
    Z j              	          S r SSKrSSKrSSKJ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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Jr  SSKJr  SSKJr  \R>                  " \ 5      r!\" SS9\ " S S\5      5       5       r"\" SS9\ " S S\5      5       5       r#\" SS9\ " S S\5      5       5       r$\" SS9\ " S S\5      5       5       r%S r&S r' " S S \RP                  5      r) " S! S"\RP                  5      r* " S# S$\RP                  5      r+SJS%\RX                  S&\-S'\.S(\RX                  4S) jjr/ " S* S+\RP                  5      r0 " S, S-\RP                  5      r1 " S. S/\RP                  5      r2 " S0 S1\RP                  5      r3 " S2 S3\RP                  5      r4 " S4 S5\RP                  5      r5 " S6 S7\RP                  5      r6 " S8 S9\5      r7 " S: S;\RP                  5      r8\ " S< S=\5      5       r9\ " S> S?\95      5       r:\" S@S9 " SA SB\95      5       r;\" SCS9 " SD SE\95      5       r<\" SFS9 " SG SH\\95      5       r=/ SIQr>g)KzPyTorch Swin Transformer model.    N)	dataclass)nn   )initialization)ACT2FN)BackboneMixinfilter_output_hidden_states)GradientCheckpointingLayer)BackboneOutput)PreTrainedModel)ModelOutputauto_docstringlogging	torch_int)can_return_tuple   )
SwinConfigzN
    Swin encoder's outputs, with potential hidden states and attentions.
    )custom_introc                       \ rS rSr% SrSr\R                  S-  \S'   Sr	\
\R                  S4   S-  \S'   Sr\
\R                  S4   S-  \S'   Sr\
\R                  S4   S-  \S'   S	rg)
SwinEncoderOutput(   a  
reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
    Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
    shape `(batch_size, hidden_size, height, width)`.

    Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
    include the spatial dimensions.
Nlast_hidden_state.hidden_states
attentionsreshaped_hidden_states )__name__
__module____qualname____firstlineno____doc__r   torchFloatTensor__annotations__r   tupler   r   __static_attributes__r       w/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/swin/modeling_swin.pyr   r   (   s}     37u((4/6:>M5**C/047>7;Je'',-4;CGE%"3"3S"89D@Gr'   r   zV
    Swin 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'   Sr	\R                  S-  \S'   Sr
\\R                  S4   S-  \S'   Sr\\R                  S4   S-  \S'   Sr\\R                  S4   S-  \S	'   S
rg)SwinModelOutput>   a  
pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*, returned when `add_pooling_layer=True` is passed):
    Average pooling of the last layer hidden-state.
reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
    Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
    shape `(batch_size, hidden_size, height, width)`.

    Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
    include the spatial dimensions.
Nr   pooler_output.r   r   r   r   )r   r   r   r    r!   r   r"   r#   r$   r,   r   r%   r   r   r&   r   r'   r(   r*   r*   >   s    	 37u((4/6.2M5$$t+2:>M5**C/047>7;Je'',-4;CGE%"3"3S"89D@Gr'   r*   z*
    Swin masked image model outputs.
    c                       \ rS rSr% SrSr\R                  S-  \S'   Sr	\R                  S-  \S'   Sr
\\R                  S4   S-  \S'   Sr\\R                  S4   S-  \S'   Sr\\R                  S4   S-  \S	'   S
rg)SwinMaskedImageModelingOutputW   a  
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided):
    Masked image modeling (MLM) loss.
reconstruction (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
    Reconstructed pixel values.
reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
    Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
    shape `(batch_size, hidden_size, height, width)`.

    Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
    include the spatial dimensions.
Nlossreconstruction.r   r   r   r   )r   r   r   r    r!   r0   r"   r#   r$   r1   r   r%   r   r   r&   r   r'   r(   r.   r.   W   s     &*D%

d
")/3NE%%,3:>M5**C/047>7;Je'',-4;CGE%"3"3S"89D@Gr'   r.   z0
    Swin outputs for image classification.
    c                       \ rS rSr% SrSr\R                  S-  \S'   Sr	\R                  S-  \S'   Sr
\\R                  S4   S-  \S'   Sr\\R                  S4   S-  \S'   Sr\\R                  S4   S-  \S	'   S
rg)SwinImageClassifierOutputr   a  
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
    Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
    Classification (or regression if config.num_labels==1) scores (before SoftMax).
reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
    Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of
    shape `(batch_size, hidden_size, height, width)`.

    Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
    include the spatial dimensions.
Nr0   logits.r   r   r   r   )r   r   r   r    r!   r0   r"   r#   r$   r5   r   r%   r   r   r&   r   r'   r(   r3   r3   r   s     &*D%

d
")'+FE$+:>M5**C/047>7;Je'',-4;CGE%"3"3S"89D@Gr'   r3   c                     U R                   u  p#pEU R                  X#U-  XU-  X5      n U R                  SSSSSS5      R                  5       R                  SXU5      nU$ )z*
Partitions the given input into windows.
r   r   r            shapeviewpermute
contiguous)input_featurewindow_size
batch_sizeheightwidthnum_channelswindowss          r(   window_partitionrG      so     /<.A.A+J!&&k);8LkM ##Aq!Q15@@BGGKfrsGNr'   c                     U R                   S   nU R                  SX!-  X1-  XU5      n U R                  SSSSSS5      R                  5       R                  SX#U5      n U $ )z7
Merges windows to produce higher resolution features.
r:   r   r   r   r7   r8   r9   r;   )rF   rA   rC   rD   rE   s        r(   window_reverserI      se     ==$Lll2v4e6JKfrsGooaAq!Q/::<AA"fUabGNr'   c            
          ^  \ rS rSrSrSU 4S jjrS\R                  S\S\S\R                  4S jr	  SS
\R                  S	-  S\R                  S	-  S\S\\R                     4S jjrSrU =r$ )SwinEmbeddings   zO
Construct the patch and position embeddings. Optionally, also the mask token.
c                   > [         TU ]  5         [        U5      U l        U R                  R                  nU R                  R
                  U l        U(       a6  [        R                  " [        R                  " SSUR                  5      5      OS U l        UR                  (       a?  [        R                  " [        R                  " SUS-   UR                  5      5      U l        OS U l        [        R                  " UR                  5      U l        [        R"                  " UR$                  5      U l        UR(                  U l        Xl        g )Nr   )super__init__SwinPatchEmbeddingspatch_embeddingsnum_patches	grid_size
patch_gridr   	Parameterr"   zeros	embed_dim
mask_tokenuse_absolute_embeddingsposition_embeddings	LayerNormnormDropouthidden_dropout_probdropout
patch_sizeconfig)selfra   use_mask_tokenrR   	__class__s       r(   rO   SwinEmbeddings.__init__   s     3F ;++77//99O]",,u{{1a9I9I'JKcg))')||EKK;QR?TZTdTd4e'fD$'+D$LL!1!12	zz&"<"<= ++r'   
embeddingsrC   rD   returnc                    UR                   S   S-
  nU R                  R                   S   S-
  n[        R                  R	                  5       (       d  XE:X  a  X#:X  a  U R                  $ U R                  SS2SS24   nU R                  SS2SS24   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[        R                  " Xg4SS9$ )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.

