# coding=utf-8
# Copyright 2022 The IDEA Authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# ------------------------------------------------------------------------------------------------
# HungarianMatcher
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------------------------------
# Modified from:
# https://github.com/Atten4Vis/ConditionalDETR/blob/GroupDETR/models/matcher.py
# ------------------------------------------------------------------------------------------------

import numpy as np
import torch
import torch.nn as nn
from scipy.optimize import linear_sum_assignment

from detrex.layers.box_ops import box_cxcywh_to_xyxy, generalized_box_iou


class GroupHungarianMatcher(nn.Module):
    """HugarianMatcher supports Group-DETR

    Args:
        cost_class (float): The relative weight of the classification error
            in the matching cost. Default: 1.
        cost_bbox (float): The relative weight of the L1 error of the bounding box
            coordinates in the matching cost. Default: 1.
        cost_giou (float): This is the relative weight of the giou loss of
            the bounding box in the matching cost. Default: 1.
    """

    def __init__(self, cost_class: float = 1, cost_bbox: float = 1, cost_giou: float = 1):
        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, group_nums=1):
        """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
            group_nums: Number of groups used for matching.
        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).sigmoid()
        )  # [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.
        alpha = 0.25
        gamma = 2.0
        neg_cost_class = (1 - alpha) * (out_prob**gamma) * (-(1 - out_prob + 1e-8).log())
        pos_cost_class = alpha * ((1 - out_prob) ** gamma) * (-(out_prob + 1e-8).log())
        cost_class = pos_cost_class[:, tgt_ids] - neg_cost_class[:, 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 = []
        g_num_queries = num_queries // group_nums
        C_list = C.split(g_num_queries, dim=1)
        for g_i in range(group_nums):
            C_g = C_list[g_i]
            indices_g = [linear_sum_assignment(c[i]) for i, c in enumerate(C_g.split(sizes, -1))]
            if g_i == 0:
                indices = indices_g
            else:
                indices = [
                    (
                        np.concatenate([indice1[0], indice2[0] + g_num_queries * g_i]),
                        np.concatenate([indice1[1], indice2[1]]),
                    )
                    for indice1, indice2 in zip(indices, indices_g)
                ]
        return [
            (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64))
            for i, j in indices
        ]