import click

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

from os.path import join
import time

from PIL import Image

@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):
  """
  """
  import tensorflow as tf
  import tensorflow_hub as hub
  
  module = hub.Module('https://tfhub.dev/deepmind/bigbigan-resnet50/1')

  # 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].
  z = tf.random.normal([8, 120])  # latent samples
  gen_samples = module(z, signature='generate', as_dict=True)['upsampled']

  for sample in gen_samples:
    img = Image.fromarray(sample, "RGB")
    fp_img_out = int(time.time() * 1000) + '.png'
    img.save(join(app_cfg.DIR_OUTPUTS, fp_img_out))
  # # 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]