
    cCi>                     p   S r SSKrSSKJr  SSKrSSKJrJr  SSKJr  SSK	J
r
JrJrJr  SSKJr  SS	KJrJr  SS
KJr  SSKJr  \R.                  " \5      r " S S\R4                  5      r " S S\R4                  5      r " S S\R4                  5      r " S S\R4                  5      r " S S\R4                  5      r " S S\R4                  5      r  " S S\R4                  5      r!\ " S S\5      5       r"\ " S S\"5      5       r#\" SS 9 " S! S"\"5      5       r$\" S#S 9 " S$ S%\"\5      5       r%/ S&Qr&g)'zPyTorch ResNet model.    N)Optional)Tensornn   )ACT2FN)BackboneOutputBaseModelOutputWithNoAttention(BaseModelOutputWithPoolingAndNoAttention$ImageClassifierOutputWithNoAttention)PreTrainedModel)auto_docstringlogging)BackboneMixin   )ResNetConfigc                   X   ^  \ rS rSr SS\S\S\S\S\4
U 4S jjjrS\S	\4S
 jrSr	U =r
$ )ResNetConvLayer'   in_channelsout_channelskernel_sizestride
activationc           	         > [         TU ]  5         [        R                  " XX4US-  SS9U l        [        R
                  " U5      U l        Ub  [        U   U l	        g [        R                  " 5       U l	        g )N   F)r   r   paddingbias)
super__init__r   Conv2dconvolutionBatchNorm2dnormalizationr   Identityr   )selfr   r   r   r   r   	__class__s         d/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/resnet/modeling_resnet.pyr   ResNetConvLayer.__init__(   sb     	99;WbfgWgns
  ^^L90:0F&,BKKM    inputreturnc                 l    U R                  U5      nU R                  U5      nU R                  U5      nU$ N)r!   r#   r   r%   r*   hidden_states      r'   forwardResNetConvLayer.forward2   s6    ''.)),7|4r)   )r   r!   r#   )r   r   relu)__name__
__module____qualname____firstlineno__intstrr   r   r0   __static_attributes____classcell__r&   s   @r'   r   r   '   sW    lrZZ.1Z@CZQTZfiZ ZV   r)   r   c                   F   ^  \ rS rSrSrS\4U 4S jjrS\S\4S jrSr	U =r
$ )	ResNetEmbeddings9   zG
ResNet Embeddings (stem) composed of a single aggressive convolution.
configc                    > [         TU ]  5         [        UR                  UR                  SSUR
                  S9U l        [        R                  " SSSS9U l	        UR                  U l        g )N   r   )r   r   r   r   r   )r   r   r   )
r   r   r   num_channelsembedding_size
hidden_actembedderr   	MaxPool2dpoolerr%   r?   r&   s     r'   r   ResNetEmbeddings.__init__>   s\    '!6!6Aa\b\m\m
 llqAF"//r)   pixel_valuesr+   c                     UR                   S   nX R                  :w  a  [        S5      eU R                  U5      nU R	                  U5      nU$ )Nr   zeMake sure that the channel dimension of the pixel values match with the one set in the configuration.)shaperB   
ValueErrorrE   rG   )r%   rJ   rB   	embeddings       r'   r0   ResNetEmbeddings.forwardF   sR    #))!,,,,w  MM,/	KK	*	r)   )rE   rB   rG   )r3   r4   r5   r6   __doc__r   r   r   r0   r9   r:   r;   s   @r'   r=   r=   9   s,    0| 0F v  r)   r=   c                   R   ^  \ rS rSrSrSS\S\S\4U 4S jjjrS\S\4S	 jrS
r	U =r
$ )ResNetShortCutQ   z
ResNet shortcut, used to project the residual features to the correct size. If needed, it is also used to
downsample the input using `stride=2`.
r   r   r   c                    > [         TU ]  5         [        R                  " XSUSS9U l        [        R
                  " U5      U l        g )Nr   F)r   r   r   )r   r   r   r    r!   r"   r#   )r%   r   r   r   r&   s       r'   r   ResNetShortCut.__init__W   s8    99[AV\chi^^L9r)   r*   r+   c                 J    U R                  U5      nU R                  U5      nU$ r-   r!   r#   r.   s      r'   r0   ResNetShortCut.forward\   s(    ''.)),7r)   rW   )r   )r3   r4   r5   r6   rP   r7   r   r   r0   r9   r:   r;   s   @r'   rR   rR   Q   s?    
:C :s :C : :
V   r)   rR   c            	       J   ^  \ rS rSrSrS
S\S\S\S\4U 4S jjjrS rS	r	U =r
$ )ResNetBasicLayerb   zG
A classic ResNet's residual layer composed by two `3x3` convolutions.
r   r   r   r   c           	        > [         TU ]  5         X:g  =(       d    US:g  nU(       a
  [        XUS9O[        R                  " 5       U l        [        R                  " [        XUS9[        X"S S95      U l        [        U   U l
        g )Nr   r   r   r   r   rR   r   r$   shortcut
Sequentialr   layerr   r   )r%   r   r   r   r   should_apply_shortcutr&   s         r'   r   ResNetBasicLayer.__init__g   ss     + ; Jv{H]N;VDcecncncp 	 ]]KfEL4H