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   Nr:         ?r   r   r7   bicubicF)sizemodealign_cornersdim)r<   rZ   r"   jit
is_tracingr`   r   reshaper>   r   
functionalinterpolater=   cat)rb   rf   rC   rD   rR   num_positionsclass_pos_embedpatch_pos_embedro   
new_height	new_widthsqrt_num_positionss               r(   interpolate_pos_encoding'SwinEmbeddings.interpolate_pos_encoding   sS    !&&q)A-0066q9A= yy##%%+*F6?+++221bqb59221ab59r".
__,	&}c'9:)11!5G]`a)11!Q1=--33(	 4 
 *11!Q1=BB1b#Nyy/;CCr'   Npixel_valuesbool_masked_posr|   c                    UR                   u  pEpgU R                  U5      u  pU R                  U5      nUR                  5       u  pnUbI  U R                  R                  XS5      nUR                  S5      R                  U5      nUSU-
  -  X-  -   nU R                  b*  U(       a  XR                  XU5      -   nOXR                  -   nU R                  U5      nX4$ )Nr:   g      ?)r<   rQ   r\   rk   rX   expand	unsqueezetype_asrZ   r|   r_   )rb   r~   r   r|   _rE   rC   rD   rf   output_dimensionsrB   seq_lenmask_tokensmasks                 r(   forwardSwinEmbeddings.forward   s     *6););&(,(=(=l(K%
YYz*
!+!2
Q&//00bIK",,R088ED#sTz2[5GGJ##/''*G*G
\a*bb
'*B*BB
\\*-
,,r'   )ra   r_   rX   r\   rQ   rT   r`   rZ   )FNF)r   r   r   r    r!   rO   r"   Tensorintr|   r#   
BoolTensorboolr%   r   r&   __classcell__rd   s   @r(   rK   rK      s    &&D5<< &D &DUX &D]b]i]i &DV 48).	-''$.- ))D0- #'	-
 
u||	- -r'   rK   c                      ^  \ rS rSrSrU 4S jrS rS\R                  S-  S\	\R                  \	\   4   4S jrS	rU =r$ )
rP      z
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
c                   > [         TU ]  5         UR                  UR                  p2UR                  UR
                  pT[        U[        R                  R                  5      (       a  UOX"4n[        U[        R                  R                  5      (       a  UOX34nUS   US   -  US   US   -  -  nX l        X0l        X@l        X`l
        US   US   -  US   US   -  4U l        [        R                  " XEX3S9U l        g )Nr   r   )kernel_sizestride)rN   rO   
image_sizer`   rE   rW   
isinstancecollectionsabcIterablerR   rS   r   Conv2d
projection)rb   ra   r   r`   rE   hidden_sizerR   rd   s          r(   rO   SwinPatchEmbeddings.__init__  s    !'!2!2F4E4EJ$*$7$79I9Ik#-j+//:R:R#S#SZZdYq
#-j+//:R:R#S#SZZdYq
!!}
15*Q-:VW=:XY$$(&$Q-:a=8*Q-:VW=:XY))L:ir'   c                 f   X0R                   S   -  S:w  aB  SU R                   S   X0R                   S   -  -
  4n[        R                  R                  X5      nX R                   S   -  S:w  aD  SSSU R                   S   X R                   S   -  -
  4n[        R                  R                  X5      nU$ )Nr   r   )r`   r   rs   pad)rb   r~   rC   rD   
pad_valuess        r(   	maybe_padSwinPatchEmbeddings.maybe_pad  s    ??1%%*T__Q/%//!:L2LLMJ==,,\FLOOA&&!+Q4??1#5QRAS8S#STJ==,,\FLr'   r~   Nrg   c                     UR                   u  p#pEU R                  XU5      nU R                  U5      nUR                   u    p$nXE4nUR                  S5      R	                  SS5      nXg4$ )Nr7   r   )r<   r   r   flatten	transpose)rb   r~   r   rE   rC   rD   rf   r   s           r(   r   SwinPatchEmbeddings.forward  sp    )5););&~~lEB__\2
(..1e#O''*44Q:
,,r'   )rS   r   rE   rR   r`   r   )r   r   r   r    r!   rO   r   r"   r#   r%   r   r   r   r&   r   r   s   @r(   rP   rP      sK    j	-E$5$5$< 	-u||UZ[^U_G_A` 	- 	-r'   rP   c            	          ^  \ rS rSrSr\R                  4S\\   S\S\R                  SS4U 4S jjjr
S	 rS
\R                  S\\\4   S\R                  4S jrSrU =r$ )SwinPatchMergingi*  a  
Patch Merging Layer.

