+hjSSKJrJrJr SSKrSSKrSSKJr SSKJr SSK J r SSK J r JrJr SSKJrJr SSKJrJrJrJrJr SS KJr SS KJrJr \R<"\5r "S S \RB5r""S S\RB5r#"SS\RB5r$"SS\RB5r%"SS\RB5r&"SS\5r'"SS\5r(g))OptionalTupleUnionN)nn) BertTokenizer)QuickGELUActivation))BaseModelOutputWithPastAndCrossAttentionsBaseModelOutputWithPooling,BaseModelOutputWithPoolingAndCrossAttentions) Blip2ConfigBlip2VisionConfig) Blip2EncoderBlip2PreTrainedModelBlip2QFormerAttentionBlip2QFormerIntermediateBlip2QFormerOutput)apply_chunking_to_forward)loggingreplace_return_docstringsc>^\rSrSrSrU4SjrSSjrSrU=r$)Blip2TextEmbeddings/z;Construct the embeddings from word and position embeddings.cH>[TU]5 [R"URUR UR S9Ul[R"URUR 5Ul [R"UR URS9Ul [R"UR5UlURS[ R""UR5R%S55 ['USS5UlXlg)N) padding_idxeps position_ids)position_embedding_typeabsolute)super__init__r Embedding vocab_size hidden_size pad_token_idword_embeddingsmax_position_embeddingsposition_embeddings LayerNormlayer_norm_epsDropouthidden_dropout_probdropoutregister_buffertorcharangeexpandgetattrr configselfr5 __class__s k/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/pipelines/blip_diffusion/modeling_blip2.pyr#Blip2TextEmbeddings.__init__2s !||F,=,=v?Q?Q_e_r_rs#%<<0N0NPVPbPb#c f&8&8f>S>STzz&"<"<=  ^U\\&:X:X-Y-`-`ah-ij'.v7PR\']$ cUbUR5SnOSnUc%URSS2XEU-24R5nUbrURU5nURS:XaUR U5nXg-nUb8UR SnURUSS5n[R"X64SS9nOUnURUR5nURU5nURU5nU$)Nrrr!dim)sizercloner(r r*shaperepeatr1cattodtyper+r/) r7 input_idsr query_embedspast_key_values_length seq_length embeddingsr* batch_sizes r9forwardBlip2TextEmbeddings.forwardBs  ")!,JJ  ,,Q0FVlIl0l-lmssuL  --i8J++z9&*&>&>|&L#'= ''--a0 +22:q!D "YY 'AqI %J]]<#5#56 ^^J/ \\*- r;)r+r5r/r r*r()NNNr) __name__ __module__ __qualname____firstlineno____doc__r#rL__static_attributes__ __classcell__r8s@r9rr/s#E$ r;rcj^\rSrSrS\4U4SjjrS\RS\R4SjrSr U=r $)Blip2VisionEmbeddingser5ct>[TU]5 XlURUlUR UlUR Ul[R"[R"SSUR55Ul [R"SURUR UR SS9Ul UR UR -S-UlURS-Ul[R"[R"SURUR55Ulg)NrF) in_channels out_channels kernel_sizestridebias)r"r#r5r& embed_dim image_size patch_sizer Parameterr1randnclass_embeddingConv2dpatch_embedding num_patches num_positionsposition_embeddingr6s r9r#Blip2VisionEmbeddings.__init__fs  ++ ++ ++!||EKK1dnn,MN!yyDOO\`\k\krw !OOt>1D!--1"$,,u{{1d>P>PRVR`R`/a"br; pixel_valuesreturncURSnURRRnURUR US95nUR S5R SS5nURRUSS5R U5n[R"XT/SS9nX`RSS2SURS52SS24R U5-nU$)NrrEr`rrr=) rArhweightrErDflatten transposerfr3r1rCrkr?)r7rmrK target_dtype patch_embeds class_embedsrJs r9rLBlip2VisionEmbeddings.forwardxs!''* ++2288 ++LOO,O,OP #++A.88A> ++22:q"EHHV YY ;C "9"9!=Qzq?Q=QST:T"U"X"XYe"ff r;) rfr5rarbrirjrhrcrk) rNrOrPrQr r#r1TensorrLrSrTrUs@r9rWrWes2c0c$ ELL U\\  r;rWcF^\rSrSrU4SjrSSjrSrU=r$)Blip2QFormerEncoderc >[TU]5 Xl[R"[ UR 5Vs/sHn[X5PM sn5UlSUl gs snf)NF) r"r#r5r ModuleListrangenum_hidden_layersBlip2QFormerLayerlayergradient_checkpointingr7r5 layer_idxr8s r9r#Blip2QFormerEncoder.