 !,r)   c                 x    UnU R                  U5      nU R                  U5      nX-  nU R                  U5      nU$ r-   rb   r`   r   r%   r/   residuals      r'   r0   ResNetBasicLayer.forwards   ?    zz,/==* |4r)   r   rb   r`   )r   r2   )r3   r4   r5   r6   rP   r7   r8   r   r0   r9   r:   r;   s   @r'   rZ   rZ   b   s9    
-C 
-s 
-C 
-Y\ 
- 
- r)   rZ   c                   Z   ^  \ rS rSrSr    SS\S\S\S\S\S\4U 4S	 jjjrS
 r	Sr
U =r$ )ResNetBottleNeckLayer|   a  
A classic ResNet's bottleneck layer composed by three `3x3` convolutions.

The first `1x1` convolution reduces the input by a factor of `reduction` in order to make the second `3x3`
convolution faster. The last `1x1` convolution remaps the reduced features to `out_channels`. If
`downsample_in_bottleneck` is true, downsample will be in the first layer instead of the second layer.
r   r   r   r   	reductiondownsample_in_bottleneckc                 N  > [         T	U ]  5         X:g  =(       d    US:g  nX%-  nU(       a
  [        XUS9O[        R                  " 5       U l        [        R                  " [        XSU(       a  UOSS9[        XU(       d  UOSS9[        XSS S95      U l        [        U   U l
        g )Nr   r]   )r   r   )r   r   r_   )
r%   r   r   r   r   ro   rp   rc   reduces_channelsr&   s
            r'   r   ResNetBottleNeckLayer.__init__   s     	 + ; Jv{'4H]N;VDcecncncp 	 ]]1OgVmn ,Umvstu,VZ[

 !,r)   c                 x    UnU R                  U5      nU R                  U5      nX-  nU R                  U5      nU$ r-   rf   rg   s      r'   r0   ResNetBottleNeckLayer.forward   rj   r)   rk   )r   r2      F)r3   r4   r5   r6   rP   r7   r8   boolr   r0   r9   r:   r;   s   @r'   rm   rm   |   sd      ).-- - 	-
 - - #'- -0 r)   rm   c                   ^   ^  \ rS rSrSr  SS\S\S\S\S\4
U 4S jjjrS	\S
\4S jr	Sr
U =r$ )ResNetStage   z,
A ResNet stage composed by stacked layers.
r?   r   r   r   depthc                 r  > [         T	U ]  5         UR                  S:X  a  [        O[        nUR                  S:X  a  U" UUUUR
                  UR                  S9nOU" X#XAR
                  S9n[        R                  " U/[        US-
  5       Vs/ s H  o" X3UR
                  S9PM     snQ76 U l
        g s  snf )N
bottleneck)r   r   rp   )r   r   r   r^   )r   r   
layer_typerm   rZ   rD   rp   r   ra   rangelayers)
r%   r?   r   r   r   r{   rb   first_layer_r&   s
            r'   r   ResNetStage.__init__   s     	)/):):l)J%P`,!,,)/)H)HK  &UfUfgKmm
dijorsjsdtudt_`5HYHYZdtu
us   B4
r*   r+   c                 @    UnU R                    H  nU" U5      nM     U$ r-   r   )r%   r*   r/   rb   s       r'   r0   ResNetStage.forward   s%    [[E .L !r)   r   )r   r   )r3   r4   r5   r6   rP   r   r7   r   r   r0   r9   r:   r;   s   @r'   ry   ry      sb     

 
 	

 
 
