Zj@SSKrSSKrSSKJrJrJr SSKJr SSKJr SSK J r SSK J r JrJr SSKJr SS KJr SS KJrJrJr SS KJr SS KJr SS KJr SSKJrJ r J!r! SSK"J#r# SSK$J%r%J&r& SSK'J(r( \!RR"\*5r+"SS\RX5r-"SS\RX5r."SS\RX5r/"SS\RX5r0"SS\RX5r1"SS\RX5r2"SS \5r3"S!S"\RX5r4"S#S$\RX5r5"S%S&\RX5r6"S'S(\RX5r7"S)S*\RX5r8"S+S,\5r9"S-S.\95r:"S/S0\9\5r;/S1Qr[TU]5 [R"URUR UR S9Ul[R"URUR 5Ul [R"UR URS9Ul [R"UR5UlURS[ R""UR5R%S5SS9 Xlg)N) padding_idxeps position_idsrF) persistent)super__init__r Embedding vocab_size hidden_size pad_token_idword_embeddingsmax_position_embeddingsposition_embeddings LayerNormlayer_norm_epsDropouthidden_dropout_probdropoutregister_buffertorcharangeexpandconfigselfr: __class__s |/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/blip/modeling_blip_text.pyr)BlipTextEmbeddings.__init__0s !||F,=,=v?Q?Q_e_r_rs#%<<0N0NPVPbPb#c f&8&8f>S>STzz&"<"<=   ELL)G)GHOOPWXej   N input_idsr$ inputs_embedspast_key_values_lengthreturnc,UbUR5nOUR5SSnUSnUcURSS2XFU-24nUcURU5nUnURU5nXx- nUR U5nUR U5nU$)Nr&r)sizer$r.r0r1r5) r<rAr$rBrC input_shape seq_length embeddingsr0s r>forwardBlipTextEmbeddings.forward?s  #..*K',,.s3K ^  ,,Q0FVlIl0l-lmL   00;M" "66|D) ^^J/ \\*- r@)r1r:r5r0r.)NNNr)__name__ __module__ __qualname____firstlineno____doc__r)r7 LongTensor FloatTensorintrrJ__static_attributes__ __classcell__r=s@r>rr-sxE ".20426&' ##d*&&-((4/  !$   r@rc^\rSrSrSU4SjjrSrSrSrSrSS\ RS \ RS-S \ RS-S \ RS-S \ S-S \ \S\\ R\ R44SjjrSrU=r$)BlipTextSelfAttention^Nc>[TU]5 XlURUR-S:wa5[ US5(d$[ SURUR4-5eURUl[URUR- 5UlURUR-Ul X0l [R"URUR5Ul U(aa[R"URUR5Ul[R"URUR5UlO`[R"URUR5Ul[R"URUR5Ul[R""UR$5Ulg)Nrembedding_sizezLThe hidden size (%d) is not a multiple of the number of attention heads (%d))r(r)r:r,num_attention_headshasattr ValueErrorrSattention_head_size all_head_size layer_idxrLinearqueryencoder_hidden_sizekeyvaluer3attention_probs_dropout_probr5r<r:is_cross_attentionrar=s r>r)BlipTextSelfAttention.__init___s_     : : :a ?PVXhHiHi^%%v'A'ABC  $*#=#= #&v'9'9Fsave_attn_gradients)BlipTextSelfAttention.save_attn_gradientsws,r@cUR$rlrmr<s r>get_attn_gradients(BlipTextSelfAttention.get_attn_gradientszs"""r@cXlgrl attention_map)r<rws r>save_attention_map(BlipTextSelfAttention.save_attention_map}s*r@cUR$rlrvrrs r>get_attention_map'BlipTextSelfAttention.get_attention_maps!!!r@ hidden_statesattention_maskencoder_hidden_statesencoder_attention_maskpast_key_valueskwargsrDc URSSn/UQSPURP7nURU5RU5R SS5n USLn U (aUOUnSn Ub][ U[ 5(aFURRUR5n U (a URn OURn OUn U (aUOUn U (aQUbNU (aGW RURRnU RURRnO/U RSSQSPURP7nURU 5RU5R SS5nUR!U 5RU5R SS5nUbSW R#XUR5upU (a.