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


class ArcMarginProduct(nn.Module):
    r"""Implement of large margin arc distance: :
    Args:
        in_features: size of each input sample
        out_features: size of each output sample
        s: norm of input feature
        m: margin
        cos(theta + m)
    """

    def __init__(
        self,
        in_features,
        out_features,
        s=30.0,
        m=0.50,
        easy_margin=False,
        ls_eps=0.0,
        alpha=0.0,
        device="cuda",
    ):
        super(ArcMarginProduct, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.s = s
        self.m = m
        self.alpha = alpha
        self.ls_eps = ls_eps  # label smoothing
        self.device = device
        self.weight = Parameter(torch.FloatTensor(out_features, in_features))
        nn.init.xavier_uniform_(self.weight)

        self.easy_margin = easy_margin
        self.cos_m = math.cos(m)
        self.sin_m = math.sin(m)
        self.th = math.cos(math.pi - m)
        self.mm = math.sin(math.pi - m) * m

    def __repr__(self):
        return (
            "{in_features}, {out_features}, s={s}, m = {m}, "
            "easy_margin = {easy_margin}, ls_eps = {ls_eps} ".format(**self.__dict__)
        )

    def update_margin(self, epoch):
        m = self.m + (self.alpha * (epoch + 1))
        self.cos_m = math.cos(m)
        self.sin_m = math.sin(m)
        self.th = math.cos(math.pi - m)
        self.mm = math.sin(math.pi - m) * m
        print(f"margin updated to : {m}")
        return None

    def forward(self, input, label):
        # --------------------------- cos(theta) & phi(theta) ---------------------------
        cosine = F.linear(input, F.normalize(self.weight))
        cosine = cosine.clamp(-1, 1)
        sine = torch.sqrt(1.0 - torch.pow(cosine, 2))
        phi = cosine * self.cos_m - sine * self.sin_m
        if self.easy_margin:
            phi = torch.where(cosine > 0, phi, cosine)
        else:
            phi = torch.where(cosine > self.th, phi, cosine - self.mm)
        # --------------------------- convert label to one-hot ---------------------------
        # one_hot = torch.zeros(cosine.size(), requires_grad=True, device='cuda')
        one_hot = torch.zeros(cosine.size(), device=self.device)
        one_hot.scatter_(1, label.view(-1, 1).long(), 1)
        if self.ls_eps > 0:
            one_hot = (1 - self.ls_eps) * one_hot + self.ls_eps / self.out_features
        # -------------torch.where(out_i = {x_i if condition_i else y_i) -------------
        output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
        output *= self.s

        return output