import os
import scipy.misc
import numpy as np
import math

from model import DCGAN
from utils import pp, visualize, to_json, show_all_variables, save_images

import tensorflow as tf

flags = tf.app.flags
flags.DEFINE_integer("epoch", 25, "Epoch to train [25]")
flags.DEFINE_float("learning_rate", 0.0002, "Learning rate of for adam [0.0002]")
flags.DEFINE_float("beta1", 0.5, "Momentum term of adam [0.5]")
flags.DEFINE_integer("train_size", np.inf, "The size of train images [np.inf]")
flags.DEFINE_integer("batch_size", 64, "The size of batch images [64]")
flags.DEFINE_integer("input_height", 108, "The size of image to use (will be center cropped). [108]")
flags.DEFINE_integer("input_width", None, "The size of image to use (will be center cropped). If None, same value as input_height [None]")
flags.DEFINE_integer("output_height", 64, "The size of the output images to produce [64]")
flags.DEFINE_integer("output_width", None, "The size of the output images to produce. If None, same value as output_height [None]")
flags.DEFINE_string("dataset", "celebA", "The name of dataset [celebA, mnist, lsun]")
flags.DEFINE_string("input_fname_pattern", "*.jpg", "Glob pattern of filename of input images [*]")
flags.DEFINE_string("checkpoint_dir", "checkpoint", "Directory name to save the checkpoints [checkpoint]")
flags.DEFINE_string("sample_dir", "samples", "Directory name to save the image samples [samples]")
flags.DEFINE_boolean("train", False, "True for training, False for testing [False]")
flags.DEFINE_boolean("crop", False, "True for training, False for testing [False]")
flags.DEFINE_boolean("visualize", False, "True for visualizing, False for nothing [False]")
FLAGS = flags.FLAGS

def main(_):
  pp.pprint(flags.FLAGS.__flags)

  if FLAGS.input_width is None:
    FLAGS.input_width = FLAGS.input_height
  if FLAGS.output_width is None:
    FLAGS.output_width = FLAGS.output_height

  if not os.path.exists(FLAGS.checkpoint_dir):
    os.makedirs(FLAGS.checkpoint_dir)
  if not os.path.exists(FLAGS.sample_dir):
    os.makedirs(FLAGS.sample_dir)

  #gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
  run_config = tf.ConfigProto()
  run_config.gpu_options.allow_growth=True

  with tf.Session(config=run_config) as sess:
    if FLAGS.dataset == 'mnist':
      dcgan = DCGAN(
          sess,
          input_width=FLAGS.input_width,
          input_height=FLAGS.input_height,
          output_width=FLAGS.output_width,
          output_height=FLAGS.output_height,
          batch_size=FLAGS.batch_size,
          sample_num=FLAGS.batch_size,
          y_dim=10,
          dataset_name=FLAGS.dataset,
          input_fname_pattern=FLAGS.input_fname_pattern,
          crop=FLAGS.crop,
          checkpoint_dir=FLAGS.checkpoint_dir,
          sample_dir=FLAGS.sample_dir)
    else:
      dcgan = DCGAN(
          sess,
          input_width=FLAGS.input_width,
          input_height=FLAGS.input_height,
          output_width=FLAGS.output_width,
          output_height=FLAGS.output_height,
          batch_size=FLAGS.batch_size,
          sample_num=FLAGS.batch_size,
          dataset_name=FLAGS.dataset,
          input_fname_pattern=FLAGS.input_fname_pattern,
          crop=FLAGS.crop,
          checkpoint_dir=FLAGS.checkpoint_dir,
          sample_dir=FLAGS.sample_dir)

    show_all_variables()

    if FLAGS.train:
      dcgan.train(FLAGS)
    else:
      if not dcgan.load(FLAGS.checkpoint_dir)[0]:
        raise Exception("[!] Train a model first, then run test mode")

    # Visualization
    x = np.random.uniform(-0.9, 0.9, size=(100)) 
    y = np.random.uniform(-0.9, 0.9, size=(100)) 
    delta = 1.0/32.0
    arrays = [p*x+(1-p)*y for p in np.arange(-16*delta, 1+16*delta-0.0001, delta)]
    z_sample = np.stack(arrays)
    samples = sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample})
    save_images(samples, [8, 8], './samples/test_me.png')

if __name__ == '__main__':
  tf.app.run()
