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_block(inputs, out_channels, residual_layers, stride, dropout=None):
    x = inputs
    for i in range(0, residual_layers):
        x = residual_layer(x, out_channels, stride, dropout)
    return x


def packnet_conv2d(inputs, out_channels, kernel_size, stride):
    x = keras.layers.Conv2D(out_channels, kernel_size,
                            stride, padding='same')(inputs)
    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, 4)
    x = keras.layers.Conv3D(features_3d, kernel_size=3, padding='same')(x)
    b, h, w, c, d = x.shape
    x = tf.reshape(x, (b, h, w, c * d))
    return packnet_conv2d(x, inputs.shape[3], kernel_size, 1)


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


# TODO: Support different size packnet for scaling up/down
# TODO: Support different channel format (right now we're supporting NHWC, we should also support NCHW)
def make_packnet(shape=(224, 224, 3), skip_add=True, features_3d=4, dropout=None):
    """
    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:
    """

    # ================ ENCODER =================
    input = keras.layers.Input(shape=shape)
    x = packnet_conv2d(input, 32, 5, 1)
    skip_1 = x
    x = packnet_conv2d(x, 32, 7, 1)
    x = pack_3d(x, 5, features_3d=features_3d)
    skip_2 = x
    x = residual_block(x, 64, 2, 1, dropout)
    x = pack_3d(x, 3, features_3d=features_3d)
    skip_3 = x
    x = residual_block(x, 128, 2, 1, dropout)
    x = pack_3d(x, 3, features_3d=features_3d)
    skip_4 = x
    x = residual_block(x, 256, 3, 1, dropout)
    x = pack_3d(x, 3, features_3d=features_3d)
    skip_5 = x
    x = residual_block(x, 512, 3, 1, dropout)
    x = pack_3d(x, 3, features_3d=features_3d)
    # ================ ENCODER =================

    x = unpack_3d(x, 512, 3, features_3d=features_3d)
    x = keras.layers.Add(
        [x, skip_5]) if skip_add else keras.layers.Concatenate([x, skip_5])
    x = packnet_conv2d(x, 512, 3, 1)
    x = unpack_3d(x, 256, 3, features_3d=features_3d)
    x = keras.layers.Add(
        [x, skip_4]) if skip_add else keras.layers.Concatenate([x, skip_4])
    x = packnet_conv2d(x, 256, 3, 1)
    # TODO: This is wrong, look at the paper
    x = packnet_inverse_depth(x, 1)
    x = keras.layers.UpSampling2D()

    # 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")