import datetime

import os

import time

import torch

import torch.utils.data

from torch import nn

import torchvision

from torchvision import transforms

import utils

try:

    from apex import amp

except ImportError:

    amp = None

def train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, print_freq, apex=False):

    model.train()

    metric_logger = utils.MetricLogger(delimiter="  ")

    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value}'))

    metric_logger.add_meter('img/s', utils.SmoothedValue(window_size=10, fmt='{value}'))

    header = 'Epoch: [{}]'.format(epoch)

    for image, target in metric_logger.log_every(data_loader, print_freq, header):

        start_time = time.time()

        image, target = image.to(device), target.to(device)

        output = model(image)

        loss = criterion(output, target)

        optimizer.zero_grad()

        if apex:

            with amp.scale_loss(loss, optimizer) as scaled_loss:

                scaled_loss.backward()

        else:

            loss.backward()

        optimizer.step()

        acc1, acc5 = utils.accuracy(output, target, topk=(1, 2))

        batch_size = image.shape[0]

        metric_logger.update(loss=loss.item(), lr=optimizer.param_groups[0]["lr"])

        metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)

        metric_logger.meters['acc2'].update(acc5.item(), n=batch_size)

        metric_logger.meters['img/s'].update(batch_size / (time.time() - start_time))

def evaluate(model, criterion, data_loader, device, print_freq=100):

    model.eval()

    metric_logger = utils.MetricLogger(delimiter="  ")

    header = 'Test:'

    with torch.no_grad():

        for image, target in metric_logger.log_every(data_loader, print_freq, header):

            image = image.to(device, non_blocking=True)

            target = target.to(device, non_blocking=True)

            output = model(image)

            loss = criterion(output, target)

            acc1, acc5 = utils.accuracy(output, target, topk=(1, 2))

            # FIXME need to take into account that the datasets

            # could have been padded in distributed setup

            batch_size = image.shape[0]

            metric_logger.update(loss=loss.item())

            metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)

            metric_logger.meters['acc2'].update(acc5.item(), n=batch_size)

    # gather the stats from all processes

    metric_logger.synchronize_between_processes()

    print(' * Acc@1 {top1.global_avg:.3f} Acc@2 {top5.global_avg:.3f}'

          .format(top1=metric_logger.acc1, top5=metric_logger.acc2))

    return metric_logger.acc1.global_avg

def _get_cache_path(filepath):

    import hashlib

    h = hashlib.sha1(filepath.encode()).hexdigest()

    cache_path = os.path.join("~", ".torch", "vision", "datasets", "imagefolder", h[:10] + ".pt")

    cache_path = os.path.expanduser(cache_path)

    return cache_path

def load_data(traindir, valdir, cache_dataset, distributed):

    # Data loading code

    print("Loading data")

    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],

                                     std=[0.229, 0.224, 0.225])

    print("Loading training data")

    st = time.time()

    cache_path = _get_cache_path(traindir)

    if cache_dataset and os.path.exists(cache_path):

        # Attention, as the transforms are also cached!

        print("Loading dataset_train from {}".format(cache_path))

        dataset, _ = torch.load(cache_path)

    else:

        #dataset = torchvision.datasets.ImageFolder(

        #    traindir,

        #    transforms.Compose([

        #        transforms.RandomResizedCrop(224),

        #        transforms.RandomHorizontalFlip(),

        #        transforms.ToTensor(),

        #        normalize,

        #    ]))

        dataset = utils.CSVDataset(

            traindir,

            transforms.Compose([

                transforms.RandomResizedCrop(224),

                transforms.RandomPerspective(),

                transforms.RandomHorizontalFlip(),

                transforms.RandomRotation(degrees=180),

                transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.0),

                transforms.ToTensor(),

                normalize,

            ]))

        if cache_dataset:

            print("Saving dataset_train to {}".format(cache_path))

            utils.mkdir(os.path.dirname(cache_path))

            utils.save_on_master((dataset, traindir), cache_path)

    print("Took", time.time() - st)

    print("Loading validation data")

    cache_path = _get_cache_path(valdir)

    if cache_dataset and os.path.exists(cache_path):

