codingutf8 2022 the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license image processor class for donut from typing import dict list optional union import numpy as np from imageprocessingutils import baseimageprocessor batchfeature getsizedict from imagetransforms import getresizeoutputimagesize pad resize tochanneldimensionformat from imageutils import imagenetstandardmean imagenetstandardstd channeldimension imageinput pilimageresampling getimagesize inferchanneldimensionformat isscaledimage makelistofimages tonumpyarray validimages from utils import tensortype logging from utils importutils import isvisionavailable logger logging getloggername if isvisionavailable import pil class donutimageprocessorbaseimageprocessor r constructs a donut image processor args doresize bool optional defaults to true whether to resize the image s height width dimensions to the specified size can be overridden by doresize in the preprocess method size dictstr int optional defaults to shortestedge 224 size of the image after resizing the shortest edge of the image is resized to sizeshortestedge 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 dothumbnail bool optional defaults to true whether to resize the image using thumbnail method doalignlongaxis 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 dopad bool optional defaults to true whether to pad the image if randompadding 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 dorescale bool optional defaults to true whether to rescale the image by the specified scale rescalefactor can be overridden by dorescale in the preprocess method rescalefactor int or float optional defaults to 1255 scale factor to use if rescaling the image can be overridden by rescalefactor in the preprocess method donormalize bool optional defaults to true whether to normalize the image can be overridden by donormalize in the preprocess method imagemean float or listfloat optional defaults to imagenetstandardmean 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 imagemean parameter in the preprocess method imagestd float or listfloat optional defaults to imagenetstandardstd image standard deviation the previous feature extractor size parameter was in width height format 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 dictstr int the size height h width w to align the long axis to dataformat str or channeldimension optional the data format of the output image if unset the same format as the input image is used inputdataformat 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 pad the image to the specified size args image np ndarray the image to be padded size dictstr int the size height h width w to pad the image to randompadding bool optional defaults to false whether to use random padding or not dataformat str or channeldimension optional the data format of the output image if unset the same format as the input image is used inputdataformat channeldimension or str optional the channel dimension format of the input image if not provided it will be inferred 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 dictstr int the size height h width w to resize the image to resample pilimageresampling optional defaults to pilimageresampling bicubic the resampling filter to use dataformat optionalunionstr channeldimension optional the data format of the output image if unset the same format as the input image is used inputdataformat channeldimension or str optional the channel dimension format of the input image if not provided it will be inferred we always resize to the smallest of either the input or output size resizes image to height width specified by size using the pil library args image np ndarray image to resize size dictstr int size of the output image resample pilimageresampling optional defaults to pilimageresampling bicubic resampling filter to use when resiizing the image dataformat str or channeldimension optional the channel dimension format of the image if not provided it will be the same as the input image inputdataformat channeldimension or str optional the channel dimension format of the input image if not provided it will be inferred 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 dorescalefalse doresize bool optional defaults to self doresize whether to resize the image size dictstr int optional defaults to self size size of the image after resizing shortest edge of the image is resized to minsizeheight sizewidth 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 doresize is set to true dothumbnail bool optional defaults to self dothumbnail whether to resize the image using thumbnail method doalignlongaxis bool optional defaults to self doalignlongaxis whether to align the long axis of the image with the long axis of size by rotating by 90 degrees dopad bool optional defaults to self dopad whether to pad the image if randompadding 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 randompadding bool optional defaults to self randompadding 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 dorescale bool optional defaults to self dorescale whether to rescale the image pixel values rescalefactor float optional defaults to self rescalefactor rescale factor to rescale the image by if dorescale is set to true donormalize bool optional defaults to self donormalize whether to normalize the image imagemean float or listfloat optional defaults to self imagemean image mean to use for normalization imagestd float or listfloat optional defaults to self imagestd image standard deviation to use for normalization returntensors 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 dataformat channeldimension or str optional defaults to channeldimension first the channel dimension format for the output image can be one of channeldimension first image in numchannels height width format channeldimension last image in height width numchannels format unset defaults to the channel dimension format of the input image inputdataformat 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 channelsfirst or channeldimension first image in numchannels height width format channelslast or channeldimension last image in height width numchannels format none or channeldimension none image in height width format previous feature extractor had size in width height format all transformations expect numpy arrays we assume that all images have the same channel dimension format coding utf 8 2022 the huggingface inc team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license image processor class for donut 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 the previous feature extractor size parameter was in width height format 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 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 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 we always resize to the smallest of either the input or output size 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 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 previous feature extractor had size in width height format all transformations expect numpy arrays we assume that all images have the same channel dimension format
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    pad,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    infer_channel_dimension_format,
    is_scaled_image,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


logger = logging.get_logger(__name__)


if is_vision_available():
    import PIL


class DonutImageProcessor(BaseImageProcessor):
    r

    model_input_names = ["pixel_values"]

    def __init__(
        self,
        do_resize: bool = True,
        size: Dict[str, int] = None,
        resample: PILImageResampling = PILImageResampling.BILINEAR,
        do_thumbnail: bool = True,
        do_align_long_axis: bool = False,
        do_pad: bool = True,
        do_rescale: bool = True,
        rescale_factor: Union[int, float] = 1 / 255,
        do_normalize: bool = True,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)

        size = size if size is not None else {"height": 2560, "width": 1920}
        if isinstance(size, (tuple, list)):
            
            size = size[::-1]
        size = get_size_dict(size)

        self.do_resize = do_resize
        self.size = size
        self.resample = resample
        self.do_thumbnail = do_thumbnail
        self.do_align_long_axis = do_align_long_axis
        self.do_pad = do_pad
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.do_normalize = do_normalize
        self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD

    def align_long_axis(
        self,
        image: np.ndarray,
        size: Dict[str, int],
        data_format: Optional[Union[str, ChannelDimension]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
    ) -> np.ndarray:
        
        input_height, input_width = get_image_size(image, channel_dim=input_data_format)
        output_height, output_width = size["height"], size["width"]

        if (output_width < output_height and input_width > input_height) or (
            output_width > output_height and input_width < input_height
        ):
            image = np.rot90(image, 3)

        if data_format is not None:
            image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)

        return image

    def pad_image(
        self,
        image: np.ndarray,
        size: Dict[str, int],
        random_padding: bool = False,
        data_format: Optional[Union[str, ChannelDimension]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
    ) -> np.ndarray:
        
        output_height, output_width = size["height"], size["width"]
        input_height, input_width = get_image_size(image, channel_dim=input_data_format)

        delta_width = output_width - input_width
        delta_height = output_height - input_height

        if random_padding:
            pad_top = np.random.randint(low=0, high=delta_height + 1)
            pad_left = np.random.randint(low=0, high=delta_width + 1)
        else:
            pad_top = delta_height // 2
            pad_left = delta_width // 2

        pad_bottom = delta_height - pad_top
        pad_right = delta_width - pad_left

        padding = ((pad_top, pad_bottom), (pad_left, pad_right))
        return pad(image, padding, data_format=data_format, input_data_format=input_data_format)

    def pad(self, *args, **kwargs):
        logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead.")
        return self.pad_image(*args, **kwargs)

    def thumbnail(
        self,
        image: np.ndarray,
        size: Dict[str, int],
        resample: PILImageResampling = PILImageResampling.BICUBIC,
        data_format: Optional[Union[str, ChannelDimension]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> np.ndarray:
        
        input_height, input_width = get_image_size(image, channel_dim=input_data_format)
        output_height, output_width = size["height"], size["width"]

        
        height = min(input_height, output_height)
        width = min(input_width, output_width)

        if height == input_height and width == input_width:
            return image

        if input_height > input_width:
            width = int(input_width * height / input_height)
        elif input_width > input_height:
            height = int(input_height * width / input_width)

        return resize(
            image,
            size=(height, width),
            resample=resample,
            reducing_gap=2.0,
            data_format=data_format,
            input_data_format=input_data_format,
            **kwargs,
        )

    def resize(
        self,
        image: np.ndarray,
        size: Dict[str, int],
        resample: PILImageResampling = PILImageResampling.BICUBIC,
        data_format: Optional[Union[str, ChannelDimension]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> np.ndarray:
        
        size = get_size_dict(size)
        shortest_edge = min(size["height"], size["width"])
        output_size = get_resize_output_image_size(
            image, size=shortest_edge, default_to_square=False, input_data_format=input_data_format
        )
        resized_image = resize(
            image,
            size=output_size,
            resample=resample,
            data_format=data_format,
            input_data_format=input_data_format,
            **kwargs,
        )
        return resized_image

    def preprocess(
        self,
        images: ImageInput,
        do_resize: bool = None,
        size: Dict[str, int] = None,
        resample: PILImageResampling = None,
        do_thumbnail: bool = None,
        do_align_long_axis: bool = None,
        do_pad: bool = None,
        random_padding: bool = False,
        do_rescale: bool = None,
        rescale_factor: float = None,
        do_normalize: bool = None,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> PIL.Image.Image:
        
        do_resize = do_resize if do_resize is not None else self.do_resize
        size = size if size is not None else self.size
        if isinstance(size, (tuple, list)):
            
            size = size[::-1]
        size = get_size_dict(size)
        resample = resample if resample is not None else self.resample
        do_thumbnail = do_thumbnail if do_thumbnail is not None else self.do_thumbnail
        do_align_long_axis = do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
        do_pad = do_pad if do_pad is not None else self.do_pad
        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
        image_mean = image_mean if image_mean is not None else self.image_mean
        image_std = image_std if image_std is not None else self.image_std

        images = make_list_of_images(images)

        if not valid_images(images):
            raise ValueError(
                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                "torch.Tensor, tf.Tensor or jax.ndarray."
            )

        if do_resize and size is None:
            raise ValueError("Size must be specified if do_resize is True.")

        if do_rescale and rescale_factor is None:
            raise ValueError("Rescale factor must be specified if do_rescale is True.")

        if do_pad and size is None:
            raise ValueError("Size must be specified if do_pad is True.")

        if do_normalize and (image_mean is None or image_std is None):
            raise ValueError("Image mean and std must be specified if do_normalize is True.")

        
        images = [to_numpy_array(image) for image in images]

        if is_scaled_image(images[0]) and do_rescale:
            logger.warning_once(
                "It 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."
            )

        if input_data_format is None:
            
            input_data_format = infer_channel_dimension_format(images[0])

        if do_align_long_axis:
            images = [self.align_long_axis(image, size=size, input_data_format=input_data_format) for image in images]

        if do_resize:
            images = [
                self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
                for image in images
            ]

        if do_thumbnail:
            images = [self.thumbnail(image=image, size=size, input_data_format=input_data_format) for image in images]

        if do_pad:
            images = [
                self.pad_image(
                    image=image, size=size, random_padding=random_padding, input_data_format=input_data_format
                )
                for image in images
            ]

        if do_rescale:
            images = [
                self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
                for image in images
            ]

        if do_normalize:
            images = [
                self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
                for image in images
            ]

        images = [
            to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
        ]

        data = {"pixel_values": images}
        return BatchFeature(data=data, tensor_type=return_tensors)