bCia SSKJr SSKJrJr SSKrSSKJrJ r J r J r SSK J r JrJrJrJr SSKJrJrJrJrJrJrJrJrJrJr SSKJrJr \R@"\!5r"S \RFS \$S \%\RF4S jr&"S S\5r'S/r(g))Iterable)OptionalUnionN)BaseImageProcessor BatchFeatureget_patch_output_sizeselect_best_resolution) PaddingModeconvert_to_rgbpadresizeto_channel_dimension_format) ChannelDimension ImageInputPILImageResamplingget_image_sizeinfer_channel_dimension_formatis_scaled_imagemake_flat_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypeloggingimage patch_sizereturnc/n[XS9upE[SXA5H\n[SXQ5HInU[R:XaXXa-2XwU-24nOUSS2XfU-2XwU-24nUR U5 MK M^ U$)aW Divides an image into patches of a specified size. Args: image (`np.ndarray`): The input image. patch_size (`int`): The size of each patch. input_data_format (`ChannelDimension` or `str`): The channel dimension format of the input image. Returns: list: A list of np.ndarray representing the patches.  channel_dimrN)rrangerLASTappend) rrinput_data_formatpatchesheightwidthijpatchs h/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/aria/image_processing_aria.pydivide_to_patchesr-.sG"5HMF 1f )q%,A $4$9$99!.0!*n2DDEa^!3QZ5GGH NN5 ! -* Nc ^\rSrSrSr/SQrSSSSSSSSS S\R4 S \\ \ S \\ \ S \ S \ S\\ \ \ \ 4S\\ S\\ S\ S\\ \ 4S\\ S\4U4SjjjrSSSSSSSSSSS\R"S4 S\\\ \4S \\\ \ \ 4S \\\ \ \ 4S \\ S \\ S\\ S\\ S\\ S\\ S\\ S\\S\\\\4S\\S\\\\44SjjrS\R.S\ S\S\R.4SjrS \ S\ 4S!jrS\R.S\ S\S\R.4S"jr\R8S#SS4S\R.S$\\ \ \ \ 4\\ \ \ 44S%\S&\\ \\ 4S\\\\4S\\\\4S\R.4S'jjrS\R.S(\ \ \ \ 4S)\ S\S\S\S\ \R.4S*jrS/S+\ S,\ 4S-jjr S.r!U=r"$)0AriaImageProcessorJa A vision processor for the Aria model that handles image preprocessing. Initialize the AriaImageProcessor. Args: image_mean (`list`, *optional*, defaults to [0.5, 0.5, 0.5]): Mean values for normalization. image_std (`list`, *optional*, defaults to [0.5, 0.5, 0.5]): Standard deviation values for normalization. max_image_size (`int`, *optional*, defaults to 980): Maximum image size. min_image_size (`int`, *optional*, defaults to 336): Minimum image size. split_resolutions (`list`, *optional*, defaults to a list of optimal,resolutions as tuples): The optimal resolutions for splitting the image. split_image (`bool`, *optional*, defaults to `False`): Whether to split the image. do_convert_rgb (`bool`, *optional*, defaults to `True`): Whether to convert the image to RGB. do_rescale (`bool`, *optional*, defaults to `True`): Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in the `preprocess` method. rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess` method. do_normalize (`bool`, *optional*, defaults to `True`): Whether to normalize the image. resample (PILImageResampling, *optional*, defaults to `BICUBIC`): The resampling filter to use if resizing the image.  pixel_values pixel_mask num_cropsNiPFTgp? image_mean image_stdmax_image_sizemin_image_sizesplit_resolutions split_imagedo_convert_rgb do_rescalerescale_factor do_normalizeresamplec  >[TU]"S0U D6 Uc/SQnUc/SQnX0lX@lXlX lX`lUc#/SQnUV s/sHoSS-U SS-4PM nn XPlXplXl Xl Xl Xl gs sn f)N)?rCrC)))rDr)rD)rD)rD)rD)rD)rErF)rEr)rErE)rErD)rrD)rrE)rFrD)rFrE)rGrD)rHrD)rIrD)rJrDrrD) super__init__r9r:r7r8r<r;r=r>r?r@rA)selfr7r8r9r:r;r<r=r>r?r@rAkwargsel __class__s r,rNAriaImageProcessor.