import argparse
import os
import copy

import torch
from torch import nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm

from models import FSRCNN
from datasets import TrainDataset, EvalDataset
from utils import AverageMeter, calc_psnr


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    # 训练文件
    parser.add_argument('--train-file', type=str,help="the dir of train data",default="https://www.ctyun.cn/zhishi/Train/91-image_x4.h5")
    # 测试集文件
    parser.add_argument('--eval-file', type=str,help="thr dir of test data ",default="https://www.ctyun.cn/zhishi/Test/Set5_x4.h5")
    # 输出的文件夹
    parser.add_argument('--outputs-dir',help="the output dir", type=str,default="https://www.ctyun.cn/zhishi/outputs")
    parser.add_argument('--weights-file', type=str)
    parser.add_argument('--scale', type=int, default=2)
    parser.add_argument('--lr', type=float, default=1e-3)
    parser.add_argument('--batch-size', type=int, default=16)
    parser.add_argument('--num-epochs', type=int, default=20)
    parser.add_argument('--num-workers', type=int, default=8)
    parser.add_argument('--seed', type=int, default=123)
    args = parser.parse_args()

    args.outputs_dir = os.path.join(args.outputs_dir, 'x{}'.format(args.scale))

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

    cudnn.benchmark = True
    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

    torch.manual_seed(args.seed)

    model = FSRCNN(scale_factor=args.scale).to(device)
    criterion = nn.MSELoss()
    optimizer = optim.Adam([
        {'params': model.first_part.parameters()},
        {'params': model.mid_part.parameters()},
        {'params': model.last_part.parameters(), 'lr': args.lr * 0.1}
    ], lr=args.lr)

    train_dataset = TrainDataset(args.train_file)
    train_dataloader = DataLoader(dataset=train_dataset,
                                  batch_size=args.batch_size,
                                  shuffle=True,
                                  num_workers=args.num_workers,
                                  pin_memory=True)
    eval_dataset = EvalDataset(args.eval_file)
    eval_dataloader = DataLoader(dataset=eval_dataset, batch_size=1)

    best_weights = copy.deepcopy(model.state_dict())
    best_epoch = 0
    best_psnr = 0.0

    for epoch in range(args.num_epochs):
        model.train()
        epoch_losses = AverageMeter()

        with tqdm(total=(len(train_dataset) - len(train_dataset) % args.batch_size), ncols=80) as t:
            t.set_description('epoch: {}/{}'.format(epoch, args.num_epochs - 1))

            for data in train_dataloader:
                inputs, labels = data

                inputs = inputs.to(device)
                labels = labels.to(device)

                preds = model(inputs)

                loss = criterion(preds, labels)

                epoch_losses.update(loss.item(), len(inputs))

                optimizer.zero_grad()
                loss.backward()
                optimizer.step()

                t.set_postfix(loss='{:.6f}'.format(epoch_losses.avg))
                t.update(len(inputs))

        torch.save(model.state_dict(), os.path.join(args.outputs_dir, 'epoch_{}.pth'.format(epoch)))

        model.eval（)
        epoch_psnr = AverageMeter()

        for data in eval_dataloader:
            inputs, labels = data

            inputs = inputs.to(device)
            labels = labels.to(device)

            with torch.no_grad():
                preds = model(inputs).clamp(0.0, 1.0)

            epoch_psnr.update(calc_psnr(preds, labels), len(inputs))

        print('eval psnr: {:.2f}'.format(epoch_psnr.avg))

        if epoch_psnr.avg > best_psnr:
            best_epoch = epoch
            best_psnr = epoch_psnr.avg
            best_weights = copy.deepcopy(model.state_dict())

    print('best epoch: {}, psnr: {:.2f}'.format(best_epoch, best_psnr))
    torch.save(best_weights, os.path.join(args.outputs_dir, 'best.pth'))