"""
paper: https://arxiv.org/abs/1802.02611
ref:
    - https://github.com/tensorflow/models/tree/master/research/deeplab
    - https://github.com/VainF/DeepLabV3Plus-Pytorch
    - https://github.com/Hyunjulie/KR-Reading-Computer-Vision-Papers/blob/master/DeepLabv3%2B/deeplabv3p.py
"""

import math
import torch
from torch import nn
from torch.functional import F


class AtrousSeparableConv1d(nn.Module):
    def __init__(
        self, inplanes, planes, kernel_size=3, stride=1, dilation=1, bias=False
    ):
        super(AtrousSeparableConv1d, self).__init__()

        self.depthwise = nn.Conv1d(
            inplanes,
            inplanes,
            kernel_size,
            stride,
            0,
            dilation,
            groups=inplanes,
            bias=bias,
        )
        self.pointwise = nn.Conv1d(inplanes, planes, 1, 1, 0, 1, 1, bias=bias)

    def forward(self, x):
        x = self.apply_fixed_padding(
            x, self.depthwise.kernel_size[0], rate=self.depthwise.dilation[0]
        )
        x = self.depthwise(x)
        x = self.pointwise(x)
        return x

    def apply_fixed_padding(self, inputs, kernel_size, rate):
        """
        해당 함수는 (dilation)rate 와 kernel_size 에 따라 output 의 크기가 input 의 크기와 동일해질 수 있도록 input 에 padding 을 적용합니다.
        다만, stride 가 2 이상인 경우에는 해당 함수를 거치더라도 input 과 output 크기가 동일해지지 않을 수 있습니다.
        이 경우는 최대한 input 과 output 크기를 맞춰주는 것에 의미가 있고, 전체 네트워크의 마지막 upsample 단계에서 최종적으로 크기를 맞춰줍니다.
        """
        kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
        pad_total = kernel_size_effective - 1
        pad_beg = pad_total // 2
        pad_end = pad_total - pad_beg
        padded_inputs = F.pad(inputs, (pad_beg, pad_end))
        return padded_inputs


class Block(nn.Module):
    def __init__(
        self,
        inplanes,
        planes,
        reps,
        kernel_size=3,
        stride=1,
        dilation=1,
        start_with_relu=True,
        grow_first=True,
        is_last=False,
    ):
        super(Block, self).__init__()

        if planes != inplanes or stride != 1:
            self.skip = nn.Conv1d(inplanes, planes, 1, stride=stride, bias=False)
            self.skipbn = nn.BatchNorm1d(planes)
        else:
            self.skip = None

        self.relu = nn.ReLU(inplace=True)
        rep = []

        filters = inplanes
        if grow_first:
            rep.append(self.relu)
            rep.append(
                AtrousSeparableConv1d(
                    inplanes, planes, kernel_size, stride=1, dilation=dilation
                )
            )
            rep.append(nn.BatchNorm1d(planes))
            filters = planes

        for _ in range(reps - 1):
            rep.append(self.relu)
            rep.append(
                AtrousSeparableConv1d(
                    filters, filters, kernel_size, stride=1, dilation=dilation
                )
            )
            rep.append(nn.BatchNorm1d(filters))

        if not grow_first:
            rep.append(self.relu)
            rep.append(
                AtrousSeparableConv1d(
                    inplanes, planes, kernel_size, stride=1, dilation=dilation
                )
            )
            rep.append(nn.BatchNorm1d(planes))

        if not start_with_relu:
            rep = rep[1:]

        if stride == 2:
            rep.append(AtrousSeparableConv1d(planes, planes, kernel_size, stride=2))
        elif stride == 1:
            if is_last:
                rep.append(AtrousSeparableConv1d(planes, planes, kernel_size, stride=1))
        else:
            raise NotImplementedError("stride must be 1 or 2 in Block.")

        self.rep = nn.Sequential(*rep)

    def forward(self, inp):
        x = self.rep(inp)

        if self.skip is not None:
            skip = self.skip(inp)
            skip = self.skipbn(skip)
        else:
            skip = inp

        x += skip

        return x


class Xception(nn.Module):
    """Modified Aligned Xception"""

    def __init__(
        self,
        inplanes=1,
        output_stride=16,
        kernel_size=3,
        middle_repeat=16,
        middle_block_rate=1,
        exit_block_rates=(1, 2),
    ):
        super(Xception, self).__init__()

        if output_stride == 16:
            entry3_stride = 2
        elif output_stride == 8:
            entry3_stride = 1
        else:
            raise NotImplementedError

        self.conv1 = nn.Conv1d(
            inplanes,
            32,
            kernel_size,
            stride=2,
            padding=(kernel_size - 1) // 2,
            bias=False,
        )
        self.bn1 = nn.BatchNorm1d(32)
        self.relu = nn.ReLU(inplace=True)

        self.conv2 = nn.Conv1d(
            32, 64, kernel_size, stride=1, padding=(kernel_size - 1) // 2, bias=False
        )
        self.bn2 = nn.BatchNorm1d(64)

        self.entry1 = Block(
            64, 128, reps=2, kernel_size=kernel_size, stride=2, start_with_relu=False
        )
        self.entry2 = Block(
            128,
            256,
            reps=2,
            kernel_size=kernel_size,
            stride=2,
            start_with_relu=True,
            grow_first=True,
        )
        self.entry3 = Block(
            256,
            728,
            reps=2,
            kernel_size=kernel_size,
            stride=entry3_stride,
            start_with_relu=True,
            grow_first=True,
            is_last=True,
        )

        self.middle = nn.Sequential(
            *[
                Block(
                    728,
                    728,
                    reps=3,
                    kernel_size=kernel_size,
                    stride=1,
                    dilation=middle_block_rate,
                    start_with_relu=True,
                    grow_first=True,
                )
                for _ in range(middle_repeat)
            ]
        )

