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# Copyright 2025 The Meta AI Authors and The HuggingFace Team. All rights reserved.
#
# 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.


import math
from copy import deepcopy
from itertools import product
from typing import Any, Optional, Union

import numpy as np
import torch
import torch.nn.functional as F
from torchvision.ops.boxes import batched_nms

from ...image_processing_utils import BatchFeature, get_size_dict
from ...image_processing_utils_fast import BaseImageProcessorFast
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    SizeDict,
    pil_torch_interpolation_mapping,
)
from ...processing_utils import ImagesKwargs, Unpack
from ...utils import TensorType, auto_docstring


class Sam3FastImageProcessorKwargs(ImagesKwargs, total=False):
    r"""
    mask_size (`dict[str, int]`, *optional*):
        The size `{"height": int, "width": int}` to resize the segmentation maps to.
    """

    mask_size: dict[str, int]


def _compute_stability_score(masks: "torch.Tensor", mask_threshold: float, stability_score_offset: int):
    # One mask is always contained inside the other.
    # Save memory by preventing unnecessary cast to torch.int64
    intersections = (
        (masks > (mask_threshold + stability_score_offset)).sum(-1, dtype=torch.int16).sum(-1, dtype=torch.int32)
    )
    unions = (masks > (mask_threshold - stability_score_offset)).sum(-1, dtype=torch.int16).sum(-1, dtype=torch.int32)
    stability_scores = intersections / unions
    return stability_scores


def _mask_to_rle(input_mask: "torch.Tensor"):
    """
    Encodes masks the run-length encoding (RLE), in the format expected by pycoco tools.
    """
    # Put in fortran order and flatten height and width
    batch_size, height, width = input_mask.shape
    input_mask = input_mask.permute(0, 2, 1).flatten(1)

    # Compute change indices
    diff = input_mask[:, 1:] ^ input_mask[:, :-1]
    change_indices = diff.nonzero()

    # Encode run length
    out = []
    for i in range(batch_size):
        cur_idxs = change_indices[change_indices[:, 0] == i, 1] + 1
        if len(cur_idxs) == 0:
            # No changes => either all 0 or all 1
            # If the entire mask is 0, RLE is [height*width] or if the entire mask is 1, RLE is [0, height*width].
            if input_mask[i, 0] == 0:
                out.append({"size": [height, width], "counts": [height * width]})
            else:
                out.append({"size": [height, width], "counts": [0, height * width]})
            continue
        btw_idxs = cur_idxs[1:] - cur_idxs[:-1]
        counts = [] if input_mask[i, 0] == 0 else [0]
        counts += [cur_idxs[0].item()] + btw_idxs.tolist() + [height * width - cur_idxs[-1].item()]
        out.append({"size": [height, width], "counts": counts})
    return out


def _batched_mask_to_box(masks: "torch.Tensor"):
    """
    Computes the bounding boxes around the given input masks. The bounding boxes are in the XYXY format which
    corresponds the following required indices:
        - LEFT: left hand side of the bounding box
        - TOP: top of the bounding box
        - RIGHT: right of the bounding box
        - BOTTOM: bottom of the bounding box

    Return [0,0,0,0] for an empty mask. For input shape channel_1 x channel_2 x ... x height x width, the output shape
    is channel_1 x channel_2 x ... x 4.

    Args:
        - masks (`torch.Tensor` of shape `(batch, nb_mask, height, width)`)
    """
    # torch.max below raises an error on empty inputs, just skip in this case

    if torch.numel(masks) == 0:
        return torch.zeros(*masks.shape[:-2], 4, device=masks.device)

    # Normalize shape to Cxheightxwidth
    shape = masks.shape
    height, width = shape[-2:]

    # Get top and bottom edges
    in_height, _ = torch.max(masks, dim=-1)
    in_height_coords = in_height * torch.arange(height, device=in_height.device)[None, :]
    bottom_edges, _ = torch.max(in_height_coords, dim=-1)
    in_height_coords = in_height_coords + height * (~in_height)
    top_edges, _ = torch.min(in_height_coords, dim=-1)

    # Get left and right edges
    in_width, _ = torch.max(masks, dim=-2)
    in_width_coords = in_width * torch.arange(width, device=in_width.device)[None, :]
    right_edges, _ = torch.max(in_width_coords, dim=-1)
    in_width_coords = in_width_coords + width * (~in_width)
    left_edges, _ = torch.min(in_width_coords, dim=-1)

    # If the mask is empty the right edge will be to the left of the left edge.
    # Replace these boxes with [0, 0, 0, 0]
    empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges)
    out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1)
    out = out * (~empty_filter).unsqueeze(-1)