Args:
    input_resolution (`tuple[int]`):
        Resolution of input feature.
    dim (`int`):
        Number of input channels.
    norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
        Normalization layer class.
input_resolutionro   
norm_layerrg   Nc                    > [         TU ]  5         Xl        X l        [        R
                  " SU-  SU-  SS9U l        U" SU-  5      U l        g )Nr8   r7   Fbias)rN   rO   r   ro   r   Linear	reductionr\   )rb   r   ro   r   rd   s       r(   rO   SwinPatchMerging.__init__7  sE     01s7AG%@q3w'	r'   c                     US-  S:H  =(       d    US-  S:H  nU(       a-  SSSUS-  SUS-  4n[         R                  R                  X5      nU$ )Nr7   r   r   )r   rs   r   )rb   r@   rC   rD   
should_padr   s         r(   r   SwinPatchMerging.maybe_pad>  sS    qjAo:519>
Q519a!<JMM--mHMr'   r@   input_dimensionsc                    Uu  p4UR                   u  pVnUR                  XSXG5      nU R                  XU5      nUS S 2SS S2SS S2S S 24   nUS S 2SS S2SS S2S S 24   n	US S 2SS S2SS S2S S 24   n
US S 2SS S2SS S2S S 24   n[        R                  " XX/S5      nUR                  USSU-  5      nU R                  U5      nU R                  U5      nU$ )Nr   r7   r   r:   r8   )r<   r=   r   r"   ru   r\   r   )rb   r@   r   rC   rD   rB   ro   rE   input_feature_0input_feature_1input_feature_2input_feature_3s               r(   r   SwinPatchMerging.forwardF  s   ((5(;(;%
%**:uS}eD'14a4Aq(89'14a4Aq(89'14a4Aq(89'14a4Aq(89		?_"fhjk%**:r1|;KL		-0}5r'   )ro   r   r\   r   )r   r   r   r    r!   r   r[   r%   r   ModulerO   r   r"   r   r   r&   r   r   s   @r(   r   r   *  s|    
 XZWcWc (s (# (299 (hl ( (U\\ U3PS8_ Y^YeYe  r'   r   input	drop_probtrainingrg   c                    US:X  d  U(       d  U $ SU-
  nU R                   S   4SU R                  S-
  -  -   nU[        R                  " X@R                  U R
                  S9-   nUR                  5         U R                  U5      U-  nU$ )z[
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).

        r   r   )r   dtypedevice)r<   ndimr"   randr   r   floor_div)r   r   r   	keep_probr<   random_tensoroutputs          r(   	drop_pathr   a  s    
 CxII[[^

Q 77E

5ELL YYMYYy!M1FMr'   c                      ^  \ rS rSrSrS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$ )SwinDropPathiq  zXDrop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).Nr   rg   c                 .   > [         TU ]  5         Xl        g N)rN   rO   r   )rb   r   rd   s     r(   rO   SwinDropPath.__init__t  s    "r'   r   c                 B    [        XR                  U R                  5      $ r   )r   r   r   rb   r   s     r(   r   SwinDropPath.forwardx  s    FFr'   c                      SU R                    3$ )Nzp=r   rb   s    r(   
extra_reprSwinDropPath.extra_repr{  s    DNN#$$r'   r   r   )r   r   r   r    r!   floatrO   r"   r   r   strr   r&   r   r   s   @r(   r   r   q  sQ    b#%$, #$ # #GU\\ Gell G%C % %r'   r   c            
          ^  \ rS rSrU 4S jr  SS\R                  S\R                  S-  S\S-  S\	\R                     4S jjr
S	 rS
rU =r$ )SwinSelfAttentioni  c                   > [         TU ]  5         X#-  S:w  a  [        SU SU S35      eX0l        [	        X#-  5      U l        U R                  U R
                  -  U l        [        U[        R                  R                  5      (       a  UOXD4U l        [        R                  " [        R                  " SU R                  S   -  S-
  SU R                  S   -  S-
  -  U5      5      U l        U R#                  SU R%                  5       5        [        R&                  " U R                  U R                  UR(                  S9U l        [        R&                  " U R                  U R                  UR(                  S9U l        [        R&                  " U R                  U R                  UR(                  S9U l        [        R0                  " UR2                  5      U l        g )	Nr   zThe hidden size (z6) is not a multiple of the number of attention heads ()r7   r   relative_position_indexr   )rN   rO   
ValueErrornum_attention_headsr   attention_head_sizeall_head_sizer   r   r   r   rA   r   rU   r"   rV   relative_position_bias_tableregister_buffercreate_relative_position_indexr   qkv_biasquerykeyvaluer]   attention_probs_dropout_probr_   rb   ra   ro   	num_headsrA   rd   s        r(   rO   SwinSelfAttention.__init__  s   ?a#C5(^_h^iijk  $- #&s#7 !558P8PP%k;??3K3KLLKS^Rl 	 -/LLKKT--a0014T=M=Ma=P9PST9TUW`a-
) 	68[8[8]^YYt1143E3EFOO\
99T//1C1C&//ZYYt1143E3EFOO\
zz&"E"EFr'   Nr   attention_maskoutput_attentionsrg   c                 v   UR                   u  pEnXESU R                  4nU R                  U5      R                  U5      R	                  SS5      nU R                  U5      R                  U5      R	                  SS5      n	U R                  U5      R                  U5      R	                  SS5      n
[        R                  " XR	                  SS5      5      nU[        R                  " U R                  5      -  nU R                  U R                  R                  S5         nUR                  U R                  S   U R                  S   -  U R                  S   U R                  S   -  S5      nUR                  SSS5      R                  5       nXR!                  S5      -   nUbm  UR                   S   nUR                  XM-  XR"                  XU5      nXR!                  S5      R!                  S5      -   nUR                  SU R"                  XU5      n[$        R&                  R)                  USS9nU R+                  U5      n[        R                  " X5      nUR                  SSSS5      R                  5       nUR-                  5       S S U R.                  4-   nUR                  U5      nU(       a  X4nU$ U4nU$ )Nr:   r   r7   r   rn   r   )r<   r   r   r=   r   r   r   r"   matmulmathsqrtr   r   rA   r>   r?   r   r   r   rs   softmaxr_   rk   r   )rb   r   r   r   rB   ro   rE   hidden_shapequery_layer	key_layervalue_layerattention_scoresrelative_position_bias
mask_shapeattention_probscontext_layernew_context_layer_shapeoutputss                     r(   r   SwinSelfAttention.forward  s    )6(;(;%
"T-E-EFjj/44\BLLQPQRHH]+00>HHAN	jj/44\BLLQPQR !<<5H5HR5PQ+dii8P8P.QQ!%!B!B4C_C_CdCdegCh!i!7!<!<Q$"2"21"55t7G7G7JTM]M]^_M`7`bd"
 "8!?!?1a!H!S!S!U+.N.Nq.QQ%'--a0J/44(*6N6NPS   02J2J12M2W2WXY2ZZ/44R9Q9QSV\ --//0@b/I ,,7_B%--aAq9DDF"/"4"4"6s";t?Q?Q>S"S%**+BC6G=2 O\M]r'   c                    [         R                  " U R                  S   5      n[         R                  " U R                  S   5      n[         R                  " [         R                  " X/SS95      n[         R
                  " US5      nUS S 2S S 2S 4   US S 2S S S 24   -
  nUR                  SSS5      R                  5       nUS S 2S S 2S4==   U R                  S   S-
  -  ss'   US S 2S S 2S4==   U R                  S   S-
  -  ss'   US S 2S S 2S4==   SU R                  S   -  S-
  -  ss'   UR                  S5      nU$ )Nr   r   ij)indexingr7   r:   )	r"   arangerA   stackmeshgridr   r>   r?   sum)rb   coords_hcoords_wcoordscoords_flattenrelative_coordsr   s          r(   r   0SwinSelfAttention.create_relative_position_index  s+   << 0 0 34<< 0 0 34U^^X,@4PQvq1(At4~aqj7QQ)11!Q:EEG1a D$4$4Q$7!$;; 1a D$4$4Q$7!$;; 1a A(8(8(;$;a$?? "1"5"5b"9&&r'   )	r   r   r_   r   r   r   r   r   rA   r   )r   r   r   r    rO   r"   r   r#   r   r%   r   r   r&   r   r   s   @r(   r   r     sc    G: 48).	1||1 ))D01  $;	1
 
