import torch
import torch.nn as nn


class CNNBlock(nn.Module):
    def __init__(self, in_channels, out_channels, bn_act=True, **kwargs):
        super().__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, bias=not bn_act, **kwargs)
        self.bn = nn.BatchNorm2d(out_channels)
        self.leaky = nn.LeakyReLU(0.1)
        self.use_bn_act = bn_act

    def forward(self, x):
        if self.use_bn_act:
            return self.leaky(self.bn(self.conv(x)))
        else:
            return self.conv(x)


class ResidualBlock(nn.Module):
    def __init__(self, channels, use_residual=True, num_repeats=1):
        super().__init__()
        self.layers = nn.ModuleList()
        for _ in range(num_repeats):
            self.layers += [
                nn.Sequential(
                    CNNBlock(channels, channels // 2, kernel_size=1),
                    CNNBlock(channels // 2, channels, kernel_size=3, padding=1),
                )
            ]

        self.use_residual = use_residual
        self.num_repeats = num_repeats

    def forward(self, x):
        for layer in self.layers:
            if self.use_residual:
                x = x + layer(x)
            else:
                x = layer(x)

        return x


class ScalePrediction(nn.Module):
    def __init__(self, in_channels, num_classes):
        super().__init__()
        self.pred = nn.Sequential(
            CNNBlock(in_channels, 2 * in_channels, kernel_size=3, padding=1),
            CNNBlock(2 * in_channels, (num_classes + 5) * 3, bn_act=False, kernel_size=1),
        )
        self.num_classes = num_classes

    def forward(self, x):
        return self.pred(x).reshape(x.shape[0], 3, self.num_classes + 5, x.shape[2], x.shape[3]).permute(0, 1, 3, 4, 2)


class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.num_classes = 12
        self.in_channels = 3
        # self.config = cfg.model_files.params.config
        # self.config = [i if i[0] != '(' else literal_eval(i) for i in self.config]
        self.config = [
            (32, 3, 1),
            (64, 3, 2),
            ['B', 1],
            (128, 3, 2),
            ['B', 2],
            (256, 3, 2),
            ['B', 8],
            (512, 3, 2),
            ['B', 8],
            (1024, 3, 2),
            ['B', 4],
            (512, 1, 1),
            (1024, 3, 1),
            'S',
            (256, 1, 1),
            'U',
            (256, 1, 1),
            (512, 3, 1),
            'S',
            (128, 1, 1),
            'U',
            (128, 1, 1),
            (256, 3, 1),
            'S',
        ]
        # print('self.config', self.config)
        self.layers = self._create_conv_layers()

    def forward(self, x):
        outputs = []  # for each scale
        route_connections = []
        for layer in self.layers:
            if isinstance(layer, ScalePrediction):
                outputs.append(layer(x))
                continue
            # print(layer, x.shape)
            x = layer(x)

            if isinstance(layer, ResidualBlock) and layer.num_repeats == 8:
                route_connections.append(x)

            elif isinstance(layer, nn.Upsample):
                x = torch.cat([x, route_connections[-1]], dim=1)
                route_connections.pop()

        return outputs

    def _create_conv_layers(self):
        layers = nn.ModuleList()
        in_channels = self.in_channels

        for module in self.config:
            # print(module, type(module))
            if isinstance(module, tuple):
                out_channels, kernel_size, stride = module
                layers.append(
                    CNNBlock(
                        in_channels,
                        out_channels,
                        kernel_size=kernel_size,
                        stride=stride,
                        padding=1 if kernel_size == 3 else 0,
                    )
                )
                in_channels = out_channels

            elif isinstance(module, list):
                num_repeats = module[1]
                layers.append(
                    ResidualBlock(
                        in_channels,
                        num_repeats=num_repeats,
                    )
                )

            elif isinstance(module, str):
                if module == 'S':
                    layers += [
                        ResidualBlock(in_channels, use_residual=False, num_repeats=1),
                        CNNBlock(in_channels, in_channels // 2, kernel_size=1),
                        ScalePrediction(in_channels // 2, num_classes=self.num_classes),
                    ]
                    in_channels = in_channels // 2

                elif module == 'U':
                    layers.append(
                        nn.Upsample(scale_factor=2),
                    )
                    in_channels = in_channels * 3

        return layers