr )r4rr\rrrrrrall_hidden_statesall_self_attentionsall_cross_attentionsr[ layer_module layer_outputss r'rIzBrosEncoder.forwards#7BD$5b4%64;;;Z;Zr`d(4 VOA|#$58H$H!(+)-&;'="3 M*!,M &9]1=M;?).,1#'* ||* ll* ))D0 * %0047 * !& 1 1D 8 *  $;* #Tk* D[*  u|| A A* * r&rcVeZdZfdZdej dej fdZxZS) BrosPoolerct|tj|j|j|_tj |_yrQ)r/r0rrbryrTanh activationrUs r'r0zBrosPooler.__init__s9 YYv1163E3EF '')r&rr8c\|dddf}|j|}|j|}|S)Nr)rr)r4rfirst_token_tensor pooled_outputs r'rIzBrosPooler.forwards6+1a40 #56  6 r&rJrMs@r'rrs#$ U\\ellr&rcVeZdZfdZdej dej fdZxZS)BrosRelationExtractorcRt||j|_|j|_|j|_|j |_tj|j |_ tj|j|j|j z|_ tj|j|j|j z|_ tjtjd|j|_y)Nr)r/r0 n_relationsrybackbone_hidden_sizehead_hidden_sizeclassifier_dropout_probrrdroprbrr Parameterr!r dummy_noderUs r'r0zBrosRelationExtractor.__init__s !--$*$6$6! & 2 2'-'E'E$JJt;;< YYt88$:J:JTMbMb:bc 99T668H8H4K`K`8`a,,u{{1d6O6O'PQr&rrc |j|j|}|jjdj d|j dd}t j||gd}|j|j|}|j|j d|j d|j|j}|j|j d|j d|j|j}t j|jdddd|jdddd}|S)Nrraxisr:r)rrr unsqueezerepeatr>r!r@rr?r rrr)r4rr dummy_vecrelation_scores r'rIzBrosRelationExtractor.forwardsjj;!78 OO--a0779>>!;LaP IIy)41= HHTYYy12 !&&   Q !1!1!!4d6F6FH]H] NN9>>!#4innQ6GIYIY[_[p[pq    1a +Y->->q!Q-J r&rJrMs@r'r r s$ R5<<ELLr&r cpeZdZUeed<dZejdejffd Z xZ S)BrosPreTrainedModelr5brosmodulect|||jj}t |t r"t j|j|yt |tryt j|jtj|jjdjdt j |j"yt |t$rUddtjd|j&d|j&z zz }t j|j(|yy) zInitialize the weights)stdr;rqrr+r,r-N)r/ _init_weightsr5initializer_rangerr initnormal_rrkcopy_rpr!r2rrzeros_rrr)r1r.)r4rrr.r6s r'r z!BrosPreTrainedModel._init_weightss f%kk++ f3 4 LL** 4  2 3 JJv**ELL9L9L9R9RSU9V,W,^,^_f,g h KK-- .  9 :%,,sF,K,KSQTZTsTsstH JJv 1 ;r&) rrrrr#base_model_prefixr!no_gradrModuler rLrMs@r'rrs4 U]]_ 2BII 2 2r&rceZdZdfd ZdZdZee ddejdzdejdzdejdzdejdzd ejdzd ejdzd ejdzd ejdzd e dzde dzde dzde eje zfdZ xZS) BrosModelct||||_t||_t ||_t||_|r t|nd|_ |jy)zv add_pooling_layer (bool, *optional*, defaults to `True`): Whether to add a pooling layer N) r/r0r5rkrr^bbox_embeddingsrencoderrpooler post_init)r4r5add_pooling_layerr6s r'r0zBrosModel.__init__sX   ,V41&9"6* ,=j(4  r&c.|jjSrQrr{)r4s r'get_input_embeddingszBrosModel.get_input_embeddings's...r&c&||j_yrQr2)r4rs r'set_input_embeddingszBrosModel.set_input_embeddings*s*/'r&NrrVrrrrprrrrrrr8c | | n|jj} | | n|jj} | | n|jj} | | t d||j } n!||j dd} n t d| t d| \}}| |j n |j }|tj| |}|pt|jdr4|jjddd|f}|j||}|}n&tj| tj|}|j|| }|jj rE|C|j \}}}||f}|tj||}|j#|}nd}|j|||| }|j$dd k(r|ddddgd f}||jj&z}|j)|}|j+|||||| | d }|d}|j,|j-|nd}t/|||j0|j2|j4S)a bbox ('torch.FloatTensor' of shape '(batch_size, num_boxes, 4)'): Bounding box coordinates for each token in the input sequence. Each bounding box is a list of four values (x1, y1, x2, y2), where (x1, y1) is the top left corner, and (x2, y2) is the bottom right corner of the bounding box. Examples: ```python >>> import torch >>> from transformers import BrosProcessor, BrosModel >>> processor = BrosProcessor.from_pretrained("jinho8345/bros-base-uncased") >>> model = BrosModel.from_pretrained("jinho8345/bros-base-uncased") >>> encoding = processor("Hello, my dog is cute", add_special_tokens=False, return_tensors="pt") >>> bbox = torch.tensor([[[0, 0, 1, 1]]]).repeat(1, encoding["input_ids"].shape[-1], 1) >>> encoding["bbox"] = bbox >>> outputs = model(**encoding) >>> last_hidden_states = outputs.last_hidden_state ```NzDYou cannot specify both input_ids and inputs_embeds at