+ jxRt^RIt^RIHt^RIt^RIHt^RIHt^RIH t H t ^RI H t ^RI Ht^RIHtHtHtHtHt^R IHt^R IHt]!4'd ^R IHtHtMR tR t]!RR7]!RR]444t]!RR7]!RR]444t]!RR7]!RR]444t!RR]P>4t !RR]P>4t!!RR]P>4t"!RR]P>4t#!R R!]P>4t$!R"R#]P>4t%!R$R%]P>4t&!R&R']P>4t'!R(R)]P>4t(!R*R+]P>4t)!R,R-]P>4t*!R.R/]P>4t+]!R0R1]44t,]!R2R3],44t-]!R4R7!R5R6],44t.]!R7R7!R8R9] ],44t/.R:Ot0R#);z9PyTorch Dilated Neighborhood Attention Transformer model.N) dataclass)nn)ACT2FN) BackboneMixinfilter_output_hidden_states)BackboneOutput)PreTrainedModel) ModelOutputOptionalDependencyNotAvailableauto_docstringis_natten_availablerequires_backends)can_return_tuple DinatConfig) natten2dav natten2dqkrpbc\4hNr argskwargss*,i/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/dinat/modeling_dinat.pyrr) ,..c\4hrrrs*,rrr,rrzO Dinat encoder's outputs, with potential hidden states and attentions. ) custom_introc@a]tRt^0toRtRtRtRtRtV3Rlt Rt Vt R#)DinatEncoderOutputa reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, hidden_size, height, width)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions. Nc<V^8dQh/S[PR,;R&S[S[PR3,R,;R&S[S[PR3,R,;R&S[S[PR3,R,;R&#)Nlast_hidden_state. hidden_states attentionsreshaped_hidden_statestorch FloatTensortuple)format __classdict__s"r __annotate__DinatEncoderOutput.__annotate__0s ((4/6!"**C/047>#$e'',-4;%&"%"3"3S"89D@G'r) __name__ __module__ __qualname____firstlineno____doc__r"r#r$r%__annotate_func____static_attributes____classdictcell__r+s@rrr0s)37:>M7;JCG'rrzW Dinat model's outputs that also contains a pooling of the last hidden states. cDa]tRt^FtoRtRtRtRtRtRt V3Rlt Rt Vt R#)DinatModelOutputa pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*, returned when `add_pooling_layer=True` is passed): Average pooling of the last layer hidden-state. reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, hidden_size, height, width)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions. Nc<<V^8dQh/S[PR,;R&S[PR,;R&S[S[PR3,R,;R&S[S[PR3,R,;R&S[S[PR3,R,;R&#)r!Nr" pooler_output.r#r$r%r&)r*r+s"rr,DinatModelOutput.__annotate__Fs$((4/6%&$$t+2'(**C/047>)*e'',-4;+,"%"3"3S"89D@G-rr.) r/r0r1r2r3r"r;r#r$r%r4r5r6r7s@rr9r9Fs. 37.2M:>M7;JCG-rr9z1 Dinat outputs for image classification. cDa]tRt^_toRtRtRtRtRtRt V3Rlt Rt Vt R#)DinatImageClassifierOutputa loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Classification (or regression if config.num_labels==1) loss. logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): Classification (or regression if config.num_labels==1) scores (before SoftMax). reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of shape `(batch_size, hidden_size, height, width)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to include the spatial dimensions. Nc<<V^8dQh/S[PR,;R&S[PR,;R&S[S[PR3,R,;R&S[S[PR3,R,;R&S[S[PR3,R,;R&#)r!Nlosslogits.r#r$r%r&)r*r+s"rr,'DinatImageClassifierOutput.__annotate___s(   d "))*    $++,**C/047>-.e'',-4;/0"%"3"3S"89D@G1rr.) r/r0r1r2r3r@rAr#r$r%r4r5r6r7s@rr>r>_s- &*D'+F:>M7;JCG1rr>cHaa]tRt^ztoRtV3RltV3RlRltRtVtV;t #)DinatEmbeddingsz. Construct the patch and position embeddings. c<\SV`4\V4Vn\P !VP 4Vn\P!VP4Vn R#r) super__init__DinatPatchEmbeddingspatch_embeddingsr LayerNorm embed_dimnormDropouthidden_dropout_probdropoutselfconfig __class__s&&rrGDinatEmbeddings.__init__sG  4V <LL!1!12 zz&"<"<= rcl<V^8dQhRS[PR,RS[S[P,/#r! pixel_valuesNreturn)r'r(r)Tensor)r*r+s"rr,DinatEmbeddings.__annotate__s/E$5$5$<u||ATrclVPV4pVPV4pVPV4pV#r)rIrLrO)rQrW embeddingss&& rforwardDinatEmbeddings.forwards4**<8 YYz* \\*- r)rOrLrI r/r0r1r2r3rGr]r5r6 __classcell__rSr+s@@rrDrDzs>rrDcHaa]tRt^toRtV3RltV3RlRltRtVtV;t #)rHz This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial `hidden_states` (patch embeddings) of shape `(batch_size, height, width, hidden_size)` to be consumed by a Transformer. c h<\SV`4VPpVPVPrCW0nV^8XdM \ R4h\ P!\ P!VPV^,RRRR7\ P!V^,VRRRR74Vn R#)z2Dinat only supports patch size of 4 at the moment.) kernel_sizestridepaddingNrir!r!rl) rFrG patch_size num_channelsrK ValueErrorr SequentialConv2d projection)rQrRrmrn hidden_sizerSs&& rrGDinatPatchEmbeddings.__init__s && $*$7$79I9Ik( ? QR R-- IId'')9vV\fl m IIkQ& PV`f g rc\<V^8dQhRS[PR,RS[P/#rV)r'r(rY)r*r+s"rr,!DinatPatchEmbeddings.__annotate__s*  E$5$5$<  rcVPwr#rEW0P8wd \R4hVPV4pVP ^^^^4pV#)zeMake sure that the channel dimension of the pixel values match with the one set in the configuration.)shapernrorrpermute)rQrW_rnheightwidthr\s&& rr]DinatPatchEmbeddings.forwardsZ)5););& ,, ,w __\2 ''1a3 r)rnrrr_ras@@rrHrHs  "   rrHcnaa]tRt^toRt]P 3V3RlV3RllltV3RlRltRt Vt V;t #)DinatDownsamplerz Convolutional Downsampling Layer. Args: dim (`int`): Number of input channels. norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`): Normalization layer class. c><V^8dQhRS[RS[PRR/#)r!dim norm_layerrXN)intrModule)r*r+s"rr,DinatDownsampler.__annotate__s&((C(RYY($(rc <\SV`4Wn\P!V^V,RRRRR7VnV!^V,4VnR#)r!F)rerfrgbiasNrhrjrk)rFrGrrrq reductionrL)rQrrrSs&&&rrGDinatDownsampler.__init__sC 3CVF\binoq3w' rcN<V^8dQhRS[PRS[P/#)r! input_featurerXr'rY)r*r+s"rr,r#U\\ellrcVPVP^^^^44P^^^^4pVPV4pV#))rryrL)rQrs&&rr]DinatDownsampler.forwardsJ}'<'