import torch
from bisect import bisect_right


class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler):
    def __init__(
        self,
        optimizer,
        milestones,
        gamma=0.1,
        warmup_factor=1.0 / 3,
        warmup_iters=5,
        warmup_method='linear',
        last_epoch=-1,
    ):
        if not milestones == sorted(milestones):
            raise ValueError(
                f'Milestones should be a list of increasing integers. '
                f'Got {milestones}',
            )

        if warmup_method not in ('constant', 'linear'):
            raise ValueError(
                f'Only "constant" or "linear" warmup_method accepted'
                f'got {warmup_method}'
            )
        self.milestones = milestones
        self.gamma = gamma
        self.warmup_factor = warmup_factor
        self.warmup_iters = warmup_iters
        self.warmup_method = warmup_method
        super(WarmupMultiStepLR, self).__init__(optimizer, last_epoch)

    def get_lr(self):
        warmup_factor = 1
        if self.last_epoch < self.warmup_iters:
            if self.warmup_method == 'constant':
                warmup_factor = self.warmup_factor
            elif self.warmup_method == 'linear':
                alpha = float(self.last_epoch) / self.warmup_iters
                warmup_factor = self.warmup_factor * (1 - alpha) + alpha
        return [
            base_lr *
            warmup_factor *
            self.gamma ** bisect_right(self.milestones, self.last_epoch)
            for base_lr in self.base_lrs
        ]
