
    Z j/A                        S r SSKrSSKrSSKJrJr  SSKJr  SSKJ	r	  SSK
Jr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  \R6                  " \5      r " S S\R<                  5      r " S S\R<                  5      r  " S S\R<                  5      r! " S S\R<                  5      r" " S S\R<                  5      r# " S S\R<                  5      r$ " S S\R<                  5      r%\ " S S\5      5       r&\ " S S\&5      5       r'\" S S!9 " S" S#\&5      5       r(\" S$S!9 " S% S&\\&5      5       r)/ S'Qr*g)(zPyTorch ResNet model.    N)Tensornn   )initialization)ACT2FN)BackboneMixinfilter_output_hidden_states)BackboneOutputBaseModelOutputWithNoAttention(BaseModelOutputWithPoolingAndNoAttention$ImageClassifierOutputWithNoAttention)PreTrainedModel)auto_docstringlogging)can_return_tuple   )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
$ )ResNetConvLayer'   in_channelsout_channelskernel_sizestride
activationc           	         > [         TU ]  5         [        R                  " XX4US-  SS9U l        [        R
                  " U5      U l        Ub  [        U   U l	        g [        R                  " 5       U l	        g )N   F)r   r   paddingbias)
super__init__r   Conv2dconvolutionBatchNorm2dnormalizationr   Identityr   )selfr   r   r   r   r   	__class__s         {/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/resnet/modeling_resnet.pyr!   ResNetConvLayer.__init__(   sb     	99;WbfgWgns
  ^^L90:0F&,BKKM    inputreturnc                 l    U R                  U5      nU R                  U5      nU R                  U5      nU$ 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   r2   __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
$ )	ResNetEmbeddings9   zG
ResNet Embeddings (stem) composed of a single aggressive convolution.
configc                    > [         TU ]  5         [        UR                  UR                  SSUR
                  S9U l        [        R                  " SSSS9U l	        UR                  U l        g )N   r   )r   r   r   r   r   )r   r   r   )
r    r!   r   num_channelsembedding_size
hidden_actembedderr   	MaxPool2dpoolerr'   rA   r(   s     r)   r!   ResNetEmbeddings.__init__>   s\    '!6!6Aa\b\m\m
 llqAF"//r+   pixel_valuesr-   c                     UR                   S   nX R                  :w  a  [        S5      eU R                  U5      nU R	                  U5      nU$ )Nr   zeMake sure that the channel dimension of the pixel values match with the one set in the configuration.)shaperD   
ValueErrorrG   rI   )r'   rL   rD   	embeddings       r)   r2   ResNetEmbeddings.forwardF   sR    #))!,,,,w  MM,/	KK	*	r+   )rG   rD   rI   )r5   r6   r7   r8   __doc__r   r!   r   r2   r;   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%   r0   s      r)   r2   ResNetShortCut.forward\   s(    ''.)),7r+   rY   )r   )r5   r6   r7   r8   rR   r9   r!   r   r2   r;   r<   r=   s   @r)   rT   rT   Q   s?    
:C :s :C : :
V   r+   rT   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
$ )ResNetBasicLayerb   zG
A classic ResNet's residual layer composed by two `3x3` convolutions.
r   r   r   r   c           	        > [         TU ]  5         X:g  =(       d    US:g  nU(       a
  [        XUS9O[        R                  " 5       U l        [        R                  " [        XUS9[        X"S S95      U l        [        U   U l
        g )Nr   r   r   r    r!   rT   r   r&   shortcut
Sequentialr   layerr   r   )r'   r   r   r   r   should_apply_shortcutr(   s         r)   r!   ResNetBasicLayer.__init__g   ss     + ; Jv{H]N;VDcecncncp 	 ]]KfEL4H

