import argparse
from tools.utils import *
import os
from tqdm import tqdm
from glob import glob
import time
import numpy as np
from net import generator
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"

class ImportGraph:
    def __init__(self, checkpoint_dir):
        self.graph = tf.Graph()
        self.sess = tf.Session(graph=self.graph, config=tf.ConfigProto(allow_soft_placement=True, gpu_options=tf.GPUOptions(allow_growth=True)))
        with self.graph.as_default():
            test_real = tf.placeholder(tf.float32, [1, None, None, 3], name='test')
            with tf.variable_scope("generator", reuse=False):
                test_generated = generator.G_net(test_real).fake
            saver = tf.train.Saver()

            ckpt = tf.train.get_checkpoint_state(checkpoint_dir)  # checkpoint file information
            if ckpt and ckpt.model_checkpoint_path:
                ckpt_name = os.path.basename(ckpt.model_checkpoint_path)  # first line
                saver.restore(self.sess, os.path.join(checkpoint_dir, ckpt_name))
                print(" [*] Success to read {}".format(os.path.join(checkpoint_dir, ckpt_name)))
            else:
                print(" [*] Failed to find a checkpoint")

    def test(self, sample_file, if_adjust_brightness, img_size=[256,256]):
        sample_image = np.asarray(load_test_data(sample_file, img_size))
        image_path = os.path.join(result_dir, '{0}'.format(os.path.basename(sample_file)))
        fake_img = sess.run(test_generated, feed_dict={test_real: sample_image})
        if if_adjust_brightness:
            save_images(fake_img, image_path, sample_file)
        else:
            save_images(fake_img, image_path, None)

        return image_path


def stats_graph(graph):
    flops = tf.profiler.profile(graph, options=tf.profiler.ProfileOptionBuilder.float_operation())
    # params = tf.profiler.profile(graph, options=tf.profiler.ProfileOptionBuilder.trainable_variables_parameter())
    print('FLOPs: {}'.format(flops.total_float_ops))

def test(checkpoint_dir, style_name, test_dir, if_adjust_brightness, img_size=[256,256]):
    # tf.reset_default_graph()
    result_dir = 'results/'+style_name
    check_folder(result_dir)
    test_files = [test_dir]

    test_real = tf.placeholder(tf.float32, [1, None, None, 3], name='test')

    with tf.variable_scope("generator", reuse=False):
        test_generated = generator.G_net(test_real).fake
    saver = tf.train.Saver()

    out_paths = []

    gpu_options = tf.GPUOptions(allow_growth=True)
    with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_options)) as sess:
        # tf.global_variables_initializer().run()
        # load model
        ckpt = tf.train.get_checkpoint_state(checkpoint_dir)  # checkpoint file information
        if ckpt and ckpt.model_checkpoint_path:
            ckpt_name = os.path.basename(ckpt.model_checkpoint_path)  # first line
            saver.restore(sess, os.path.join(checkpoint_dir, ckpt_name))
            print(" [*] Success to read {}".format(os.path.join(checkpoint_dir, ckpt_name)))
        else:
            print(" [*] Failed to find a checkpoint")
            return
        # stats_graph(tf.get_default_graph())

        begin = time.time()
        for sample_file  in tqdm(test_files) :
            # print('Processing image: ' + sample_file)
            sample_image = np.asarray(load_test_data(sample_file, img_size))
            image_path = os.path.join(result_dir,'{0}'.format(os.path.basename(sample_file)))
            fake_img = sess.run(test_generated, feed_dict = {test_real : sample_image})
            if if_adjust_brightness:
                save_images(fake_img, image_path, sample_file)
            else:
                save_images(fake_img, image_path, None)
				
            out_paths.append(image_path)
        end = time.time()
        print(f'test-time: {end-begin} s')
		
    return out_paths
    
if __name__ == '__main__':
    arg = parse_args()
    print(arg.checkpoint_dir)
    test(arg.checkpoint_dir, arg.save_dir, arg.test_dir, arg.if_adjust_brightness)