
    cCi_                        S r SSKrSSKrSSKJr  SSKJrJrJrJ	r	  SSK
rSSKrSSKJr  SSKJr  SSKJr  SS	KJr  SS
KJr  SSKJrJrJr  SSKJrJr  SSKJr  SSKJ r J!r!J"r"J#r#J$r$J%r%  SSK&J'r'  SSK(J)r)J*r*J+r+  S r, SNS\RZ                  S\.S\.S\.S\.S\RZ                  4S jjr/SOS jr0S r1S r2\\"" SS9 " S S \ 5      5       5       r3\\"" S!S9 " S" S#\ 5      5       5       r4\\" " S$ S%\ 5      5       5       r5 " S& S'\Rl                  5      r7 " S( S)\Rl                  5      r8 SPS*\Rl                  S+\RZ                  S,\RZ                  S-\RZ                  S.\\RZ                     S/\.S0\.4S1 jjr9 " S2 S3\Rl                  5      r: " S4 S5\Rl                  5      r; " S6 S7\5      r<\" " S8 S9\5      5       r= " S: S;\Rl                  5      r> " S< S=\Rl                  5      r?\"" S>S9 " S? S@\=5      5       r@ " SA SB\Rl                  5      rA " SC SD\Rl                  5      rB\"" SES9 " SF SG\=5      5       rC\" " SH SI\=5      5       rD\"" SJS9 " SK SL\=5      5       rE/ SMQrFg)QzPyTorch Siglip model.    N)	dataclass)AnyCallableOptionalUnion)nn)_calculate_fan_in_and_fan_out   )ACT2FN)_prepare_4d_attention_mask)GradientCheckpointingLayer)BaseModelOutputBaseModelOutputWithPoolingImageClassifierOutput)ALL_ATTENTION_FUNCTIONSPreTrainedModel)Unpack)ModelOutputTransformersKwargsauto_docstringcan_return_tuplefilter_out_non_signature_kwargs	torch_int)check_model_inputs   )SiglipConfigSiglipTextConfigSiglipVisionConfigc                    S nXSU-  -
  :  d  XSU-  -   :  a  [         R                  " SSS9  U" X1-
  U-  5      nU" XA-
  U-  5      nU R                  SU-  S-
  SU-  S-
  5        U R                  5         U R	                  U[
        R                  " S5      -  5        U R                  U5        U R                  X4S9  g )Nc                 h    S[         R                  " U [         R                  " S5      -  5      -   S-  $ )N      ?       @)matherfsqrt)xs    d/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/siglip/modeling_siglip.pynorm_cdf _trunc_normal_.<locals>.norm_cdf0   s(    dhhq499S>122c99       zjmean is more than 2 std from [a, b] in nn.init.trunc_normal_. The distribution of values may be incorrect.)
stacklevelr   r"   )minmax)	warningswarnuniform_erfinv_mul_r#   r%   add_clamp_)tensormeanstdabr(   lus           r'   _trunc_normal_r=   -   s    : 	1s7{1s7{ 2;	
 	!(c!"A!(c!"A OOAEAIq1uqy) NN KKdiin$%
KK MMaMr*   r6   r7   r8   r9   r:   returnc                     [         R                  " 5          [        U SSX45        U R                  U5      R	                  U5        SSS5        g! , (       d  f       g= f)a=  Fills the input Tensor with values drawn from a truncated
normal distribution. The values are effectively drawn from the
normal distribution :math:`\mathcal{N}(     ext{mean},      ext{std}^2)`
with values outside :math:`[a, b]` redrawn until they are within
the bounds. The method used for generating the random values works
best when :math:`a \leq     ext{mean} \leq b`.

NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the
bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0
and the result is subsequently scaled and shifted by the mean and std args.