 
4V   r)   ry   c            	       P   ^  \ rS rSrS\4U 4S jjr S
S\S\S\S\4S jjr	S	r
U =r$ )ResNetEncoder   r?   c           
        > [         TU ]  5         [        R                  " / 5      U l        U R                  R                  [        UUR                  UR                  S   UR                  (       a  SOSUR                  S   S95        [        UR                  UR                  SS  5      n[        X!R                  SS  5       H+  u  u  p4nU R                  R                  [        XXES95        M-     g )Nr   r   r   )r   r{   )r{   )r   r   r   
ModuleListstagesappendry   rC   hidden_sizesdownsample_in_first_stagedepthszip)r%   r?   in_out_channelsr   r   r{   r&   s         r'   r   ResNetEncoder.__init__   s    mmB'%%##A&"<<q!mmA&	
 f1163F3Fqr3JK25o}}UVUWGX2Y.'[KK{6Z[ 3Zr)   r/   output_hidden_statesreturn_dictr+   c                     U(       a  SOS nU R                    H  nU(       a  XA4-   nU" U5      nM     U(       a  XA4-   nU(       d  [        S X4 5       5      $ [        UUS9$ )N c              3   .   #    U  H  oc  M  Uv   M     g 7fr-   r   ).0vs     r'   	<genexpr>(ResNetEncoder.forward.<locals>.<genexpr>   s     S$Aq$As   	)last_hidden_statehidden_states)r   tupler	   )r%   r/   r   r   r   stage_modules         r'   r0   ResNetEncoder.forward   sk     3 KKL# - ?'5L	 (  )O;MS\$ASSS-*'
 	
r)   )r   )FT)r3   r4   r5   r6   r   r   r   rw   r	   r0   r9   r:   r;   s   @r'   r   r      sB    \| \$ ]a
"
:>
UY
	'
 
r)   r   c                   6    \ rS rSr% \\S'   SrSrSS/rS r	Sr
g	)
ResNetPreTrainedModel   r?   resnetrJ   r   rR   c                 b   [        U[        R                  5      (       a*  [        R                  R	                  UR
                  SSS9  g [        U[        R                  5      (       a  [        R                  R                  UR
                  [        R                  " S5      S9  UR                  by  [        R                  R                  UR
                  5      u  p#US:  a  S[        R                  " U5      -  OSn[        R                  R                  UR                  U* U5        g g [        U[        R                  [        R                  45      (       aU  [        R                  R                  UR
                  S5        [        R                  R                  UR                  S5        g g )Nfan_outr2   )modenonlinearity   )ar   r   )
isinstancer   r    initkaiming_normal_weightLinearkaiming_uniform_mathsqrtr   _calculate_fan_in_and_fan_outuniform_r"   	GroupNorm	constant_)r%   modulefan_inr   bounds        r'   _init_weights#ResNetPreTrainedModel._init_weights   s   fbii((GG##FMM	PV#W		**GG$$V]]diil$C{{&GGAA&--P	17!DIIf--  ufe< '  >??GGfmmQ/GGfkk1- @r)   r   N)r3   r4   r5   r6   r   __annotations__base_model_prefixmain_input_name_no_split_modulesr   r9   r   r)   r'   r   r      s$     $O*,<=.r)   r   c            
       ^   ^  \ rS rSrU 4S jr\ S	S\S\\   S\\   S\	4S jj5       r
SrU =r$ )
ResNetModeli  c                    > [         TU ]  U5        Xl        [        U5      U l        [        U5      U l        [        R                  " S5      U l	        U R                  5         g )N)r   r   )r   r   r?   r=   rE   r   encoderr   AdaptiveAvgPool2drG   	post_initrH   s     r'   r   ResNetModel.__init__  sI     (0$V,**62r)   rJ   r   r   r+   c                 &   Ub  UOU R                   R                  nUb  UOU R                   R                  nU R                  U5      nU R	                  XBUS9nUS   nU R                  U5      nU(       d	  Xg4USS  -   $ [        UUUR                  S9$ )Nr   r   r   r   )r   pooler_outputr   )r?   r   use_return_dictrE   r   rG   r
   r   )r%   rJ   r   r   embedding_outputencoder_outputsr   pooled_outputs           r'   r0   ResNetModel.forward  s    
 %9$D $++JjJj 	 &1%<k$++B]B]==6,,U` ' 
 ,A.$56%58KKK7/')77
 	
r)   )r?   rE   r   rG   NN)r3   r4   r5   r6   r   r   r   r   rw   r
   r0   r9   r:   r;   s   @r'   r   r     sI     os
"
:B4.
^fgk^l
	1
 
