
    <hK              	          S r SSKJrJr  SSKrSSKrSSKJr  SSKJr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  \R6                  " \5      rS+S\R<                  S\S\ S\R<                  4S jjr! " S S\RD                  5      r# " S S\RD                  5      r$ " S S\RD                  5      r% " S S\RD                  5      r& " S S\RD                  5      r' " S S\RD                  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 ConvNext model.    )OptionalUnionN)nn)BCEWithLogitsLossCrossEntropyLossMSELoss   )ACT2FN)BackboneOutputBaseModelOutputWithNoAttention(BaseModelOutputWithPoolingAndNoAttention$ImageClassifierOutputWithNoAttention)PreTrainedModel)auto_docstringlogging)BackboneMixin   )ConvNextConfiginput	drop_probtrainingreturnc                    US:X  d  U(       d  U $ SU-
  nU R                   S   4SU R                  S-
  -  -   nU[        R                  " X@R                  U R
                  S9-   nUR                  5         U R                  U5      U-  nU$ )a*  
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
argument.
        r   r   )r   )dtypedevice)shapendimtorchrandr   r   floor_div)r   r   r   	keep_probr   random_tensoroutputs          f/var/www/html/shao/venv/lib/python3.13/site-packages/transformers/models/convnext/modeling_convnext.py	drop_pathr'   )   s     CxII[[^

Q 77E

5ELL YYMYYy!M1FM    c                      ^  \ rS rSrSrSS\\   SS4U 4S jjjrS\R                  S\R                  4S jr
S\4S	 jrS
rU =r$ )ConvNextDropPath>   zXDrop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).Nr   r   c                 .   > [         TU ]  5         Xl        g N)super__init__r   )selfr   	__class__s     r&   r/   ConvNextDropPath.__init__A   s    "r(   hidden_statesc                 B    [        XR                  U R                  5      $ r-   )r'   r   r   r0   r3   s     r&   forwardConvNextDropPath.forwardE   s    FFr(   c                      SU R                    3$ )Nzp=r   )r0   s    r&   
extra_reprConvNextDropPath.extra_reprH   s    DNN#$$r(   r9   r-   )__name__
__module____qualname____firstlineno____doc__r   floatr/   r   Tensorr6   strr:   __static_attributes____classcell__r1   s   @r&   r*   r*   >   sQ    b#(5/ #T # #GU\\ Gell G%C % %r(   r*   c                   j   ^  \ rS rSrSrSU 4S jjrS\R                  S\R                  4S jrSr	U =r
$ )	ConvNextLayerNormL   a5  LayerNorm that supports two data formats: channels_last (default) or channels_first.
The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height,
width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width).
c                 V  > [         TU ]  5         [        R                  " [        R
                  " U5      5      U l        [        R                  " [        R                  " U5      5      U l        X l	        X0l
        U R                  S;  a  [        SU R                   35      eU4U l        g )N)channels_lastchannels_firstzUnsupported data format: )r.   r/   r   	Parameterr   onesweightzerosbiasepsdata_formatNotImplementedErrornormalized_shape)r0   rU   rR   rS   r1   s       r&   r/   ConvNextLayerNorm.__init__R   s    ll5::.>#?@LL-=!>?	&#FF%(A$BRBRAS&TUU!1 3r(   xr   c                 P   U R                   S:X  aV  [        R                  R                  R	                  XR
                  U R                  U R                  U R                  5      nU$ U R                   S:X  a  UR                  nUR                  5       nUR                  SSS9nX-
  R                  S5      R                  SSS9nX-
  [        R                  " X@R                  -   5      -  nUR                  US9nU R                  S S 2S S 4   U-  U R                  S S 2S S 4   -   nU$ )NrK   rL   r   T)keepdim   )r   )rS   r   r   
functional
layer_normrU   rO   rQ   rR   r   rA   meanpowsqrtto)r0   rW   input_dtypeuss        r&   r6   ConvNextLayerNorm.forward\   s   .##..q2G2GVZV_V_aeaiaijA  !11''K	Aq$'AA##At#4A%**Q\22A;'AAtTM*Q.1dD=1IIAr(   )rQ   rS   rR   rU   rO   )ư>rK   )r<   r=   r>   r?   r@   r/   r   rB   r6   rD   rE   rF   s   @r&   rH   rH   L   s-    
4 %,,  r(   rH   c                   f   ^  \ rS rSrSrU 4S jrS\R                  S\R                  4S jr	Sr
U =r$ )ConvNextEmbeddingsj   zThis class is comparable to (and inspired by) the SwinEmbeddings class
found in src/transformers/models/swin/modeling_swin.py.
c                   > [         TU ]  5         [        R                  " UR                  UR
                  S   UR                  UR                  S9U l        [        UR
                  S   SSS9U l	        UR                  U l        g )Nr   kernel_sizestridere   rL   rR   rS   )
r.   r/   r   Conv2dnum_channelshidden_sizes
patch_sizepatch_embeddingsrH   	layernormr0   configr1   s     r&   r/   ConvNextEmbeddings.__init__o   sr     "		!4!4Q!7VEVEV_e_p_p!
 +6+>+>q+AtYij"//r(   pixel_valuesr   c                     UR                   S   nX R                  :w  a  [        S5      eU R                  U5      nU R	                  U5      nU$ )Nr   zeMake sure that the channel dimension of the pixel values match with the one set in the configuration.)r   ro   
ValueErrorrr   rs   )r0   rw   ro   
embeddingss       r&   r6   ConvNextEmbeddings.forwardw   sT    #))!,,,,w  **<8
^^J/
r(   )rs   ro   rr   r<   r=   r>   r?   r@   r/   r   FloatTensorrB   r6   rD   rE   rF   s   @r&   rg   rg   j   s/    0E$5$5 %,,  r(   rg   c                   j   ^  \ rS rSrSrSU 4S jjrS\R                  S\R                  4S jr	Sr
U =r$ )	ConvNextLayer   a  This corresponds to the `Block` class in the original implementation.