    # Return to original shape
    out = out.reshape(*shape[:-2], 4)
    return out


def _is_box_near_crop_edge(boxes, crop_box, orig_box, atol=20.0):
    """Filter masks at the edge of a crop, but not at the edge of the original image."""
    crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device)
    orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device)

    left, top, _, _ = crop_box
    offset = torch.tensor([[left, top, left, top]], device=boxes.device)
    # Check if boxes has a channel dimension
    if len(boxes.shape) == 3:
        offset = offset.unsqueeze(1)
    boxes = (boxes + offset).float()

    near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0)
    near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0)
    near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge)
    return torch.any(near_crop_edge, dim=1)


def _pad_masks(masks, crop_box: list[int], orig_height: int, orig_width: int):
    left, top, right, bottom = crop_box
    if left == 0 and top == 0 and right == orig_width and bottom == orig_height:
        return masks
    # Coordinate transform masks
    pad_x, pad_y = orig_width - (right - left), orig_height - (bottom - top)
    pad = (left, pad_x - left, top, pad_y - top)
    return torch.nn.functional.pad(masks, pad, value=0)


def _generate_crop_boxes(
    image,
    target_size: int,  # Is it tuple here?
    crop_n_layers: int = 0,
    overlap_ratio: float = 512 / 1500,
    points_per_crop: int | None = 32,
    crop_n_points_downscale_factor: list[int] | None = 1,
) -> tuple[list[list[int]], list[int]]:
    """
    Generates a list of crop boxes of different sizes. Each layer has (2**i)**2 boxes for the ith layer.

    Args:
        image (Union[`numpy.ndarray`, `PIL.Image`, `torch.Tensor`]):
            Image to generate crops for.
        target_size (`int`):
            Size of the smallest crop.
        crop_n_layers (`int`, *optional*):
            If `crops_n_layers>0`, mask prediction will be run again on crops of the image. Sets the number of layers
            to run, where each layer has 2**i_layer number of image crops.
        overlap_ratio (`int`, *optional*):
            Sets the degree to which crops overlap. In the first crop layer, crops will overlap by this fraction of the
            image length. Later layers with more crops scale down this overlap.
        points_per_crop (`int`, *optional*):
            Number of points to sam3ple per crop.
        crop_n_points_downscale_factor (`int`, *optional*):
            The number of points-per-side sam3pled in layer n is scaled down by crop_n_points_downscale_factor**n.
        input_data_format (`str` or `ChannelDimension`, *optional*):
            The channel dimension format of the input image. If not provided, it will be inferred.
    """

    if isinstance(image, list):
        raise ValueError("Only one image is allowed for crop generation.")
    original_size = image.shape[-2:]

    points_grid = []
    for i in range(crop_n_layers + 1):
        n_points = int(points_per_crop / (crop_n_points_downscale_factor**i))
        points_grid.append(_build_point_grid(n_points))

    crop_boxes, layer_idxs = _generate_per_layer_crops(crop_n_layers, overlap_ratio, original_size)

    cropped_images, point_grid_per_crop = _generate_crop_images(
        crop_boxes, image, points_grid, layer_idxs, target_size, original_size
    )
    crop_boxes = torch.tensor(crop_boxes)
    crop_boxes = crop_boxes.float()
    points_per_crop = torch.stack(point_grid_per_crop)
    points_per_crop = points_per_crop.unsqueeze(0).permute(0, 2, 1, 3)
    cropped_images = torch.stack(cropped_images)

    input_labels = torch.ones_like(points_per_crop[:, :, :, 0], dtype=torch.int64)

    return crop_boxes, points_per_crop, cropped_images, input_labels


def _generate_per_layer_crops(crop_n_layers, overlap_ratio, original_size):
    """
    Generates 2 ** (layers idx + 1) crops for each crop_n_layers. Crops are in the XYWH format : The XYWH format
    consists of the following required indices:
        - X: X coordinate of the top left of the bounding box
        - Y: Y coordinate of the top left of the bounding box
        - W: width of the bounding box
        - H: height of the bounding box
    """
    crop_boxes, layer_idxs = [], []
    im_height, im_width = original_size
    short_side = min(im_height, im_width)

