ZjSSSKrSSKJr SSKJrJr SSKrSSKrSSK J r SSK J r SSK JrJrJr SSKJrJrJrJrJrJr SS KJrJr SS KJrJrJr \"5(aSS KJ r SS K!J"r# "S S\SS9r$\"SS9S\%S\&\'\%\%44Sj5r(S\%S\%S\%S\%S\%S\'\%\%44 Sjr)\"SS9S\%S\%S\%S\%S\'\%\%44 Sj5r*S\SS4Sjr+S r,S-S!\&\&\'\%\%4S\%S"\S#SS$4S%jjr-S&\&\&S$S\%S\'S$\&\&\%44S'jr.S-S!\&\&\'\%\%4S\%S"\S#SS$4S(jjr/S)\S\4S*jr0\"S+S,\ 55r1S,/r2g).N) lru_cache)OptionalUnion)TorchvisionBackend) BatchFeature)group_images_by_shapereorder_imagessplit_to_tiles)IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STD ImageInputPILImageResamplingSizeDictmake_nested_list_of_images) ImagesKwargsUnpack) TensorTypeauto_docstringis_vision_available)Image) functionalc$\rSrSr%Sr\\S'Srg)MllamaImageProcessorKwargs+zO max_image_tiles (`int`, *optional*): The maximum number of tiles allowed. max_image_tilesN)__name__ __module__ __qualname____firstlineno____doc__int__annotations____static_attributes__rڃ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/mllama/image_processing_mllama.pyrr+s r&rF)total )maxsizerreturnc/n[SUS-5H5n[SUS-5HnX#-U::dM URX#45 M! M7 U$)a Computes all allowed aspect ratios for a given maximum number of input tiles. This function calculates all possible arrangements of tiles that can be formed within the constraint of the maximum number of tiles. Each arrangement is represented by its aspect ratio (width/height) and the corresponding tile configuration. Args: max_image_tiles (`int`): The maximum number of tiles allowed. Returns: `list[tuple[int, int]]`: A list of tuples, each tuple representing a valid (width, height) configuration in terms of number of tiles. Example: >>> get_all_supported_aspect_ratios(4) [(1, 1), (1, 2), (1, 3), (1, 4), (2, 1), (2, 2), (3, 1), (4, 1)] )rangeappend)r aspect_ratioswidthheights r'get_all_supported_aspect_ratiosr34sW,Mq/A-.A23F~0$$e_54/ r& image_height image_width canvas_height canvas_width tile_sizec8[R"XU5n[R"XU5nX`- nXQ- nX:a0Un [[R"X-5=(d SU5n X4$Un [[R"X-5=(d SU5n X4$)a Calculates the new size of an image to fit within a canvas while maintaining aspect ratio. This function calculates the optimal size for an image to fit within a canvas defined by canvas_height and canvas_width, while ensuring that the image dimensions are not smaller than tile_size. If the image is larger than the canvas, the returned size will fit within the canvas. If the image already fits within the canvas, the size remains unchanged. The aspect ratio of the original image is preserved as much as possible. Args: image_height (`int`): The height of the original image. image_width (`int`): The width of the original image. canvas_height (`int`): The height of the canvas. canvas_width (`int`): The width of the canvas. tile_size (`int`): The tile size. Returns: `tuple[int, int]`: A tuple containing the new height and width of the image. r-)npclipminmathfloor) r4r5r6r7r8 target_width target_heightscale_hscale_w new_width new_heights r'get_image_size_fit_to_canvasrERsB77;<@LGGL]CM*G(G L$:;@q-P   #  ;#89>Q M   r&dc[U5n[R"U5U-n[R"U5RupgX`- nXq- n [R"X:X5n XS:n [ U 5S:a[R "U 5n OXS:n [R"U 5n XZU :Hn[ U5S:a/USS2S4USS2S4-n[R"U5nUUnOUSn[U5$)ar Determines the best canvas based on image and tile size and maximum number of tiles. First, calculates possible resolutions based on the maximum number of tiles and tile size. For each possible resolution, calculates the scaling factors for width and height, and selects the smallest one. If upscaling is possible, picks the smallest upscaling factor > 1. If upscaling is not possible, picks the largest scaling factor <= 1. If there are