        # Attention, as the transforms are also cached!

        print("Loading dataset_test from {}".format(cache_path))

        dataset_test, _ = torch.load(cache_path)

    else:

        #dataset_test = torchvision.datasets.ImageFolder(

        #    valdir,

        #    transforms.Compose([

        #        transforms.Resize(256),

        #        transforms.CenterCrop(224),

        #        transforms.ToTensor(),

        #        normalize,

        #    ]))

        dataset_test = utils.CSVDataset(

            valdir,

            transforms.Compose([

                transforms.Resize(256),

                transforms.CenterCrop(224),

                transforms.ToTensor(),

                normalize,

            ]))

        if cache_dataset:

            print("Saving dataset_test to {}".format(cache_path))

            utils.mkdir(os.path.dirname(cache_path))

            utils.save_on_master((dataset_test, valdir), cache_path)

    print("Creating data loaders")

    if distributed:

        train_sampler = torch.utils.data.distributed.DistributedSampler(dataset)

        test_sampler = torch.utils.data.distributed.DistributedSampler(dataset_test)

    else:

        train_sampler = torch.utils.data.RandomSampler(dataset)

        test_sampler = torch.utils.data.SequentialSampler(dataset_test)

    return dataset, dataset_test, train_sampler, test_sampler

def main(args):

    if args.apex and amp is None:

        raise RuntimeError("Failed to import apex. Please install apex from https://www.github.com/nvidia/apex "

                           "to enable mixed-precision training.")

    if args.output_dir:

        utils.mkdir(args.output_dir)

    utils.init_distributed_mode(args)

    print(args)

    device = torch.device(args.device)

    torch.backends.cudnn.benchmark = True

    #train_dir = os.path.join(args.data_path, 'train')

    #val_dir = os.path.join(args.data_path, 'val')

    train_dir = args.train_file

    val_dir = args.val_file

    dataset, dataset_test, train_sampler, test_sampler = load_data(train_dir, val_dir,

                                                                   args.cache_dataset, args.distributed)

    data_loader = torch.utils.data.DataLoader(

        dataset, batch_size=args.batch_size,

        sampler=train_sampler, num_workers=args.workers, pin_memory=True)

    data_loader_test = torch.utils.data.DataLoader(

        dataset_test, batch_size=args.batch_size,

        sampler=test_sampler, num_workers=args.workers, pin_memory=True)

    print("Creating model")

    #model = torchvision.models.__dict__[args.model](pretrained=args.pretrained)

    model = torchvision.models.densenet169(pretrained=args.pretrained)

    # modify the last layer for objective task

    num_ftrs = model.features.norm5.num_features

    model.classifier = nn.Linear(num_ftrs, args.num_classes)

    model.to(device)

    if args.distributed and args.sync_bn:

        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)

    if args.focal_loss:

        import pytorch_toolbelt.losses

        criterion = pytorch_toolbelt.losses.FocalLoss()

    else:

        criterion = nn.CrossEntropyLoss()

    optimizer = torch.optim.SGD(

        model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)

    if args.apex:

        model, optimizer = amp.initialize(model, optimizer,

                                          opt_level=args.apex_opt_level

                                          )

    lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=args.lr_step_size, gamma=args.lr_gamma)

    model_without_ddp = model

    if args.distributed:

        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])

        model_without_ddp = model.module

    if args.resume:

        checkpoint = torch.load(args.resume, map_location='cpu')

        model_without_ddp.load_state_dict(checkpoint['model'])

        optimizer.load_state_dict(checkpoint['optimizer'])

        lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])

        args.start_epoch = checkpoint['epoch'] + 1

    if args.test_only:

        evaluate(model, criterion, data_loader_test, device=device)

        return

    print("Start training")

    start_time = time.time()