    # Original image
    crop_boxes.append([0, 0, im_width, im_height])
    layer_idxs.append(0)
    for i_layer in range(crop_n_layers):
        n_crops_per_side = 2 ** (i_layer + 1)
        overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side))

        crop_width = int(math.ceil((overlap * (n_crops_per_side - 1) + im_width) / n_crops_per_side))
        crop_height = int(math.ceil((overlap * (n_crops_per_side - 1) + im_height) / n_crops_per_side))

        crop_box_x0 = [int((crop_width - overlap) * i) for i in range(n_crops_per_side)]
        crop_box_y0 = [int((crop_height - overlap) * i) for i in range(n_crops_per_side)]

        for left, top in product(crop_box_x0, crop_box_y0):
            box = [left, top, min(left + crop_width, im_width), min(top + crop_height, im_height)]
            crop_boxes.append(box)
            layer_idxs.append(i_layer + 1)

    return crop_boxes, layer_idxs


def _build_point_grid(n_per_side: int) -> torch.Tensor:
    """Generates a 2D grid of points evenly spaced in [0,1]x[0,1]."""
    offset = 1 / (2 * n_per_side)
    points_one_side = torch.linspace(offset, 1 - offset, n_per_side)
    points_x = torch.tile(points_one_side[None, :], (n_per_side, 1))
    points_y = torch.tile(points_one_side[:, None], (1, n_per_side))
    points = torch.stack([points_x, points_y], dim=-1).reshape(-1, 2)
    return points


def _generate_crop_images(
    crop_boxes, image, points_grid, layer_idxs, target_size, original_size, input_data_format=None
):
    """
    Takes as an input bounding boxes that are used to crop the image. Based in the crops, the corresponding points are
    also passed.
    """
    cropped_images = []
    total_points_per_crop = []
    for i, crop_box in enumerate(crop_boxes):
        left, top, right, bottom = crop_box
        cropped_im = image[:, top:bottom, left:right]

        cropped_images.append(cropped_im)

        cropped_im_size = cropped_im.shape[-2:]
        points_scale = torch.tensor(cropped_im_size).flip(dims=(0,)).unsqueeze(0)

        points = points_grid[layer_idxs[i]] * points_scale
        normalized_points = _normalize_coordinates(target_size, points, original_size)
        total_points_per_crop.append(normalized_points)

    return cropped_images, total_points_per_crop


def _normalize_coordinates(
    target_size: int, coords: torch.Tensor, original_size: tuple[int, int], is_bounding_box=False
) -> torch.Tensor:
    """
    Expects a numpy array of length 2 in the final dimension. Requires the original image size in (height, width)
    format.
    """
    old_height, old_width = original_size

    scale = target_size * 1.0 / max(old_height, old_width)
    new_height, new_width = old_height * scale, old_width * scale
    new_width = int(new_width + 0.5)
    new_height = int(new_height + 0.5)

    coords = deepcopy(coords).float()

    if is_bounding_box:
        coords = coords.reshape(-1, 2, 2)

    coords[..., 0] = coords[..., 0] * (new_width / old_width)
    coords[..., 1] = coords[..., 1] * (new_height / old_height)

    if is_bounding_box:
        coords = coords.reshape(-1, 4)

    return coords


def _rle_to_mask(rle: dict[str, Any]) -> torch.Tensor:
    """Compute a binary mask from an uncompressed RLE."""
    height, width = rle["size"]
    mask = torch.empty(height * width, dtype=bool)
    idx = 0
    parity = False
    for count in rle["counts"]:
        mask[idx : idx + count] = parity
        idx += count
        parity = not parity
    mask = mask.reshape(width, height)
    return mask.transpose(0, 1)  # Reshape to original shape


def _post_process_for_mask_generation(rle_masks, iou_scores, mask_boxes, amg_crops_nms_thresh=0.7):
    """
    Perform NMS (Non Maximum Suppression) on the outputs.

    Args:
            rle_masks (`torch.Tensor`):
                binary masks in the RLE format
            iou_scores (`torch.Tensor` of shape (nb_masks, 1)):
                iou_scores predicted by the model
            mask_boxes (`torch.Tensor`):
                The bounding boxes corresponding to segmentation masks
            amg_crops_nms_thresh (`float`, *optional*, defaults to 0.7):
                NMS threshold.
    """
    keep_by_nms = batched_nms(
        boxes=mask_boxes.float(),
        scores=iou_scores,
        idxs=torch.zeros(mask_boxes.shape[0]),
        iou_threshold=amg_crops_nms_thresh,
    )

    iou_scores = iou_scores[keep_by_nms]
    rle_masks = [rle_masks[i] for i in keep_by_nms]
    mask_boxes = mask_boxes[keep_by_nms]
    masks = [_rle_to_mask(rle) for rle in rle_masks]

    return masks, iou_scores, rle_masks, mask_boxes


def _scale_boxes(boxes, target_sizes):
    """
    Scale batch of bounding boxes to the target sizes.

