Zj5r?\5"SS9"SS\ 55r@\5"SS9"SS\ 55rA\@rBg))Iterable) lru_cache)AnyOptionalUnionN) BatchFeature)BaseImageProcessor) center_crop)convert_to_rgbdivide_to_patchesget_resize_output_image_sizeget_size_with_aspect_ratiogroup_images_by_shapereorder_images) normalize)rescale)resize) ChannelDimension ImageInput ImageTypeSizeDictget_image_size#get_image_size_for_max_height_widthget_image_typeget_max_height_widthinfer_channel_dimension_formatis_valid_imageload_image_as_tensor) ImagesKwargsUnpack) TensorTypeis_torch_availableis_torchvision_availableis_vision_availablelogging)is_rocm_platformis_torchdynamo_compilingrequires)PILImageResampling) functional)pil_torch_interpolation_mappingtorch_pil_interpolation_mapping)torch torchvision)backendsc r^\rSrSrSrS\\4U4Sjjr\S\ 4Sj5r \S\ 4Sj5r S\ \ \ -\ \ \ -4S jrS;S \S \ S -S \ \-S -S\SS\\SS4 SjjrS \S\4SjrSS,\ S -S-\\ \-S -S.\\ \-S -S/\ S -S0\S -S\SS\4S1jj5r!SSS/\ S0\S,\ S-\\ \-S.\\ \-SS4S2jr"S SS\SS4S3jr#S\ SS4\ S\SSS5\ S6\S/\ S0\S,\ S-\\ \-S -S.\\ \-S -S7\ S -S\S -S\ S -S8\ \$-S -S\%4 S9jr&S:r'U=r($)?TorchvisionBackendUzATorchvision backend for GPU-accelerated batched image processing.kwargsc J>[TU]"S0UD6 UR"S0UD6 gNsuper__init___set_attributesselfr4 __class__s w/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/image_processing_backends.pyr:TorchvisionBackend.__init__Y$ "6" &v&returnc.[RS5 g) `bool`: Whether or not this image processor is using the fast (Torchvision) backend. The `is_fast` property is deprecated and will be removed in v5.3 of Transformers. Use the `backend` attribute instead (e.g., `processor.backend == "torchvision"`). The `is_fast` property is deprecated and will be removed in v5.3 of Transformers. Use the `backend` attribute instead (e.g., `processor.backend == 'torchvision'`).Tlogger warning_oncer=s r?is_fastTorchvisionBackend.is_fast]s  ` rBcg)2 `str`: The backend used by this image processor. r/r7rJs r?backendTorchvisionBackend.backendjs rBimage_url_or_urlsc[U[[45(a!UVs/sHo RU5PM sn$[U[5(a [ U5$[ U5(aU$[S[U535es snf)z Convert a single or a list of URLs / paths into `torch.Tensor` objects. Already-valid image objects (tensors, numpy arrays, PIL Images) are passed through unchanged so that callers who pre-load images are unaffected. z=only a single or a list of entries is supported but got type=) isinstancelisttuple fetch_imagesstrrr TypeErrortype)r=rQxs r?rVTorchvisionBackend.fetch_imagesqs '$ 7 72CD2CQ%%a(2CD D )3 / /'(9: : - . .