import torch
import torch.nn
import torch.utils.model_zoo

import thelper.nn
import thelper.nn.coordconv


class Module(torch.nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, coordconv=False, radius_channel=True):
        super().__init__()
        self.inplanes = inplanes
        self.planes = planes
        self.stride = stride
        self.downsample = downsample
        self.coordconv = coordconv
        self.radius_channel = radius_channel

    def _make_conv2d(self, *args, **kwargs):
        if self.coordconv:
            return thelper.nn.coordconv.CoordConv2d(*args, radius_channel=self.radius_channel, **kwargs)
        else:
            return torch.nn.Conv2d(*args, **kwargs)


class BasicBlock(Module):

    def __init__(self, inplanes, planes, stride=1, downsample=None, coordconv=False, radius_channel=True):
        super().__init__(inplanes, planes, stride, downsample, coordconv, radius_channel)
        self.conv1 = self._make_conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = torch.nn.BatchNorm2d(planes)
        self.relu = torch.nn.ReLU(inplace=True)
        self.conv2 = self._make_conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = torch.nn.BatchNorm2d(planes)

    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        if self.downsample is not None:
            residual = self.downsample(x)
        out += residual
        out = self.relu(out)
        return out


class Bottleneck(Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, coordconv=False, radius_channel=True):
        super().__init__(inplanes, planes, stride, downsample, coordconv, radius_channel)
        self.conv1 = self._make_conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = torch.nn.BatchNorm2d(planes)
        self.conv2 = self._make_conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = torch.nn.BatchNorm2d(planes)
        self.conv3 = self._make_conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = torch.nn.BatchNorm2d(planes * self.expansion)
        self.relu = torch.nn.ReLU(inplace=True)

    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)
        out = self.conv3(out)
        out = self.bn3(out)
        if self.downsample is not None:
            residual = self.downsample(x)
        out += residual
        out = self.relu(out)
        return out


class SqueezeExcitationLayer(torch.nn.Module):

    def __init__(self, channel, reduction=16):
        super().__init__()
        self.pool = torch.nn.AdaptiveAvgPool2d(1)
        self.fc = torch.nn.Sequential(
            torch.nn.Linear(channel, channel // reduction),
            torch.nn.ReLU(inplace=True),
            torch.nn.Linear(channel // reduction, channel),
            torch.nn.Sigmoid()
        )

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        return x * y


class SqueezeExcitationBlock(Module):

    def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=16, coordconv=False, radius_channel=True):
        super().__init__(inplanes, planes, stride, downsample, coordconv, radius_channel)
        self.conv1 = self._make_conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = torch.nn.BatchNorm2d(planes)
        self.relu = torch.nn.ReLU(inplace=True)
        self.conv2 = self._make_conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = torch.nn.BatchNorm2d(planes)
        self.se = SqueezeExcitationLayer(planes, reduction)

    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.se(out)
        if self.downsample is not None:
            residual = self.downsample(x)
        out += residual
        out = self.relu(out)
        return out


class ResNet(thelper.nn.Module):

