import numpy as np
import scipy.ndimage as nd

import torch
import torch.nn as nn
import torch.nn.functional as F

from utils.seg_opr.lovasz_losses import lovasz_softmax

class JSD(nn.Module):
    def __init__(self):
        super(JSD, self).__init__()
        self.kl = nn.KLDivLoss(reduction='batchmean', log_target=True)

    def forward(self, p: torch.tensor, q: torch.tensor):
        p = F.softmax(p, dim=1)
        q = F.softmax(q, dim=1)
        m = (0.5 * (p + q)).log()
        return 0.5 * (self.kl(m, p.log()) + self.kl(m, q.log()))

class MSE(nn.Module):
    def __init__(self):
        super(MSE, self).__init__()
        self.mse = nn.MSELoss(reduction="mean")
    
    def forward(self, p: torch.tensor, q: torch.tensor):
        p = F.softmax(p, dim=1)
        q = F.softmax(q, dim=1)
        return self.mse(p, q)

class ProbOhemCrossEntropy2d(nn.Module):
    def __init__(self, ignore_label, reduction='mean', thresh=0.6, min_kept=256,
                 down_ratio=1, use_weight=False):
        super(ProbOhemCrossEntropy2d, self).__init__()
        self.ignore_label = ignore_label
        self.thresh = float(thresh)
        self.min_kept = int(min_kept)
        self.down_ratio = down_ratio
        if use_weight:
            weight = torch.FloatTensor(
                [0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489,
                 0.8786, 1.0023, 0.9539, 0.9843, 1.1116, 0.9037, 1.0865, 1.0955,
                 1.0865, 1.1529, 1.0507])
            self.criterion = torch.nn.CrossEntropyLoss(reduction=reduction,
                                                       weight=weight,
                                                       ignore_index=ignore_label)
        else:
            self.criterion = torch.nn.CrossEntropyLoss(reduction=reduction,
                                                       ignore_index=ignore_label)

    def forward(self, pred, target):
        b, c, h, w = pred.size()
        target = target.view(-1)
        valid_mask = target.ne(self.ignore_label)
        target = target * valid_mask.long()
        num_valid = valid_mask.sum()

        prob = F.softmax(pred, dim=1)
        prob = (prob.transpose(0, 1)).reshape(c, -1)

        if self.min_kept > num_valid:
            print('Labels: {} < {}'.format(num_valid, self.min_kept))
        elif num_valid > 0:
            prob = prob.masked_fill_(~valid_mask, 1)
            mask_prob = prob[
                target, torch.arange(len(target), dtype=torch.long)]
            threshold = self.thresh
            if self.min_kept > 0:
                index = mask_prob.argsort()
                threshold_index = index[min(len(index), self.min_kept) - 1]
                if mask_prob[threshold_index] > self.thresh:
                    threshold = mask_prob[threshold_index]
                kept_mask = mask_prob.le(threshold)     # 概率小于阈值的挖出来 (The probability is less than the threshold to be dug out)
                target = target * kept_mask.long()
                valid_mask = valid_mask * kept_mask
                # logger.info('Valid Mask: {}'.format(valid_mask.sum()))

        target = target.masked_fill_(~valid_mask, self.ignore_label)
        target = target.view(b, h, w)
        
        return self.criterion(pred, target)
    
class FocalLoss(nn.Module):
    def __init__(self, gamma=2, alpha=None, ignore_label=255, size_average=True):
        super(FocalLoss, self).__init__()
        self.gamma = gamma
        self.size_average = size_average
        self.CE_loss = nn.CrossEntropyLoss(reduce=False, ignore_index=ignore_label, weight=alpha)

    def forward(self, output, target):
        logpt = self.CE_loss(output, target)
        pt = torch.exp(-logpt)
        loss = ((1-pt)**self.gamma) * logpt
        if self.size_average:
            return loss.mean()
        return loss.sum()
    
class LovaszSoftmax(nn.Module):
    def __init__(self, classes='present', per_image=False, ignore_index=255):
        super(LovaszSoftmax, self).__init__()
        self.smooth = classes
        self.per_image = per_image
        self.ignore_index = ignore_index
    
    def forward(self, output, target):
        logits = F.softmax(output, dim=1)
        loss = lovasz_softmax(logits, target, ignore=self.ignore_index)
        return loss