from typing import Union, Tuple

import torch
import torch.nn as nn

from .layers import LayerInfo, Layer


class ResBlock(Layer):
    def __init__(self, in_channels: int, out_channels: int, kernel_size: int = 3,
                 activation=None, **kwargs):
        super().__init__(activation if activation is not None else 0, False)

        self.seq = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=False, **kwargs),
            nn.BatchNorm2d(
                out_channels,
                momentum=Layer.BATCH_NORM_MOMENTUM,
                track_running_stats=not Layer.BATCH_NORM_TRAINING),
            torch.nn.LeakyReLU(),
            nn.Conv2d(out_channels, out_channels, kernel_size=kernel_size, bias=False, padding=1),
            nn.BatchNorm2d(
                out_channels,
                momentum=Layer.BATCH_NORM_MOMENTUM,
                track_running_stats=not Layer.BATCH_NORM_TRAINING))
        self.batch_norm = nn.BatchNorm2d(
            out_channels,
            momentum=Layer.BATCH_NORM_MOMENTUM,
            track_running_stats=not Layer.BATCH_NORM_TRAINING) if self.batch_norm else None

    def forward(self, input_data: torch.Tensor) -> torch.Tensor:
        return super().forward(input_data + self.seq(input_data))


class ResBottleneck(Layer):
    def __init__(self, in_channels: int, out_channels: int, planes: int = 1, kernel_size: int = 3,
                 stride: Union[int, Tuple[int, int]] = 1, activation=None, **kwargs):
        super().__init__(activation if activation is not None else 0, False)
        self.batch_norm = None

        self.seq = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
            nn.BatchNorm2d(
                out_channels,
                momentum=Layer.BATCH_NORM_MOMENTUM,
                track_running_stats=not Layer.BATCH_NORM_TRAINING),
            torch.nn.LeakyReLU(),
            nn.Conv2d(out_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=False, **kwargs),
            nn.BatchNorm2d(
                out_channels,
                momentum=Layer.BATCH_NORM_MOMENTUM,
                track_running_stats=not Layer.BATCH_NORM_TRAINING),
            torch.nn.LeakyReLU(),
            nn.Conv2d(out_channels, planes * out_channels, kernel_size=1, bias=False),
            nn.BatchNorm2d(
                out_channels,
                momentum=Layer.BATCH_NORM_MOMENTUM,
                track_running_stats=not Layer.BATCH_NORM_TRAINING))
        self.downsample = nn.Sequential(
            nn.Conv2d(in_channels, planes * out_channels, stride=stride, kernel_size=1),
            nn.BatchNorm2d(
                planes * out_channels,
                momentum=Layer.BATCH_NORM_MOMENTUM,
                track_running_stats=not Layer.BATCH_NORM_TRAINING))

    def forward(self, input_data: torch.Tensor) -> torch.Tensor:
        return super().forward(self.downsample(input_data) + self.seq(input_data))