rhWdZddlmZmZddlZddlmZmZm Z ddl m Z m Z m Z mZmZddlmZmZmZmZmZmZmZmZmZmZmZmZddlmZmZm Z dd l!m"Z"m#Z#e jHe%Z&e"rddl'Z'e#d Gd d eZ(d gZ)y)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_list_of_imagesto_numpy_array valid_imagesvalidate_preprocess_arguments) TensorTypefilter_out_non_signature_kwargslogging)is_vision_availablerequires)vision)backendsc%$eZdZdZdgZddej ddddddddf dedee e e fd ed ed ed ed ede e e fdedee e ee fdee e ee fddffd Z d dej"de e e fdee e efdee e efdej"f dZ d!dej"de e e fdedee e efdee e efdej"f dZdZej,ddfdej"de e e fd edee e efdee e efdej"f dZej,ddfdej"de e e fd edee e efdee e efdej"f dZedddddddddddddej4dfdedeedee e e fd ed eed eed eeded eedee deedee e ee fdee e ee fdee e efdeedee 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 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  Nt |di| ||nddd}t|ttfr|ddd}t |}||_||_||_||_ ||_ ||_ ||_ ||_ | |_| | nt|_| | |_yt"|_y)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 /var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/models/donut/image_processing_donut.pyr6zDonutImageProcessor.__init__^s "6"'tt-L dUDM *":DT""   ("4 $,((2(>*DZ&/&;AVimage data_formatinput_data_formatc@t||\}}|d|d}}| t|}|tjk(rd} n$|tjk(rd} nt d|||kr||kDs ||kDr||krt j|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_dimr1r2)r)rEzUnsupported data format: r)axesinput_channel_dim) rrrLASTFIRST ValueErrornprot90r ) r:r?r%r@rA input_height input_width output_height output_widthrot_axess r=align_long_axisz#DonutImageProcessor.align_long_axiss.%35FW$X! k&*8nd7m|  $ >u E   0 5 5 5H "2"8"8 8H89J8KLM M = ([<-G = ([<-GHHUAH5E  "/{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. r1r2rCrrE)lowhighrF)r@rA)rrMrandomrandintr )r:r?r%rUr@rArQrRrOrP delta_width delta_heightpad_toppad_left pad_bottom pad_rightpaddings r= pad_imagezDonutImageProcessor.pad_images.'+8nd7m| $25FW$X! k"[0 $|3 ii''AL14D'EGyy((Q[1_(EH"a'G"a'H!G+ (* Z(8Y*?@5'{N_``r>cPtjd|j|i|S)NzTpad is deprecated and will be removed in version 4.27. Please use pad_image instead.)loggerinforb)r:argsr;s r=r zDonutImageProcessor.pads% jkt~~t.v..r>c t||\}}|d|d} } t|| } t|| } | |k(r| |k(r|S||kDrt|| z|z } n||kDrt|| z|z } t|f| | f|d||d|S)as Resize the image to make a thumbnail. The image is resized so that no dimension is larger than any corresponding dimension of the specified size. Args: image (`np.ndarray`): The image to be resized. size (`dict[str, int]`): The size `{"height": h, "width": w}` to resize the image to. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): The resampling filter to use. data_format (`Optional[Union[str, ChannelDimension]]`, *optional*): The data format of the output image. If unset, the same format as the input image is used. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. rCr1r2g@)r%r& reducing_gapr@rA)rminintr ) r:r?r%r&r@rAr;rOrPrQrRr1r2s r= thumbnailzDonutImageProcessor.thumbnails2%35FW$X! k&*8nd7m| \=1K. \ !e{&:L + % f,|;c t|}t|d|d}t||d|}t|f||||d|} | S)a Resizes `image` to `(height, width)` specified by `size` using the PIL library. Args: image (`np.ndarray`): Image to resize. size (`dict[str, int]`): Size of the output image. resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`): Resampling filter to use when resiizing the image. data_format (`str` or `ChannelDimension`, *optional*): The channel dimension format of the image. If not provided, it will be the same as the input image. input_data_format (`ChannelDimension` or `str`, *optional*): The channel dimension format of the input image. If not provided, it will be inferred. r1r2F)r%default_to_squarerA)r%r&r@rA)rrir r ) r:r?r%r&r@rAr; shortest_edge output_size resized_images r=r zDonutImageProcessor.resizesh0T"DNDM: 2  Rc   #/    r>imagesreturn_tensorsc F||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'|}}|Dcgc] }t)|}}| r#t+|drt,j/d|t1|d}|r!|Dcgc]}|j3|||}}|r"|Dcgc]}|j5||||}}|r!|Dcgc]}|j7||| }}|r"|Dcgc]}|j9|||| }}| r!|Dcgc]}|j;|| | }}| r"|Dcgc]}|j=|| | | }}|Dcgc]}t?||| }}d|i}tA||Scc}wcc}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 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. Nr3zkInvalid 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$r%r&rzIt looks like you are trying to rescale already rescaled images. If the input images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again.)r%rA)r?r%r&rA)r?r%rA)r?r%rUrA)r?scalerA)r?meanstdrArHr#)data tensor_type)!r$r%r7r8r9rr&r'r(r)r*r+r,r-r.rrrLrr rrrd warning_oncerrTr rkrbrescale normalizer r)r:rqr$r%r&r'r(r)rUr*r+r,r-r.rrr@rAr?rxs r= preprocesszDonutImageProcessor.preprocess7s=V"+!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 $U^opF ou ej '{N_ `  '>BBi==w w   s6I6,I;;JJJ %J J,JJ)NN)FNN)!__name__ __module__ __qualname____doc__model_input_namesrBILINEARboolrdictstrrjrfloatr9r6rMndarrayrrTrbr BICUBICrkr rrKrrPILImager} __classcell__)r<s@r=r"r"6s"H(()-'9'B'B!#(,3!:>9=!W!WtCH~&!W% !W  !W ! !W!W!Wc5j)!W!WU5$u+#567!WE%e"456!W !WN?CDH -zz-38n-eC)9$9:; - $E#/?*?$@A -  -f %>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%&%))-'+'+-1!%$%)*.'+:>9=;?2B2H2HDH#bCbCD>bCtCH~& bC % bC tn bC%TNbCbCbCTNbC!bCtnbCU5$u+#567bCE%e"456bC!sJ!78bC ./!bC"$E#/?*?$@A#bC$ %bC'bCr>r")*rtypingrrnumpyrMimage_processing_utilsrrrimage_transformsr r r r r image_utilsrrrrrrrrrrrrutilsrrrutils.import_utilsrr get_loggerr~rdrr"__all__r4r>r=rs'"UU    JI?   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