    def __init__(self, task, block="thelper.nn.resnet.BasicBlock", layers=[3, 4, 6, 3], strides=[1, 2, 2, 2], input_channels=3,
                 flexible_input_res=False, pool_size=7, head_type=None, coordconv=False, radius_channel=True, pretrained=False):
        # note: must always forward args to base class to keep backup
        super().__init__(task, **{k: v for k, v in vars().items() if k not in ["self", "task", "__class__"]})
        if isinstance(block, str):
            block = thelper.utils.import_class(block)
        if not issubclass(block, Module):
            raise AssertionError("block type must be subclass of thelper.nn.resnet.Module")
        if not isinstance(layers, list) or not isinstance(strides, list):
            raise AssertionError("expected layers/strides to be provided as list of ints")
        if len(layers) != len(strides):
            raise AssertionError("layer/strides length mismatch")
        self.input_channels = input_channels
        self.flexible_input_res = flexible_input_res
        self.pool_size = pool_size
        self.head_type = head_type
        self.coordconv = coordconv
        self.radius_channel = radius_channel
        self.pretrained = pretrained
        self.inplanes = 64
        self.conv1 = self._make_conv2d(in_channels=input_channels, out_channels=self.inplanes,
                                       kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = torch.nn.BatchNorm2d(self.inplanes)
        self.relu = torch.nn.ReLU(inplace=True)
        self.maxpool = torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0], stride=strides[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2])
        self.layer4 = self._make_layer(block, 512, layers[3], stride=strides[3])
        self.out_features = 512
        self.layer5 = None
        if len(layers) > 4:
            self.layer5 = self._make_layer(block, 1024, layers[4], stride=strides[4])
            self.out_features = 1024
        if flexible_input_res:
            self.avgpool = torch.nn.AdaptiveAvgPool2d(1)
        else:
            if pool_size < 1:
                raise AssertionError("invalid avg pool size for non-flex resolution")
            self.avgpool = torch.nn.AvgPool2d(pool_size, stride=1)
        self.out_features *= block.expansion
        self.fc = torch.nn.Linear(self.out_features, 1000)  # output type/count will be specialized by task after init
        for m in self.modules():
            if isinstance(m, torch.nn.Conv2d):
                torch.nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, thelper.nn.coordconv.CoordConv2d):
                torch.nn.init.kaiming_normal_(m.conv.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, torch.nn.BatchNorm2d):
                torch.nn.init.constant_(m.weight, 1)
                torch.nn.init.constant_(m.bias, 0)
        if pretrained:
            # note: if using a non-default setup in the constructor, loading the pre-trained weights will most
            # likely fail as the weights are downloaded from the pytorch model zoo for the regular resnet impls
            import torchvision
            default_weights_mapping = {
                str([2, 2, 2, 2]) + str("BasicBlock"): "resnet18",
                str([3, 4, 6, 3]) + str("BasicBlock"): "resnet34",
                str([3, 4, 6, 3]) + str("Bottleneck"): "resnet50",
                str([3, 4, 23, 3]) + str("Bottleneck"): "resnet101",
                str([3, 8, 36, 3]) + str("Bottleneck"): "resnet152"
            }
            tag = str(layers) + block.__name__
            assert tag in default_weights_mapping, "could not find corresponding weight url"
            weights_url = torchvision.models.resnet.model_urls[default_weights_mapping[tag]]
            state_dict = torchvision.models.utils.load_state_dict_from_url(weights_url)
            self.load_state_dict(state_dict)
        if isinstance(task, thelper.tasks.Segmentation):
            # if base task is already associated with segmentation, add head attribute
            assert isinstance(head_type, str) and head_type in ["fcn", "deeplabv3"], \
                f"unrecognized head type ('{head_type}') for segmentation resnet"
            # note: head below will be fully instantiated when the task is assigned
            self.fc = None
        self.set_task(task)

    def _make_conv2d(self, *args, **kwargs):
        if self.coordconv:
            return thelper.nn.coordconv.CoordConv2d(*args, radius_channel=self.radius_channel, **kwargs)
        else:
            return torch.nn.Conv2d(*args, **kwargs)

    def _make_layer(self, block, planes, blocks, stride):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = torch.nn.Sequential(
                self._make_conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
                torch.nn.BatchNorm2d(planes * block.expansion),
            )
        layers = [block(self.inplanes, planes, stride, downsample)]
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))
        return torch.nn.Sequential(*layers)

    def get_embedding(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        if self.layer5 is not None:
            x = self.layer5(x)
        return x

    def forward(self, x):
        x = self.get_embedding(x)
        if isinstance(self.task, thelper.tasks.Classification):
            x = self.avgpool(x)
            x = x.view(x.size(0), -1)
            x = self.fc(x)
        elif isinstance(self.task, thelper.tasks.Segmentation):
            x = self.fc(x)
        return x

    def set_task(self, task):
        assert isinstance(task, (thelper.tasks.Classification, thelper.tasks.Segmentation)), \
            "missing impl for non-classif task type"
        num_classes = len(task.class_names)
        if isinstance(task, thelper.tasks.Classification):
            if self.fc.out_features != num_classes:
                self.fc = torch.nn.Linear(self.out_features, num_classes)
        elif isinstance(task, thelper.tasks.Segmentation):
            import torchvision.models.segmentation
            # note: heads below will be fully reinstantiated when the output class count changes
            if self.fc is None or self.fc[len(self.fc) - 1].out_channels != num_classes:
                if self.head_type == "fcn":
                    self.fc = torchvision.models.segmentation.fcn.FCNHead(self.out_features, num_classes)
                else:
                    self.fc = torchvision.models.segmentation.deeplabv3.DeepLabHead(self.out_features, num_classes)
        self.task = task


class ConvTailNet(torch.nn.Module):
    """DEPRECATED. Will be removed in a future version."""

