""" Plateau Scheduler

Adapts PyTorch plateau scheduler and allows application of noise, warmup.

Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from typing import Any, Dict, List, Optional, Tuple, Union

from .scheduler import Scheduler


class PlateauLRScheduler(Scheduler):
    """Decay the LR by a factor every time the validation loss plateaus."""

    def __init__(
            self,
            optimizer: torch.optim.Optimizer,
            decay_rate: float = 0.1,
            patience_t: int = 10,
            threshold: float = 1e-4,
            cooldown_t: int = 0,
            warmup_t: int = 0,
            warmup_lr_init: float = 0.,
            lr_min: float = 0.,
            mode: str = 'max',
            noise_range_t: Union[List[int], Tuple[int, int], int, None] = None,
            noise_type: str = 'normal',
            noise_pct: float = 0.67,
            noise_std: float = 1.0,
            noise_seed: Optional[int] = None,
            initialize: bool = True,
    ) -> None:
        super().__init__(
            optimizer,
            'lr',
            noise_range_t=noise_range_t,
            noise_type=noise_type,
            noise_pct=noise_pct,
            noise_std=noise_std,
            noise_seed=noise_seed,
            initialize=initialize,
        )

        self.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
            self.optimizer,
            patience=patience_t,
            factor=decay_rate,
            threshold=threshold,
            cooldown=cooldown_t,
            mode=mode,
            min_lr=lr_min,
        )

        self.warmup_t = warmup_t
        self.warmup_lr_init = warmup_lr_init
        if self.warmup_t:
            self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values]
            super().update_groups(self.warmup_lr_init)
        else:
            self.warmup_steps = [1 for _ in self.base_values]
        self.restore_lr = None

    def state_dict(self) -> Dict[str, Any]:
        return {
            'best': self.lr_scheduler.best,
            'last_epoch': self.lr_scheduler.last_epoch,
        }

    def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
        self.lr_scheduler.best = state_dict['best']
        if 'last_epoch' in state_dict:
            self.lr_scheduler.last_epoch = state_dict['last_epoch']

    # override the base class step fn completely
    def step(self, epoch: int, metric: Optional[float] = None) -> None:
        if epoch <= self.warmup_t:
            lrs = [self.warmup_lr_init + epoch * s for s in self.warmup_steps]
            super().update_groups(lrs)
        else:
            if self.restore_lr is not None:
                # restore actual LR from before our last noise perturbation before stepping base
                for i, param_group in enumerate(self.optimizer.param_groups):
                    param_group['lr'] = self.restore_lr[i]
                self.restore_lr = None

            # step the base scheduler if metric given
            if metric is not None:
                self.lr_scheduler.step(metric)

            if self._is_apply_noise(epoch):
                self._apply_noise(epoch)

    def step_update(self, num_updates: int, metric: Optional[float] = None):
        return None

    def _apply_noise(self, epoch: int) -> None:
        noise = self._calculate_noise(epoch)

        # apply the noise on top of previous LR, cache the old value so we can restore for normal
        # stepping of base scheduler
        restore_lr = []
        for i, param_group in enumerate(self.optimizer.param_groups):
            old_lr = float(param_group['lr'])
            restore_lr.append(old_lr)
            new_lr = old_lr + old_lr * noise
            param_group['lr'] = new_lr
        self.restore_lr = restore_lr

    def _get_lr(self, t: int) -> List[float]:
        assert False, 'should not be called as step is overridden'