Z j1 S SK r S SKJs Js Jr SSKJr SSKJ r SSK
J r J r SSK
JrJrJr SSKJrJr SSKJrJrJr SS KJr SS
KJr \R8 " \ 5 r \\" S S 9 " S
S\5 5 5 r S/r g) N )TorchvisionBackend) BatchFeature)group_images_by_shapereorder_images)IMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STDSizeDict) ImagesKwargsUnpack)auto_docstringis_torchdynamo_compilinglogging)
TensorType)requires)torch)backendsc ^ \ rS rSrSr\r\rSSS.r SSS.r
Sr Sr Sr
SrSrSSS\S S4S
jrS \S S \S\S
SS\S\S\S\S\S\\\ - S- S\\\ - S- S\S- S\S- S\S- S\\- S- S \4 S jrS \\ 4U 4S jjrS r S r S r U =r $ )!SLANeXtImageProcessor& i )heightwidthTimagez torch.Tensorsizereturnc f
UR u p4pVUR X4- XV5 nUR n[ UR UR
5 [ XV5 - n[
XX- 5 n [
Xh- 5 n
[ R " U
[ R US9n U S- [ U5 [ U
5 - - S-
n U R 5 R [ R 5 n
XR 5 -
n[ R " U
S: [ R " U5 U5 n[ R " U
S: [ R " U
5 U
5 n
[ R " XS-
: [ R " U5 U5 n[ R " XS-
: [ R" " XS-
5 U
5 n
US- S- R 5 R [ R 5 nSU-
n[ R " U [ R US9nUS- [ U5 [ U 5 - - S-
nUR 5 R [ R 5 nUUR 5 -
n[ R " US: [ R " U5 U5 n[ R " US: [ R " U5 U5 n[ R " UUS-
: [ R " U5 U5 n[ R " UUS-
: [ R" " UUS-
5 U5 nUS- S- R 5 R [ R 5 nSU-
nUR% SS5 R [ R& 5 nUR [ R 5 nU
R) 5 nU
S- R) 5 nUR) 5 nUS- R) 5 n US S 2US S 2S 4 US S S 24 4 n US S 2US S 2S 4 US S S 24 4 n US S 2U S S 2S 4 US S S 24 4 n US S 2U S S 2S 4 US S S 24 4 n UR SU S5 n!UR SU S5 n"UR SSU
5 n#UR SSU
5 n$U"U$U - U#U - - - U!U$U - U#U - - - - n%U%S- S - n%U%R% SS5 R [ R& 5 n&U&R X4X5 R UR* S
9$ ) N)dtypedeviceg ?r r i i )r )shapeviewr maxr r roundr arangefloat32floatfloortoint32where
zeros_like ones_like full_likeclampuint8longr )'selfr r
batch_sizechannelsr r r scale
target_height target_width
target_colsrc_col
src_col_floor src_col_frac weight_right weight_left
target_rowsrc_row
src_row_floor src_row_frac
weight_bottom
weight_top image_uint8 image_int32col_left col_rightrow_top
row_bottompixel_top_leftpixel_top_rightpixel_bottom_leftpixel_bottom_rightweight_bottom_3d
weight_top_3dweight_right_3dweight_left_3dinterpresults' څ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/slanext/image_processing_slanext.py_resize SLANeXtImageProcessor._resize4 s
/4kk+
f
:0&@ DKK,s6/AA fn-
U]+ \\,emmFS
#e u\7J(JKcQ
**5;;7
!4!4!66 {{=1#4e6F6F|6TVbc MA$5u7G7G
7VXef
{{=AI#=u|?\^jk QY &
qy(QS`
%t+c188:==ekkJ \) \\-u}}VT
#f
m8L(LMPSS
**5;;7
!4!4!66 {{=1#4e6F6F|6TVbc MA$5u7G7G
7VXef
{{=FQJ#>P\@]_kl VaZ ' QR
)SUb
&,s299;>>u{{K
M)
kk!S),,U[[9 !nnU[[1 %%'"Q&,,. $$&#a'--/
$Q4(8(47:K%KL%aD)99T1W;M&MN ':ag+>q@Q(Q R (Jq$w,?4QR7AS)S T(--a B"=!<
&++Aq,?$))!Q = ^ +o.O O
1B B_WiEi i jk G$+ a%((5{{: MPPW\WbWbPcc images do_resizeresamplez"tvF.InterpolationMode | int | Nonedo_center_crop crop_size
do_rescalerescale_factor do_normalize
image_meanN image_stddo_padpad_sizedisable_groupingreturn_tensorsc Ub$ [ 5 ( d [ R S5 [ X S9u nn0 nUR 5 H" u nnU( a U R UUS9nUUU' M$ [
UU5 n[ UUS9u nn0 nUR 5 H8 u nnU( a U R UU5 nU R UXxXU 5 nUUU' M: [