    def __init__(self, n_inputs, num_classes):
        super(ConvTailNet, self).__init__()
        self.conv1 = torch.nn.Conv2d(n_inputs, n_inputs, kernel_size=1, bias=False)
        self.relu = torch.nn.ReLU(True)
        self.conv2 = torch.nn.Conv2d(n_inputs, n_inputs, kernel_size=1, bias=False)
        self.conv3 = torch.nn.Conv2d(n_inputs, num_classes, kernel_size=1, bias=False)

    def forward(self, x):
        x0 = x
        x = self.conv1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.relu(x)
        x = torch.add(x0, x)
        x = self.conv3(x)
        return x


class ResNetFullyConv(ResNet):
    """DEPRECATED. Will be removed in a future version. Use the torchvision segmentation models or the ResNet above instead."""

    def __init__(self, task, block="thelper.nn.resnet.BasicBlock", layers=[3, 4, 6, 3], strides=[1, 2, 2, 2], input_channels=3,
                 flexible_input_res=False, pool_size=7, coordconv=False, radius_channel=True, pretrained=False):
        super().__init__(task=task, block=block, layers=layers, strides=strides, input_channels=input_channels,
                         flexible_input_res=flexible_input_res, pool_size=pool_size, coordconv=coordconv,
                         radius_channel=radius_channel, pretrained=pretrained)
        self.set_task(task)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        if self.layer5 is not None:
            x = self.layer5(x)
        x = self.avgpool(x)
        x = self.fc(x)
        x = torch.squeeze(x)
        return x

    def set_task(self, task):
        assert isinstance(task, thelper.tasks.Classification), "missing impl for non-classif task type"
        num_classes = len(task.class_names)
        self.fc = ConvTailNet(self.out_features, num_classes)
        self.task = task


class FCResNet(ResNet):
    """Fully Convolutional ResNet converter for pre-trained classification models."""

    def __init__(self, task, ckptdata, map_location="cpu", avgpool_size=0):
        if isinstance(ckptdata, str):
            ckptdata = thelper.utils.load_checkpoint(ckptdata, map_location=map_location)
        model_type = ckptdata["model_type"]
        if model_type != "thelper.nn.resnet.ResNet":
            raise AssertionError("cannot convert non-resnet model to fully conv with this impl")
        model_params = ckptdata["model_params"]
        if isinstance(ckptdata["task"], str):
            old_model_task = thelper.tasks.create_task(ckptdata["task"])
        else:
            old_model_task = ckptdata["task"]
        self.task = None
        self.avgpool_size = avgpool_size
        super().__init__(old_model_task, **model_params)
        self.load_state_dict(ckptdata["model"], strict=False)  # assumes model always stored as weight dict
        self.finallayer = torch.nn.Conv2d(self.out_features, self.fc.out_features, kernel_size=1)
        self.finallayer.weight = torch.nn.Parameter(self.fc.weight.view(self.fc.out_features, self.out_features, 1, 1))
        self.finallayer.bias = torch.nn.Parameter(self.fc.bias)
        self.set_task(task)

    def forward(self, x):
        x = self.get_embedding(x)
        if self.avgpool_size > 0:
            x = torch.nn.functional.avg_pool2d(x, kernel_size=self.avgpool_size, stride=1)
        x = self.finallayer(x)
        return x

    def set_task(self, task):
        assert isinstance(task, (thelper.tasks.Segmentation, thelper.tasks.Classification)), \
            "missing impl for non-segm/classif task type"
        num_classes = len(task.class_names)
        if self.fc.out_features != num_classes:
            self.fc = torch.nn.Linear(self.out_features, num_classes)
            self.finallayer = torch.nn.Conv2d(self.out_features, num_classes, kernel_size=1)
            self.finallayer.weight = torch.nn.Parameter(self.fc.weight.view(self.fc.out_features, self.out_features, 1, 1))
            self.finallayer.bias = torch.nn.Parameter(self.fc.bias)
        self.task = task