u||	1f' 'r'   r   c                   z   ^  \ rS rSrU 4S jrS\R                  S\R                  S\R                  4S jrSrU =r	$ )SwinSelfOutputi  c                    > [         TU ]  5         [        R                  " X"5      U l        [        R
                  " UR                  5      U l        g r   )rN   rO   r   r   denser]   r   r_   rb   ra   ro   rd   s      r(   rO   SwinSelfOutput.__init__  s4    YYs(
zz&"E"EFr'   r   input_tensorrg   c                 J    U R                  U5      nU R                  U5      nU$ r   r  r_   )rb   r   r  s      r(   r   SwinSelfOutput.forward  s$    

=1]3r'   r  
r   r   r   r    rO   r"   r   r   r&   r   r   s   @r(   r  r    s7    G
U\\  RWR^R^  r'   r  c            
          ^  \ rS rSrU 4S jr  S
S\R                  S\R                  S-  S\S-  S\	\R                     4S jjr
S	rU =r$ )SwinAttentioni  c                 d   > [         TU ]  5         [        XX45      U l        [	        X5      U l        g r   )rN   rO   r   rb   r  r   r   s        r(   rO   SwinAttention.__init__  s(    %f9J	$V1r'   Nr   r   r   rg   c                 f    U R                  XU5      nU R                  US   U5      nU4USS  -   nU$ )Nr   r   )rb   r   )rb   r   r   r   self_outputsattention_outputr  s          r(   r   SwinAttention.forward  sC     yy@QR;;|AF#%QR(88r'   )r   rb   r   )r   r   r   r    rO   r"   r   r#   r   r%   r   r&   r   r   s   @r(   r  r    s\    2 48).		||	 ))D0	  $;		
 
u||		 	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	$ )SwinIntermediatei  c                   > [         TU ]  5         [        R                  " U[	        UR
                  U-  5      5      U l        [        UR                  [        5      (       a  [        UR                     U l        g UR                  U l        g r   )rN   rO   r   r   r   	mlp_ratior  r   
hidden_actr   r   intermediate_act_fnr  s      r(   rO   SwinIntermediate.__init__  sd    YYsC(8(83(>$?@
f''--'-f.?.?'@D$'-'8'8D$r'   r   rg   c                 J    U R                  U5      nU R                  U5      nU$ r   r  r*  r   s     r(   r   SwinIntermediate.forward  s&    

=100?r'   r-  r  r   s   @r(   r&  r&    s(    9U\\ ell  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	$ )
SwinOutputi
  c                    > [         TU ]  5         [        R                  " [	        UR
                  U-  5      U5      U l        [        R                  " UR                  5      U l	        g r   )
rN   rO   r   r   r   r(  r  r]   r^   r_   r  s      r(   rO   SwinOutput.__init__  sF    YYs6#3#3c#9:C@
zz&"<"<=r'   r   rg   c                 J    U R                  U5      nU R                  U5      nU$ r   r  r   s     r(   r   SwinOutput.forward  s$    

