import torch
import copy
from torch import nn, optim
from torch.optim import lr_scheduler
import time


def train(dataloaders, dataset_sizes, model, device):
    def train_model(model, criterion, optimizer, scheduler, num_epochs=10):
        since = time.time()

        best_model_wts = copy.deepcopy(model.state_dict())
        best_acc = 0.0

        for epoch in range(num_epochs):
            print("Start of an epoch")
            print('Epoch {}/{}'.format(epoch, num_epochs - 1))
            print('-' * 10)

            # Each epoch has a training and validation phase
            for phase in ['train', 'val']:
                if phase == 'train':
                    print("Train")
                    model.train()  # Set model to training mode
                else:
                    print("Validation")
                    model.eval()  # Set model to evaluate mode

                running_loss = 0.0
                running_corrects = 0.0

                # Iterate over data.
                for inputs, labels in dataloaders[phase]:
                    inputs = inputs.to(device)
                    labels = labels.to(device)

                    optimizer.zero_grad()

                    with torch.set_grad_enabled(phase == 'train'):
                        outputs = model(inputs)
                        _, preds = torch.max(outputs, 1)
                        loss = criterion(outputs, labels)
                        
                        if phase == 'train':
                            loss.backward()
                            optimizer.step()

                    
                    running_loss += loss.item() * inputs.size(0)
                    running_corrects += torch.sum(preds == labels.data)
                if phase == 'train':
                    scheduler.step()

                epoch_loss = running_loss / dataset_sizes[phase]
                epoch_acc = running_corrects.double() / dataset_sizes[phase]

                print('{} Loss: {:.4f} Acc: {:.4f}'.format(
                    phase, epoch_loss, epoch_acc))
                
                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

    # Training Hyperparameters
    print("Prefered epochs is 20")
    num_epochs = int(input("Enter your desired epochs: \t"))
    model = model.to(device)
    loss_fn = nn.CrossEntropyLoss()

    # Optimisation of model parameters
    optimizer_ft = optim.Adam(model.parameters(), lr=0.001)
    
    # Decay LR by a factor of 0.1 every 7 epochs
    exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
    model_ft = train_model(model, loss_fn, optimizer_ft, exp_lr_scheduler, num_epochs)
    return model_ft, optimizer_ft