Args:
    tensor: an n-dimensional `torch.Tensor`
    mean: the mean of the normal distribution
    std: the standard deviation of the normal distribution
    a: the minimum cutoff value
    b: the maximum cutoff value
r   r!   N)torchno_gradr=   r3   r4   )r6   r7   r8   r9   r:   s        r'   trunc_normal_tf_rB   Q   s<    * 
vq#q,Cd# 
s   /A
Ac                 F   [        U 5      u  pEUS:X  a  UnOUS:X  a  UnOUS:X  a  XE-   S-  nUW-  nUS:X  a"  [        U [        R                  " U5      S-  S9  g US:X  aB  [        R
                  " 5          U R                  [        R                  " U5      S9  S S S 5        g US	:X  aK  [        R                  " S
U-  5      n[        R
                  " 5          U R                  U* U5        S S S 5        g [        SU 35      e! , (       d  f       g = f! , (       d  f       g = f)Nfan_infan_outfan_avgr+   truncated_normalg۶%?r8   normaluniformr
   zinvalid distribution )	r	   rB   r#   r%   r@   rA   normal_r1   
ValueError)	r6   scalemodedistributionrD   rE   denomvariancebounds	            r'   variance_scaling_rS   k   s    3F;OFx				!Q&u}H))TYYx%8;N%NO		!]]_NNtyy2N3 _		"		!h,']]_OOUFE* _ 0?@@ _ _s   5$DD
D
D c                     [        U SSS9  g )NrD   rG   rN   rO   rS   r6   s    r'   lecun_normal_rX      s    f8:LMr*   c                     [        U SSS9  g )NrD   rI   rU   rV   rW   s    r'   default_flax_embed_initrZ      s    f8(Cr*   z}
    Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
    )custom_introc                       \ rS rSr% SrSr\\R                     \	S'   Sr
\\R                     \	S'   Sr\\\R                  S4      \	S'   Sr\\\R                  S4      \	S'   S	rg)
SiglipVisionModelOutput   z
image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
    The image embeddings obtained by applying the projection layer to the pooler_output.
Nimage_embedslast_hidden_state.hidden_states
attentions )__name__
__module____qualname____firstlineno____doc__r_   r   r@   FloatTensor__annotations__r`   ra   tuplerb   __static_attributes__rc   r*   r'   r]   r]      sr    
 15L(5,,-459x 1 129=AM8E%"3"3S"89:A:>Ju00#567>r*   r]   ze
    Base class for text model's outputs that also contains a pooling of the last hidden states.
    c                       \ rS rSr% SrSr\\R                     \	S'   Sr
\\R                     \	S'   Sr\\\R                  S4      \	S'   Sr\\\R                  S4      \	S'   S	rg)
SiglipTextModelOutput   z
text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
    The text embeddings obtained by applying the projection layer to the pooler_output.
Ntext_embedsr`   .ra   rb   rc   )rd   re   rf   rg   rh   rp   r   r@   ri   rj   r`   ra   rk   rb   rl   rc   r*   r'   rn   rn      sr    
 04K%++,359x 1 129=AM8E%"3"3S"89:A:>Ju00#567>r*   rn   c                      \ rS rSr% SrSr\\R                     \	S'   Sr
\\R                     \	S'   Sr\\R                     \	S'   Sr\\R                     \	S'   Sr\\R                     \	S'   Sr\\	S	'   Sr\\	S
'   S\\   4S jrSrg)SiglipOutput   am  
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
    Contrastive loss for image-text similarity.
logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
    The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
    similarity scores.
logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`):
    The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
    similarity scores.
text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):
    The text embeddings obtained by applying the projection layer to the pooled output of [`SiglipTextModel`].
image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`):
    The image embeddings obtained by applying the projection layer to the pooled output of [`SiglipVisionModel`].
text_model_output (`BaseModelOutputWithPooling`):
    The output of the [`SiglipTextModel`].
vision_model_output (`BaseModelOutputWithPooling`):
    The output of the [`SiglipVisionModel`].
Nlosslogits_per_imagelogits_per_textrp   r_   text_model_outputvision_model_outputr>   c                 J   ^  [        U 4S jT R                  5        5       5      $ )Nc              3   n   >#    U  H*  nUS ;  a  TU   O[        TU5      R                  5       v   M,     g7f))rw   rx   N)getattrto_tuple).0kselfs     r'   	<genexpr>(SiglipOutput.to_tuple.<locals>.<genexpr>   s<      
   LLDGRYZ^`aRbRkRkRmm s   25)rk   keysr   s   `r'   r|   SiglipOutput.to_tuple   s#     
YY[
 
 	
r*   rc   )rd   re   rf   rg   rh   rt   r   r@   ri   rj   ru   rv   rp   r_   rw   r   rx   rk   r   r|   rl   rc   r*   r'   rr   rr      s    & )-D(5$$
%,48hu001837OXe//07/3K%++,304L(5,,-448186:3:
%* 
r*   rr   c                      ^  \ rS rSrS\4U 4S jjrS\R                  S\S\S\R                  4S jr	SS	\R                  S\R                  4S
 jjrSrU =r$ )SiglipVisionEmbeddings   configc                 ^  > [         TU ]  5         Xl        UR                  U l        UR
                  U l        UR                  U l        [        R                  " UR                  U R                  U R                  U R                  SS9U l
        U R
                  U R                  -  S-  U l        U R                  U l        [        R                  " U R                  U R                  5      U l        U R                  S[         R"                  " U R                  5      R%                  S5      SS9  g )Nvalid)in_channelsout_channelskernel_sizestridepaddingr+   position_idsr   F
persistent)super__init__r   hidden_size	embed_dim
image_size
patch_sizer   Conv2dnum_channelspatch_embeddingnum_patchesnum_positions	Embeddingposition_embeddingregister_bufferr@   arangeexpandr   r   	__class__s     r'   r   SiglipVisionEmbeddings.__init__   s    ++ ++ ++!yy++?? 
 !OOt>1D!--"$,,t/A/A4>>"R^U\\$:L:L-M-T-TU\-]jopr*   
embeddingsheightwidthr>   c                    UR                   S   nU R                  R                  R                   S   n[        R                  R                  5       (       d%  XE:X  a   X#:X  a  U R                  U R                  5      $ U R                  R                  R                  S5      nUR                   S   nX R                  -  nX0R                  -  n	[        US-  5      n
UR                  SXU5      nUR                  SSSS5      n[        R                  R                  UX4SSS	9nUR                  SSSS5      R                  SSU5      nU$ )
a  
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
images. This method is also adapted to support torch.jit tracing and no class embeddings.

