import click

from app.utils import click_utils
from app.settings import app_cfg

from os.path import join
import time
import numpy as np

from PIL import Image

def image_to_uint8(x):
  """Converts [-1, 1] float array to [0, 255] uint8."""
  x = np.asarray(x)
  x = (256. / 2.) * (x + 1.)
  x = np.clip(x, 0, 255)
  x = x.astype(np.uint8)
  return x

@click.command('')
# @click.option('-i', '--input', 'opt_dir_in', required=True, 
#   help='Path to input image glob directory')
# @click.option('-r', '--recursive', 'opt_recursive', is_flag=True)
@click.pass_context
def cli(ctx):
  """
  Generate a random BigGAN image
  """
  import tensorflow as tf
  import tensorflow_hub as hub

  print("Loading module...")
  module = hub.Module('https://tfhub.dev/deepmind/biggan-128/2')
  # module = hub.Module('https://tfhub.dev/deepmind/biggan-256/2')
  # module = hub.Module('https://tfhub.dev/deepmind/biggan-512/2')

  batch_size = 8
  truncation = 0.5  # scalar truncation value in [0.02, 1.0]

  z = truncation * tf.random.truncated_normal([batch_size, 120])  # noise sample

  y_index = tf.random.uniform([batch_size], maxval=1000, dtype=tf.int32)
  y = tf.one_hot(y_index, 1000)

  outputs = module(dict(y=y, z=z, truncation=truncation))

  with tf.Session() as sess:
    sess.run(tf.compat.v1.global_variables_initializer())
    sess.run(tf.compat.v1.tables_initializer())
    results = sess.run(outputs)

  print(results)

  for sample in results:
    sample = image_to_uint8(sample)
    img = Image.fromarray(sample, "RGB")
    fp_img_out = "{}.png".format(int(time.time() * 1000))
    img.save(join(app_cfg.DIR_OUTPUTS, fp_img_out))