from __future__ import print_function
import argparse

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

from dataset_tool import train_loader, test_loader

# Softmax type can either be pc_softmax or trad_softmax
softmax_type = 'pc_softmax'


def get_label_proportions(a_dataloader):
    label_count_dict = {}
    rtn = []
    data_all_len = 0
    for batch_idx, (data, target) in enumerate(a_dataloader):
        data_all_len += len(target)
        for a_target in target:
            a_target_item = a_target.item()
            if a_target_item not in label_count_dict.keys():
                label_count_dict[a_target_item] = 1
            else:
                label_count_dict[a_target_item] += 1

    for a_key in label_count_dict.keys():
        label_count_dict[a_key] = label_count_dict[a_key] / data_all_len

    for a_key in sorted(label_count_dict.keys()):
        rtn.append(label_count_dict[a_key])
    print('rtn: ', rtn)
    return torch.tensor(rtn).to(device='cuda')


def own_softmax(x, y, label_proportions):
    x_exp = torch.exp(x)
    # Switch these two

    if softmax_type == 'pc_softmax':
        weighted_x_exp = x_exp * label_proportions
    else:
        weighted_x_exp = x_exp

    x_exp_sum = torch.sum(weighted_x_exp, 1, keepdim=True)

    return x_exp / x_exp_sum


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5, 1)
        self.conv2 = nn.Conv2d(20, 50, 5, 1)
        self.fc1 = nn.Linear(4 * 4 * 50, 500)
        self.fc2 = nn.Linear(500, 10)

    def forward(self, x, y, label_proportions):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2, 2)
        x = x.view(-1, 4 * 4 * 50)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return torch.log(own_softmax(x, y, label_proportions))


def train(args, model, device, train_loader, optimizer, epoch, label_proportions):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data, target, label_proportions)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % args.log_interval == 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(args, model, device, test_loader, label_proportions):
    confusion_matrix = torch.zeros(10, 10)
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data, target, label_proportions)
            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss
            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
            correct += pred.eq(target.view_as(pred)).sum().item()
            for t, p in zip(target.view(-1), pred.view(-1)):
                confusion_matrix[t.long(), p.long()] += 1

    test_loss /= len(test_loader.dataset)

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

    # print('confusion matrix: ', confusion_matrix)
    per_class_acc = confusion_matrix.diag() / confusion_matrix.sum(1)
    print('per class accuracy: ', per_class_acc)
    print('macro avg accuracy: ', torch.mean(per_class_acc))


def main():
    # Training settings
    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
    parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                        help='input batch size for training (default: 64)')
    parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                        help='input batch size for testing (default: 1000)')
    parser.add_argument('--epochs', type=int, default=8, metavar='N',
                        help='number of epochs to train (default: 10)')
    parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
                        help='learning rate (default: 0.01)')
    parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
                        help='SGD momentum (default: 0.5)')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='disables CUDA training')
    parser.add_argument('--seed', type=int, default=1, metavar='S',
                        help='random seed (default: 1)')
    parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                        help='how many batches to wait before logging training status')
    parser.add_argument('--save-model', action='store_true', default=False,
                        help='For Saving the current Model')
    args = parser.parse_args()
    use_cuda = not args.no_cuda and torch.cuda.is_available()

    # torch.manual_seed(args.seed)

    device = torch.device("cuda" if use_cuda else "cpu")

    kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}

    model = Net().to(device)
    optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)

    label_proportions = get_label_proportions(train_loader)

    for epoch in range(1, args.epochs + 1):
        train(args, model, device, train_loader, optimizer, epoch, label_proportions)
        test(args, model, device, test_loader, label_proportions)

    if (args.save_model):
        torch.save(model.state_dict(), "mnist_cnn.pt")


if __name__ == '__main__':
    main()
