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((9(9: h. !)):): ; h."%h.$D>h.!.h. u|| h.Sh.r@rXcz^\rSrSrU4SjrS\R S\R S\R 4SjrSrU=r $)BlipTextSelfOutputc(>[TU]5 [R"URUR5Ul[R "URURS9Ul[R"UR5Ul gNr ) r'r(rrer+denser0r1r2r3r4r;s r>r(BlipTextSelfOutput.__init__s` YYv1163E3EF f&8&8f>S>STzz&"<"<= r@r input_tensorrDcpURU5nURU5nURX-5nU$rprr4r0r<rrs r>rJBlipTextSelfOutput.forward5 =1  ]3 }'CD r@r0rr4 rLrMrNrOr(r6rrJrTrUrVs@r>rr6> U\\RWR^R^r@rc$^\rSrSrSU4SjjrSr\"SSSS9SS\RS \ \RS \ \RS \ \RS\ \ S \ \ S \ \RS\ \R4Sjj5rSrU=r$)BlipTextAttentionic~>[TU]5 [XUS9Ul[ U5Ul[ 5Ulg)Nrd)r'r(rXr<routputset pruned_headsrls r>r(BlipTextAttention.__init__s3 )&PYZ (0 Er@c6[U5S:Xag[XRRURRUR 5up[ URRU5URl[ URRU5URl[ URRU5URl [ URRUSS9URl URR[U5- URlURRURR-URl UR RU5Ulg)Nrrr)lenrr<r_rbrrrfrhrirrrcunion)r<headsindexs r> prune_headsBlipTextAttention.prune_heads s  u:? 7 9900$))2O2OQUQbQb  -TYY__eD *499==%@ ,TYY__eD .t{{/@/@%QO )- (E(EE (R %"&))"?"?$))B_B_"_  --33E:r@rrrrrrrrrrrDc lURUUUUUUUS9nURUSU5n U 4USS-n U $)Nrrrrrrrr)r<r) r<rrrrrrr self_outputsattention_outputoutputss r>rJBlipTextAttention.forwards\yy )"7+/)!  ;;|A F#% QR(88r@)rrr<)FN)NNNNFN)rLrMrNrOr(rrr6rrrRr rrrJrTrUrVs@r>rrs" ;$%0A6R7;15=A+/,115||!!2!23E--.  ((9(9:  "% $D>!. u|| Sr@rcb^\rSrSrU4SjrS\R S\R 4SjrSrU=r $)BlipTextIntermediatei8c>[TU]5 [R"URUR 5Ul[UR[5(a[URUl gURUl grp) r'r(rrer+intermediate_sizerr hidden_actstrr intermediate_act_fnr;s r>r(BlipTextIntermediate.__init__9s` YYv1163K3KL f'' - -'-f.?.?'@D $'-'8'8D $r@rrDcJURU5nURU5nU$rprrr<rs r>rJBlipTextIntermediate.forwardAs& =1 00? r@rrrVs@r>rr8s(9U\\ellr@rcz^\rSrSrU4SjrS\R S\R S\R 4SjrSrU=r $)BlipTextOutputiHc(>[TU]5 [R"URUR 5Ul[R"UR URS9Ul[R"UR5Ul gr) r'r(rrerr+rr0r1r2r3r4r;s r>r(BlipTextOutput.__init__Is` YYv779K9KL f&8&8f>S>STzz&"<"<= r@rrrDcpURU5nURU5nURX-5nU$rprrs r>rJBlipTextOutput.forwardOrr@rrrVs@r>rrHrr@rc@^\rSrSrU4Sjr\"SSSS9SS\RS\\RS \\RS \\RS \\RS\\ S \\ S \\RS\ \R4Sjj5r SrSrU=r$) BlipTextLayeriVcB>[TU]5 XlURUlSUl[ XS9UlX lURR(a#[ XRRUS9Ul [U5Ul [U5Ul g)Nrr)rmrd)r'r(r:chunk_size_feed_forward seq_len_dimr attention layer_num is_decodercrossattentionr intermediaterr)r<r:rr=s r>r(BlipTextLayer.__init__Ws~  '-'E'E$*6G" ;; ! !"3;;+A+AY#D 18$V, r@rrrrrrrrrrrrDc URUUUUUUS9n U Sn U SSn Ub!URU UUUUUUS9n U Sn XSS-n [URURUR U 5n U 4U -$)N)rrrrrrrr)rrrfeed_forward_chunkrr)r<rrrrrrrrself_attention_outputsrrcross_attention_outputs layer_outputs r>rJBlipTextLayer.forwardes"& )/+) "0" 2!4(, ,&*&9&9 5#&; /"3-':' # 7q9  ;;G0  # #T%A%A4CSCSUe ((r@cJURU5nURX!5nU$rp)rr)r<rintermediate_outputrs r>r BlipTextLayer.feed_forward_chunks)"//0@A{{#6I r@)rrr:rrrrrr)rLrMrNrOr(rr6rrrRr rrrJrrTrUrVs@r>rrVs -%0A6R7;15=A>B+/,115%)||%)!!2!23%)E--. %) ((9(9: %) !)):): ; %)"%%)$D>%)!.