__init__ls "6"  (J  'I,,$"&  $!y FW XFWQ%#+r!us{!;FW  X!2,$,( !Ys B ptimagesreturn_tensors data_formatr%c UbUO URnUbUO URnUbUO URnUbUO URnUbUO URnUbUO UR nUbUO UR nU bU O URn U bU O URn U bU O URn US;a [S5eURU5n[U5n[U5(d [S5e[U UUU UU S9 U(aUVs/sHn[U5PM nnUVs/sHn[!U5PM nnU(a([#US5(a[$R'S5 Uc[)US5n/n/nSnUGHnU(aUR+UUR,UU UUS9nOU/nUb[/U5U:a [/U5nUGH.n[1U5unnU[3UU5- nUU:a[3[5UU-5U5U4nOU[3[5UU-5U54n[7UUU UUS 9nUUS- UUS - nn[9USU4SU44UUS9n[:R<"XD4[>S 9nS USUS2SUS 24'URAU5 U(aURCUXS 9nU (a8UREUURURUUS9nU b [GUX5OUnURAU5 GM1 GM [I[:RJ"USS 9[:RJ"USS 9US.U S9$s snfs snf)a Process a list of images. Args: images (ImageInput or list of ImageInput): The input image or a list of images. image_mean (`list`, *optional*, defaults to [0.5, 0.5, 0.5]): Mean values for normalization. image_std (`list`, *optional*, defaults to [0.5, 0.5, 0.5]): Standard deviation values for normalization. max_image_size (`int`, *optional*, defaults to `self.max_image_size` (980)): Maximum image size. min_image_size (`int`, *optional*, defaults to `self.min_image_size` (336)): Minimum image size. split_image (`bool`, *optional*, defaults to `self.split_image` (False)): Whether to split the image. do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb` (True)): Whether to convert the image to RGB. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image. rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`): Rescale factor to rescale the image by if `do_rescale` is set to `True`. do_normalize (`bool`, *optional*, defaults to `self.do_normalize` (True)): Whether to normalize the image. resample (PILImageResampling, *optional*, defaults to `self.resample` (BICUBIC)): The resampling filter to use if resizing the image. return_tensors (`str` or `TensorType`, *optional*, defaults to "pt"): The type of tensor to return. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format for the output image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. If unset, will use same as the input image. input_data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format for the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. If unset, will use the inferred format of the input image. Returns: BatchFeature: A BatchFeature object containing: - 'pixel_values': Tensor of processed image pixel values. - 'pixel_mask': Boolean pixel mask. This mask is a 2D tensor of shape (max_image_size, max_image_size) where: - True (1) values indicate pixels that belong to the original resized image. - False (0) values indicate pixels that are part of the padding. The mask helps distinguish between actual image content and padded areas in subsequent processing steps. - 'num_crops': The maximum number of crops across all images. N)rKr6z(max_image_size must be either 490 or 980zkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.)r@r7r8rAr>r?rzIt looks like you are trying to rescale already rescaled images. If the input images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again.)rWr%)rArWr%rD)dtype)rscaler%)axisr2)data tensor_type)&r7r8r9r:r<r=r>r?r@rA ValueError fetch_imagesrrrr rrlogger warning_oncerget_image_patchesr;lenrmaxintrr npzerosboolr$rescale normalizerrstack)rOrUr7r8r9r:r<r=r>r?r@rArVrWr%rr3 pixel_masksr5 crop_images crop_imagehwrZnew_sizecrop_image_resizedpadding_bottom padding_rightcrop_image_paddedr4s r, preprocessAriaImageProcessor.preprocesssR$.#9Zt !*!6IDNN +9+E4K^K^+9+E4K^K^%0%vay I    E"44**" 1&7 5  %g  C $4y$@ , ) %j11&Q26 #CE NN C^TH .CE NN0STH%+% 1&7 &"1?!0Ln_ghi_jNj $'&(1m*<= 1&7 %! XX~&FdS ;< =Xa[=-HQK-78"":.(, /~)5)% (,)$5*; )7)%'244E{f.