import warnings
import argparse
import builtins
import os
import random
import shutil
import time
import math
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
from PIL import Image
from hcsc.loader import GaussianBlur
from utils.utils import mkdir
from utils.utils import accuracy, concat_all_gather

from utils.options import parse_args_lincls_places



warnings.filterwarnings('ignore')
model_names = sorted(name for name in models.__dict__
                     if name.islower() and not name.startswith("__")
                     and callable(models.__dict__[name]))

best_acc1 = 0

class Places205(torch.utils.data.Dataset):
    def __init__(self, root, split, transform=None, target_transform=None):
        self.root = os.path.expanduser(root)
        self.data_folder  = os.path.join(self.root, 'data', 'vision', 'torralba', 'deeplearning', 'images256')
        self.split_folder = os.path.join(self.root, 'trainvalsplit_places205')
        assert(split=='train' or split=='val')
        split_csv_file = os.path.join(self.split_folder, split+'_places205.csv')

        self.transform = transform
        self.target_transform = target_transform
        self.img_files = []
        self.labels = []
        with open(split_csv_file, 'r') as file:
            line=file.readline()
            while line:
                line=line.strip("\n")
                split_result=line.split()
                self.img_files.append(split_result[0])
                self.labels.append(int(split_result[1]))
                line=file.readline()

    def __getitem__(self, index):
        """
        Args:
            index (int): Index
        Returns:
            tuple: (image, target) where target is index of the target class.
        """
        image_path = os.path.join(self.data_folder, self.img_files[index])
        img = Image.open(image_path).convert('RGB')
        target = self.labels[index]

        if self.transform is not None:
            img = self.transform(img)
        if self.target_transform is not None:
            target = self.target_transform(target)
        return img, target

    def __len__(self):
        return len(self.labels)

def main():
    args = parse_args_lincls_places()
    choose = args.choose
    if choose is not None:
        os.environ['CUDA_VISIBLE_DEVICES'] = choose
    if args.seed is not None:
        random.seed(args.seed)
        torch.manual_seed(args.seed)
        cudnn.deterministic = True
        warnings.warn('You have chosen to seed training. '
                      'This will turn on the CUDNN deterministic setting, '
                      'which can slow down your training considerably! '
                      'You may see unexpected behavior when restarting '
                      'from checkpoints.')

    if args.gpu is not None:
        warnings.warn('You have chosen a specific GPU. This will completely '
                      'disable data parallelism.')

    if args.dist_url == "env://" and args.world_size == -1:
        args.world_size = int(os.environ["WORLD_SIZE"])

    args.distributed = args.world_size > 1 or args.multiprocessing_distributed
    params = vars(args)
    data_path = args.data  # the path stored
    args.data = data_path
    ngpus_per_node = torch.cuda.device_count()
    if args.multiprocessing_distributed:
        # Since we have ngpus_per_node processes per node, the total world_size
        # needs to be adjusted accordingly
        args.world_size = ngpus_per_node * args.world_size
        # Use torch.multiprocessing.spawn to launch distributed processes: the
        # main_worker process function
        mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
    else:
        # Simply call main_worker function
        main_worker(args.gpu, ngpus_per_node, args)


def main_worker(gpu, ngpus_per_node, args):
    global best_acc1
    args.gpu = gpu
    params = vars(args)
    # suppress printing if not master
    if args.multiprocessing_distributed and args.gpu != 0:
        def print_pass(*args):
            pass

        builtins.print = print_pass

    if args.gpu is not None:
        print("Use GPU: {} for training".format(args.gpu))

    if args.distributed:
        if args.dist_url == "env://" and args.rank == -1:
            args.rank = int(os.environ["RANK"])
        if args.multiprocessing_distributed:
            # For multiprocessing distributed training, rank needs to be the
            # global rank among all the processes
            args.rank = args.rank * ngpus_per_node + gpu
        dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                world_size=args.world_size, rank=args.rank)
    # create model
    print("=> creating model '{}'".format(args.arch))
    if args.dataset == "Place205":
        num_classes = 205
    else:
        num_classes = 1000
    
    model = models.__dict__[args.arch](num_classes=num_classes)

    # freeze all layers but the last fc
    for name, param in model.named_parameters():
        if name not in ['fc.weight', 'fc.bias']:
            param.requires_grad = False

