"""Callbacks for learning rate schedulers."""
from typing import Callable, Dict, List, Optional, Type

from torch.optim.swa_utils import update_bn
from training.trainer.callbacks import Callback

from text_recognizer.models import Model


class StepLR(Callback):
    """Callback for StepLR."""

    def __init__(self) -> None:
        """Initializes the callback."""
        super().__init__()
        self.lr_scheduler = None

    def set_model(self, model: Type[Model]) -> None:
        """Sets the model and lr scheduler."""
        self.model = model
        self.lr_scheduler = self.model.lr_scheduler

    def on_epoch_end(self, epoch: int, logs: Optional[Dict] = None) -> None:
        """Takes a step at the end of every epoch."""
        self.lr_scheduler.step()


class MultiStepLR(Callback):
    """Callback for MultiStepLR."""

    def __init__(self) -> None:
        """Initializes the callback."""
        super().__init__()
        self.lr_scheduler = None

    def set_model(self, model: Type[Model]) -> None:
        """Sets the model and lr scheduler."""
        self.model = model
        self.lr_scheduler = self.model.lr_scheduler

    def on_epoch_end(self, epoch: int, logs: Optional[Dict] = None) -> None:
        """Takes a step at the end of every epoch."""
        self.lr_scheduler.step()


class ReduceLROnPlateau(Callback):
    """Callback for ReduceLROnPlateau."""

    def __init__(self) -> None:
        """Initializes the callback."""
        super().__init__()
        self.lr_scheduler = None

    def set_model(self, model: Type[Model]) -> None:
        """Sets the model and lr scheduler."""
        self.model = model
        self.lr_scheduler = self.model.lr_scheduler

    def on_epoch_end(self, epoch: int, logs: Optional[Dict] = None) -> None:
        """Takes a step at the end of every epoch."""
        val_loss = logs["val_loss"]
        self.lr_scheduler.step(val_loss)


class CyclicLR(Callback):
    """Callback for CyclicLR."""

    def __init__(self) -> None:
        """Initializes the callback."""
        super().__init__()
        self.lr_scheduler = None

    def set_model(self, model: Type[Model]) -> None:
        """Sets the model and lr scheduler."""
        self.model = model
        self.lr_scheduler = self.model.lr_scheduler

    def on_train_batch_end(self, batch: int, logs: Optional[Dict] = None) -> None:
        """Takes a step at the end of every training batch."""
        self.lr_scheduler.step()


class OneCycleLR(Callback):
    """Callback for OneCycleLR."""

    def __init__(self) -> None:
        """Initializes the callback."""
        super().__init__()
        self.lr_scheduler = None

    def set_model(self, model: Type[Model]) -> None:
        """Sets the model and lr scheduler."""
        self.model = model
        self.lr_scheduler = self.model.lr_scheduler

    def on_train_batch_end(self, batch: int, logs: Optional[Dict] = None) -> None:
        """Takes a step at the end of every training batch."""
        self.lr_scheduler.step()


class CosineAnnealingLR(Callback):
    """Callback for Cosine Annealing."""

    def __init__(self) -> None:
        """Initializes the callback."""
        super().__init__()
        self.lr_scheduler = None

    def set_model(self, model: Type[Model]) -> None:
        """Sets the model and lr scheduler."""
        self.model = model
        self.lr_scheduler = self.model.lr_scheduler

    def on_epoch_end(self, epoch: int, logs: Optional[Dict] = None) -> None:
        """Takes a step at the end of every epoch."""
        self.lr_scheduler.step()


class SWA(Callback):
    """Stochastic Weight Averaging callback."""

    def __init__(self) -> None:
        """Initializes the callback."""
        super().__init__()
        self.swa_scheduler = None

    def set_model(self, model: Type[Model]) -> None:
        """Sets the model and lr scheduler."""
        self.model = model
        self.swa_start = self.model.swa_start
        self.swa_scheduler = self.model.lr_scheduler
        self.lr_scheduler = self.model.lr_scheduler

    def on_epoch_end(self, epoch: int, logs: Optional[Dict] = None) -> None:
        """Takes a step at the end of every training batch."""
        if epoch > self.swa_start:
            self.model.swa_network.update_parameters(self.model.network)
            self.swa_scheduler.step()
        else:
            self.lr_scheduler.step()

    def on_fit_end(self) -> None:
        """Update batch norm statistics for the swa model at the end of training."""
        if self.model.swa_network:
            update_bn(
                self.model.val_dataloader(),
                self.model.swa_network,
                device=self.model.device,
            )