'''
CVPR 2020 submission, Paper ID 6791
Source code for 'Learning to Cartoonize Using White-Box Cartoon Representations'
'''


import layers
import tensorflow as tf
import numpy as np
import tensorflow.contrib.slim as slim

from tqdm import tqdm



def resblock(inputs, out_channel=32, name='resblock'):
    
    with tf.variable_scope(name):
        
        x = slim.convolution2d(inputs, out_channel, [3, 3], 
                               activation_fn=None, scope='conv1')
        x = tf.nn.leaky_relu(x)
        x = slim.convolution2d(x, out_channel, [3, 3], 
                               activation_fn=None, scope='conv2')
        
        return x + inputs
        


def generator(inputs, channel=32, num_blocks=4, name='generator', reuse=False):
    with tf.variable_scope(name, reuse=reuse):
        
        x = slim.convolution2d(inputs, channel, [7, 7], activation_fn=None)
        x = tf.nn.leaky_relu(x)
        
        x = slim.convolution2d(x, channel*2, [3, 3], stride=2, activation_fn=None)
        x = slim.convolution2d(x, channel*2, [3, 3], activation_fn=None)
        x = tf.nn.leaky_relu(x)
       
        x = slim.convolution2d(x, channel*4, [3, 3], stride=2, activation_fn=None)
        x = slim.convolution2d(x, channel*4, [3, 3], activation_fn=None)
        x = tf.nn.leaky_relu(x)
        
        for idx in range(num_blocks):
            x = resblock(x, out_channel=channel*4, name='block_{}'.format(idx))
            
        x = slim.conv2d_transpose(x, channel*2, [3, 3], stride=2, activation_fn=None)
        x = slim.convolution2d(x, channel*2, [3, 3], activation_fn=None)
        
        x = tf.nn.leaky_relu(x)
        
        x = slim.conv2d_transpose(x, channel, [3, 3], stride=2, activation_fn=None)
        x = slim.convolution2d(x, channel, [3, 3], activation_fn=None)
        x = tf.nn.leaky_relu(x)
        
        x = slim.convolution2d(x, 3, [7, 7], activation_fn=None)
        #x = tf.clip_by_value(x, -0.999999, 0.999999)
        
        return x
    

def unet_generator(inputs, channel=32, num_blocks=4, name='generator', reuse=False):
    with tf.variable_scope(name, reuse=reuse):
        
        x0 = slim.convolution2d(inputs, channel, [7, 7], activation_fn=None)
        x0 = tf.nn.leaky_relu(x0)
        
        x1 = slim.convolution2d(x0, channel, [3, 3], stride=2, activation_fn=None)
        x1 = tf.nn.leaky_relu(x1)
        x1 = slim.convolution2d(x1, channel*2, [3, 3], activation_fn=None)
        x1 = tf.nn.leaky_relu(x1)
        
        x2 = slim.convolution2d(x1, channel*2, [3, 3], stride=2, activation_fn=None)
        x2 = tf.nn.leaky_relu(x2)
        x2 = slim.convolution2d(x2, channel*4, [3, 3], activation_fn=None)
        x2 = tf.nn.leaky_relu(x2)
        
        for idx in range(num_blocks):
            x2 = resblock(x2, out_channel=channel*4, name='block_{}'.format(idx))
            
        x2 = slim.convolution2d(x2, channel*2, [3, 3], activation_fn=None)
        x2 = tf.nn.leaky_relu(x2)
        
        h1, w1 = tf.shape(x2)[1], tf.shape(x2)[2]
        x3 = tf.image.resize_bilinear(x2, (h1*2, w1*2))
        x3 = slim.convolution2d(x3+x1, channel*2, [3, 3], activation_fn=None)
        x3 = tf.nn.leaky_relu(x3)
        x3 = slim.convolution2d(x3, channel, [3, 3], activation_fn=None)
        x3 = tf.nn.leaky_relu(x3)

        h2, w2 = tf.shape(x3)[1], tf.shape(x3)[2]
        x4 = tf.image.resize_bilinear(x3, (h2*2, w2*2))
        x4 = slim.convolution2d(x4+x0, channel, [3, 3], activation_fn=None)
        x4 = tf.nn.leaky_relu(x4)
        x4 = slim.convolution2d(x4, 3, [7, 7], activation_fn=None)
        #x4 = tf.clip_by_value(x4, -1, 1)
        return x4
        
    
    
def disc_bn(x, scale=1, channel=32, is_training=True, 
            name='discriminator', patch=True, reuse=False):
    
    with tf.variable_scope(name, reuse=reuse):
        
        for idx in range(3):
            x = slim.convolution2d(x, channel*2**idx, [3, 3], stride=2, activation_fn=None)
            x = slim.batch_norm(x, is_training=is_training, center=True, scale=True)
            x = tf.nn.leaky_relu(x)
            
            x = slim.convolution2d(x, channel*2**idx, [3, 3], activation_fn=None)
            x = slim.batch_norm(x, is_training=is_training, center=True, scale=True)
            x = tf.nn.leaky_relu(x)

        if patch == True:
            x = slim.convolution2d(x, 1, [1, 1], activation_fn=None)
        else:
            x = tf.reduce_mean(x, axis=[1, 2])
            x = slim.fully_connected(x, 1, activation_fn=None)
        
        return x

    


def disc_sn(x, scale=1, channel=32, patch=True, name='discriminator', reuse=False):
    with tf.variable_scope(name, reuse=reuse):

        for idx in range(3):
            x = layers.conv_spectral_norm(x, channel*2**idx, [3, 3], 
                                          stride=2, name='conv{}_1'.format(idx))
            x = tf.nn.leaky_relu(x)
            
            x = layers.conv_spectral_norm(x, channel*2**idx, [3, 3], 
                                          name='conv{}_2'.format(idx))
            x = tf.nn.leaky_relu(x)
        
        
        if patch == True:
            x = layers.conv_spectral_norm(x, 1, [1, 1], name='conv_out'.format(idx))
            
        else:
            x = tf.reduce_mean(x, axis=[1, 2])
            x = slim.fully_connected(x, 1, activation_fn=None)
        
        return x


def disc_ln(x, channel=32, is_training=True, name='discriminator', patch=True, reuse=False):
    with tf.variable_scope(name, reuse=reuse):

        for idx in range(3):
            x = slim.convolution2d(x, channel*2**idx, [3, 3], stride=2, activation_fn=None)
            x = tf.contrib.layers.layer_norm(x)
            x = tf.nn.leaky_relu(x)
            
            x = slim.convolution2d(x, channel*2**idx, [3, 3], activation_fn=None)
            x = tf.contrib.layers.layer_norm(x)
            x = tf.nn.leaky_relu(x)

        if patch == True:
            x = slim.convolution2d(x, 1, [1, 1], activation_fn=None)
        else:
            x = tf.reduce_mean(x, axis=[1, 2])
            x = slim.fully_connected(x, 1, activation_fn=None)
        
        return x

        

            
if __name__ == '__main__':
    pass