=1]3r'   r  r  r   s   @r(   r0  r0  
  s(    >
U\\ ell  r'   r0  c                      ^  \ rS rSrSU 4S jjrS rS rS r  SS\R                  S\
\\4   S\S	-  S
\S	-  S\
\R                  \R                  4   4
S jjrSrU =r$ )	SwinLayeri  c                   > [         TU ]  5         UR                  U l        X`l        UR                  U l        X0l        [        R                  " X!R                  S9U l	        [        XX@R                  S9U l        US:  a  [        U5      O[        R                  " 5       U l        [        R                  " X!R                  S9U l        [!        X5      U l        [%        X5      U l        g )Neps)rA   r   )rN   rO   chunk_size_feed_forward
shift_sizerA   r   r   r[   layer_norm_epslayernorm_beforer  	attentionr   Identityr   layernorm_afterr&  intermediater0  r   )rb   ra   ro   r   r   drop_path_rater;  rd   s          r(   rO   SwinLayer.__init__  s    '-'E'E$$!-- 0 "S6K6K L&vIK[K[\9G#9Mn5SUS^S^S`!||C5J5JK,V9 -r'   c                    [        U5      U R                  ::  an  [        S5      U l        [        R
                  R                  5       (       a*  [        R                   " [        R                  " U5      5      O
[        U5      U l        g g Nr   )minrA   r   r;  r"   rp   rq   tensor)rb   r   s     r(   set_shift_and_window_size#SwinLayer.set_shift_and_window_size$  s_     D$4$44'lDO=BYY=Q=Q=S=S		%,,'789Y\]mYn  5r'   c           	         U R                   S:  Gae  [        R                  " SXS4X4S9n[        SU R                  * 5      [        U R                  * U R                   * 5      [        U R                   * S 5      4n[        SU R                  * 5      [        U R                  * U R                   * 5      [        U R                   * S 5      4nSnU H  n	U H  n
XS S 2XS S 24'   US-  nM     M     [        XPR                  5      nUR                  SU R                  U R                  -  5      nUR                  S5      UR                  S5      -
  nUR                  US:g  S5      R                  US:H  S5      nU$ S nU$ )Nr   r   r   r:   r7   g      Yr   )	r;  r"   rV   slicerA   rG   r=   r   masked_fill)rb   rC   rD   r   r   img_maskheight_sliceswidth_slicescountheight_slicewidth_slicemask_windows	attn_masks                r(   get_attn_maskSwinLayer.get_attn_mask,  sy   ??Q{{Ava#8UHa$***+t'''$//)9:t&-M a$***+t'''$//)9:t&-L
 E -#/K@EQ1<=QJE $0 !.
 ,H6F6FGL',,R1A1ADDTDT1TUL$..q1L4J4J14MMI!--i1nfEQQR[_`R`befI  Ir'   c                     U R                   X0R                   -  -
  U R                   -  nU R                   X R                   -  -
  U R                   -  nSSSUSU4n[        R                  R                  X5      nX4$ rE  )rA   r   rs   r   )rb   r   rC   rD   	pad_right
pad_bottomr   s          r(   r   SwinLayer.maybe_padH  sy    %%0@0@(@@DDTDTT	&&2B2B)BBdFVFVV
Ay!Z8
))-D((r'   r   r   r   Nalways_partitionrg   c                    U(       d  U R                  U5        O Uu  pVUR                  5       u  pxn	Un
U R                  U5      nUR                  XuXi5      nU R	                  XU5      u  pUR
                  u  ppU R                  S:  a.  [        R                  " XR                  * U R                  * 4SS9nOUn[        XR                  5      nUR                  SU R                  U R                  -  U	5      nU R                  XUR                  UR                  S9nU R                  UUUS9nUS   nUR                  SU R                  U R                  U	5      n[        UU R                  X5      nU R                  S:  a-  [        R                  " UU R                  U R                  4SS9nOUnUS   S:  =(       d    US   S:  nU(       a  US S 2S U2S U2S S 24   R!                  5       nUR                  XuU-  U	5      nXR#                  U5      -   nU R%                  U5      nU R'                  U5      nXR)                  U5      -   nU(       a	  UUS	   4nU$ U4nU$ )
Nr   )r   r7   )shiftsdimsr:   r   )r   r   r9   r   )rH  rk   r=  r=   r   r<   r;  r"   rollrG   rA   rU  r   r   r>  rI   r?   r   r@  rA  r   )rb   r   r   r   r[  rC   rD   rB   r   channelsshortcutr   
height_pad	width_padshifted_hidden_stateshidden_states_windowsrT  attention_outputsr#  attention_windowsshifted_windows
was_paddedlayer_outputlayer_outputss                           r(   r   SwinLayer.forwardO  s{     **+;<("/"4"4"6
x --m<%**:uO %)NN=%$P!&3&9&9#y??Q$)JJ}FVY]YhYhXhEipv$w!$1! !11FHXHX Y 5 : :2t?O?ORVRbRb?bdl m&&)<)<EZEaEa ' 
	 !NN+@)_pNq,Q/,11"d6F6FHXHXZbc():D<L<Ljd ??Q %

?DOOUYUdUdCelr s /]Q&;*Q-!*;
 1!WfWfufa2G H S S U-22:~xX >>2C#DD++M:((6${{<'@@@Q'8';< YeWfr'   )
r>  r:  r   r   rA  r@  r=  r   r;  rA   )r   r   FF)r   r   r   r    rO   rH  rU  r   r"   r   r%   r   r   r   r&   r   r   s   @r(   r6  r6    s    .8) */(->||>  S/>  $;	>
 +> 
u||U\\)	*> >r'   r6  c                      ^  \ rS rSrU 4S jr  SS\R                  S\\\4   S\	S-  S\	S-  S\\R                     4
S	 jjr
S
rU =r$ )	SwinStagei  c                 R  > [         T	U ]  5         Xl        X l        [        R
                  " [        U5       Vs/ s H+  n[        UUUUXh   US-  S:X  a  SOUR                  S-  S9PM-     sn5      U l	        Ub  U" X2[        R                  S9U l        OS U l        SU l        g s  snf )Nr7   r   )ra   ro   r   r   rB  r;  )ro   r   F)rN   rO   ra   ro   r   
ModuleListranger6  rA   blocksr[   
downsamplepointing)
rb   ra   ro   r   depthr   r   rt  ird   s
            r(   rO   SwinStage.__init__  s    mm u
 &A !%5'#,<%&UaZqf6H6HA6M &