$ $[\`ar\s[tuv v EsBNimagedo_convert_rgbinput_data_formatdevicez torch.device torch.Tensorc [U5nU[R[R[R4;a[ SU35eU(aUR U5nU[R:Xa[R"U5nO8U[R:Xa$[R"U5R5nURS:XaURS5nUc [U5nU[R :Xa!UR#SSS5R5nUbUR%U5nU$)z/Process a single image for torchvision backend.Unsupported input image type rr)rrPILTORCHNUMPY ValueErrorr tvF pil_to_tensorr. from_numpy contiguousndim unsqueezerrLASTpermuteto)r=r\r]r^r_r4 image_types r? process_image TorchvisionBackend.process_images$E* immY__iooN Nu E   0 5 5 5MM!Q*557E  HHV$E rBc[U5$zConvert an image to RGB format.r r=r\s r?r !TorchvisionBackend.convert_to_rgb e$$rBimagespad_size fill_value padding_mode return_maskdisable_grouping is_nested)r`r`c UbJUR(aUR(d[SUS35eURUR4nO [U5n[ XUS9up0n 0n U R 5HupUR SSnUSUS- nUSUS- nUS:dUS:a[SUS US35eX:waSSUU4n[R"UUX4S 9nXU 'U(dMv[R"U[RS 9S SSS2SS24nSUS SUS2SUS24'UX'M [XUS 9nU(a[XUS 9nUU4$U$)z5Pad images using Torchvision with batched operations.NCPad size must contain 'height' and 'width' keys only. Got pad_size=.)rrrrzrPadding dimensions are negative. Please make sure that the `pad_size` is larger than the image size. Got pad_size=z , image_size=)fillr}dtype.)r) heightwidthrgrritemsshaperhpadr. zeros_likeint64r)r=rzr{r|r}r~rrr4grouped_imagesgrouped_images_indexprocessed_images_groupedprocessed_masks_groupedrstacked_images image_sizepadding_height padding_widthpadding stacked_masksprocessed_imagesprocessed_maskss r?rTorchvisionBackend.pads  OO #fgofppq!rss 8H+F3H/D 0 ,$& "$%3%9%9%; !E'--bc2J%a[:a=8N$QK*Q-7M!]Q%6 008zzlRSU%a?!$z!m.'.#&<&**Bdmn ,-DfopO#_4 4rBsizeresamplez7PILImageResampling | tvF.InterpolationMode | int | None antialiasc Ub([U[[45(a [UnOUnO[R R nU[R R:Xa/[RS5 [R RnUR(aDUR(a3[UR5SSURUR5nOUR(a%[UURS[ R"S9nOUR$(aDUR&(a3[)UR5SSUR$UR&5nOJUR*(a*UR,(aUR*UR,4nO[/SUS35e[15(a![35(aUR5XXd5$[R6"XXdS9$) z"Resize an image using Torchvision.Na You have used a torchvision backend image processor with LANCZOS resample which not yet supported for torch.Tensor. BICUBIC resample will be used as an alternative. Please fall back to a pil backend image processor if you want full consistency with the original model.rFrdefault_to_squarer^jSize must contain 'height' and 'width' keys, or 'max_height' and 'max_width', or 'shortest_edge' key. Got r interpolationr)rSr*intr,rhInterpolationModeBILINEARLANCZOSrHrIBICUBIC shortest_edge longest_edgerrrrFIRST max_height max_widthrrrrgr(r'_compile_friendly_resizer)r=r\rrrr4rnew_sizes r?rTorchvisionBackend.resizes  (%7$=>> ? I ( 11::M C1199 9   A   1199M   $"3"31 RS!""!!H   3''"'"2"8"8 H __:5::<;Ldoo_c_m_mnH [[TZZ TZZ0H6  $ % %*:*<*<00-[ [zz%\\rBrrztvF.InterpolationModecUR[R:XaUR5S- n[R "XX#S9nUS-n[R "US:SU5n[R "US:SU5nUR5R[R5nU$[R "XX#S9nU$)zOA wrapper around tvF.resize for torch.compile compatibility with uint8 tensors.rr) rr.uint8floatrhrwhereroundrp)r\rrrs r?r+TorchvisionBackend._compile_friendly_resizes ;;%++ %KKMC'EJJumaECKEKK S%8EKK 1e4EKKM$$U[[1E JJumaE rBscalec X-$)z5Rescale an image by a scale factor using Torchvision.r7r=r\rr4s r?rTorchvisionBackend.rescale"s }rBmeanstdc 0[R"XU5$)z%Normalize an image using Torchvision.)rhrr=r\rrr4s r?rTorchvisionBackend.normalize+s}}U#..rB )maxsize do_normalize image_mean image_std do_rescalerescale_factorcU(a=U(a6[R"X&S9SU- -n[R"X6S9SU- -nSnX#U4$)N)r_g?F)r.tensor)r=rrrrrr_s r?!