    Args:
        boxes (`torch.Tensor` of shape `(batch_size, num_boxes, 4)`):
            Bounding boxes to scale. Each box is expected to be in (x1, y1, x2, y2) format.
        target_sizes (`list[tuple[int, int]]` or `torch.Tensor` of shape `(batch_size, 2)`):
            Target sizes to scale the boxes to. Each target size is expected to be in (height, width) format.

    Returns:
        `torch.Tensor` of shape `(batch_size, num_boxes, 4)`: Scaled bounding boxes.
    """

    if isinstance(target_sizes, (list, tuple)):
        image_height = torch.tensor([i[0] for i in target_sizes])
        image_width = torch.tensor([i[1] for i in target_sizes])
    elif isinstance(target_sizes, torch.Tensor):
        image_height, image_width = target_sizes.unbind(1)
    else:
        raise TypeError("`target_sizes` must be a list, tuple or torch.Tensor")

    scale_factor = torch.stack([image_width, image_height, image_width, image_height], dim=1)
    scale_factor = scale_factor.unsqueeze(1).to(boxes.device)
    boxes = boxes * scale_factor
    return boxes


@auto_docstring
class Sam3ImageProcessorFast(BaseImageProcessorFast):
    resample = PILImageResampling.BILINEAR
    image_mean = IMAGENET_STANDARD_MEAN
    image_std = IMAGENET_STANDARD_STD
    size = {"height": 1008, "width": 1008}
    mask_size = {"height": 288, "width": 288}
    do_resize = True
    do_rescale = True
    do_normalize = True
    do_convert_rgb = True

    valid_kwargs = Sam3FastImageProcessorKwargs

    # modular artefacts
    do_pad = None
    pad_size = None
    mask_pad_size = None

    def __init__(self, **kwargs: Unpack[Sam3FastImageProcessorKwargs]):
        super().__init__(**kwargs)

    def _further_process_kwargs(
        self,
        size: SizeDict | None = None,
        mask_size: SizeDict | None = None,
        default_to_square: bool | None = None,
        image_mean: float | list[float] | None = None,
        image_std: float | list[float] | None = None,
        data_format: ChannelDimension | None = None,
        **kwargs,
    ) -> dict:
        """
        Update kwargs that need further processing before being validated
        Can be overridden by subclasses to customize the processing of kwargs.
        """
        if kwargs is None:
            kwargs = {}
        if size is not None:
            size = SizeDict(**get_size_dict(size=size, default_to_square=default_to_square))
        if mask_size is not None:
            mask_size = SizeDict(**get_size_dict(mask_size, param_name="mask_size"))
        if isinstance(image_mean, list):
            image_mean = tuple(image_mean)
        if isinstance(image_std, list):
            image_std = tuple(image_std)
        if data_format is None:
            data_format = ChannelDimension.FIRST

        kwargs["size"] = size
        kwargs["mask_size"] = mask_size
        kwargs["image_mean"] = image_mean
        kwargs["image_std"] = image_std
        kwargs["data_format"] = data_format

        # torch resize uses interpolation instead of resample
        # Check if resample is an int before checking if it's an instance of PILImageResampling
        # because if pillow < 9.1.0, resample is an int and PILImageResampling is a module.
        # Checking PILImageResampling will fail with error `TypeError: isinstance() arg 2 must be a type or tuple of types`.
        resample = kwargs.pop("resample")
        kwargs["interpolation"] = (
            pil_torch_interpolation_mapping[resample] if isinstance(resample, (PILImageResampling, int)) else resample
        )

        return kwargs

    @auto_docstring
    def preprocess(
        self,
        images: ImageInput,
        segmentation_maps: ImageInput | None = None,
        **kwargs: Unpack[Sam3FastImageProcessorKwargs],
    ) -> BatchFeature:
        r"""
        segmentation_maps (`ImageInput`, *optional*):
            The segmentation maps to preprocess.
        """
        return super().preprocess(images, segmentation_maps, **kwargs)

    def _preprocess_image_like_inputs(
        self,
        images: ImageInput,
        segmentation_maps: ImageInput | None,
        do_convert_rgb: bool,
        input_data_format: ChannelDimension,
        device: Union[str, "torch.device"] | None = None,
        **kwargs: Unpack[Sam3FastImageProcessorKwargs],
    ) -> BatchFeature:
        """
        Preprocess image-like inputs.
        """
        images = self._prepare_image_like_inputs(
            images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, device=device
        )
        original_sizes = [image.shape[-2:] for image in images]
        images_kwargs = kwargs.copy()
        pixel_values = self._preprocess(images, **images_kwargs)
        data = {
            "pixel_values": pixel_values,
            "original_sizes": original_sizes,
        }