multiple resolutions with the same max scale, picks the one with the lowest area. Args: image_height (`int`): The height of the image. image_width (`int`): The width of the image. max_image_tiles (`int`): The maximum number of tiles any image can be split into. tile_size (`int`): The tile size. Returns: `tuple[int, int]`: The best canvas resolution [height, width] for the given image. r-rN) r3r:arrayTwherelenr<maxargmintuple)r4r5rr8possible_tile_arrangementspossible_canvas_sizestarget_heights target_widthsrArBscalesupscaling_optionsselected_scaledownscaling_options chosen_canvasareas optimal_idxoptimal_canvass r'get_optimal_tiled_canvasr[s :"A!QHH%?@9L$&HH-B$C$E$E!N+G)G XXg' :F{+ ! 12$aZ0 34)N*BCM =Aad#mAqD&99ii& &{3&q)   r&sizecUR(aUR(d[SU35eURUR:wa[SU35eg)NzJArgument `size` must be a dictionary with keys 'height' and 'width'. Got: z9Argument `size` must have the same height and width, got )r2r1 ValueError)r\s r'_validate_sizer_sN KKDJJefjeklmm {{djj TUYTZ[\\!r&cU(d [S5eU(d [S5eUbUS::a[SUS35e[U5 g)Nz9MllamaImageProcessor doesn't support `do_pad=False` mode.z*build_aspect_ratio_mask..;]cS]dtyperer-Nr)rKrLtorchzeroslong enumerate) r0rre batch_sizemax_num_imagesaspect_ratio_maskisample_aspect_ratiosj num_tiles_w num_tiles_hs r'build_aspect_ratio_maskrs*]#J;];;N Z$QY^YcYclrs "#aAg$-]#;-67K-L )A) CD $?k&?$?? @.M$< r& batch_imagesc[U5n[SU55nUVVs/sHoDHoURPM M nnnUSupxp[R"X#XX4[R USSR S9n /n [U5HTup/n[U5H-upURSnX[XSU24'URU5 M/ U RU5 MV X4$s snnf)a Stack a list of lists of images with variable lengths into a torch.Tensor, applying zero padding as needed. Each list in the input represents a batch sample, and each image within a list is expected to be pre-split into tiles. The resulting array will have a shape of (batch_size, max_num_images, max_image_tiles, channels, tile_height, tile_width). Args: batch_images (`List[List[torch.Tensor]]`): A list of lists of image tiles. Each inner list represents a batch sample containing multiple images, where each image is pre-split into tiles. The shape of each tile array is (num_tiles, channels, tile_height, tile_width). max_image_tiles (int): The maximum number of tiles any image was potantially split. Returns: `Tuple[torch.Tensor, List[List[int]]]`: A tuple containing: - stacked_images (`torch.Tensor`): A numpy array of stacked images with shape (batch_size, max_num_images, max_image_tiles, channels, tile_height, tile_width). - all_num_tiles (`List[List[int]]`): A list of lists containing the number of tiles for each image in each batch sample. c38# UHn[U5v M g7frirj)rlimagess r'rn(pad_batches_and_tiles..s@<V.Frprqrrr-)rKrLr3rtrurvrwindex) r0rrerxrysupported_aspect_ratiosaspect_ratios_idsr{r|r}rr~s r'convert_aspect_ratios_to_idsr,s2]#J;];;N=oN Z$@ [ab#,]#;-67K-L )A) &=&C&C[D^&_bc&c d #.M$< r&rc>[5(a[U[R5(dU$URS:XaU$UR S5n[R "SUR S5n[R"X!5nUR S5nU$)z| Converts an image to RGB format. Only converts if the image is of type PIL.Image.Image, otherwise returns the image as is. RGBRGBA)rr)r isinstancermodeconvertnewr\alpha_composite)r image_rgba backgroundrs r'convert_to_rgbrQs}  5%++(F(F  zzU v&J6:??ODJ++JCO%--e4O r&c^\rSrSr\R r\r\ r SSS.r Sr Sr SrSrSrSr\r/SQrS\\4U4SjjrU4S jr\S \S\\S \4U4S jj5rS%S \S \S \4SjjrS\S \4SjrSSS\S\ \\4S S4Sjr!S&SSS\S\S\"SS\#S \$S\ \\444 U4Sjjjr%S \&\&SS\SSS\#S\'S\#S\'\&\'-S-S \'\&\'-S-S\S-S!\(\)-S-S"\#S-S \4S#jr*S$r+U=r,$)'MllamaImageProcessoricr2r1T) pixel_valuesraspect_ratio_idsrzkwargsc >[TU]"S0UD6 [URURUR UR 5 gNr)super__init__rdrbr\rcrselfr __class__s r'rMllamaImageProcessor.