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import pathlib
from typing import Any, Optional, Union

import torch
from torchvision.io import read_image
from torchvision.transforms.v2 import functional as F

from ...image_processing_utils import BatchFeature, get_size_dict
from ...image_processing_utils_fast import (
    BaseImageProcessorFast,
    DefaultFastImageProcessorKwargs,
    SizeDict,
    get_image_size_for_max_height_width,
    get_max_height_width,
    safe_squeeze,
)
from ...image_transforms import center_to_corners_format, corners_to_center_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    AnnotationFormat,
    AnnotationType,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    validate_annotations,
)
from ...processing_utils import Unpack
from ...utils import TensorType, auto_docstring, logging
from ...utils.import_utils import requires


logger = logging.get_logger(__name__)


class YolosFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
    r"""
    format (`str`, *optional*, defaults to `AnnotationFormat.COCO_DETECTION`):
        Data format of the annotations. One of "coco_detection" or "coco_panoptic".
    do_convert_annotations (`bool`, *optional*, defaults to `True`):
        Controls whether to convert the annotations to the format expected by the YOLOS model. Converts the
        bounding boxes to the format `(center_x, center_y, width, height)` and in the range `[0, 1]`.
        Can be overridden by the `do_convert_annotations` parameter in the `preprocess` method.
    return_segmentation_masks (`bool`, *optional*, defaults to `False`):
        Whether to return segmentation masks.
    """

    format: Optional[Union[str, AnnotationFormat]]
    do_convert_annotations: Optional[bool]
    return_segmentation_masks: Optional[bool]


SUPPORTED_ANNOTATION_FORMATS = (AnnotationFormat.COCO_DETECTION, AnnotationFormat.COCO_PANOPTIC)


# inspired by https://github.com/facebookresearch/yolos/blob/master/datasets/coco.py#L33
def convert_coco_poly_to_mask(segmentations, height: int, width: int, device: torch.device) -> torch.Tensor:
    """
    Convert a COCO polygon annotation to a mask.

    Args:
        segmentations (`list[list[float]]`):
            List of polygons, each polygon represented by a list of x-y coordinates.
        height (`int`):
            Height of the mask.
        width (`int`):
            Width of the mask.
    """
    try:
        from pycocotools import mask as coco_mask
    except ImportError:
        raise ImportError("Pycocotools is not installed in your environment.")

    masks = []
    for polygons in segmentations:
        rles = coco_mask.frPyObjects(polygons, height, width)
        mask = coco_mask.decode(rles)
        if len(mask.shape) < 3:
            mask = mask[..., None]
        mask = torch.as_tensor(mask, dtype=torch.uint8, device=device)
        mask = torch.any(mask, axis=2)
        masks.append(mask)
    if masks:
        masks = torch.stack(masks, axis=0)
    else:
        masks = torch.zeros((0, height, width), dtype=torch.uint8, device=device)

    return masks


# inspired by https://github.com/facebookresearch/yolos/blob/master/datasets/coco.py#L50
def prepare_coco_detection_annotation(
    image,
    target,
    return_segmentation_masks: bool = False,
    input_data_format: Optional[Union[ChannelDimension, str]] = None,
):
    """
    Convert the target in COCO format into the format expected by YOLOS.
    """
    image_height, image_width = image.size()[-2:]

    image_id = target["image_id"]
    image_id = torch.as_tensor([image_id], dtype=torch.int64, device=image.device)