There are two equivalent implementations: [DwConv, LayerNorm (channels_first), Conv, GELU,1x1 Conv]; all in (N, C,
H, W) (2) [DwConv, Permute to (N, H, W, C), LayerNorm (channels_last), Linear, GELU, Linear]; Permute back

The authors used (2) as they find it slightly faster in PyTorch.

Args:
    config ([`ConvNextConfig`]): Model configuration class.
    dim (`int`): Number of input channels.
    drop_path (`float`): Stochastic depth rate. Default: 0.0.
c                    > [         TU ]  5         [        R                  " X"SSUS9U l        [        USS9U l        [        R                  " USU-  5      U l        [        UR                     U l        [        R                  " SU-  U5      U l        UR                  S:  a6  [        R                  " UR                  [        R                   " U5      -  SS	9OS U l        US
:  a  [%        U5      U l        g [        R&                  " 5       U l        g )N   r	   )rk   paddinggroupsre   rR      r   T)requires_gradr   )r.   r/   r   rn   dwconvrH   rs   Linearpwconv1r
   
hidden_actactpwconv2layer_scale_init_valuerM   r   rN   layer_scale_parameterr*   Identityr'   )r0   ru   dimr'   r1   s       r&   r/   ConvNextLayer.__init__   s    iia3O*3D9yya#g.&++,yyS#. ,,q0 LL66CHX\] 	"
 9BC))4R[[]r(   r3   r   c                 b   UnU R                  U5      nUR                  SSSS5      nU R                  U5      nU R                  U5      nU R	                  U5      nU R                  U5      nU R                  b  U R                  U-  nUR                  SSSS5      nX R                  U5      -   nU$ )Nr   rZ   r	   r   )r   permuters   r   r   r   r   r'   )r0   r3   r   rW   s       r&   r6   ConvNextLayer.forward   s    KK&IIaAq!NN1LLOHHQKLLO%%1**Q.AIIaAq!NN1%%r(   )r   r'   r   r   rs   r   r   )r   r|   rF   s   @r&   r   r      s0    [U%6%6 5<<  r(   r   c                   j   ^  \ rS rSrSrSU 4S jjrS\R                  S\R                  4S jr	Sr
U =r$ )	ConvNextStage   a}  ConvNeXT stage, consisting of an optional downsampling layer + multiple residual blocks.

