import argparse
import os
import shutil
import time

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
from torch.utils.tensorboard import SummaryWriter
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import bayesian_torch.models.bayesian.resnet_variational as resnet
import numpy as np

model_names = sorted(
    name for name in resnet.__dict__
    if name.islower() and not name.startswith("__")
    and name.startswith("resnet") and callable(resnet.__dict__[name]))

print(model_names)
len_trainset = 50000
len_testset = 10000

parser = argparse.ArgumentParser(description='CIFAR10')
parser.add_argument('--arch',
                    '-a',
                    metavar='ARCH',
                    default='resnet20',
                    choices=model_names,
                    help='model architecture: ' + ' | '.join(model_names) +
                    ' (default: resnet20)')
parser.add_argument('-j',
                    '--workers',
                    default=8,
                    type=int,
                    metavar='N',
                    help='number of data loading workers (default: 8)')
parser.add_argument('--epochs',
                    default=200,
                    type=int,
                    metavar='N',
                    help='number of total epochs to run')
parser.add_argument('--start-epoch',
                    default=0,
                    type=int,
                    metavar='N',
                    help='manual epoch number (useful on restarts)')
parser.add_argument('-b',
                    '--batch-size',
                    default=128,
                    type=int,
                    metavar='N',
                    help='mini-batch size (default: 128)')
parser.add_argument('--lr',
                    '--learning-rate',
                    default=0.001,
                    type=float,
                    metavar='LR',
                    help='initial learning rate')
parser.add_argument('--momentum',
                    default=0.9,
                    type=float,
                    metavar='M',
                    help='momentum')
parser.add_argument('--weight-decay',
                    '--wd',
                    default=5e-4,
                    type=float,
                    metavar='W',
                    help='weight decay (default: 5e-4)')
parser.add_argument('--print-freq',
                    '-p',
                    default=50,
                    type=int,
                    metavar='N',
                    help='print frequency (default: 20)')
parser.add_argument('--resume',
                    default='',
                    type=str,
                    metavar='PATH',
                    help='path to latest checkpoint (default: none)')
parser.add_argument('-e',
                    '--evaluate',
                    dest='evaluate',
                    action='store_true',
                    help='evaluate model on validation set')
parser.add_argument('--pretrained',
                    dest='pretrained',
                    action='store_true',
                    help='use pre-trained model')
parser.add_argument('--half',
                    dest='half',
                    action='store_true',
                    help='use half-precision(16-bit) ')
parser.add_argument('--save-dir',
                    dest='save_dir',
                    help='The directory used to save the trained models',
                    default='./checkpoint/bayesian',
                    type=str)
parser.add_argument(
    '--save-every',
    dest='save_every',
    help='Saves checkpoints at every specified number of epochs',
    type=int,
    default=10)
parser.add_argument('--mode', type=str, required=True, help='train | test')
parser.add_argument(
    '--num_monte_carlo',
    type=int,
    default=20,
    metavar='N',
    help='number of Monte Carlo samples to be drawn during inference')
parser.add_argument('--num_mc',
                    type=int,
                    default=1,
                    metavar='N',
                    help='number of Monte Carlo runs during training')
parser.add_argument(
    '--tensorboard',
    type=bool,
    default=True,
    metavar='N',
    help='use tensorboard for logging and visualization of training progress')
parser.add_argument(
    '--log_dir',
    type=str,
    default='./logs/cifar/bayesian',
    metavar='N',
    help='use tensorboard for logging and visualization of training progress')

best_prec1 = 0


def MOPED_layer(layer, det_layer, delta):
    """
    Set the priors and initialize surrogate posteriors of Bayesian NN with Empirical Bayes
    MOPED (Model Priors with Empirical Bayes using Deterministic DNN)
    Reference:
    [1] Ranganath Krishnan, Mahesh Subedar, Omesh Tickoo.
        Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes. AAAI 2020.
    """

    if (str(layer) == 'Conv2dReparameterization()'):
        #set the priors
        print(str(layer))
        layer.prior_weight_mu = det_layer.weight.data
        if layer.prior_bias_mu is not None:
            layer.prior_bias_mu = det_layer.bias.data

