import os.path as osp
import logging
import time
import argparse
from collections import OrderedDict
import torch

import options.options as option
import utils.util as util
from data.util import bgr2ycbcr
from data import create_dataset, create_dataloader
from models import create_model

#### options
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, required=True, help='Path to options YMAL file.')
opt = option.parse(parser.parse_args().opt, is_train=False)
opt = option.dict_to_nonedict(opt)

util.mkdirs(
    (path for key, path in opt['path'].items()
     if not key == 'experiments_root' and 'pretrain_model' not in key and 'resume' not in key))
util.setup_logger('base', opt['path']['log'], 'test_' + opt['name'], level=logging.INFO,
                  screen=True, tofile=True)
logger = logging.getLogger('base')
logger.info(option.dict2str(opt))

#### Create test dataset and dataloader
test_loaders = []
for phase, dataset_opt in sorted(opt['datasets'].items()):
    test_set = create_dataset(dataset_opt)
    test_loader = create_dataloader(test_set, dataset_opt)
    logger.info('Number of test images in [{:s}]: {:d}'.format(dataset_opt['name'], len(test_set)))
    test_loaders.append(test_loader)

model = create_model(opt)
for test_loader in test_loaders:
    test_set_name = test_loader.dataset.opt['name']
    logger.info('\nTesting [{:s}]...'.format(test_set_name))
    test_start_time = time.time()
    dataset_dir = osp.join(opt['path']['results_root'], test_set_name)
    util.mkdir(dataset_dir)

    test_results = OrderedDict()
    test_results['psnr'] = []
    test_results['ssim'] = []
    test_results['psnr_y'] = []
    test_results['ssim_y'] = []

    cond = test_loader.dataset.opt['cond']
    cond_norm = test_loader.dataset.opt['cond_norm']
    for data in test_loader:
        need_GT = False if test_loader.dataset.opt['dataroot_GT'] is None else True

        if cond is not None:
            for i in range(len(cond)):
                cond[i] = cond[i] / cond_norm[i]
            data['cond'] = torch.Tensor(cond).view(1, -1)
            need_cond = True
        elif test_loader.dataset.opt['mode'] in ['LQGT_cond']:
            need_cond = True
        else:
            need_cond = False
            
        model.feed_data(data, need_GT=need_GT, need_cond=need_cond)

        img_path = data['LQ_path'][0]
        img_name = osp.splitext(osp.basename(img_path))[0]

        model.test()
        visuals = model.get_current_visuals(need_GT=need_GT)

        sr_img = util.tensor2img(visuals['rlt'])  # uint8

        # save images
        suffix = opt['suffix']
        if suffix:
            save_img_path = osp.join(dataset_dir, img_name + suffix + '.png')
        else:
            save_img_path = osp.join(dataset_dir, img_name + '.png')
        util.save_img(sr_img, save_img_path)

        # calculate PSNR and SSIM
        if need_GT:
            gt_img = util.tensor2img(visuals['GT'])
            psnr = util.calculate_psnr(sr_img, gt_img)
            ssim = util.calculate_ssim(sr_img, gt_img)
            test_results['psnr'].append(psnr)
            test_results['ssim'].append(ssim)

            if gt_img.shape[2] == 3:  # RGB image
                sr_img_y = bgr2ycbcr(sr_img / 255., only_y=True)
                gt_img_y = bgr2ycbcr(gt_img / 255., only_y=True)

                psnr_y = util.calculate_psnr(sr_img_y * 255, gt_img_y * 255)
                ssim_y = util.calculate_ssim(sr_img_y * 255, gt_img_y * 255)
                test_results['psnr_y'].append(psnr_y)
                test_results['ssim_y'].append(ssim_y)
                logger.info(
                    '{:20s} - PSNR: {:.6f} dB; SSIM: {:.6f}; PSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}.'.
                    format(img_name, psnr, ssim, psnr_y, ssim_y))
            else:
                logger.info('{:20s} - PSNR: {:.6f} dB; SSIM: {:.6f}.'.format(img_name, psnr, ssim))
        else:
            logger.info(img_name)

    if need_GT:  # metrics
        # Average PSNR/SSIM results
        ave_psnr = sum(test_results['psnr']) / len(test_results['psnr'])
        ave_ssim = sum(test_results['ssim']) / len(test_results['ssim'])
        logger.info(
            '----Average PSNR/SSIM results for {}----\n\tPSNR: {:.6f} dB; SSIM: {:.6f}\n'.format(
                test_set_name, ave_psnr, ave_ssim))
        if test_results['psnr_y'] and test_results['ssim_y']:
            ave_psnr_y = sum(test_results['psnr_y']) / len(test_results['psnr_y'])
            ave_ssim_y = sum(test_results['ssim_y']) / len(test_results['ssim_y'])
            logger.info(
                '----Y channel, average PSNR/SSIM----\n\tPSNR_Y: {:.6f} dB; SSIM_Y: {:.6f}\n'.
                format(ave_psnr_y, ave_ssim_y))