Args:
    config ([`ConvNextConfig`]): Model configuration class.
    in_channels (`int`): Number of input channels.
    out_channels (`int`): Number of output channels.
    depth (`int`): Number of residual blocks.
    drop_path_rates(`list[float]`): Stochastic depth rates for each layer.
c                 |  > [         T	U ]  5         X#:w  d  US:  a9  [        R                  " [	        USSS9[        R
                  " X#XES95      U l        O[        R                  " 5       U l        U=(       d    S/U-  n[        R                  " [        U5       Vs/ sH  n[        XXx   S9PM     sn6 U l
        g s  snf )Nr   re   rL   rm   rj   r   )r   r'   )r.   r/   r   
SequentialrH   rn   downsampling_layerr   ranger   layers)
r0   ru   in_channelsout_channelsrk   rl   depthdrop_path_ratesjr1   s
            r&   r/   ConvNextStage.__init__   s    &&1*&(mm!+4EUV		+\'D#
 ')kkmD#):cUU]mm]bch]ij]iXYmF@RS]ij
js   B9r3   r   c                 J    U R                  U5      nU R                  U5      nU$ r-   r   r   r5   s     r&   r6   ConvNextStage.forward   s&    //>M2r(   r   )rZ   rZ   rZ   Nr|   rF   s   @r&   r   r      s/    
U%6%6 5<<  r(   r   c                   t   ^  \ rS rSrU 4S jr  S	S\R                  S\\   S\\   S\	\
\4   4S jjrSrU =r$ )
ConvNextEncoder   c           
      ,  > [         TU ]  5         [        R                  " 5       U l        [
        R                  " SUR                  [        UR                  5      SS9R                  UR                  5       Vs/ sH  nUR                  5       PM     nnUR                  S   n[        UR                  5       HT  nUR                  U   n[        UUUUS:  a  SOSUR                  U   X5   S9nU R                  R!                  U5        UnMV     g s  snf )Nr   cpu)r   rZ   r   )r   r   rl   r   r   )r.   r/   r   
ModuleListstagesr   linspacedrop_path_ratesumdepthssplittolistrp   r   
num_stagesr   append)	r0   ru   rW   r   prev_chsiout_chsstager1   s	           r&   r/   ConvNextEncoder.__init__   s    mmo ^^Av'<'<c&-->PY^_eeflfsfst
t HHJt 	 
 &&q)v(()A))!,G!$$EqqmmA& / 2E KKu%H *
s   9Dr3   output_hidden_statesreturn_dictr   c                     U(       a  SOS n[        U R                  5       H  u  pVU(       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>*ConvNextEncoder.forward.<locals>.<genexpr>   s     X$Fq$Fs   	)last_hidden_stater3   )	enumerater   tupler   )r0   r3   r   r   all_hidden_statesr   layer_modules          r&   r6   ConvNextEncoder.forward   sw     #7BD(5OA#$58H$H!(7M	  6   14D DX]$FXXX-++
 	
r(   )r   )FT)r<   r=   r>   r?   r/   r   r}   r   boolr   r   r   r6   rD   rE   rF   s   @r&   r   r      sY    0 05&*	
((
 'tn
 d^	