the same timer;z5You have to specify either input_ids or inputs_embedszYou have to specify bbox)rurrrs)rrprrr)rrr:rr:rrrT)r\rrrrrrr)r pooler_outputrrr)r5rruse_return_dictrr>rur!onesrrrrrrrget_extended_attention_maskrinvert_attention_maskr bbox_scaler,r-r.r rrr)r4rrVrrrrprrrrrrkwargsrrrrurrextended_attention_maskencoder_batch_sizeencoder_sequence_lengthrencoder_hidden_shapeencoder_extended_attention_maskembedding_output scaled_bboxbbox_position_embeddingsencoder_outputssequence_outputrs r'rIzBrosModel.forward-sP2C1N-TXT_T_TqTq$8$D $++JjJj &1%cd d  "#..*K  &',,.s3KTU U <78 8!, J%.%:!! @T@T  !"ZZ FCN  !t(89*.//*H*HKZK*X'3J3Q3QR\^h3i0!A!&[ SY!Z150P0PQ_al0m ;; ! !&;&G=R=W=W=Y :  7$68O#P %-).4HQW)X&.2.H.HI_.` +.2 +??%)' +  ::b>Q 1667DT[[333 #'#7#7 #D ,, 12"7#B/!5'  *!,8< 8O O4UY ;-')77&11,==   r&)T NNNNNNNNNNN)rrrr0r3r5rrr!rKrr$r rIrLrMs@r'r*r*s8 /0*.$(.2.2,0-1596:)-,0#'u <<$&u llT!u  t+ u  t+ u llT) u ||d*u  %||d2u !& t 3u  $;u #Tku D[u  u|| K Ku u r&r*ceZdZdgZfdZee ddejdzdejdzdejdzdejdzdejdzd ejdzd ejdzd ejdzd e dzd e dzde dzde eje zfdZ xZ S)BrosForTokenClassificationr.c`t|||j|_t||_t |dr |j n |j}tj||_ tj|j|j|_ |jyNclassifier_dropout)r/r0 num_labelsr*rrrNrrrrrbry classifierr/r4r5rNr6s r'r0z#BrosForTokenClassification.__init__s   ++f% )09M)NF % %TZTnTn zz"45 ))F$6$68I8IJ r&NrrVrbbox_first_token_maskrrrprlabelsrrrr8c | | n|jj} |j||||||| | d } | d}|j|}|j |}d}|t }|J|j d}||j d|j||j d|}n2||j d|j|j d}t||| j| jS)a bbox ('torch.FloatTensor' of shape '(batch_size, num_boxes, 4)'): Bounding box coordinates for each token in the input sequence. Each bounding box is a list of four values (x1, y1, x2, y2), where (x1, y1) is the top left corner, and (x2, y2) is the bottom right corner of the bounding box. bbox_first_token_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to indicate the first token of each bounding box. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. Examples: ```python >>> import torch >>> from transformers import BrosProcessor, BrosForTokenClassification >>> processor = BrosProcessor.from_pretrained("jinho8345/bros-base-uncased") >>> model = BrosForTokenClassification.from_pretrained("jinho8345/bros-base-uncased") >>> encoding = processor("Hello, my dog is cute", add_special_tokens=False, return_tensors="pt") >>> bbox = torch.tensor([[[0, 0, 1, 1]]]).repeat(1, encoding["input_ids"].shape[-1], 1) >>> encoding["bbox"] = bbox >>> outputs = model(**encoding) ```NT)rVrrrrprrrrrr;rlogitsrr) r5r9rrrPrr?rOr rr)r4rrVrrRrrrprrSrrrr>rrHrVrloss_fcts r'rIz"BrosForTokenClassification.forwardsZ&1% bbox ('torch.FloatTensor' of shape '(batch_size, num_boxes, 4)'): Bounding box coordinates for each token in the input sequence. Each bounding box is a list of four values (x1, y1, x2, y2), where (x1, y1) is the top left corner, and (x2, y2) is the bottom right corner of the bounding box. bbox_first_token_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to indicate the first token of each bounding box. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. initial_token_labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for the initial token classification. subsequent_token_labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for the subsequent token classification. Examples: ```python >>> import torch >>> from transformers import BrosProcessor, BrosSpadeEEForTokenClassification >>> processor = BrosProcessor.from_pretrained("jinho8345/bros-base-uncased") >>> model = BrosSpadeEEForTokenClassification.from_pretrained("jinho8345/bros-base-uncased") >>> encoding = processor("Hello, my dog is cute", add_special_tokens=False, return_tensors="pt") >>> bbox = torch.tensor([[[0, 0, 1, 1]]]).repeat(1, encoding["input_ids"].shape[-1], 1) >>> encoding["bbox"] = bbox >>> outputs = model(**encoding) ```NT rrVrrrrprrrrrrrrurtr;)rrrrr)r5r9rrgrr^r_squeezerrur!r@rtor masked_fillfinfortmineyer?rrOrrr)r4rrVrrRrrrprr`rarrrr>rlast_hidden_statesrrinv_attention_maskrmax_seq_lengthruinvalid_token_maskself_token_masksubsequent_token_maskrrWinitial_token_losssubsequent_token_losss r'rIz)BrosSpadeEEForTokenClassification.forward0sd&1%  +0G0S')H$8#<#?&" &..B.G.GDOO.\^r%s"&=&B&B2&F #$,',,R!1CDEZ['(=>% ! &(==D!5$;!//))   r&) NNNNNNNNNNNN)rrrrYr0rrr!rKrr$rrIrLrMs@r'r[r[ sN+4&2*.$(.259.2,0-1487;)-,0#'n <<$&n llT!n  t+ n %||d2 n  t+ n llT)n ||d*n $llT1n "'!4n  $;n #Tkn D[n  u||  .n n r&r[z Bros Model with a token classification head on top (a entity_linker layer on top of the hidden-states output) e.g. for Entity-Linking. The entity_linker is used to predict intra-entity links (one entity to another entity). ceZdZdgZfdZee ddejdzdejdzdejdzdejdzdejdzd ejdzd ejdzd ejdzd e dzd e dzde dzde eje zfdZ xZ S)!BrosSpadeELForTokenClassificationr.c@t||||_|j|_|j|_|j |_t||_t|dr |jn |jt||_ |jyrM)r/r0r5rOr ryr r*rrrNrr  entity_linkerr/rUs r'r0z*BrosSpadeELForTokenClassification.__init__s    ++!--$*$6$6!f% &-f6J&K " "QWQkQk26: r&NrrVrrRrrrprrSrrrr8c | | n|jj} |j||||||| | d } | d}|jddj }|j ||j d}d}|et}|j\}}|j}tj||dzj|tj}|jd}tj|tj |dgtj|gd }|j#|dddddftj$|j&j(}|j#|dddddftj$|j&j(}||jd|dz||jd|}t+||| j,| j. S) a bbox ('torch.FloatTensor' of shape '(batch_size, num_boxes, 4)'): Bounding box coordinates for each token in the input sequence. Each bounding box is a list of four values (x1, y1, x2, y2), where (x1, y1) is the top left corner, and (x2, y2) is the bottom right corner of the bounding box. bbox_first_token_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to indicate the first token of each bounding box. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. Examples: ```python >>> import torch >>> from transformers import BrosProcessor, BrosSpadeELForTokenClassification >>> processor = BrosProcessor.from_pretrained("jinho8345/bros-base-uncased") >>> model = BrosSpadeELForTokenClassification.from_pretrained("jinho8345/bros-base-uncased") >>> encoding = processor("Hello, my dog is cute", add_special_tokens=False, return_tensors="pt") >>> bbox = torch.tensor([[[0, 0, 1, 1]]]).repeat(1, encoding["input_ids"].shape[-1], 1) >>> encoding["bbox"] = bbox >>> outputs = model(**encoding) ```NTrcrrrdr;rsrrU)r5r9rrgrrvrerrrur!rjrfrr?r@rrgrhrtrir rr)r4rrVrrRrrrprrSrrrr>rrkrVrrWrrmruromasks r'rIz)BrosSpadeELForTokenClassification.forwardsX&1%?Ev{{SUW[G\]D$!//))   r&rIrXrMs@r'rtrts8+4& *.$(.259.2,0-1&*)-,0#'X <<$&X llT!X  t+ X %||d2 X  t+ X llT)X ||d*X  t#X  $;X #TkX D[X  u|| 4 4X X r&rt)rr*rKr[rt)6r r dataclassesrr!rtorch.nnrrr" activationsrmodeling_layersr modeling_outputsr r r modeling_utilsr pytorch_utilsrutilsrrrrconfiguration_brosr get_loggerrloggerrr(r)rOr^rkrrrrrrrrr rr*rKr[rt__all__r%r&r'rs  ! %&!9 .6KK*   H %  7k7  7" * &  ;;|O Of RYY BII: ryy   G*GT1 "))1 j  BIID2/22*N #N N b` !4` ` FL (;L L ^ k (;k  k \ r&