+ j1n ^RIt^RIt^RIHt^RIt^RIHt^RIHuHt ^RI H t ^RI HtHt^RIHt^RIHt^RIHtHt^RIHt^R IHt^R IHtHtHt^R IH t ^R I!H"t"^R I#H$t$H%t%!RR]PL4t'!RR]PL4t(!RR]PL4t)!RR]4t*!RR]PL4t+!RR]4t,]!RR7]!RR]444t-!RR]PL4t.!R R!]P^4t0!R"R#]PL4t1!R$R%]*4t2!R&R']*4t3!R(R)]*4t4]]!R*R+]444t5]!R,R7!R-R.]*44t6.R/Ot7R#)0N) dataclass)initialization)ACT2CLSACT2FN)filter_output_hidden_states)GradientCheckpointingLayer)BaseModelOutput ModelOutput)PreTrainedModel)Unpack)TransformersKwargsauto_docstringcan_return_tuple)merge_with_config_defaults)capture_outputs) SLANeXtConfigSLANeXtVisionConfigcpaa]tRt^+toRtV3RltV3RlRltV3RlRltR V3RlRlltR t Vt V;t #) SLANeXtVisionAttentionz=Multi-head Attention block with relative position embeddings.c<\SV`4V^8Xd;VPVP,VPVP,3MW"3pVPVnVP VP,pVR,VnVPVn\P!VP VP ^,VPR7Vn \P!VP VP 4Vn VPVnVP'dVf \R4h\P !\"P$!^V^,,^, V44Vn\P !\"P$!^V^,,^, V44VnR#R#)rbiasNzBInput size must be provided if using relative positional encoding.g)super__init__ image_size patch_sizenum_attention_heads hidden_sizescaleattention_dropoutdropoutnnLinearqkv_biasqkvproj use_rel_pos ValueError Parametertorchzeros rel_pos_h rel_pos_w)selfconfig window_size input_sizehead_dim __class__s&&& m/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/slanext/modeling_slanext.pyrSLANeXtVisionAttention.__init__.sY a  &"3"3 3V5F5F&J[J[5[ \+  $*#=#= %%)C)CCt^ // 99V//1C1Ca1Gfoo^IIf00&2D2DE !--    ! !eff \\%++a*Q-6G!6KX*VWDN\\%++a*Q-6G!6KX*VWDN cZ<V^8dQhRS[RS[RS[PRS[P/#)q_sizek_sizerel_posreturn)intr*Tensor)format __classdict__s"r4 __annotate__#SLANeXtVisionAttention.__annotate__Gs177#7s7U\\7ell7r6c v\^\W4,^, 4p\P!VP ^VP ^,R4P ^^4VRR7pVP RV4P^^4p\P!V4R,\W!, R4,p\P!V4R,\W, R4,pWg, V^, \W, R4,,pWXP4,#)aa Get relative positional embeddings according to the relative positions of query and key sizes. Args: q_size (int): size of the query. k_size (int): size of key k. rel_pos (`torch.Tensor`): relative position embeddings (L, channel). Returns: Extracted positional embeddings according to relative positions. linear)sizemode?)NNNN)NrI) r=maxF interpolatereshapeshape transposepermuter*arangelong) r.r9r:r; max_rel_distrel_pos_resizedq_coordsk_coordsrelative_coordss &&&& r4 get_rel_pos"SLANeXtVisionAttention.get_rel_posGs 1s622Q67 -- OOAw}}Q/ 4 > >q! D  *11"lCKKAqQ<<'03v3LL<<'03v3LL#.6A:V_VYAZ2ZZ33566r6c <V^8dQhRS[PRS[PRS[PRS[S[S[3,RS[S[S[3,RS[P/#)r8queryr,r-r9r:r<)r*r>tupler=)r?r@s"r4rArBgsg("("||("<<("<< (" c3h (" c3h (" ("r6c *VwrgVwrVPWhV4p VPWyV4p VPwrpVPWW~4p\P!RW4p\P!RW4pVR,VR,,pV#)a& Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py Args: query (`torch.Tensor`): query q in the attention layer with shape (batch_size, query_height * query_width, channel). rel_pos_h (`torch.Tensor`): relative position embeddings (Lh, channel) for height axis. rel_pos_w (`torch.Tensor`): relative position embeddings (Lw, channel) for width axis. q_size (tuple): spatial sequence size of query q with (query_height, query_width). k_size (tuple): spatial sequence size of key k with (key_height, key_width). Returns: decomposed_rel_pos (`torch.Tensor`): decomposed relative position embeddings. zbhwc,hkc->bhwkzbhwc,wkc->bhwk)rIrIrIrIN)rIrIrINrI)rXrNrMr*einsum)r.r[r,r-r9r: query_height query_width key_height key_widthrelative_position_heightrelative_position_width batch_size_dimreshaped_queryrel_hrel_wdecomposed_rel_poss&&&&&& r4get_decomposed_rel_pos-SLANeXtVisionAttention.get_decomposed_rel_posgs8%+! & #'#3#3Li#X "&"2"2;9"U"[[ szR -~X -~W"#34u=M7NN!!r6cx<V^8dQhRS[PRS[S[PS[P3,/#r8 hidden_statesr<)r*r>r\)r?r@s"r4rArBs7))U\\)eTYT`T`bgbnbnTnNo)r6cVPwr4rVVPV4PW4V,^VPR4P ^^^^^4pVP^W0P,WE,R4P ^4wrp WP ,V PRR4,p VP'dDVPWPVPWE3WE34p V PV 4p W,p \PPP!V \P"RR7P%VP&4p \PP)WP(VP*R7p W,PW0PWER4pVP ^^^^^4PW4VR4pVP-V4pW3#))dtyperg)ptrainingrH)rNr%rMrrPunbindrrOr'rlr,r- reshape_asr*r" functionalsoftmaxfloat32torsr!rur&)r.rpoutput_attentionsreheightwidthrfr%r[keyvalue attn_weightsrk attn_probs attn_outputs&&& r4forwardSLANeXtVisionAttention.forwards'4':':$ E HH] # WZ%D4L4Lb Q WQ1a #  KK:8P8P+PRXR`bdellmnoE *cmmB.CC    !%!!>|!L '00=GGJ,? ::&67::&67yy!1q NQQRbRhRhi #--a3 ,,|:BB1EG#:A>= **r6)rrrr rrrrrrrrrrs@@r4rrsH+++r6rc<aa]tRt^toRV3RlltRtRtVtV;t#) SLANeXtMLPc<\SV`4\P!W4Vn\P!W4VnVf\P !4VnR#\V,!4VnR#r) rrr"r#fc1fc2Identityract_fn)r.r out_channels activationr3s&&&&r4rSLANeXtMLP.__init__sN 99[699[7'1'9bkkm wz?R?T r6clVPV4pVPV4pVPV4pV#r)rrrr.rps&&r4rSLANeXtMLP.forwards2/ /  M2 r6)rrrrrrs@@r4rrsU r6rc~aa]tRt^toRtRtR tRtRR.t] P!4V3Rl4t V3Rlt Rt VtV;t#) SLANeXtPreTrainedModelbackbone pixel_valuesTstructure_attention_cellstructure_generatorc n<\SV`V4\V\4'd0VPe"\ P !VPR4\V\4'dUVP'dC\ P !VPR4\ P !VPR4\V\P4'dVP^8d(R\P!VP4, M^p\ P !VP"V)V4\ P !VP$V)V4VP&e$\ P !VP&V)V4VP(e$\ P !VP(V)V4\V\*4'dR\P!VP,PR,4, pVP.3FpVP14F{p\V\P24'gK%\ P !VP4V)V4VP6fKX\ P !VP6V)V4K} K