import time
from options.train_options import TrainOptions
from data.data_loader import CreateDataLoader
from models.models import create_model
from util.visualizer import Visualizer
from util import html
import copy
from test_function import test_func
import os

opt = TrainOptions().parse()
data_loader = CreateDataLoader(opt)
dataset = data_loader.load_data()
dataset_size = len(data_loader)
print('#training images = %d' % dataset_size)

model = create_model(opt)
visualizer = Visualizer(opt)
total_steps = 0

opt.results_dir = os.path.join(os.path.dirname(opt.checkpoints_dir), 'results')
web_dir = os.path.join(opt.results_dir, opt.name)
webpage = html.HTML(web_dir, 'Experiment = %s' % (opt.name))

# model.ttt()
#
# import matplotlib.pyplot as plt
# import numpy as np
#
# def save_img(image_tensor):
#     image_numpy = image_tensor.float().numpy()
#     image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
#     image_numpy = image_numpy.astype(np.uint8)
#     return image_numpy
#
# # plt.show()
# while True:
#     for i, data in enumerate(dataset):
#         plt.subplot(1, 2, 1)
#         plt.imshow(save_img(data['A'][0]))
#         plt.title(data['A_paths'])
#         plt.axis("off")
#         plt.subplot(1, 2, 2)
#         # plt.imshow(save_img(data['B'][0] * data['M'][0]))
#         plt.imshow(save_img(data['B'][0]))
#         plt.title(data['B_paths'])
#         plt.axis("off")
#         plt.pause(1)
#         print(data['A'].size(), data['B'].size())

# while True:
#     for i, data in enumerate(dataset):
#         print(data['A'].size())


for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
    epoch_start_time = time.time()
    epoch_iter = 0
    for i, data in enumerate(dataset):
        if epoch == 1:
            save_data = data
        else:
            save_data = save_data
        iter_start_time = time.time()
        total_steps += opt.batchSize
        epoch_iter += opt.batchSize
        model.set_input(data)
        model.optimize_parameters()

        if total_steps % opt.display_freq == 0:
            print("Saving images......")
            model.set_input(save_data)
            model.forward()
            visuals, start_points = model.get_current_visuals()
            visualizer.display_current_results(visuals, start_points, epoch)

        if total_steps % opt.print_freq == 0:
            errors = model.get_current_errors()
            t = (time.time() - iter_start_time) / opt.batchSize
            visualizer.print_current_errors(epoch, epoch_iter, errors, t)
            # if opt.display_id > 0:
            #     visualizer.plot_current_errors(epoch, float(epoch_iter)/dataset_size, opt, errors)

        if total_steps % opt.save_latest_freq == 0:
            print('saving the latest model (epoch %d, total_steps %d)' %
                  (epoch, total_steps))
            model.save('latest')
            test_func(opt, webpage, epoch=str(total_steps))

    if epoch % opt.save_epoch_freq == 0:
        print('saving the model at the end of epoch %d, iters %d' %
              (epoch, total_steps))
        model.save('latest')
        model.save(epoch)
        test_func(opt, webpage, epoch=str(epoch))

    # print('End of epoch %d / %d \t Time Taken: %d sec' %
    #       (epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))

    if epoch > opt.niter:
        model.update_learning_rate()
