pyi%$SrSSKJr SSKrSSKJs Jr SSKJr SSK J r SSK J r SSKJrJr SS KJrJrJrJrJrJr SS KJrJr SS KJrJrJr "S S \SS9r\"SS\ 55r S/r!g)zImage processor class for BEiT.)UnionN) functional)TorchvisionBackend) BatchFeature)group_images_by_shapereorder_images)IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDChannelDimension ImageInputPILImageResamplingSizeDict) ImagesKwargsUnpack) TensorTypeauto_docstringis_torch_availablec$\rSrSr%Sr\\S'Srg)BeitImageProcessorKwargs%aW do_reduce_labels (`bool`, *optional*, defaults to `self.do_reduce_labels`): Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255. do_reduce_labelsN)__name__ __module__ __qualname____firstlineno____doc__bool__annotations____static_attributes__rk/home/mande/repo/quber/.venv/lib/python3.13/site-packages/transformers/models/beit/image_processing_beit.pyrr%sr"rF)totalc^\rSrSrSr\r\Rr \ r \ r SSS.rSrSSS.rSrSrSrSrSrS\\4U4Sjjr\S(S \S \S -S\\S \4U4S jjj5rS(S \S \S -S\S\S\\-S -S\ \S4S -S \4Sjjr!S\"SS \"S4Sjr#S)S \"SS\S\$SSS\S\$S\S\%S\S \%\"\%-S -S!\%\"\%-S -S"\S -S#\S \"S4S$jjr&S(S%\"\'S -4S&jjr(S'r)U=r*$)*BeitImageProcessor0z/PIL backend for BEiT with reduce_label support.)heightwidthTFkwargsc &>[TU]"S0UD6 g)Nr)super__init__)selfr+ __class__s r#r.BeitImageProcessor.__init__Bs "6"r"Nimagessegmentation_mapsreturnc &>[TU]"X40UD6$)zX segmentation_maps (`ImageInput`, *optional*): The segmentation maps to preprocess. )r- preprocess)r/r2r3r+r0s r#r6BeitImageProcessor.preprocessEsw!&FvFFr"do_convert_rgbinput_data_formatreturn_tensorsdevicez torch.devicec URXXFS9nUR5nSUS'0n UR"U40UD6U S'UbURUSS[RS9n UR5n U R SSS.5 UR"S SU 0U D6n U V s/sH1n U R S 5R[R5PM3 n n XS '[XS 9$s sn f) z"Handle extra inputs beyond images.)r2r8r9r;Fr pixel_values)r2expected_ndimsr8r9) do_normalize do_rescaler2rlabels)data tensor_typer) _prepare_image_like_inputscopy _preprocessr FIRSTupdatesqueezetotorchint64r) r/r2r3r8r9r:r;r+ images_kwargsrCprocessed_segmentation_mapssegmentation_maps_kwargsprocessed_segmentation_maps r#_preprocess_image_like_inputs0BeitImageProcessor._preprocess_image_like_inputsRs!00L]1  ,1 ()#//H-H^  (*.*I*I( $"2"8"8 +J+ '(.{{} $ $ + +URW,X Y*.*:*:+2+6N+ '3N+2M.+221588E2M (+9NBB +s8C$rBz torch.Tensorc b[[U55HnXn[R"US:H[R"SUR UR S9U5nUS- n[R"US:H[R"SUR UR S9U5nX1U'M U$)z/Reduce label values by 1, replacing 0 with 255.r)dtyper;)rangelenrLwheretensorrVr;)r/rBidxlabels r# reduce_labelBeitImageProcessor.reduce_label~sV%CKEKK ELLEKKX]XdXd,eglmEAIEKK ell3ekkZ_ZfZf.ginoE3K &  r" do_resizesizeresamplez7PILImageResampling | tvF.InterpolationMode | int | Nonedo_center_crop crop_sizerArescale_factorr@ image_mean image_stddisable_groupingrc U (aURU5n[XS9unn0nUR5H$unnU(aURUX45nUUU'M& [ UU5n[UU S9unn0nUR5H8unnU(aUR UU5nUR UXxXU 5nUUU'M: [ UU5nU$)zCustom preprocessing for BEiT.)ri)r_ritemsresizer center_croprescale_and_normalize)r/r2rarbrcrdrerArfr@rgrhrirr+grouped_imagesgrouped_images_indexresized_images_groupedshapestacked_imagesresized_imagesprocessed_images_groupedprocessed_imagess r#rGBeitImageProcessor._preprocesss$ &&v.F0EV/o,,!#%3%9%9%; !E>!%^T!L,: "5 )&<((>@TU0E^fv/w,,#% %3%9%9%; !E>!%!1!1.)!L!77 LV_N/= $U +&<**BDXYr" target_sizescL[5(d [S5eURnUb[U5[U5:wa [ S5e[ U[ R5(aUR5n/n[[U55HNn[R"X5RSS9X%SSS9nUSRSS9nURU5 MP U$URSS9n[URS5Vs/sHoUPM nnU$s snf) a Converts the output of [`BeitForSemanticSegmentation`] into semantic segmentation maps. Args: outputs ([`BeitForSemanticSegmentation`]): Raw outputs of the model. target_sizes (`list[Tuple]` of length `batch_size`, *optional*): List of tuples corresponding to the requested final size (height, width) of each prediction. If unset, predictions will not be resized. Returns: semantic_segmentation: `list[torch.Tensor]` of length `batch_size`, where each item is a semantic segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each `torch.Tensor` correspond to a semantic class id. z:PyTorch is required for post_process_semantic_segmentationzTMake sure that you pass in as many target sizes as the batch dimension of the logitsr)dimbilinearF)rbmode align_cornersrW)r ImportErrorlogitsrZ ValueError isinstancerLTensornumpyrYF interpolate unsqueezeargmaxappendrr) r/outputsrxrsemantic_segmentationr]resized_logits semantic_mapis r#"post_process_semantic_segmentation5BeitImageProcessor.post_process_semantic_segmentations+ "##Z[ [  #6{c,// j, 55+113 $& !S[)!"K))a)0|7Hzin" .a077A7> %,,\: *%$%+MMaM$8 !GLMbMhMhijMkGl$mGl!1%=Gl !$m$$%nsD!r)N)F)+rrrrrr valid_kwargsrBICUBICrcr rgr rhrbdefault_to_squarererardrAr@rrr.rr rr6rr strrrrRlistr_rfloatrGtuplerr! __classcell__)r0s@r#r&r&0s#9+L!))H'J%IC (D-IINJL#(@!A#04 G G&, G12 G  G G&59*C*C&,*C *C , *C j(4/ *Cc>)*T1*C *CX4#7Drso& 7;2E5CC|5p%+p%p%f r"