Adapted from:
- https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
- https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
r   r   r   g      ?r
   r+   bicubicF)sizerN   align_corners)shaper   weightr@   jit
is_tracingr   	unsqueezer   r   reshapepermuter   
functionalinterpolateview)r   r   r   r   r   r   patch_pos_embeddim
new_height	new_widthsqrt_num_positionss              r'   interpolate_pos_encoding/SiglipVisionEmbeddings.interpolate_pos_encoding   s:    !&&q)//66<<Q? yy##%%+*F6?**4+<+<==1188BB1Er".
__,	&}c'9:)11!5G]`a)11!Q1=--33(	 4 
 *11!Q1=BB1b#Nr*   pixel_valuesc                 X   UR                   u    p4nU R                  R                  R                  nU R                  UR	                  US95      nUR                  S5      R                  SS5      nU(       a  XR                  XU5      -   nU$ XR                  U R                  5      -   nU$ )N)dtyper+   r   )
r   r   r   r   toflatten	transposer   r   r   )	r   r   r   _r   r   target_dtypepatch_embedsr   s	            r'   forwardSiglipVisionEmbeddings.forward  s    *001e++2288++LOO,O,OP!))!,66q!<
##&C&CJX]&^^J  $&=&=d>O>O&PPJr*   )r   r   r   r   r   r   r   r   F)rd   re   rf   rg   r   r   r@   Tensorintr   ri   r   rl   __classcell__r   s   @r'   r   r      se    q1 q($5<< $ $UX $]b]i]i $L
E$5$5 
Z_ZfZf 
 
r*   r   c            	          ^  \ rS rSrS\4U 4S jjr   S
S\\R                     S\\R                     S\\R                     S\R                  4S jjrS	rU =r$ )SiglipTextEmbeddingsi!  r   c                 N  > [         TU ]  5         UR                  n[        R                  " UR
                  U5      U l        [        R                  " UR                  U5      U l        U R                  S[        R                  " UR                  5      R                  S5      SS9  g )Nr   r   Fr   )r   r   r   r   r   
vocab_sizetoken_embeddingmax_position_embeddingsr   r   r@   r   r   r   r   r   r   s      r'   r   SiglipTextEmbeddings.__init__"  s    &&	!||F,=,=yI"$,,v/M/My"Y 	ELL)G)GHOOPWXej 	 	
r*   	input_idsr   inputs_embedsr>   c                 <   Ub  UR                   S   OUR                   S   nU R                  R                  R                   S   nXE:  a  [        SU SU 35      eUc  U R                  S S 2S U24   nUc  U R                  U5      nU R                  U5      nX6-   nU$ )Nr   r   zRSequence length must be less than max_position_embeddings (got `sequence length`: z and max_position_embeddings: )r   r   r   rL   r   r   )r   r   r   r   
seq_lengthmax_position_embeddingposition_embeddingsr   s           r'   r   SiglipTextEmbeddings.forward.  s     -6,AY__R(}GZGZ[]G^
!%!8!8!?!?!E!Ea!H.d,<=S<TV 
 ,,Q^<L  00;M"55lC"8
r*   )r   r   NNN)rd   re   rf   rg   r   r   r   r@   
LongTensorri   r   r   rl   r   r   s   @r'   r   r   !  sp    

/ 

 153759	E,,- u//0   1 12	
 
 r*   r   modulequerykeyvalueattention_maskscalingdropoutc                    [         R                  " XR                  SS5      5      U-  nUb  X-   n[        R                  R                  US[         R                  S9R                  UR                  5      n[        R                  R                  XU R                  S9n[         R                  " X5      n	U	R                  SS5      R                  5       n	X4$ )Nr   r   )r   r   )ptrainingr   r+   )r@   matmulr   r   r   softmaxfloat32r   r   r   r   
contiguous)
r   r   r   r   r   r   r   kwargsattn_weightsattn_outputs
             r'   eager_attention_forwardr   I  s     <<}}R'<=GL!#4==((2U]](SVVW\WbWbcL==((6??([L,,|3K''1-88:K$$r*   c            
          ^  \ rS rSrSrU 4S jr S	S\R                  S\\R                     S\	\R                  \\R                     4   4S jjr
SrU =r$ )
SiglipAttentioni`  z=Multi-headed attention from 'Attention Is All You Need' paperc                    > [         TU ]  5         Xl        UR                  U l        UR
                  U l        U R                  U R                  -  U l        U R                  U R                  -  U R                  :w  a&  [        SU R                   SU R                   S35      eU R                  S-  U l	        UR                  U l        SU l        [        R                  " U R                  U R                  5      U l        [        R                  " U R                  U R                  5      U l        [        R                  " U R                  U R                  5      U l        [        R                  " U R                  U R                  5      U l        g )Nz;embed_dim must be divisible by num_heads (got `embed_dim`: z and `num_heads`: z).      F)r   r   r   r   r   num_attention_heads	num_headshead_dimrL   rM   attention_dropoutr   	is_causalr   Lineark_projv_projq_projout_projr   s     r'   r   SiglipAttention.__init__c  s   ++33$..8==4>>)T^^;MdnnM] ^NN#2'  ]]D(
//ii?ii?ii?		$..$..Ar*   ra   r   r>   c                 2   UR                   u  pEnU R                  U5      nU R                  U5      nU R                  U5      n	UR	                  XEU R
                  U R                  5      R                  SS5      nUR	                  XEU R
                  U R                  5      R                  SS5      nU	R	                  XEU R
                  U R                  5      R                  SS5      n	[        n
U R                  R                  S:w  a  [        U R                  R                     n
U
" U UUU	UU R                  U R                  U R                  (       d  SOU R                  S9u  pUR!                  XEU5      R#                  5       nU R%                  U5      nX4$ )z#Input shape: Batch x Time x Channelr   r+   eager        )r   r   r   )r   r   r   r   r   r   r   r   r   r   _attn_implementationr   r   rM   r   r   r   r   r  )r   ra   r   r   
batch_sizer   r   queriesr   valuesattention_interfacer   r   s                r'   r   SiglipAttention.forwardw  sS    -:,?,?)
	++m,{{=)]+,,zt~~t}}U__`acdeyyOYYZ[]^_ZT^^T]]S]]^_abc(?;;++w6"9$++:Z:Z"[$7nnJJ#}}C$,,	%
! "))*)LWWYmmK0((r*   )r   r   r   r   r   r   r   r  r   rM   r   N)rd   re   rf   rg   rh   r   r@   r   r   rk   r   rl   r   r   s   @r'   r   r   `  s[    GB. 26$)||$) !.$)
 