%) u|| %)S%)Nr@rcR^\rSrSrU4SjrSS\R S\\RS\\RS\\RS\\RS\\ S \\ S \\ S \\ S \\ S \\R S\ \ \R \ 44SjjrSrU=r$)BlipTextEncoderic >[TU]5 Xl[R"[ UR 5Vs/sHn[X5PM sn5UlSUl gs snf)NF) r'r(r:r ModuleListrangenum_hidden_layersrlayergradient_checkpointing)r<r:ir=s r>r(BlipTextEncoder.__init__sR  ]]eFLdLdFe#fFeM&$ *BlipTextEncoder.forward..s" As )last_hidden_staterr attentionscross_attentions)rtrainingloggerwarningrr warning_oncer from_legacy_cacher r:rrrr)r<rrrrrrrrrrrall_hidden_statesall_self_attentionsall_cross_attentionsr layer_modulelayer_head_mask layer_outputss r>rJBlipTextEncoder.forwards  & &4==p" /511##` #6"G"G"XO\::"5o|[_[f[fGg"h ("5  4l$++6V##7BD$5b4%6;P;\rbft{{445A::a=L#$58H$H!.7.CilO(%&! 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W ((9(9: W !)):): ; W "%W D>W $D>W 'tnW d^W !.W  uU\\"$MM NW W r@r cb^\rSrSrU4SjrS\R S\R 4SjrSrU=r $)BlipTextPooleric>[TU]5 [R"URUR5Ul[R "5Ulgrp)r'r(rrer+rTanh activationr;s r>r(BlipTextPooler.__init__s9 YYv1163E3EF '')r@rrDc\USS2S4nURU5nURU5nU$)Nr)rr2)r<rfirst_token_tensor pooled_outputs r>rJBlipTextPooler.forwards6+1a40 #56  6 r@)r2rrrVs@r>r/r/s($ U\\ellr@r/cb^\rSrSrU4SjrS\R S\R 4SjrSrU=r $)BlipTextPredictionHeadTransformicp>[TU]5 [R"URUR5Ul[ UR[5(a[URUl OURUl [R"URURS9Ul gr) r'r(rrer+rrrrr transform_act_fnr0r1r;s r>r((BlipTextPredictionHeadTransform.__init__s~ YYv1163E3EF f'' - -$*6+<+<$=D !$*$5$5D !f&8&8f>S>STr@rrDclURU5nURU5nURU5nU$rp)rr;r0rs r>rJ'BlipTextPredictionHeadTransform.forwards4 =1 --m< }5 r@)r0rr;rrVs@r>r9r9s)UU\\ellr@r9c4^\rSrSrU4SjrSrSrSrU=r$)BlipTextLMPredictionHeadicH>[TU]5 [U5Ul[R "UR URSS9Ul[R"[R"UR55Ul URURl g)NF)bias) r'r(r9 transformrrer+r*decoder Parameterr6zerosrBr;s r>r(!BlipTextLMPredictionHead.__init__sm 8@yy!3!3V5F5FUS LLV->->!?@ !II r@c:URURlgrp)rBrDrvs r> _tie_weights%BlipTextLMPredictionHead._tie_weights&s II r@cJURU5nURU5nU$rp)rCrDrs r>rJ BlipTextLMPredictionHead.forward)s$}5  ]3 r@)rBrDrC) rLrMrNrOr(rIrJrTrUrVs@r>r@r@s &&r@r@cb^\rSrSrU4SjrS\R S\R 4SjrSrU=r $)BlipTextOnlyMLMHeadi0cB>[TU]5 [U5Ulgrp)r'r(r@ predictionsr;s r>r(BlipTextOnlyMLMHead.__init__1s 3F;r@sequence_outputrDc(URU5nU$rprP)r<rRprediction_scoress r>rJBlipTextOnlyMLMHead.forward5s ,,_=  r@rTrrVs@r>rNrN0s(<!u||! !!r@rNc2\rSrSr%Sr\\S'Sr/rSr Sr g)BlipTextPreTrainedModeli;zz An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. r:bertcH[U[R[R45(a9URR R SURRS9 Oh[U[R5(aIURR R5 URR RS5 [U[R5(a3URb%URR R5 ggg)zInitialize the weightsg)meanstd?N) rrrer)weightdatanormal_r:initializer_ranger0rBzero_fill_)r<modules