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

from .utils import _SimpleSegmentationModel


__all__ = ["DeepLabV3"]
batchnorm_grad = False


class DeepLabV3(_SimpleSegmentationModel):
    """
    Implements DeepLabV3 model from
    `"Rethinking Atrous Convolution for Semantic Image Segmentation"
    <https://arxiv.org/abs/1706.05587>`_.

    Arguments:
        backbone (nn.Module): the network used to compute the features for the model.
            The backbone should return an OrderedDict[Tensor], with the key being
            "out" for the last feature map used, and "aux" if an auxiliary classifier
            is used.
        classifier (nn.Module): module that takes the "out" element returned from
            the backbone and returns a dense prediction.
        aux_classifier (nn.Module, optional): auxiliary classifier used during training
    """

    pass


class DeepLabHeadV3Plus(nn.Module):
    def __init__(
        self, in_channels, low_level_channels, num_classes, aspp_dilate=[12, 24, 36]
    ):
        super(DeepLabHeadV3Plus, self).__init__()
        self.project = nn.Sequential(
            nn.Conv2d(low_level_channels, 48, 1, bias=False),
            nn.BatchNorm2d(48),
            nn.ReLU(inplace=True),
        )

        self.aspp = ASPP(in_channels, aspp_dilate)

        self.classifier = nn.Sequential(
            nn.Conv2d(304, 256, 3, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, num_classes, 1),
        )
        self._init_weight()

    def forward(self, feature):
        low_level_feature = self.project(feature["low_level"])
        output_feature = self.aspp(feature["out"])
        output_feature = F.interpolate(
            output_feature,
            size=low_level_feature.shape[2:],
            mode="bilinear",
            align_corners=False,
        )
        return self.classifier(torch.cat([low_level_feature, output_feature], dim=1))

    def _init_weight(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight)
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                for i in m.parameters():
                    i.requires_grad &= batchnorm_grad
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)


class DeepLabHead(nn.Module):
    def __init__(self, in_channels, num_classes, aspp_dilate=[12, 24, 36]):
        super(DeepLabHead, self).__init__()

        self.classifier = nn.Sequential(
            ASPP(in_channels, aspp_dilate),
            nn.Conv2d(256, 256, 3, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, num_classes, 1),
        )
        self._init_weight()

    def forward(self, feature):

        return self.classifier(feature)
        # return self.classifier( feature['out'] )

    def _init_weight(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight)
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                for i in m.parameters():
                    i.requires_grad &= batchnorm_grad
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)


class AtrousSeparableConvolution(nn.Module):
    """Atrous Separable Convolution"""

    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        stride=1,
        padding=0,
        dilation=1,
        bias=True,
    ):
        super(AtrousSeparableConvolution, self).__init__()
        self.body = nn.Sequential(
            # Separable Conv
            nn.Conv2d(
                in_channels,
                in_channels,
                kernel_size=kernel_size,
                stride=stride,
                padding=padding,
                dilation=dilation,
                bias=bias,
                groups=in_channels,
            ),
            # PointWise Conv
            nn.Conv2d(
                in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=bias
            ),
        )

        self._init_weight()

    def forward(self, x):
        return self.body(x)

    def _init_weight(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight)
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)


class ASPPConv(nn.Sequential):
    def __init__(self, in_channels, out_channels, dilation):
        modules = [
            nn.Conv2d(
                in_channels,
                out_channels,
                3,
                padding=dilation,
                dilation=dilation,
                bias=False,
            ),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
        ]
        super(ASPPConv, self).__init__(*modules)


class ASPPPooling(nn.Sequential):
    def __init__(self, in_channels, out_channels):
        super(ASPPPooling, self).__init__(
            nn.AdaptiveAvgPool2d(1),
            nn.Conv2d(in_channels, out_channels, 1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
        )

    def forward(self, x):
        size = x.shape[-2:]
        x = super(ASPPPooling, self).forward(x)
        return F.interpolate(x, size=size, mode="bilinear", align_corners=False)


class ASPP(nn.Module):
    def __init__(self, in_channels, atrous_rates):
        super(ASPP, self).__init__()
        out_channels = 256
        modules = []
        modules.append(
            nn.Sequential(
                nn.Conv2d(in_channels, out_channels, 1, bias=False),
                nn.BatchNorm2d(out_channels),
                nn.ReLU(inplace=True),
            )
        )

        rate1, rate2, rate3 = tuple(atrous_rates)
        modules.append(ASPPConv(in_channels, out_channels, rate1))
        modules.append(ASPPConv(in_channels, out_channels, rate2))
        modules.append(ASPPConv(in_channels, out_channels, rate3))
        modules.append(ASPPPooling(in_channels, out_channels))

        self.convs = nn.ModuleList(modules)

        self.project = nn.Sequential(
            nn.Conv2d(5 * out_channels, out_channels, 1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Dropout(0.1),
        )

    def forward(self, x):
        res = []
        for conv in self.convs:
            res.append(conv(x))
        res = torch.cat(res, dim=1)
        return self.project(res)


def convert_to_separable_conv(module):
    new_module = module
    if isinstance(module, nn.Conv2d) and module.kernel_size[0] > 1:
        new_module = AtrousSeparableConvolution(
            module.in_channels,
            module.out_channels,
            module.kernel_size,
            module.stride,
            module.padding,
            module.dilation,
            module.bias,
        )
    for name, child in module.named_children():
        new_module.add_module(name, convert_to_separable_conv(child))
    return new_module
