from torch.utils.data import Dataset, DataLoader
import numpy as np
import torchvision.models as models
from torchvision import datasets, transforms
import torchvision
import torch.optim as optim
import torch.nn.functional as F
import torch.nn as nn
import torch
import os
import utils as utils


def train(model, train_loader, optimizer, epoch=0):
    model.train()
    batchsize = int(train_loader.sampler.num_samples /
                    train_loader.batch_size / 5)+1
    for batch_idx, (data, target) in enumerate(train_loader):
        data = utils.SetDevice(data)
        target = utils.SetDevice(target)
        optimizer.zero_grad()

        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()

        if batch_idx % batchsize == 0 and batch_idx > 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'
                  .format(epoch, batch_idx * len(data),
                          len(train_loader.dataset),
                          100. * batch_idx /
                          len(train_loader),
                          loss.item()))


def test(model, test_loader):
    with torch.no_grad():
        model.eval()
        test_loss = 0
        correct = 0
        for data, target in test_loader:
            data = utils.SetDevice(data)
            target = utils.SetDevice(target)
            output = model(data)

            # sum up batch loss
            test_loss += F.nll_loss(output, target, reduction='sum').item()
            # get the index of the max log-probability
            pred = output.max(1, keepdim=True)[1]
            correct += pred.eq(target.view_as(pred)).sum().item()

        test_loss /= len(test_loader.dataset)
        accu = 100. * correct / len(test_loader.dataset)
        print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'
              .format(test_loss, correct, len(test_loader.dataset), accu))
        return accu