ZjB SrSSKrSSKJr SSKJr SSKrSSKJr SSK J r SSK J r SS KJrJrJr SS KJrJrJr SS KJrJr SS KJrJr S \S\\4SjrS\\\4S\\\4S\\\44Sjr \"SS9S\S\S\RB4Sj5r"S%SSS\\\4S\\\\\4-SS4Sjjr#S&S\\\4S\RBS\$S\\\44Sjjr%"S S!\SS"9r&\"S#S$\ 55r'S$/r(g)'z!Image processor class for Llama4.N) defaultdict) lru_cache) functional)TorchvisionBackend) BatchFeature)group_images_by_shapereorder_imagessplit_to_tiles) ImageInputPILImageResamplingSizeDict) ImagesKwargsUnpack) TensorTypeauto_docstringdividendreturnc[5n[S[US-5S-5H1nX-S:XdM URU5 URX-5 M3 U$)a Calculate all factors of a given number, i.e. a divisor that leaves no remainder. For example, if dividend=12, it will return {1, 2, 3, 4, 6, 12}. Args: dividend (int): The number to find factors for. Returns: set: A set containing all factors of the number. ?r)setrangeintadd)r factors_setis ڃ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/llama4/image_processing_llama4.py get_factorsrsV%K 1c(C-(1, - <1  OOA  OOHM *.  image_size target_sizecUup#UupEXS- nXB- nXg:a'Un[[R"X&-5U5n X4$Un [[R"X7-5U5nX4$)a Determines the maximum resolution to which an image can be resized to without distorting its aspect ratio, based on the target resolution. Args: image_size (tuple[int, int]): The original resolution of the image (height, width). target_resolution (tuple[int, int]): The desired resolution to fit the image into (height, width). Returns: tuple[int, int]: The optimal dimensions (height, width) to which the image should be resized. Example: >>> _get_max_res_without_distortion([200, 300], target_size = [450, 200]) (134, 200) >>> _get_max_res_without_distortion([800, 600], target_size = [450, 1300]) (450, 338) )minmathfloor) r!r"original_heightoriginal_width target_height target_widthscale_wscale_h new_width new_heights rget_max_res_without_distortionr/3sz('1#O"-M+G-G O$=> N   #  >#;>> max_num_chunks = 5 >>> patch_size = 224 >>> find_supported_resolutions(max_num_chunks, patch_size) tensor([(224, 896), (448, 448), (224, 224), (896, 224), (224, 672), (672, 224), (224, 448), (448, 224)]) Given max_num_chunks=4, patch_size=224, it will create a dictionary: { 0.25: [(1, 4)], 1.0: [(2, 2), (1, 1)], 4.0: [(4, 1)], 0.33: [(1, 3)], 3.0: [(3, 1)], 0.5: [(1, 2)], 2.0: [(2, 1)] } and return the resolutions multiplied by the patch_size: [(1*224, 4*224), (2*224, 2*224), ..., (2*224, 1*224)] z`size` must be square.r) heightwidth ValueErrorrlistrsortedrappendvalues) r1r2r5r6asp_dict chunk_size_factorsfactor _asp_ratios ratio_floatpossible_resolutionsvaluedepths rfind_supported_resolutionsrEWsD%%z'7'7E 122J4 HNAr2 +j12DLMH& 45H M(MF .K  ! ( (& 9)3""MF ' ')P(Q R## Ns!Cimages torch.Tensorbackground_colorcb[UR5S:XaURSOURSn[U[5(aU/S/US- --nO[U5U:wa[ SUS35eURSSupEUupgXu- nXd- n [ R "USSX/US9n U $) a Pads an image to fit the target size. Args: images (`np.ndarray`): The images to pad. background_color (`int` or `tuple[int, int, int]`, *optional*, defaults to 0): The color to use for the padding. Can be an integer for single channel or a tuple of integers representing for multi-channel images. If passed as integer in multi-channel mode, it will default to `0` in subsequent channels. Returns: `torch.Tensor`: The padded images. rrz(background_color must have no more than z) elements to match the number of channelsN)paddingfill)lenshape isinstancerr7tvFpad) rFr"rH num_channelsr5r6r)r* paste_x_right paste_y_right padded_imagess rpad_to_best_fitrWs&'*&,,&71&<6<<?