+ UV-jpA:^RIHtHtHt^RIHt^RIHtRt !RR]P4t !RR]P4t !RR ]P4t !R R ]P4t!R R ]P4tRRltRRltRRltRRltR#))OptionalTupleTypeNc$VPpVP^,pW18wdkTP^^^^4pVP\P 4p^RIHpV!WAV3RR7PV4pVP^^^^4pV#V#)z:Interpolate absolute positional embeddings to target size.)bicubic_interpolateT)size antialias)dtypeshape transposeastypemxfloat32kernelsr)abs_postgt_sizer src_size old_pos_embedr new_pos_embeds&& o/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/mlx_vlm/models/deepseekocr/sam.pyget_abs_pos_samrs MME}}QH))!Q15 %,,RZZ8  2+ 8 4 &-  &//1a; cnaa]tRt^toRt]P 3V3RlV3RllltV3RlRltRt Vt V;t #)MLPBlockzMLP block with GELU activation.cT<V^8dQhRS[RS[RS[S[P,RR/#) embedding_dimmlp_dimactreturnN)intrnnModule)format __classdict__s"r __annotate__MLPBlock.__annotate__"s;    "))_   rc<\SV`4\P!W4Vn\P!W!4VnV!4VnR#N)super__init__r"Linearlin1lin2r)selfrrr __class__s&&&&rr+MLPBlock.__init__"s9 IIm5 IIg5 5rcN<V^8dQhRS[PRS[P/#rxr rarray)r$r%s"rr&r'-s#11"((1rxx1rc`VPVPVPV444#r))r.rr-r/r4s&&r__call__MLPBlock.__call__-s"yy$))A,/00r)rr-r.) __name__ __module__ __qualname____firstlineno____doc__r"GELUr+r9__static_attributes____classdictcell__ __classcell__r0r%s@@rrrs*) "ww  111rrcXaa]tRt^1toRtRV3RlV3RllltV3RlRltRtVtV;t #) Attentionz=Multi-head Attention block with relative position embeddings.c b<V^8dQhRS[RS[RS[RS[RS[S[S[S[3,,RR/#)rdim num_headsqkv_bias use_rel_pos input_sizer N)r!boolrr)r$r%s"rr&Attention.__annotate__4s\ I I  I I I  I U38_- I  Irc <\SV`4W nW,pVR,Vn\P !W^,VR7Vn\P !W4VnW@nVP'drVfQR4h\P!^V^,,^, V34Vn \P!^V^,,^, V34Vn R#R#)a~ Args: dim (int): Number of input channels. num_heads (int): Number of attention heads. qkv_bias (bool): If True, add a learnable bias to query, key, value. use_rel_pos (bool): If True, add relative positional embeddings to the attention map. input_size (tuple(int, int) or None): Input resolution for calculating the relative positional parameter size. )biasNzBInput size must be provided if using relative positional encoding.g) r*r+rIscaler"r,qkvprojrKrzeros rel_pos_h rel_pos_w)r/rHrIrJrKrLhead_dimr0s&&&&&& rr+Attention.__init__4s" "#t^ 99S'9IIc' &    & TS T& XXq:a='81'>4>>A6A6LE IIa 3 IIa 3 IIa 3    MM>>5;;q>5;;q>5;;q>EMM>>5;;q>5;;q>5;;q>E//>>5;;q>5;;q>EKKPQN3RI44azz 5A44Q1JJ4OA IIar 2 Yq!Q1 % WQ1b ! IIaLr)rIrSrRrUrVrQrK)TFN r;r<r=r>r?r+r9rArBrCrDs@@rrFrF1s$G I ID333rrFcaa]tRt^toRtRR]P ]PR^R3V3RlV3RllltV3RlR lt R t Vt V;t #) BlockzMTransformer blocks with support of window attention and residual propagation.@TFNc<V^8dQhRS[RS[RS[RS[RS[S[P ,RS[S[P ,RS[RS[R S[S[S[S[3,,R R / #) rrHrI mlp_ratiorJ norm_layer act_layerrK window_sizerLr N)r!floatrMrr"r#rr)r$r%s"rr&Block.