    # init the fc layer
    model.fc.weight.data.normal_(mean=0.0, std=0.01)
    model.fc.bias.data.zero_()

    # load from pre-trained, before DistributedDataParallel constructor
    if args.pretrained:

        if os.path.isfile(args.pretrained):
            print("=> loading checkpoint '{}'".format(args.pretrained))

            checkpoint = torch.load(args.pretrained, map_location="cpu")
            state_dict = checkpoint['state_dict']
            for k in list(state_dict.keys()):
                # retain only encoder_q up to before the embedding layer
                if k.startswith('module.encoder_q') and not k.startswith('module.encoder_q.fc'):
                    # remove prefix
                    state_dict[k[len("module.encoder_q."):]] = state_dict[k]
                    # delete renamed or unused k
                    del state_dict[k]

            args.start_epoch = 0
            msg = model.load_state_dict(state_dict, strict=False)
            assert set(msg.missing_keys) == {"fc.weight", "fc.bias"}

            print("=> loaded pre-trained model '{}'".format(args.pretrained))
        else:
            print("=> no checkpoint found at '{}'".format(args.pretrained))

    if args.dropout != 0.0:
        model.fc = nn.Sequential(nn.Dropout(args.dropout), model.fc)
    if args.distributed:
        # For multiprocessing distributed, DistributedDataParallel constructor
        # should always set the single device scope, otherwise,
        # DistributedDataParallel will use all available devices.
        if args.gpu is not None:
            torch.cuda.set_device(args.gpu)
            model.cuda(args.gpu)
            # When using a single GPU per process and per
            # DistributedDataParallel, we need to divide the batch size
            # ourselves based on the total number of GPUs we have
            args.batch_size = int(args.batch_size / ngpus_per_node)
            args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)
            model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
        else:
            model.cuda()
            # DistributedDataParallel will divide and allocate batch_size to all
            # available GPUs if device_ids are not set
            model = torch.nn.parallel.DistributedDataParallel(model)
    elif args.gpu is not None:
        torch.cuda.set_device(args.gpu)
        model = model.cuda(args.gpu)
    else:
        # DataParallel will divide and allocate batch_size to all available GPUs
        if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
            model.features = torch.nn.DataParallel(model.features)
            model.cuda()
        else:
            model = torch.nn.DataParallel(model).cuda()

    # define loss function (criterion) and optimizer
    criterion = nn.CrossEntropyLoss().cuda(args.gpu)

    # optimize only the linear classifier
    parameters = list(filter(lambda p: p.requires_grad, model.parameters()))
    assert len(parameters) == 2  # fc.weight, fc.bias
    optimizer = torch.optim.SGD(parameters, args.lr,
                                momentum=args.momentum,
                                weight_decay=args.weight_decay)

    # optionally resume from a checkpoint
    if args.resume:
        if os.path.isfile(args.resume):
            print("=> loading checkpoint '{}'".format(args.resume))
            if args.gpu is None:
                checkpoint = torch.load(args.resume)
            else:
                # Map model to be loaded to specified single gpu.
                loc = 'cuda:{}'.format(args.gpu)
                checkpoint = torch.load(args.resume, map_location=loc)

            args.start_epoch = checkpoint['epoch']
            best_acc1 = torch.tensor(checkpoint['best_acc1'])
            if args.gpu is not None:
                # best_acc1 may be from a checkpoint from a different GPU
                best_acc1 = best_acc1.to(args.gpu)
            model.load_state_dict(checkpoint['state_dict'])
            optimizer.load_state_dict(checkpoint['optimizer'])
            print("=> loaded checkpoint '{}' (epoch {})"
                  .format(args.resume, checkpoint['epoch']))
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))

    cudnn.benchmark = True

    # Data loading code


    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                        std=[0.229, 0.224, 0.225])
    if args.train_strong:
        if args.randcrop:
            transform_train = transforms.Compose([
                transforms.RandomCrop(224),
                transforms.RandomApply([
                    transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)  # not strengthened
                ], p=0.8),
                transforms.RandomGrayscale(p=0.2),
                transforms.RandomApply([GaussianBlur([.1, 2.])], p=0.5),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                normalize
            ])
        else:
            transform_train = transforms.Compose([
                transforms.RandomResizedCrop(224, scale=(0.2, 1.)),
                transforms.RandomApply([
                    transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)  # not strengthened
                ], p=0.8),
                transforms.RandomGrayscale(p=0.2),
                transforms.RandomApply([GaussianBlur([.1, 2.])], p=0.5),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                normalize
            ])
    else:
        if args.randcrop:
            transform_train = transforms.Compose([
                transforms.RandomCrop(224),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                normalize, ])

        else:
            transform_train = transforms.Compose([
                transforms.RandomResizedCrop(224),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                normalize, ])
    # waiting to add 10 crop
    transform_valid = transforms.Compose([
        transforms.Resize([256, 256]),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        normalize,
    ])

    train_dataset = Places205(args.data, 'train', transform_train)
    valid_dataset = Places205(args.data, 'val', transform_valid)
    if args.distributed:
        train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
        val_sampler = torch.utils.data.distributed.DistributedSampler(valid_dataset, shuffle=False)
        #             val_sampler = None
    else:
        train_sampler = None
        val_sampler = None

