sgTdZddlmZmZmZmZddlZddlm Z m Z m Z ddl m Z mZmZmZddlmZmZmZmZmZmZmZmZmZmZmZmZddlmZm Z m!Z!dd l"m#Z#e!jHe%Z&e#rddl'Z'Gd d e Z(y) z Image processor class for Donut.)DictListOptionalUnionN)BaseImageProcessor BatchFeature get_size_dict)get_resize_output_image_sizepadresizeto_channel_dimension_format) IMAGENET_STANDARD_MEANIMAGENET_STANDARD_STDChannelDimension ImageInputPILImageResamplingget_image_sizeinfer_channel_dimension_formatis_scaled_imagemake_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargslogging)is_vision_availablec%eZdZdZdgZddej ddddddddf dedee e fd ed ed ed ed ede e e fdede e e ee fde e e ee fddffd Z d dej"dee e fde e e efde e e efdej"f dZ d!dej"dee e fdede e e efde e e efdej"f dZdZej,ddfdej"dee e fd ede e e efde e e efdej"f dZej,ddfdej"dee e fd ede e e efde e e efdej"f dZedddddddddddddej4dfdededee e fd ed ed ed eded ede dede e e ee fde e e ee fde e e efde ede e e efdej<j<f"dZxZ S)"DonutImageProcessora 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 amont 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  Nt |di| ||nddd}t|ttfr|ddd}t |}||_||_||_||_ ||_ ||_ ||_ ||_ | |_| | nt|_| | |_yt"|_y)Ni i)heightwidth)super__init__ isinstancetuplelistr r"r#r$r%r&r'r(r)r*rr+rr,)selfr"r#r$r%r&r'r(r)r*r+r,kwargs __class__s c/var/www/html/venv/lib/python3.12/site-packages/transformers/models/donut/image_processing_donut.pyr4zDonutImageProcessor.__init__\s "6"'tt-L dUDM *":DT""   ("4 $,((2(>*DZ&/&;AVimage data_formatinput_data_formatct||\}}|d|d}}||kr||kDs ||kDr||krtj|d}|t|||}|S)a Align the long axis of the image to the longest axis of the specified size. Args: image (`np.ndarray`): The image to be aligned. size (`Dict[str, int]`): The size `{"height": h, "width": w}` to align the long axis to. data_format (`str` or `ChannelDimension`, *optional*): The data format of the output image. If unset, the same format as the input image is used. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. Returns: `np.ndarray`: The aligned image.  channel_dimr/r0rinput_channel_dim)rnprot90r) r8r=r#r>r? input_height input_width output_height output_widths r;align_long_axisz#DonutImageProcessor.align_long_axisso.%35FW$X! k&*8nd7m| = ([<-G = ([<-GHHUA&E  "/{VghE r<random_paddingc6|d|d}}t||\}} || z } ||z } |rItjjd| dz} tjjd| dz} n | dz} | dz} | | z }| | z }| |f| |ff}t ||||S) a 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. r/r0rAr)lowhigh)r>r?)rrErandomrandintr )r8r=r#rLr>r?rIrJrGrH delta_width delta_heightpad_toppad_left pad_bottom pad_rightpaddings r; pad_imagezDonutImageProcessor.pad_images.'+8nd7m| $25FW$X! k"[0 $|3 ii''AL14D'EGyy((Q[1_(EH"a'G"a'H!G+ (* Z(8Y*?