R#R#)zInitialize the weightsNgrG)r _init_weights isinstanceSLANeXtVisionEncoder pos_embedinit constant_rr'r,r-r"rrmathsqrtuniform_ weight_ih weight_hhbias_ihbias_hhSLANeXtSLAHeadr/rchildrenr#weightr)r.modulestd generatorlayerr3s&& r4r$SLANeXtPreTrainedModel._init_weightss f% f2 3 3+v//5 f4 5 5!!!v//5v//5 fbjj ) )9?9K9Ka9O# &"4"455UVC MM&**SD# 6 MM&**SD# 6~~) fnnsdC8~~) fnnsdC8 fn - - $++"9"9C"?@@C$88: &//1E!%33 ellSD#> ::1 MM%**sdC@ 2; .r6c&<V^8dQh/S[;R&#r8r/r)r?r@s"r4rA#SLANeXtPreTrainedModel.__annotate__s r6image)rrrrbase_model_prefixmain_input_nameinput_modalitiessupports_gradient_checkpointing_keep_in_fp32_modules_strictr*no_gradr__annotate_func__rrrrs@@r4rrsI"$O!&*#$>@U#V  ]]_"A"Ar6rcDaa]tRtRtoV3RltV3RlRltRtVtV;t#)SLANeXtMLPBlocki c<\SV`4\P!VPVP 4Vn\P!VP VP4Vn\VP,Vn R#r) rrr"r#rmlp_dimlin1lin2r hidden_actactr.r/r3s&&r4rSLANeXtMLPBlock.__init__sX IIf00&..A IIfnnf.@.@A &++,r6cN<V^8dQhRS[PRS[P/#ror*r>)r?r@s"r4rASLANeXtMLPBlock.__annotate__s#U\\ellr6clVPV4pVPV4pVPV4pV#r)rrrrs&&r4rSLANeXtMLPBlock.forwards2 -0 /  -0 r6)rrrrrs@@r4rr s- r6rchaa]tRtRtoV3RltV3RlRltV3RlRltV3RlRltR tVt V;t #) SLANeXtVisionLayeric4<\SV`4\P!VPVP R7Vn\W4Vn\P!VPVP R7Vn \V4Vn W n R#))epsN) rrr" LayerNormrlayer_norm_eps layer_norm1rattn layer_norm2rmlpr0)r.r/r0r3s&&&r4rSLANeXtVisionLayer.__init__sj <<(:(:@U@UV*6? <<(:(:@U@UV"6*&r6c <V^8dQhRS[PRS[RS[S[PS[S[S[3,3,/#)r8rpr0r<r*r>r=r\)r?r@s"r4rASLANeXtVisionLayer.__annotate__$sE00ell00QVW\WcWcejknpsksetWtQu0r6c  VPwr4rVW$V,, V,pW%V,, V,p\P!V^^^V^V34pWG,WX,rVPW9V,W*V,W&4pVP ^^^^^^4P 4PRW"V4p WV 33#)aw Args: Partition into non-overlapping windows with padding if needed. hidden_states (tensor): input tokens with [batch_size, height, width, channel]. window_size (int): window size. Returns: windows: windows after partition with [batch_size * num_windows, window_size, window_size, channel]. (pad_height, pad_width): padded height and width before partition rH)rNrKpadrMrP contiguous) r.rpr0rer~rchannelpad_hpad_w pad_height pad_widthwindowss &&& r4window_partition#SLANeXtVisionLayer.window_partition$s.;-@-@* E 33{B{22kAmaAua-GH & I%-- k1;[@XZe  ''1aAq9DDFNNrS^mtuY///r6c <V^8dQhRS[PRS[RS[S[S[3,RS[S[S[3,RS[P/#)r8r r0 padding_shapeoriginal_shaper<r)r?r@s"r4rAr<sR||25FKCQTHoglmprumugv r6c \VwrVVwrxVP^,WV,V,V,,p VPWV,Wb,W"R4p V P^^^^^^4P4PWVR4p V RRV1RV1R3,P4p V #)a Args: Window unpartition into original sequences and removing