u||Xell33	4$) $)r*   r   c                   b   ^  \ rS rSrU 4S jrS\R                  S\R                  4S jrSrU =r	$ )	SiglipMLPi  c                   > [         TU ]  5         Xl        [        UR                     U l        [        R                  " UR                  UR                  5      U l
        [        R                  " UR                  UR                  5      U l        g r  )r   r   r   r   
hidden_actactivation_fnr   r   r   intermediate_sizefc1fc2r   s     r'   r   SiglipMLP.__init__  sb    #F$5$5699V//1I1IJ99V55v7I7IJr*   ra   r>   c                 l    U R                  U5      nU R                  U5      nU R                  U5      nU$ r  )r  r  r  )r   ra   s     r'   r   SiglipMLP.forward  s4    /**=9/r*   )r  r   r  r  )
rd   re   rf   rg   r   r@   r   r   rl   r   r   s   @r'   r  r    s)    KU\\ ell  r*   r  c            	          ^  \ rS rSrS\\\4   4U 4S jjr\S\	R                  S\	R                  S\\   S\	R                  4S j5       rS	rU =r$ )
SiglipEncoderLayeri  r   c                 <  > [         TU ]  5         UR                  U l        [        R
                  " U R                  UR                  S9U l        [        U5      U l	        [        R
                  " U R                  UR                  S9U l
        [        U5      U l        g Neps)r   r   r   r   r   	LayerNormlayer_norm_epslayer_norm1r   	self_attnlayer_norm2r  mlpr   s     r'   r   SiglipEncoderLayer.__init__  sm    ++<<F<Q<QR(0<<F<Q<QRV$r*   ra   r   r   r>   c                     UnU R                  U5      nU R                  " SUUS.UD6u  pXA-   nUnU R                  U5      nU R                  U5      nXA-   nU$ )N)ra   r   rc   )r   r!  r"  r#  )r   ra   r   r   residualr   s         r'   r   SiglipEncoderLayer.forward  sz     !((7>> 
')
 

 !0 ((7/ 0r*   )r   r   r"  r#  r!  )rd   re   rf   rg   r   r   r   r   r   r@   r   r   r   ri   r   rl   r   r   s   @r'   r  r    si    %u%79I%IJ % ||  +,	
 