&,,q/L"C((,-|a7G0HH  , .6|nDm n  LL%MF"-M (M!*MGGFQ=,PWghM r FrBresize_to_max_canvascUup4USS2S4USS2S4peXd- nXS- n[R"X:Xx5n XS:n [U 5S:a5U(a[R"U 5n O4[R"U 5n OXS:n [R"U 5n XU :Hn [U 5S:a/U SS2S4U SS2S4-n[R "U5nXnU$U SnU$)a Determines the best canvas possible from a list of possible resolutions to, without distortion, resize an image to. For each possible resolution, calculates the scaling factors for width and height, and selects the smallest one, which is the limiting side. E.g. to match the canvas you can upscale height by 2x, and width by 1.5x, therefore, the maximum upscaling you can do is min(2, 1.5) = 1.5. If upscaling is possible (any of the scaling factors is greater than 1), then picks the smallest upscaling factor > 1, unless resize_to_max_canvas is True. If upscaling is not possible, then picks the largest scaling factor <= 1, i.e. reduce downscaling as much as possible. If there are multiple resolutions with the same max scale, we pick the one with the lowest area, to minimize padding. E.g., the same image can be upscaled to 224x224 and 224x448, but the latter has more padding. Args: image_size (tuple[int, int]): A tuple containing the height and width of the image. possible_resolutions (torch.Tensor): A tensor of shape (N, 2) where each row represents a possible resolution (height, width). resize_to_max_canvas (bool): If True, will return the largest upscaling resolution. Returns: list[int]: The best resolution [height, width] for the given image. Example: >>> image_size = (200, 300) >>> possible_resolutions = torch.tensor([[224, 672], ... [672, 224], ... [224, 448], ... [448, 224], ... [224, 224]]) >>> get_best_fit(image_size, possible_resolutions) [224, 448] We have: scale_w = tensor([2.2400, 0.7467, 1.4933, 0.7467, 0.7467]) scale_h = tensor([1.1200, 3.3600, 1.1200, 2.2400, 1.1200]) scales = tensor([1.1200, 0.7467, 1.1200, 0.7467, 0.7467]) Only one of the scales > 1: upscaling_possible = tensor([1.1200, 1.1200]) smallest_rescale = tensor(1.1200) So we pick the resolution with the smallest smallest area: areas = tensor([150528, 100352]) # [672, 224], [224, 448] optimal_canvas = tensor([224, 448]) Nrr)torchwhererNmaxr$argmin)r!rBrXr'r(target_heights target_widthsr+r,scalesupscaling_optionsselected_scaledownscaling_options chosen_canvasareas optimal_idxoptimal_canvass r get_best_fitrhsn'1#O QT"QT"" ,G.G[[*G =F{+ ! "YY'89N"YY'89N%aZ0#67)>)ABM =Aad#mAqD&99ll5) &3 'q) r c.