@5'{N_``r<cPtjd|j|i|S)NzTpad is deprecated and will be removed in version 4.27. Please use pad_image instead.)loggerinfor[)r8argsr9s r;r zDonutImageProcessor.pads% jkt~~t.v..r<c t||\}}|d|d} } t|| } t|| } | |k(r| |k(r|S||kDrt|| z|z } n||kDrt|| z|z } t|f| | f|d||d|S)as Resize the image to make a thumbnail. The image is resized so that no dimension is larger than any corresponding dimension of the specified size. Args: image (`np.ndarray`): The image to be resized. size (`Dict[str, int]`): The size `{"height": h, "width": w}` to resize the image to. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): The resampling filter to use. data_format (`Optional[Union[str, ChannelDimension]]`, *optional*): The data format of the output image. If unset, the same format as the input image is used. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. rAr/r0g@)r#r$ reducing_gapr>r?)rminintr ) r8r=r#r$r>r?r9rGrHrIrJr/r0s r; thumbnailzDonutImageProcessor.thumbnails2%35FW$X! k&*8nd7m| \=1K. \ !e{&:L + % f,|;r?)r rbr r ) r8r=r#r$r>r?r9 shortest_edge output_size resized_images r;r zDonutImageProcessor.resizesh0T"DNDM: 2  Rc   #/    r<imagesreturn_tensorsc  ||n |j}||n |j}t|ttfr|ddd}t |}||n |j }||n |j}||n |j}||n |j}| | n |j} | | n |j} | | n |j} | | n |j} | | n |j} t|}t!|s t#dt%| | | | | ||||| |Dcgc] }t'|}}t)|dr| rt*j-d|t/|d}|r!|Dcgc]}|j1|||}}|r"|Dcgc]}|j3||||}}|r!|Dcgc]}|j5||| }}|r"|Dcgc]}|j7|||| }}| r!|Dcgc]}|j9|| | }}| r"|Dcgc]}|j;|| | | }}|Dcgc]}t=||| }}d|i}t?||Scc}wcc}wcc}wcc}wcc}wcc}wcc}wcc}w)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 amont 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. Nr1zkInvalid image type. Must be of type PIL.Image.Image, numpy.ndarray, torch.Tensor, tf.Tensor or jax.ndarray.) r(r)r*r+r,r'size_divisibilityr"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#r?)r=r#r$r?)r=r#r?)r=r#rLr?)r=scaler?)r=meanstdr?rCr!)data tensor_type) r"r#r5r6r7r r$r%r&r'r(r)r*r+r,rr ValueErrorrrrr] warning_oncerrKr rdr[rescale normalizerr )r8rjr"r#r$r%r&r'rLr(r)r*r+r,rkr>r?r=rqs r; preprocesszDonutImageProcessor.preprocess*sV"+!6IDNN 'tTYY dUDM *":DT"'38'3'?|TEVEV 3E3Q/W[WnWn!-4;;#-#9Zt +9+E4K^K^'3'?|TEVEV #-#9Zt !*!6IDNN $V,F#:  &!)%!" 6<vay I  ouvfkd**5tO`*avFv $ %dXYj kF ouvfkdnn5tO`navFv  $ d>]nF $ 5Rc dF $U^opF ou ej '{N_ `  '>BBc=w w   s0I#I#I(*I- I21I7I<6J)NN)FNN)!__name__ __module__ __qualname____doc__model_input_namesrBILINEARboolrstrrcrfloatrrr4rEndarrayrrKr[r BICUBICrdr rFIRSTrrPILImagerw __classcell__)r:s@r;r r 5si"H((#'9'B'B!#(,3!:>9=!W!W38n!W% !W  !W ! !W!W!Wc5j)!W!WU5$u+#567!WE%e"456!W !WN?CDH "zz"38n"eC)9$9:; " $E#/?*?$@A "  "P %>BDH (azz(a38n(a (a eC)9$9:; (a $E#/?*?$@A (a (aT/(:'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%&#'+!#'$ $!:>9=;?2B2H2HDH#`C`C`C38n `C % `C  `C!`C`C`C`C`C`CU5$u+#567`CE%e"456`C!sJ!78`C ./!`C"$E#/?*?$@A#`C$ %`C'`Cr<r ))r{typingrrrrnumpyrEimage_processing_utilsrr r image_transformsr r r r image_utilsrrrrrrrrrrrrutilsrrrutils.import_utilsr get_loggerrxr]rr r2r<r;rss'..UU    JI5   H %VC,VCr<