        self.exit = Block(
            728,
            1024,
            reps=2,
            kernel_size=kernel_size,
            stride=1,
            dilation=exit_block_rates[0],
            start_with_relu=True,
            grow_first=False,
            is_last=True,
        )

        self.conv3 = AtrousSeparableConv1d(
            1024, 1536, kernel_size, stride=1, dilation=exit_block_rates[1]
        )
        self.bn3 = nn.BatchNorm1d(1536)

        self.conv4 = AtrousSeparableConv1d(
            1536, 1536, kernel_size, stride=1, dilation=exit_block_rates[1]
        )
        self.bn4 = nn.BatchNorm1d(1536)

        self.conv5 = AtrousSeparableConv1d(
            1536, 2048, kernel_size, stride=1, dilation=exit_block_rates[1]
        )
        self.bn5 = nn.BatchNorm1d(2048)

    def forward(self, x: torch.Tensor):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)

        low_level = x = self.entry1(x)

        x = self.entry2(x)
        x = self.entry3(x)

        x = self.middle(x)

        x = self.exit(x)
        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu(x)

        x = self.conv4(x)
        x = self.bn4(x)
        x = self.relu(x)

        x = self.conv5(x)
        x = self.bn5(x)
        x = self.relu(x)

        return x, low_level


class ASPP(nn.Module):
    """Atrous Spatial Pyramid Pooling"""

    def __init__(self, inplanes, planes, rate, kernel_size=3):
        super(ASPP, self).__init__()
        if rate == 1:
            kernel_size = 1
            padding = 0
        else:
            padding = rate * (kernel_size - 1) // 2
        self.atrous_convolution = nn.Conv1d(
            inplanes,
            planes,
            kernel_size=kernel_size,
            stride=1,
            padding=padding,
            dilation=rate,
            bias=False,
        )
        self.bn = nn.BatchNorm1d(planes)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.atrous_convolution(x)
        x = self.bn(x)

        return self.relu(x)


class DeepLabV3Plus(nn.Module):
    def __init__(self, config):
        super(DeepLabV3Plus, self).__init__()

        self.config = config
        # output_stride: (input's spatial resolution / output's resolution)
        output_stride = int(config.output_stride)
        kernel_size = int(config.kernel_size)
        middle_block_rate = int(config.middle_block_rate)
        exit_block_rates: list = config.exit_block_rates
        middle_repeat = int(config.middle_repeat)
        self.interpolate_mode = str(config.interpolate_mode)
        aspp_channel = int(config.aspp_channel)
        aspp_rate: list = config.aspp_rate
        output_size = config.output_size  # 3(p, qrs, t)

        self.xception_features = Xception(
            output_stride=output_stride,
            kernel_size=kernel_size,
            middle_repeat=middle_repeat,
            middle_block_rate=middle_block_rate,
            exit_block_rates=exit_block_rates,
        )

        # ASPP
        self.aspp1 = ASPP(
            2048, aspp_channel, rate=aspp_rate[0], kernel_size=kernel_size
        )
        self.aspp2 = ASPP(
            2048, aspp_channel, rate=aspp_rate[1], kernel_size=kernel_size
        )
        self.aspp3 = ASPP(
            2048, aspp_channel, rate=aspp_rate[2], kernel_size=kernel_size
        )
        self.aspp4 = ASPP(
            2048, aspp_channel, rate=aspp_rate[3], kernel_size=kernel_size
        )

        self.relu = nn.ReLU()

        self.global_avg_pool = nn.Sequential(
            nn.AdaptiveAvgPool1d(1),
            nn.Conv1d(2048, aspp_channel, 1, stride=1, bias=False),
            nn.BatchNorm1d(aspp_channel),
            nn.ReLU(),
        )

        self.conv1 = nn.Conv1d(aspp_channel * 5, aspp_channel, 1, bias=False)
        self.bn1 = nn.BatchNorm1d(aspp_channel)

        # adopt [1x1, 48] for channel reduction.
        self.conv2 = nn.Conv1d(128, 48, 1, bias=False)
        self.bn2 = nn.BatchNorm1d(48)

        self.last_conv = nn.Sequential(
            nn.Conv1d(
                aspp_channel + 48,
                256,
                kernel_size=kernel_size,
                stride=1,
                padding=(kernel_size - 1) // 2,
                bias=False,
            ),
            nn.BatchNorm1d(256),
            nn.ReLU(),
            nn.Conv1d(
                256,
                256,
                kernel_size=kernel_size,
                stride=1,
                padding=(kernel_size - 1) // 2,
                bias=False,
            ),
            nn.BatchNorm1d(256),
            nn.ReLU(),
            nn.Conv1d(256, output_size, kernel_size=1, stride=1),
        )

    def forward(self, input):
        x, low_level_features = self.xception_features(input)

        x1 = self.aspp1(x)
        x2 = self.aspp2(x)
        x3 = self.aspp3(x)
        x4 = self.aspp4(x)
        x5 = self.global_avg_pool(x)
        x5 = F.interpolate(x5, size=x4.shape[2:], mode=self.interpolate_mode)

        x = torch.cat((x1, x2, x3, x4, x5), dim=1)

        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = F.interpolate(
            x, size=int(math.ceil(input.shape[-1] / 4)), mode=self.interpolate_mode
        )

        low_level_features = self.conv2(low_level_features)
        low_level_features = self.bn2(low_level_features)
        low_level_features = self.relu(low_level_features)

        x = torch.cat((x, low_level_features), dim=1)
        x = self.last_conv(x)
        x = F.interpolate(x, size=input.shape[2:], mode=self.interpolate_mode)

        return x