 !()9r||\DO"DO'
s   2B$r   r   r   Nr[  rg   c                     Uu  pV[        U R                  5       H  u  pxU" XX45      n	U	S   nM     Un
U R                  b%  US-   S-  US-   S-  pXVX4nU R                  X5      nOXVXV4nXU4nU(       a  UW	SS  -  nU$ )Nr   r   r7   )	enumeraters  rt  )rb   r   r   r   r[  rC   rD   rw  layer_modulerk  !hidden_states_before_downsamplingheight_downsampledwidth_downsampledr   stage_outputss                  r(   r   SwinStage.forward  s     )(5OA(J[nM)!,M  6
 -:)??&5;aZA4EPQ	VWGW 1!'0B V OO,M`M!' >&K\]]12..Mr'   )rs  ra   ro   rt  ru  rm  )r   r   r   r    rO   r"   r   r%   r   r   r   r&   r   r   s   @r(   ro  ro    sg    < */(-||  S/  $;	
 + 
u||	 r'   ro  c                      ^  \ rS rSrU 4S jr     SS\R                  S\\\4   S\	S-  S\	S-  S\	S-  S	\	S-  S
\	S-  S\\
-  4S jjrSrU =r$ )SwinEncoderi  c                   > [         TU ]  5         [        UR                  5      U l        Xl        [        R                  " SUR                  [        UR                  5      SS9 Vs/ s H  o3R                  5       PM     nn[        R                  " [        U R                  5       Vs/ s H  n[        U[        UR                   SU-  -  5      US   SU-  -  US   SU-  -  4UR                  U   UR"                  U   U[        UR                  S U 5      [        UR                  S US-    5       XPR                  S-
  :  a  [$        OS S9PM     sn5      U l        SU l        g s  snf s  snf )Nr   cpu)r   r7   r   )ra   ro   r   rv  r   r   rt  F)rN   rO   lendepths
num_layersra   r"   linspacerB  r  itemr   rq  rr  ro  r   rW   r   r   layersgradient_checkpointing)rb   ra   rS   xdpri_layerrd   s         r(   rO   SwinEncoder.__init__  sQ   fmm,!&63H3H#fmmJ\ej!kl!kAvvx!klmm  %T__5  6G !F,,q'z9:&/lq'z&BIaLUVX_U_D`%a --0$..w7!#fmmHW&=">V]]S`U\_`U`EaAbc4;ooPQ>Q4Q/X\  6
 ',#! ms   &E&(B*E+r   r   r   Noutput_hidden_states(output_hidden_states_before_downsamplingr[  return_dictrg   c                    U(       a  SOS nU(       a  SOS n	U(       a  SOS n
U(       aB  UR                   u  pnUR                  " U/UQUP76 nUR                  SSSS5      nX4-  nX4-  n	[        U R                  5       H  u  nnU" XX65      nUS   nUS   nUS   nUS   US   4nU(       aS  U(       aL  UR                   u  pnUR                  " U/US   US   4QUP76 nUR                  SSSS5      nUU4-  nX4-  n	OPU(       aI  U(       dB  UR                   u  pnUR                  " U/UQUP76 nUR                  SSSS5      nX4-  nX4-  n	U(       d  M  U
USS  -  n
M     U(       d  [        S XU
4 5       5      $ [        UUU
U	S	9$ )
Nr   r   r   r   r7   r   r:   c              3   .   #    U  H  oc  M  Uv   M     g 7fr   r   ).0vs     r(   	<genexpr>&SwinEncoder.forward.<locals>.<genexpr>  s     m$[q$[s   	)r   r   r   r   )r<   r=   r>   rz  r  r%   r   )rb   r   r   r   r  r  r[  r  all_hidden_statesall_reshaped_hidden_statesall_self_attentionsrB   r   r   reshaped_hidden_staterw  r{  rk  r|  r   s                       r(   r   SwinEncoder.forward  s    #7BD+?RT"$5b4)6)<)<&J;$1$6$6z$bDT$bVa$b!$9$A$A!Q1$M!!11&*BB&(5OA|(J[nM)!,M0=a0@- -a 0 1" 57H7LM#(P-N-T-T*
{ )J(N(N)"3A"68I!8L!M)OZ)% )>(E(EaAq(Q%!&G%II!*.FF*%.V-:-@-@*
{(5(:(::(fHX(fZe(f%(=(E(EaAq(Q%!%55!*.FF*  #}QR'88#9  6< m]GZ$[mmm ++*#=	
 	
r'   )ra   r  r  r  )FFFFT)r   r   r   r    rO   r"   r   r%   r   r   r   r   r&   r   r   s   @r(   r  r    s    ,4 */,1@E(-#'<
||<
  S/<
  $;	<

 #Tk<
 37+<
 +<
 D[<
 
"	"<
 <
r'   r  c                   r   ^  \ rS rSr% \\S'   SrSrSrSr	S/r
\R                  " 5       U 4S j5       rS	rU =r$ )
SwinPreTrainedModeli  ra   swinr~   )imageTro  c                   > [         TU ]  U5        [        U[        5      (       a\  UR                  b   [
        R                  " UR                  5        UR                  b!  [
        R                  " UR                  5        gg[        U[        5      (       aP  [
        R                  " UR                  5        [
        R                  " UR                  UR                  5       5        gg)zInitialize the weightsN)rN   _init_weightsr   rK   rX   initzeros_rZ   r   r   copy_r   r   )rb   modulerd   s     r(   r  !SwinPreTrainedModel._init_weights&  s     	f%fn--  ,F--.))5F667 6 122KK;;<JJv55v7\7\7^_ 3r'   r   )r   r   r   r    r   r$   base_model_prefixmain_input_nameinput_modalitiessupports_gradient_checkpointing_no_split_modulesr"   no_gradr  r&   r   r   s   @r(   r  r    sB    $O!&*#$
]]_
` 
`r'   r  c                      ^  \ rS rSrSU 4S jjrS r\      SS\R                  S-  S\R                  S-  S\
S-  S\
S-  S	\
S
\
S-  S\\-  4S jj5       rSrU =r$ )	SwinModeli4  c                   > [         TU ]  U5        Xl        [        UR                  5      U l        [        UR                  SU R
                  S-
  -  -  5      U l        [        XS9U l
        [        XR                  R                  5      U l        [        R                  " U R                  UR                   S9U l        U(       a  [        R$                  " S5      OSU l        U R)                  5         g)z
add_pooling_layer (`bool`, *optional*, defaults to `True`):
    Whether or not to apply pooling layer.
use_mask_token (`bool`, *optional*, defaults to `False`):
    Whether or not to create and apply mask tokens in the embedding layer.
r7   r   )rc   r8  N)rN   rO   ra   r  r  r  r   rW   num_featuresrK   rf   r  rT   encoderr   r[   r<  	layernormAdaptiveAvgPool1dpooler	post_init)rb   ra   add_pooling_layerrc   rd   s       r(   rO   SwinModel.__init__6  s     	 fmm, 0 0119L3M MN(O"6??+E+EFd&7&7V=R=RS1Bb**1- 	r'   c                 .    U R                   R                  $ r   rf   rQ   r   s    r(   get_input_embeddingsSwinModel.get_input_embeddingsK      ///r'   Nr~   r   r   r  r|   r  rg   c                 >   Ub  UOU R                   R                  nUb  UOU R                   R                  nUb  UOU R                   R                  nUc  [	        S5      eU R                  XUS9u  pU R                  UU	UUUS9n
U
S   nU R                  U5      nSnU R                  b8  U R                  UR                  SS5      5      n[        R                  " US5      nU(       d  X4U
SS -   nU$ [        UUU
R                  U
R                  U
R                  S9$ )	z
bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*):
    Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
Nz You have to specify pixel_values)r   r|   )r   r  r  r   r   r7   )r   r,   r   r   r   )ra   r   r  r  r   rf   r  r  r  r   r"   r   r*   r   r   r   )rb   r~   r   r   r  r|   r  kwargsembedding_outputr   encoder_outputssequence_outputpooled_outputr   s                 r(   r   SwinModel.forwardN  sC    2C1N-TXT_T_TqTq$8$D $++JjJj 	 &1%<k$++BYBY?@@-1__Tl .= .
* ,,/!5# ' 
 *!,..9;;" KK(A(A!Q(GHM!MM-;M%58KKFM-')77&11#2#I#I
 	