    # Get all COCO annotations for the given image.
    annotations = target["annotations"]
    classes = []
    area = []
    boxes = []
    keypoints = []
    for obj in annotations:
        if "iscrowd" not in obj or obj["iscrowd"] == 0:
            classes.append(obj["category_id"])
            area.append(obj["area"])
            boxes.append(obj["bbox"])
            if "keypoints" in obj:
                keypoints.append(obj["keypoints"])

    classes = torch.as_tensor(classes, dtype=torch.int64, device=image.device)
    area = torch.as_tensor(area, dtype=torch.float32, device=image.device)
    iscrowd = torch.zeros_like(classes, dtype=torch.int64, device=image.device)
    # guard against no boxes via resizing
    boxes = torch.as_tensor(boxes, dtype=torch.float32, device=image.device).reshape(-1, 4)
    boxes[:, 2:] += boxes[:, :2]
    boxes[:, 0::2] = boxes[:, 0::2].clip(min=0, max=image_width)
    boxes[:, 1::2] = boxes[:, 1::2].clip(min=0, max=image_height)

    keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0])

    new_target = {
        "image_id": image_id,
        "class_labels": classes[keep],
        "boxes": boxes[keep],
        "area": area[keep],
        "iscrowd": iscrowd[keep],
        "orig_size": torch.as_tensor([int(image_height), int(image_width)], dtype=torch.int64, device=image.device),
    }

    if keypoints:
        keypoints = torch.as_tensor(keypoints, dtype=torch.float32, device=image.device)
        # Apply the keep mask here to filter the relevant annotations
        keypoints = keypoints[keep]
        num_keypoints = keypoints.shape[0]
        keypoints = keypoints.reshape((-1, 3)) if num_keypoints else keypoints
        new_target["keypoints"] = keypoints

    if return_segmentation_masks:
        segmentation_masks = [obj["segmentation"] for obj in annotations]
        masks = convert_coco_poly_to_mask(segmentation_masks, image_height, image_width, device=image.device)
        new_target["masks"] = masks[keep]

    return new_target


def masks_to_boxes(masks: torch.Tensor) -> torch.Tensor:
    """
    Compute the bounding boxes around the provided panoptic segmentation masks.

    Args:
        masks: masks in format `[number_masks, height, width]` where N is the number of masks

    Returns:
        boxes: bounding boxes in format `[number_masks, 4]` in xyxy format
    """
    if masks.numel() == 0:
        return torch.zeros((0, 4), device=masks.device)

    h, w = masks.shape[-2:]
    y = torch.arange(0, h, dtype=torch.float32, device=masks.device)
    x = torch.arange(0, w, dtype=torch.float32, device=masks.device)
    # see https://github.com/pytorch/pytorch/issues/50276
    y, x = torch.meshgrid(y, x, indexing="ij")

    x_mask = masks * torch.unsqueeze(x, 0)
    x_max = x_mask.view(x_mask.shape[0], -1).max(-1)[0]
    x_min = (
        torch.where(masks, x.unsqueeze(0), torch.tensor(1e8, device=masks.device)).view(masks.shape[0], -1).min(-1)[0]
    )

    y_mask = masks * torch.unsqueeze(y, 0)
    y_max = y_mask.view(y_mask.shape[0], -1).max(-1)[0]
    y_min = (
        torch.where(masks, y.unsqueeze(0), torch.tensor(1e8, device=masks.device)).view(masks.shape[0], -1).min(-1)[0]
    )

    return torch.stack([x_min, y_min, x_max, y_max], 1)


# 2 functions below adapted from https://github.com/cocodataset/panopticapi/blob/master/panopticapi/utils.py
# Copyright (c) 2018, Alexander Kirillov
# All rights reserved.
def rgb_to_id(color):
    """
    Converts RGB color to unique ID.
    """
    if isinstance(color, torch.Tensor) and len(color.shape) == 3:
        if color.dtype == torch.uint8:
            color = color.to(torch.int32)
        return color[:, :, 0] + 256 * color[:, :, 1] + 256 * 256 * color[:, :, 2]
    return int(color[0] + 256 * color[1] + 256 * 256 * color[2])


def prepare_coco_panoptic_annotation(
    image: torch.Tensor,
    target: dict,
    masks_path: Union[str, pathlib.Path],
    return_masks: bool = True,
    input_data_format: Union[ChannelDimension, str] = None,
) -> dict:
    """
    Prepare a coco panoptic annotation for YOLOS.
    """
    image_height, image_width = get_image_size(image, channel_dim=input_data_format)
    annotation_path = pathlib.Path(masks_path) / target["file_name"]