__annotate__s)')' )')' )'  )' O )' ?)')')'U38_-)' )'rc  <\S V`4V!VRR7Vn\TTTTV^8XdT MW3R7VnV!VRR7Vn\ V\W,4VR7VnWn R#)a Args: dim (int): Number of input channels. num_heads (int): Number of attention heads in each ViT block. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. qkv_bias (bool): If True, add a learnable bias to query, key, value. norm_layer (nn.Module): Normalization layer. act_layer (nn.Module): Activation layer. use_rel_pos (bool): If True, add relative positional embeddings to the attention map. window_size (int): Window size for window attention blocks. If it equals 0, then use global attention. input_size (tuple(int, int) or None): Input resolution for calculating the relative positional parameter size. ư>eps)rIrJrKrL)rrrN) r*r+norm1rFattnnorm2rr!mlprw) r/rHrIrtrJrurvrKrwrLr0s &&&&&&&&&&rr+Block.__init__sw4 .  #%0A%5zK;U    . s3?'; 'rcN<V^8dQhRS[PRS[P/#r3r5)r$r%s"rr&rys#"((rxxrcTpVPV4pVP^8d=VP^,VP^,rC\WP4wrVP V4pVP^8d\ WPXXX34pW!,pWP VPV44,pV#)r)r~rwr window_partitionrwindow_unpartitionrr)r/r4shortcutrcrdpad_hws&& rr9Block.__call__s JJqM   a 771:qwwqzq(,<,<=IA IIaL   a "1&6&6AGA L A' 'r)rrr~rrw r;r<r=r>r?r" LayerNormr@r+r9rArBrCrDs@@rrqrqsAW &(ll%'WW!04)')'VrrqcXaa]tRt^toRtRV3RlV3RllltV3RlRltRtVtV;t #) PatchEmbedzImage to Patch Embedding.c b<V^8dQhRS[S[S[3,RS[S[S[3,RS[RS[RR/#)r kernel_sizestridein_chans embed_dimr N)rr!)r$r%s"rr&PatchEmbed.__annotate__sM  38_ c3h       rc ^<\SV`4\P!W4WR7VnR#)z Args: kernel_size (Tuple): kernel size of the projection layer. stride (Tuple): stride of the projection layer. in_chans (int): Number of input image channels. embed_dim (int): Patch embedding dimension. )rrN)r*r+r"Conv2drS)r/rrrrr0s&&&&&rr+PatchEmbed.__init__s% II [  rcN<V^8dQhRS[PRS[P/#r3r5)r$r%s"rr&rs#"((rxxrc(VPV4pV#r)rSr8s&&rr9PatchEmbed.__call__s IIaLrr)rrr[rorDs@@rrrs!#  &rrcaa]tRt^toRtR^^R^ ^ RRR]P ]PRR^R R3V3RlV3RllltV3R lR lt R t Vt V;t #) SAMEncoderz5Vision Transformer encoder based on SAM architecture.irrrTc"<V^8dQhRS[RS[RS[RS[RS[RS[RS[RS[R S[R S[S[P ,R S[S[P ,R S[R S[RS[RS[S[R3,RS[RR/#)rimg_size patch_sizerrdepthrIrt out_chansrJrurv use_abs_posrKrwglobal_attn_indexes.final_out_chansr N)r!rxrMrr"r#r)r$r%s"rr&SAMEncoder.__annotate__sR R R R  R  R  R R R R R OR  ?R R R R #38_!R "#R $ %R rc <\SV`4Wn\W"3W"3VVR7VnWnV 'd,\ P!^W,W,V34Vn.Vn \V4FIp\TTTT T T T VV9dTM^W,W,3R7 pVPPV4KK \P!WH^RR7\P!VRR7\P!W^^RR7\P!VRR7.Vn\P!RR ^^^RR 7Vn\P!R V^^^RR 7VnR #) a Args: img_size (int): Input image size. patch_size (int): Patch size. in_chans (int): Number of input image channels. embed_dim (int): Patch embedding dimension. depth (int): Depth of ViT. num_heads (int): Number of attention heads in each ViT block. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. out_chans (int): Output channels for neck. qkv_bias (bool): If True, add a learnable bias to query, key, value. norm_layer (nn.Module): Normalization layer. act_layer (nn.Module): Activation layer. use_abs_pos (bool): If True, use absolute positional embeddings. use_rel_pos (bool): If True, add relative positional embeddings to the attention map. window_size (int): Window size for window attention blocks. global_attn_indexes (tuple): Indexes for blocks using global attention. final_out_chans (int): Final output channels after net_3 (1024 for OCR, 896 for OCR-2). )rrrr) rHrIrtrJrurvrKrwrLF)rrPr{r|)rpaddingrPri)rrrrPN)r*r+rr patch_embedrrrT pos_embedblocksrangerqappendr"rrnecknet_2net_3)r/rrrrrrIrtrrJrurvrrKrwrriblockr0s&&&&&&&&&&&&&&&&& rr+SAMEncoder.__init__sJL  %#0+  ' XXH*H,BINDN uA##!%#'+,4G+GKQ$2H4JK E KK  u % IIi F LL - IIi15 Q LL -   YYsCQq!RWX YY a15  rcN<V^8dQhRS[PRS[P/#r3r5)r$r%s"rr&rAs#"((rxxrcXVPV4pVP'd/V\VPVP^,4,pVP F pV!V4pK VP F pV!V4pK VPV4pVPV4pV#)) rrrrr rrrr)r/r4blkns&& rr9SAMEncoder.__call__As   Q     ODNNAGGAJ??A;;CAAA!A JJqM JJqMr)rrrrrrrr)rrn rrDs@@rrrs^?&(ll%'WW  /<##R R hrrc V^8dQhR\PR\R\\P\\\3,3,/#)rr4rwr rr6r!r)r$s"rr&r&[s<suRXXuSRUX=V7WrcVPwr#rEWV,, V,pWV,, V,pV^8gV^8d \P!VR^V3^V3R.4pW6,WG,rVPW(V,WV,W4pVP ^^^^^^4PRWV4p WV 33#)a8 Partition into non-overlapping windows with padding if needed. Args: x (mx.array): input tokens with [B, H, W, C]. window_size (int): window size. Returns: windows: windows after partition with [B * num_windows, window_size, window_size, C]. (Hp, Wp): padded height and width before partition )rrr])r rpadr^r ) r4rwrbrcrdCpad_hpad_wHpWpwindowss && rrr[sJA! {? *k 9E {? *k 9E qyEAI FF1v5zAu:v> ? Y  !;& ;5F WAkk!Q1a+33B RSTG H rc V^8dQhR\PR\R\\\3,R\\\3,R\P/#)rrrwrhwr r)r$s"rr&r&wsR  XX   #s(O  c3h  XX  rc<VwrEVwrgVP^,WE,V,V,,pVPWV,WQ,WR4p V P^^^^^^4PWVR4p WF8gWW8dV RRV1RV1R3,p V #)az Window unpartition into original sequences and removing padding. Args: windows (mx.array): input tokens with [B * num_windows, window_size, window_size, C]. window_size (int): window size. pad_hw (Tuple): padded height and width (Hp, Wp). hw (Tuple): original height and width (H, W) before padding. Returns: x: unpartitioned sequences with [B, H, W, C]. NNNNr])r r^r ) rrwrrrrrcrdrbr4s &&&& rrrws$FB DA aRW 3{BCA  b/2 A Aq!Q1%--aRWWHyyrzz27;c&/SV>WWH*vzSRU=V.VVO 11"((; <bhwkzbhwc,wkc->bhwk).N).Nr)rr r^reinsum)rgrUrVrrq_hq_wk_hk_wRhRwrbrerHr_qrjrks&&&&& rr_r_s(HCHC Sy )B Sy )BIA# ))AC %C II& 0E II& 0E ) E , E MM!3YQ /E MM!3Y3 /E %<r)typingrrrmlx.corecorermlx.nnr"rr#rrFrqrrrrrr_rrrs((01ryy1$X Xv@BII@F6kkb8 B*=Z$r