		 r*   r  c                   P    \ rS rSr% \\S'   SrSr/ SQrSr	Sr
SrSr\\S.rS rSrg	)
SiglipPreTrainedModeli  r   siglipT)r   r   r  #SiglipMultiheadAttentionPoolingHead)ra   rb   c                 V   [        U[        5      (       a  [        U R                  [        5      (       a   U R                  R                  R
                  OU R                  R
                  n[        R                  R                  UR                  R                  S[        R                  " U5      -  S9  g[        U[        R                  5      (       a  [        UR                  5        g[        U[        5      (       Ga  [        R                  R!                  UR"                  R                  5        [        R                  R!                  UR$                  R                  5        [        R                  R!                  UR&                  R                  5        [        R                  R!                  UR(                  R                  5        [        R                  R+                  UR"                  R,                  5        [        R                  R+                  UR$                  R,                  5        [        R                  R+                  UR&                  R,                  5        [        R                  R+                  UR(                  R,                  5        g[        U[.        5      (       a  [        R                  R!                  UR0                  R                  5        [        R                  R!                  UR2                  R                  5        [        R                  R                  UR0                  R,                  SS9  [        R                  R                  UR2                  R,                  SS9  g[        U[4        5      (       a  [        R                  R!                  UR6                  R8                  5        [        R                  R!                  UR:                  R<                  R8                  5        [        R                  R+                  UR:                  R>                  R8                  5        g[        U[@        5      (       at  [B        RD                  " [B        RF                  " S5      5      nURH                  R8                  RK                  U5        URL                  R8                  RO                  5         g[        U[P        5      (       ak  [        R                  R                  URR                  R                  U R                  R                  R
                  S-  U R                  RT                  -  S9  g[        U[        RV                  [        RX                  45      (       aM  [[        UR                  5        UR,                  b*  [        R                  R+                  UR,                  5        gg[        U[        R\                  5      (       aJ  UR,                  R8                  RO                  5         UR                  R8                  RK                  S5        gg)zInitialize the weightsr   rH   gư>r!   r   N)/
isinstancer   r   r   vision_configr   r   initrK   r   r   npr%   r   rZ   r   xavier_uniform_r   r   r   r  zeros_biasr  r  r  r+  probedata	attentionin_proj_weightin_proj_biasSiglipModelr@   logr6   logit_scalefill_
logit_biaszero_SiglipForImageClassification
classifierinitializer_factorr   r   rX   r  )r   r   r   logit_scale_inits       r'   _init_weights#SiglipPreTrainedModel._init_weights  s   f455 dkk<88 ))55[[,, 
 GGOOF55<<!bggenBTOU--#FMM200GG##FMM$8$89GG##FMM$8$89GG##FMM$8$89GG##FOO$:$:;GGNN6==--.GGNN6==--.GGNN6==--.GGNN6??//0	**GG##FJJ$5$56GG##FJJ$5$56GGOOFJJOOO6GGOOFJJOOO6 CDDGG##FLL$5$56GG##F$4$4$C$C$H$HIGGNN6++88==>,,$yyc):;##))*:;""((* <==GGOO!!((KK--994?$++B`B``   BII 677&--({{&v{{+ '--KK""$MM$$S) .r*   rc   N)rd   re   rf   rg   r   rj   base_model_prefixsupports_gradient_checkpointing_no_split_modules_supports_flash_attn_supports_sdpa_supports_flex_attn_supports_attention_backendr  r   _can_record_outputsrC  rl   rc   r*   r'   r)  r)    sJ     &*#  N"& ,%
,*r*   r)  c                   z   ^  \ rS rSrSrS\4U 4S jjr\ S
S\\	R                     S\\   S\4S jj5       rS	rU =r$ )SiglipEncoderi  z
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`SiglipEncoderLayer`].

Args:
    config: SiglipConfig
r   c                    > [         TU ]  5         Xl        [        R                  " [        UR                  5       Vs/ s H  n[        U5      PM     sn5      U l        SU l	        g s  snf )NF)
r   r   r   r   
ModuleListrangenum_hidden_layersr  layersgradient_checkpointing)r   r   r   r   s      r'   r   SiglipEncoder.__init__  sS    mmvOgOgIh$iIhA%7%?Ih$ij&+# %js   A&r   r   r>   c                 R    UnU R                    H  nU" UU40 UD6nM     [        US9$ )N)r`   )rS  r   )r   r   r   r   ra   encoder_layers         r'   r   SiglipEncoder.forward&  s>     &![[M) M ) ??r*   )r   rT  rS  r  )rd   re   rf   rg   rh   r   r   r   r   r@   r   r   r   r   r   rl   r   r   s   @r'   rN  rN    s_    ,| ,  26@ !.@ +,	@
 
@ @r*   rN  c                      ^  \ rS rSrS\4U 4S jjr\\   SS\\	R                     S\\	R                     S\\	R                     S\\   S\4
S	 jj5       5       rS
rU =r$ )SiglipTextTransformeri8  r   c                   > [         TU ]  5         Xl        UR                  n[	        U5      U l        [        U5      U l        [        R                  " X!R                  S9U l        [        R                  " X!R                  5      U l        g r  )r   r   r   r   r   r   rN  encoderr   r  r  final_layer_normr   projection_sizeheadr   s      r'   r   SiglipTextTransformer.__init__9  sc    &&	.v6$V, "Y<Q<Q RIIi)?)?@	r*   r   r   r   r   r>   c                    Uc  [        S5      eUR                  5       nUR                  SUS   5      nU R                  XS9nSU R                  R
                  ;   nU(       a  S nOUb  U(       d  [        X&R                  5      nU R                  " SUUS.UD6nUR                  n	U R                  U	5      n	U	S S 2SS S 24   n
U R                  U
5      n
[        U	U
S9$ )NzYou have to specify input_idsr   )r   r   flash)r   r   r`   pooler_outputrc   )rL   r   r   r   r   r  r   r   r\  r`   r]  r_  r   )r   r   r   r   r   input_shapera   uses_flash_attentionencoder_outputsr`   pooled_outputs              r'   r   SiglipTextTransformer.forwardC  s     <==nn&NN2{27	)W  '$++*J*JJ!N'0D7H[H[\N+/<< ,
'),
 ,
 ,== 112CD *!R(3		-0)/'
 	
r*   )r   r   r\  r]  r_  r   )rd   re   rf   rg   r   r   r   r   r   r@   r   r   r   r   r   rl   r   r   s   @r'   rZ  rZ  8  s    A/ A  -115/3	(
ELL)(
 !.(
 u||,	(

