import torch
from torch import Tensor
from typing import Tuple
from ._box_convert import _box_cxcywh_to_xyxy, _box_xyxy_to_cxcywh, _box_xywh_to_xyxy, _box_xyxy_to_xywh
import torchvision
from torchvision.extension import _assert_has_ops


def nms(boxes: Tensor, scores: Tensor, iou_threshold: float) -> Tensor:
    """
    Performs non-maximum suppression (NMS) on the boxes according
    to their intersection-over-union (IoU).

    NMS iteratively removes lower scoring boxes which have an
    IoU greater than iou_threshold with another (higher scoring)
    box.

    If multiple boxes have the exact same score and satisfy the IoU
    criterion with respect to a reference box, the selected box is
    not guaranteed to be the same between CPU and GPU. This is similar
    to the behavior of argsort in PyTorch when repeated values are present.

    Args:
        boxes (Tensor[N, 4])): boxes to perform NMS on. They
            are expected to be in ``(x1, y1, x2, y2)`` format with ``0 <= x1 < x2`` and
            ``0 <= y1 < y2``.
        scores (Tensor[N]): scores for each one of the boxes
        iou_threshold (float): discards all overlapping boxes with IoU > iou_threshold

    Returns:
        keep (Tensor): int64 tensor with the indices
            of the elements that have been kept
            by NMS, sorted in decreasing order of scores
    """
    _assert_has_ops()
    return torch.ops.torchvision.nms(boxes, scores, iou_threshold)


@torch.jit._script_if_tracing
def batched_nms(
    boxes: Tensor,
    scores: Tensor,
    idxs: Tensor,
    iou_threshold: float,
) -> Tensor:
    """
    Performs non-maximum suppression in a batched fashion.

    Each index value correspond to a category, and NMS
    will not be applied between elements of different categories.

    Args:
        boxes (Tensor[N, 4]): boxes where NMS will be performed. They
            are expected to be in ``(x1, y1, x2, y2)`` format with ``0 <= x1 < x2`` and
            ``0 <= y1 < y2``.
        scores (Tensor[N]): scores for each one of the boxes
        idxs (Tensor[N]): indices of the categories for each one of the boxes.
        iou_threshold (float): discards all overlapping boxes with IoU > iou_threshold

    Returns:
        keep (Tensor): int64 tensor with the indices of
            the elements that have been kept by NMS, sorted
            in decreasing order of scores
    """
    if boxes.numel() == 0:
        return torch.empty((0,), dtype=torch.int64, device=boxes.device)
    # strategy: in order to perform NMS independently per class.
    # we add an offset to all the boxes. The offset is dependent
    # only on the class idx, and is large enough so that boxes
    # from different classes do not overlap
    else:
        max_coordinate = boxes.max()
        offsets = idxs.to(boxes) * (max_coordinate + torch.tensor(1).to(boxes))
        boxes_for_nms = boxes + offsets[:, None]
        keep = nms(boxes_for_nms, scores, iou_threshold)
        return keep


def remove_small_boxes(boxes: Tensor, min_size: float) -> Tensor:
    """
    Remove boxes which contains at least one side smaller than min_size.

    Args:
        boxes (Tensor[N, 4]): boxes in ``(x1, y1, x2, y2)`` format
            with ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
        min_size (float): minimum size

    Returns:
        keep (Tensor[K]): indices of the boxes that have both sides
            larger than min_size
    """
    ws, hs = boxes[:, 2] - boxes[:, 0], boxes[:, 3] - boxes[:, 1]
    keep = (ws >= min_size) & (hs >= min_size)
    keep = torch.where(keep)[0]
    return keep


def clip_boxes_to_image(boxes: Tensor, size: Tuple[int, int]) -> Tensor:
    """
    Clip boxes so that they lie inside an image of size `size`.

    Args:
        boxes (Tensor[N, 4]): boxes in ``(x1, y1, x2, y2)`` format
            with ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
        size (Tuple[height, width]): size of the image

    Returns:
        clipped_boxes (Tensor[N, 4])
    """
    dim = boxes.dim()
    boxes_x = boxes[..., 0::2]
    boxes_y = boxes[..., 1::2]
    height, width = size

    if torchvision._is_tracing():
        boxes_x = torch.max(boxes_x, torch.tensor(0, dtype=boxes.dtype, device=boxes.device))
        boxes_x = torch.min(boxes_x, torch.tensor(width, dtype=boxes.dtype, device=boxes.device))
        boxes_y = torch.max(boxes_y, torch.tensor(0, dtype=boxes.dtype, device=boxes.device))
        boxes_y = torch.min(boxes_y, torch.tensor(height, dtype=boxes.dtype, device=boxes.device))
    else:
        boxes_x = boxes_x.clamp(min=0, max=width)
        boxes_y = boxes_y.clamp(min=0, max=height)

    clipped_boxes = torch.stack((boxes_x, boxes_y), dim=dim)
    return clipped_boxes.reshape(boxes.shape)


def box_convert(boxes: Tensor, in_fmt: str, out_fmt: str) -> Tensor:
    """
    Converts boxes from given in_fmt to out_fmt.
    Supported in_fmt and out_fmt are:

    'xyxy': boxes are represented via corners, x1, y1 being top left and x2, y2 being bottom right.

