cCiVSrSSKJrJr SSKrSSKJrJrJ r SSK J r J r J r JrJr SSKJrJrJrJrJrJrJrJrJrJrJrJr SSKJrJrJ r SS K!J"r"J#r# \ RH"\%5r&\""5(aSSK'r'\#"S S 9"S S \55r(S /r)g)z Image processor class for Donut.)OptionalUnionN)BaseImageProcessor BatchFeature get_size_dict)convert_to_rgbget_resize_output_image_sizepadresizeto_channel_dimension_format) IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDChannelDimension ImageInputPILImageResamplingget_image_sizeinfer_channel_dimension_formatis_scaled_imagemake_flat_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargslogging)is_vision_availablerequires)vision)backendsc%F^\rSrSrSrS/rSS\RSSSSSSSS4 S\S \ \ \ \ 4S \S \S \S \S\S\ \ \4S\S\ \ \\\4S\ \ \\\4SS4U4SjjjrS!S\R$S \ \ \ 4S\ \ \ \4S\ \ \ \4S\R$4 SjjrS"S\R$S \ \ \ 4S\S\ \ \ \4S\ \ \ \4S\R$4 Sjjr\R,SS4S\R$S \ \ \ 4S \S\ \ \ \4S\ \ \ \4S\R$4 Sjjr\R,SS4S\R$S \ \ \ 4S \S\ \ \ \4S\ \ \ \4S\R$4 Sjjr\"5SSSSSSSSSSSSS\R4S4S\S\ \S \ \ \ \ 4S \ \S \ \S \ \S \ \S\S\ \S\ \S\ \S\ \ \\\4S\ \ \\\4S\ \ \ \4S\ \S\ \ \ \4S\R<R<4"Sjj5rS r U=r!$)#DonutImageProcessor6am Constructs a Donut image processor. Args: do_resize (`bool`, *optional*, defaults to `True`): Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by `do_resize` in the `preprocess` method. size (`dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`): Size of the image after resizing. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge resized to keep the input aspect ratio. Can be overridden by `size` in the `preprocess` method. resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`): Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method. do_thumbnail (`bool`, *optional*, defaults to `True`): Whether to resize the image using thumbnail method. do_align_long_axis (`bool`, *optional*, defaults to `False`): Whether to align the long axis of the image with the long axis of `size` by rotating by 90 degrees. do_pad (`bool`, *optional*, defaults to `True`): Whether to pad the image. If `random_padding` is set to `True` in `preprocess`, each image is padded with a random amount of padding on each size, up to the largest image size in the batch. Otherwise, all images are padded to the largest image size in the batch. 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. Can be overridden by `do_normalize` in the `preprocess` method. image_mean (`float` or `list[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`): Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method. image_std (`float` or `list[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`): Image standard deviation. pixel_valuesTNFgp? do_resizesizeresample do_thumbnaildo_align_long_axisdo_pad do_rescalerescale_factor do_normalize image_mean image_stdreturnc  L>[T U]"S0U D6 UbUOSSS.n[U[[45(aUSSS2n[ U5nXlX lX0lX@l XPl X`l Xpl Xl XlU bU O[UlU bXlg["Ulg)Ni i)heightwidth)super__init__ isinstancetuplelistrr%r&r'r(r)r*r+r,r-rr.rr/)selfr%r&r'r(r)r*r+r,r-r.r/kwargs __class__s j/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/donut/image_processing_donut.pyr7DonutImageProcessor.__init__^s "6"'tt-L dUDM * *":DT""  ("4 $,((2(>*DZ&/&;AVimage data_formatinput_data_formatc0[XS9upVUSUSpUc [U5nU[R:XaSn O%U[R:XaSn O[ SU35eX:aXe:d X:aXe:a[ R"USU S9nUb [XUS 9nU$) aa Align the long axis of the image to the longest axis of the specified size. Args: image (`np.ndarray`): The image to be aligned. size (`dict[str, int]`): The size `{"height": h, "width": w}` to align the long axis to. data_format (`str` or `ChannelDimension`, *optional*): The data format of the output image. If unset, the same format as the input image is used. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. Returns: `np.ndarray`: The aligned image.  channel_dimr2r3)r)rGzUnsupported data format: r)axesinput_channel_dim) rrrLASTFIRST ValueErrornprot90r ) r;rAr&rBrC input_height input_width output_height output_widthrot_axess r>align_long_axis#DonutImageProcessor.align_long_axiss.%35$X! &*8nd7m|  $ >u E   0 5 5 5H "2"8"8 8H89J8KLM M  ([-G  ([-GHHUAH5E  "/VghE r@random_paddingcUSUSpv[XS9upXy- n Xh- n U(aC[RRSU S-S9n [RRSU S-S9n O U S-n U S-n X- nX- nX4X44n[ UUXES9$) ap Pad the image to the specified size. Args: image (`np.ndarray`): The image to be padded. size (`dict[str, int]`): The size `{"height": h, "width": w}` to pad the image to. random_padding (`bool`, *optional*, defaults to `False`): Whether to use random padding or not. data_format (`str` or `ChannelDimension`, *optional*): The data format of the output image. If unset, the same format as the input image is used. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. r2r3rErrG)lowhighrH)rBrC)rrOrandomrandintr )r;rAr&rXrBrCrSrTrQrR delta_width delta_heightpad_toppad_left pad_bottom pad_rightpaddings r> pad_imageDonutImageProcessor.pad_images.'+8nd7m|$25$X! "0 $3 ii''AL14D'EGyy((Q[1_(EH"a'G"a'H!+ * (8*?@5'{``r@c [XS9upxUSUSp[Xy5n [X5n X:XaX:XaU$Xx:a[X-U- 5n OX:a[X|-U- 5n [U4X4USUUS.UD6$)a Resize the image to make a thumbnail. The image is resized so that no dimension is larger than any corresponding dimension of the specified size. Args: image (`np.ndarray`): The image to be resized. size (`dict[str, int]`): The size `{"height": h, "width": w}` to resize the image to. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): The resampling filter to use. data_format (`Optional[Union[str, ChannelDimension]]`, *optional*): The data format of the output image. If unset, the same format as the input image is used. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. rEr2r3g@)r&r' reducing_gaprBrC)rminintr ) r;rAr&r'rBrCr<rQrRrSrTr2r3s r> thumbnailDonutImageProcessor.thumbnails2%35$X! &*8nd7m|\1K.  !e&:L  % ,|;r DonutImageProcessor.resize sf0T"DNDM: 2 Rc   #/    r@imagesreturn_tensorsc UbUO URnUbUO URn[U[[45(aUSSS2n[ U5nUbUO UR nUbUO URnUbUO URnUbUO URnU bU O URn U bU O URn U bU O URn U bU O URn U bU O URn [U5n[!U5(d [#S5e[%U U U U U UUUS9 UVs/sHn['U5PM nnUVs/sHn[)U5PM nnU (a([+US5(a[,R/S5 Uc[1US5nU(a!UVs/sHnUR3UUUS9PM nnU(a!UVs/sHnUR5UX4US9PM nnU(a!UVs/sHnUR7UUUS 9PM nnU(a!UVs/sHnUR9UX8US 9PM nnU (a!UVs/sHnUR;UU US 9PM nnU (a!UVs/sHnUR=UXUS 9PM nnUVs/sHn[?UUUS 9PM nnSU0n[AUUS9$s snfs snfs snfs snfs snfs snfs snfs snfs snf)a Preprocess an image or batch of images. Args: images (`ImageInput`): Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. do_resize (`bool`, *optional*, defaults to `self.do_resize`): Whether to resize the image. size (`dict[str, int]`, *optional*, defaults to `self.size`): Size of the image after resizing. Shortest edge of the image is resized to min(size["height"], size["width"]) with the longest edge resized to keep the input aspect ratio. resample (`int`, *optional*, defaults to `self.resample`): Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only has an effect if `do_resize` is set to `True`. do_thumbnail (`bool`, *optional*, defaults to `self.do_thumbnail`): Whether to resize