        if segmentation_maps is not None:
            processed_segmentation_maps = self._prepare_image_like_inputs(
                images=segmentation_maps,
                expected_ndims=2,
                do_convert_rgb=False,
                input_data_format=ChannelDimension.FIRST,
            )

            segmentation_maps_kwargs = kwargs.copy()
            segmentation_maps_kwargs.update(
                {
                    "do_normalize": False,
                    "do_rescale": False,
                    "interpolation": pil_torch_interpolation_mapping[PILImageResampling.NEAREST],
                    "size": segmentation_maps_kwargs.pop("mask_size"),
                }
            )
            processed_segmentation_maps = self._preprocess(
                images=processed_segmentation_maps, **segmentation_maps_kwargs
            )
            data["labels"] = processed_segmentation_maps.squeeze(1).to(torch.int64)

        return BatchFeature(data=data, tensor_type=kwargs["return_tensors"])

    def _preprocess(
        self,
        images: list["torch.Tensor"],
        return_tensors: str | TensorType | None,
        **kwargs,
    ) -> "torch.Tensor":
        return super()._preprocess(images, return_tensors=return_tensors, **kwargs).pixel_values

    def generate_crop_boxes(
        self,
        image: "torch.Tensor",
        target_size,
        crop_n_layers: int = 0,
        overlap_ratio: float = 512 / 1500,
        points_per_crop: int | None = 32,
        crop_n_points_downscale_factor: list[int] | None = 1,
        device: Optional["torch.device"] = None,
    ):
        """
        Generates a list of crop boxes of different sizes. Each layer has (2**i)**2 boxes for the ith layer.

        Args:
            image (`torch.Tensor`):
                Input original image
            target_size (`int`):
                Target size of the resized image
            crop_n_layers (`int`, *optional*, defaults to 0):
                If >0, mask prediction will be run again on crops of the image. Sets the number of layers to run, where
                each layer has 2**i_layer number of image crops.
            overlap_ratio (`float`, *optional*, defaults to 512/1500):
                Sets the degree to which crops overlap. In the first crop layer, crops will overlap by this fraction of
                the image length. Later layers with more crops scale down this overlap.
            points_per_crop (`int`, *optional*, defaults to 32):
                Number of points to sam3ple from each crop.
            crop_n_points_downscale_factor (`list[int]`, *optional*, defaults to 1):
                The number of points-per-side sam3pled in layer n is scaled down by crop_n_points_downscale_factor**n.
            device (`torch.device`, *optional*, defaults to None):
                Device to use for the computation. If None, cpu will be used.
            input_data_format (`str` or `ChannelDimension`, *optional*):
                The channel dimension format of the input image. If not provided, it will be inferred.
            return_tensors (`str`, *optional*, defaults to `pt`):
                If `pt`, returns `torch.Tensor`.
        """
        image = self._process_image(image)
        crop_boxes, points_per_crop, cropped_images, input_labels = _generate_crop_boxes(
            image,
            target_size,
            crop_n_layers,
            overlap_ratio,
            points_per_crop,
            crop_n_points_downscale_factor,
        )
        if device is None:
            device = torch.device("cpu")
        crop_boxes = crop_boxes.to(device)
        points_per_crop = points_per_crop.to(device)
        # cropped_images stays as torch.Tensor
        input_labels = input_labels.to(device)

        return crop_boxes, points_per_crop, cropped_images, input_labels

    def filter_masks(
        self,
        masks,
        iou_scores,
        original_size,
        cropped_box_image,
        pred_iou_thresh=0.88,
        stability_score_thresh=0.95,
        mask_threshold=0,
        stability_score_offset=1,
    ):
        """
        Filters the predicted masks by selecting only the ones that meets several criteria. The first criterion being
        that the iou scores needs to be greater than `pred_iou_thresh`. The second criterion is that the stability
        score needs to be greater than `stability_score_thresh`. The method also converts the predicted masks to
        bounding boxes and pad the predicted masks if necessary.

        Args:
            masks (`torch.Tensor`):
                Input masks.
            iou_scores (`torch.Tensor`):
                List of IoU scores.
            original_size (`tuple[int,int]`):
                Size of the original image.
            cropped_box_image (`torch.Tensor`):
                The cropped image.
            pred_iou_thresh (`float`, *optional*, defaults to 0.88):
                The threshold for the iou scores.
            stability_score_thresh (`float`, *optional*, defaults to 0.95):
                The threshold for the stability score.
            mask_threshold (`float`, *optional*, defaults to 0):
                The threshold for the predicted masks.
            stability_score_offset (`float`, *optional*, defaults to 1):
                The offset for the stability score used in the `_compute_stability_score` method.

