import torch
import numpy as np


def bbox_overlaps(boxes, query_boxes):
    """
    Parameters
    ----------
    boxes: (N, 4) ndarray or tensor or variable
    query_boxes: (K, 4) ndarray or tensor or variable
    Returns
    -------
    overlaps: (N, K) overlap between boxes and query_boxes
    """
    if isinstance(boxes, np.ndarray):
        boxes = torch.from_numpy(boxes)
        query_boxes = torch.from_numpy(query_boxes)
        out_fn = lambda x: x.numpy()  # If input is ndarray, turn the overlaps back to ndarray when return
    else:
        out_fn = lambda x: x

    box_areas = (boxes[:, 2] - boxes[:, 0] + 1) * \
            (boxes[:, 3] - boxes[:, 1] + 1)
    query_areas = (query_boxes[:, 2] - query_boxes[:, 0] + 1) * \
            (query_boxes[:, 3] - query_boxes[:, 1] + 1)

    iw = (torch.min(boxes[:, 2:3], query_boxes[:, 2:3].t()) - torch.max(
        boxes[:, 0:1], query_boxes[:, 0:1].t()) + 1).clamp(min=0)
    ih = (torch.min(boxes[:, 3:4], query_boxes[:, 3:4].t()) - torch.max(
        boxes[:, 1:2], query_boxes[:, 1:2].t()) + 1).clamp(min=0)
    ua = box_areas.view(-1, 1) + query_areas.view(1, -1) - iw * ih
    overlaps = iw * ih / ua
    return out_fn(overlaps)