& ##$56c*B "A > hh{;& '   iA=s L>'Mrtarget_resolutionrc:[XU5upV[XU4X4S9nU$)a Resizes an image to a target resolution while maintaining aspect ratio. Args: image (np.ndarray): The input image. target_resolution (tuple): The target resolution (height, width) of the image. resample (`PILImageResampling`): Resampling filter to use if resizing the image. input_data_format (`ChannelDimension` or `str`): The channel dimension format of the input image. Returns: np.ndarray: The resized and padded image. rAr%)r r)rOrrxrAr% new_height new_width resized_images r,_resize_for_patching'AriaImageProcessor._resize_for_patchingWs-&!6ePa b u9&=v r.original_resolutioncjUup4UupV[Xd- S5upx[XS- S5upXU -4XwU-44$)NrE)divmod) rOrrxoriginal_heightoriginal_width target_height target_widthpaste_xr_xpaste_yr_ys r,_get_padding_size$AriaImageProcessor._get_padding_sizeqsM*='&7# l;Q? m=qA 3''S=)AAAr.c^[XU5nURXB5nURXS9nU$)zE Pad an image to a target resolution while maintaining aspect ratio. )padding)r rr )rOrrxr%new_resolutionr padded_images r,_pad_for_patching$AriaImageProcessor._pad_for_patchingxs7 /uIZ[((Kxxx7 r.grmodeconstant_valuesc^[U[5(d[U5S:wa [XX4XV5$Uc [ U5n[ R S[ RS[ RS[ RS0n[R"XXsUS9nUb[XU5nU$UnU$)a Pads the `image` with the specified `padding` and `mode`. Padding can be in the (`height`, `width`) dimension of in the (`num_patches`) dimension. In the second case an iterable if tuples is expected as input. Args: image (`np.ndarray`): The image to pad. padding (`int` or `tuple[int, int]` or `Iterable[tuple[int, int]]`): Padding to apply to the edges of the height, width axes. Can be one of three formats: - `((before_height, after_height), (before_width, after_width))` unique pad widths for each axis. - `((before, after),)` yields same before and after pad for height and width. - `(pad,)` or int is a shortcut for before = after = pad width for all axes. mode (`PaddingMode`): The padding mode to use. Can be one of: - `"constant"`: pads with a constant value. - `"reflect"`: pads with the reflection of the vector mirrored on the first and last values of the vector along each axis. - `"replicate"`: pads with the replication of the last value on the edge of the array along each axis. - `"symmetric"`: pads with the reflection of the vector mirrored along the edge of the array. constant_values (`float` or `Iterable[float]`, *optional*): The value to use for the padding if `mode` is `"constant"`. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format for the output image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. If unset, will use same as the input image. input_data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format for the input image. Can be one of: - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. If unset, will use the inferred format of the input image. Returns: `np.ndarray`: The padded image. rFconstantreflectedge symmetric)rr) isinstancerercr rr CONSTANTREFLECT REPLICATE SYMMETRICrfr)rOrrrrrWr%padding_mode_mappings r,r AriaImageProcessor.pads` gs # #s7|q'8utk] ]  $ >u E   *     ! !6  ! !;   u,@,FXghR]Ri 'PTPfPf(g%*a 1?0PS`0`dr0r r.) r=r@r>r7r8r9r:rAr?r<r;)N)#__name__ __module__ __qualname____firstlineno____doc__model_input_namesrBICUBICrrfloatretuplerhrrNrFIRSTrstrrrvrfndarrayr~rrr rrr rbr__static_attributes__ __classcell__)rRs@r,r0r0Js>D-1+/!!=A&+)-,3'+'9'A'A"!T%[)"!DK("! "!  "! $DsCx$9: "!d^"!!"!"!c5j)"!tn"!%"!"!N;?9=(,(,&*)-%)*.'+15;?2B2H2HDHE j$z"223E U5$u+#567E E%e"456 E ! E ! E d^E !E TNE !E tnE -.E !sJ!78E ./E $E#/?*?$@AE NZZ49Xh 4BUBuB ZZ 49 N^   "(009<>BDH@zz@sE#s(OXeCHo-FFG@ @ uhuo56 @ eC)9$9:; @$E#/?*?$@A@ @D0zz0U38_-0 0 % 0 & 0,0 bjj 0d#cr.r0))collections.abcrtypingrrnumpyrfimage_processing_utilsrrr r image_transformsr r r rr image_utilsrrrrrrrrrrutilsrr get_loggerrr`rrerr-r0__all__rLr.r,rs*%"uuee   )   H %RZZSPTUWU_U_P`8B+BJ r.