_fuse_mean_std_and_rescale_factor4TorchvisionBackend._fuse_mean_std_and_rescale_factor5sI ,j@C.DXYJ Y>#BVWIJj00rBc URUUUUUURS9upVnU(a/URUR[R S9XV5nU$U(aUR X5nU$)zFRescale and normalize images using Torchvision (fused for efficiency).)rrrrrr_r)rr_rrpr.float32r)r=rzrrrrrs r?rescale_and_normalize(TorchvisionBackend.rescale_and_normalizeFsx-1,R,R%!!)== -S- ) z ^^FIIEMMI$BJZF \\&9F rBc URb URc[SUR535eURSSupEURURpvXu:dXd:anXu:aXu- S-OSXd:aXd- S-OSXu:a Xu- S-S-OSXd:a Xd- S-S-OS/n[ R "XSS9nURSSupEXu:XaXd:XaU$[XF- S- 5n [XW- S- 5n [ R"XXU5$) z'Center crop an image using Torchvision.N=The size dictionary must have keys 'height' and 'width'. Got rrcrr)rg@) rrrgkeysrrhrrcrop) r=r\rr4 image_height image_width crop_height crop_width padding_ltrbcrop_top crop_lefts r?r TorchvisionBackend.center_crop_s+ ;; $**"4\]a]f]f]h\ijk k$)KK$4! "&++tzzZ  #{'A3=3K)a/QR5@5O+1UV7A7O)A-!3UV9D9S+a/A5YZ L GGEa8E(- BC(8 %L([-H  2c9:1S89 xxLLrB do_resizedo_center_crop crop_sizedo_padreturn_tensorsc [XS9unn0nUR5H"unnU(aURUX4S9nUUU'M$ [UU5n[UUS9unn0nUR5H8unnU(aUR UU5nUR UXxXU 5nUUU'M: [UU5nU (aUR UXS9n[SU0US9$)z=Preprocess using Torchvision backend (fast, GPU-accelerated).)rr\rr)r{r pixel_valuesdata tensor_type)rrrrr rrr )r=rzrrrrrrrrrrrr{rrr4rrresized_images_groupedrrresized_imagesrrs r? _preprocessTorchvisionBackend._preprocess{s*0EV/o,,!#%3%9%9%; !E>!%>!`,: "5 )&<((>@TU0E^fv/w,,#% %3%9%9%; !E>!%!1!1.)!L!77 LV_N/= $U +&<**BDXY #xx(88xo .2B!CQ_``rBr7)NNN)NrconstantFFF)NT)NNNNNN))__name__ __module__ __qualname____firstlineno____doc__r!r r:propertyboolrKrWrOrTrVrrrrrr rrrrUrr staticmethodrrrrrrrrr r"r r__static_attributes__ __classcell__r>s@r?r2r2UsK' !5'    wcDIoT#Y.Ow&'+;?+/ !!t !!11D8 ! ( ! & ! !F%J%:%"!"#-!(-!&0 ^$0 0 $J 0 Dj 0  0 +0 $;0  u34nD E0 lOS 3]3]3]L 3]  3] 3]j<@ S/ 78   $  //huo%/Xe_ $ /  /r%)1504"&'++/1Tk1DK'$.14;&- 1 4K 1  1(1 11     DK' 4;& 2MMM  M8-a^$-a-a -a L -a  -a-a-a-a-aDK'$.-a4;&--at -aT/-a+-a j(4/!-a$ %-a-arBr2)visioncd^\rSrSrSrS\\4U4Sjjr\S\ 4Sj5r \S\ 4Sj5r S,S \ S \ S-S \ \-S-S\\S\R 4 S jjrS \ S\ 4S jrS-S\\R S\S\S-S\ S-S\ S\\\R \\R 4\\R -4 SjjrS,S \R S\SSS\S-S\R 4 SjjrS \R S\S\R 4SjrS \R S\\\-S\\\-S\R 4SjrS \R S\S\R 4SjrS\\R S\ S\SSS \ S!\S"\ S#\S$\ S%\\\-S-S&\\\-S-S'\ S-S\S-S(\ \-S-S\4S)jr S\!\ \"44U4S*jjr#S+r$U=r%$). PilBackendiz9PIL/NumPy backend for portable CPU-only image processing.r4c J>[TU]"S0UD6 UR"S0UD6 gr6r8r<s r?r:PilBackend.__init__rArBrCc.