    new_target = {}
    new_target["image_id"] = torch.as_tensor(
        [target["image_id"] if "image_id" in target else target["id"]], dtype=torch.int64, device=image.device
    )
    new_target["size"] = torch.as_tensor([image_height, image_width], dtype=torch.int64, device=image.device)
    new_target["orig_size"] = torch.as_tensor([image_height, image_width], dtype=torch.int64, device=image.device)

    if "segments_info" in target:
        masks = read_image(annotation_path).permute(1, 2, 0).to(dtype=torch.int32, device=image.device)
        masks = rgb_to_id(masks)

        ids = torch.as_tensor([segment_info["id"] for segment_info in target["segments_info"]], device=image.device)
        masks = masks == ids[:, None, None]
        masks = masks.to(torch.bool)
        if return_masks:
            new_target["masks"] = masks
        new_target["boxes"] = masks_to_boxes(masks)
        new_target["class_labels"] = torch.as_tensor(
            [segment_info["category_id"] for segment_info in target["segments_info"]],
            dtype=torch.int64,
            device=image.device,
        )
        new_target["iscrowd"] = torch.as_tensor(
            [segment_info["iscrowd"] for segment_info in target["segments_info"]],
            dtype=torch.int64,
            device=image.device,
        )
        new_target["area"] = torch.as_tensor(
            [segment_info["area"] for segment_info in target["segments_info"]],
            dtype=torch.float32,
            device=image.device,
        )

    return new_target


def get_size_with_aspect_ratio(
    image_size: tuple[int, int], size: int, max_size: Optional[int] = None, mod_size: int = 16
) -> tuple[int, int]:
    """
    Computes the output image size given the input image size and the desired output size with multiple of divisible_size.

    Args:
        image_size (`tuple[int, int]`):
            The input image size.
        size (`int`):
            The desired output size.
        max_size (`int`, *optional*):
            The maximum allowed output size.
        mod_size (`int`, *optional*):
            The size to make multiple of mod_size.
    """
    height, width = image_size
    raw_size = None
    if max_size is not None:
        min_original_size = float(min((height, width)))
        max_original_size = float(max((height, width)))
        if max_original_size / min_original_size * size > max_size:
            raw_size = max_size * min_original_size / max_original_size
            size = int(round(raw_size))

    if width < height:
        ow = size
        if max_size is not None and raw_size is not None:
            oh = int(raw_size * height / width)
        else:
            oh = int(size * height / width)
    elif (height <= width and height == size) or (width <= height and width == size):
        oh, ow = height, width
    else:
        oh = size
        if max_size is not None and raw_size is not None:
            ow = int(raw_size * width / height)
        else:
            ow = int(size * width / height)

    if mod_size is not None:
        ow_mod = torch.remainder(torch.tensor(ow), mod_size).item()
        oh_mod = torch.remainder(torch.tensor(oh), mod_size).item()
        ow = ow - ow_mod
        oh = oh - oh_mod

    return (oh, ow)


@auto_docstring
@requires(backends=("torchvision", "torch"))
class YolosImageProcessorFast(BaseImageProcessorFast):
    resample = PILImageResampling.BILINEAR
    image_mean = IMAGENET_DEFAULT_MEAN
    image_std = IMAGENET_DEFAULT_STD
    format = AnnotationFormat.COCO_DETECTION
    do_resize = True
    do_rescale = True
    do_normalize = True
    do_pad = True
    size = {"shortest_edge": 800, "longest_edge": 1333}
    default_to_square = False
    model_input_names = ["pixel_values", "pixel_mask"]
    valid_kwargs = YolosFastImageProcessorKwargs

    def __init__(self, **kwargs: Unpack[YolosFastImageProcessorKwargs]) -> None:
        if "pad_and_return_pixel_mask" in kwargs:
            kwargs["do_pad"] = kwargs.pop("pad_and_return_pixel_mask")

        size = kwargs.pop("size", None)
        if "max_size" in kwargs:
            logger.warning_once(
                "The `max_size` parameter is deprecated and will be removed in v4.26. "
                "Please specify in `size['longest_edge'] instead`.",
            )
            max_size = kwargs.pop("max_size")
        else:
            max_size = None if size is None else 1333

        size = size if size is not None else {"shortest_edge": 800, "longest_edge": 1333}
        self.size = get_size_dict(size, max_size=max_size, default_to_square=False)