    'xywh' : boxes are represented via corner, width and height, x1, y2 being top left, w, h being width and height.

    'cxcywh' : boxes are represented via centre, width and height, cx, cy being center of box, w, h
    being width and height.

    Args:
        boxes (Tensor[N, 4]): boxes which will be converted.
        in_fmt (str): Input format of given boxes. Supported formats are ['xyxy', 'xywh', 'cxcywh'].
        out_fmt (str): Output format of given boxes. Supported formats are ['xyxy', 'xywh', 'cxcywh']

    Returns:
        boxes (Tensor[N, 4]): Boxes into converted format.
    """

    allowed_fmts = ("xyxy", "xywh", "cxcywh")
    if in_fmt not in allowed_fmts or out_fmt not in allowed_fmts:
        raise ValueError("Unsupported Bounding Box Conversions for given in_fmt and out_fmt")

    if in_fmt == out_fmt:
        return boxes.clone()

    if in_fmt != 'xyxy' and out_fmt != 'xyxy':
        # convert to xyxy and change in_fmt xyxy
        if in_fmt == "xywh":
            boxes = _box_xywh_to_xyxy(boxes)
        elif in_fmt == "cxcywh":
            boxes = _box_cxcywh_to_xyxy(boxes)
        in_fmt = 'xyxy'

    if in_fmt == "xyxy":
        if out_fmt == "xywh":
            boxes = _box_xyxy_to_xywh(boxes)
        elif out_fmt == "cxcywh":
            boxes = _box_xyxy_to_cxcywh(boxes)
    elif out_fmt == "xyxy":
        if in_fmt == "xywh":
            boxes = _box_xywh_to_xyxy(boxes)
        elif in_fmt == "cxcywh":
            boxes = _box_cxcywh_to_xyxy(boxes)
    return boxes


def _upcast(t: Tensor) -> Tensor:
    # Protects from numerical overflows in multiplications by upcasting to the equivalent higher type
    if t.is_floating_point():
        return t if t.dtype in (torch.float32, torch.float64) else t.float()
    else:
        return t if t.dtype in (torch.int32, torch.int64) else t.int()


def box_area(boxes: Tensor) -> Tensor:
    """
    Computes the area of a set of bounding boxes, which are specified by its
    (x1, y1, x2, y2) coordinates.

    Args:
        boxes (Tensor[N, 4]): boxes for which the area will be computed. They
            are expected to be in (x1, y1, x2, y2) format with
            ``0 <= x1 < x2`` and ``0 <= y1 < y2``.

    Returns:
        area (Tensor[N]): area for each box
    """
    boxes = _upcast(boxes)
    return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])


# implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py
# with slight modifications
def _box_inter_union(boxes1: Tensor, boxes2: Tensor) -> Tuple[Tensor, Tensor]:
    area1 = box_area(boxes1)
    area2 = box_area(boxes2)

    lt = torch.max(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]
    rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]

    wh = _upcast(rb - lt).clamp(min=0)  # [N,M,2]
    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]

    union = area1[:, None] + area2 - inter

    return inter, union


def box_iou(boxes1: Tensor, boxes2: Tensor) -> Tensor:
    """
    Return intersection-over-union (Jaccard index) of boxes.

    Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with
    ``0 <= x1 < x2`` and ``0 <= y1 < y2``.

    Args:
        boxes1 (Tensor[N, 4])
        boxes2 (Tensor[M, 4])

    Returns:
        iou (Tensor[N, M]): the NxM matrix containing the pairwise IoU values for every element in boxes1 and boxes2
    """
    inter, union = _box_inter_union(boxes1, boxes2)
    iou = inter / union
    return iou


# Implementation adapted from https://github.com/facebookresearch/detr/blob/master/util/box_ops.py
def generalized_box_iou(boxes1: Tensor, boxes2: Tensor) -> Tensor:
    """
    Return generalized intersection-over-union (Jaccard index) of boxes.

    Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with
    ``0 <= x1 < x2`` and ``0 <= y1 < y2``.

    Args:
        boxes1 (Tensor[N, 4])
        boxes2 (Tensor[M, 4])

    Returns:
        generalized_iou (Tensor[N, M]): the NxM matrix containing the pairwise generalized_IoU values
        for every element in boxes1 and boxes2
    """

    # degenerate boxes gives inf / nan results
    # so do an early check
    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()

    inter, union = _box_inter_union(boxes1, boxes2)
    iou = inter / union

    lti = torch.min(boxes1[:, None, :2], boxes2[:, :2])
    rbi = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])

    whi = _upcast(rbi - lti).clamp(min=0)  # [N,M,2]
    areai = whi[:, :, 0] * whi[:, :, 1]

    return iou - (areai - union) / areai