[RS5 g)rErFFrGrJs r?rKPilBackend.is_fasts  ` rBcg)rNpilr7rJs r?rOPilBackend.backends rBNr\r]r^c [U5nU[R[R[R4;a[ SU35eU(aUR U5nU[R:Xa<[R"U5nURS:aUc[ROUnO$U[R:XaUR5nURS:Xa[R"USS9nUc [U5nU[R:Xa6[U[R 5(a[R""US5nU$)z'Process a single image for PIL backend.rbrcr)axis)rcrr)rrrdrerfrgr nparrayrlrrnnumpy expand_dimsrrSndarray transpose)r=r\r]r^r4rqs r?rrPilBackend.process_images$E* immY__iooN Nu E   0 5 5 5%,, UI6 rBc[U5$rurvrws r?r PilBackend.convert_to_rgbryrBrzr{r|r}r~c UbIUR(aUR(d[SUS35eURURpO [U5upx/n /n UHn [ U [ R S9upX|- nX- nUS:dUS:a[SUSUSU SU S 3 5eX:wdX:wa=S SU4SU44nUS :Xa[R"U US US 9n O[R"U UUS 9n U RU 5 U(dM[R"Xx4[RS9nSUSU 2SU 24'U RU5 M U(aX4$U $)z)Pad images to specified size using NumPy.Nrr channel_dimrzsPadding dimensions are negative. Please make sure that the `pad_size` is larger than the image size. Got pad_size=(z, z), image_size=(z).)rrr)modeconstant_values)rrr) rrrgrrrrrrappendzerosr)r=rzr{r|r}r~r4 target_height target_widthrrr\rrrr pad_widthmasks r?rPilBackend.pads  OO #fgofppq!rss*2//8>><*>v*F 'ME*5>N>T>TUMF*3N(0M!]Q%6 11>r,_e^ffhinhooqs &%*?$a%81m:LM :-FF5)*V`aEFF5),GE  # #E *{xx =RXXN()WfWfuf_%&&t,36 #4 4rBrrzPILImageResampling | None reducing_gapc UbF[U[[45(d+[bU[;a [UnO[RnUbUO[RnUR (aNUR (a=[U[RS9upg[Xg4UR UR 5nOUR (a%[UUR S[RS9nOUR(aNUR(a=[U[RS9upg[Xg4URUR5nOJUR(a*UR (aURUR 4nO[#SUS35e[%UUUU[R[RS9$)z Resize an image using PIL/NumPy.rFrrr)rrr data_formatr^)rSr*rr-rrrrrrrrrrrrrrg np_resize) r=r\rrrr4rrrs r?rPilBackend.resize"sp   8>PRU=V(W(W.:xKj?j:8D-66'389K9T9T   $"3"3*5>N>T>TUMF1""!!H   3''"'"2"8"8 H __*5>N>T>TUMF:F?DOO]a]k]klH [[TZZ TZZ0H6   %(...44   rBrc R[UU[R[RS9$)z/Rescale an image by a scale factor using NumPy.)rrr^) np_rescalerrrs r?rPilBackend.rescaleUs) (...44   rBrrc T[UUU[R[RS9$)zNormalize an image using NumPy.)rrrr^) np_normalizerrrs r?rPilBackend.normalizecs, (...44   rBc URb URc[SUR535e[ UURUR4[ R [ R S9$)z!Center crop an image using NumPy.r)rrr^)rrrgrnp_center_croprr)r=r\rr4s r?r PilBackend.center_cropssg ;; $**"4\]a]f]f]h\ijk k ++tzz*(...44   rBrrrrrrrrrrc D/nUHvnU(aURUX4S9nU(aURUU5nU(aURUU5nU (aURUX5nUR U5 Mx U (aUR UU S9n[ SU0US9$)z2Preprocess using PIL backend (portable, CPU-only).r)r{rr)rr rrrrr )r=rzrrrrrrrrrrrr{rr4rr\s r?rPilBackend._preprocesss&E %d N(( : UN;ujD  # #E * #xx(88xL .2B!CQ_``rBc>[TU]5nURSS5RS5(a USSSUS'U$)Nimage_processor_typePil)r9to_dictgetendswith)r=processor_dictr>s r?r0PilBackend.to_dictsN*   4b 9 B B5 I I5CDZ5[\_]_5`N1 2rBr7)NN)NrrF)&rrrrrr!r r:rrrKrWrOrrrr rrr rTrrrUrrrrrrr r"r rdictrr0rrrs@r?rrsC' !5'   '+;? ""t "!11D8 " & "  "H%J%:%"!"#-! 1 RZZ 1 1 $J 1 Dj 1  1  tBJJbjj!11 2T"**5E E1 n15#' 1 zz1 1 . 1 Dj 1  1 f  zz       zz huo% Xe_ $     zz    ""aRZZ "a"a "a . "a  "a"a"a"a"aDK'$."a4;&-"at "aT/"aj(4/"a" #"aHc3hrBr)Ccollections.abcr functoolsrtypingrrrrrimage_processing_baser image_processing_utilsr image_transformsr r'r r rrrrrr$rr!rr image_utilsrrrrrrrrrrrprocessing_utilsr r!utilsr"r#r$r%r&utils.import_utilsr'r(r)r*r.torchvision.transforms.v2r+rhr,r- get_loggerrrHr2rBaseImageProcessorFastr7rBr?rCs%''/6    3UT/;]]&*#&*#   H % +,Ra+Ra-Raj  ;A#A AJ,rB