import tensorflow as tf
import tensorflow.keras as keras
import tensorflow.keras.layers as layers
from tensorflow import nn

import group_norm


def pack_layer():
    pass


def residual_layer(inputs, out_channels, stride, dropout=None):
    """
    Keras implementation of the Residual block (ResNet) as used in PackNet
    :param inputs:
    :param out_channels:
    :param stride:
    :param dropout:
    :return:
    """
    x = layers.Conv2D(out_channels, 3, padding='same', stride=stride)(inputs)
    x = layers.Conv2D(out_channels, 3, padding='same')(x)
    shortcut = layers.Conv2D(out_channels, 3, padding='same', stride=stride)(inputs)
    if dropout:
        shortcut = keras.layers.SpatialDropout2D(dropout)(shortcut)
    x = keras.layers.Concatenate()([x, shortcut])
    x = group_norm.GroupNormalization(16)(x)
    return keras.layers.ELU()(x)


# Packnet usually expects more than one layer per block (2,2,3,3)
def residual_bock(inputs, out_channels, residual_layers, stride, dropout=None):
    pass


def packnet_conv2d(inputs, out_channels, kernel_size, stride):
    x = keras.layers.Conv2D(out_channels, kernel_size, stride, padding='same')
    x = group_norm.GroupNormalization(16)(x)
    return keras.layers.ELU()(x)


def packnet_inverse_depth(inputs, out_channels=1, min_depth=0.5):
    x = packnet_conv2d(inputs, out_channels, kernel_size=3, stride=1)
    return keras.activations.sigmoid(x) / min_depth


def pack_3d(inputs, kernel_size, r=2, features_3d=8):
    """
    Implementatino of the 3d packing block proposed here: https://arxiv.org/abs/1905.02693
    :param inputs:
    :param kernel_size:
    :param r:
    :param features_3d:
    :return:
    """
    # Data format for single image in nyu is HWC (space_to_depth uses NHWC as default)
    x = nn.space_to_depth(inputs, r)
    x = tf.expand_dims(x, 1)
    x = keras.layers.Conv3D(features_3d, kernel_size=3, padding='same')
    b, c, d, h, w = x.shape
    x = tf.reshape(x, (b, c * d, h, w))
    return packnet_conv2d(x, inputs.shape[1], kernel_size, 1)


def unpack_3d(inputs, out_channels, kernel_size, r=3, features_3d=8):
    x = packnet_conv2d(inputs, out_channels * (r ** 2) // features_3d, kernel_size, 1)
    x = tf.expand_dims(x, 1)  # B x D x 4(out)/D x H/2 x W/2
    x = keras.layers.Conv3D(features_3d, kernel_size=3, padding='same')
    b, c, d, h, w = x.shape
    x = tf.reshape(x, [b, c * d, h, w])
    return nn.depth_to_space(x, r)


# TODO: Support different size packnet for scaling up/down
def make_packnet(shape=(224, 224, 3), skip_add=True, features_3d=4):
    """
    Make the PackNet depth network.
    :param shape: Input shape of the image
    :param skip_add: Set to use add rather than concat skip connections, defaults to True
    :return:
    """
    input = keras.layers.Input(shape=shape)
    x = packnet_conv2d(input, 32, 5, 1)
    skip_1 = x
    x = packnet_conv2d(input, 32, 7, 1)
    x = pack_3d(x, 5, features_3d)
    x = residual_layer(x, 64, )

    # TODO: Skip connection
    if skip_add:
        x = keras.layers.Add([x, ])
    else:
        x = keras.layers.Concatenate([x, ])

    x = packnet_conv2d(x, 32, 3, 1)
    x = packnet_inverse_depth(x)
    return keras.Model(inputs=input, outputs=x, name="PackNet")