 !,r+   c                 x    UnU R                  U5      nU R                  U5      nX-  nU R                  U5      nU$ r/   rd   rb   r   r'   r1   residuals      r)   r2   ResNetBasicLayer.forwards   ?    zz,/==* |4r+   r   rd   rb   )r   r4   )r5   r6   r7   r8   rR   r9   r:   r!   r2   r;   r<   r=   s   @r)   r\   r\   b   s9    
-C 
-s 
-C 
-Y\ 
- 
- r+   r\   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   ra   )
r'   r   r   r   r   rq   rr   re   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/   rh   ri   s      r)   r2   ResNetBottleNeckLayer.forward   rl   r+   rm   )r   r4      F)r5   r6   r7   r8   rR   r9   r:   boolr!   r2   r;   r<   r=   s   @r)   ro   ro   |   sd      ).-- - 	-
 - - #'- -0 r+   ro   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$ )ResNetStage   z,
A ResNet stage composed by stacked layers.
rA   r   r   r   depthc                 r  > [         T	U ]  5         UR                  S:X  a  [        O[        nUR                  S:X  a  U" UUUUR
                  UR                  S9nOU" X#XAR
                  S9n[        R                  " U/[        US-
  5       Vs/ s H  o" X3UR
                  S9PM     snQ76 U l
        g s  snf )N
bottleneck)r   r   rr   )r   r   r   r`   )r    r!   
layer_typero   r\   rF   rr   r   rc   rangelayers)
r'   rA   r   r   r   r}   rd   first_layer_r(   s
            r)   r!   ResNetStage.__init__   s     	)/):):l)J%P`,!,,)/)H)HK  &UfUfgKmm
dijorsjsdtudt_`5HYHYZdtu
us   B4
r,   r-   c                 @    UnU R                    H  nU" U5      nM     U$ r/   r   )r'   r,   r1   rd   s       r)   r2   ResNetStage.forward   s%    [[E .L !r+   r   )r   r   )r5   r6   r7   r8   rR   r   r9   r!   r   r2   r;   r<   r=   s   @r)   r{   r{      sb     

 
 	

 
 
 
4V   r+   r{   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$ )ResNetEncoder   rA   c           
        > [         TU ]  5         [        R                  " / 5      U l        U R                  R                  [        UUR                  UR                  S   UR                  (       a  SOSUR                  S   S95        [        UR                  UR                  SS  5      n[        X!R                  SS  5       H+  u  u  p4nU R                  R                  [        XXES95        M-     g )Nr   r   r   )r   r}   )r}   )r    r!   r   
ModuleListstagesappendr{   rE   hidden_sizesdownsample_in_first_stagedepthszip)r'   rA   in_out_channelsr   r   r}   r(   s         r)   r!   ResNetEncoder.__init__   s    mmB'%%##A&"<<q!mmA&	
 f1163F3Fqr3JK25o}}UVUWGX2Y.'[KK{6Z[ 3Zr+   r1   output_hidden_statesreturn_dictr-   c                     U(       a  SOS nU R                    H  nU(       a  XA4-   nU" U5      nM     U(       a  XA4-   nU(       d  [        S X4 5       5      $ [        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$As   	)last_hidden_statehidden_states)r   tupler   )r'   r1   r   r   r   stage_modules         r)   r2   ResNetEncoder.forward   sk     3 KKL# - ?'5L	 (  )O;MS\$ASSS-*'
 	
r+   )r   )FT)r5   r6   r7   r8   r   r!   r   ry   r   r2   r;   r<   r=   s   @r)   r   r      sB    \| \$ ]a
"
:>
UY
	'
 
r+   r   c                   b    \ rS rSr% \\S'   SrSrSrSS/r	\
R                  " 5       S 5       rS	rg
)ResNetPreTrainedModel   rA   resnetrL   )imager   rT   c                    [        U[        R                  5      (       a!  [        R                  " UR
                  SSS9  g [        U[        R                  5      (       a  [        R                  " UR
                  [        R                  " S5      S9  UR                  bz  [        R                  R                  R                  UR
                  5      u  p#US:  a  S[        R                  " U5      -  OSn[        R                  " UR                  U* U5        g g SUR                  R                  ;   a  [        R                   " UR
                  5        [        R"                  " UR                  5        [        R"                  " UR$                  5        [        R                   " UR&                  5        [)        US	S 5      b!  [        R"                  " UR*                  5        g g g )
Nfan_outr4   )modenonlinearity   )ar   r   	BatchNormnum_batches_tracked)
isinstancer   r"   initkaiming_normal_weightLinearkaiming_uniform_mathsqrtr   torch_calculate_fan_in_and_fan_outuniform_r(   r5   ones_zeros_running_meanrunning_vargetattrr   )r'   modulefan_inr   bounds        r)   _init_weights#ResNetPreTrainedModel._init_weights   s;   fbii((  YVT		**!!&--499Q<@{{&!HHMMGGV	17!DIIf--fkkE659 '
 F,,555JJv}}%KK$KK++,JJv))*v4d;GF667 H 6r+   r   N)r5   r6   r7   r8   r   __annotations__base_model_prefixmain_input_nameinput_modalities_no_split_modulesr   no_gradr   r;   r   r+   r)   r   r      s=     $O!*,<=
]]_8 8r+   r   c            
       `   ^  \ rS rSrU 4S jr\  S
S\S\S-  S\S-  S\4S jj5       r	S	r
U =r$ )ResNetModeli  c                    > [         TU ]  U5        Xl        [        U5      U l        [        U5      U l        [        R                  " S5      U l	        U R                  5         g )N)r   r   )r    r!   rA   r?   rG   r   encoderr   AdaptiveAvgPool2drI   	post_initrJ   s     r)   r!   ResNetModel.__init__  sI     (0$V,**62r+   NrL   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	                  XRUS9nUS   nU R                  U5      nU(       d	  Xx4USS  -   $ [        UUUR                  S9$ )Nr   r   r   r   )r   pooler_outputr   )rA   r   r   rG   r   rI   r   r   )	r'   rL   r   r   kwargsembedding_outputencoder_outputsr   pooled_outputs	            r)   r2   ResNetModel.forward  s     %9$D $++JjJj 	 &1%<k$++BYBY==6,,U` ' 
 ,A.$56%58KKK7/')77
 	