padding. hidden_states (tensor): input tokens with [batch_size * num_windows, window_size, window_size, channel]. window_size (int): window size. padding_shape (Tuple): padded height and width (pad_height, pad_width). original_shape (Tuple): original height and width (height, width) before padding. Returns: hidden_states: unpartitioned sequences with [batch_size, height, width, channel]. rINrH)rNrMrPr) r.r r0rrr r r~rrerps &&&&& r4window_unpartition%SLANeXtVisionLayer.window_unpartition<s"!. & ]]1%**@K*OS^*^_  k193K[gi   ! !!Q1a 3 > > @ H Hajln o &a&&5&!&;<GGI r6c^<V^8dQhRS[PRS[S[P,/#ro)r*r>r\r)r?r@s"r4rArZs*U\\eE e'',-4;!r6r) rrrrrrr rpr!rrrr@s@r4rros( .2L26:>M7;J!r6rc<aa]tRtRtoRtV3RltRtRtVtV;t #)SLANeXtPatchEmbeddingsiz This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a Transformer. c <\SV`4VPVPr2VPVP rT\ V\PP4'dTMW"3p\ V\PP4'dTMW33pV^,V^,,V^,V^,,,pW nW0nW@nW`n \P!WEW3R7Vn R#)r) kernel_sizestrideN)rrrr num_channelsrr collectionsabcIterable num_patchesr"Conv2d projection)r.r/rrr)rr-r3s&& r4rSLANeXtPatchEmbeddings.__init__s !'!2!2F4E4EJ$*$7$79K9Kk#-j+//:R:R#S#SZZdYq #-j+//:R:R#S#SZZdYq !!} 1 5*Q-:VW=:XY $$(&))L:ir6c pVPwr#rEW0P8wd \R4hW@P^,8wgWPP^,8wd;\RV RV RVP^, RVP^, R2 4hVP V4P ^^^^4pV#)zeMake sure that the channel dimension of the pixel values match with the one set in the configuration.zInput image size (*z) doesn't match model (z).)rNr)r(rr/rP)r.rrer)r~r embeddingss&& r4rSLANeXtPatchEmbeddings.forwards2>2D2D/ & ,, ,w  __Q' '5OOA4F+F$VHAeW4KDOO\]L^K__`aeapapqras`ttvw __\2::1aAF r6)rr)r-rr/ rrrrrrrrrrrs@@r4r%r%s j  r6r%cZaa]tRtRtoRtRRRR/V3RlltV3RlV3R lltR tVtV;t #) SLANeXtLayerNormia5LayerNorm that supports two data formats: channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height, width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width). rgư> data_format channels_lastc d<\SV`!V3RV/VBVR9d\RV 24hW0nR#)rzUnsupported data format: N)r9channels_first)rrNotImplementedErrorr8)r.normalized_shaperr8rr3s&&$$,r4rSLANeXtLayerNorm.__init__s= )=s=f= A A%(A+&OP P&r6cN<V^8dQhRS[PRS[P/#)r8featuresr<r)r?r@s"r4rASLANeXtLayerNorm.__annotate__s#    r6c <VPR8Xd:VP^^^^4p\SV` V4pVP^^^^4pV#\SV` V4pV#)zt Args: features: Tensor of shape (batch_size, channels, height, width) OR (batch_size, height, width, channels) r;)r8rPrr)r.r@r3s&&r4rSLANeXtLayerNorm.forwardsj   / /''1a3Hwx0H''1a3Hwx0Hr6r8r5rs@@r4r7r7s, ''/'   r6r7cDaa]tRtRtoV3RlV3RlltRtRtVtV;t#)SLANeXtVisionNeckic <V^8dQhRS[/#rr)r?r@s"r4rASLANeXtVisionNeck.