UU5 nU ( a U R UXS9n[ SU0US9$ )Nz&Resampling is not supported in SLANeXt)rf )r r )re rf pixel_values)data tensor_type) r logger warning_oncer itemsrW r center_croprescale_and_normalizepadr )r4 rZ r[ r r\ r] r^ r_ r` ra rb rc rd re rf rg kwargsgrouped_imagesgrouped_images_indexresized_images_groupedr# stacked_imagesresized_imagesprocessed_images_groupedprocessed_imagess rV _preprocess!SLANeXtImageProcessor._preprocessv s/ & (@(B(B H I 0EV/o,,!# %3%9%9%; !E>!% N !N,: "5 ) &< ((>@TU 0E^fv/w,,#% %3%9%9%; !E> !%!1!1.)!L ! 7 7
LV_ N /= $U + &< **BDXY #xx(88x o .2B!CQ_``rY rr c F > [ TU ] " S0 UD6 U R 5 g )N )super__init__ init_decoder)r4 rr __class__s rV r SLANeXtImageProcessor.__init__ s
"6" rY c / SQnU[ S5 Vs/ s H nSUS- S3PM sn-
nU[ S5 Vs/ s H nSUS- S3PM sn-
nSU; a UR S5 SU; a UR S5 S /U- S
/- n[ U5 VVs0 s H u p#X2_M snnU l Xl / S QU l U R S U l U R S
U l g s snf s snf s snnf )
a
Initialize the decoder vocabulary for table structure recognition.
Builds a character dictionary mapping HTML table structure tokens (e.g., ``, `
`, `
`, colspan/
rowspan attributes) to integer indices. The dictionary includes special `"sos"` (start-of-sequence) and
`"eos"` (end-of-sequence) tokens. Merged `
` tokens are used in place of standalone `
` tokens
when applicable.
)
zzz
zz
z
z
z
colspan="r "z
rowspan="
r soseos)r r r N) rangeappendremove enumeratedict charactertd_tokenbos_ideos_id)r4 dict_characterichars rV r "SLANeXtImageProcessor.init_decoder s
%)D)QZAwa0)DD%)D)QZAwa0)DD n , ! !+ . ^ # ! !& ) >1UG;,5n,EF,ETW,EF '4
ii& ii& ED Gs C&C+C0c UR U l U R SS n[ U R 5 [ U R 5 /n[ U R 5 nUR SS9nUR
SS9R n/ nUR S n[ U5 H n/ n / n
[ UR S 5 H[ n [ XXU 4 5 n U S: a X:X a O@X; a M& U R U n
U R U
5 U
R X(U 4 5 M] UR U 5 [ R " U
5 R 5 R 5 nM / SQUS - / SQ- nUWS.$ )a
Post-process the raw model outputs to decode the predicted table structure into an HTML token sequence.
Converts the model's predicted probability distributions over the structure vocabulary into a sequence of
HTML tokens representing the table structure. The decoded tokens are wrapped with ``, ``, and
`
` tags to form a complete HTML table structure.
Args:
outputs ([`SLANeXtForTableRecognitionOutput`]):
Raw outputs from the SLANeXt model. The `last_hidden_state` field contains the predicted probability
distributions over the structure vocabulary at each decoding step, with shape
`(batch_size, max_text_length, num_classes)`.
Returns:
`dict`: A dictionary containing:
- **structure** (`list[str]`): The predicted HTML table structure as a list of tokens, wrapped with
``, ``, and `
` tags.
- **structure_score** (`float`): The mean confidence score across all predicted tokens.
r r r )dim)zzz
)z
zz) structurestructure_score)last_hidden_statepredintr r argmaxr% valuesr# r r r r stackmeanitem)r4 outputsstructure_probsignored_tokensend_idx
structure_idxstructure_str_listr5 batch_indexstructure_list
score_listpositionchar_idxtextr r s rV post_process_table_recognition4SLANeXtImageProcessor.post_process_table_recognition sn ( -- ))Aa. dkk*C ,<=dkk"'..1.5
)--!-4;; "((+
,K NJ !-"5"5a"8 9 }(-B CDar sz ,
2 2 ; 2 E P P 4 F F ' *
H % : VL . VL VLr #
#rY