        #initialize surrogate posteriors
        layer.mu_kernel.data = det_layer.weight.data
        layer.rho_kernel.data = get_rho(det_layer.weight.data, delta)
        if layer.mu_bias is not None:
            layer.mu_bias.data = det_layer.bias.data
            layer.rho_bias.data = get_rho(det_layer.bias.data, delta)

    elif (isinstance(layer, nn.Conv2d)):
        print(str(layer))
        layer.weight.data = det_layer.weight.data
        if layer.bias is not None:
            layer.bias.data = det_layer.bias.data

    elif (str(layer) == 'LinearReparameterization()'):
        print(str(layer))
        layer.prior_weight_mu = det_layer.weight.data
        if layer.prior_bias_mu is not None:
            layer.prior_bias_mu = det_layer.bias.data

        #initialize the surrogate posteriors
        layer.mu_weight.data = det_layer.weight.data
        layer.rho_weight.data = get_rho(det_layer.weight.data, delta)
        if layer.mu_bias is not None:
            layer.mu_bias.data = det_layer.bias.data
            layer.rho_bias.data = get_rho(det_layer.bias.data, delta)

    elif str(layer).startswith('Batch'):
        #initialize parameters
        print(str(layer))
        layer.weight.data = det_layer.weight.data
        if layer.bias is not None:
            layer.bias.data = det_layer.bias.data
        layer.running_mean.data = det_layer.running_mean.data
        layer.running_var.data = det_layer.running_var.data
        layer.num_batches_tracked.data = det_layer.num_batches_tracked.data


def main():
    global args, best_prec1
    args = parser.parse_args()

    # Check the save_dir exists or not
    if not os.path.exists(args.save_dir):
        os.makedirs(args.save_dir)

    model = torch.nn.DataParallel(resnet.__dict__[args.arch]())
    if torch.cuda.is_available():
        model.cuda()
    else:
        model.cpu()

    # optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            print("=> loading checkpoint '{}'".format(args.resume))
            checkpoint = torch.load(args.resume)
            args.start_epoch = checkpoint['epoch']
            best_prec1 = checkpoint['best_prec1']
            model.load_state_dict(checkpoint['state_dict'])
            print("=> loaded checkpoint '{}' (epoch {})".format(
                args.evaluate, checkpoint['epoch']))
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))

    cudnn.benchmark = True

    tb_writer = None
    if args.tensorboard:
        logger_dir = os.path.join(args.log_dir, 'tb_logger')
        if not os.path.exists(logger_dir):
            os.makedirs(logger_dir)
        tb_writer = SummaryWriter(logger_dir)

    normalize = transforms.Normalize(mean=[0.4914, 0.4822, 0.4465],
                                     std=[0.2023, 0.1994, 0.2010])

    train_loader = torch.utils.data.DataLoader(datasets.CIFAR10(
        root='./data',
        train=True,
        transform=transforms.Compose([
            transforms.RandomHorizontalFlip(),
            transforms.RandomCrop(32, 4),
            transforms.ToTensor(),
            normalize,
        ]),
        download=True),
                                               batch_size=args.batch_size,
                                               shuffle=True,
                                               num_workers=args.workers,
                                               pin_memory=True)

    val_loader = torch.utils.data.DataLoader(datasets.CIFAR10(
        root='./data',
        train=False,
        transform=transforms.Compose([
            transforms.ToTensor(),
            normalize,
        ])),
                                             batch_size=args.batch_size,
                                             shuffle=False,
                                             num_workers=args.workers,
                                             pin_memory=True)

    if not os.path.exists(args.save_dir):
        os.makedirs(args.save_dir)

    if torch.cuda.is_available():
        criterion = nn.CrossEntropyLoss().cuda()
    else:
        criterion = nn.CrossEntropyLoss().cpu()

    if args.half:
        model.half()
        criterion.half()

