import tensorflow as tf
import os
import numpy as np
import module as mm

#suppress tensorflow deprecation warnings
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)

class InpaintNN:

	def __init__(self, input_height=256, input_width=256, batch_size = 1, bar_model_name=None, bar_checkpoint_name=None, mosaic_model_name=None, mosaic_checkpoint_name = None, is_mosaic=False):
		self.bar_model_name = bar_model_name
		self.bar_checkpoint_name = bar_checkpoint_name
		self.mosaic_model_name = mosaic_model_name
		self.mosaic_checkpoint_name = mosaic_checkpoint_name
		self.is_mosaic = is_mosaic

		self.input_height = input_height
		self.input_width = input_width
		self.batch_size = batch_size

		self.check_model_file()
		self.build_model()

	def check_model_file(self):
		if not os.path.exists(self.bar_model_name) or not os.path.exists(self.mosaic_model_name) :
			print("\nMissing Train Model, download train model")
			print("Read : https://github.com/deeppomf/DeepCreamPy/blob/master/docs/INSTALLATION.md#run-code-yourself \n")
			exit(-1)

	def build_model(self):
		# ------- variables

		self.X = tf.placeholder(tf.float32, [self.batch_size, self.input_height, self.input_width, 3])
		self.Y = tf.placeholder(tf.float32, [self.batch_size, self.input_height, self.input_width, 3])

		self.MASK = tf.placeholder(tf.float32, [self.batch_size, self.input_height, self.input_width, 3])
		IT = tf.placeholder(tf.float32)

		# ------- structure

		input = tf.concat([self.X, self.MASK], 3)

		vec_en = mm.encoder(input, reuse=False, name='G_en')

		vec_con = mm.contextual_block(vec_en, vec_en, self.MASK, 3, 50.0, 'CB1', stride=1)

		I_co = mm.decoder(vec_en, self.input_height, self.input_height, reuse=False, name='G_de')
		I_ge = mm.decoder(vec_con, self.input_height, self.input_height, reuse=True, name='G_de')

		self.image_result = I_ge * (1-self.MASK) + self.Y*self.MASK

		D_real_red = mm.discriminator_red(self.Y, reuse=False, name='disc_red')
		D_fake_red = mm.discriminator_red(self.image_result, reuse=True, name='disc_red')

		# ------- Loss

		Loss_D_red = tf.reduce_mean(tf.nn.relu(1+D_fake_red)) + tf.reduce_mean(tf.nn.relu(1-D_real_red))

		Loss_D = Loss_D_red

		Loss_gan_red = -tf.reduce_mean(D_fake_red)

		Loss_gan = Loss_gan_red

		Loss_s_re = tf.reduce_mean(tf.abs(I_ge - self.Y))
		Loss_hat = tf.reduce_mean(tf.abs(I_co - self.Y))

		A = tf.image.rgb_to_yuv((self.image_result+1)/2.0)
		A_Y = tf.to_int32(A[:, :, :, 0:1]*255.0)

		B = tf.image.rgb_to_yuv((self.Y+1)/2.0)
		B_Y = tf.to_int32(B[:, :, :, 0:1]*255.0)

		ssim = tf.reduce_mean(tf.image.ssim(A_Y, B_Y, 255.0))

		alpha = IT/1000000

		Loss_G = 0.1*Loss_gan + 10*Loss_s_re + 5*(1-alpha) * Loss_hat

		# --------------------- variable & optimizer

		var_D = [v for v in tf.global_variables() if v.name.startswith('disc_red')]
		var_G = [v for v in tf.global_variables() if v.name.startswith('G_en') or v.name.startswith('G_de') or v.name.startswith('CB1')]

		update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)

		with tf.control_dependencies(update_ops):
		    optimize_D = tf.train.AdamOptimizer(learning_rate=0.0004, beta1=0.5, beta2=0.9).minimize(Loss_D, var_list=var_D)
		    optimize_G = tf.train.AdamOptimizer(learning_rate=0.0001, beta1=0.5, beta2=0.9).minimize(Loss_G, var_list=var_G)

		config = tf.ConfigProto()
		# config.gpu_options.per_process_gpu_memory_fraction = 0.4
		# config.gpu_options.allow_growth = False

		self.sess = tf.Session(config=config)

		init = tf.global_variables_initializer()
		self.sess.run(init)
		saver = tf.train.Saver()

		if self.is_mosaic:
			Restore = tf.train.import_meta_graph(self.mosaic_model_name)
			Restore.restore(self.sess, tf.train.latest_checkpoint(self.mosaic_checkpoint_name))
		else:
			Restore = tf.train.import_meta_graph(self.bar_model_name)
			Restore.restore(self.sess, tf.train.latest_checkpoint(self.bar_checkpoint_name))

	def predict(self, censored, unused, mask):
		img_sample = self.sess.run(self.image_result, feed_dict={self.X: censored, self.Y: unused, self.MASK: mask})

		return img_sample