from distutils.version import LooseVersion
from typing import Union

import torch
from torch.optim.lr_scheduler import _LRScheduler
from typeguard import check_argument_types

from espnet2.schedulers.abs_scheduler import AbsBatchStepScheduler


class WarmupLR(_LRScheduler, AbsBatchStepScheduler):
    """The WarmupLR scheduler

    This scheduler is almost same as NoamLR Scheduler except for following difference:

    NoamLR:
        lr = optimizer.lr * model_size ** -0.5
             * min(step ** -0.5, step * warmup_step ** -1.5)
    WarmupLR:
        lr = optimizer.lr * warmup_step ** 0.5
             * min(step ** -0.5, step * warmup_step ** -1.5)

    Note that the maximum lr equals to optimizer.lr in this scheduler.

    """

    def __init__(
        self,
        optimizer: torch.optim.Optimizer,
        warmup_steps: Union[int, float] = 25000,
        last_epoch: int = -1,
    ):
        if LooseVersion(torch.__version__) < LooseVersion("1.1.0"):
            raise NotImplementedError(f"Require PyTorch>=1.1.0: {torch.__version__}")

        assert check_argument_types()
        self.warmup_steps = warmup_steps

        # __init__() must be invoked before setting field
        # because step() is also invoked in __init__()
        super().__init__(optimizer, last_epoch)

    def __repr__(self):
        return f"{self.__class__.__name__}(warmup_steps={self.warmup_steps})"

    def get_lr(self):
        step_num = self.last_epoch + 1
        return [
            lr
            * self.warmup_steps ** 0.5
            * min(step_num ** -0.5, step_num * self.warmup_steps ** -1.5)
            for lr in self.base_lrs
        ]