import argparse
import os
import random
import shutil
import time
import warnings

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
import numpy as np

from util import AverageMeter, ProgressMeter, accuracy, parse_gpus
from checkpoint import save_checkpoint, load_checkpoint
from thop import profile
from networks.imagenet import create_net


parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('data', metavar='DIR',
                    help='path to dataset')
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18',
                    help='model architecture (default: resnet18)')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=90, 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=256, type=int,
                    metavar='N',
                    help='mini-batch size (default: 256), this is the total '
                         'batch size of all GPUs on the current node when '
                         'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
                    metavar='LR', help='initial learning rate', dest='lr')
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('-p', '--print-freq', default=10, type=int,
                    metavar='N', help='print frequency (default: 10)')
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('--world-size', default=-1, type=int,
                    help='number of nodes for distributed training')
parser.add_argument('--rank', default=-1, type=int,
                    help='node rank for distributed training')
parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
                    help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='nccl', type=str,
                    help='distributed backend')
parser.add_argument('--seed', default=None, type=int,
                    help='seed for initializing training. ')
parser.add_argument('--gpu', default="0",
                    help='GPU id to use.')
parser.add_argument('--multiprocessing-distributed', action='store_true',
                    help='Use multi-processing distributed training to launch '
                         'N processes per node, which has N GPUs. This is the '
                         'fastest way to use PyTorch for either single node or '
                         'multi node data parallel training')

parser.add_argument("--ckpt", default="./ckpts/", 
                    help="folder to output checkpoints")
parser.add_argument("--attention_type", type=str, default="none",
                    help="attention type (possible choices none | se | cbam | simam)")
parser.add_argument("--attention_param", type=float, default=4,
                    help="attention parameter (reduction factor in se and cbam, e_lambda in simam)")
parser.add_argument("--log_freq", type=int, default=500,
                    help="log frequency to file")
parser.add_argument("--cos_lr", action='store_true',
                    help='use cosine learning rate')
parser.add_argument("--save_weights", default=None, type=str, metavar='PATH',
                    help='save weights by CPU for mmdetection')


best_acc1 = 0


def main():
    args = parser.parse_args()

    args.ckpt += "imagenet"
    args.ckpt += "-" + args.arch
    if args.attention_type.lower() != "none":
        args.ckpt += "-" + args.attention_type
    if args.attention_type.lower() != "none":
        args.ckpt += "-param" + str(args.attention_param)


    args.gpu = parse_gpus(args.gpu)
    if args.gpu is not None:
        args.device = torch.device("cuda:{}".format(args.gpu[0]))
    else:
        args.device = torch.device("cpu")


    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.')
        args.ckpt += '-seed' + str(args.seed)

    if not os.path.isdir(args.ckpt):
        os.makedirs(args.ckpt)

    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

    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

    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
    model = create_net(args)


    x = torch.randn(1, 3, 224, 224)
    flops, params = profile(model, inputs=(x,))

    print("model [%s] - params: %.6fM" % (args.arch, params / 1e6))
    print("model [%s] - FLOPs: %.6fG" % (args.arch, flops / 1e9))

    log_file = os.path.join(args.ckpt, "log.txt")

    if os.path.exists(log_file):
        args.log_file = open(log_file, mode="a")
    else:
        args.log_file = open(log_file, mode="w")
        args.log_file.write("Network - " + args.arch + "\n")
        args.log_file.write("Attention Module - " + args.attention_type + "\n")
        args.log_file.write("Params - " % str(params) + "\n")
        args.log_file.write("FLOPs - " % str(flops) + "\n")
        args.log_file.write("--------------------------------------------------" + "\n")

    args.log_file.close()


    if not torch.cuda.is_available():
        print('using CPU, this will be slow')
    elif 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.device)
            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.device)
        model = model.to(args.gpu[0])
        model = torch.nn.DataParallel(model, args.gpu) 

    print(model)