the image using thumbnail method. do_align_long_axis (`bool`, *optional*, defaults to `self.do_align_long_axis`): Whether to align the long axis of the image with the long axis of `size` by rotating by 90 degrees. do_pad (`bool`, *optional*, defaults to `self.do_pad`): Whether to pad the image. If `random_padding` is set to `True`, each image is padded with a random amount of padding on each size, up to the largest image size in the batch. Otherwise, all images are padded to the largest image size in the batch. random_padding (`bool`, *optional*, defaults to `self.random_padding`): Whether to use random padding when padding the image. If `True`, each image in the batch with be padded with a random amount of padding on each side up to the size of the largest image in the batch. do_rescale (`bool`, *optional*, defaults to `self.do_rescale`): Whether to rescale the image pixel values. 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`): Whether to normalize the image. image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`): Image mean to use for normalization. image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`): Image standard deviation to use for normalization. return_tensors (`str` or `TensorType`, *optional*): The type of tensors to return. Can be one of: - Unset: Return a list of `np.ndarray`. - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): The channel dimension format for the output image. Can be one of: - `ChannelDimension.FIRST`: image in (num_channels, height, width) format. - `ChannelDimension.LAST`: image in (height, width, num_channels) format. - Unset: defaults to the channel dimension format of the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format for the input image. If unset, the channel dimension format is inferred from 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. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. Nr4zkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.)r+r,r-r.r/r%r&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.)r&rC)rAr&r'rC)rAr&rC)rAr&rXrC)rAscalerC)rAmeanstdrCrJr$)data tensor_type)!r%r&r8r9r:rr'r(r)r*r+r,r-r.r/rrrNrr rrlogger warning_oncerrVr rkrerescale normalizer r)r;rsr%r&r'r(r)r*rXr+r,r-r.r/rtrBrCrArys r> preprocessDonutImageProcessor.preprocess3sTV"+!6IDNN 'tTYY dUDM * *":DT"'38'3'?|TEVEV 3E3Q/W[WnWn!-4;;#-#9Zt +9+E4K^K^'3'?|TEVEV #-#9Zt !*!6IDNN )&1F##:  &!)%! 6<vay I  ouvoufkd**5tO`*aouFv $#E %dYj k#  ouvoufkdnn5tO`naouFv  $ $Ed]n$  $#E 5Rc d#  $#EU^op#  ou ntej '{N_ `nt  '>BBi==w w   s6$J/?J4!J9 J>1KKK )K K) r)r-r*r+r%r(r.r/r'r,r&)NN)FNN)"__name__ __module__ __qualname____firstlineno____doc__model_input_namesrBILINEARboolrdictstrrjrfloatr:r7rOndarrayrrVreBICUBICrkr rrMrrPILImager__static_attributes__ __classcell__)r=s@r>r"r"6s"H(()-'9'B'B!#(,3!:>9=!W!WtCH~&!W% !W  !W ! !W!W!Wc5j)!W!WU5$u+#567!WE%e"456!W !W!WN?CDH -zz-38n-eC)9$9:; - $E#/?*?$@A -  -f %>BDH (azz(a38n(a (a eC)9$9:; (a $E#/?*?$@A (a (a\(:'A'A>BDH 0 zz0 38n0 % 0 eC)9$9:; 0 $E#/?*?$@A 0  0 l(:'A'A>BDH %zz%38n%% % eC)9$9:; % $E#/?*?$@A % %N%&%))-15'+-1!%$%)*.'+:>9=;?2B2H2HDH#`C`CD>`CtCH~& `C -. `C tn `C%TN`C`C`CTN`C!`Ctn`CU5$u+#567`CE%e"456`C!sJ!78`C ./!`C"$E#/?*?$@A#`C$ %`C'`Cr@r")*rtypingrrnumpyrOimage_processing_utilsrrrimage_transformsr r r r r image_utilsrrrrrrrrrrrrutilsrrrutils.import_utilsrr get_loggerrr{rr"__all__r5r@r>rs'"UU    JI?   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