r'   )ra   rf   r  r  r  r  r  )TFNNNNFN)r   r   r   r    rO   r  r   r"   r#   r   r   r%   r*   r   r&   r   r   s   @r(   r  r  4  s    *0  2637)-,0).#'6
''$.6
 ))D06
  $;	6

 #Tk6
 #'6
 D[6
 
	 6
 6
r'   r  ad  
    Swin Model with a decoder on top for masked image modeling, as proposed in [SimMIM](https://huggingface.co/papers/2111.09886).

    <Tip>

    Note that we provide a script to pre-train this model on custom data in our [examples
    directory](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining).

    </Tip>
    c                      ^  \ rS rSrU 4S jr\      SS\R                  S-  S\R                  S-  S\	S-  S\	S-  S\	S	\	S-  S
\
\-  4S jj5       rSrU =r$ )SwinForMaskedImageModelingi  c                   > [         TU ]  U5        [        USSS9U l        [	        UR
                  SUR                  S-
  -  -  5      n[        R                  " [        R                  " X!R                  S-  UR                  -  SS9[        R                  " UR                  5      5      U l        U R                  5         g )NFT)r  rc   r7   r   )in_channelsout_channelsr   )rN   rO   r  r  r   rW   r  r   
Sequentialr   encoder_striderE   PixelShuffledecoderr  )rb   ra   r  rd   s      r(   rO   #SwinForMaskedImageModeling.__init__  s     fdS	6++aF4E4E4I.JJK}}II(7L7La7ORXReRe7est OOF112	
 	r'   Nr~   r   r   r  r|   r  rg   c           	         Ub  UOU R                   R                  nU R                  UUUUUUS9nUS   n	U	R                  SS5      n	U	R                  u  pn[
        R                  " US-  5      =pU	R                  XX5      n	U R                  U	5      nSnUGb  U R                   R                  U R                   R                  -  nUR                  SUU5      nUR                  U R                   R                  S5      R                  U R                   R                  S5      R                  S5      R                  5       n[        R                  R!                  XSS	9nUU-  R#                  5       UR#                  5       S
-   -  U R                   R$                  -  nU(       d  U4USS -   nUb  U4U-   $ U$ ['        UUUR(                  UR*                  UR,                  S9$ )a  
bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`):
    Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).

Examples:
```python
>>> from transformers import AutoImageProcessor, SwinForMaskedImageModeling
>>> 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()))

>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/swin-base-simmim-window6-192")
>>> model = SwinForMaskedImageModeling.from_pretrained("microsoft/swin-base-simmim-window6-192")

>>> num_patches = (model.config.image_size // model.config.patch_size) ** 2
>>> pixel_values = image_processor(images=image, return_tensors="pt").pixel_values
>>> # create random boolean mask of shape (batch_size, num_patches)
>>> bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool()

>>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
>>> loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction
>>> list(reconstructed_pixel_values.shape)
[1, 3, 192, 192]
```N)r   r   r  r|   r  r   r   r7   ri   r:   none)r   gh㈵>)r0   r1   r   r   r   )ra   r  r  r   r<   r   floorrr   r  r   r`   repeat_interleaver   r?   r   rs   l1_lossr  rE   r.   r   r   r   )rb   r~   r   r   r  r|   r  r  r  r  rB   rE   sequence_lengthrC   rD   reconstructed_pixel_valuesmasked_im_lossrk   r   reconstruction_lossr   s                        r(   r   "SwinForMaskedImageModeling.forward  s   P &1%<k$++BYBY))+/!5%=#  
 "!*)33Aq94C4I4I1
/OS$899)11*FZ &*\\/%B"&;;))T[[-C-CCD-55b$EO11$++2H2H!L""4;;#9#91=1	  #%--"7"7lr"7"s1D8==?488:PTCTUX\XcXcXpXppN02WQR[@F3A3M^%.YSYY,5!//))#*#A#A
 	
r'   )r  r  r  )r   r   r   r    rO   r   r"   r#   r   r   r%   r.   r   r&   r   r   s   @r(   r  r    s       2637)-,0).#'S
''$.S
 ))D0S
  $;	S

 #TkS
 #'S
 D[S
 
.	.S
 S
r'   r  a  
    Swin Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
    the [CLS] token) e.g. for ImageNet.

    <Tip>

        Note that it's possible to fine-tune Swin on higher resolution images than the ones it has been trained on, by
        setting `interpolate_pos_encoding` to `True` in the forward of the model. This will interpolate the pre-trained
        position embeddings to the higher resolution.

