import numpy as np
import pycocotools.mask as mask_util

from detectron2.structures import BoxMode


def instances_to_coco_json_with_logits(instances, img_id):
    """
    Dump an "Instances" object to a COCO-format json that's used for evaluation.

    Args:
        instances (Instances):
        img_id (int): the image id

    Returns:
        list[dict]: list of json annotations in COCO format.
    """
    num_instance = len(instances)
    if num_instance == 0:
        return []

    boxes = instances.pred_boxes.tensor.numpy()
    boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
    boxes = boxes.tolist()
    scores = instances.scores.tolist()
    classes = instances.pred_classes.tolist()
    pred_inds = instances.pred_inds.tolist()

    has_mask = instances.has("pred_masks")
    if has_mask:
        # use RLE to encode the masks, because they are too large and takes memory
        # since this evaluator stores outputs of the entire dataset
        rles = [
            mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0]
            for mask in instances.pred_masks
        ]
        for rle in rles:
            # "counts" is an array encoded by mask_util as a byte-stream. Python3's
            # json writer which always produces strings cannot serialize a bytestream
            # unless you decode it. Thankfully, utf-8 works out (which is also what
            # the pycocotools/_mask.pyx does).
            rle["counts"] = rle["counts"].decode("utf-8")

    has_keypoints = instances.has("pred_keypoints")
    if has_keypoints:
        keypoints = instances.pred_keypoints

    results = []
    for k in range(num_instance):
        result = {
            "image_id": img_id,
            "category_id": classes[k],
            "bbox": boxes[k],
            "score": scores[k],
            "pred_ind": pred_inds[k],
        }
        if has_mask:
            result["segmentation"] = rles[k]
        if has_keypoints:
            # In COCO annotations,
            # keypoints coordinates are pixel indices.
            # However our predictions are floating point coordinates.
            # Therefore we subtract 0.5 to be consistent with the annotation format.
            # This is the inverse of data loading logic in `datasets/coco.py`.
            keypoints[k][:, :2] -= 0.5
            result["keypoints"] = keypoints[k].flatten().tolist()
        results.append(result)
    return results