import sys, time, copy

import torch

import torch.nn as nn

import tqdm

from timm.models import create_model

from timm.scheduler import create_scheduler

from timm.optim import create_optimizer

from timm.utils import NativeScaler

def prepare_training(args):

    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

    model = create_model('eegt').to(device)

    optimizer = create_optimizer(args, model)

    lr_scheduler, _ = create_scheduler(args, optimizer)

    criterion = nn.CrossEntropyLoss()

    loss_scaler = NativeScaler()

    print(device)

    return model, optimizer, lr_scheduler, criterion, device, loss_scaler

def train_model(model, criterion, optimizer, scheduler, device, dataloaders, args={'dataset_sizes': {'train': 1000, 'val': 197, 'test':200}}):

    since = time.time()

    best_model_wts = copy.deepcopy(model.state_dict())

    best_acc = 0.0

    for epoch in range(50):

        sys.stdout.flush()

        print('Epoch {}/{}'.format(epoch+1, 50))

        print('-' * 10)

        # Each epoch has a training and validation phase

        for phase in ['train', 'val', 'test']:

            if phase == 'train':

                model.train()  # Set model to training mode

            else:

                model.eval()   # Set model to evaluate mode

            running_loss = 0.0

            running_corrects = 0

            # Iterate over data.

            for inputs, labels in tqdm.tqdm(dataloaders[phase]):

                inputs = inputs.type(torch.cuda.FloatTensor).to(device)

                labels = labels.type(torch.cuda.LongTensor).to(device).squeeze(1)

                # print(labels)

                # zero the parameter gradients

                optimizer.zero_grad()

                # forward

                # track history if only in train

                with torch.set_grad_enabled(phase == 'train'):

                    outputs = model(inputs)

                    _, preds = torch.max(outputs, 1)

                    loss = criterion(outputs, labels)

                    # backward + optimize only if in training phase

                    if phase == 'train':

                        loss.backward()

                        optimizer.step()

                # statistics

                running_loss += loss.item() * inputs.size(0)

                running_corrects += torch.sum(preds == labels.data)

            if phase == 'train':

                scheduler.step(epoch=epoch)

            epoch_loss = running_loss / args['dataset_sizes'][phase]

            epoch_acc = running_corrects.double() / args['dataset_sizes'][phase]

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(

                phase, epoch_loss, epoch_acc))

            # deep copy the model

            if phase == 'val' and epoch_acc > best_acc:

                best_acc = epoch_acc

                best_model_wts = copy.deepcopy(model.state_dict())

        print()

    time_elapsed = time.time() - since

    print('Training complete in {:.0f}m {:.0f}s'.format(

        time_elapsed // 60, time_elapsed % 60))

    print('Best val Acc: {:4f}'.format(best_acc))

    # load best model weights

    model.load_state_dict(best_model_wts)

    return model