#Code from: https://github.com/epalu/mmvaeplus

import torch
import torch.nn as nn
import hydra 
import numpy as np

class Flatten(torch.nn.Module):
    def forward(self, x):
        return x.view(x.size(0), -1)

def actvn(x):
    out = torch.nn.functional.leaky_relu(x, 2e-1)
    return out


class ResnetBlock(nn.Module):
    def __init__(self, fin, fout, fhidden=None, is_bias=True):
        super().__init__()
        # Attributes
        self.is_bias = is_bias
        self.learned_shortcut = fin != fout
        self.fin = fin
        self.fout = fout
        if fhidden is None:
            self.fhidden = min(fin, fout)
        else:
            self.fhidden = fhidden

        # Submodules
        self.conv_0 = nn.Conv2d(self.fin, self.fhidden, 3, stride=1, padding=1)
        self.conv_1 = nn.Conv2d(
            self.fhidden, self.fout, 3, stride=1, padding=1, bias=is_bias
        )
        if self.learned_shortcut:
            self.conv_s = nn.Conv2d(
                self.fin, self.fout, 1, stride=1, padding=0, bias=False
            )

    def forward(self, x):
        x_s = self._shortcut(x)
        dx = self.conv_0(actvn(x))
        dx = self.conv_1(actvn(dx))
        out = x_s + 0.1 * dx

        return out

    def _shortcut(self, x):
        if self.learned_shortcut:
            x_s = self.conv_s(x)
        else:
            x_s = x
        return x_s

class VariationalEncoder(nn.Module):
    """
    Adopted from:
    https://www.cs.toronto.edu/~lczhang/360/lec/w05/autoencoder.html
    """

    def __init__(self, w_dim, u_dim, **kwargs):
        super().__init__()
        self.latent_dim = u_dim
        s0 = self.s0 = 7  # kwargs['s0']
        nf = self.nf = 64  # nfilter
        nf_max = self.nf_max = 1024  # nfilter_max
        size = 28

        # Submodules
        nlayers = int(np.log2(size / s0))
        self.nf0 = min(nf_max, nf * 2**nlayers)

        blocks_w = [ResnetBlock(nf, nf)]

        blocks_u = [ResnetBlock(nf, nf)]

        for i in range(nlayers):
            nf0 = min(nf * 2**i, nf_max)
            nf1 = min(nf * 2 ** (i + 1), nf_max)
            blocks_w += [
                nn.AvgPool2d(3, stride=2, padding=1),
                ResnetBlock(nf0, nf1),
            ]
            blocks_u += [
                nn.AvgPool2d(3, stride=2, padding=1),
                ResnetBlock(nf0, nf1),
            ]

        self.conv_img_w = nn.Conv2d(3, 1 * nf, 3, padding=1)
        self.resnet_w = nn.Sequential(*blocks_w)
        self.fc_mu_w = nn.Linear(self.nf0 * s0 * s0, w_dim)
        self.fc_lv_w = nn.Linear(self.nf0 * s0 * s0, w_dim)

        self.conv_img_u = nn.Conv2d(3, 1 * nf, 3, padding=1)
        self.resnet_u = nn.Sequential(*blocks_u)
        self.fc_mu_u = nn.Linear(self.nf0 * s0 * s0, u_dim)
        self.fc_lv_u = nn.Linear(self.nf0 * s0 * s0, u_dim)

    def forward(self, x):
        # batch_size = x.size(0)
        out_w = self.conv_img_w(x)
        out_w = self.resnet_w(out_w)
        out_w = out_w.view(out_w.size()[0], self.nf0 * self.s0 * self.s0)
        lv_w = self.fc_lv_w(out_w)

        out_u = self.conv_img_u(x)
        out_u = self.resnet_u(out_u)
        out_u = out_u.view(out_u.size()[0], self.nf0 * self.s0 * self.s0)
        lv_u = self.fc_lv_u(out_u)

        return self.fc_mu_u(out_u), lv_u, self.fc_mu_w(out_w), lv_w

class Decoder(nn.Module):
    """Generate a SVHN image given a sample from the latent space."""

    def __init__(self, 
        z_dim,
        dec_dist,
        **kwargs):

        super().__init__()
        self.dec_dist = dec_dist
        s0 = self.s0 = 7  # kwargs['s0']
        nf = self.nf = 64  # nfilter
        nf_max = self.nf_max = 512  # nfilter_max
        size = 28

        # Submodules
        nlayers = int(np.log2(size / s0))
        self.nf0 = min(nf_max, nf * 2**nlayers)

        self.fc = nn.Linear(z_dim, self.nf0 * s0 * s0)

        blocks = []
        for i in range(nlayers):
            nf0 = min(nf * 2 ** (nlayers - i), nf_max)
            nf1 = min(nf * 2 ** (nlayers - i - 1), nf_max)
            blocks += [ResnetBlock(nf0, nf1), nn.Upsample(scale_factor=2)]

        blocks += [
            ResnetBlock(nf, nf),
        ]

        self.resnet = nn.Sequential(*blocks)
        self.conv_img = nn.Conv2d(nf, 3, 3, padding=1)

    def forward(self, z):
        out = self.fc(z)
        out = out.view(-1, self.nf0, self.s0, self.s0)
        out = self.resnet(out)
        out = self.conv_img(actvn(out))
        
        if len(z.size()) == 2:
            out = out.view(*z.size()[:1], *out.size()[1:])
        else:
            out = out.view(*z.size()[:2], *out.size()[1:])

        return hydra.utils.instantiate(self.dec_dist, x=out)