    if args.arch in ['resnet110']:
        for param_group in optimizer.param_groups:
            param_group['lr'] = args.lr * 0.1

    if args.evaluate:
        validate(val_loader, model, criterion)
        return

    if args.mode == 'train':

        for epoch in range(args.start_epoch, args.epochs):

            lr = args.lr
            if (epoch >= 80 and epoch < 120):
                lr = 0.1 * args.lr
            elif (epoch >= 120 and epoch < 160):
                lr = 0.01 * args.lr
            elif (epoch >= 160 and epoch < 180):
                lr = 0.001 * args.lr
            elif (epoch >= 180):
                lr = 0.0005 * args.lr

            optimizer = torch.optim.Adam(model.parameters(), lr)

            # train for one epoch
            print('current lr {:.5e}'.format(optimizer.param_groups[0]['lr']))
            train(args, train_loader, model, criterion, optimizer, epoch,
                  tb_writer)

            prec1 = validate(args, val_loader, model, criterion, epoch,
                             tb_writer)

            is_best = prec1 > best_prec1
            best_prec1 = max(prec1, best_prec1)

            if is_best:
                save_checkpoint(
                    {
                        'epoch': epoch + 1,
                        'state_dict': model.state_dict(),
                        'best_prec1': best_prec1,
                    },
                    is_best,
                    filename=os.path.join(
                        args.save_dir,
                        'bayesian_{}_cifar.pth'.format(args.arch)))

    elif args.mode == 'test':
        checkpoint_file = args.save_dir + '/bayesian_{}_cifar.pth'.format(
            args.arch)
        if torch.cuda.is_available():
            checkpoint = torch.load(checkpoint_file)
        else:
            checkpoint = torch.load(checkpoint_file,
                                    map_location=torch.device('cpu'))
        model.load_state_dict(checkpoint['state_dict'])
        evaluate(args, model, val_loader)


def train(args,
          train_loader,
          model,
          criterion,
          optimizer,
          epoch,
          tb_writer=None):
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()

    # switch to train mode
    model.train()

    end = time.time()
    for i, (input, target) in enumerate(train_loader):

        # measure data loading time
        data_time.update(time.time() - end)

        if torch.cuda.is_available():
            target = target.cuda()
            input_var = input.cuda()
            target_var = target
        else:
            target = target.cpu()
            input_var = input.cpu()
            target_var = target

        if args.half:
            input_var = input_var.half()

        # compute output
        output_ = []
        kl_ = []
        for mc_run in range(args.num_mc):
            output, kl = model(input_var)
            output_.append(output)
            kl_.append(kl)
        output = torch.mean(torch.stack(output_), dim=0)
        kl = torch.mean(torch.stack(kl_), dim=0)
        cross_entropy_loss = criterion(output, target_var)
        scaled_kl = kl / args.batch_size 
        #ELBO loss
        loss = cross_entropy_loss + scaled_kl

        # compute gradient and do SGD step
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        output = output.float()
        loss = loss.float()
        # measure accuracy and record loss
        prec1 = accuracy(output.data, target)[0]
        losses.update(loss.item(), input.size(0))
        top1.update(prec1.item(), input.size(0))

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % args.print_freq == 0:
            print('Epoch: [{0}][{1}/{2}]\t'
                  'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                  'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
                  'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                  'Prec@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
                      epoch,
                      i,
                      len(train_loader),
                      batch_time=batch_time,
                      data_time=data_time,
                      loss=losses,
                      top1=top1))

        if tb_writer is not None:
            tb_writer.add_scalar('train/cross_entropy_loss',
                                 cross_entropy_loss.item(), epoch)
            tb_writer.add_scalar('train/kl_div', scaled_kl.item(), epoch)
            tb_writer.add_scalar('train/elbo_loss', loss.item(), epoch)
            tb_writer.add_scalar('train/accuracy', prec1.item(), epoch)
            tb_writer.flush()


def validate(args, val_loader, model, criterion, epoch, tb_writer=None):
    batch_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()

