# SPDX-License-Identifier: Apache-2.0

import os
import sys
import unittest
from os.path import abspath, dirname

import keras_contrib
import mock_keras2onnx
import numpy as np
from mock_keras2onnx import set_converter
from mock_keras2onnx.proto import keras

sys.path.insert(0, os.path.join(dirname(abspath(__file__)), '../../keras2onnx_tests/'))
from test_utils import convert_InstanceNormalizationLayer, run_onnx_runtime

Activation = keras.layers.Activation
BatchNormalization = keras.layers.BatchNormalization
Concatenate = keras.layers.Concatenate
Conv2D = keras.layers.Conv2D
Dense = keras.layers.Dense
Dropout = keras.layers.Dropout
Embedding = keras.layers.Embedding
Flatten = keras.layers.Flatten
InstanceNormalization = keras_contrib.layers.InstanceNormalization
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

# From https://github.com/eriklindernoren/Keras-GAN/blob/master/ccgan/ccgan.py
class CCGAN():
    def __init__(self):
        self.img_rows = 32
        self.img_cols = 32
        self.channels = 1
        self.img_shape = (self.img_rows, self.img_cols, self.channels)
        self.mask_height = 10
        self.mask_width = 10
        self.num_classes = 10

        # Number of filters in first layer of generator and discriminator
        self.gf = 32
        self.df = 32

        # Build and compile the discriminator
        self.discriminator = self.build_discriminator()

        # Build the generator
        self.generator = self.build_generator()

        # The generator takes noise as input and generates imgs
        masked_img = Input(shape=self.img_shape)
        gen_img = self.generator(masked_img)

        # For the combined model we will only train the generator
        self.discriminator.trainable = False

        # The valid takes generated images as input and determines validity
        valid, _ = self.discriminator(gen_img)
        # The combined model  (stacked generator and discriminator)
        # Trains the generator to fool the discriminator
        self.combined = Model(masked_img , valid)

    def build_generator(self):
        """U-Net Generator"""

        def conv2d(layer_input, filters, f_size=4, bn=True):
            """Layers used during downsampling"""
            d = Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input)
            d = LeakyReLU(alpha=0.2)(d)
            if bn:
                d = BatchNormalization(momentum=0.8)(d)
            return d

        def deconv2d(layer_input, skip_input, filters, f_size=4, dropout_rate=0):
            """Layers used during upsampling"""
            u = UpSampling2D(size=2)(layer_input)
            u = Conv2D(filters, kernel_size=f_size, strides=1, padding='same', activation='relu')(u)
            if dropout_rate:
                u = Dropout(dropout_rate)(u)
            u = BatchNormalization(momentum=0.8)(u)
            u = Concatenate()([u, skip_input])
            return u

        img = Input(shape=self.img_shape)

        # Downsampling
        d1 = conv2d(img, self.gf, bn=False)
        d2 = conv2d(d1, self.gf*2)
        d3 = conv2d(d2, self.gf*4)
        d4 = conv2d(d3, self.gf*8)

        # Upsampling
        u1 = deconv2d(d4, d3, self.gf*4)
        u2 = deconv2d(u1, d2, self.gf*2)
        u3 = deconv2d(u2, d1, self.gf)

        u4 = UpSampling2D(size=2)(u3)
        output_img = Conv2D(self.channels, kernel_size=4, strides=1, padding='same', activation='tanh')(u4)

        return Model(img, output_img)

    def build_discriminator(self):

        img = Input(shape=self.img_shape)

        model = Sequential()
        model.add(Conv2D(64, kernel_size=4, strides=2, padding='same', input_shape=self.img_shape))
        model.add(LeakyReLU(alpha=0.8))
        model.add(Conv2D(128, kernel_size=4, strides=2, padding='same'))
        model.add(LeakyReLU(alpha=0.2))
        model.add(InstanceNormalization())
        model.add(Conv2D(256, kernel_size=4, strides=2, padding='same'))
        model.add(LeakyReLU(alpha=0.2))
        model.add(InstanceNormalization())

        features = model(img)

        validity = Conv2D(1, kernel_size=4, strides=1, padding='same')(features)

        label = Flatten()(features)
        label = Dense(self.num_classes+1, activation="softmax")(label)

        return Model(img, [validity, label])


set_converter(keras_contrib.layers.InstanceNormalization, convert_InstanceNormalizationLayer)


class TestCCGAN(unittest.TestCase):

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

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

    def test_CCGAN(self):
        keras_model = CCGAN().combined
        x = np.random.rand(2, 32, 32, 1).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, rtol=1.e-2, atol=1.e-4))


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