import tensorflow.keras as keras
import tensorflow.keras.layers as layers


def wrap_mobilenet_nnconv5_for_utrain(model):
    """
    Wraps a fast depth model for use in unsupervised training.

    This just exposes the lower disparity layers as outputs, so they can be used to train at different scales/image
    resolutions.
    :param model:
    :return:
    """
    input = model.input
    disp_1 = model.get_layer('conv_pw_%d_relu' % 15).output
    disp_2 = model.get_layer('conv_pw_%d_relu' % 16).output
    disp_3 = model.get_layer('conv_pw_%d_relu' % 17).output
    return keras.Model(input, outputs=[disp_1, disp_2, disp_3, model.output])


def res_layer(inputs, out_channels, down_sample=None, stride=1, normalisation=layers.BatchNormalization,
              activation=layers.ReLU, name=None):
    x = layers.Conv2D(out_channels, 3, padding='same', strides=stride)(inputs)
    x = normalisation()(x)
    x = activation()(x)
    x = layers.Conv2D(out_channels, 3, padding='same', strides=1)(x)
    x = normalisation()(x)
    # Residual skip connection. Downsample inputs if necessary
    if down_sample is not None:
        inputs = down_sample
    x = layers.Add()([x, inputs])
    x = activation(name=name)(x)
    return x


def res_block(inputs, out_channels, num_blocks=1, stride=1, normalisation=layers.BatchNormalization,
              activation=layers.ReLU, name=None):
    down_sample = None
    if stride != 1 or inputs.shape[-1] != out_channels:
        down_sample = layers.Conv2D(out_channels, 1, stride, padding='same')(inputs)
        down_sample = normalisation()(down_sample)

    x = res_layer(inputs, out_channels, down_sample, stride, normalisation, activation)
    for i in range(1, num_blocks):
        x = res_layer(x, out_channels, None, 1, normalisation, activation, name)
    return x


def resnet_18(shape=(224, 224, 6)):
    """
    Build the ResNet 18 network (encoder for the pose network)
    :param shape: Input shape. Note this should support 2 images for the pose net, so 6 channels in that case
    :return: Resnet encoder (ResNet18)
    """
    inputs = layers.Input(shape)
    x = keras.layers.Conv2D(64, 7, 2, padding='same')(inputs)
    x = layers.BatchNormalization()(x)
    skip_1 = layers.ReLU(name="res_1")(x)
    x = layers.MaxPooling2D(3, 2, 'same')(skip_1)
    skip_2 = res_block(x, 64, 2, name="res_2")
    skip_3 = res_block(skip_2, 128, 2, 2, name="res_3")
    skip_4 = res_block(skip_3, 256, 2, 2, name="res_4")
    skip_5 = res_block(skip_4, 512, 2, 2, name="res_5")
    return keras.Model(inputs=inputs, outputs=[skip_1, skip_2, skip_3, skip_4, skip_5])


# TODO Monodepth and sfm learner both solve the posenet on all source images. So for the case of monodepth, it would need
#   9 as the input (3 images - target and 2 source images) and would produce 2 6DOF poses
def pose_net(shape=(224, 224, 6), encoder=resnet_18):
    resnet = encoder(shape=shape)

    for layer in resnet.layers:
        layer.trainable = True

    # Concatenate every skip connection
    cat_skips = [layers.ReLU()(layers.Conv2D(256, 1)(encode_output[-1])) for encode_output in resnet.outputs]
    cat_skips = layers.Concatenate(1)(cat_skips)

    x = layers.Conv2D(256, 3, padding='same')(cat_skips)
    x = layers.ReLU()(x)

    x = layers.Conv2D(256, 3, padding='same')
    x = layers.ReLU()(x)

    x = layers.Conv2D(256, 12, 1)(x)

    # Decoder

    pass


if __name__ == '__main__':
    # import fast_depth_functional as fd
    # wrap_mobilenet_nnconv5_for_utrain(fd.mobilenet_nnconv5()).summary()
    pose_net()