        """
        original_height, original_width = original_size
        iou_scores = iou_scores.flatten(0, 1)
        masks = masks.flatten(0, 1)

        if masks.shape[0] != iou_scores.shape[0]:
            raise ValueError("masks and iou_scores must have the sam3e batch size.")

        if masks.device != iou_scores.device:
            iou_scores = iou_scores.to(masks.device)

        batch_size = masks.shape[0]

        keep_mask = torch.ones(batch_size, dtype=torch.bool, device=masks.device)

        if pred_iou_thresh > 0.0:
            keep_mask = keep_mask & (iou_scores > pred_iou_thresh)

        # compute stability score
        if stability_score_thresh > 0.0:
            stability_scores = _compute_stability_score(masks, mask_threshold, stability_score_offset)
            keep_mask = keep_mask & (stability_scores > stability_score_thresh)

        scores = iou_scores[keep_mask]
        masks = masks[keep_mask]

        # binarize masks
        masks = masks > mask_threshold
        converted_boxes = _batched_mask_to_box(masks)

        keep_mask = ~_is_box_near_crop_edge(
            converted_boxes, cropped_box_image, [0, 0, original_width, original_height]
        )

        scores = scores[keep_mask]
        masks = masks[keep_mask]
        converted_boxes = converted_boxes[keep_mask]

        masks = _pad_masks(masks, cropped_box_image, original_height, original_width)
        # conversion to rle is necessary to run non-maximum suppression
        masks = _mask_to_rle(masks)

        return masks, scores, converted_boxes

    def post_process_masks(
        self,
        masks,
        original_sizes,
        mask_threshold=0.0,
        binarize=True,
        max_hole_area=0.0,
        max_sprinkle_area=0.0,
        apply_non_overlapping_constraints=False,
        **kwargs,
    ):
        """
        Remove padding and upscale masks to the original image size.

        Args:
            masks (`Union[torch.Tensor, List[torch.Tensor], np.ndarray, List[np.ndarray]]`):
                Batched masks from the mask_decoder in (batch_size, num_channels, height, width) format.
            original_sizes (`Union[torch.Tensor, List[Tuple[int,int]]]`):
                The original sizes of each image before it was resized to the model's expected input shape, in (height,
                width) format.
            mask_threshold (`float`, *optional*, defaults to 0.0):
                Threshold for binarization and post-processing operations.
            binarize (`bool`, *optional*, defaults to `True`):
                Whether to binarize the masks.
            max_hole_area (`float`, *optional*, defaults to 0.0):
                The maximum area of a hole to fill.
            max_sprinkle_area (`float`, *optional*, defaults to 0.0):
                The maximum area of a sprinkle to fill.
            apply_non_overlapping_constraints (`bool`, *optional*, defaults to `False`):
                Whether to apply non-overlapping constraints to the masks.

        Returns:
            (`torch.Tensor`): Batched masks in batch_size, num_channels, height, width) format, where (height, width)
            is given by original_size.
        """
        if isinstance(original_sizes, (torch.Tensor, np.ndarray)):
            original_sizes = original_sizes.tolist()
        # TODO: add connected components kernel for postprocessing
        output_masks = []
        for i, original_size in enumerate(original_sizes):
            if isinstance(masks[i], np.ndarray):
                masks[i] = torch.from_numpy(masks[i])
            elif not isinstance(masks[i], torch.Tensor):
                raise TypeError("Input masks should be a list of `torch.tensors` or a list of `np.ndarray`")
            interpolated_mask = F.interpolate(masks[i], original_size, mode="bilinear", align_corners=False)
            if apply_non_overlapping_constraints:
                interpolated_mask = self._apply_non_overlapping_constraints(interpolated_mask)
            if binarize:
                interpolated_mask = interpolated_mask > mask_threshold
            output_masks.append(interpolated_mask)

        return output_masks

    def post_process_for_mask_generation(self, all_masks, all_scores, all_boxes, crops_nms_thresh):
        """
        Post processes mask that are generated by calling the Non Maximum Suppression algorithm on the predicted masks.

