text: import argparse
  import os
  import uuid
  import random
  import sys
  import tensorflow as tf
  import hypergan as hg
  import hyperchamber as hc
  import numpy as np
  from hypergan.generators import *
  from hypergan.gans.base_gan import BaseGAN
  from hypergan.gans.standard_gan import StandardGAN
  from hypergan.samplers.aligned_sampler import AlignedSampler
  from hypergan.viewer import GlobalViewer
  from hypergan.gans.autoencoder_gan import AutoencoderGAN
  from hypergan.search.alphagan_random_search import AlphaGANRandomSearch
  
  from examples.common import *
  
  arg_parser = ArgumentParser("Colorize an image")
  arg_parser.add_image_arguments()
  args = arg_parser.parse_args()
  
  width, height, channels = parse_size(args.size)
  
  config = lookup_config(args)
  if args.action == 'search':
      config = AlphaGANRandomSearch({}).random_config()
  
  save_file = "save/model.ckpt"
  inputs = hg.inputs.image_loader.ImageLoader(args.batch_size)
  inputs.create(args.directory,
                channels=channels, 
                format=args.format,
                crop=args.crop,
                width=width,
                height=height,
                resize=True)
  
  
  def setup_gan(config, inputs, args):
      gan = AutoencoderGAN(config=config, inputs=inputs)
      gan.create()
  
      if(args.action != 'search' and os.path.isfile(save_file+".meta")):
          gan.load(save_file)
  
   
      tf.train.start_queue_runners(sess=gan.session)
      config_name = args.config
      title = "[hypergan] colorizer " + config_name
      GlobalViewer.title = title
      GlobalViewer.enabled = args.viewer
  
      return gan
  
  def train(config, inputs, args):
      gan = setup_gan(config, inputs, args)
      sampler = lookup_sampler(args.sampler or 'autoencode')(gan)
  
      metrics = [accuracy(gan.inputs.x, gan.generator.sample), batch_diversity(gan.generator.sample)]
      sum_metrics = [0 for metric in metrics]
  
      for i in range(args.steps):
          gan.step()
  
          if i > args.steps * 9.0/10:
              for k, metric in enumerate(gan.session.run(metrics)):
                  print("Metric "+str(k)+" "+str(metric))
                  sum_metrics[k] += metric 
              
          if i % args.sample_every == 0:
              print("sampling "+str(i))
              sample_file = "samples/"+str(i)+".png"
              sampler.sample(sample_file, args.save_samples)
  
          if args.action == 'train' and i % args.save_every == 0 and i > 0:
              print("saving " + save_file)
              gan.save(save_file)
  
      return sum_metrics
  
  def sample(config, inputs, args):
      gan = setup_gan(config, inputs, args)
      sampler = lookup_sampler(args.sampler or 'random_walk')(gan)
      for i in range(args.steps):
          sample_file = "samples/"+str(i)+".png"
          sampler.sample(sample_file, False)
  
  def search(config, inputs, args):
      metrics = train(config, inputs, args)
  
      config_filename = "autoencode-"+str(uuid.uuid4())+'.json'
      hc.Selector().save(config_filename, config)
      with open(args.search_output, "a") as myfile:
          myfile.write(config_filename+","+",".join([str(x) for x in metrics])+"\n")
  
  if args.action == 'train':
      metrics = train(config, inputs, args)
      print("Resulting metrics:", metrics)
  elif args.action == 'sample':
      sample(config, inputs, args)
  elif args.action == 'search':
      search(config, inputs, args)
  else:
      print("Unknown action: "+args.action)
  