import math
from bisect import bisect_right
from typing import List

import torch

# NOTE: PyTorch's LR scheduler interface uses names that assume the LR changes
# only on epoch boundaries. We typically use iteration based schedules instead.
# As a result, "epoch" (e.g., as in self.last_epoch) should be understood to
# mean "iteration" instead.

__all__ = ["NoOpLR", "WarmupMultiStepLR", "WarmupCosineLR"]


class NoOpLR(torch.optim.lr_scheduler._LRScheduler):
    def __init__(self, optimizer, last_epoch=-1):
        super().__init__(optimizer, last_epoch)

    def get_lr(self) -> List[float]:
        return self.base_lrs

    def _compute_values(self) -> List[float]:
        # The new interface
        return self.get_lr()


class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler):
    """

    Adapted from
    https://github.com/facebookresearch/detectron2
    """

    def __init__(
        self,
        optimizer: torch.optim.Optimizer,
        milestones: List[int],
        gamma: float = 0.1,
        warmup_factor: float = 0.001,
        warmup_iters: int = 1000,
        warmup_method: str = "linear",
        last_epoch: int = -1,
    ):
        if not list(milestones) == sorted(milestones):
            raise ValueError(
                "Milestones should be a list of" " increasing integers. Got {}", milestones
            )
        self.milestones = milestones
        self.gamma = gamma
        self.warmup_factor = warmup_factor
        self.warmup_iters = warmup_iters
        self.warmup_method = warmup_method
        super().__init__(optimizer, last_epoch)

    def get_lr(self) -> List[float]:
        warmup_factor = _get_warmup_factor_at_iter(
            self.warmup_method, self.last_epoch, self.warmup_iters, self.warmup_factor
        )
        return [
            base_lr * warmup_factor * self.gamma ** bisect_right(self.milestones, self.last_epoch)
            for base_lr in self.base_lrs
        ]

    def _compute_values(self) -> List[float]:
        # The new interface
        return self.get_lr()


class WarmupCosineLR(torch.optim.lr_scheduler._LRScheduler):
    def __init__(
        self,
        optimizer: torch.optim.Optimizer,
        max_iters: int,
        warmup_factor: float = 0.001,
        warmup_iters: int = 1000,
        warmup_method: str = "linear",
        last_epoch: int = -1,
    ):
        """
        Adapted from
        https://github.com/facebookresearch/detectron2
        """
        self.max_iters = max_iters
        self.warmup_factor = warmup_factor
        self.warmup_iters = warmup_iters
        self.warmup_method = warmup_method
        super().__init__(optimizer, last_epoch)

    def get_lr(self) -> List[float]:
        warmup_factor = _get_warmup_factor_at_iter(
            self.warmup_method, self.last_epoch, self.warmup_iters, self.warmup_factor
        )
        # Different definitions of half-cosine with warmup are possible. For
        # simplicity we multiply the standard half-cosine schedule by the warmup
        # factor. An alternative is to start the period of the cosine at warmup
        # iters instead of at 0. In the case that warmup_iters << max_iters the
        # two are very close to each other.
        return [
            base_lr
            * warmup_factor
            * 0.5
            * (1.0 + math.cos(math.pi * self.last_epoch / self.max_iters))
            for base_lr in self.base_lrs
        ]

    def _compute_values(self) -> List[float]:
        # The new interface
        return self.get_lr()


def _get_warmup_factor_at_iter(
    method: str, iter: int, warmup_iters: int, warmup_factor: float
) -> float:
    """
    Return the learning rate warmup factor at a specific iteration.
    See https://arxiv.org/abs/1706.02677 for more details.

    Args:
        method (str): warmup method; either "constant" or "linear".
        iter (int): iteration at which to calculate the warmup factor.
        warmup_iters (int): the number of warmup iterations.
        warmup_factor (float): the base warmup factor (the meaning changes
            according to the method used).

    Returns:
        float: the effective warmup factor at the given iteration.
    """
    if iter >= warmup_iters:
        return 1.0

    if method == "constant":
        return warmup_factor
    elif method == "linear":
        alpha = iter / warmup_iters
        return warmup_factor * (1 - alpha) + alpha
    else:
        raise ValueError("Unknown warmup method: {}".format(method))