[ U[ 5(aSURUR'[$R&"XR SS55nU[(R*"UR5- nUbUUR-UR.5-n[0R2"SS9"U5nUR5U5n[$R&"UU5nUR7SSSS 5R95nUR;5SSUR<4-nUR"U6nUU4$) Nr&rFT)dimrr)shaper_rcview transpose isinstancer is_updatedgetracross_attention_cacheself_attention_cachelayerskeysvaluesrerfupdater7matmulmathsqrttorrSoftmaxr5permute contiguousrFr`)r<r}r~rrrrrG hidden_shape query_layerrircurr_past_key_valuescurrent_states key_layer value_layerkv_shapeattention_scoresattention_probsattention_probs_dropped context_layernew_context_layer_shapes r>rJBlipTextSelfAttention.forwards$))#2. CCbC$*B*BC jj/44\BLLQPQR 3$>3E/>  &/+>??,77;;DNNK %+:+P+P(+:+O+O('6$2D.- /"=*,33DNNCHHI.55dnnELLKQ--cr2QBQ8P8PQH055h?II!QOI**^499(CMMaQRSK*)=)D)DY]a]k]k)l& %*_FY*Z*ZAEO..t~~>!<< 5H5HR5PQ+dii8P8P.QQ  %/.2C2CDTD[D[2\\ **,-=>#',,"? %S"S%**,CD o--r@) r`r_rwrnr:r5rerar\rcrfrlNNNN)rLrMrNrOr)rorsrxr{r7rrRr rrtuplerJrTrUrVs@r>rXrX^sG0-#+" 48:>;?(, D.||D.))D0D. %0047 D. !& 1 1D 8 D.  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Gomez, Lukasz Kaiser and Illia Polosukhin. argument and `is_decoder` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. c>[TU]U5 Xl[U5Ul[ U5UlU(a [U5OSUlUR5 grl) r(r)r:rrIrencoderrpooler post_init)r<r:add_pooling_layerr=s r>r)BlipTextModel.__init__sG   ,V4&v. 0AnV,t  r@c.URR$rlrIr.rrs r>get_input_embeddings"BlipTextModel.get_input_embeddingss...r@c$XRlgrlr>)r<rfs r>set_input_embeddings"BlipTextModel.set_input_embeddingss */'r@r~rGrrrDcUR5S:XaUSS2SSS2SS24nGO1UR5S:XGaU(aUupg[R"XsS9nUSSSS24RXgS5USSS2S4:*n U R UR 5n U R SUR S:aUUR SU R S- n [R"[R"XgU 4X9R S9U /SS9n U SS2SSS2SS24USS2SSSS24-nO*USS2SSSS24nO[S US UR S 35eUR UR S 9nS U- S-nU$)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`. rNrrr)rdtyper&)axisz!Wrong shape for input_ids (shape z) or attention_mask (shape ))rFg?g) rr7r8repeatrrFrcatonesr^) r<r~rGrrextended_attention_mask batch_sizerHseq_ids causal_maskprefix_seq_lens r>get_extended_attention_mask)BlipTextModel.get_extended_attention_masks&    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@NrAr$rBencoder_embedsrrrrrc U (dSn UbUb [S5eUb2URX5 UR5n U upURnOYUb$UR5SSn U upURnO2Ub$UR5SSn U upURnO [S5eUcSOUR 5nUc)[ R "XU-45RU5nURX,X5nUb[U[5(aUSR5unnnOUR5unnnUU4n[U[5(a"UVs/sHnURU5PM nnO>Uc'[ R "UUS9nURU5nOURU5nOSnUcURUUUUS9nOUnUR"U4UUUUU S .U D6nURnURbURU5OSn[!UUUR"UR$UR&UR(S 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`). FNzDYou cannot specify both input_ids and inputs_embeds at the same timer&zGYou have to specify either input_ids or inputs_embeds or encoder_embedsrrE)rAr$rBrC)r~rrrr)r pooler_outputrr}r(r))r^%warn_if_padding_and_no_attention_maskrFrget_seq_lengthr7rKrrQrlistinvert_attention_maskrIr8rr9rrr}r(r))r<rAr~r$rBrSrrrrrrrGrMrHrrCrLencoder_batch_sizeencoder_sequence_lengthrencoder_hidden_shapemaskencoder_extended_attention_maskembedding_outputencoder_outputsrrs r>rJBlipTextModel.forward$sDI  ]%>cd d  "  6 6y Q#..*K%0 "J%%F  &',,.s3K%0 "J"))F  '(--/4K%0 "J#**Ffg g&5&=?CaCaCc  !"ZZBX5X(YZ]]^deN150P0P 1  ! ,/66AVWXAYA^A^A`>"$;QAVA[A[A]>"$;Q$68O#P 0$77`v2w`vX\43M3Md3S`v/2w/'/).4HQW)X&262L2LMc2d/262L2LMc2d/.2 +  !