\rSrSr%Sr\\S'\\S'Srg)Llama4ImageProcessorKwargsia( max_patches (`int`, *optional*, defaults to 16): The maximum number of patches to be extracted from the image. Can be overridden by the `max_patches` parameter in the `preprocess` method. resize_to_max_canvas (`bool`, *optional*, defaults to False): Whether to resize the image to the maximum canvas size. If True, picks the canvas the allows the largest resizing without distortion. If False, downsample as little as possible, including no resizing at all, but never upsample, unless the image is smaller than the patch size. max_patchesrXN) __name__ __module__ __qualname____firstlineno____doc__r__annotations__bool__static_attributes__rlr rrjrjs r rj)totalc^\rSrSr\R r/SQr/SQrSSS.r Sr Sr Sr Sr SrSr\rS\\4U4S jjr\S \S\\S \4U4S jj5r\R2R4S S S\S\S\S\\\-S\\\-S S 4Sj5rS \S S\S\SSS\S\S\S\\\-S-S\\\-S-S\ S\S\S-S\!\"-S-S \4Sjr#Sr$U=r%$)Llama4ImageProcessori')rrriPr5r6TFkwargsc &>[TU]"S0UD6 g)Nrl)super__init__)selfrz __class__s rr}Llama4ImageProcessor.__init__5s "6"r rFrc &>[TU]"U40UD6$)N)r| preprocess)r~rFrzrs rrLlama4ImageProcessor.preprocess8sw!&3F33r rG do_rescalerescale_factor do_normalize image_mean image_stdcU(a;U(a4UR[RS9U-nURXU5nU$U(aX-nU$U(aURXU5nU$)z Rescale and normalize images. Override to rescale and normalize the images in torch.bfloat16 as in the original implementation )dtype)torZbfloat16 normalize)r~rFrrrrrs rrescale_and_normalize*Llama4ImageProcessor.rescale_and_normalize=sg ,YYU^^Y4~EF^^F BF  ,F ^^F BF r do_resizesizeresamplez7PILImageResampling | tvF.InterpolationMode | int | NoneNrkrXdisable_groupingreturn_tensorsc f[XS9n[R"XSRS9n[ XS9unn0n0nUR 5GHunnUR SSn[UXS9nU (aSO URnUb=[[USU5US5n[[USU5US5nUU4nOUn[UU5n[[USS5[USS5S9nURUUUS 9n[UU5nURUXVXxU 5nUSUR-USUR -nn[#UUU5nUUU'[R"UU//UR S-USRS9UU'UU-S:dGMRURUUUS 9n URU XVXxU 5n [R$"UU R'S5/SS 9UU'GM [)UU5n[)UU5n!U (a[R$"USS 9OUn[+UU!S .U S 9$) N)r1r2r)device)rrK)rXrrx)r)dim) pixel_values aspect_ratios)data tensor_type)rErZtensorrr itemsrOrhr5r$r\r/rresizerWrr6r cat unsqueezer r)"r~rFrrrrrrrrrkrXrrrzrBgrouped_imagesgrouped_images_indexgrouped_processed_imagesgrouped_aspect_ratiosrOstacked_imagesr!r"max_upscaling_sizenew_target_heightnew_target_widthtarget_size_without_distortionnew_size_without_distortionprocessed_imagesratio_hratio_w global_tilesrs" r _preprocess Llama4ImageProcessor._preprocessUs" :f$||,@PQIYIYZ/DV/o,,#% "%3%9%9%; !E>'--bc2J&z3GsK)=4;; !-$'JqM;M(NP[\]P^$_!#&s:a=:L'M{[\~#^ 2CEU1V.1<.+IUs*t '*26q91=SIdefIgijEk+ ' ${{+! +   //?M #99 *lXa   A$++-A$**,G ..>Q .> $U ++0<<7#$~';';A'>>vayGWGW, !% (  1$#{{"% +  $99 *lXa  38))=M|OeOefgOhRS AO599%51=Ue"2]Sao  r rl)&rmrnrorpr BILINEARrrrrrrrdo_convert_rgbrkrXrj valid_kwargsrr}rr rrrZcompilerdisablersfloatr8rrrstrrrrt __classcell__)rs@rrwrw's!**H JIC (DIJLNK -L#(B!C#44v>X7Y4^j44 ^^    DK' 4;& .Q ^$Q Q  Q L Q  Q Q Q DK'$.Q 4;&-Q Q #Q +Q j(4/Q !Q Q r rw)r)F))rqr% collectionsr functoolsrrZtorchvision.transforms.v2rrQimage_processing_backendsrimage_processing_utilsrimage_transformsr r r image_utilsr r rprocessing_utilsrrutilsrrrrrtupler/TensorrErWrsrhrjrw__all__rlr rrsz( # 7;2UUCC4/##c((!!c3h!!sCx!! 38_!!H 15 s5 5 U\\5 5 v45! !sCx!E#sC-00! !N"'`c3h`,,`` 38_ `F U  ~ -~ ~ B " "r