pyi;(SSKrSSKJr SSKJr SSKJrJrJ r SSK J r SSK J r JrJrJr SSKJrJr SS KJrJr "S S \S S 9r\"SS\55rS/rg)N) functional)TorchvisionBackend) BatchFeatureget_patch_output_sizeselect_best_resolution)divide_to_patches)ChannelDimensionPILImageResamplingSizeDictget_image_size) ImagesKwargsUnpack) TensorTypeauto_docstringcN\rSrSr%Sr\\S'\\S'\\\\S'\\S'Sr g) AriaImageProcessorKwargsa max_image_size (`int`, *optional*, defaults to `self.max_image_size`): Maximum image size. Must be either 490 or 980. min_image_size (`int`, *optional*, defaults to `self.min_image_size`): Minimum image size. Images smaller than this in any dimension will be scaled up. split_resolutions (`list[list[int]]`, *optional*, defaults to `self.split_resolutions`): A list of possible resolutions as (height, width) pairs for splitting high-resolution images into patches. split_image (`bool`, *optional*, defaults to `self.split_image`): Whether to split the image into patches using the best matching resolution from `split_resolutions`. max_image_sizemin_image_sizesplit_resolutions split_imageN) __name__ __module__ __qualname____firstlineno____doc__int__annotations__listbool__static_attributes__rk/home/mande/repo/quber/.venv/lib/python3.13/site-packages/transformers/models/aria/image_processing_aria.pyrrs) DI&r$rF)totalc^\rSrSr/SQr\r\Rr /SQr /SQr Sr Sr SrSrSrSrSrS \\4U4S jjrS \S \S \\4SjrSSS \SSS S4SjrSSS \S S4SjrSSS\\\S\SSS \S4 SjrS)S\SS\S\S\S\\\-S-S\\\-S-S\S-S\\-S-S \S!\S"\\\S-S#\SSS \ 4S$jjr!S*S%\S&\4S'jjr"S(r#U=r$$)+AriaImageProcessor1 pixel_values pixel_mask num_crops)?r.r.PFNTkwargsc >URS5c'/SQnUVs/sHo3SS-USS-/PM snUS'[TU]"S0UD6 gs snf)Nr)))r3r)r3)r3)r3)r3)r3)r4r5)r4r)r4r4)r4r3)rr3)rr4)r5r3)r5r4)r6r3)r7r3)r8r3)r9r3rr3r)getsuper__init__)selfr1default_resolutionsel __class__s r%r=AriaImageProcessor.__init__Asa ::) * 2#{ Pc*dPc"qECKA+EPc*dF& ' "6"+esA original_resolutiontarget_resolutionreturncdUup4UupV[Xd- S5upx[XS- S5upXyXx-X-/$)zNGet padding size for patching, returns [left, top, right, bottom] for tvF.pad.r4)divmod) r>rCrDoriginal_heightoriginal_width target_height target_widthpaste_xr_xpaste_yr_ys r%_get_padding_size$AriaImageProcessor._get_padding_sizeGsE*='&7# l;Q? m=qA '-??r$imagez torch.Tensorresamplez7PILImageResampling | tvF.InterpolationMode | int | Nonecj[X[RS9upEURU[ XES9U5$)zFResize an image to a target resolution while maintaining aspect ratio.input_data_formatheightwidth)rr FIRSTresizer )r>rRrDrS new_height new_widths r%_resize_for_patching'AriaImageProcessor._resize_for_patchingOs8!6 8H8N8N!  {{5(*"NPXYYr$c|[X[RS9nURX25n[R "XS9$)zCPad an image to a target resolution while maintaining aspect ratio.rU)padding)rr rZrPtvFpad)r>rRrDnew_resolutionras r%_pad_for_patching$AriaImageProcessor._pad_for_patching[s6 /u[k[q[qr((Kwwu..r$grid_pinpoints patch_sizec[U[5(d [S5e[U[R S9n[ XR5nURXU5nURXv5n[XS9n U $)a: Process an image with variable resolutions by dividing it into patches. Args: image (`torch.Tensor`): The input image to be processed (channels-first format). grid_pinpoints (`list[list[int]]`): A list of possible resolutions as (height, width) pairs. patch_size (`int`): Size of each square patch to divide the image into. resample (`PILImageResampling | tvF.InterpolationMode | int | None`): Resampling filter to use when resizing. Returns: `list[torch.Tensor]`: A list of image patches in channels-first format. z6grid_pinpoints must be a list of possible resolutions.) channel_dim)rh) isinstancer! TypeErrorr r rZrr^rer ) r>rRrgrhrS image_sizebest_resolution resized_image padded_imagepatchess r%get_image_patches$AriaImageProcessor.get_image_patchesesk..$//TU U#E7G7M7MN 0L11%(S --mM #LHr$images do_rescalerescale_factor do_normalize image_mean image_stddisable_groupingreturn_tensorsrrrrc XU S;a [S5e/n/nSnUGHAnU (aURUXU 5nOU/nUb[U5U:a [U5nUHnURSURSnnU [ UU5- nUU:a[ [ UU-5U 5nU nOU n[ [ UU-5U 5nUR U[UUS9U 5nU U- nU U- n[R"USSUU/5n[R"X4[RS9nSUSU2SU24'URU5 URU5 M GMD [R"USS 9nURUX#XEU5n[R"USS 9n[!UUUS .US 9$) N)r:r/z(max_image_size must be either 490 or 980rWr)dtypeT)dimr*)data tensor_type) ValueErrorrrlenshapemaxrr[r rbrctorchzerosr"appendstackrescale_and_normalizer)r>rtrurvrwrxryrzr{rrrrrSr1 pixel_masksprocessed_cropsr-rR crop_images crop_imagehwscalenew_hnew_wpadding_bottom padding_rightr,stacked_images stacked_maskss r% _preprocessAriaImageProcessor._preprocesss"  +GH H  E"44U*"":.&&z2'*<_!<33 J R[  KQ7  .+& '   r$rXrYcURSUR5nURSUR5n[X4UR5upgU(dSnU$Xe-U-U-nU$)aU A utility that returns number of image patches for a given image size. Args: height (`int`): Height of the input image. width (`int`): Width of the input image. images_kwargs (`dict`, *optional*): Any kwargs to override defaults of the image processor. Returns: `int`: Number of patches per image. rrr3)r;rrrr) r>rXrY images_kwargsrrresized_height resized_width num_patchess r%get_number_of_image_patches.AriaImageProcessor.get_number_of_image_patchessv$'' t7G7GH &**+;T=P=PQ(>PTPfPf(g%*a 1?0PS`0`dr0r r$r)r/r0NFN)N)%rrrrmodel_input_namesr valid_kwargsr BICUBICrSrxryrrrrdo_convert_rgbrurwrr=tupler!rrPr^rerrr"floatstrrrrrr# __classcell__)rAs@r%r(r(1sC+L!))H JINNKNJL#(@!A# @U@u@Y]^aYb@ Z Z! ZL Z  Z//!/  /T#Y  L  n  V"!48!NRC ^$C C  C  C DK'$. C 4;&-C +C j(4/C C C  S ?T1C C LC !C J#cr$r()rtorchvision.transforms.v2rrbimage_processing_backendsrimage_processing_utilsrrrimage_transformsr image_utilsr r r r processing_utilsrrutilsrrrr(__all__rr$r%rs]( 7;aa1YY4/|5$m+mm` r$