        # Backwards compatibility
        do_convert_annotations = kwargs.get("do_convert_annotations")
        do_normalize = kwargs.get("do_normalize")
        if do_convert_annotations is None and getattr(self, "do_convert_annotations", None) is None:
            self.do_convert_annotations = do_normalize if do_normalize is not None else self.do_normalize

        super().__init__(**kwargs)

    @classmethod
    def from_dict(cls, image_processor_dict: dict[str, Any], **kwargs):
        """
        Overrides the `from_dict` method from the base class to make sure parameters are updated if image processor is
        created using from_dict and kwargs e.g. `YolosImageProcessorFast.from_pretrained(checkpoint, size=600,
        max_size=800)`
        """
        image_processor_dict = image_processor_dict.copy()
        if "max_size" in kwargs:
            image_processor_dict["max_size"] = kwargs.pop("max_size")
        if "pad_and_return_pixel_mask" in kwargs:
            image_processor_dict["pad_and_return_pixel_mask"] = kwargs.pop("pad_and_return_pixel_mask")
        return super().from_dict(image_processor_dict, **kwargs)

    def prepare_annotation(
        self,
        image: torch.Tensor,
        target: dict,
        format: Optional[AnnotationFormat] = None,
        return_segmentation_masks: Optional[bool] = None,
        masks_path: Optional[Union[str, pathlib.Path]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
    ) -> dict:
        """
        Prepare an annotation for feeding into YOLOS model.
        """
        format = format if format is not None else self.format

        if format == AnnotationFormat.COCO_DETECTION:
            return_segmentation_masks = False if return_segmentation_masks is None else return_segmentation_masks
            target = prepare_coco_detection_annotation(
                image, target, return_segmentation_masks, input_data_format=input_data_format
            )
        elif format == AnnotationFormat.COCO_PANOPTIC:
            return_segmentation_masks = True if return_segmentation_masks is None else return_segmentation_masks
            target = prepare_coco_panoptic_annotation(
                image,
                target,
                masks_path=masks_path,
                return_masks=return_segmentation_masks,
                input_data_format=input_data_format,
            )
        else:
            raise ValueError(f"Format {format} is not supported.")
        return target

    def resize(
        self,
        image: torch.Tensor,
        size: SizeDict,
        interpolation: Optional["F.InterpolationMode"] = None,
        **kwargs,
    ) -> torch.Tensor:
        """
        Resize the image to the given size. Size can be `min_size` (scalar) or `(height, width)` tuple. If size is an
        int, smaller edge of the image will be matched to this number.

