# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch


class Matcher(object):
    """
    This class assigns to each predicted "element" (e.g., a box) a ground-truth
    element. Each predicted element will have exactly zero or one matches; each
    ground-truth element may be assigned to zero or more predicted elements.

    Matching is based on the MxN match_quality_matrix, that characterizes how well
    each (ground-truth, predicted)-pair match. For example, if the elements are
    boxes, the matrix may contain box IoU overlap values.

    The matcher returns a tensor of size N containing the index of the ground-truth
    element m that matches to prediction n. If there is no match, a negative value
    is returned.
    """

    BELOW_LOW_THRESHOLD = -1
    BETWEEN_THRESHOLDS = -2

    def __init__(self, high_threshold, low_threshold, allow_low_quality_matches=False):
        """
        Args:
            high_threshold (float): quality values greater than or equal to
                this value are candidate matches.
            low_threshold (float): a lower quality threshold used to stratify
                matches into three levels:
                1) matches >= high_threshold
                2) BETWEEN_THRESHOLDS matches in [low_threshold, high_threshold)
                3) BELOW_LOW_THRESHOLD matches in [0, low_threshold)
            allow_low_quality_matches (bool): if True, produce additional matches
                for predictions that have only low-quality match candidates. See
                set_low_quality_matches_ for more details.
        """
        assert low_threshold <= high_threshold
        self.high_threshold = high_threshold
        self.low_threshold = low_threshold
        self.allow_low_quality_matches = allow_low_quality_matches

    def __call__(self, match_quality_matrix):
        """
        Args:
            match_quality_matrix (Tensor[float]): an MxN tensor, containing the
            pairwise quality between M ground-truth elements and N predicted elements.

        Returns:
            matches (Tensor[int64]): an N tensor where N[i] is a matched gt in
            [0, M - 1] or a negative value indicating that prediction i could not
            be matched.
        """
        if match_quality_matrix.numel() == 0:
            # empty targets or proposals not supported during training
            if match_quality_matrix.shape[0] == 0:
                raise ValueError(
                    "No ground-truth boxes available for one of the images " "during training"
                )
            else:
                raise ValueError(
                    "No proposal boxes available for one of the images " "during training"
                )

        # match_quality_matrix is M (gt) x N (predicted)
        # Max over gt elements (dim 0) to find best gt candidate for each prediction
        matched_vals, matches = match_quality_matrix.max(dim=0)
        if self.allow_low_quality_matches:
            all_matches = matches.clone()

        # Assign candidate matches with low quality to negative (unassigned) values
        below_low_threshold = matched_vals < self.low_threshold
        between_thresholds = (matched_vals >= self.low_threshold) & (
            matched_vals < self.high_threshold
        )
        matches[below_low_threshold] = Matcher.BELOW_LOW_THRESHOLD
        matches[between_thresholds] = Matcher.BETWEEN_THRESHOLDS

        if self.allow_low_quality_matches:
            self.set_low_quality_matches_(matches, all_matches, match_quality_matrix)

        return matches

    def set_low_quality_matches_(self, matches, all_matches, match_quality_matrix):
        """
        Produce additional matches for predictions that have only low-quality matches.
        Specifically, for each ground-truth find the set of predictions that have
        maximum overlap with it (including ties); for each prediction in that set, if
        it is unmatched, then match it to the ground-truth with which it has the highest
        quality value.
        """
        # For each gt, find the prediction with which it has highest quality
        highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1)
        # Find highest quality match available, even if it is low, including ties
        gt_pred_pairs_of_highest_quality = torch.nonzero(
            match_quality_matrix == highest_quality_foreach_gt[:, None]
        )
        # Example gt_pred_pairs_of_highest_quality:
        #   tensor([[    0, 39796],
        #           [    1, 32055],
        #           [    1, 32070],
        #           [    2, 39190],
        #           [    2, 40255],
        #           [    3, 40390],
        #           [    3, 41455],
        #           [    4, 45470],
        #           [    5, 45325],
        #           [    5, 46390]])
        # Each row is a (gt index, prediction index)
        # Note how gt items 1, 2, 3, and 5 each have two ties

        pred_inds_to_update = gt_pred_pairs_of_highest_quality[:, 1]
        matches[pred_inds_to_update] = all_matches[pred_inds_to_update]