r> _init_weights%BlipTextPreTrainedModel._init_weightsEs fryy",,7 8 8 MM   & &CT[[5R5R & S  - - KK   " " $ MM   $ $S ) fbii ( (V[[-D KK   " " $.E (r@rN) rLrMrNrOrPr__annotations__base_model_prefix_no_split_modulesrerTrr@r>rXrX;s   %r@rXc#^\rSrSrSrSU4SjjrSrSrSrS\ S\ \ S \ S \ S \ 4 S jrSS \\RS\\RS\\RS\\RS\\RS\\RS\\RS\\RS\\S\\ S\\ S\\ S\\ S \\ S\\RS \\ \R\44 SjjrSrU=r$) BlipTextModeliSa 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](https://huggingface.co/papers/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. argument and `is_decoder` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. c>[TU]U5 Xl[U5Ul[ U5UlU(a [U5OSUlUR5 grp) r'r(r:rrIr encoderr/pooler post_init)r<r:add_pooling_layerr=s r>r(BlipTextModel.__init__\sG   ,V4&v. 0AnV,t  r@c.URR$rprIr-rvs r>get_input_embeddings"BlipTextModel.get_input_embeddingsfs...r@c$XRlgrprs)r<ris r>set_input_embeddings"BlipTextModel.set_input_embeddingsis */'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)itemsrmrrr)r<heads_to_prunerrs r> _prune_headsBlipTextModel._prune_headsms< +002LE LL  u % / / ; ;E B3r@rrGrrrDcUR5S:XaUSS2SSS2SS24nGO1UR5S:XGaU(aUupg[R"XsS9nUSSSS24RXgS5USSS2S4:*n U R UR 5n U R SUR S:aUUR SU R S- n [R"[R"XgU 4X9R S9U /SS9n U SS2SSS2SS24USS2SSSS24-nO*USS2SSSS24nO[S US UR S 35eUR UR S 9nS U- S-nU$)a Makes broadcastable attention and causal masks so that future and masked tokens are ignored. Arguments: attention_mask (`torch.Tensor`): Mask with ones indicating tokens to attend to, zeros for tokens to ignore. input_shape (`tuple[int]`): The shape of the input to the model. device (`torch.device`): The device of the input to the model. Returns: `torch.Tensor` The extended attention mask, with a the same dtype as `attention_mask.dtype`. r Nr^rr)rrr#)axisz!Wrong shape for input_ids (shape z) or attention_mask (shape )rr]g) rr6r7repeatrrrcatonesra) r<rrGrrextended_attention_maskrrHseq_ids causal_maskprefix_seq_lens r>get_extended_attention_mask)BlipTextModel.get_extended_attention_maskus&    1 $&4Qa]&C #    !Q &)4& ,,zA%dD!m4;;JTUVZabfhikoboZpp )nn^-A-AB $$Q'.*>*>q*AA%3%9%9!%<{?P?PQR?S%SN"'))!JJ!+ HQW_p_p(   #K+6aq!m*D~VWY]_cefVfGg*g'*8D$9I*J'3K=@[\j\p\p[qqrs #:"<"<4::"<"N#&)@#@H"L&&r@rAr"rrBencoder_embedsrrrrrrrrc:U bU OURRn U bU OURRn U bU OURRn U(aU bU OURRn OSn UbUb [ S5eUb3UR X5 UR5nUunnURnO[Ub%UR5SSnUunnURnO3Ub%UR5SSnUunnURnO [ S5eSnU b:[U [5(dU SSRSOU R5nUc*[R"UUU-45RU5nUR!UUUU5nUb[U["5(aUSR5unnnOUR5unnnUU4n[U["5(a"UVs/sHnUR%U5PM nnO>Uc'[R"UUS9nUR%U5nOUR%U5nOSnUR'X@RR(5nUcUR+UUUUS 9nOUnUR-UUUUUU U U U U US 9 nUSnUR.bUR/U5OSnU (d UU4US S-$[1UUUR2UR4UR6UR8S 9$s snf) a encoder_hidden_states (`torch.FloatTensor`, *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`, *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 (`Cache`, *optional*): 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#zGYou have to specify either input_ids or inputs_embeds or encoder_embedsrrr)rAr"rBrC) rrrrrrrrrrr)r pooler_outputrrr r!)r:rruse_return_dictrra%warn_if_padding_and_no_attention_maskrFrrr rget_seq_lengthr6rrrlistinvert_attention_mask get_head_maskrrIrmrnrrrr r!) r<rArr"rrBrrrrrrrrrrrGrrHrrCrencoder_batch_sizeencoder_sequence_lengthrencoder_hidden_shapemaskencoder_extended_attention_maskembedding_outputencoder_outputsrRr6s r>rJBlipTextModel.forwardsH2C1N-TXT_T_TqTq$8$D $++JjJj &1%cd d  "  6 6y Q#..*K%0 "J %%F  &',,.s3K%0 "J "))F  '(--/4K%0 "J #**Ffg g!"  &"/599 "1%++B/$335 #  !"ZZZBX5X(YZ]]^deN150P0P K1  ! ,/66AVWXAYA^A^A`>"$;QAVA[A[A]>"$;Q$68O#P 0$77`v2w`vX\43M3Md3S`v/2w/'/).4HQW)X&262L2LMc2d/262L2LMc2d/.2 +&&y++2O2OP  !##)+'= /   . ,, 2"7#B+/!5#)'  *!,8< 8O O4UY #]3oab6II I;-'+;;)77&11,==   [3xs:L)r:rIrmrn)T)NNNNNNNNNNNNNFN)rLrMrNrOrPr(rtrwr|rrrSrrrrr6r rrrJrTrUrVs@r>rkrkSs/0C<'$<'38:<'GM<'[_<' <'@-115/3,004158<9=+/$(,0/3&*%*15!P ELL)P !.P u||, P ELL) P   - P !.P  ( 5P !) 6P "%P D>P $D>P 'tnP d^P TNP !.!P " uU\\"$PP Q#P P r@rkc'^\rSrSrSS/rU4SjrSrSrSrSr SS \ \ RS \ \ RS \ \ RS \ \ RS \ \ RS\ \ RS\ \ RS\ \ RS\ \ S\ \S\ \S\ \S\ \S\ \S\ \S\ \S\ \ RS\\\ R\44$SjjrSU4SjjrSrU=r$) BlipTextLMHeadModeliGzcls.predictions.decoder.weightzcls.predictions.decoder.biasc>[TU]U5 [USS9Ul[ U5UlUR Ulg)NF)rp)r'r(rkrYrNclslabel_smoothingr;s r>r(BlipTextLMHeadModel.__init__Js8  !&EB &v.%55r@c6URR5$rp)rYrtrvs r>rt(BlipTextLMHeadModel.get_input_embeddingsQsyy--//r@c:URRU5 grp)rYrwr<new_embeddingss r>rw(BlipTextLMHeadModel.set_input_embeddingsTs &&~6r@cBURRR$rp)rrPrDrvs r>get_output_embeddings)BlipTextLMHeadModel.get_output_embeddingsWsxx##+++r@cXRRlURURRlgrp)rrPrDrBrs r>set_output_embeddings)BlipTextLMHeadModel.set_output_embeddingsZs*'5$$2$7$7!r@rArr"rrBrrlabelsrrrrr return_logitsr reductionrrDc@U bU OURRn UbSn URUUUUUUUU U U U U UUS9nUSnURU5nU(aUSS2SS2SS24R 5$SnUbUSS2SS2SS24R 5nUSS2SS24R 5R UR 5n[UURS9nU"URSURR5URS55nUS:Xa0URURS5S5RS5nU (dU4US S-nUbU4U-$U$[UUURURUR UR"S 9$) a encoder_hidden_states (`torch.FloatTensor`, *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`, *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**. labels (`torch.LongTensor`, *optional*): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]` past_key_values (`Cache`, *optional*): 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`). 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