    </Tip>
    c                      ^  \ rS rSrU 4S jr\      SS\R                  S-  S\R                  S-  S\	S-  S\	S-  S\	S	\	S-  S
\
\-  4S jj5       rSrU =r$ )SwinForImageClassificationi  c                 D  > [         TU ]  U5        UR                  U l        [        U5      U l        UR                  S:  a5  [
        R                  " U R                  R                  UR                  5      O[
        R                  " 5       U l	        U R                  5         g rE  )rN   rO   
num_labelsr  r  r   r   r  r?  
classifierr  )rb   ra   rd   s     r(   rO   #SwinForImageClassification.__init__  sx      ++f%	 EKDUDUXYDYBIIdii,,f.?.?@_a_j_j_l 	
 	r'   Nr~   labelsr   r  r|   r  rg   c                 X   Ub  UOU R                   R                  nU R                  UUUUUS9nUS   n	U R                  U	5      n
SnUb  U R	                  X*U R                   5      nU(       d  U
4USS -   nUb  U4U-   $ U$ [        UU
UR                  UR                  UR                  S9$ )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).
N)r   r  r|   r  r   r7   )r0   r5   r   r   r   )	ra   r  r  r  loss_functionr3   r   r   r   )rb   r~   r  r   r  r|   r  r  r  r  r5   r0   r   s                r(   r   "SwinForImageClassification.forward  s    " &1%<k$++BYBY))/!5%=#  
  
/%%fdkkBDY,F)-)9TGf$EvE(!//))#*#A#A
 	
r'   )r  r  r  r  )r   r   r   r    rO   r   r"   r#   
LongTensorr   r%   r3   r   r&   r   r   s   @r(   r  r    s      26*.)-,0).#',
''$.,
   4',
  $;	,

 #Tk,
 #',
 D[,
 
*	*,
 ,
r'   r  zM
    Swin backbone, to be used with frameworks like DETR and MaskFormer.
    c                      ^  \ rS rSrS\4U 4S jjrS r\\   SS\	R                  S\S-  S\S-  S	\S-  S
\4
S jj5       5       rSrU =r$ )SwinBackboneiI  ra   c           	      (  > [         TU ]  U5        UR                  /[        [	        UR
                  5      5       Vs/ s H  n[        UR                  SU-  -  5      PM      sn-   U l        [        U5      U l	        [        XR                  R                  5      U l        0 n[        U R                  U R                  5       H  u  pE[         R"                  " U5      X4'   M     [         R$                  " U5      U l        U R)                  5         g s  snf )Nr7   )rN   rO   rW   rr  r  r  r   r  rK   rf   r  rT   r  zipout_featuresr`  r   r[   
ModuleDicthidden_states_normsr  )rb   ra   rw  r  stagerE   rd   s         r(   rO   SwinBackbone.__init__O  s     #--.X]^abhbobo^pXq1rXqST#f6F6FA6M2NXq1rr(0"6??+E+EF !#&t'8'8$--#HE)+l)C& $I#%==1D#E  	 2ss   %Dc                 .    U R                   R                  $ r   r  r   s    r(   r  !SwinBackbone.get_input_embeddings_  r  r'   Nr~   r  r   r  rg   c           
      2   Ub  UOU R                   R                  nUb  UOU R                   R                  nUb  UOU R                   R                  nU R	                  U5      u  pgU R                  UUUSSSSS9nUR                  n	Sn
[        U R                  U	5       H  u  pXR                  ;   d  M  UR                  u  pnnUR                  SSSS5      R                  5       nUR                  XU-  U5      nU R                  U   " U5      nUR                  XUU5      nUR                  SSSS5      R                  5       nX4-  n
M     U(       d  U
4nU(       a  UUR                  4-  nU$ [!        U
U(       a  UR                  OSUR"                  S	9$ )
a  
Returns:

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("shi-labs/nat-mini-in1k-224")
>>> model = AutoBackbone.from_pretrained(
...     "microsoft/swin-tiny-patch4-window7-224", 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, 768, 7, 7]
```NT)r   r  r  r[  r  r   r   r7   r   r   )feature_mapsr   r   )ra   r  r  r   rf   r  r   r  stage_namesr  r<   r>   r?   r=   r  r   r   r   )rb   r~   r  r   r  r  r  r   r  r   r  r  hidden_staterB   rE   rC   rD   r   s                     r(   r   SwinBackbone.forwardb  s   J &1%<k$++BYBY$8$D $++JjJj 	 2C1N-TXT_T_TqTq-1__\-J*,,/!%59!  
  66#&t'7'7#GE))):F:L:L7
&%+33Aq!Q?JJL+00e^\Z#77>|L+00ULY+33Aq!Q?JJL/ $H "_F#70022M%3G'//T))
 	
r'   )rf   r  r  r  )NNN)r   r   r   r    r   rO   r  r   r	   r"   r   r   r   r   r&   r   r   s   @r(   r  r  I  s    z  0   -1)-#'L
llL
 #TkL
  $;	L

 D[L
 
L
 ! L
r'   r  )r  r  r  r  r  )r   F)?r!   collections.abcr   r   dataclassesr   r"   r    r   r  activationsr   backbone_utilsr   r	   modeling_layersr
   modeling_outputsr   modeling_utilsr   utilsr   r   r   r   utils.genericr   configuration_swinr   
get_loggerr   loggerr   r*   r.   r3   rG   rI   r   rK   rP   r   r   r   r   r   r   r   r  r  r&  r0  r6  ro  r  r  r  r  r  r  __all__r   r'   r(   <module>r     s   &   !   & ! H 9 . - D D - * 
		H	% 
 H H H  
 Hk H H& 
 HK H H* 
 H H H*	Y-RYY Y-x(-")) (-V3ryy 3nU\\ e T V[VbVb  %299 %Z'		 Z'z
RYY 
BII $ryy 	 	w		 wt4* 4nS
")) S
l `/ ` `, P
# P
 P
f 	e
!4 e
e
P <
!4 <
<
~ 
b
="5 b

b
Jr'   