__annotate__sbb2br6cn<\SV`4Wn\P!VP VP ^RR7Vn\VP RR7Vn \P!VP VP ^^RR7Vn \VP RR7Vn R#)rF)r'rr;rD)r'paddingrN) rrr/r"r.routput_channelsconv1r7rconv2rrs&&r4rSLANeXtVisionNeck.__init__s  YYv1163I3IWX_de +F,B,BP`aYYv55v7M7M[\fgnst +F,B,BP`ar6cVP^^^^4pVPV4pVPV4pVPV4pVP V4pV#)r)rPrMrrNrrs&&r4rSLANeXtVisionNeck.forwardsZ%--aAq9  =1 ((7  =1 ((7 r6)r/rMrNrrrrs@@r4rFrFsbbr6rFcaa]tRtRtoR]R]/tR tV3RlV3RlltRt ] ] !RR7R V3R lR ll44t R t VtV;t#)rirpr!c <V^8dQhRS[/#rrH)r?r@s"r4rA!SLANeXtVisionEncoder.__annotate__s2r6c<\SV`V4WnVPVn\ V4VnRVnVP'ds\P!\P!^VPVP,VPVP,VP44Vn\P!4Vn\!VP"4FGp\%TW!P&9d VP(M^R7pVPP+V4KI \-V4VnRVnVP34R#)N)r0F)rrr/rr% patch_embedr use_abs_posr"r)r*r+rr ModuleListlayersrangenum_hidden_layersrglobal_attn_indexesr0appendrFneckgradient_checkpointing post_init)r.r/irr3s&& r4rSLANeXtVisionEncoder.__init__s    ++1&9    \\ %%):)::%%):)::&& DNmmo v//0A&23;U;U2UF..[\E KK  u % 1&f- &+# r6cVP#r)rV)r.s&r4get_input_embeddings)SLANeXtVisionEncoder.get_input_embeddingssr6F)tie_last_hidden_statescn<V^8dQhRS[PR,RS[S[,RS[S[,/#)r8rNrr<)r*rr r r\r)r?r@s"r4rArTs<  !--4 GMN`Ga + + r6c Vf \R4hVPV4pVPeW0P,pVPF pV!V4pK VP V4p\ VR7#)Nz You have to specify pixel_values)r )r(rVrrYr^r)r.rrrp layer_modules&&, r4rSLANeXtVisionEncoder.forwardsr  ?@ @((6 >> %)NN:M KKL(7M( -0 )+  r6)r/r_rrYr^rVrrr)rrrrrr_can_record_outputsrrrdrrrrrrrs@@r4rrsV*,> Nde!>  E2  3   r6rcTaa]tRtRtoRV3RlV3RllltV3RlRltRtVtV;t#)SLANeXtBackbonei c.<V^8dQhRS[R,/#r8r/Ndict)r?r@s"r4rASLANeXtBackbone.__annotate__ s  t  r6c <\SV`V4\VP4Vn\ P !VPVP^^^RR7Vn VP4R#)rrF)r'r(rKrN) rrr vision_config vision_towerr"r.post_conv_in_channelspost_conv_out_channels post_convr`r.r/rr3s&&,r4rSLANeXtBackbone.__init__ s^  01E1EF  ( (&*G*GUV_`jkrw  r6cJ<V^8dQhRS[PRS[S[,/#)r8rpr)r*r>r r )r?r@s"r4rArrs%  U\\ VDV=W r6c VP!V3/VBpVPVP4pVP^4P ^^4p\ VVP VPR7#)r8)r rpr!)rurxr flattenrOr rpr!)r.rpr vision_outputs&&, r4rSLANeXtBackbone.forwardsj))-B6B }'F'FG %--a0::1a@ +'55$//  r6)rxrurrrs@@r4rmrm s     r6rmc~aa]tRtRtoR]/tRV3RlV3Rlllt]]] RV3RlRll444t Rt Vt V;t #) ri$r!c.<V^8dQhRS[R,/#rorp)r?r@s"r4rASLANeXtSLAHead.__annotate__)s  t  r6c <\SV`V4\VPVPVP 4Vn\VPVP 4VnVP4R#r) rrrrwrrrrrr`rys&&,r4rSLANeXtSLAHead.__init__)s_  (?  ) )6+=+=v?R?R) %$.f.@.@&BUBU#V  r6cr<V^8dQhRS[PRS[PR,RS[S[,/#)r8rptargetsNr)r*rr>r r )r?r@s"r4rAr:sCff((f$f+, fr6c n\P!VP^,VPP3\P VP R7p\P!VP^,.