# -*- coding: UTF-8 -*-
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.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as torchmodels
import models
# from thop import profile

from torch import Tensor
from typing import Callable, Any, Optional, List



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


parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('data', metavar='DIR',
                    help='path to dataset')
parser.add_argument('--arch', '-a', metavar='ARCH', default='rla_mobilenet_k6',
                    choices=model_names,
                    help='model architecture: ' +
                        ' | '.join(model_names) +
                        ' (default: rla_mobilenetv2_k12)')
parser.add_argument('--work-dir', default='work_dirs', type=str, 
                    help='the dir to save logs and models')
parser.add_argument('-j', '--workers', default=12, type=int, metavar='N',
                    help='number of data loading workers (default: 4)')
parser.add_argument('--epochs', default=400, 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=96, type=int,
                    metavar='N', help='mini-batch size (default: 96)')
parser.add_argument('--lr', '--learning-rate', default=0.045, 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('--weight-decay', '--wd', default=4e-5, type=float,
                    metavar='W', help='weight decay (default: 4e-5)', 
                    dest='weight_decay')
parser.add_argument('--print-freq', '-p', default=50, 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('-pre', '--pretrained_conv1', dest='pretrained_conv1', action='store_true',
                    help='use pre-trained conv1')
parser.add_argument('--world-size', default=1, type=int,
                    help='number of distributed processes')
parser.add_argument('--dist-url', default='tcp://127.0.0.1:12345', type=str,
                    help='url used to set up distributed training')
parser.add_argument('--dist-backend', default='gloo', type=str,
                    help='distributed backend')
parser.add_argument('--seed', default=None, type=int,
                    help='seed for initializing training. ')
parser.add_argument('--gpu', default=None, type=int,
                    help='GPU id to use.')
parser.add_argument('--rla', default=None, type=int,
                    help='Manually specify the channels (filters) of the rla module')
parser.add_argument('--action', default='', type=str,
                    help='other information.')
                    

best_acc1 = 0


def main():
    global args, best_acc1
    args = parser.parse_args()

    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.')

    args.distributed = args.world_size > 1

    if args.distributed:
        dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                world_size=args.world_size)

    # # create model
    # if args.pretrained:
    #     print("=> using pre-trained model '{}'".format(args.arch))
    #     model = models.__dict__[args.arch](pretrained=True)
    # else:
    #     print("=> creating model '{}'".format(args.arch))
    #     model = models.__dict__[args.arch]()
    print("=> creating model '{}' for training".format(args.arch))
    if args.rla == None:
        model = models.__dict__[args.arch]()
    else:
        model = models.__dict__[args.arch](rla_channel=args.rla)


    # ------------- use pretrained conv1 --------------
    if args.pretrained_conv1:
        pretrain_model = None
        arch = args.arch
        if arch.find('mobilenetv2') > -1:
            pretrain_model = torchmodels.mobilenet_v2(pretrained=True)
        
        if pretrain_model is not None:
            model.conv1[0].weight = pretrain_model.features[0][0].weight
            model.conv1[0].weight.requires_grad = False
            print("[!] Using pretrained conv1")
            
    if args.gpu is not None:
        model = model.cuda(args.gpu)
    elif args.distributed:
        model.cuda()
        model = torch.nn.parallel.DistributedDataParallel(model)

    else:
        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()

    # print(model)

    # get the number of models parameters
    print('Number of models parameters: {}'.format(
        sum([p.data.nelement() for p in model.parameters()])))
    # with torch.cuda.device(0):
    #     net = models.__dict__[args.arch]()
    #     # flops, params = get_model_complexity_info(net, (3, 224, 224), as_strings=True, print_per_layer_stat=True)
    #     flops, params = profile(net, inputs=(torch.randn(1,3,224,224),))
    #     print('Flops:  ' + str(flops))
    #     print('Params: ' + str(params))
    #     del net, flops, params

    # 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)

    # 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_acc1 = checkpoint['best_acc1']
            model.load_state_dict(checkpoint['state_dict'])
            optimizer.load_state_dict(checkpoint['optimizer'])
            print("=> loaded checkpoint '{}' (epoch {})"
                  .format(args.resume, checkpoint['epoch']))
            del checkpoint
        else:
            print("=> no checkpoint found at '{}'".format(args.resume))

    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.evaluate:
        m = time.time()
        _, _ =validate(val_loader, model, criterion)
        n = time.time()
        print("evaluate_time (h): ", (n-m)/3600)
        return
    
    # create folder
    if not os.path.exists(os.path.abspath(args.work_dir)):
        os.mkdir(os.path.abspath(args.work_dir))  
    # directory = "runs/%s/"%(args.arch + '_' + args.action)
    directory = "%s/%s/"%(args.work_dir, args.arch + '_' + args.action)
    if not os.path.exists(directory):
        os.mkdir(directory)
    print ("save checkpoint file and log in: ", directory)

    loss_plot = {}
    train_acc1_plot = {}
    train_acc5_plot = {}
    val_acc1_plot = {}
    val_acc5_plot = {}

    for epoch in range(args.start_epoch, args.epochs):
        start_time = time.time()
        if args.distributed:
            train_sampler.set_epoch(epoch)
        adjust_learning_rate(optimizer, epoch)