##)+'= /   . EI\\ F 2"7#B+ F F *;;8< 8O O4UY ;-'+;;)77&11,==   ?3xsI )r:rIr8r9)T) NNNNNNNNNF)rLrMrNrOrPr)r?rBrrrSrrrQrrr7r rrrrJrTrUrVs@r>r6r6sZ/0<'$<'38:<'GM<'[_<' <'| *..2,0-1.2596:(,!%"'p <<$&p  t+p llT) p ||d* p  t+ p  %||d2p !& t 3p p $;p 4Kp +,p  6p  p r@r6c ^\rSrSrSSS.rU4SjrSrSrSrS r \ SS \ RS -S \ RS -S \ RS -S\ RS -S\ RS -S\ RS -S\ RS -S\ S -S\S -S\S -S\S -S\S -S\\ R-S\\S\4Sjj5rSU4SjjrSrU=r$)BlipTextLMHeadModelizcls.predictions.biasz&bert.embeddings.word_embeddings.weight)zcls.predictions.decoder.biaszcls.predictions.decoder.weightc>[TU]U5 [USS9Ul[ U5UlUR UlUR5 g)NF)r;)r(r)r6r%rclslabel_smoothingr:r;s r>r)BlipTextLMHeadModel.__init__sB  !&EB &v.%55 r@c6URR5$rl)r%r?rrs r>r?(BlipTextLMHeadModel.get_input_embeddingssyy--//r@c:URRU5 grl)r%rBr<new_embeddingss r>rB(BlipTextLMHeadModel.set_input_embeddingss &&~6r@cBURRR$rl)rerrrrs r>get_output_embeddings)BlipTextLMHeadModel.get_output_embeddingssxx##+++r@cXRRlURURRlgrl)rerrrrks r>set_output_embeddings)BlipTextLMHeadModel.set_output_embeddingss*'5$$2$7$7!r@NrAr~r$rBrrlabelsrr return_logitsr reductionlogits_to_keeprrDc <UbSn UR"U4UUUUUUU U S.UD6nURn[U [5(a [ U *S5OU nUR USS2USS245nU (aUSS2SS2SS24R 5$SnUbUSS2SS2SS24R 5nUSS2SS24R 5RUR5n[XRS9nU"URSURR5URS55nU S:Xa0URURS5S5RS5n[!UUUR"UR$UR&UR(S 9$) a encoder_hidden_states (`torch.FloatTensor`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. labels (`torch.LongTensor`, *optional*): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]` past_key_values (`Cache`, *optional*): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). NF)r~r$rBrrrrrr&r)rvrfnoner)losslogitsrr}r(r))r%rrrSslicererrrrrfrr:r+rFsumrrr}r(r))r<rAr~r$rBrrrtrrrurrvrwroutputsr} slice_indicesr!lm_lossshifted_prediction_scoresloss_fcts r>rJBlipTextLMHeadModel.forwardsP  I@D  A )%'"7#9+! A  A  11 8B>SV8W8W~ot4]k  HH]1mQ3F%GH $QQY/::< <  (9!SbS!)(D(O(O(Q %AqrE]--/223L3S3STF')MaMabH8==b$++BXBXY[a[f[fgi[jkGF"!,,'8'='=a'@"EII!L0$#33!//))$55   r@c :>[TU]"U4UUS.UD6nSUS'U$)N)rr~Tr)r(prepare_inputs_for_generation)r<rArr~ model_kwargs model_inputsr=s r>r1BlipTextLMHeadModel.prepare_inputs_for_generation s>w<  +)   &* \"r@)r%rerf) NNNNNNNNNFTmeanr)NN)rLrMrNrO_tied_weights_keysr)r?rBrorrrr7rr rrrSrrrrJrrTrUrVs@r>rcrcsy(>*R 07,8*..2,0-1596:&*(,!%%*"& &-.P <<$&P  t+P llT) P ||d* P %||d2 P !& t 3P  t#P P $;P d{P 4KP :P ell*P +,P +!P P d  r@rc)r6rcr$)=rr7rrrtorch.nnrrr, activationsr cache_utilsr r r generationr modeling_layersrmodeling_outputsrrrmodeling_utilsrprocessing_utilsr pytorch_utilsrutilsrrr utils.genericrutils.output_capturingrrconfiguration_blipr get_loggerrLrModulerrXrrrrrrrrrrr$r6rc__all__r0r@r>rsY  $$%&!CC)9 .&6BB7E.   H %--bi.BIIi.Z  1 12 299  RYY -.-b/ bii/ f RYY  bii$ ryy "!"))!ioi4I +I Zz1?zz Nr@