# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from scipy.optimize import linear_sum_assignment

import torch
import torch.nn as nn

from cvpods.layers.box_ops import box_cxcywh_to_xyxy, generalized_box_iou


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 matched to zero or more predicted elements.

    The matching is determined by the MxN match_quality_matrix, that characterizes
    how well each (ground-truth, prediction)-pair match each other. For example,
    if the elements are boxes, this matrix may contain box intersection-over-union
    overlap values.

    The matcher returns (a) a vector of length N containing the index of the
    ground-truth element m in [0, M) that matches to prediction n in [0, N).
    (b) a vector of length N containing the labels for each prediction.
    """

    def __init__(self, thresholds, labels, allow_low_quality_matches=False):
        """
        Args:
            thresholds (list): a list of thresholds used to stratify predictions
                into levels.
            labels (list): a list of values to label predictions belonging at
                each level. A label can be one of {-1, 0, 1} signifying
                {ignore, negative class, positive class}, respectively.
            allow_low_quality_matches (bool): if True, produce additional matches
                for predictions with maximum match quality lower than high_threshold.
                See set_low_quality_matches_ for more details.

            For example,
                thresholds = [0.3, 0.5]
                labels = [0, -1, 1]
                All predictions with iou < 0.3 will be marked with 0 and
                thus will be considered as false positives while training.
                All predictions with 0.3 <= iou < 0.5 will be marked with -1 and
                thus will be ignored.
                All predictions with 0.5 <= iou will be marked with 1 and
                thus will be considered as true positives.
        """
        # Add -inf and +inf to first and last position in thresholds
        thresholds = thresholds[:]
        assert thresholds[0] > 0
        thresholds.insert(0, -float("inf"))
        thresholds.append(float("inf"))
        assert all(low <= high for (low, high) in zip(thresholds[:-1], thresholds[1:]))
        assert all(l in [-1, 0, 1] for l in labels)
        assert len(labels) == len(thresholds) - 1
        self.thresholds = thresholds
        self.labels = labels
        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. All elements must be >= 0 (due to the us of `torch.nonzero`
                for selecting indices in :meth:`set_low_quality_matches_`).

        Returns:
            matches (Tensor[int64]): a vector of length N, where matches[i] is a matched
                ground-truth index in [0, M)
            match_labels (Tensor[int8]): a vector of length N, where pred_labels[i] indicates
                whether a prediction is a true or false positive or ignored
        """
        assert match_quality_matrix.dim() == 2
        if match_quality_matrix.numel() == 0:
            default_matches = match_quality_matrix.new_full(
                (match_quality_matrix.size(1),), 0, dtype=torch.int64
            )
            # When no gt boxes exist, we define IOU = 0 and therefore set labels
            # to `self.labels[0]`, which usually defaults to background class 0
            # To choose to ignore instead, can make labels=[-1,0,-1,1] + set appropriate thresholds
            default_match_labels = match_quality_matrix.new_full(
                (match_quality_matrix.size(1),), self.labels[0], dtype=torch.int8
            )
            return default_matches, default_match_labels

        assert torch.all(match_quality_matrix >= 0)

        # 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)

        match_labels = matches.new_full(matches.size(), 1, dtype=torch.int8)

        for (l, low, high) in zip(self.labels, self.thresholds[:-1], self.thresholds[1:]):
            low_high = (matched_vals >= low) & (matched_vals < high)
            match_labels[low_high] = l

        if self.allow_low_quality_matches:
            self.set_low_quality_matches_(match_labels, match_quality_matrix)           # 

        return matches, match_labels

    def set_low_quality_matches_(self, match_labels, match_quality_matrix):
        """
        Produce additional matches for predictions that have only low-quality matches.
        Specifically, for each ground-truth G 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 G.

        This function implements the RPN assignment case (i) in Sec. 3.1.2 of the
        Faster R-CNN paper: https://arxiv.org/pdf/1506.01497v3.pdf.
        """
        # For each gt, find the prediction with which it has highest quality
        highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1)
        # Find the highest quality match available, even if it is low, including ties.
        # Note that the matches qualities must be positive due to the use of
        # `torch.nonzero`.
        gt_pred_pairs_of_highest_quality = torch.nonzero(
            match_quality_matrix == highest_quality_foreach_gt[:, None], as_tuple=False
        )
        # 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]
        match_labels[pred_inds_to_update] = 1


class MatcherIgnore(Matcher):
    """
    Matcher with Ignore labels. (e.g. Crowd Human)
    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 matched to zero or more predicted elements.

    The matching is determined by the MxN match_quality_matrix, that characterizes
    how well each (ground-truth, prediction)-pair match each other. For example,
    if the elements are boxes, this matrix may contain box intersection-over-union
    overlap values.

    The matcher returns (a) a vector of length N containing the index of the
    ground-truth element m in [0, M) that matches to prediction n in [0, N).
    (b) a vector of length N containing the labels for each prediction.
    """

    def __call__(self, match_quality_matrix, match_quality_ignore=None):
        """
        Args:
            match_quality_matrix (Tensor[float]): an MxN tensor, containing the
                pairwise quality between M ground-truth elements and N predicted
                elements. All elements must be >= 0 (due to the us of `torch.nonzero`
                for selecting indices in :meth:`set_low_quality_matches_`).

