# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from bisect import bisect_right
from dataset import num_iters_per_epoch
import torch


# FIXME ideally this would be achieved with a CombinedLRScheduler,
# separating MultiStepLR with WarmupLR
# but the current LRScheduler design doesn't allow it
class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler):
    def __init__(
        self,
        optimizer,
        milestones,
        gamma=0.1,
        warmup_factor=1.0 / 3,
        warmup_iters=500,
        warmup_method="linear",
        last_epoch=-1,
    ):
        if not list(milestones) == sorted(milestones):
            raise ValueError(
                "Milestones should be a list of" " increasing integers. Got {}",
                milestones,
            )

        if warmup_method not in ("constant", "linear"):
            raise ValueError(
                "Only 'constant' or 'linear' warmup_method accepted"
                "got {}".format(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
        ]



class WarmupLinearLR(torch.optim.lr_scheduler._LRScheduler):
    def __init__(
        self,
        optimizer,
        final_lr,
        final_iters,
        warmup_factor=1.0 / 3,
        warmup_iters=500,
        warmup_method="linear",
        last_epoch=-1,
    ):
        assert final_iters > warmup_iters
        self.final_lr = final_lr
        self.final_iters = final_iters
        self.warmup_factor = warmup_factor
        self.warmup_iters = max(warmup_iters, 0)
        self.warmup_method = warmup_method
        super(WarmupLinearLR, self).__init__(optimizer, last_epoch)

    #   last_epoch == 0:            base_lr * warmup_factor
    #   last_epoch == warmup_iters: base_lr
    #   last_epoch == final_iters:  final_lr

    def get_lr(self):
        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
            else:
                raise ValueError(
                    "Only 'constant' or 'linear' warmup_method accepted"
                    "got {}".format(self.warmup_method)
                )
            return [
                base_lr
                * warmup_factor
                for base_lr in self.base_lrs
            ]
        else:
            return [
                base_lr - (base_lr - self.final_lr) * float(self.last_epoch - self.warmup_iters) / (
                            self.final_iters - self.warmup_iters)
                for base_lr in self.base_lrs
            ]

#   LR scheduler should work according the number of iterations
def get_lr_scheduler(cfg, optimizer):
    it_ep = num_iters_per_epoch(cfg)
    if cfg.linear_final_lr is None:
        lr_iter_boundaries = [it_ep * ep for ep in cfg.lr_epoch_boundaries]
        return WarmupMultiStepLR(
            optimizer, lr_iter_boundaries, cfg.lr_decay_factor,
            warmup_factor=cfg.warmup_factor,
            warmup_iters=cfg.warmup_epochs * it_ep,
            warmup_method=cfg.warmup_method, )
    else:
        return WarmupLinearLR(optimizer, final_lr=cfg.linear_final_lr,
                              final_iters=cfg.max_epochs * it_ep,
                              warmup_factor=cfg.warmup_factor,
                              warmup_iters=cfg.warmup_epochs * it_ep,
                              warmup_method=cfg.warmup_method,)