# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Tuple, Union

import numpy as np
import torch
from mmengine.model import BaseModule
from torch import Tensor

from mmdet.registry import MODELS
from .mse_loss import mse_loss


@MODELS.register_module()
class MarginL2Loss(BaseModule):
    """L2 loss with margin.

    Args:
        neg_pos_ub (int, optional): The upper bound of negative to positive
            samples in hard mining. Defaults to -1.
        pos_margin (float, optional): The similarity margin for positive
            samples in hard mining. Defaults to -1.
        neg_margin (float, optional): The similarity margin for negative
            samples in hard mining. Defaults to -1.
        hard_mining (bool, optional): Whether to use hard mining. Defaults to
            False.
        reduction (str, optional): The method to reduce the loss.
            Options are "none", "mean" and "sum". Defaults to "mean".
        loss_weight (float, optional): The weight of loss. Defaults to 1.0.
    """

    def __init__(self,
                 neg_pos_ub: int = -1,
                 pos_margin: float = -1,
                 neg_margin: float = -1,
                 hard_mining: bool = False,
                 reduction: str = 'mean',
                 loss_weight: float = 1.0):
        super(MarginL2Loss, self).__init__()
        self.neg_pos_ub = neg_pos_ub
        self.pos_margin = pos_margin
        self.neg_margin = neg_margin
        self.hard_mining = hard_mining
        self.reduction = reduction
        self.loss_weight = loss_weight

    def forward(self,
                pred: Tensor,
                target: Tensor,
                weight: Optional[Tensor] = None,
                avg_factor: Optional[float] = None,
                reduction_override: Optional[str] = None) -> Tensor:
        """Forward function.

        Args:
            pred (torch.Tensor): The prediction.
            target (torch.Tensor): The learning target of the prediction.
            weight (torch.Tensor, optional): The weight of loss for each
                prediction. Defaults to None.
            avg_factor (float, optional): Average factor that is used to
                average the loss. Defaults to None.
            reduction_override (str, optional): The reduction method used to
                override the original reduction method of the loss.
                Defaults to None.
        """
        assert reduction_override in (None, 'none', 'mean', 'sum')
        reduction = (
            reduction_override if reduction_override else self.reduction)
        pred, weight, avg_factor = self.update_weight(pred, target, weight,
                                                      avg_factor)
        loss_bbox = self.loss_weight * mse_loss(
            pred,
            target.float(),
            weight.float(),
            reduction=reduction,
            avg_factor=avg_factor)
        return loss_bbox

    def update_weight(self, pred: Tensor, target: Tensor, weight: Tensor,
                      avg_factor: float) -> Tuple[Tensor, Tensor, float]:
        """Update the weight according to targets.

        Args:
            pred (torch.Tensor): The prediction.
            target (torch.Tensor): The learning target of the prediction.
            weight (torch.Tensor): The weight of loss for each prediction.
            avg_factor (float): Average factor that is used to average the
                loss.

        Returns:
            tuple[torch.Tensor]: The updated prediction, weight and average
            factor.
        """
        if weight is None:
            weight = target.new_ones(target.size())

        invalid_inds = weight <= 0
        target[invalid_inds] = -1
        pos_inds = target == 1
        neg_inds = target == 0

        if self.pos_margin > 0:
            pred[pos_inds] -= self.pos_margin
        if self.neg_margin > 0:
            pred[neg_inds] -= self.neg_margin
        pred = torch.clamp(pred, min=0, max=1)

        num_pos = int((target == 1).sum())
        num_neg = int((target == 0).sum())
        if self.neg_pos_ub > 0 and num_neg / (num_pos +
                                              1e-6) > self.neg_pos_ub:
            num_neg = num_pos * self.neg_pos_ub
            neg_idx = torch.nonzero(target == 0, as_tuple=False)

            if self.hard_mining:
                costs = mse_loss(
                    pred, target.float(),
                    reduction='none')[neg_idx[:, 0], neg_idx[:, 1]].detach()
                neg_idx = neg_idx[costs.topk(num_neg)[1], :]
            else:
                neg_idx = self.random_choice(neg_idx, num_neg)

            new_neg_inds = neg_inds.new_zeros(neg_inds.size()).bool()
            new_neg_inds[neg_idx[:, 0], neg_idx[:, 1]] = True

            invalid_neg_inds = torch.logical_xor(neg_inds, new_neg_inds)
            weight[invalid_neg_inds] = 0

        avg_factor = (weight > 0).sum()
        return pred, weight, avg_factor

    @staticmethod
    def random_choice(gallery: Union[list, np.ndarray, Tensor],
                      num: int) -> np.ndarray:
        """Random select some elements from the gallery.

        It seems that Pytorch's implementation is slower than numpy so we use
        numpy to randperm the indices.

        Args:
            gallery (list | np.ndarray | torch.Tensor): The gallery from
                which to sample.
            num (int): The number of elements to sample.
        """
        assert len(gallery) >= num
        if isinstance(gallery, list):
            gallery = np.array(gallery)
        cands = np.arange(len(gallery))
        np.random.shuffle(cands)
        rand_inds = cands[:num]
        if not isinstance(gallery, np.ndarray):
            rand_inds = torch.from_numpy(rand_inds).long().to(gallery.device)
        return gallery[rand_inds]