 +,(
 
$(
  (
r*   rZ  zK
    The text model from SigLIP without any head or projection on top.
    c                      ^  \ rS rSr% \\S'   S\4U 4S jjrS\R                  4S jr	S r
\" SS9\   SS	\\R                     S
\\R                     S\\R                     S\\   S\4
S jj5       5       rSrU =r$ )SiglipTextModelip  r   c                 d   > [         TU ]  U5        [        U5      U l        U R	                  5         g r  )r   r   rZ  
text_model	post_initr   s     r'   r   SiglipTextModel.__init__x  s&     /7r*   r>   c                 B    U R                   R                  R                  $ r  rm  r   r   r   s    r'   get_input_embeddings$SiglipTextModel.get_input_embeddings~  s    ))999r*   c                 8    XR                   R                  l        g r  rq  )r   r   s     r'   set_input_embeddings$SiglipTextModel.set_input_embeddings  s    5:""2r*   Ftie_last_hidden_statesr   r   r   r   c                 .    U R                   " SUUUS.UD6$ )aT  
Examples:

```python
>>> from transformers import AutoTokenizer, SiglipTextModel

>>> model = SiglipTextModel.from_pretrained("google/siglip-base-patch16-224")
>>> tokenizer = AutoTokenizer.from_pretrained("google/siglip-base-patch16-224")

>>> # important: make sure to set padding="max_length" as that's how the model was trained
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding="max_length", return_tensors="pt")

>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output  # pooled (EOS token) states
```r   r   r   rc   rm  )r   r   r   r   r   s        r'   r   SiglipTextModel.forward  s-    4  
)%
 	
 	
r*   r{  r   )rd   re   rf   rg   r   rj   r   r   Modulerr  ru  r   r   r   r@   r   r   r   r   r   rl   r   r   s   @r'   rk  rk  p  s     / :bii :; u5 -115/3	
ELL)
 !.
 u||,	

 +,
 
$
  6
r*   rk  c                   b   ^  \ rS rSrS\4U 4S jjr\ S	S\\   S\	\
   S\4S jj5       rSrU =r$ )
SiglipVisionTransformeri  r   c                 X  > [         TU ]  5         Xl        UR                  n[	        U5      U l        [        U5      U l        [        R                  " X!R                  S9U l        [        US5      (       d  SOUR                  U l        U R                  (       a  [        U5      U l        g g )Nr  vision_use_headT)r   r   r   r   r   r   rN  r\  r   r  r  post_layernormhasattrr  use_headr+  r_  r   s      r'   r    SiglipVisionTransformer.__init__  s    &&	08$V, ll9:O:OP$+F4E$F$FFLbLb==;FCDI r*   r   r   r>   c                     U R                  XS9nU R                  " SSU0UD6nUR                  nU R                  U5      nU R                  (       a  U R                  U5      OS n[        UUS9$ )N)r   r   rc  rc   )r   r\  r`   r  r  r_  r   )r   r   r   r   ra   rg  r`   rd  s           r'   r   SiglipVisionTransformer.forward  s|     h+/<< ,
',
,

 ,== //0AB8<		"344)/'
 	
r*   )r   r   r\  r_  r  r  r   )rd   re   rf   rg   r   r   r   r   boolr   r   r   r   rl   r   r   s   @r'   r  r    sS    
D1 
D  49
 #+4.
 +,	

 
$
 
r*   r  c                   :   ^  \ rS rSrSrS\4U 4S jjrS rSrU =r	$ )r+  i  zMultihead Attention Pooling.r   c                   > [         TU ]  5         [        R                  " [        R
                  " SSUR                  5      5      U l        [        R                  R                  UR                  UR                  SS9U l
        [        R                  " UR                  UR                  S9U l        [        U5      U l        g )Nr   T)batch_firstr  )r   r   r   	Parameterr@   randnr   r4  MultiheadAttentionr   r6  r  r  	layernormr  r#  r   s     r'   r   ,SiglipMultiheadAttentionPoolingHead.__init__  s    \\%++aF4F4F"GH
44V5G5GIcIcqu4vf&8&8f>S>STV$r*   c                     UR                   S   nU R                  R                  USS5      nU R                  X1U5      S   nUnU R	                  U5      nX@R                  U5      -   nUS S 2S4   $ )Nr   r   )r   r4  repeatr6  r  r#  )r   hidden_stater  r4  r&  s        r'   r   +SiglipMultiheadAttentionPoolingHead.forward  sr    !''*


!!*a3~~e<HK~~l3((<"88AqD!!r*   )r6  r  r#  r4  )
rd   re   rf   rg   rh   r   r   r   rl   r   r   s   @r'   r+  r+    s    &%1 %
" 
"r*   r+  zM
    The vision model from SigLIP without any head or projection on top.
    c            	          ^  \ rS rSr% \\S'   SrS\4U 4S jjrS\R                  4S jr
\" SS9\ SS	\S
\\   S\4S jj5       5       rSrU =r$ )SiglipVisionModeli  r   r   c                 d   > [         TU ]  U5        [        U5      U l        U R	                  5         g r  )r   r   r  vision_modelrn  r   s     r'   r   SiglipVisionModel.__init__  s)     3F; 	r*   r>   c                 B    U R                   R                  R                  $ r  )r  r   r   r   s    r'   rr  &SiglipVisionModel.get_input_embeddings  s      ++;;;r*   Frw  r   r   c                 ,    U R                   " SUUS.UD6$ )an  
Examples:

```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, SiglipVisionModel

>>> model = SiglipVisionModel.from_pretrained("google/siglip-base-patch16-224")
>>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224")

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)

>>> inputs = processor(images=image, return_tensors="pt")

>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output  # pooled features
```r   r   rc   r  )r   r   r   r   s       r'   r   SiglipVisionModel.forward  s,    :    
%%=
 