        # train for one epoch
        # train(train_loader, model, criterion, optimizer, epoch)
        loss_temp, train_acc1_temp, train_acc5_temp = train(train_loader, model, criterion, optimizer, epoch)
        loss_plot[epoch] = loss_temp
        train_acc1_plot[epoch] = train_acc1_temp
        train_acc5_plot[epoch] = train_acc5_temp

        # evaluate on validation set
        # acc1 = validate(val_loader, model, criterion)
        acc1, acc5 = validate(val_loader, model, criterion)
        val_acc1_plot[epoch] = acc1
        val_acc5_plot[epoch] = acc5

        # remember best acc@1 and save checkpoint
        is_best = acc1 > best_acc1
        best_acc1 = max(acc1, best_acc1)
        save_checkpoint({
            'epoch': epoch + 1,
            'arch': args.arch,
            'state_dict': model.state_dict(),
            'best_acc1': best_acc1,
            'optimizer' : optimizer.state_dict(),
        }, is_best)
        
        # save loss,train_acc1,train_acc5,val_acc1,val_acc5 in .txt files
        data_save(directory + 'loss_plot.txt', loss_plot)
        data_save(directory + 'train_acc1.txt', train_acc1_plot)
        data_save(directory + 'train_acc5.txt', train_acc5_plot)
        data_save(directory + 'val_acc1.txt', val_acc1_plot)
        data_save(directory + 'val_acc5.txt', val_acc5_plot)

        end_time = time.time()
        time_value = (end_time - start_time) / 3600
        print("-" * 80)
        print("epoch {} train_time (h): {}".format(epoch, time_value))
        print("-" * 80)


def train(train_loader, model, criterion, optimizer, epoch):
    batch_time = AverageMeter()
    data_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()
    losses_batch = {}
    # switch to train mode
    model.train()

    directory = "%s/%s/"%(args.work_dir, args.arch + '_' + args.action)
    
    end = time.time()
    for i, (input, target) in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)

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

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

        # measure accuracy and record loss
        acc1, acc5 = accuracy(output, target, topk=(1, 5))
        losses.update(loss.item(), input.size(0))
        top1.update(acc1[0], input.size(0))
        top5.update(acc5[0], input.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 args.gpu is None or args.gpu == 0:
            if i % args.print_freq == 0:
                lr_temp = args.lr * (0.98 ** epoch)
                print('Epoch: [{0}][{1}/{2}]\t'
                    'LR {3:.5f}\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'
                    'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                    'Acc@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                    epoch, i, len(train_loader), lr_temp, 
                    batch_time=batch_time, data_time=data_time, 
                    loss=losses, top1=top1, top5=top5))
                # losses_batch[epoch] = losses.avg
                # loss_batch = open(directory + "loss_batch.txt", 'a')
                # for line in losses_batch:
                #     loss_batch.write(str(epoch) + " " + str(line) + " " + str(losses_batch[line]) + '\n')
                # loss_batch.close()

    return losses.avg, top1.avg, top5.avg


def validate(val_loader, model, criterion):
    batch_time = AverageMeter()
    losses = AverageMeter()
    top1 = AverageMeter()
    top5 = AverageMeter()

    # switch to evaluate mode
    model.eval()

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

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

            # measure accuracy and record loss
            acc1, acc5 = accuracy(output, target, topk=(1, 5))
            losses.update(loss.item(), input.size(0))
            top1.update(acc1[0], input.size(0))
            top5.update(acc5[0], 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'
                      'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
                      'Acc@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
                       i, len(val_loader), batch_time=batch_time, loss=losses,
                       top1=top1, top5=top5))

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

    return top1.avg, top5.avg


def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
    # directory = "runs/%s/"%(args.arch + '_' + args.action)
    directory = "%s/%s/"%(args.work_dir, args.arch + '_' + args.action)
    
    filename = directory + filename
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, directory + 'model_best.pth.tar')


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 adjust_learning_rate(optimizer, epoch):
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    # lr = args.lr * (0.1 ** (epoch // 30))
    lr = args.lr * (0.98 ** epoch)
    print('lr = ', lr)
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


def accuracy(output, target, topk=(1,)):
    """Computes the accuracy@k for the specified values of k"""
    with torch.no_grad():
        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, keepdim=True)
            correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
            res.append(correct_k.mul_(100.0 / batch_size))
        return res


def data_save(root, file):
    if not os.path.exists(root):
        os.mknod(root)
    file_temp = open(root, 'r')
    lines = file_temp.readlines()
    if not lines:
        epoch = -1
    else:
        epoch = lines[-1][:lines[-1].index(' ')]
    epoch = int(epoch)
    file_temp.close()
    file_temp = open(root, 'a')
    for line in file:
        if line > epoch:
            file_temp.write(str(line) + " " + str(file[line]) + '\n')
    file_temp.close()


if __name__ == '__main__':
    main()
    