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

        if args.distributed:

            train_sampler.set_epoch(epoch)

        train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, args.print_freq, args.apex)

        lr_scheduler.step()

        evaluate(model, criterion, data_loader_test, device=device)

        if args.output_dir:

            checkpoint = {

                'model': model_without_ddp.state_dict(),

                'optimizer': optimizer.state_dict(),

                'lr_scheduler': lr_scheduler.state_dict(),

                'epoch': epoch,

                'args': args}

            utils.save_on_master(

                checkpoint,

                os.path.join(args.output_dir, 'model_{}.pth'.format(epoch)))

            utils.save_on_master(

                checkpoint,

                os.path.join(args.output_dir, 'checkpoint.pth'))

    total_time = time.time() - start_time

    total_time_str = str(datetime.timedelta(seconds=int(total_time)))

    print('Training time {}'.format(total_time_str))

def parse_args():

    import argparse

    parser = argparse.ArgumentParser(description='PyTorch Classification Training')

    #parser.add_argument('--data-path', default='/datasets01/imagenet_full_size/061417/', help='dataset')

    parser.add_argument('--train-file', help='training set')

    parser.add_argument('--val-file', help='validation set')

    parser.add_argument('--num-classes', help='number of classes for the objective task', type=int)

    parser.add_argument(

        "--focal-loss", help="Use focal loss",action="store_true")

    #parser.add_argument('--model', default='resnet18', help='model')

    parser.add_argument('--device', default='cuda', help='device')

    parser.add_argument('-b', '--batch-size', default=32, type=int)

    parser.add_argument('--epochs', default=90, type=int, metavar='N',

                        help='number of total epochs to run')

    parser.add_argument('-j', '--workers', default=16, type=int, metavar='N',

                        help='number of data loading workers (default: 16)')

    parser.add_argument('--lr', default=0.1, type=float, help='initial learning rate')

    parser.add_argument('--momentum', default=0.9, type=float, metavar='M',

                        help='momentum')

    parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,

                        metavar='W', help='weight decay (default: 1e-4)',

                        dest='weight_decay')

    parser.add_argument('--lr-step-size', default=30, type=int, help='decrease lr every step-size epochs')

    parser.add_argument('--lr-gamma', default=0.1, type=float, help='decrease lr by a factor of lr-gamma')

    parser.add_argument('--print-freq', default=10, type=int, help='print frequency')

    parser.add_argument('--output-dir', default='.', help='path where to save')

    parser.add_argument('--resume', default='', help='resume from checkpoint')

    parser.add_argument('--start-epoch', default=0, type=int, metavar='N',

                        help='start epoch')

    parser.add_argument(

        "--cache-dataset",

        dest="cache_dataset",

        help="Cache the datasets for quicker initialization. It also serializes the transforms",

        action="store_true",

    )

    parser.add_argument(

        "--sync-bn",

        dest="sync_bn",

        help="Use sync batch norm",

        action="store_true",

    )

    parser.add_argument(

        "--test-only",

        dest="test_only",

        help="Only test the model",

        action="store_true",

    )

    parser.add_argument(

        "--pretrained",

        dest="pretrained",

        help="Use pre-trained models from the modelzoo",

        action="store_true",

    )

    # Mixed precision training parameters

    parser.add_argument('--apex', action='store_true',

                        help='Use apex for mixed precision training')

    parser.add_argument('--apex-opt-level', default='O1', type=str,

                        help='For apex mixed precision training'

                             'O0 for FP32 training, O1 for mixed precision training.'

                             'For further detail, see https://github.com/NVIDIA/apex/tree/master/examples/imagenet'

                        )

    # distributed training parameters

    parser.add_argument('--world-size', default=1, type=int,

                        help='number of distributed processes')

    parser.add_argument('--dist-url', default='env://', help='url used to set up distributed training')

    args = parser.parse_args()

    return args

if __name__ == "__main__":

    args = parse_args()

    main(args)