j|. (SSKrSSKJr SSKrSSKJr SSKJr SSK J r SSK J r J r SSKJrJrJr SS KJrJr SS KJrJr SS KJrJr SS KJr "S S\SS9rSSS\SS4SjrS\S\S\S-S\S-S\ \\44 Sjr!\"SS\55r"S/r#g)N) Annotated) functional)TorchvisionBackend) BatchFeature)group_images_by_shapereorder_images) ImageInputPILImageResamplingSizeDict) ImagesKwargsUnpack) TensorTypeauto_docstring)IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STD) positive_intc\\rSrSr%Sr\\\"54\S'\\S-\"54\S'Sr g)NeoMMEImageProcessorKwargs%z patch_size (`int`, *optional*, defaults to `self.patch_size`): Side, in pixels, of one patch token. The image is padded to a whole multiple of it. max_side (`int`, *optional*): Longest-side cap in pixels. Unset means no longest-side resize. patch_sizeNmax_side) __name__ __module__ __qualname____firstlineno____doc__rintr__annotations____static_attributes__rC:\Users\johnn\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbz5n2kfra8p0\LocalCache\local-packages\Python313\site-packages\transformers/models/neomme/image_processing_neomme.pyrr%s1#|~-..d LN233r"rF)totalimages torch.TensorrreturncURup#pEXA-nXQ-nURX#XaXq5nURSSSSSS5nURX&U-S5nU$)z Convert 3D array image of shape (image_height, image_width, num_channels) into 2D array of patches of shape (num_patches_height * num_patches_width, patch_size * patch_size * num_channels). rr)shapereshapepermute) r%r batch_size num_channels image_height image_widthnum_patches_heightnum_patches_width patched_images r#convert_image_to_patchesr81sw ;A,,7Jl%3#1NN"4BSM"))!Q1a;M!))*K\6\^`aM r"heightwidthrsizecUb UROSnUb UROSnUbX-U:a X@U-- S-OSnUb[Xb[X5- 5nUb[XeX-- S-5nUS:XaX4$[S[ X-55n[S[ X-55nUSL=(a [Xx5U:=(d USL=(a Xx-U:n U (av[S[ R "X-55n[S[ R "X-55nUb,Xx-U:a$Xx:a[SXX-5nXx4$[SXW-5nXx4$UbX-U:aXx-U:aXx4$[S[ R"X-55n [S[ R"X-55n USL=(a [X5U:=(d USL=(a X-U:n U (dX4$Xx4$)zHCompute integer height and width from the configured image size targets.Ng?g?r,) min_pixels max_pixelsminmaxroundmathfloorceil) r9r:rr;r=r>scaleresized_height resized_width exceeds_cap ceiled_height ceiled_widths r#get_resize_output_sizerKAs$($4$J$($4$J6@6LQWQ_blQlZE> *s 2ruEEc&&889E&.9cAB |}E&.12N5/0M4'YC,NQY,Y$N>#AJ#NQ 6> :;Atzz%-89  !n&Dz&Q.!$Q (C!D,,!$Az'C D ,,&.:"=  )Z 7!0 0Atyy89 1dii 67 t+[M0PS[0[ d " P}'Cj'P  . .  ((r"c^\rSrSrSr\r\Rr \ r \ r SrSrSrSrSrSrSrSrSS/rS \\4U4S jjr\S \S \\S \4U4S jj5rS \4U4SjjrSSS\S-S\ S-SSS S4 Sjr!SSS\S \S\\44Sjr"S \#SS\$S\S\S-S\ S-SSS\$S\%S\$S\%\#\%-S-S\%\#\%-S-S\$S-S\&\'-S-S \4S jr(S%S!\S"\S \4S#jjr)S$r*U=r+$)&NeoMMEImageProcessorja{ Constructs an image processor for NeoMME. The processor converts each image to RGB and splits it into row-major patches. It preserves the original size by default, but `max_side` or a pixel-area `size` can resize images. It pads the bottom and right edges to complete the final patch. A batch returns one concatenated patch tensor and one grid height and width pair per image. Tgp? N pixel_values image_grid_hwkwargsc &>[TU]"S0UD6 g)Nr)super__init__)selfrR __class__s r#rUNeoMMEImageProcessor.__init__s "6"r"r%r'c &>[TU]"U40UD6$)z Returns: [`BatchFeature`] with `pixel_values` of shape `(total_patches, 3 * patch_size ** 2)` and `image_grid_hw` of shape `(batch_size, 2)`. `pixel_values` concatenates patches from every image in the batch. )rT preprocess)rVr%rRrWs r#rZNeoMMEImageProcessor.preprocesssw!&3F33r"c >USnUb%[[U55SS1:wa [S5eUScURSS5 [TU]"S0UD6$)Nr;r=r>z4size must contain exactly min_pixels and max_pixels. do_resizer)setdict ValueErrorpoprT_validate_preprocess_kwargs)rVrRr;rWs r#rb0NeoMMEImageProcessor._validate_preprocess_kwargss]f~  DJL,3O OST T &> ! JJ{D )w2vh(]%  *vo =L~C{{HPT{UUr"rcURSURSpCU*U-*U*U-*peXR-U- nXb-U- nU(dU(a[R"USSX/SS9nXU4$)Nrgr-r)fill)r.tvFpad) rVrdrr9r: grid_height grid_width pad_height pad_widths r#_pad_to_patch_grid'NeoMMEImageProcessor._pad_to_patch_gridsw BR%+Gz$9":uf >R!%!7!7X`!a,: "5 )&<((>@TU/D^fv/w,,#%  %3%9%9%; !E>7;6M6Mn^h6i 3NK!77 LV_N/G~Wa.b $U +).z*+c..AA*  &&<&&>@TU &':!&]!;'   r"r9r:c\U=(d 0nURSUR5nURSUR5nURURSUR5S9SnURSUR 5(a[ XXV5upU*U-*U*U-*-$)z Return the number of image patches for one image, excluding row markers. The method applies the effective resize settings, then rounds each image dimension up to a whole patch. Values in `images_kwargs` override the processor settings. rrr;)r;r])getrr_standardize_kwargsr;r]rK)rVr9r: images_kwargsrrr;s r#get_number_of_image_patches0NeoMMEImageProcessor.get_number_of_image_patchess&+ "&&|T__E  $$Z?'']->->vtyy-Q'RSYZ   [$.. 9 926(QMFJ&'eVz-A+BCCr"r)N),rrrrrr valid_kwargsr BILINEARrerryrrzdo_convert_rgbr]rvrwrxrrr;model_input_namesrrUrr rrZtuplerbrr rjrtlistboolfloatstrrrrr! __classcell__)rWs@r#rMrMjs.L!**H'J%INIJNLJH D'9#(B!C#44v>X7Y4^j44=u= V V* Vo V L V  V..C.ER`begjRjLk.0 ^$0 0  0 * 0 o 0 L0 0 0 0 DK'$.0 4;&-0 +0 j(4/0 !0 dD#DcDZ]DDr"rM)$rBtypingrrtorchvision.transforms.v2rrnimage_processing_backendsrimage_processing_utilsrimage_transformsrr image_utilsr r r processing_utilsr rutilsrrutils.constantsrrutils.type_validatorsrrrr8rrKrM__all__rr"r#rs*  7;2ECC4/L1 4U 4 ^    &)3&)s&)cDj&)PX[_P_&)dijmorjrds&)RJD-JDJDZ " "r"