        Args:
            image (`torch.Tensor`):
                Image to resize.
            size (`SizeDict`):
                Size of the image's `(height, width)` dimensions after resizing. Available options are:
                    - `{"height": int, "width": int}`: The image will be resized to the exact size `(height, width)`.
                        Do NOT keep the aspect ratio.
                    - `{"shortest_edge": int, "longest_edge": int}`: The image will be resized to a maximum size respecting
                        the aspect ratio and keeping the shortest edge less or equal to `shortest_edge` and the longest edge
                        less or equal to `longest_edge`.
                    - `{"max_height": int, "max_width": int}`: The image will be resized to the maximum size respecting the
                        aspect ratio and keeping the height less or equal to `max_height` and the width less or equal to
                        `max_width`.
            interpolation (`InterpolationMode`, *optional*, defaults to `InterpolationMode.BILINEAR`):
                Resampling filter to use if resizing the image.
        """
        interpolation = interpolation if interpolation is not None else F.InterpolationMode.BILINEAR
        if size.shortest_edge and size.longest_edge:
            # Resize the image so that the shortest edge or the longest edge is of the given size
            # while maintaining the aspect ratio of the original image.
            new_size = get_size_with_aspect_ratio(
                image.size()[-2:],
                size["shortest_edge"],
                size["longest_edge"],
            )
        elif size.max_height and size.max_width:
            new_size = get_image_size_for_max_height_width(image.size()[-2:], size["max_height"], size["max_width"])
        elif size.height and size.width:
            new_size = (size["height"], size["width"])
        else:
            raise ValueError(
                "Size must contain 'height' and 'width' keys or 'shortest_edge' and 'longest_edge' keys. Got"
                f" {size.keys()}."
            )

        image = F.resize(
            image,
            size=new_size,
            interpolation=interpolation,
            **kwargs,
        )
        return image

    def resize_annotation(
        self,
        annotation: dict[str, Any],
        orig_size: tuple[int, int],
        target_size: tuple[int, int],
        threshold: float = 0.5,
        interpolation: Optional["F.InterpolationMode"] = None,
    ):
        """
        Resizes an annotation to a target size.

        Args:
            annotation (`dict[str, Any]`):
                The annotation dictionary.
            orig_size (`tuple[int, int]`):
                The original size of the input image.
            target_size (`tuple[int, int]`):
                The target size of the image, as returned by the preprocessing `resize` step.
            threshold (`float`, *optional*, defaults to 0.5):
                The threshold used to binarize the segmentation masks.
            resample (`InterpolationMode`, defaults to `F.InterpolationMode.NEAREST_EXACT`):
                The resampling filter to use when resizing the masks.
        """
        interpolation = interpolation if interpolation is not None else F.InterpolationMode.NEAREST_EXACT
        ratio_height, ratio_width = [target / orig for target, orig in zip(target_size, orig_size)]

        new_annotation = {}
        new_annotation["size"] = target_size

        for key, value in annotation.items():
            if key == "boxes":
                boxes = value
                scaled_boxes = boxes * torch.as_tensor(
                    [ratio_width, ratio_height, ratio_width, ratio_height], dtype=torch.float32, device=boxes.device
                )
                new_annotation["boxes"] = scaled_boxes
            elif key == "area":
                area = value
                scaled_area = area * (ratio_width * ratio_height)
                new_annotation["area"] = scaled_area
            elif key == "masks":
                masks = value[:, None]
                masks = [F.resize(mask, target_size, interpolation=interpolation) for mask in masks]
                masks = torch.stack(masks).to(torch.float32)
                masks = masks[:, 0] > threshold
                new_annotation["masks"] = masks
            elif key == "size":
                new_annotation["size"] = target_size
            else:
                new_annotation[key] = value

        return new_annotation

    def normalize_annotation(self, annotation: dict, image_size: tuple[int, int]) -> dict:
        image_height, image_width = image_size
        norm_annotation = {}
        for key, value in annotation.items():
            if key == "boxes":
                boxes = value
                boxes = corners_to_center_format(boxes)
                boxes /= torch.as_tensor(
                    [image_width, image_height, image_width, image_height], dtype=torch.float32, device=boxes.device
                )
                norm_annotation[key] = boxes
            else:
                norm_annotation[key] = value
        return norm_annotation

    def _update_annotation_for_padded_image(
        self,
        annotation: dict,
        input_image_size: tuple[int, int],
        output_image_size: tuple[int, int],
        padding,
        update_bboxes,
    ) -> dict:
        """
        Update the annotation for a padded image.
        """
        new_annotation = {}
        new_annotation["size"] = output_image_size
        ratio_height, ratio_width = (input / output for output, input in zip(output_image_size, input_image_size))

