# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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

import numpy as np


class OhemCELoss(nn.Module):
    def __init__(self, thresh, n_min, ignore_lb=255, *args, **kwargs):
        super(OhemCELoss, self).__init__()
        self.thresh = -torch.log(torch.tensor(thresh, dtype=torch.float)).cuda()
        self.n_min = n_min
        self.ignore_lb = ignore_lb
        self.criteria = nn.CrossEntropyLoss(ignore_index=ignore_lb, reduction='none')

    def forward(self, logits, labels):
        N, C, H, W = logits.size()
        loss = self.criteria(logits, labels).view(-1)
        loss, _ = torch.sort(loss, descending=True)
        if loss[self.n_min] > self.thresh:
            loss = loss[loss > self.thresh]
        else:
            loss = loss[:self.n_min]
        return torch.mean(loss)


class SoftmaxFocalLoss(nn.Module):
    def __init__(self, gamma, ignore_lb=255, *args, **kwargs):
        super(SoftmaxFocalLoss, self).__init__()
        self.gamma = gamma
        self.nll = nn.NLLLoss(ignore_index=ignore_lb)

    def forward(self, logits, labels):
        scores = F.softmax(logits, dim=1)
        factor = torch.pow(1. - scores, self.gamma)
        log_score = F.log_softmax(logits, dim=1)
        log_score = factor * log_score
        loss = self.nll(log_score.cpu(), labels.cpu())
        return loss


class ParsingRelationLoss(nn.Module):
    def __init__(self):
        super(ParsingRelationLoss, self).__init__()

    def forward(self, logits):
        n, c, h, w = logits.shape
        loss_all = []
        for i in range(0, h - 1):
            loss_all.append(logits[:, :, i, :] - logits[:, :, i + 1, :])
        # loss0 : n,c,w
        loss = torch.cat(loss_all)
        return torch.nn.functional.smooth_l1_loss(loss, torch.zeros_like(loss))


class ParsingRelationDis(nn.Module):
    def __init__(self):
        super(ParsingRelationDis, self).__init__()
        self.l1 = torch.nn.L1Loss()
        # self.l1 = torch.nn.MSELoss()

    def forward(self, x):
        n, dim, num_rows, num_cols = x.shape
        x = torch.nn.functional.softmax(x[:, :dim - 1, :, :], dim=1)
        embedding = torch.Tensor(np.arange(dim - 1)).float().to(x.device).view(1, -1, 1, 1)
        pos = torch.sum(x * embedding, dim=1)

        diff_list1 = []
        for i in range(0, num_rows // 2):
            diff_list1.append(pos[:, i, :] - pos[:, i + 1, :])

        loss = 0
        for i in range(len(diff_list1) - 1):
            loss += self.l1(diff_list1[i], diff_list1[i + 1])
        loss /= len(diff_list1) - 1
        return loss