 	
r*   r  r   )rd   re   rf   rg   r   rj   main_input_namer   r   r}  rr  r   r   r  r   r   r   r   rl   r   r   s   @r'   r  r    sw     $O1 <bii < u5 */
 #'
 +,	

 
$
  6
r*   r  c                     ^  \ rS rSr% \\S'   S\4U 4S jjr\" 5       \  SS\	R                  S\\	R                     S\\	R                     S\	R                  4S jj5       5       r\" 5       \ SS	\	R                  S
\S\\   S\	R                  4S jj5       5       r\\      SS\\	R&                     S	\\	R                     S\\	R                     S\\	R&                     S\\   S
\S\\   S\4S jj5       5       rSrU =r$ )r9  i  r   c                   > [         TU ]  U5        [        UR                  [        5      (       d"  [        S[        UR                  5       S35      e[        UR                  [        5      (       d"  [        S[        UR                  5       S35      eUR                  nUR                  n[        R                  U5      n[        R                  U5      nUR                  U l        UR                  U l        [        R                  " [         R"                  " S5      5      U l        [        R                  " [         R"                  " S5      5      U l        U R)                  5         g )NzMconfig.text_config is expected to be of type SiglipTextConfig but is of type .zQconfig.vision_config is expected to be of type SiglipVisionConfig but is of type r   )r   r   r-  text_configr   	TypeErrortyper.  r   rk  _from_configr  rm  r  r   r  r@   r  r;  r=  rn  )r   r   r  r.  rm  r  r   s         r'   r   SiglipModel.__init__   s"    &,,.>??++,-Q0 
 &..0BCC--./q2 
 ((,, %11+>
(55mD %//(55<<A7,,u{{1~6 	r*   r   r   r   r>   c                 @    U R                  UUUS9nUR                  nU$ )a  
Returns:
    text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by
    applying the projection layer to the pooled output of [`SiglipTextModel`].

Examples:

```python
>>> from transformers import AutoTokenizer, AutoModel
>>> import torch

>>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224")
>>> tokenizer = AutoTokenizer.from_pretrained("google/siglip-base-patch16-224")

>>> # important: make sure to set padding="max_length" as that's how the model was trained
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding="max_length", return_tensors="pt")
>>> with torch.no_grad():
...     text_features = model.get_text_features(**inputs)
```rz  )rm  rd  )r   r   r   r   text_outputsrh  s         r'   get_text_featuresSiglipModel.get_text_features@  s4    6 48??)% 4C 4

 %22r*   r   r   r   c                 H    U R                   " SUUS.UD6nUR                  nU$ )a  
Returns:
    image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by
    applying the projection layer to the pooled output of [`SiglipVisionModel`].

Examples:

```python
>>> import torch
>>> from transformers import AutoProcessor, AutoModel
>>> from transformers.image_utils import load_image

>>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224")
>>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224")

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = load_image(url)

>>> inputs = processor(images=image, return_tensors="pt")

>>> with torch.no_grad():
...     image_features = model.get_image_features(**inputs)
```r  rc   )r  rd  )r   r   r   r   vision_outputsrh  s         r'   get_image_featuresSiglipModel.get_image_featuresd  s<    > 6:5F5F 6
%%=6
 6

 '44r*   return_lossc           
         U R                   " SUUS.UD6nU R                  " SUUUS.UD6n	UR                  n
U	R                  nXR                  SSSS9-  n
XR                  SSSS9-  n[        R
                  " XR                  5       R                  UR                  5      5      nU R                  R                  UR                  5      U R                  R                  UR                  5      pXR                  5       -  U-   nUR                  5       nSnU(       a  [        R                  " UR                  S5      UR                  S	9n[        R                  " U5      * SU-  -   n[        R                  R                   R#                  UU-  5      n[        R$                  " USS
9* nUR'                  5       n[)        UUUUU
U	US9$ )a  
return_loss (`bool`, *optional*):
    Whether or not to return the contrastive loss.