        for key, value in annotation.items():
            if key == "masks":
                masks = value
                masks = F.pad(
                    masks,
                    padding,
                    fill=0,
                )
                masks = safe_squeeze(masks, 1)
                new_annotation["masks"] = masks
            elif key == "boxes" and update_bboxes:
                boxes = value
                boxes *= torch.as_tensor([ratio_width, ratio_height, ratio_width, ratio_height], device=boxes.device)
                new_annotation["boxes"] = boxes
            elif key == "size":
                new_annotation["size"] = output_image_size
            else:
                new_annotation[key] = value
        return new_annotation

    def pad(
        self,
        image: torch.Tensor,
        padded_size: tuple[int, int],
        annotation: Optional[dict[str, Any]] = None,
        update_bboxes: bool = True,
        fill: int = 0,
    ):
        original_size = image.size()[-2:]
        padding_bottom = padded_size[0] - original_size[0]
        padding_right = padded_size[1] - original_size[1]
        if padding_bottom < 0 or padding_right < 0:
            raise ValueError(
                f"Padding dimensions are negative. Please make sure that the padded size is larger than the "
                f"original size. Got padded size: {padded_size}, original size: {original_size}."
            )
        if original_size != padded_size:
            padding = [0, 0, padding_right, padding_bottom]
            image = F.pad(image, padding, fill=fill)
            if annotation is not None:
                annotation = self._update_annotation_for_padded_image(
                    annotation, original_size, padded_size, padding, update_bboxes
                )

        # Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding.
        pixel_mask = torch.zeros(padded_size, dtype=torch.int64, device=image.device)
        pixel_mask[: original_size[0], : original_size[1]] = 1

        return image, pixel_mask, annotation

    @auto_docstring
    def preprocess(
        self,
        images: ImageInput,
        annotations: Optional[Union[AnnotationType, list[AnnotationType]]] = None,
        masks_path: Optional[Union[str, pathlib.Path]] = None,
        **kwargs: Unpack[YolosFastImageProcessorKwargs],
    ) -> BatchFeature:
        r"""
        annotations (`AnnotationType` or `list[AnnotationType]`, *optional*):
            List of annotations associated with the image or batch of images. If annotation is for object
            detection, the annotations should be a dictionary with the following keys:
            - "image_id" (`int`): The image id.
            - "annotations" (`list[Dict]`): List of annotations for an image. Each annotation should be a
                dictionary. An image can have no annotations, in which case the list should be empty.
            If annotation is for segmentation, the annotations should be a dictionary with the following keys:
            - "image_id" (`int`): The image id.
            - "segments_info" (`list[Dict]`): List of segments for an image. Each segment should be a dictionary.
                An image can have no segments, in which case the list should be empty.
            - "file_name" (`str`): The file name of the image.
        masks_path (`str` or `pathlib.Path`, *optional*):
            Path to the directory containing the segmentation masks.
        """
        if "pad_and_return_pixel_mask" in kwargs:
            kwargs["do_pad"] = kwargs.pop("pad_and_return_pixel_mask")
            logger.warning_once(
                "The `pad_and_return_pixel_mask` argument is deprecated and will be removed in a future version, "
                "use `do_pad` instead."
            )

        if "max_size" in kwargs:
            logger.warning_once(
                "The `max_size` argument is deprecated and will be removed in a future version, use"
                " `size['longest_edge']` instead."
            )
            kwargs["size"] = kwargs.pop("max_size")

        return super().preprocess(images, annotations, masks_path, **kwargs)

    def _preprocess(
        self,
        images: list["torch.Tensor"],
        annotations: Optional[Union[AnnotationType, list[AnnotationType]]],
        masks_path: Optional[Union[str, pathlib.Path]],
        return_segmentation_masks: bool,
        do_resize: bool,
        size: SizeDict,
        interpolation: Optional["F.InterpolationMode"],
        do_rescale: bool,
        rescale_factor: float,
        do_normalize: bool,
        do_convert_annotations: bool,
        image_mean: Optional[Union[float, list[float]]],
        image_std: Optional[Union[float, list[float]]],
        do_pad: bool,
        pad_size: Optional[SizeDict],
        format: Optional[Union[str, AnnotationFormat]],
        return_tensors: Optional[Union[str, TensorType]],
        **kwargs,
    ) -> BatchFeature:
        """
        Preprocess an image or a batch of images so that it can be used by the model.
        """
        if annotations is not None and isinstance(annotations, dict):
            annotations = [annotations]