r+   )rA   rG   r   rI   NN)r5   r6   r7   r8   r!   r   r   ry   r   r2   r;   r<   r=   s   @r)   r   r     sR      -1#'	

 #Tk
 D[	
 
2
 
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-  S\R                  S-  S\	S-  S\	S-  S\
4
S	 jj5       rS
rU =r$ )ResNetForImageClassificationi>  c                   > [         TU ]  U5        UR                  U l        [        U5      U l        [
        R                  " [
        R                  " 5       UR                  S:  a.  [
        R                  " UR                  S   UR                  5      O[
        R                  " 5       5      U l        U R                  5         g )Nr   )r    r!   
num_labelsr   r   r   rc   Flattenr   r   r&   
classifierr   rJ   s     r)   r!   %ResNetForImageClassification.__init__E  s      ++!&)--JJLEKEVEVYZEZBIIf))"-v/@/@A`b`k`k`m

 	r+   NrL   labelsr   r   r-   c                 J   Ub  UOU R                   R                  nU R                  XUS9nU(       a  UR                  OUS   nU R	                  U5      nSn	Ub  U R                  X(U R                   5      n	U(       d  U4USS -   n
U	b  U	4U
-   $ 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   )rA   r   r   r   r   loss_functionr   r   )r'   rL   r   r   r   r   outputsr   r   r   outputs              r)   r2   $ResNetForImageClassification.forwardQ  s     &1%<k$++BYBY++lcn+o1<--'!*/%%fdkkBDY,F'+'7D7V#CVC3\c\q\qrrr+   )r   r   r   )NNNN)r5   r6   r7   r8   r!   r   r   FloatTensor
LongTensorry   r   r2   r;   r<   r=   s   @r)   r   r   >  s    
  26*.,0#'s''$.s   4's #Tk	s
 D[s 
.s sr+   r   zO
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    c                   x   ^  \ rS rSrSrU 4S jr\\\  SS\	S\
S-  S\
S-  S\4S	 jj5       5       5       rS
rU =r$ )ResNetBackboneis  Fc                    > [         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!   rE   r   num_featuresr?   rG   r   r   r   rJ   s     r)   r!   ResNetBackbone.__init__{  sP     #223f6I6II(0$V, 	r+   NrL   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  XU	   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 httpx
>>> from io import BytesIO

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> with httpx.stream("GET", url) as response:
...     image = Image.open(BytesIO(response.read()))

>>> 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)
rA   r   r   rG   r   r   	enumeratestage_namesout_featuresr
   )r'   rL   r   r   r   r   r   r   r   idxstager   s               r)   r2   ResNetBackbone.forward  s    H &1%<k$++BYBY$8$D $++JjJj 	  ==6,,/dX\,]--#D$4$45JC)))s!3 55 6 "_F#0022M%3G'//
 	
MQ
 	
r+   )rG   r   r   r   )r5   r6   r7   r8   has_attentionsr!   r   r	   r   r   ry   r
   r2   r;   r<   r=   s   @r)   r   r   s  si     N   -1#'	;
;
 #Tk;
 D[	;
 
;
  ! ;
r+   r   )r   r   r   r   )+rR   r   r   r   r    r   r   activationsr   backbone_utilsr   r	   modeling_outputsr
   r   r   r   modeling_utilsr   utilsr   r   utils.genericr   configuration_resnetr   
get_loggerr5   loggerModuler   r?   rT   r\   ro   r{   r   r   r   r   r   __all__r   r+   r)   <module>r
     sO       & ! H  . , - . 
		H	%bii $ryy 0RYY "ryy 4'BII 'T#")) #L&
BII &
R 8O 8 88 (
' (
 (
V ,s#8 ,s,s^ 
K
]$9 K

K
\ er+   