# -*- coding: utf-8 -*-
"""
   Copyright 2019 Petr Masopust, Aprar s.r.o.

   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.

   Adopted code from https://github.com/ronghuaiyang/arcface-pytorch
"""

import math

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


class AngleLinear(nn.Module):
    def __init__(self, in_features, out_features):
        super(AngleLinear, self).__init__()
        self.W = Parameter(torch.FloatTensor(out_features, in_features))
        nn.init.xavier_uniform_(self.W)

    def forward(self, input):
        x = F.normalize(input)
        W = F.normalize(self.W)
        return F.linear(x, W)


class AdaCos(nn.Module):
    def __init__(self, num_classes, m=0.50, is_cuda=True):
        super(AdaCos, self).__init__()
        self.n_classes = num_classes
        self.s = math.sqrt(2) * math.log(num_classes - 1)
        self.base_s = self.s
        self.m = m
        self.criterion = nn.CrossEntropyLoss()
        if is_cuda:
            self.criterion = self.criterion.cuda()

    def forward(self, input, label):
        # changed to fixed adacos - faster and more stable
        #        theta = torch.acos(torch.clamp(input, -1.0 + 1e-7, 1.0 - 1e-7))
        #        one_hot = torch.zeros_like(input)
        #        one_hot.scatter_(1, label.view(-1, 1).long(), 1)
        #        with torch.no_grad():
        #            B_avg = torch.where(one_hot < 1, torch.exp(self.s * input), torch.zeros_like(input))
        #            B_avg = torch.sum(B_avg) / input.size(0)
        #            theta_med = torch.median(theta)
        #            self.s = torch.log(B_avg) / torch.cos(torch.min(math.pi/4 * torch.ones_like(theta_med), theta_med))
        #            # TODO why converge to infinity ?
        #            self.s = torch.clamp(self.s, self.base_s / 2, self.base_s * 2)
        #            print(self.s)
        output = self.s * input

        return self.criterion(output, label)


class ArcFace(nn.Module):
    def __init__(self, s=30.0, m=0.50, is_cuda=True):
        super(ArcFace, self).__init__()
        self.s = s
        self.m = m
        self.criterion = nn.CrossEntropyLoss()
        if is_cuda:
            self.criterion = self.criterion.cuda()

    def forward(self, input, label):
        theta = torch.acos(torch.clamp(input, -1.0 + 1e-7, 1.0 - 1e-7))
        target_logits = torch.cos(theta + self.m)
        one_hot = torch.zeros_like(input)
        one_hot.scatter_(1, label.view(-1, 1).long(), 1)
        output = input * (1 - one_hot) + target_logits * one_hot
        output *= self.s

        return self.criterion(output, label)


class SphereFace(nn.Module):
    def __init__(self, s=30.0, m=1.35, is_cuda=True):
        super(SphereFace, self).__init__()
        self.s = s
        self.m = m
        self.criterion = nn.CrossEntropyLoss()
        if is_cuda:
            self.criterion = self.criterion.cuda()

    def forward(self, input, label):
        theta = torch.acos(torch.clamp(input, -1.0 + 1e-7, 1.0 - 1e-7))
        target_logits = torch.cos(self.m * theta)
        one_hot = torch.zeros_like(input)
        one_hot.scatter_(1, label.view(-1, 1).long(), 1)
        output = input * (1 - one_hot) + target_logits * one_hot
        output *= self.s

        return self.criterion(output, label)


class CosFace(nn.Module):
    def __init__(self, s=30.0, m=0.35, is_cuda=True):
        super(CosFace, self).__init__()
        self.s = s
        self.m = m
        self.criterion = nn.CrossEntropyLoss()
        if is_cuda:
            self.criterion = self.criterion.cuda()

    def forward(self, input, label):
        target_logits = input - self.m
        one_hot = torch.zeros_like(input)
        one_hot.scatter_(1, label.view(-1, 1).long(), 1)
        output = input * (1 - one_hot) + target_logits * one_hot
        output *= self.s

        return self.criterion(output, label)