import math

import ipdb
import torch
from torch import nn
from torch.nn import functional as F

from pysgg.config import cfg
from pysgg.structures.boxlist_ops import squeeze_tensor


class FocalLoss(nn.Module):
    def __init__(
        self, alpha=1.0, gamma=2.0, logits=False, reduce=True, ignored_label_idx=None
    ):
        super(FocalLoss, self).__init__()
        self.alpha = alpha
        self.gamma = gamma
        self.logits = logits
        self.reduce = reduce
        self.ignored_label_idx = ignored_label_idx

    def forward(self, inputs, targets):
        if self.logits:#true
            BCE_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
        else:
            BCE_loss = F.binary_cross_entropy(inputs, targets, reduction="none")
        pt = torch.exp(-BCE_loss)
        F_loss = self.alpha * (1.0 - pt) ** self.gamma * BCE_loss
        if self.ignored_label_idx:
            F_loss = F_loss[targets != self.ignored_label_idx]

        if self.reduce:
            return torch.mean(F_loss)
        else:
            return F_loss


class FocalLossMultiTemplate(nn.Module):
    def __init__(self, alpha=1.0, gamma=2.0, logits=True):
        super(FocalLossMultiTemplate, self).__init__()
        self.alpha = alpha
        self.gamma = gamma

        self.focal_loss = FocalLoss(alpha, gamma, logits, reduce=False)

    def forward(self, inputs, targets):

        loss = self.focal_loss(inputs, targets).sum(-1).mean(-1)

        return loss


class FocalLossFGBGNormalization(nn.Module):
    def __init__(self, alpha=1.0, gamma=2.0, logits=True, fgbgnorm=True):
        super(FocalLossFGBGNormalization, self).__init__()
        self.alpha = alpha
        self.gamma = gamma
        self.focal_loss = FocalLoss(alpha, gamma, logits, reduce=False)



    def forward(self, inputs, targets, reduce=True):
        loss = self.focal_loss(inputs, targets)
        
        loss = loss.sum(-1)
        loss /= (len(torch.nonzero(targets)) + 1)

        return loss.mean(-1)



class WrappedBCELoss(nn.Module):
    def __init__(self):
        super(WrappedBCELoss, self).__init__()
        self.loss = F.binary_cross_entropy_with_logits

    def forward(self, inputs, targets, reduce=True):
        return self.loss(inputs, targets)

def loss_eval_bincls_single_level(pre_cls_logits, rel_labels, loss):

    bin_logits = pre_cls_logits[rel_labels != -1]

    selected_labels = rel_labels[rel_labels != -1].long()

    onehot = torch.zeros_like(bin_logits)
    onehot[selected_labels > 0] = 1
    loss_val = loss[0](inputs=bin_logits, targets=onehot, reduce=True)

    return loss_val


def loss_eval_mulcls_single_level(pre_cls_logits, rel_labels, loss):

    selected_cls_logits = pre_cls_logits[rel_labels != -1]

    selected_labels = rel_labels[rel_labels != -1].long()
    onehot = torch.zeros_like(selected_cls_logits)

    if len(onehot.shape) > 1:
        selected_fg_idx = squeeze_tensor(torch.nonzero(selected_labels > 0))
        onehot[selected_fg_idx, selected_labels[selected_fg_idx] - 1] = 1
    else:
        onehot[selected_labels > 0] = 1

    onehot = onehot.view(-1)
    selected_cls_logits = selected_cls_logits.view(-1)

    loss_val = loss(inputs=selected_cls_logits, targets=onehot)

    return loss_val

def loss_eval_hybrid_level(pre_cls_logits, rel_labels, loss):#loss分为两部分，第一部分是50rel分类，第二部分计算relateless,两个都是bce，所以2target可以
    selected_cls_logits = pre_cls_logits[rel_labels != -1]

    mulitlabel_logits = selected_cls_logits[:, :-1]
    bin_logits = selected_cls_logits[:, -1]#todo 和上面修改保持一致

    selected_labels = rel_labels[rel_labels != -1].long()

    onehot = torch.zeros_like(mulitlabel_logits)
    selected_fg_idx = squeeze_tensor(torch.nonzero(selected_labels > 0))#只是编号
    onehot[selected_fg_idx, selected_labels[selected_fg_idx] - 1] = 1#todo 和line 117矛盾？onehot[N,50]即不考虑no_rel，其他fg的one hot

    loss_val_mulabel = loss[0](inputs=mulitlabel_logits, targets=onehot, reduce=True)

    onehot = torch.zeros_like(bin_logits)
    onehot[selected_labels > 0] = 1#新的onehot,只考虑这个rel是不是有rel的onehot
    loss_val_bin = loss[1](inputs=bin_logits, targets=onehot, reduce=True)#loss_val_bin：scalar

    # return loss_val_bin
    return loss_val_bin * 0.8 + loss_val_mulabel * 0.2


class RelAwareLoss(nn.Module):
    def __init__(self, cfg):
        super(RelAwareLoss, self).__init__()
        alpha = cfg.MODEL.ROI_RELATION_HEAD.RELATION_PROPOSAL_MODEL.FOCAL_LOSS_ALPHA
        gamma = cfg.MODEL.ROI_RELATION_HEAD.RELATION_PROPOSAL_MODEL.FOCAL_LOSS_GAMMA

        self.pre_clser_loss_type = (
            cfg.MODEL.ROI_RELATION_HEAD.RELATION_PROPOSAL_MODEL.PRE_CLSER_LOSS
        )

        self.predictor_type = (
            cfg.MODEL.ROI_RELATION_HEAD.RELATION_PROPOSAL_MODEL.REL_AWARE_PREDICTOR_TYPE
        )

        fgbgnorm = False
        if "fgbg_norm" in self.pre_clser_loss_type:
            fgbgnorm = True

        if "focal" in self.pre_clser_loss_type:
            self.loss_module = (
                FocalLossFGBGNormalization(alpha, gamma, fgbgnorm=fgbgnorm),
                FocalLossFGBGNormalization(alpha, gamma, fgbgnorm=fgbgnorm),
            )
        elif "bce" in self.pre_clser_loss_type:
            self.loss_module = (
                WrappedBCELoss(),
                WrappedBCELoss(),
            )

    def forward(self, pred_logit, rel_labels):
        if "focal" in self.pre_clser_loss_type:
            if self.predictor_type == "single":
                return loss_eval_mulcls_single_level(pred_logit, rel_labels, self.loss_module[0])

            elif self.predictor_type == "hybrid":#ddefault
                return loss_eval_hybrid_level(pred_logit, rel_labels, self.loss_module)

        if 'bce' in self.pre_clser_loss_type:
            return  loss_eval_bincls_single_level(pred_logit, rel_labels, self.loss_module)
