import os
from tqdm import tqdm
import torch
from torch.utils.data import DataLoader
from logger import Logger, Visualizer
import numpy as np
import imageio


def reconstruction(config, inpainting_network, kp_detector, bg_predictor, dense_motion_network, checkpoint, log_dir, dataset):
    png_dir = os.path.join(log_dir, 'reconstruction/png')
    log_dir = os.path.join(log_dir, 'reconstruction')

    if checkpoint is not None:
        Logger.load_cpk(checkpoint, inpainting_network=inpainting_network, kp_detector=kp_detector,
                         bg_predictor=bg_predictor, dense_motion_network=dense_motion_network)
    else:
        raise AttributeError("Checkpoint should be specified for mode='reconstruction'.")
    dataloader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=1)

    if not os.path.exists(log_dir):
        os.makedirs(log_dir)

    if not os.path.exists(png_dir):
        os.makedirs(png_dir)
    
    loss_list = []

    inpainting_network.eval()
    kp_detector.eval()
    dense_motion_network.eval()
    if bg_predictor:
        bg_predictor.eval()

    for it, x in tqdm(enumerate(dataloader)):
        with torch.no_grad():
            predictions = []
            visualizations = []
            if torch.cuda.is_available():
                x['video'] = x['video'].cuda()
            kp_source = kp_detector(x['video'][:, :, 0])
            for frame_idx in range(x['video'].shape[2]):
                source = x['video'][:, :, 0]
                driving = x['video'][:, :, frame_idx]
                kp_driving = kp_detector(driving)
                bg_params = None
                if bg_predictor:
                    bg_params = bg_predictor(source, driving)
                
                dense_motion = dense_motion_network(source_image=source, kp_driving=kp_driving,
                                                    kp_source=kp_source, bg_param = bg_params, 
                                                    dropout_flag = False)
                out = inpainting_network(source, dense_motion)
                out['kp_source'] = kp_source
                out['kp_driving'] = kp_driving

                predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0])

                visualization = Visualizer(**config['visualizer_params']).visualize(source=source,
                                                                                   driving=driving, out=out)
                visualizations.append(visualization)
                loss = torch.abs(out['prediction'] - driving).mean().cpu().numpy()
                
                loss_list.append(loss)
            # print(np.mean(loss_list))
            predictions = np.concatenate(predictions, axis=1)
            imageio.imsave(os.path.join(png_dir, x['name'][0] + '.png'), (255 * predictions).astype(np.uint8))

    print("Reconstruction loss: %s" % np.mean(loss_list))