import torch

from torch import nn
from torch.nn import functional as F, init

import utils


class ResidualBlock(nn.Module):
    """A general-purpose residual block. Works only with 1-dim inputs."""

    def __init__(self,
                 features,
                 context_features,
                 activation=F.relu,
                 dropout_probability=0.,
                 use_batch_norm=False,
                 zero_initialization=True):
        super().__init__()
        self.activation = activation

        self.use_batch_norm = use_batch_norm
        if use_batch_norm:
            self.batch_norm_layers = nn.ModuleList([
                nn.BatchNorm1d(features, eps=1e-3)
                for _ in range(2)
            ])
        if context_features is not None:
            self.context_layer = nn.Linear(context_features, features)
        self.linear_layers = nn.ModuleList([
            nn.Linear(features, features)
            for _ in range(2)
        ])
        self.dropout = nn.Dropout(p=dropout_probability)
        if zero_initialization:
            init.uniform_(self.linear_layers[-1].weight, -1e-3, 1e-3)
            init.uniform_(self.linear_layers[-1].bias, -1e-3, 1e-3)

    def forward(self, inputs, context=None):
        temps = inputs
        if self.use_batch_norm:
            temps = self.batch_norm_layers[0](temps)
        temps = self.activation(temps)
        temps = self.linear_layers[0](temps)
        if self.use_batch_norm:
            temps = self.batch_norm_layers[1](temps)
        temps = self.activation(temps)
        temps = self.dropout(temps)
        temps = self.linear_layers[1](temps)
        if context is not None:
            temps = F.glu(
                torch.cat(
                    (temps, self.context_layer(context)),
                    dim=1
                ),
                dim=1
            )
        return inputs + temps


class ResidualNet(nn.Module):
    """A general-purpose residual network. Works only with 1-dim inputs."""

    def __init__(self,
                 in_features,
                 out_features,
                 hidden_features,
                 context_features=None,
                 num_blocks=2,
                 activation=F.relu,
                 dropout_probability=0.,
                 use_batch_norm=False):
        super().__init__()
        self.hidden_features = hidden_features
        self.context_features = context_features
        if context_features is not None:
            self.initial_layer = nn.Linear(in_features + context_features, hidden_features)
        else:
            self.initial_layer = nn.Linear(in_features, hidden_features)
        self.blocks = nn.ModuleList([
            ResidualBlock(
                features=hidden_features,
                context_features=context_features,
                activation=activation,
                dropout_probability=dropout_probability,
                use_batch_norm=use_batch_norm,
            ) for _ in range(num_blocks)
        ])
        self.final_layer = nn.Linear(hidden_features, out_features)

    def forward(self, inputs, context=None):
        if context is None:
            temps = self.initial_layer(inputs)
        else:
            temps = self.initial_layer(
                torch.cat((inputs, context), dim=1)
            )
        for block in self.blocks:
            temps = block(temps, context=context)
        outputs = self.final_layer(temps)
        return outputs


class ConvResidualBlock(nn.Module):
    def __init__(self,
                 channels,
                 context_channels=None,
                 activation=F.relu,
                 dropout_probability=0.,
                 use_batch_norm=False,
                 zero_initialization=True):
        super().__init__()
        self.activation = activation

        if context_channels is not None:
            self.context_layer = nn.Conv2d(
                in_channels=context_channels,
                out_channels=channels,
                kernel_size=1,
                padding=0
            )
        self.use_batch_norm = use_batch_norm
        if use_batch_norm:
            self.batch_norm_layers = nn.ModuleList([
                nn.BatchNorm2d(channels, eps=1e-3)
                for _ in range(2)
            ])
        self.conv_layers = nn.ModuleList([
            nn.Conv2d(channels, channels, kernel_size=3, padding=1)
            for _ in range(2)
        ])
        self.dropout = nn.Dropout(p=dropout_probability)
        if zero_initialization:
            init.uniform_(self.conv_layers[-1].weight, -1e-3, 1e-3)
            init.uniform_(self.conv_layers[-1].bias, -1e-3, 1e-3)

    def forward(self, inputs, context=None):
        temps = inputs
        if self.use_batch_norm:
            temps = self.batch_norm_layers[0](temps)
        temps = self.activation(temps)
        temps = self.conv_layers[0](temps)
        if self.use_batch_norm:
            temps = self.batch_norm_layers[1](temps)
        temps = self.activation(temps)
        temps = self.dropout(temps)
        temps = self.conv_layers[1](temps)
        if context is not None:
            temps = F.glu(
                torch.cat(
                    (temps, self.context_layer(context)),
                    dim=1
                ),
                dim=1
            )
        return inputs + temps


class ConvResidualNet(nn.Module):
    def __init__(self,
                 in_channels,
                 out_channels,
                 hidden_channels,
                 context_channels=None,
                 num_blocks=2,
                 activation=F.relu,
                 dropout_probability=0.,
                 use_batch_norm=False
                 ):
        super().__init__()
        self.context_channels = context_channels
        self.hidden_channels = hidden_channels
        if context_channels is not None:
            self.initial_layer = nn.Conv2d(
                in_channels=in_channels + context_channels,
                out_channels=hidden_channels,
                kernel_size=1,
                padding=0
            )
        else:
            self.initial_layer = nn.Conv2d(
                in_channels=in_channels,
                out_channels=hidden_channels,
                kernel_size=1,
                padding=0
            )
        self.blocks = nn.ModuleList([
            ConvResidualBlock(
                channels=hidden_channels,
                context_channels=context_channels,
                activation=activation,
                dropout_probability=dropout_probability,
                use_batch_norm=use_batch_norm,
            ) for _ in range(num_blocks)
        ])
        self.final_layer = nn.Conv2d(hidden_channels, out_channels, kernel_size=1, padding=0)

    def forward(self, inputs, context=None):
        if context is None:
            temps = self.initial_layer(inputs)
        else:
            temps = self.initial_layer(
                torch.cat((inputs, context), dim=1)
            )
        for block in self.blocks:
            temps = block(temps, context)
        outputs = self.final_layer(temps)
        return outputs


def main():
    batch_size, channels, height, width = 100, 12, 64, 64
    inputs = torch.rand(batch_size, channels, height, width)
    context = torch.rand(batch_size, channels // 2, height, width)
    net = ConvResidualNet(
        in_channels=channels,
        out_channels=2 * channels,
        hidden_channels=32,
        context_channels=channels // 2,
        num_blocks=2,
        dropout_probability=0.1,
        use_batch_norm=True
    )
    print(utils.get_num_parameters(net))
    outputs = net(inputs, context)
    print(outputs.shape)


if __name__ == '__main__':
    main()
