import time
import copy
import torch

from data_loaders import load_dataset, get_dataset_sizes, get_dataloaders


def train_model(model, criterion, optimizer, scheduler, num_epochs, batch_size, device):
    since = time.time()

    # Load data
    data_set = load_dataset()
    dataset_sizes = get_dataset_sizes(data_set)
    dataloaders = get_dataloaders(data_set, batch_size)

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0
    best_loss = 10000.0  # Large arbitrary number
    best_acc_train = 0.0
    best_loss_train = 10000.0  # Large arbitrary number
    print("Training started:")

    time_elapsed = time.time() - since
    print("Data Loading Completed in {:.0f}m {:.0f}s".format(time_elapsed // 60, time_elapsed % 60))

    for epoch in range(num_epochs):

        # Each epoch has a training and validation phase
        for phase in ["train", "validation"]:
            if phase == "train":
                # Set model to training mode
                model.train()
            else:
                # Set model to evaluate mode
                model.eval()
            running_loss = 0.0
            running_corrects = 0

            # Iterate over data.
            n_batches = dataset_sizes[phase] // batch_size
            it = 0
            for inputs, labels in dataloaders[phase]:
                since_batch = time.time()
                batch_size_ = len(inputs)
                inputs = inputs.to(device)
                labels = labels.to(device)
                optimizer.zero_grad()

                # Track/compute gradient and make an optimization step only when training
                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()

                # Print iteration results
                running_loss += loss.item() * batch_size_
                batch_corrects = torch.sum(preds == labels.data).item()
                running_corrects += batch_corrects
                print(
                    "Phase: {} Epoch: {}/{} Iter: {}/{} Batch time: {:.4f}".format(
                        phase,
                        epoch + 1,
                        num_epochs,
                        it + 1,
                        n_batches + 1,
                        time.time() - since_batch,
                    ),
                    end="\r",
                    flush=True,
                )
                it += 1

            # Print epoch results
            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects / dataset_sizes[phase]
            print(
                "Phase: {} Epoch: {}/{} Loss: {:.4f} Acc: {:.4f}        ".format(
                    "train" if phase == "train" else "validation  ",
                    epoch + 1,
                    num_epochs,
                    epoch_loss,
                    epoch_acc,
                )
            )

            # Check if this is the best model wrt previous epochs
            if phase == "validation" and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())
            if phase == "validation" and epoch_loss < best_loss:
                best_loss = epoch_loss
            if phase == "train" and epoch_acc > best_acc_train:
                best_acc_train = epoch_acc
            if phase == "train" and epoch_loss < best_loss_train:
                best_loss_train = epoch_loss

            # Update learning rate
            if phase == "train":
                scheduler.step()

    # Print final results
    model.load_state_dict(best_model_wts)
    time_elapsed = time.time() - since
    print("Training Completed in {:.0f}m {:.0f}s".format(time_elapsed // 60, time_elapsed % 60))
    print("Best test loss: {:.4f} | Best test accuracy: {:.4f}".format(best_loss, best_acc))
    return model