 
u44	5
 
r(   r   c                   4    \ rS rSr% \\S'   SrSrS/rS r	Sr
g)	ConvNextPreTrainedModel   ru   convnextrw   r   c                    [        U[        R                  [        R                  45      (       ak  UR                  R
                  R                  SU R                  R                  S9  UR                  b%  UR                  R
                  R                  5         gg[        U[        R                  [        45      (       aJ  UR                  R
                  R                  5         UR                  R
                  R                  S5        g[        U[        5      (       aH  UR                  b:  UR                  R
                  R                  U R                  R                   5        ggg)zInitialize the weightsr   )r]   stdNg      ?)
isinstancer   r   rn   rO   datanormal_ru   initializer_rangerQ   zero_	LayerNormrH   fill_r   r   r   )r0   modules     r&   _init_weights%ConvNextPreTrainedModel._init_weights  s    fryy"))455 MM&&CT[[5R5R&S{{&  &&( '/@ ABBKK""$MM$$S)..++7,,11778Z8Z[ 8 /r(   r   N)r<   r=   r>   r?   r   __annotations__base_model_prefixmain_input_name_no_split_modulesr   rD   r   r(   r&   r   r      s"    "$O()\r(   r   c                      ^  \ rS rSrU 4S jr\   S	S\\R                     S\\	   S\\	   S\
\\4   4S jj5       rSrU =r$ )
ConvNextModeli  c                    > [         TU ]  U5        Xl        [        U5      U l        [        U5      U l        [        R                  " UR                  S   UR                  S9U l        U R                  5         g )Nr   )r.   r/   ru   rg   rz   r   encoderr   r   rp   layer_norm_epsrs   	post_initrt   s     r&   r/   ConvNextModel.__init__  s^     ,V4&v. f&9&9"&=6CXCXY 	r(   rw   r   r   r   c                 f   Ub  UOU R                   R                  nUb  UOU R                   R                  nUc  [        S5      eU R	                  U5      nU R                  UUUS9nUS   nU R                  UR                  SS/5      5      nU(       d	  Xg4USS  -   $ [        UUUR                  S9$ )Nz You have to specify pixel_valuesr   r   r   r   r   )r   pooler_outputr3   )
ru   r   use_return_dictry   rz   r   rs   r]   r   r3   )r0   rw   r   r   embedding_outputencoder_outputsr   pooled_outputs           r&   r6   ConvNextModel.forward$  s     %9$D $++JjJj 	 &1%<k$++B]B]?@@??<8,,!5# ' 
 ,A. '8'='=r2h'GH%58KKK7/')77
 	
r(   )ru   rz   r   rs   )NNN)r<   r=   r>   r?   r/   r   r   r   r}   r   r   r   r   r6   rD   rE   rF   s   @r&   r   r     sk      59/3&*	"
u001"
 'tn"
 d^	"

 
u>>	?"
 "
r(   r   z
    ConvNext 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   4
S jj5       rS	rU =r$ )ConvNextForImageClassificationiJ  c                 6  > [         TU ]  U5        UR                  U l        [        U5      U l        UR                  S:  a.  [
        R                  " UR                  S   UR                  5      O[
        R                  " 5       U l	        U R                  5         g )Nr   r   )r.   r/   
num_labelsr   r   r   r   rp   r   
classifierr   rt   s     r&   r/   'ConvNextForImageClassification.__init__Q  sy      ++%f- FLEVEVYZEZBIIf))"-v/@/@A`b`k`k`m 	
 	r(   rw   labelsr   r   r   c                 2   Ub  UOU R                   R                  nU R                  XUS9nU(       a  UR                  OUS   nU R	                  U5      nSnUGb  U R                   R
                  c  U R                  S:X  a  SU R                   l        OoU R                  S:  aN  UR                  [        R                  :X  d  UR                  [        R                  :X  a  SU R                   l        OSU R                   l        U R                   R
                  S:X  aI  [        5       n	U R                  S:X  a&  U	" UR                  5       UR                  5       5      nOU	" Xr5      nOU R                   R
                  S:X  a=  [        5       n	U	" UR                  SU R                  5      UR                  S5      5      nO,U R                   R
                  S:X  a  [        5       n	U	" Xr5      nU(       d  U4USS -   n
Ub  U4U
-   $ U
$ [!        UUUR"                  S	9$ )
ab  
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).
Nr   r   
regressionsingle_label_classificationmulti_label_classificationr   rZ   )losslogitsr3   )ru   r   r   r   r   problem_typer   r   r   longintr   squeezer   viewr   r   r3   )r0   rw   r   r   r   outputsr   r   r   loss_fctr%   s              r&   r6   &ConvNextForImageClassification.forward_  s    &1%<k$++B]B]--ep-q1<--'!*/{{''/??a'/;DKK,__q(fllejj.HFLL\a\e\eLe/LDKK,/KDKK,{{''<7"9??a'#FNN$4fnn6FGD#F3D))-JJ+-B @&++b/R))-II,./Y,F)-)9TGf$EvE3!//
 	