Examples:

```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, AutoModel
>>> import torch

>>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224")
>>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224")

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)

>>> texts = ["a photo of 2 cats", "a photo of 2 dogs"]
>>> # important: we pass `padding=max_length` since the model was trained with this
>>> inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt")

>>> with torch.no_grad():
...     outputs = model(**inputs)

>>> logits_per_image = outputs.logits_per_image
>>> probs = torch.sigmoid(logits_per_image) # these are the probabilities
>>> print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'")
31.9% that image 0 is 'a photo of 2 cats'
```r  rz  r+   r   T)r   r   keepdimNr   )devicer   )rt   ru   rv   rp   r_   rw   rx   rc   )r  rm  rd  normr@   r   tr   r  r;  r=  expeyer   	ones_liker   r   
logsigmoidsumr7   rr   )r   r   r   r   r   r  r   r   r  r  r_   rp   rv   r;  r=  ru   rt   r  m1_diag1logliknlls                        r'   r   SiglipModel.forward  s   T 6:5F5F 6
%%=6
 6
 48?? 4
)%4
 	4
 &33"00 $&7&7!T&7&RR!$4$4qb$$4$OO  ,,{NN4D4G4GHZHZ4[\"&"2"2"5"5k6H6H"I4??K]K]^i^p^pKqZ)OO,==
J*,,.))O003O<R<RSC881s7BHXX((33H4NOF99V,,C88:D-+#%* .
 	
r*   )r=  r;  rm  r  )NNr   )NNNNNF)rd   re   rf   rg   r   rj   r   r   r   r@   r   r   ri   r  r  r   r   r  r   r   rr   r   rl   r   r   s   @r'   r9  r9    s   | @ %& 26/3	 <<  !.  u||,	 
 
		   ' D %& */$''$ #'$ +,	$
 
		$  '$N  15481537&*).U
E,,-U
 u001U
 !.	U

 u//0U
 d^U
 #'U
 +,U
 
U
  U
r*   r9  z
    SigLIP vision encoder with an image classification head on top (a linear layer on top of the pooled final hidden states of
    the patch tokens) e.g. for ImageNet.
    c                      ^  \ rS rSrSrS\SS4U 4S jjr\" 5       \   SS\	\
R                     S\	\
R                     S\S	\\   S\4
S
 jj5       5       rSrU =r$ )r?  i  r   r   r>   Nc                   > [         TU ]  U5        UR                  U l        [        R	                  UR
                  5      nUR                  U l        UR                  S:  a5  [        R                  " UR
                  R                  UR                  5      O[        R                  " 5       U l        U R                  5         g )Nr   )r   r   
num_labelsr  r  r.  r  r   r   r   Identityr@  rn  )r   r   r  r   s      r'   r   %SiglipForImageClassification.__init__  s      ++ )55f6J6JK(55 OUN_N_bcNcBIIf**668I8IJikititiv 	
 	r*   labelsr   r   c                     U R                   " U4SU0UD6nUR                  n[        R                  " USS9nU R	                  U5      nSnUb  U R                  X'U R                  5      n[        UUS9$ )a\  
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
    Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
    config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
    `config.num_labels > 1` a classification loss is computed (Cross-Entropy).

Examples:

```python
>>> from transformers import AutoImageProcessor, SiglipForImageClassification
>>> import torch
>>> from PIL import Image
>>> import requests

>>> torch.manual_seed(3)  # doctest: +IGNORE_RESULT
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)

>>> # note: we are loading a `SiglipModel` from the hub here,
>>> # so the head will be randomly initialized, hence the predictions will be random if seed is not set above.
>>> image_processor = AutoImageProcessor.from_pretrained("google/siglip-base-patch16-224")
>>> model = SiglipForImageClassification.from_pretrained("google/siglip-base-patch16-224")

>>> inputs = image_processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
>>> # model predicts one of the two classes
>>> predicted_class_idx = logits.argmax(-1).item()
>>> print("Predicted class:", model.config.id2label[predicted_class_idx])
Predicted class: LABEL_1
```r   r   r  N)rt   logits)r  r`   r@   r7   r@  loss_functionr   r   )	r   r   r  r   r   outputssequence_outputr  rt   s	            r'   r   $SiglipForImageClassification.forward  s    P /3.?.?/
%=/
 /
 "33  **_!<1%%fdkkBD$
 	
r*   )r@  r  r  )NNF)rd   re   rf   rg   r  r   r   r   r   r   r@   r   r  r   r   r   r   rl   r   r   s   @r'   r?  r?    s     %O|  $  04)-).	:
u||,:
 &:
 #'	:

 +,:
 
:
  :
r*   r?  )r9  r)  rk  r  r?  )r  r!   g       r"   )r!   rD   rI   )r  )Grh   r#   r/   dataclassesr   typingr   r   r   r   numpyr0  r@   r   torch.nn.initr	   activationsr   modeling_attn_mask_utilsr   modeling_layersr   modeling_outputsr   r   r   modeling_utilsr   r   processing_utilsr   utilsr   r   r   r   r   r   utils.genericr   configuration_siglipr   r   r   r=   r   floatrB   rS   rX   rZ   r]   rn   rr   r}  r   r   r   r   r  r  r)  rN  rZ  rk  r  r+  r  r9  r?  __all__rc   r*   r'   <module>r     s      ! 1 1    7 ! B 9 b b F &  0 T T! J \_$LL$ %$27$BG$SX$
\\$4A2ND 	?k 	? 	? 	?K 	? 	?  
;  
   
FERYY ER%299 %^ %II%<<% 
% <<	%
 U\\*% % %.;)bii ;)~		 3 D A*O A* A*J@BII @D5
BII 5
p 
.
+ .

.
b#
bii #
L"")) "0 
0
- 0

0
f G
' G
 G
T Q
#8 Q
Q
hr*   