__init__sX  ]]CHIaIaCb cCbi v 1Cb c  ',# dsA&c U (aSOSn U(aSOSn U(aSOSnU(aSOSn[URR5HnURUnU (aX4-n UbUUOSnUbUUOSn[ URSS5(aR[ R "5(a8U(a[RS5 SnURUUUUUUUUU 5 nOU"UUUUUUUU 5nUSnU(a UUS4- nU(dMU US4-n UR(dMUUS4-nM U (aX4-n U (d[S UUU U U455$[UUU U US 9$) NrFzZ`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...rrrr`c30# UH nUcMUv M g7fNr).0vs r9 .Blip2QFormerEncoder.forward..s" As )last_hidden_statepast_key_values hidden_states attentionscross_attentions) r~r5rrr4r1is_grad_enabledloggerwarning_gradient_checkpointing_funchas_cross_attentiontupler )r7rattention_mask head_maskencoder_hidden_statesencoder_attention_maskr use_cacheoutput_attentionsoutput_hidden_states return_dict query_lengthall_hidden_statesall_self_attentionsall_cross_attentionsnext_decoder_cachei layer_modulelayer_head_maskpast_key_value layer_outputss r9rLBlip2QFormerEncoder.forwards#7BD$5b4%6rD#,R$t{{445A::a=L#$58H$H!.7.CilO3B3N_Q/TXNt{{$[TU]5 URUlSUl[ U5UlX lX!R-S:Xa[ USS9UlSUl OSUl [U5Ul [U5Ul [U5Ul[U5Ulg)NrrT)is_cross_attentionF)r"r#chunk_size_feed_forward seq_len_dimr attentionrcross_attention_frequencycrossattentionrr intermediateintermediate_queryr output_queryoutputrs r9r#Blip2QFormerLayer.__init__s '-'E'E$.v6" 77 71 <"7SW"XD '+D $',D $4V<":6"B.v6(0 r;c UbUSSOSn URUUUUU S9n U Sn U SSn U Sn US:aU SS2SU2SS24nUR(a.Uc [S5eURUUUUUUS9nUSnXSS-n [ UR UR URU5nU RSU:aO[ URUR URU SS2US2SS245n[R"UU/SS9nO,[ URUR URU 5nU4U -n X4-n U $) Nr`)rrrrrz>encoder_hidden_states must be given for cross-attention layers)rr=) rr ValueErrorrrfeed_forward_chunk_queryrrrAfeed_forward_chunkr1rC)r7rrrrrrrrself_attn_past_key_valueself_attention_outputsattention_outputoutputspresent_key_valuequery_attention_outputcross_attention_outputs layer_outputlayer_output_texts r9rLBlip2QFormerLayer.forwards:H9S>"1#5Y] !%   /3 "0" 2!4(2.226 ! %5a,6I%J "''(0$%eff*.*=*=*")*&7 +>+'*A)C&!Ab$AA4--,,  & L %%a(<7$=++00$$$Q q%89 %! %yy,8I)JPQR 4'',,   L  /G+00r;cJURU5nURX!5nU$r)rrr7rintermediate_outputrs r9r$Blip2QFormerLayer.feed_forward_chunkDs)"//0@A{{#6I r;cJURU5nURX!5nU$r)rrrs r9r*Blip2QFormerLayer.feed_forward_chunk_queryIs+"556FG(()<O r;) rrrrrrrrrr)NNNNNFr) rNrOrPrQr#rLrrrSrTrUs@r9rrs51,"#EN r;rc2^\rSrSrSU4SjjrSrSrU=r$) ProjLayeriPc>[TU]5 [R"X5Ul[ 5Ul[R"X25Ul[R"U5Ul [R"X%S9Ul gNr) r"r#rLineardense1 QuickGELUact_fndense2r-r/r+)r7in_dimout_dim hidden_dimdrop_prr8s r9r#ProjLayer.__init__QsY ii3 k ii 4 zz&) g7r;c UnURU5nURURURUR U5555U-nU$r)r+r/rrr)r7xx_ins r9rLProjLayer.forward\sG NN1  LLT[[Q%@A BT Ir;)r+rrrr/)皙?-q=rrUs@r9rrPs 8r;rc^\rSrSrSr\rS\4U4Sjjr\"\ \S9S S\ \ RS\ \ S\ \ S\ \ S \\\ 44 S jj5rS rS rU=r$)Blip2VisionModelifrmr5c4>[TU]U5 XlURn[ U5Ul[ R"X!RS9Ul [U5Ul [ R"X!RS9Ul UR5 gr)r"r#r5r&rWrJrr+r, pre_layernormrencoderpost_layernorm post_init)r7r5rar8s r9r#Blip2VisionModel.__init__jsp   && /7\\)9N9NO#F+  ll9:O:OP r;) output_type config_classrrrrncUbUOURRnUbUOURRnUbUOURRnUc [ S5eUR U5nUR U5nURUUUUS9nUSnURU5nUSS2SSS24nURU5nU(d Xx4USS-$[UUURURS9$)z Returns: Nz You have to specify pixel_values) inputs_embedsrrrrr)r pooler_outputrr) r5rruse_return_dictrrJrrrr rr) r7rmrrrrencoder_outputsr pooled_outputs r9rLBlip2VisionModel.forwardus2C1N-TXT_T_TqTq$8$D $++JjJj &1%* 'tn * d^ * u00 1 * g* Xr;rc ^\rSrSrSrS\4U4SjjrSrSrSr SS\ RS \ \ S \ RS \S \ R4 S jjrSSjrSrU=r$)Blip2QFormerModeliz2 Querying Transformer (Q-Former), used in BLIP-2. r5c >[TU]U5 Xl[UR5Ul[ UR5Ul[R"[R"SURURR55Ul[!US5(a UR"c[$R&"SSS9UlO$[$R&"UR"SS9UlUR"R)SS05 [+URRURRURRS-S S S 9Ul[/UR5UlUR35 g) Nr tokenizerzbert-base-uncasedright)truncation_side bos_tokenz[DEC]rr)rrrrr)r"r#r5rqformer_configrJr vision_configvisual_encoderrrdr1zerosnum_query_tokensr& query_tokenshasattrrrfrom_pretrainedadd_special_tokensr proj_layerrzrrr6s r9r#Blip2QFormerModel.