r(   )r   r   r   )NNNN)r<   r=   r>   r?   r/   r   r   r   r}   
LongTensorr   r   r   r   r6   rD   rE   rF   s   @r&   r   r   J  s      59-1/3&*3
u0013
 ))*3
 'tn	3

 d^3
 
u::	;3
 3
r(   r   zQ
    ConvNeXt backbone, to be used with frameworks like DETR and MaskFormer.
    c            
       t   ^  \ rS rSrU 4S jr\  S	S\R                  S\\	   S\\	   S\
4S jj5       rSrU =r$ )
ConvNextBackbonei  c                   > [         TU ]  U5        [         TU ]	  U5        [        U5      U l        [        U5      U l        UR                  S   /UR                  -   U l        0 n[        U R                  U R                  5       H  u  p4[        USS9X#'   M     [        R                  " U5      U l        U R!                  5         g )Nr   rL   )rS   )r.   r/   _init_backbonerg   rz   r   r   rp   num_featureszip_out_featureschannelsrH   r   
ModuleDicthidden_states_normsr   )r0   ru   r  r   ro   r1   s        r&   r/   ConvNextBackbone.__init__  s     v&,V4&v.#0034v7J7JJ !#&t'9'94==#IE):<Ue)f& $J#%==1D#E  	r(   rw   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US9nU(       a  UR
                  OUS   nSn[        U R                  U5       H0  u  pXR                  ;   d  M  U R                  U   " U	5      n	Xy4-  nM2     U(       d  U4n
U(       a  X4-  n
U
$ [        UU(       a  USS9$ 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("facebook/convnext-tiny-224")
>>> model = AutoBackbone.from_pretrained("facebook/convnext-tiny-224")

>>> inputs = processor(image, return_tensors="pt")
>>> outputs = model(**inputs)
```NTr   r   r   )feature_mapsr3   
attentions)ru   r   r   rz   r   r3   r  stage_namesout_featuresr  r   )r0   rw   r   r   r   r  r3   r  r   hidden_stater%   s              r&   r6   ConvNextBackbone.forward  s   2 &1%<k$++B]B]$8$D $++JjJj 	  ??<8,,!%#  
 2=--'!*#&t'7'7#GE)))#77>|L/ $H
 "_F#**M%+?-
 	
EI
 	
r(   )rz   r   r  r  )NN)r<   r=   r>   r?   r/   r   r   rB   r   r   r   r6   rD   rE   rF   s   @r&   r  r    sV    "  04&*	7
ll7
 'tn7
 d^	7

 
7
 7
r(   r  )r   r   r   r  )r   F).r@   typingr   r   r   torch.utils.checkpointr   torch.nnr   r   r   activationsr
   modeling_outputsr   r   r   r   modeling_utilsr   utilsr   r   utils.backbone_utilsr   configuration_convnextr   
get_loggerr<   loggerrB   rA   r   r'   Moduler*   rH   rg   r   r   r   r   r   r   r  __all__r   r(   r&   <module>r&     sq    "    A A !  . , 1 2 
		H	%U\\ e T V[VbVb *%ryy %		 < 0)BII )XBII @-
bii -
` \o \ \, 1
+ 1
 1
h C
%< C
C
L 
J
. J

J
Z mr(   