import os
import sys
import argparse

import tensorflow as tf
from skimage import transform

import hyperparameters as hp
from losses import YourModel
# from tensorboard_utils import \
#         ImageLabelingLogger, ConfusionMatrixLogger, CustomModelSaver

from skimage.io import imread, imsave
from matplotlib import pyplot as plt
import numpy as np


def parse_args():
    """ Perform command-line argument parsing. """

    parser = argparse.ArgumentParser(
        description="Let's train some neural nets!",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument(
        '--content',
        required=True,
        help='''Content image filepath''')
    parser.add_argument(
        '--style',
        required=True,
        help='Style image filepath')
    parser.add_argument(
        '--savefile',
        required=True,
        help='Filename to save image')


    return parser.parse_args()

def train(model):
    for i in range(hp.num_epochs):
        print('batch', i)
        model.train_step()

def main():
    """ Main function. """
    if os.path.exists(ARGS.content):
        ARGS.content = os.path.abspath(ARGS.content)
    if os.path.exists(ARGS.style):
        ARGS.style = os.path.abspath(ARGS.style)
    os.chdir(sys.path[0])
    print('this is',ARGS.content)

    content_image = imread(ARGS.content)
    style_image = imread(ARGS.style)
    style_image = transform.resize(style_image, content_image.shape, anti_aliasing=True)
    
    my_model = YourModel(content_image=content_image, style_image=style_image)
    my_model.vgg16.build([1, 255, 255, 3])
    my_model.vgg16.load_weights('vgg16_imagenet.h5', by_name=True)
    train(my_model)
    
    final_image = tf.squeeze(my_model.x)

    plt.imshow(final_image)

    imsave(ARGS.savefile, final_image)


ARGS = parse_args()
main()