__init__s-   -f.C.CD.v/C/CDLLQ8O8OQWQfQfQrQr)stv{++v/?/?/G*::;N`ghDN*::6;K;K]deDN ));*@A#((44))55,,881<  +6+@+@A  r;c.URR$rrJr(rs r9r&Blip2QFormerModel.get_input_embeddingss...r;c$XRlgrr)r7values r9set_input_embeddings&Blip2QFormerModel.set_input_embeddingss */'r;cUR5H7up#URRURR U5 M9 g)z Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel N)itemsrrr prune_heads)r7heads_to_prunerheadss r9 _prune_headsBlip2QFormerModel._prune_headss< +002LE LL  u % / / ; ;E B3r;r input_shapedevice has_queryrncUR5S:XaUSS2SSS2SS24nOFUR5S:XaUSS2SSSS24nO$[SRX!R55eUR UR S9nSU- 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`. rZNr`zAWrong shape for input_ids (shape {}) or attention_mask (shape {})rpg?g)r>rformatrArDrE)r7rrrrextended_attention_masks r9get_extended_attention_mask-Blip2QFormerModel.get_extended_attention_masks.    1 $&4Qa]&C #    !Q &'5QdA5E&F #SZZ!5!5 #:"<"<4::"<"N#&)@#@H"L&&r;c URUSSS9n U RUR5n U Rn U RSn [ R "XRR5S4[ RS9RUR5n[ R"XR/SS9nUbUOURRnU bU OURRn U bU OURRn Ub,USSRS URR - OSnURRSnUR#U URUS 9nUR5SS nUun nURnUR%U5R&nUnUc[ R "U UU-4US 9nUR)UUU5nUb[+U[,5(aUSR5unnnOUR5unnnUU4n[+U[,5(a"UVs/sHnUR/U5PM nnO>Uc'[ R "UUS 9nUR/U5nOUR/U5nOSnUR1X0RR2R45nUR7UUUUUUUUU U US 9 nUSnUSS2SSS24n U (dUR9USS2SU2SS245$[;UU UR<UR>UR@URBS9$s snf)ay encoder_hidden_states (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, `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.Tensor` of shape `(batch_size, sequence_length)`, `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 (`tuple(tuple(torch.Tensor))` of length `config.n_layers` with each tuple having 4 tensors of: shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): 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`). ptT)return_tensorspaddingrrrpr=Nr`)rFrGrHr)r) rrrrrrrrrr)rrrrrr)"rrDrrFrAr1onesrr?longrCrr5rrrrrJrrr! isinstancelistinvert_attention_mask get_head_maskrrrr r rrrr)!r7 text_input image_inputrrrrrrrrtextrFrK query_attsrrHrembedding_outputrrIrimage_embeds_frozenr encoder_batch_sizeencoder_sequence_length_encoder_hidden_shapemaskencoder_extended_attention_maskrsequence_outputrs! r9rLBlip2QFormerModel.forwards~B~~jt~Lwwt{{#NN __Q' ZZ->->-C-C-Ea-H IQVQ[Q[\__`d`k`kl J0C0C#D!L1B1N-TXT_T_TqTq$8$D $++JjJj &1%PP 3  !"ZZ*jCY6Y)ZdjkN#'"B"B>S^`f"g ! ,/66AVWXAYA^A^A`>"$;QAVA[A[A]>"$;Q$68O#P 0$77`v2w`vX\43M3Md3S`v/2w/'/).4HQW)X&262L2LMc2d/262L2LMc2d/.2 +&&y++2L2L2^2^_ ,, 2"7#B+/!5#%'  *!,'1a0 ???1m|mQ3F#GH H;-'+;;)77&11,==   G3xsM)r5rJrr rrr)F) NNNNNNNNNN)rNrOrPrQrRr r#rrrr1rxrintrrr!rLrSrTrUs@r9rrs{./0C +' +'3Z+' +'  +'  +'^"#!A A r;r))typingrrrr1torch.utils.checkpointr transformersrtransformers.activationsrrtransformers.modeling_outputsr r r /transformers.models.blip_2.configuration_blip_2r r *transformers.models.blip_2.modeling_blip_2rrrrrtransformers.pytorch_utilsrtransformers.utilsrr get_loggerrNrModulerrWrzrrrrrr;r9rGs*) &E [A   H % 2"))2lBII@` "))` Hc cN ,=+=BX ,X r;