from os import listdir
import os
from time import time

import torch.utils.data as data
import torchvision.transforms as transforms
from torch.utils.data import DataLoader

def printProgressBar(iteration, total, prefix='', suffix='', decimals=1, length=100,
                     fill='=', empty=' ', tip='>', begin='[', end=']', done="[DONE]", clear=True):
    percent = ("{0:." + str(decimals) + "f}").format(100 * (iteration / float(total)))
    filledLength = int(length * iteration // total)
    bar = fill * filledLength
    if iteration != total:
        bar = bar + tip
    bar = bar + empty * (length - filledLength - len(tip))
    display = '\r{prefix}{begin}{bar}{end} {percent}%{suffix}' \
        .format(prefix=prefix, begin=begin, bar=bar, end=end, percent=percent, suffix=suffix)
    print(display, end=''),  # comma after print() required for python 2
    if iteration == total:  # print with newline on complete
        if clear:  # display given complete message with spaces to 'erase' previous progress bar
            finish = '\r{prefix}{done}'.format(prefix=prefix, done=done)
            if hasattr(str, 'decode'):  # handle python 2 non-unicode strings for proper length measure
                finish = finish.decode('utf-8')
                display = display.decode('utf-8')
            clear = ' ' * max(len(display) - len(finish), 0)
            print(finish + clear)
        else:
            print('')


class DatasetFromFolder(data.Dataset):
    def __init__(self, image_dir):
        super(DatasetFromFolder, self).__init__()
        self.photo_path = os.path.join(image_dir, "a")
        self.sketch_path = os.path.join(image_dir, "b")
        self.image_filenames = [x for x in listdir(self.photo_path) if is_image_file(x)]

        transform_list = [transforms.ToTensor(),
                          transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]

        self.transform = transforms.Compose(transform_list)

    def __getitem__(self, index):
        # Load Image
        input = load_img(os.path.join(self.photo_path, self.image_filenames[index]))
        input = self.transform(input)
        target = load_img(os.path.join(self.sketch_path, self.image_filenames[index]))
        target = self.transform(target)

        return input, target

    def __len__(self):
        return len(self.image_filenames)

if __name__ == '__main__':
    dataset = DatasetFromFolder("./dataset/facades/train")
    dataloader = DataLoader(dataset=dataset, num_workers=8, batch_size=1, shuffle=True)
    total = len(dataloader)
    for epoch in range(20):
        t0 = time()
        for i, batch in enumerate(dataloader):
            real_a, real_b = batch[0], batch[1]
            printProgressBar(i + 1, total + 1,
                             length=20,
                             prefix='Epoch %s ' % str(1),
                             suffix=', d_loss: %d' % 1)
        printProgressBar(total, total,
                         done='Epoch [%s] ' % str(epoch) +
                              ', time: %.2f s' % (time() - t0)
                         )