    train_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
        num_workers=args.workers, pin_memory=True, sampler=train_sampler)

    val_loader = torch.utils.data.DataLoader(
        valid_dataset, sampler=val_sampler,
        batch_size=args.batch_size,
        num_workers=args.workers, pin_memory=True)

    if args.evaluate:
        validate(val_loader, model, criterion, args)
        return
    import datetime
    today = datetime.date.today()
    formatted_today = today.strftime('%y%m%d')
    now = time.strftime("%H:%M:%S")
    save_path = os.path.join(args.save_path, args.log_path)
    log_path = os.path.join(save_path, 'Finetune_log')
    log_path = os.path.join(log_path, formatted_today + now)
    if not args.multiprocessing_distributed or (args.multiprocessing_distributed
                                                and args.rank % ngpus_per_node == 0):
        mkdir(log_path)

    lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, args.sgdr_t0, args.sgdr_t_mult)
    for epoch in range(args.start_epoch, args.epochs):
        if args.distributed:
            train_sampler.set_epoch(epoch)
        if args.sgdr == 0:
            adjust_learning_rate(optimizer, epoch, args)
        train(train_loader, model, criterion, optimizer, epoch, args, lr_scheduler)
        # evaluate on validation set
        acc1 = validate(val_loader, model, criterion, args)
        # remember best acc@1 and save checkpoint
        is_best = acc1 > best_acc1
        best_acc1 = max(acc1, best_acc1)

        if not args.multiprocessing_distributed or (args.multiprocessing_distributed
                                                    and args.rank % ngpus_per_node == 0):
            # add timestamp
            tmp_save_path = os.path.join(log_path, 'checkpoint.pth.tar')
            save_checkpoint({
                'epoch': epoch + 1,
                'arch': args.arch,
                'state_dict': model.state_dict(),
                'best_acc1': best_acc1,
                'optimizer': optimizer.state_dict(),
            }, is_best, filename=tmp_save_path)

            if abs(args.epochs - epoch) <= 20:
                tmp_save_path = os.path.join(log_path, 'model_%d.pth.tar' % epoch)
                save_checkpoint({
                    'epoch': epoch + 1,
                    'arch': args.arch,
                    'state_dict': model.state_dict(),
                    'best_acc1': best_acc1,
                    'optimizer': optimizer.state_dict(),
                }, False, filename=tmp_save_path)
                


def train(train_loader, model, criterion, optimizer, epoch, args, lr_scheduler):
    batch_time = AverageMeter('Time', ':6.3f')
    data_time = AverageMeter('Data', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    mAP = AverageMeter("mAP", ":6.2f")
    progress = ProgressMeter(
        len(train_loader),
        [batch_time, data_time, losses, top1, top5, mAP],
        prefix="Epoch: [{}]".format(epoch))

    """
    Switch to eval mode:
    Under the protocol of linear classification on frozen features/models,
    it is not legitimate to change any part of the pre-trained model.
    BatchNorm in train mode may revise running mean/std (even if it receives
    no gradient), which are part of the model parameters too.
    """
    model.eval()
    batch_total = len(train_loader)
    end = time.time()
    for i, (images, target) in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)

        if args.gpu is not None:
            images = images.cuda(args.gpu, non_blocking=True)

        target = target.cuda(args.gpu, non_blocking=True)

        # compute output
        output = model(images)
        loss = criterion(output, target)

        # measure accuracy and record loss
        acc1, acc5 = accuracy(output, target, topk=(1, 5))
        losses.update(loss.item(), images.size(0))
        top1.update(acc1.item(), images.size(0))
        top5.update(acc5.item(), images.size(0))

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

        if args.sgdr != 0:
            lr_scheduler.step(epoch + i / batch_total)

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

        if i % args.print_freq == 0:
            progress.display(i)

def validate(val_loader, model, criterion, args):
    batch_time = AverageMeter('Time', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    mAP = AverageMeter("mAP", ":6.2f")
    progress = ProgressMeter(
        len(val_loader),
        [batch_time, losses, top1, top5, mAP],
        prefix='Test: ')

    # switch to evaluate mode
    model.eval()
    with torch.no_grad():
        end = time.time()
        for i, (images, target) in enumerate(val_loader):
            target = target.cuda(args.gpu, non_blocking=True)
            output = model(images)
            acc1, acc5 = accuracy(output, target, topk=(1, 5))
            acc1 = torch.mean(concat_all_gather(acc1.unsqueeze(0)), dim=0, keepdim=True)
            acc5 = torch.mean(concat_all_gather(acc5.unsqueeze(0)), dim=0, keepdim=True)
            top1.update(acc1.item(), images.size(0))
            top5.update(acc5.item(), images.size(0))
            loss = criterion(output, target)
            losses.update(loss.item(), images.size(0))
            # measure elapsed time
            batch_time.update(time.time() - end)
            end = time.time()

            if i % args.print_freq == 0:
                progress.display(i)

