+ jVB^Rt^RIt^RIt^RIHt^RIt^RIHuHuH t ^RI H t H t Ht^RIHt^RIHtHt^RIHtHt^RIHtHtHt^RIHtHt^R IHtH t ^R I!H"t"^R I#H$t$R t%!R R]RR7t&RRRllt']PPPRR4t*] !RR]44t+R.t,R#)z%Image processor class for Pix2Struct.N)Union)Image ImageDraw ImageFont)TorchvisionBackend) BatchFeature get_size_dict)group_images_by_shapereorder_images)ChannelDimension ImageInputSizeDict) ImagesKwargsUnpack) TensorTypeauto_docstring)hf_api)requires_backendszybelkada/fontsc0a]tRt^%toRtV3RltRtVtR#)Pix2StructImageProcessorKwargsa] max_patches (`int`, *optional*): Maximum number of patches to extract. patch_size (`dict[str, int]`, *optional*, defaults to `{"height": 16, "width": 16}`): The patch size to use for the image. According to Pix2Struct paper and code, the patch size is 16x16. is_vqa (`bool`, *optional*, defaults to `False`): Whether or not the image processor is for the VQA task. If `True` and `header_text` is passed in, text is rendered onto the input images. header_text (`Union[list[str], str]`, *optional*): Text to render as a header. Only has an effect if `image_processor.is_vqa` is `True`. c<V^8dQh/S[;R&S[S[S[3,;R&S[;R&S[S[,S[,R,;R&#) max_patches patch_sizeis_vqaN header_text)intdictstrboollist)format __classdict__s"{/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/pix2struct/image_processing_pix2struct.py __annotate__+Pix2StructImageProcessorKwargs.__annotate__%sMS#X L cS4''!N)__name__ __module__ __qualname____firstlineno____doc____annotate_func____static_attributes____classdictcell__)r"s@r#rr%s r&rF)totalcV^8dQhR\R\R\R\R\R\R\R\R \R ,R \R ,R \P/ #) rtext text_size text_colorbackground_color left_padding right_padding top_paddingbottom_padding font_bytesN font_pathreturn)rrbytesr)r!s"r#r$r$9sCC CCC C  C  CCC CTzC [[Cr&c \\R4\P!^PR7p V P VR7p RP V 4p VeV f\ P!V4p M%V eT p M\4P\R4p \P!V RVR7p \P!RR 4p\P !V4pVP#R WR 7wpppVV,V,pVV,V,p\P!RVV3V4p\P !V4pVP%WF3WV R 7V#) aC Render text. This script is entirely adapted from the original script that can be found here: https://github.com/google-research/pix2struct/blob/main/pix2struct/preprocessing/preprocessing_utils.py Args: text (`str`, *optional*, defaults to ): Text to render. text_size (`int`, *optional*, defaults to 36): Size of the text. text_color (`str`, *optional*, defaults to `"black"`): Color of the text. background_color (`str`, *optional*, defaults to `"white"`): Color of the background. left_padding (`int`, *optional*, defaults to 5): Padding on the left. right_padding (`int`, *optional*, defaults to 5): Padding on the right. top_padding (`int`, *optional*, defaults to 5): Padding on the top. bottom_padding (`int`, *optional*, defaults to 5): Padding on the bottom. font_bytes (`bytes`, *optional*): Bytes of the font to use. If `None`, the default font will be used. font_path (`str`, *optional*): Path to the font to use. If `None`, the default font will be used. vision)width)r2 z Arial.TTFzUTF-8)encodingsizeRGB)font)fillrE)rGrI)r render_texttextwrap TextWrapperwrapjoinioBytesIOrhf_hub_downloadDEFAULT_FONT_PATHrtruetypernewrDrawtextbboxr2)r2r3r4r5r6r7r8r9r:r;wrapperlines wrapped_textrEtemp_img temp_draw_wh text_width text_heightimgdraws&&&&&&&&&& r#rJrJ9s(Lk8,"",G LLdL #E99U#L)"3zz*%  x''(9;G   dW9 EDyy'Hx(I##FL#DJAq!Q\!M1Jk/N2K ))EJ 46F GC >># DII|)O1O%PRYZ  ++Y,JKVT (())@AAGXCYZ""9-008 EKK \\^e+F