from typing import Sequence, Union

import torch
import torch.nn as nn

from monai.networks.blocks.convolutions import Convolution, ResidualUnit
from monai.networks.layers.factories import Act, Norm
from monai.networks.layers.simplelayers import SkipConnection
from monai.utils import alias, export

class UNet_double(nn.Module):
    def __init__(
        self,
        dimensions: int,
        in_channels: int,
        out_channels: int,
        channels: Sequence[int],
        strides: Sequence[int],
        kernel_size: Union[Sequence[int], int] = 3,
        up_kernel_size: Union[Sequence[int], int] = 3,
        num_res_units: int = 0,
        act=Act.PRELU,
        norm=Norm.INSTANCE,
        dropout=0.0,) -> None:

        super().__init__()

        self.dimensions = dimensions
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.channels = channels
        self.strides = strides
        self.kernel_size = kernel_size
        self.up_kernel_size = up_kernel_size
        self.num_res_units = num_res_units
        self.act = act
        self.norm = norm
        self.dropout = dropout

        def _create_block(
            inc: int, outc: int, channels: Sequence[int], strides: Sequence[int], is_top: bool) -> nn.Sequential:
            
            c = channels[0]
            s = strides[0]

            subblock: nn.Module

            if len(channels) > 2:
                subblock1, subblock2 = _create_block(c, c, channels[1:], strides[1:], False)  # continue recursion down
                upc = c * 2
            else:
                # the next layer is the bottom so stop recursion, create the bottom layer as the sublock for this layer
                subblock = self._get_bottom_layer(c, channels[1])
                upc = c + channels[1]

                down = self._get_down_layer(inc, c, s, is_top)  # create layer in downsampling path
                up1 = self._get_up_layer(upc, outc, s, is_top)  # create layer in upsampling path
                up2 = self._get_up_layer(upc, outc, s, is_top)

                return nn.Sequential(down, SkipConnection(subblock), up1), nn.Sequential(down, SkipConnection(subblock), up2)

            down = self._get_down_layer(inc, c, s, is_top)  # create layer in downsampling path
            up1 = self._get_up_layer(upc, outc, s, is_top)  # create layer in upsampling path
            up2 = self._get_up_layer(upc, outc, s, is_top)

            return nn.Sequential(down, SkipConnection(subblock1), up1), nn.Sequential(down, SkipConnection(subblock2), up2)

        self.model1, self.model2 = _create_block(in_channels, out_channels, self.channels, self.strides, True)
        self.activation = nn.Sigmoid()

    def _get_down_layer(self, in_channels: int, out_channels: int, strides: int, is_top: bool) -> nn.Module:
        """
        Args:
            in_channels: number of input channels.
            out_channels: number of output channels.
            strides: convolution stride.
            is_top: True if this is the top block.
        """
        if self.num_res_units > 0:
            return ResidualUnit(
                self.dimensions,
                in_channels,
                out_channels,
                strides=strides,
                kernel_size=self.kernel_size,
                subunits=self.num_res_units,
                act=self.act,
                norm=self.norm,
                dropout=self.dropout,
            )
        return Convolution(
            self.dimensions,
            in_channels,
            out_channels,
            strides=strides,
            kernel_size=self.kernel_size,
            act=self.act,
            norm=self.norm,
            dropout=self.dropout,
        )

    def _get_bottom_layer(self, in_channels: int, out_channels: int) -> nn.Module:
        """
        Args:
            in_channels: number of input channels.
            out_channels: number of output channels.
        """
        return self._get_down_layer(in_channels, out_channels, 1, False)

    def _get_up_layer(self, in_channels: int, out_channels: int, strides: int, is_top: bool) -> nn.Module:
        """
        Args:
            in_channels: number of input channels.
            out_channels: number of output channels.
            strides: convolution stride.
            is_top: True if this is the top block.
        """
        conv: Union[Convolution, nn.Sequential]

        conv = Convolution(
            self.dimensions,
            in_channels,
            out_channels,
            strides=strides,
            kernel_size=self.up_kernel_size,
            act=self.act,
            norm=self.norm,
            dropout=self.dropout,
            conv_only=is_top and self.num_res_units == 0,
            is_transposed=True,
        )

        if self.num_res_units > 0:
            ru = ResidualUnit(
                self.dimensions,
                out_channels,
                out_channels,
                strides=1,
                kernel_size=self.kernel_size,
                subunits=1,
                act=self.act,
                norm=self.norm,
                dropout=self.dropout,
                last_conv_only=is_top,
            )
            conv = nn.Sequential(conv, ru)

        return conv

    def forward(self, x, box = None) -> torch.Tensor:
        return self.activation(self.model1(x))