__init__rs4 "6"-dnndiiVZVjVjkr&c >[TU]"S0UD6 [URURUR UR 5 gr)r_validate_preprocess_kwargsrdrbr\rcrrs r'r0MllamaImageProcessor._validate_preprocess_kwargsvs4 +5f5-dnndiiVZVjVjkr&rr+c &>[TU]"U40UD6$ri)r preprocess)rrrrs r'rMllamaImageProcessor.preprocesszsw!&3F33r&expected_ndimsc6URU5n[XS9$)z1Prepare a nested images structure for processing.)r) fetch_imagesr)rrrs r'_prepare_images_structure.MllamaImageProcessor._prepare_images_structure~s""6*)&PPr&rc[U5$)z Converts an image to RGB format.)r)rrs r'r#MllamaImageProcessor.convert_to_rgbs e$$r&rfr\ aspect_ratiocURSSupEUupgXbR-nXrR-n SSX- X- 4n [R"XSS9nU$)a Pad an image to the `size` x `aspect_ratio`. For example, if size is {height: 224, width: 224} and aspect ratio is (1, 2), the image will be padded to 224x448. Args: image (`torch.Tensor`): Image to pad. size (`Dict[str, int]`): Size of the output image. aspect_ratio (`Tuple[int, int]`): The aspect ratio of the image. Returns: `torch.Tensor`: The padded image. Nr)fill)rr2r1tvFpad) rrr\rr4r5num_tiles_heightnum_tiles_width padded_height padded_widthpad_sizes r'rMllamaImageProcessor.padsa*%*KK$4! ,8)(;;6 &3 q,4m6RSa0 r&NrresampleztvF.InterpolationMode antialiasc>URSSupgURn[UUUUS9upX-n X-n [UUU U US9up[TU]U[ XS9XES9nXU 44$)a Resizes an image to fit within a tiled canvas while maintaining its aspect ratio. The optimal canvas size is calculated based on the maximum number of tiles and the tile size. The function first determines the best tile arrangement for the image, then resizes the image to fit within this canvas. The resized image and the number of tiles along the height and width dimensions are returned. Args: image (`torch.Tensor`): Image to resize. size (`Dict[str, int]`): Size of the output image. max_image_tiles (`int`): The maximum number of tiles to split the image into. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`): Resampling filter to use when resizing the image. Returns: `Union[torch.Tensor, Tuple[int, int]]`: The resized image and a tuple containing the number of tiles along the height and width dimensions. rN)r4r5rr8)r4r5r6r7r8r)rr)rr2r[rErresizer)rrr\rrrr4r5r8r6r7rrrDrCrs r'rMllamaImageProcessor.resizes<%*KK$4! KK &>%#+ ' # )5&3 <%#'% !   8:?( 999r&z7PILImageResampling | tvF.InterpolationMode | int | None do_rescalerescale_factor do_normalize image_mean image_stdreturn_tensorsdisable_groupingc [USU S9up0n0nU R5HhunnURUX#U S9unnURUUUS9nUunnU/[ U5-UU'[ UUU5nUR UXEXgU5nUUU'Mj [XSS9n[UUSS9n[UU 5unnURn[UU US9n[UU US9n[UUUS.U S9nUUS 'U$) NT) is_nestedr)rr\rr)rr\r)r)rre)rrrz)data tensor_typer) r itemsrrrKr rescale_and_normalizer rrerrr)rrr\rrrrrrrrrrgrouped_imagesgrouped_images_indexsplit_images_groupedaspect_ratio_groupedrrrrr split_imagesr0rpixel_values_devicerrzencoded_inputss r' _preprocess MllamaImageProcessor._preprocessss 0E d5E0 , "!%3%9%9%; !E>+/;;$4Tc,7, (NL"XX$)&N 1= - o+7.3~;N*N  ').:JO\L 55j,T]L+7  '%&<(&&:\`a &';=Q]ab "7 o"V i+117 ?CV 4 ?CV & ,$4%6 '  '0{#r&r)r)NT)-rrr r!rBILINEARrr rr rr\rbrrdo_convert_rgbrcrr valid_kwargsmodel_input_namesrrrrrrrr#rrrrNrrboolrrlistfloatstrrrr% __classcell__)rs@r'rrcs'!**H'J%IC (DIJLN FO-L^l(B!Cll44v>X7Y4^j44Q QCQXbQ %J%:%CHo   F7; 6:6:6: 6: 23 6:  6: ~uS#X. /6:6:pAT.)*AAL A  A  AADK'$.A4;&-AtAj(4/A+A AAr&rri)3r= functoolsrtypingrrnumpyr:rtimage_processing_backendsrimage_processing_utilsrimage_transformsr r r image_utilsr r rrrrprocessing_utilsrrutilsrrrPILrtorchvision.transforms.v2rrrr#rrNr3rEr[r_rdrrrrr__all__rr&r'rs@ " ;2UU5DD7U 2ST%S/=R:0!0!0!0! 0!  0!  38_ 0!f 36!6!6!6! 6!  38_ 6!6!r]]d](,$U38_-.$$ ^ $$ $N2)tN+,2)2) >4S ? *+2)p(,!U38_-.!! ^ $! !J*$z-zzz " "r&