        Args:
            all_masks (`torch.Tensor`):
                List of all predicted segmentation masks
            all_scores (`torch.Tensor`):
                List of all predicted iou scores
            all_boxes (`torch.Tensor`):
                List of all bounding boxes of the predicted masks
            crops_nms_thresh (`float`):
                Threshold for NMS (Non Maximum Suppression) algorithm.
        """
        return _post_process_for_mask_generation(all_masks, all_scores, all_boxes, crops_nms_thresh)

    def _apply_non_overlapping_constraints(self, pred_masks: torch.Tensor) -> torch.Tensor:
        """
        Apply non-overlapping constraints to the object scores in pred_masks. Here we
        keep only the highest scoring object at each spatial location in pred_masks.
        """
        batch_size = pred_masks.size(0)
        if batch_size == 1:
            return pred_masks

        device = pred_masks.device
        # "max_obj_inds": object index of the object with the highest score at each location
        max_obj_inds = torch.argmax(pred_masks, dim=0, keepdim=True)
        # "batch_obj_inds": object index of each object slice (along dim 0) in `pred_masks`
        batch_obj_inds = torch.arange(batch_size, device=device)[:, None, None, None]
        keep = max_obj_inds == batch_obj_inds
        # suppress overlapping regions' scores below -10.0 so that the foreground regions
        # don't overlap (here sigmoid(-10.0)=4.5398e-05)
        pred_masks = torch.where(keep, pred_masks, torch.clamp(pred_masks, max=-10.0))
        return pred_masks

    def post_process_semantic_segmentation(
        self, outputs, target_sizes: list[tuple] | None = None, threshold: float = 0.5
    ):
        """
        Converts the output of [`Sam3Model`] into semantic segmentation maps.

        Args:
            outputs ([`Sam3ImageSegmentationOutput`]):
                Raw outputs of the model containing semantic_seg.
            target_sizes (`list[tuple]` of length `batch_size`, *optional*):
                List of tuples corresponding to the requested final size (height, width) of each prediction. If unset,
                predictions will not be resized.
            threshold (`float`, *optional*, defaults to 0.5):
                Threshold for binarizing the semantic segmentation masks.

        Returns:
            semantic_segmentation: `list[torch.Tensor]` of length `batch_size`, where each item is a semantic
            segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is
            specified). Each entry is a binary mask (0 or 1).
        """
        # Get semantic segmentation output
        # semantic_seg has shape (batch_size, 1, height, width)
        semantic_logits = outputs.semantic_seg

        if semantic_logits is None:
            raise ValueError(
                "Semantic segmentation output is not available in the model outputs. "
                "Make sure the model was run with semantic segmentation enabled."
            )

        # Apply sigmoid to convert logits to probabilities
        semantic_probs = semantic_logits.sigmoid()

        # Resize and binarize semantic segmentation maps
        if target_sizes is not None:
            if len(semantic_logits) != len(target_sizes):
                raise ValueError(
                    "Make sure that you pass in as many target sizes as the batch dimension of the logits"
                )

            semantic_segmentation = []

            for idx in range(len(semantic_logits)):
                resized_probs = torch.nn.functional.interpolate(
                    semantic_probs[idx].unsqueeze(dim=0),
                    size=target_sizes[idx],
                    mode="bilinear",
                    align_corners=False,
                )
                # Binarize: values > threshold become 1, otherwise 0
                semantic_map = (resized_probs[0, 0] > threshold).to(torch.long)
                semantic_segmentation.append(semantic_map)
        else:
            # Binarize without resizing
            semantic_segmentation = (semantic_probs[:, 0] > threshold).to(torch.long)
            semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

        return semantic_segmentation

    def post_process_object_detection(self, outputs, threshold: float = 0.3, target_sizes: list[tuple] | None = None):
        """
        Converts the raw output of [`Sam3Model`] into final bounding boxes in (top_left_x, top_left_y,
        bottom_right_x, bottom_right_y) format.

        Args:
            outputs ([`Sam3ImageSegmentationOutput`]):
                Raw outputs of the model containing pred_boxes, pred_logits, and optionally presence_logits.
            threshold (`float`, *optional*, defaults to 0.3):
                Score threshold to keep object detection predictions.
            target_sizes (`list[tuple[int, int]]`, *optional*):
                List of tuples (`tuple[int, int]`) containing the target size `(height, width)` of each image in the
                batch. If unset, predictions will not be resized.