        Returns:
            matches (Tensor[int64]): a vector of length N, where matches[i] is a matched
                ground-truth index in [0, M)
            match_labels (Tensor[int8]): a vector of length N, where pred_labels[i] indicates
                whether a prediction is a true or false positive or ignored
        """
        assert match_quality_matrix.dim() == 2
        if match_quality_matrix.numel() == 0:
            default_matches = match_quality_matrix.new_full(
                (match_quality_matrix.size(1),), 0, dtype=torch.int64
            )
            # When no gt boxes exist, we define IOU = 0 and therefore set labels
            # to `self.labels[0]`, which usually defaults to background class 0
            # To choose to ignore instead, can make labels=[-1,0,-1,1] + set appropriate thresholds
            default_match_labels = match_quality_matrix.new_full(
                (match_quality_matrix.size(1),), self.labels[0], dtype=torch.int8
            )
            return default_matches, default_match_labels

        assert torch.all(match_quality_matrix >= 0)

        # 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)
        matched_vals_ign, matches_ign = match_quality_ignore.max(dim=0)

        assert len(self.thresholds) == 3
        high = self.thresholds[1]
        ign_mask = ((matched_vals_ign > matched_vals) & (matched_vals < high)).float()
        matched_vals = matched_vals * (1 - ign_mask) + matched_vals_ign * ign_mask
        matches = (matches * (1 - ign_mask) + matches_ign * ign_mask).long()

        match_labels = matches.new_full(matches.size(), 1, dtype=torch.int8)

        for (l, low, high) in zip(self.labels, self.thresholds[:-1], self.thresholds[1:]):
            low_high = (matched_vals >= low) & (matched_vals < high)
            match_labels[low_high] = l

        if self.allow_low_quality_matches:
            self.set_low_quality_matches_(match_labels, match_quality_matrix)

        return matches, match_labels


class HungarianMatcher(nn.Module):
    """This class computes an assignment between the targets and the predictions of the network
    For efficiency reasons, the targets don't include the no_object. Because of this, in general,
    there are more predictions than targets. In this case, we do a 1-to-1 matching of the best
    predictions, while the others are un-matched (and thus treated as non-objects).
    """

    def __init__(self, cost_class: float = 1, cost_bbox: float = 1, cost_giou: float = 1):
        """Creates the matcher
        Params:
            cost_class: This is the relative weight of the classification error in the matching cost
            cost_bbox: This is the relative weight of the L1 error of the bounding box coordinates
            in the matching cost cost_giou: This is the relative weight of the giou loss of the
            bounding box in the matching cost
        """
        super().__init__()
        self.cost_class = cost_class
        self.cost_bbox = cost_bbox
        self.cost_giou = cost_giou
        assert cost_class != 0 or cost_bbox != 0 or cost_giou != 0, "all costs cant be 0"

    @torch.no_grad()
    def forward(self, outputs, targets):
        """ Performs the matching
        Params:
            outputs: This is a dict that contains at least these entries:
                 "pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the
                            classification logits
                 "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted
                            box coordinates
            targets: This is a list of targets (len(targets) = batch_size), where each target
                            is a dict containing:
                 "labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number
                            of ground-truth objects in the target) containing the class labels
                 "boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates
        Returns:
            A list of size batch_size, containing tuples of (index_i, index_j) where:
                - index_i is the indices of the selected predictions (in order)
                - index_j is the indices of the corresponding selected targets (in order)
            For each batch element, it holds:
                len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
        """
        bs, num_queries = outputs["pred_logits"].shape[:2]

        # We flatten to compute the cost matrices in a batch
        out_prob = (
            outputs["pred_logits"].flatten(0, 1).softmax(-1)
        )  # [batch_size * num_queries, num_classes]
        out_bbox = outputs["pred_boxes"].flatten(0, 1)  # [batch_size * num_queries, 4]

        # Also concat the target labels and boxes
        tgt_ids = torch.cat([v["labels"] for v in targets])
        tgt_bbox = torch.cat([v["boxes"] for v in targets])

        # Compute the classification cost. Contrary to the loss, we don't use the NLL,
        # but approximate it in 1 - proba[target class].
        # The 1 is a constant that doesn't change the matching, it can be ommitted.
        cost_class = -out_prob[:, tgt_ids]

        # Compute the L1 cost between boxes
        cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1)

        # Compute the giou cost betwen boxes
        cost_giou = -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox), box_cxcywh_to_xyxy(tgt_bbox))

        # Final cost matrix
        C = self.cost_bbox * cost_bbox + self.cost_class * cost_class + self.cost_giou * cost_giou
        C = C.view(bs, num_queries, -1).cpu()

        sizes = [len(v["boxes"]) for v in targets]
        indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))]
        return [
            (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64))
            for i, j in indices
        ]