    # switch to evaluate mode
    model.eval()

    end = time.time()
    with torch.no_grad():
        for i, (input, target) in enumerate(val_loader):
            if torch.cuda.is_available():
                target = target.cuda()
                input_var = input.cuda()
                target_var = target.cuda()
            else:
                target = target.cpu()
                input_var = input.cpu()
                target_var = target.cpu()

            if args.half:
                input_var = input_var.half()

            # compute output
            output_ = []
            kl_ = []
            for mc_run in range(args.num_mc):
                output, kl = model(input_var)
                output_.append(output)
                kl_.append(kl)
            output = torch.mean(torch.stack(output_), dim=0)
            kl = torch.mean(torch.stack(kl_), dim=0)
            cross_entropy_loss = criterion(output, target_var)
            scaled_kl = kl / args.batch_size 
            #ELBO loss
            loss = cross_entropy_loss + scaled_kl

            output = output.float()
            loss = loss.float()

            # measure accuracy and record loss
            prec1 = accuracy(output.data, target)[0]
            losses.update(loss.item(), input.size(0))
            top1.update(prec1.item(), input.size(0))

            # measure elapsed time
            batch_time.update(time.time() - end)
            end = time.time()

            if i % args.print_freq == 0:
                print('Test: [{0}/{1}]\t'
                      'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                      'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                      'Prec@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
                          i,
                          len(val_loader),
                          batch_time=batch_time,
                          loss=losses,
                          top1=top1))

            if tb_writer is not None:
                tb_writer.add_scalar('val/cross_entropy_loss',
                                     cross_entropy_loss.item(), epoch)
                tb_writer.add_scalar('val/kl_div', scaled_kl.item(), epoch)
                tb_writer.add_scalar('val/elbo_loss', loss.item(), epoch)
                tb_writer.add_scalar('val/accuracy', prec1.item(), epoch)
                tb_writer.flush()

    print(' * Prec@1 {top1.avg:.3f}'.format(top1=top1))

    return top1.avg


def evaluate(args, model, val_loader):
    pred_probs_mc = []
    test_loss = 0
    correct = 0
    output_list = []
    labels_list = []
    model.eval()
    with torch.no_grad():
        begin = time.time()
        for data, target in val_loader:
            if torch.cuda.is_available():
                data, target = data.cuda(), target.cuda()
            else:
                data, target = data.cpu(), target.cpu()
            output_mc = []
            for mc_run in range(args.num_monte_carlo):
                output, _ = model.forward(data)
                output_mc.append(output)
            output_ = torch.stack(output_mc)
            output_list.append(output_)
            labels_list.append(target)
        end = time.time()
        print("inference throughput: ", len_testset / (end - begin),
              " images/s")

        output = torch.stack(output_list)
        output = output.permute(1, 0, 2, 3)
        output = output.contiguous().view(args.num_monte_carlo, len_testset,
                                          -1)
        output = torch.nn.functional.softmax(output, dim=2)
        labels = torch.cat(labels_list)
        pred_mean = output.mean(dim=0)
        Y_pred = torch.argmax(pred_mean, axis=1)
        print('Test accuracy:',
              (Y_pred.data.cpu().numpy() == labels.data.cpu().numpy()).mean() *
              100)
        np.save('./probs_cifar_mc.npy', output.data.cpu().numpy())
        np.save('./cifar_test_labels_mc.npy', labels.data.cpu().numpy())


def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
    """
    Save the training model
    """
    torch.save(state, filename)


class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count


def accuracy(output, target, topk=(1, )):
    """Computes the precision@k for the specified values of k"""
    maxk = max(topk)
    batch_size = target.size(0)

    _, pred = output.topk(maxk, 1, True, True)
    pred = pred.t()
    correct = pred.eq(target.view(1, -1).expand_as(pred))

    res = []
    for k in topk:
        correct_k = correct[:k].view(-1).float().sum(0)
        res.append(correct_k.mul_(100.0 / batch_size))
    return res


if __name__ == '__main__':
    main()