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

    optimizer = torch.optim.SGD(model.parameters(), args.lr,
                                momentum=args.momentum,
                                weight_decay=args.weight_decay)
    if args.resume:
        model, optimizer, best_acc1, start_epoch = load_checkpoint(args, model, optimizer)
        args.start_epoch = start_epoch

    cudnn.benchmark = True

    # Data loading code
    traindir = os.path.join(args.data, 'train')
    valdir = os.path.join(args.data, 'val')
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])

    train_dataset = datasets.ImageFolder(
        traindir,
        transforms.Compose([
            transforms.RandomResizedCrop(224),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            normalize,
        ]))

    if args.distributed:
        train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
    else:
        train_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(
        datasets.ImageFolder(valdir, transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            normalize,
        ])),
        batch_size=args.batch_size, shuffle=False,
        num_workers=args.workers, pin_memory=True)

    if args.save_weights is not None: # "deparallelize" saved weights
        print("=> saving 'deparallelized' weights [%s]" % args.save_weights)
        model = model.module
        model = model.cpu()
        torch.save({'state_dict': model.state_dict()}, args.save_weights, _use_new_zipfile_serialization=False)
        return


    if args.evaluate:
        args.log_file = open(log_file, mode="a")
        validate(val_loader, model, criterion, args)
        args.log_file.close()
        return
        
    if args.cos_lr:
        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, args.epochs)
        for epoch in range(args.start_epoch):
            scheduler.step()


    for epoch in range(args.start_epoch, args.epochs):
        
        args.log_file = open(log_file, mode="a")

        if args.distributed:
            train_sampler.set_epoch(epoch)

        if(not args.cos_lr):
            adjust_learning_rate(optimizer, epoch, args)
        else:
            scheduler.step()
            print('[%03d] %.5f'%(epoch, scheduler.get_lr()[0]))


        # train for one epoch
        train(train_loader, model, criterion, optimizer, epoch, args)

        # 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)

        args.log_file.close()

        if not args.multiprocessing_distributed or (args.multiprocessing_distributed
                and args.rank % ngpus_per_node == 0):

            save_checkpoint({
                "epoch": epoch + 1,
                "arch": args.arch,
                "state_dict": model.state_dict(),
                "best_acc": best_acc1,
                "optimizer" : optimizer.state_dict(),
                }, is_best, epoch, save_path=args.ckpt)


def train(train_loader, model, criterion, optimizer, epoch, args):
    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')
    progress = ProgressMeter(
        len(train_loader),
        [batch_time, data_time, losses, top1, top5],
        prefix="Epoch: [{}]".format(epoch))

    param_groups = optimizer.param_groups[0]
    curr_lr = param_groups["lr"]

    # switch to train mode
    model.train()

    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.to(args.device, non_blocking=True)
        if torch.cuda.is_available():
            target = target.to(args.device, 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[0], images.size(0))
        top5.update(acc5[0], images.size(0))

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

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

        if i % args.print_freq == 0:
            epoch_msg = progress.get_message(i)
            epoch_msg += ("\tLr  {:.4f}".format(curr_lr))
            print(epoch_msg)

        if i % args.log_freq == 0:
            args.log_file.write(epoch_msg + "\n")


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')

    progress = ProgressMeter(
        len(val_loader),
        [batch_time, losses, top1, top5],
        prefix='Test: ')

    # switch to evaluate mode
    model.eval()

    with torch.no_grad():
        end = time.time()
        for i, (images, target) in enumerate(val_loader):
           
            if args.gpu is not None:
                images = images.to(args.device, non_blocking=True)
            if torch.cuda.is_available():
                target = target.to(args.device, non_blocking=True)

            # compute outputs
            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[0], images.size(0))
            top5.update(acc5[0], images.size(0))

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

            if i % args.print_freq == 0:
                epoch_msg = progress.get_message(i)
                print(epoch_msg)

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

        epoch_msg = '----------- Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f} -----------'.format(top1=top1, top5=top5)

        print(epoch_msg)

        args.log_file.write(epoch_msg + "\n")


    return top1.avg

def adjust_learning_rate(optimizer, epoch, args):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    lr = args.lr * (0.1 ** (epoch // 30))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


if __name__ == '__main__':
    main()