\PVP R7p.p.p\VPP^,4EF p\P!WPPP4P4p VPWAP4V 4wrHVPV4p V P!^R7pVP#V 4VP#V4\P$!V^R7P'VPP^, 4P)R4P+4'gEK M \P,!\P$!V^R7R\P R7P/VP04p \3WR7#)r)rsdevice)rErsr)rgr)r rprH)r*r+rNr/rr{rrRrZmax_text_lengthrKone_hotrfloatrrargmaxr]stackeqanyallrzr|rsr ) r.rprrr@predicted_charsstructure_preds_liststructure_ids_listrfembedding_featurestructure_stepstructure_predss &&&, r4rSLANeXtSLAHead.forward7s;;   #T[[%<%< =U]][h[o[o  ++M,?,?,B+C5::^k^r^rs!t{{22Q67A ! /;;;S;S T Z Z \ 77BUBUBWYjkKH!55h?N,333:O ' ' 7  % %o 6{{-15889Q9QTU9UVZZ[]^bbdd8))EKK0D!$LRT\a\i\ijmm    eer6)rrr)rrrrrrkrrrrrrrrrs@@r4rr$sO-    ff! ffr6rc8a]tRtRtoRtRtRtV3RltRtVt R#) SLANeXtForTableRecognitionOutputiXaY head_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Hidden-states of the SLANeXtSLAHead at each prediction step, varies up to max `self.config.max_text_length` states (depending on early exits). head_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Attentions of the SLANeXtSLAHead at each prediction step, varies up to max `self.config.max_text_length` attentions (depending on early exits). Ncv<V^8dQh/S[PR,;R&S[PR,;R&#)r8Nhead_hidden_stateshead_attentions)r*r)r?r@s"r4rA-SLANeXtForTableRecognitionOutput.__annotate__Xs5))D07&&-4r6r) rrrrrrrrrrr#s@r4rrXs4804Or6rz SLANeXt Table Recognition model for table recognition tasks. Wraps the core SLANeXtPreTrainedModel and returns outputs compatible with the Transformers table recognition API. cdaa]tRtRtoV3RlV3Rllt]]V3RlRl44tRtVt V;t #)SLANeXtForTableRecognitionifc <V^8dQhRS[/#rr)r?r@s"r4rA'SLANeXtForTableRecognition.__annotate__ms}r6c<\SV`V4\VR7Vn\ VR7VnVP 4R#))r/N)rrrmrrheadr`rs&&r4r#SLANeXtForTableRecognition.__init__ms2  'v6 "&1  r6c<V^8dQhRS[PRS[S[,RS[S[P,S[,/#)r8rrr<)r*rr r r\r)r?r@s"r4rArusB   !--  9?@R9S  u  !$D D  r6c VP!V3/VBpVP!VP3/VBp\VPVPVP VPVP R7#))r rpr!rr)rrr rrpr!)r.rrbackbone_outputs head_outputss&&, r4r"SLANeXtForTableRecognition.forwardssl  ==@@yy!1!C!CNvN /*<<*88'22+99(33   r6)rr) rrrrrrrrrrrrs@@r4rrfs0      r6r)rrmrr)8r*r dataclassesrr*torch.nnr"torch.nn.functionalryrKrr activationsrrbackbone_utilsrmodeling_layersrmodeling_outputsr r modeling_utilsr processing_utilsr utilsr rr utils.genericrutils.output_capturingrconfiguration_slanextrrModulerrrrrrrr%rr7rFrrmrrr__all__rr6r4rs, ! &*99<-&II75EB)RYYB)J+bii+B  +A_+A\ bii Q3Qh   < <  < RYY Fr||4 (6 16 r , 01f+1fh  5 5  5  !7   . hr6