        # TODO: this should also be done with the ProgressMeter
        print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f} mAP {mAP.avg:.3f} '
              .format(top1=top1, top5=top5, mAP=mAP))

    return top1.avg


def testing(val_loader, model, criterion, args):
    batch_time = AverageMeter('Time', ':6.3f')
    losses = AverageMeter('Loss', ':.4e')
    top1 = AverageMeter('Acc@1', ':6.2f')
    top5 = AverageMeter('Acc@5', ':6.2f')
    mAP = AverageMeter("mAP", ":6.2f")
    progress = ProgressMeter(
        len(val_loader),
        [batch_time, losses, top1, top5, mAP],
        prefix='Test: ')

    # switch to evaluate mode
    model.eval()
    correct_count = 0
    count_all = 0
    # implement our own random crop
    with torch.no_grad():
        end = time.time()
        for i, (images, target) in enumerate(val_loader):
            target = target.cuda(args.gpu, non_blocking=True)
            output_list = []
            for image in images:
                output = model(image)
                output = torch.softmax(output, dim=1)
                output_list.append(output)
            output_list = torch.stack(output_list, dim=0)
            output_list, max_index = torch.max(output_list, dim=0)
            output = output_list
            images = images[0]
            acc1, acc5 = accuracy(output, target, topk=(1, 5))
            acc1 = torch.mean(concat_all_gather(acc1.unsqueeze(0)), dim=0, keepdim=True)
            acc5 = torch.mean(concat_all_gather(acc5.unsqueeze(0)), dim=0, keepdim=True)
            correct_count += float(acc1[0]) * images.size(0)
            count_all += images.size(0)
            top1.update(acc1.item(), images.size(0))
            top5.update(acc5.item(), images.size(0))
            loss = criterion(output, target)
            losses.update(loss.item(), images.size(0))
            # measure elapsed time
            batch_time.update(time.time() - end)
            end = time.time()

            if i % args.print_freq == 0:
                progress.display(i)

        print(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f} mAP {mAP.avg:.3f} '
              .format(top1=top1, top5=top5, mAP=mAP))
        final_accu = correct_count / count_all
        print("$$our final calculated accuracy %.7f" % final_accu)
    return top1.avg

def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
    torch.save(state, filename)
    if is_best:
        root_path = os.path.split(filename)[0]
        best_path = os.path.join(root_path, "model_best.pth.tar")
        shutil.copyfile(filename, best_path)


def sanity_check(state_dict, pretrained_weights):
    """
    Linear classifier should not change any weights other than the linear layer.
    This sanity check asserts nothing wrong happens (e.g., BN stats updated).
    """
    print("=> loading '{}' for sanity check".format(pretrained_weights))
    checkpoint = torch.load(pretrained_weights, map_location="cpu")
    state_dict_pre = checkpoint['state_dict']

    for k in list(state_dict.keys()):
        # only ignore fc layer
        if 'fc.weight' in k or 'fc.bias' in k:
            continue

        # name in pretrained model
        k_pre = 'module.encoder_q.' + k[len('module.'):] \
            if k.startswith('module.') else 'module.encoder_q.' + k

        assert ((state_dict[k].cpu() == state_dict_pre[k_pre]).all()), \
            '{} is changed in linear classifier training.'.format(k)

    print("=> sanity check passed.")


class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self, name, fmt=':f'):
        self.name = name
        self.fmt = fmt
        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 __str__(self):
        fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
        return fmtstr.format(**self.__dict__)


class ProgressMeter(object):
    def __init__(self, num_batches, meters, prefix=""):
        self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
        self.meters = meters
        self.prefix = prefix

    def display(self, batch):
        entries = [self.prefix + self.batch_fmtstr.format(batch)]
        entries += [str(meter) for meter in self.meters]
        print('\t'.join(entries))

    def _get_batch_fmtstr(self, num_batches):
        num_digits = len(str(num_batches // 1))
        fmt = '{:' + str(num_digits) + 'd}'
        return '[' + fmt + '/' + fmt.format(num_batches) + ']'

def adjust_learning_rate(optimizer, epoch, args):
    """Decay the learning rate based on schedule"""
    lr = args.lr
    end_lr = args.final_lr
    if args.cos:
        lr = 0.5 * (1. + math.cos(math.pi * epoch / args.epochs)) * (lr - end_lr) + end_lr
    else:
        for milestone in args.schedule:
            lr *= 0.1 if epoch >= milestone else 1.
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr



if __name__ == '__main__':
    main()
