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):
  """
  """
  print("Loading Tensorflow....")
  import tensorflow as tf
  import tensorflow_hub as hub

  #tf.compat.v1.disable_eager_execution()
  #g = tf.compat.v1.get_default_graph()

  # Sample a batch of 8 random latent vectors (z) from the Gaussian prior. Then
  # call the generator on the latent samples to generate a batch of images with
  # shape [8, 128, 128, 3] and range [-1, 1].
  #recons = module(z, signature='generate', as_dict=True)['upsampled']

  #info = module.get_input_info_dict('encode')['x']
  #enc_ph = tf.placeholder(dtype=info.dtype, shape=info.get_shape())

  #z = bigbigan.encode(enc_ph, return_all_features=True)['z_mean']
  #recons = bigbigan.generate(z, upsample=True)
  #recons = outputs['upsampled']

  #if return_all_features else outputs['z_sample']

  #fp_img_out = "{}.png".format(int(time.time() * 1000))
  print("Loading module...")
  module = hub.Module('https://tfhub.dev/deepmind/bigbigan-resnet50/1')
  z = tf.random.normal([8, 120])  # latent samples
  outputs = module(z, signature='generate', as_dict=True)

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

  for sample in results['upsampled']:
    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))
    #print(result)

  #tf.keras.preprocessing.image.save_img(
  #  join(app_cfg.DIR_OUTPUTS, fp_img_out),
  #  gen_samples,
  #)
  #with tf.Session() as sess:
  #  gen_samples = gen_samples.eval()
  #  print(gen_samples)

  # # Given a batch of 256x256 RGB images in range [-1, 1], call the encoder to
  # # compute predicted latents z and other features (e.g. for use in downstream
  # # recognition tasks).
  # images = tf.placeholder(tf.float32, shape=[None, 256, 256, 3])
  # features = module(images, signature='encode', as_dict=True)

  # # Get the predicted latent sample `z_sample` from the dict of features.
  # # Other available features include `avepool_feat` and `bn_crelu_feat`, used in
  # # the representation learning results.
  # z_sample = features['z_sample']  # shape [?, 120]

  # # Compute reconstructions of the input `images` by passing the encoder's output
  # # `z_sample` back through the generator. Note that raw generator outputs are
  # # half the resolution of encoder inputs (128x128). To get upsampled generator
  # # outputs matching the encoder input resolution (256x256), instead use:
  # #     recons = module(z_sample, signature='generate', as_dict=True)['upsampled']
  # recons = module(z_sample, signature='generate')  # shape [?, 128, 128, 3]