import numpy as np
import torch
from torch.cuda.amp import autocast
from torchvision.utils import make_grid

from base import BaseTrainer
from utils import MetricTracker, inf_loop


class Trainer(BaseTrainer):
    """
    Trainer class
    """

    def __init__(
        self,
        model,
        criterion,
        metric_ftns,
        optimizer,
        config,
        device,
        data_loader,
        valid_data_loader=None,
        lr_scheduler=None,
        len_epoch=None,
        amp=False,
    ):
        super().__init__(model, criterion, metric_ftns, optimizer, config)
        self.config = config
        self.device = device
        self.data_loader = data_loader
        self.amp = amp
        if len_epoch is None:
            # epoch-based training
            self.len_epoch = len(self.data_loader)
        else:
            # iteration-based training
            self.data_loader = inf_loop(data_loader)
            self.len_epoch = len_epoch
        self.valid_data_loader = valid_data_loader
        self.do_validation = self.valid_data_loader is not None
        self.lr_scheduler = lr_scheduler
        self.log_step = int(np.sqrt(data_loader.batch_size))

        self.train_metrics = MetricTracker(
            "loss", *[m.__name__ for m in self.metric_ftns], writer=self.writer
        )
        self.valid_metrics = MetricTracker(
            "loss", *[m.__name__ for m in self.metric_ftns], writer=self.writer
        )

    def _train_epoch(self, epoch):
        """
        Training logic for an epoch

        :param epoch: Integer, current training epoch.
        :return: A log that contains average loss and metric in this epoch.
        """
        self.model.train()
        self.train_metrics.reset()
        for batch_idx, (data, target) in enumerate(self.data_loader):
            data, target = data.to(self.device), target.to(self.device)

            self.optimizer.zero_grad()

            if self.config["amp"]:
                # AMP!
                with autocast():
                    output = self.model(data)
                    loss = self.criterion(output, target)
            else:
                output = self.model(data)
                loss = self.criterion(output, target)

            loss.backward()
            self.optimizer.step()

            self.writer.set_step((epoch - 1) * self.len_epoch + batch_idx)
            self.train_metrics.update("loss", loss.item())
            for met in self.metric_ftns:
                self.train_metrics.update(met.__name__, met(output, target))

            if batch_idx % self.log_step == 0:
                self.logger.debug(
                    "Train Epoch: {} {} Loss: {:.6f}".format(
                        epoch, self._progress(batch_idx), loss.item()
                    )
                )
                self.writer.add_image(
                    "input", make_grid(data.cpu(), nrow=8, normalize=True)
                )

            if batch_idx == self.len_epoch:
                break
        log = self.train_metrics.result()

        if self.do_validation:
            val_log = self._valid_epoch(epoch)
            log.update(**{"val_" + k: v for k, v in val_log.items()})

        if self.lr_scheduler is not None:
            self.lr_scheduler.step()
        return log

    def _valid_epoch(self, epoch):
        """
        Validate after training an epoch

        :param epoch: Integer, current training epoch.
        :return: A log that contains information about validation
        """
        self.model.eval()
        self.valid_metrics.reset()
        with torch.no_grad():
            for batch_idx, (data, target) in enumerate(self.valid_data_loader):
                data, target = data.to(self.device), target.to(self.device)

                output = self.model(data)
                loss = self.criterion(output, target)

                self.writer.set_step(
                    (epoch - 1) * len(self.valid_data_loader) + batch_idx, "valid"
                )
                self.valid_metrics.update("loss", loss.item())
                for met in self.metric_ftns:
                    self.valid_metrics.update(met.__name__, met(output, target))
                self.writer.add_image(
                    "input", make_grid(data.cpu(), nrow=8, normalize=True)
                )

        # add histogram of model parameters to the tensorboard
        for name, p in self.model.named_parameters():
            self.writer.add_histogram(name, p, bins="auto")
        return self.valid_metrics.result()

    def _progress(self, batch_idx):
        base = "[{}/{} ({:.0f}%)]"
        if hasattr(self.data_loader, "n_samples"):
            current = batch_idx * self.data_loader.batch_size
            total = self.data_loader.n_samples
        else:
            current = batch_idx
            total = self.len_epoch
        return base.format(current, total, 100.0 * current / total)