        Returns:
            `list[dict]`: A list of dictionaries, each dictionary containing the following keys:
                - **scores** (`torch.Tensor`): The confidence scores for each predicted box on the image.
                - **boxes** (`torch.Tensor`): Image bounding boxes in (top_left_x, top_left_y, bottom_right_x,
                  bottom_right_y) format.
        """
        pred_logits = outputs.pred_logits  # (batch_size, num_queries)
        pred_boxes = outputs.pred_boxes  # (batch_size, num_queries, 4) in xyxy format
        presence_logits = outputs.presence_logits  # (batch_size, 1) or None

        batch_size = pred_logits.shape[0]

        if target_sizes is not None and len(target_sizes) != batch_size:
            raise ValueError("Make sure that you pass in as many target sizes as images")

        # Compute scores: combine pred_logits with presence_logits if available
        batch_scores = pred_logits.sigmoid()
        if presence_logits is not None:
            presence_scores = presence_logits.sigmoid()  # (batch_size, 1)
            batch_scores = batch_scores * presence_scores  # Broadcast multiplication

        # Boxes are already in xyxy format from the model
        batch_boxes = pred_boxes

        # Convert from relative [0, 1] to absolute [0, height/width] coordinates
        if target_sizes is not None:
            batch_boxes = _scale_boxes(batch_boxes, target_sizes)

        results = []
        for scores, boxes in zip(batch_scores, batch_boxes):
            keep = scores > threshold
            scores = scores[keep]
            boxes = boxes[keep]
            results.append({"scores": scores, "boxes": boxes})

        return results

    def post_process_instance_segmentation(
        self,
        outputs,
        threshold: float = 0.3,
        mask_threshold: float = 0.5,
        target_sizes: list[tuple] | None = None,
    ):
        """
        Converts the raw output of [`Sam3Model`] into instance segmentation predictions with bounding boxes and masks.

        Args:
            outputs ([`Sam3ImageSegmentationOutput`]):
                Raw outputs of the model containing pred_boxes, pred_logits, pred_masks, and optionally
                presence_logits.
            threshold (`float`, *optional*, defaults to 0.3):
                Score threshold to keep instance predictions.
            mask_threshold (`float`, *optional*, defaults to 0.5):
                Threshold for binarizing the predicted masks.
            target_sizes (`list[tuple[int, int]]`, *optional*):
                List of tuples (`tuple[int, int]`) containing the target size `(height, width)` of each image in the
                batch. If unset, predictions will not be resized.

        Returns:
            `list[dict]`: A list of dictionaries, each dictionary containing the following keys:
                - **scores** (`torch.Tensor`): The confidence scores for each predicted instance on the image.
                - **boxes** (`torch.Tensor`): Image bounding boxes in (top_left_x, top_left_y, bottom_right_x,
                  bottom_right_y) format.
                - **masks** (`torch.Tensor`): Binary segmentation masks for each instance, shape (num_instances,
                  height, width).
        """
        pred_logits = outputs.pred_logits  # (batch_size, num_queries)
        pred_boxes = outputs.pred_boxes  # (batch_size, num_queries, 4) in xyxy format
        pred_masks = outputs.pred_masks  # (batch_size, num_queries, height, width)
        presence_logits = outputs.presence_logits  # (batch_size, 1) or None

        batch_size = pred_logits.shape[0]

        if target_sizes is not None and len(target_sizes) != batch_size:
            raise ValueError("Make sure that you pass in as many target sizes as images")

        # Compute scores: combine pred_logits with presence_logits if available
        batch_scores = pred_logits.sigmoid()
        if presence_logits is not None:
            presence_scores = presence_logits.sigmoid()  # (batch_size, 1)
            batch_scores = batch_scores * presence_scores  # Broadcast multiplication

        # Apply sigmoid to mask logits
        batch_masks = pred_masks.sigmoid()

        # Boxes are already in xyxy format from the model
        batch_boxes = pred_boxes

        # Scale boxes to target sizes if provided
        if target_sizes is not None:
            batch_boxes = _scale_boxes(batch_boxes, target_sizes)

        results = []
        for idx, (scores, boxes, masks) in enumerate(zip(batch_scores, batch_boxes, batch_masks)):
            # Filter by score threshold
            keep = scores > threshold
            scores = scores[keep]
            boxes = boxes[keep]
            masks = masks[keep]  # (num_keep, height, width)

            # Resize masks to target size if provided
            if target_sizes is not None:
                target_size = target_sizes[idx]
                if len(masks) > 0:
                    masks = torch.nn.functional.interpolate(
                        masks.unsqueeze(0),  # (1, num_keep, height, width)
                        size=target_size,
                        mode="bilinear",
                        align_corners=False,
                    ).squeeze(0)  # (num_keep, target_height, target_width)

            # Binarize masks
            masks = (masks > mask_threshold).to(torch.long)

            results.append({"scores": scores, "boxes": boxes, "masks": masks})

        return results


__all__ = ["Sam3ImageProcessorFast"]