r&c"<V^8dQhRRRR/#)rimagesrr<r')r!r"s"r#r$rzs111>1r&c ~VPRRR7pVPRRR7pVP^,VP^,,VP^,,pRVR,, p\P!V\P !WSP R74pW, V, #)z Normalize batched images using per-image mean and standard deviation. Args: images (`torch.Tensor`): Batched float image tensor of shape (B, C, H, W). Returns: `torch.Tensor`: Normalized images of shape (B, C, H, W). T)dimkeepdimg??r)rGr)meanstdrfrgmaximumtensorr)rrrrnum_elements_per_imagemin_stdadjusted_stddevs&& r# normalize"Pix2StructImageProcessor.normalizes{{y${7jjYj5!'a6<<?!BV\\RS_!T.33--U\\'**-UV 00r&c.<V^8dQhRRRS[RS[RR/#)rrrrrr<)rr )r!r"s"r#r$rzs9CCCC C  Cr&c  HVPVPrTVPwrgrW$V, ,WY, ,R,p \\ \ W,V, 4V4^4p \\ \ W,V, 4V4^4p \W,^4p \W,^4p\ WR7pVPW\PPRR7p\WV4pVPwppppVPVVV,V4p\P!VVPR7PV^4P!^V4P^VV,^4p\P!VVPR7P^V4P!V^4P^VV,^4pVP#VRR4pVP#VRR4pV^,P%4pV^,P%4p\P&!VVV.RR7p\P(P*P-V^^^VVV,, .4P%4pV#)a Extract flattened patches from a batch of images. Args: images (`torch.Tensor`): Batched images tensor of shape (batch, channels, height, width). max_patches (`int`): Maximum number of patches to extract. patch_size (`SizeDict`): Dictionary containing patch height and width. Returns: `torch.Tensor`: Batched flattened patches with row/column IDs of shape (batch, max_patches, patch_dim). r)rrr@T)rrCresample antialiasrrre)rrr@rfrminrr rrInterpolationModeBILINEARrtrkrgarangerrepeatexpandrcatrhripad)rrrrrnrorprq image_height image_widthscalenum_feasible_rowsnum_feasible_colsresized_height resized_width resize_sizersrowscolumnsdepthrow_idscol_idsrs&&&& r#extract_flattened_patches2Pix2StructImageProcessor.extract_flattened_patchess=(%/$5$5z7G7Gk:@,,7 l| ;< @YZ_bbC(<|(K$Lk Z\]^C(;k(I$JK XZ[\.=qA-;Q? nJ 6cF[F[FdFdptu(kJ+2==( D'5//*dWneD LLfmm 4 < )*T1 &278&2 &2r&c VPVVVVR7pVPRVP4pV'dVf \R4hVP RR4pVP RR4p \ V\ 4'dV.\V4,p\V4U U u.uFwrVPWV ,WR7NK! pp p VP!V3/VB#uup p i)z# Preprocess images for Pix2Struct. )rrrrrNz.A header text must be provided for VQA models.r:r;r) _prepare_image_like_inputsgetr ValueErrorpoprrlen enumerater _preprocess) rrrrrrrrr:r;irs &&&&&&, r#_preprocess_image_like_inputs6Pix2StructImageProcessor._preprocess_image_like_inputsXs00)/ 1 Hdkk2 " !QRRL$7J ;5I+s++*mc&k9 !*& 1 1HA""5a.Z"e 1  1&11 s$%Ccp<V^8dQhRS[R,RS[RS[RS[RS[S[,R,RS[R S[/#) rrr do_normalizerrreturn_tensorsNdisable_groupingr<)r rrr rrr)r!r"s"r#r$rzs`/ / ^$/ /  /  / j(4/ / /  / r&c "\WR7wr/p /p VP4FwrV P\P8XdV P 4p V'dVP V 4p VPWVR7pVPRR7^8gP 4pWV &WV &K \W4p\W4pV'd1\P!V^R7p\P!V^R7p\RVRV/VR7#)z1 Preprocess images to extract flattened patches. )r)rrrrrwrx)data tensor_typere) r itemsrrgrrrrsumr stackr)rrrrrrrrgrouped_imagesgrouped_images_indexflattened_patches_groupedattention_masks_groupedrfstacked_imagesrsmasksrwattention_maskss&&&&&&&, r#r$Pix2StructImageProcessor._preprocesss0EV/o,$&!"$%3%9%9%; !E##u{{2!/!5!5!7!%!?44%:5G[[R[(A-446E/6e ,-2E *&<"++D[()@W  % ,=1 E #kk/qAO%'8:JO\&  r&r')N)NN)r(r)r*r+rescale_factorrrrrrr valid_kwargsmodel_input_namesr~rrrrrrr FIRSTrrr.r/ __classcell__)rr"s@@r#rvrvsNLNB,JK F1L,.>?  66p11*CCJ M M M/3#.>.D.D48 &2&2P/ / / r&rv) $blackrrrrNN)-r,rOrKtypingrrg$torchvision.transforms.v2.functional transformsv2rirPILrrrimage_processing_backendsrimage_processing_utilsrrimage_transformsr r image_utilsr r r processing_utilsrrutilsrr utils.hubrutils.import_utilsrrRrrJcompilerdisablertrv__all__r'r&r#r s,  22++;AEAA4/3%(\((CN*X 1X X v & &r&