        if annotations is not None and len(images) != len(annotations):
            raise ValueError(
                f"The number of images ({len(images)}) and annotations ({len(annotations)}) do not match."
            )

        format = AnnotationFormat(format)
        if annotations is not None:
            validate_annotations(format, SUPPORTED_ANNOTATION_FORMATS, annotations)

        if (
            masks_path is not None
            and format == AnnotationFormat.COCO_PANOPTIC
            and not isinstance(masks_path, (pathlib.Path, str))
        ):
            raise ValueError(
                "The path to the directory containing the mask PNG files should be provided as a"
                f" `pathlib.Path` or string object, but is {type(masks_path)} instead."
            )

        data = {}

        processed_images = []
        processed_annotations = []
        pixel_masks = []  # Initialize pixel_masks here
        for image, annotation in zip(images, annotations if annotations is not None else [None] * len(images)):
            # prepare (COCO annotations as a list of Dict -> YOLOS target as a single Dict per image)
            if annotations is not None:
                annotation = self.prepare_annotation(
                    image,
                    annotation,
                    format,
                    return_segmentation_masks=return_segmentation_masks,
                    masks_path=masks_path,
                    input_data_format=ChannelDimension.FIRST,
                )

            if do_resize:
                resized_image = self.resize(image, size=size, interpolation=interpolation)
                if annotations is not None:
                    annotation = self.resize_annotation(
                        annotation,
                        orig_size=image.size()[-2:],
                        target_size=resized_image.size()[-2:],
                    )
                image = resized_image
            # Fused rescale and normalize
            image = self.rescale_and_normalize(image, do_rescale, rescale_factor, do_normalize, image_mean, image_std)
            if do_convert_annotations and annotations is not None:
                annotation = self.normalize_annotation(annotation, get_image_size(image, ChannelDimension.FIRST))

            processed_images.append(image)
            processed_annotations.append(annotation)
        images = processed_images
        annotations = processed_annotations if annotations is not None else None

        if do_pad:
            # depends on all resized image shapes so we need another loop
            if pad_size is not None:
                padded_size = (pad_size.height, pad_size.width)
            else:
                padded_size = get_max_height_width(images)

            padded_images = []
            padded_annotations = []
            for image, annotation in zip(images, annotations if annotations is not None else [None] * len(images)):
                # Pads images and returns their mask: {'pixel_values': ..., 'pixel_mask': ...}
                if padded_size == image.size()[-2:]:
                    padded_images.append(image)
                    pixel_masks.append(torch.ones(padded_size, dtype=torch.int64, device=image.device))
                    padded_annotations.append(annotation)
                    continue
                image, pixel_mask, annotation = self.pad(
                    image, padded_size, annotation=annotation, update_bboxes=do_convert_annotations
                )
                padded_images.append(image)
                padded_annotations.append(annotation)
                pixel_masks.append(pixel_mask)
            images = padded_images
            annotations = padded_annotations if annotations is not None else None
            data.update({"pixel_mask": torch.stack(pixel_masks, dim=0)})

        data.update({"pixel_values": torch.stack(images, dim=0)})
        encoded_inputs = BatchFeature(data, tensor_type=return_tensors)
        if annotations is not None:
            encoded_inputs["labels"] = [
                BatchFeature(annotation, tensor_type=return_tensors) for annotation in annotations
            ]
        return encoded_inputs

    def post_process(self, outputs, target_sizes):
        """
        Converts the raw output of [`YolosForObjectDetection`] into final bounding boxes in (top_left_x,
        top_left_y, bottom_right_x, bottom_right_y) format. Only supports PyTorch.

