import tensorflow as tf
from tensorflow import keras

import tensorflow_datasets as tfds


class DecodeConv(keras.layers.Layer):
    def __init__(self, out_filters, **kwargs):
        super().__init__(**kwargs)
        # Should be depthwise followed by batchnorm and relu.
        self.depthwise = keras.layers.DepthwiseConv2D(5)
        self.batch_norm = keras.layers.BatchNormalization()
        self.relu = keras.layers.ReLU(6.)
        self.pointwise = keras.layers.Conv2D(out_filters, 1)
        self.pointwise_bn = keras.layers.BatchNormalization()
        self.pointwise_rl = keras.layers.ReLU(6.)

    def call(self, inputs, **kwargs):
        inputs = self.depthwise(inputs, **kwargs)
        inputs = self.batch_norm(inputs, **kwargs)
        inputs = self.relu(inputs, **kwargs)
        inputs = self.pointwise(inputs, **kwargs)
        inputs = self.pointwise_bn(inputs, **kwargs)
        return self.pointwise_rl(inputs, **kwargs)


class FastDepth(keras.Model):
    def get_config(self):
        # TODO: What to put here?
        pass

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.mobile_net = keras.applications.MobileNet(include_top=False)

        # TODO: Try keras.layers.SeparableConv2D as well, should do the same thing if relu is used as activation
        # It probably doesn't, since
        self.decode_conv1 = DecodeConv(512)
        self.decode_conv2 = DecodeConv(256)
        self.decode_conv3 = DecodeConv(128)
        self.decode_conv4 = DecodeConv(64)
        self.decode_conv5 = DecodeConv(32)

        self.final_pointwise = keras.layers.Conv2D(1, 1)
        self.final_pointwise_bn = keras.layers.BatchNormalization()
        self.final_pointwise_relu = keras.layers.ReLU()

    def call(self, inputs, is_training=False, **kwargs):
        # Go through mobilenet, then each decode layer, including skip connections using:
        # keras.layers.Add()
        inputs = self.mobile_net(inputs, is_training=is_training, **kwargs)

        # FastDepth Additive Decoder
        inputs = self.decode_conv1(inputs, is_training=is_training, **kwargs)
        inputs = self.decode_conv2(inputs, is_training=is_training, **kwargs)
        inputs = inputs + self.mobile_net.get_layer('conv_pw_5_relu').output
        inputs = self.decode_conv3(inputs, is_training=is_training, **kwargs)
        inputs = inputs + self.mobile_net.get_layer('conv_pw_3_relu').output
        inputs = self.decode_conv4(inputs, is_training=is_training, **kwargs)
        inputs = inputs + self.mobile_net.get_layer('conv_pw_1_relu').output
        inputs = self.decode_conv5(inputs, is_training=is_training, **kwargs)

        inputs = self.final_pointwise(inputs, is_training=is_training, **kwargs)
        inputs = self.final_pointwise_bn(inputs, is_training=is_training, **kwargs)
        return self.final_pointwise_relu(inputs, is_training=is_training, **kwargs)


def load_nyu():
    builder = tfds.builder('nyu_depth_v2')
    builder.download_and_prepare(download_dir='../nyu')
    return builder.as_dataset(split='train', shuffle_files=True)


def print_hi(name):
    # Use a breakpoint in the code line below to debug your script.
    print(f'Hi, {name}')  # Press Ctrl+F8 to toggle the breakpoint.


# Press the green button in the gutter to run the script.
if __name__ == '__main__':
    load_nyu()

# See PyCharm help at https://www.jetbrains.com/help/pycharm/