import math

from torch.optim import lr_scheduler


class WarmRestart(lr_scheduler.CosineAnnealingLR):
    """This class implements Stochastic Gradient Descent with Warm Restarts(SGDR): https://arxiv.org/abs/1608.03983.

    Set the learning rate of each parameter group using a cosine annealing schedule, When last_epoch=-1, sets initial lr as lr.
    This can't support scheduler.step(epoch). please keep epoch=None.
    """

    def __init__(self, optimizer, T_max=30, T_mult=1, eta_min=0, last_epoch=-1):
        """implements SGDR

        Parameters:
        ----------
        T_max : int
            Maximum number of epochs.
        T_mult : int
            Multiplicative factor of T_max.
        eta_min : int
            Minimum learning rate. Default: 0.
        last_epoch : int
            The index of last epoch. Default: -1.
        """
        self.T_mult = T_mult
        super().__init__(optimizer, T_max, eta_min, last_epoch)

    def get_lr(self):
        if self.last_epoch == self.T_max:
            self.last_epoch = 0
            self.T_max *= self.T_mult
        return [self.eta_min + (base_lr - self.eta_min) * (1 + math.cos(math.pi * self.last_epoch / self.T_max)) / 2 for
                base_lr in self.base_lrs]


class LinearDecay(lr_scheduler._LRScheduler):
    """This class implements LinearDecay

    """

    def __init__(self, optimizer, num_epochs, start_epoch=0, min_lr=0, last_epoch=-1):
        """implements LinearDecay

        Parameters:
        ----------

        """
        self.num_epochs = num_epochs
        self.start_epoch = start_epoch
        self.min_lr = min_lr
        super().__init__(optimizer, last_epoch)

    def get_lr(self):
        if self.last_epoch < self.start_epoch:
            return self.base_lrs
        return [base_lr - ((base_lr - self.min_lr) / self.num_epochs) * (self.last_epoch - self.start_epoch) for
                base_lr in self.base_lrs]