        Args:
            outputs ([`YolosObjectDetectionOutput`]):
                Raw outputs of the model.
            target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
                Tensor containing the size (height, width) of each image of the batch. For evaluation, this must be the
                original image size (before any data augmentation). For visualization, this should be the image size
                after data augment, but before padding.
        Returns:
            `list[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
            in the batch as predicted by the model.
        """
        logger.warning_once(
            "`post_process` is deprecated and will be removed in v5 of Transformers, please use"
            " `post_process_object_detection` instead, with `threshold=0.` for equivalent results.",
        )

        out_logits, out_bbox = outputs.logits, outputs.pred_boxes

        if len(out_logits) != len(target_sizes):
            raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
        if target_sizes.shape[1] != 2:
            raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")

        prob = out_logits.sigmoid()
        topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), 100, dim=1)
        scores = topk_values
        topk_boxes = torch.div(topk_indexes, out_logits.shape[2], rounding_mode="floor")
        labels = topk_indexes % out_logits.shape[2]
        boxes = center_to_corners_format(out_bbox)
        boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4))

        # and from relative [0, 1] to absolute [0, height] coordinates
        img_h, img_w = target_sizes.unbind(1)
        scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
        boxes = boxes * scale_fct[:, None, :]

        results = [{"scores": s, "labels": l, "boxes": b} for s, l, b in zip(scores, labels, boxes)]

        return results

    def post_process_object_detection(
        self, outputs, threshold: float = 0.5, target_sizes: Union[TensorType, list[tuple]] = None, top_k: int = 100
    ):
        """
        Converts the raw output of [`YolosForObjectDetection`] into final bounding boxes in (top_left_x,
        top_left_y, bottom_right_x, bottom_right_y) format. Only supports PyTorch.

        Args:
            outputs ([`YolosObjectDetectionOutput`]):
                Raw outputs of the model.
            threshold (`float`, *optional*):
                Score threshold to keep object detection predictions.
            target_sizes (`torch.Tensor` or `list[tuple[int, int]]`, *optional*):
                Tensor of shape `(batch_size, 2)` or list of tuples (`tuple[int, int]`) containing the target size
                (height, width) of each image in the batch. If left to None, predictions will not be resized.
            top_k (`int`, *optional*, defaults to 100):
                Keep only top k bounding boxes before filtering by thresholding.

        Returns:
            `list[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
            in the batch as predicted by the model.
        """
        out_logits, out_bbox = outputs.logits, outputs.pred_boxes

        if target_sizes is not None:
            if len(out_logits) != len(target_sizes):
                raise ValueError(
                    "Make sure that you pass in as many target sizes as the batch dimension of the logits"
                )

        prob = out_logits.sigmoid()
        prob = prob.view(out_logits.shape[0], -1)
        k_value = min(top_k, prob.size(1))
        topk_values, topk_indexes = torch.topk(prob, k_value, dim=1)
        scores = topk_values
        topk_boxes = torch.div(topk_indexes, out_logits.shape[2], rounding_mode="floor")
        labels = topk_indexes % out_logits.shape[2]
        boxes = center_to_corners_format(out_bbox)
        boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4))

        # and from relative [0, 1] to absolute [0, height] coordinates
        if target_sizes is not None:
            if isinstance(target_sizes, list):
                img_h = torch.Tensor([i[0] for i in target_sizes])
                img_w = torch.Tensor([i[1] for i in target_sizes])
            else:
                img_h, img_w = target_sizes.unbind(1)
            scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
            boxes = boxes * scale_fct[:, None, :]

        results = []
        for s, l, b in zip(scores, labels, boxes):
            score = s[s > threshold]
            label = l[s > threshold]
            box = b[s > threshold]
            results.append({"scores": score, "labels": label, "boxes": box})

        return results


__all__ = ["YolosImageProcessorFast"]
