# SPDX-License-Identifier: Apache-2.0

import os
import sys
import unittest
import mock_keras2onnx
import onnx
import numpy as np
from mock_keras2onnx.proto import keras
from os.path import dirname, abspath
sys.path.insert(0, os.path.join(dirname(abspath(__file__)), '../../keras2onnx_tests/'))
from test_utils import run_onnx_runtime
from keras.layers.merge import _Merge

Activation = keras.layers.Activation
BatchNormalization = keras.layers.BatchNormalization
Conv2D = keras.layers.Conv2D
Dense = keras.layers.Dense
Dropout = keras.layers.Dropout
Embedding = keras.layers.Embedding
Flatten = keras.layers.Flatten
Input = keras.layers.Input
LeakyReLU = keras.layers.LeakyReLU
multiply = keras.layers.multiply
Reshape = keras.layers.Reshape
UpSampling2D = keras.layers.UpSampling2D
ZeroPadding2D = keras.layers.ZeroPadding2D

Sequential = keras.models.Sequential
Model = keras.models.Model
K = keras.backend

class RandomWeightedAverage(_Merge):
    """Provides a (random) weighted average between real and generated image samples"""
    def _merge_function(self, inputs):
        alpha = K.random_uniform((32, 1, 1, 1))
        return (alpha * inputs[0]) + ((1 - alpha) * inputs[1])

# From https://github.com/eriklindernoren/Keras-GAN/blob/master/wgan_gp/wgan_gp.py
class WGANGP():
    def __init__(self):
        self.img_rows = 28
        self.img_cols = 28
        self.channels = 1
        self.img_shape = (self.img_rows, self.img_cols, self.channels)
        self.latent_dim = 100

        # Following parameter and optimizer set as recommended in paper
        self.n_critic = 5

        # Build the generator and critic
        self.generator = self.build_generator()
        self.critic = self.build_critic()

        #-------------------------------
        # Construct Computational Graph
        #       for the Critic
        #-------------------------------

        # Freeze generator's layers while training critic
        self.generator.trainable = False

        # Image input (real sample)
        real_img = Input(shape=self.img_shape)

        # Noise input
        z_disc = Input(shape=(self.latent_dim,))
        # Generate image based of noise (fake sample)
        fake_img = self.generator(z_disc)

        # Discriminator determines validity of the real and fake images
        fake = self.critic(fake_img)
        valid = self.critic(real_img)

        # Construct weighted average between real and fake images
        interpolated_img = RandomWeightedAverage()([real_img, fake_img])
        # Determine validity of weighted sample
        validity_interpolated = self.critic(interpolated_img)

        self.critic_model = Model(inputs=[real_img, z_disc],
                            outputs=[valid, fake, validity_interpolated])
        #-------------------------------
        # Construct Computational Graph
        #         for Generator
        #-------------------------------

        # For the generator we freeze the critic's layers
        self.critic.trainable = False
        self.generator.trainable = True

        # Sampled noise for input to generator
        z_gen = Input(shape=(self.latent_dim,))
        # Generate images based of noise
        img = self.generator(z_gen)
        # Discriminator determines validity
        valid = self.critic(img)
        # Defines generator model
        self.generator_model = Model(z_gen, valid)


    def build_generator(self):

        model = Sequential()

        model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
        model.add(Reshape((7, 7, 128)))
        model.add(UpSampling2D())
        model.add(Conv2D(128, kernel_size=4, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Activation("relu"))
        model.add(UpSampling2D())
        model.add(Conv2D(64, kernel_size=4, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Activation("relu"))
        model.add(Conv2D(self.channels, kernel_size=4, padding="same"))
        model.add(Activation("tanh"))

        noise = Input(shape=(self.latent_dim,))
        img = model(noise)

        return Model(noise, img)

    def build_critic(self):

        model = Sequential()

        model.add(Conv2D(16, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same"))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.25))
        model.add(Conv2D(32, kernel_size=3, strides=2, padding="same"))
        model.add(ZeroPadding2D(padding=((0,1),(0,1))))
        model.add(BatchNormalization(momentum=0.8))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.25))
        model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.25))
        model.add(Conv2D(128, kernel_size=3, strides=1, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.25))
        model.add(Flatten())
        model.add(Dense(1))

        img = Input(shape=self.img_shape)
        validity = model(img)

        return Model(img, validity)


class TestWGANGP(unittest.TestCase):

    def setUp(self):
        self.model_files = []

    def tearDown(self):
        for fl in self.model_files:
            os.remove(fl)

    def test_WGANGP(self):
        keras_model = WGANGP().generator_model
        x = np.random.rand(5, 100).astype(np.float32)
        expected = keras_model.predict(x)
        onnx_model = mock_keras2onnx.convert_keras(keras_model, keras_model.name)
        self.assertTrue(run_onnx_runtime(onnx_model.graph.name, onnx_model, x, expected, self.model_files))


if __name__ == "__main__":
    unittest.main()