r)   r   z
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    )custom_introc                      ^  \ rS rSrU 4S jr\    S
S\\R                     S\\R                     S\\
   S\\
   S\4
S jj5       rS	rU =r$ )ResNetForImageClassificationi3  c                   > [         TU ]  U5        UR                  U l        [        U5      U l        [
        R                  " [
        R                  " 5       UR                  S:  a.  [
        R                  " UR                  S   UR                  5      O[
        R                  " 5       5      U l        U R                  5         g )Nr   )r   r   
num_labelsr   r   r   ra   Flattenr   r   r$   
classifierr   rH   s     r'   r   %ResNetForImageClassification.__init__:  s      ++!&)--JJLEKEVEVYZEZBIIf))"-v/@/@A`b`k`k`m

 	r)   rJ   labelsr   r   r+   c                 J   Ub  UOU R                   R                  nU R                  XUS9nU(       a  UR                  OUS   nU R	                  U5      nSnUb  U R                  X'U R                   5      nU(       d  U4USS -   n	Ub  U4U	-   $ U	$ [        XUR                  S9$ )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 classification loss is computed (Cross-Entropy).
Nr   r   r   )losslogitsr   )r?   r   r   r   r   loss_functionr   r   )
r%   rJ   r   r   r   outputsr   r   r   outputs
             r'   r0   $ResNetForImageClassification.forwardF  s     &1%<k$++B]B]++lcn+o1<--'!*/%%fdkkBDY,F'+'7D7V#CVC3\c\q\qrrr)   )r   r   r   )NNNN)r3   r4   r5   r6   r   r   r   torchFloatTensor
LongTensorrw   r   r0   r9   r:   r;   s   @r'   r   r   3  s    
  59-1/3&*su001s ))*s 'tn	s
 d^s 
.s sr)   r   zO
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    c            
       b   ^  \ rS rSrSrU 4S jr\ S
S\S\\	   S\\	   S\
4S jj5       rS	rU =r$ )ResNetBackboneig  Fc                    > [         TU ]  U5        [         TU ]	  U5        UR                  /UR                  -   U l        [        U5      U l        [        U5      U l	        U R                  5         g r-   )r   r   _init_backbonerC   r   num_featuresr=   rE   r   r   r   rH   s     r'   r   ResNetBackbone.__init__o  s]     v&#223f6I6II(0$V, 	r)   rJ   r   r   r+   c                    Ub  UOU R                   R                  nUb  UOU R                   R                  nU R                  U5      nU R	                  USSS9nUR
                  nSn[        U R                  5       H  u  pXR                  ;   d  M  XvU   4-  nM      U(       d  U4n
U(       a  XR
                  4-  n
U
$ [        UU(       a  UR
                  SS9$ SSS9$ )a  
Examples:

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

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

>>> processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
>>> model = AutoBackbone.from_pretrained(
...     "microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]
... )

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

>>> outputs = model(**inputs)
>>> feature_maps = outputs.feature_maps
>>> list(feature_maps[-1].shape)
[1, 2048, 7, 7]
```NTr   r   )feature_mapsr   
attentions)
r?   r   r   rE   r   r   	enumeratestage_namesout_featuresr   )r%   rJ   r   r   r   r   r   r   idxstager   s              r'   r0   ResNetBackbone.forwardz  s    8 &1%<k$++B]B]$8$D $++JjJj 	  ==6,,/dX\,]--#D$4$45JC)))s!3 55 6 "_F#0022M%3G'//
 	
MQ
 	
r)   )rE   r   r   r   )r3   r4   r5   r6   has_attentionsr   r   r   r   rw   r   r0   r9   r:   r;   s   @r'   r   r   g  sP     N	 os5
"5
:B4.5
^fgk^l5
	5
 5
r)   r   )r   r   r   r   )'rP   r   typingr   r   r   r   activationsr   modeling_outputsr   r	   r
   r   modeling_utilsr   utilsr   r   utils.backbone_utilsr   configuration_resnetr   
get_loggerr3   loggerModuler   r=   rR   rZ   rm   ry   r   r   r   r   r   __all__r   r)   r'   <module>r      sL        !  . , 1 . 
		H	%bii $ryy 0RYY "ryy 4'BII 'T#")) #L&
BII &
R .O . .* $
' $
 $
N +s#8 +s+s\ 
D
*M D

D
N er)   