import torch
from torch.optim import Optimizer

from timm.scheduler import CosineLRScheduler


# We need to subclass torch.optim.lr_scheduler._LRScheduler, or Pytorch-lightning will complain
class TimmCosineLRScheduler(CosineLRScheduler, torch.optim.lr_scheduler._LRScheduler):
    """ Wrap timm.scheduler.CosineLRScheduler so we can call scheduler.step() without passing in epoch.
    It supports resuming as well.
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._last_epoch = -1
        self.step(epoch=0)

    def step(self, epoch=None):
        if epoch is None:
            self._last_epoch += 1
        else:
            self._last_epoch = epoch
        # We call either step or step_update, depending on whether we're using the scheduler every
        # epoch or every step.
        # Otherwise, lightning will always call step (i.e., meant for each epoch), and if we set
        # scheduler interval to "step", then the learning rate update will be wrong.
        if self.t_in_epochs:
            super().step(epoch=self._last_epoch)
        else:
            super().step_update(num_updates=self._last_epoch)