import torch
from torch import nn as nn

from pl_bolts.utils import _TORCHVISION_AVAILABLE
from pl_bolts.utils.warnings import warn_missing_pkg

if _TORCHVISION_AVAILABLE:
    #from torchvision.models.utils import load_state_dict_from_url
    from torch.hub import load_state_dict_from_url
else:  # pragma: no cover
    warn_missing_pkg('torchvision')

__all__ = [
    'ResNet',
    'resnet18',
    'resnet34',
    'resnet50',
    'resnet101',
    'resnet152',
    'resnext50_32x4d',
    'resnext101_32x8d',
    'wide_resnet50_2',
    'wide_resnet101_2',
]

MODEL_URLS = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
    'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
    'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
    'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
    'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
}


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1) -> nn.Conv2d:
    """
    3x3 convolution with padding

    Args:
        in_planes (int): input channels
        out_planes (int): output channels
        stride (int, optional): stride. Defaults to 1.
        groups (int, optional): groups. Defaults to 1.
        dilation (int, optional): dilation. Defaults to 1.

    Returns:
        nn.Conv2d: convolutional layer
    """
    return nn.Conv2d(
        in_planes,
        out_planes,
        kernel_size=3,
        stride=stride,
        padding=dilation,
        groups=groups,
        bias=False,
        dilation=dilation
    )


def conv1x1(in_planes, out_planes, stride=1) -> nn.Conv2d:
    """
    1x1 convolution
    
    Args:
        in_planes (int): input channels
        out_planes (int): output channels
        stride (int, optional): stride. Defaults to 1.
    
    Returns:
        nn.Conv2d: convolutional layer
    """
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    """
    Basic block for ResNet
    
    Args:
        inplanes (int): input channels
        planes (int): output channels
        stride (int, optional): stride. Defaults to 1.
        downsample (nn.Module, optional): downsample. Defaults to None.
        groups (int, optional): groups. Defaults to 1.
        base_width (int, optional): base width. Defaults to 64.
        dilation (int, optional): dilation. Defaults to 1.
        norm_layer ([type], optional): normalization layer. Defaults to None.
    """
    expansion = 1

    def __init__(
        self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None
    ):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x) -> torch.Tensor:
        """
        Forward pass

        Args:
            x (torch.Tensor): input tensor

        Returns:
            torch.Tensor: output tensor
        """
        identity = 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:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    """
    Bottleneck block for ResNet

    Args:
        inplanes (int): input channels
        planes (int): output channels
        stride (int, optional): stride. Defaults to 1.
        downsample (nn.Module, optional): downsample. Defaults to None.
        groups (int, optional): groups. Defaults to 1.
        base_width (int, optional): base width. Defaults to 64.
        dilation (int, optional): dilation. Defaults to 1.
        norm_layer ([type], optional): normalization layer. Defaults to None.
    """
    expansion = 4

    def __init__(
        self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None
    ):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x) -> torch.Tensor:
        """
        Forward pass

        Args:
            x (torch.Tensor): input tensor

        Returns:
            torch.Tensor: output tensor
        """
        identity = 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:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class ResNet(nn.Module):
    """
    ResNet model

    Args:
        block (nn.Module): block type
        layers (list): list of layers
        num_classes (int, optional): number of classes. Defaults to 1000.
        zero_init_residual (bool, optional): zero init residual. Defaults to False.
        groups (int, optional): groups. Defaults to 1.
        width_per_group (int, optional): width per group. Defaults to 64.
        replace_stride_with_dilation (list, optional): replace stride with dilation. Defaults to None.
        norm_layer ([type], optional): normalization layer. Defaults to None.
        return_all_feature_maps (bool, optional): return all feature maps. Defaults to False.
    """

    def __init__(
        self,
        block,
        layers,
        num_classes=1000,
        zero_init_residual=False,
        groups=1,
        width_per_group=64,
        replace_stride_with_dilation=None,
        norm_layer=None,
        return_all_feature_maps=False,
        first_conv=True,
        maxpool1=True,
        res_remove=False #  decide for last residual block
    ):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer
        self.return_all_feature_maps = return_all_feature_maps

        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError(
                "replace_stride_with_dilation should be None "
                f"or a 3-element tuple, got {replace_stride_with_dilation}"
            )
        self.groups = groups
        self.base_width = width_per_group

        if first_conv:
            self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)
        else:
            self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)

        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)

        if maxpool1:
            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        else:
            self.maxpool = nn.MaxPool2d(kernel_size=1, stride=1)

        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1])
        if res_remove == False:
            self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2])
            
        #self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2])
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        '''
         this is where random weight initialization takes place - 
         all the layers defined can be accessed using self.modules() in pytorch
         if the layer matches with conv2d, use a kaiming_normal weight for that perticula layer (m.weight)
         if it is a batchnorm or groupnorm, use a constant weight 
        '''
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') # understand the parameters of kaiming_normal
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False) -> nn.Sequential:
        """
        Make layer

        Args:
            block (nn.Module): block type
            planes (int): output channels
            blocks (int): number of blocks
            stride (int, optional): stride. Defaults to 1.
            dilate (bool, optional): dilate. Defaults to False.

        Returns:
            nn.Sequential: sequential layer
        """
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(
            block(
                self.inplanes,
                planes,
                stride,
                downsample,
                self.groups,
                self.base_width,
                previous_dilation,
                norm_layer,
            )
        )
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(
                block(
                    self.inplanes,
                    planes,
                    groups=self.groups,
                    base_width=self.base_width,
                    dilation=self.dilation,
                    norm_layer=norm_layer
                )
            )

        return nn.Sequential(*layers)

    def forward(self, x) -> torch.Tensor:
        """
        Forward pass

        Args:
            x (torch.Tensor): input tensor

        Returns:
            torch.Tensor: output tensor
        """
        # convert list input to tensor
        #print(type(x))
        #x = torch.tensor(x[0]) # - comment it for non-temporal models
        #x = torch.tensor(x[1]) # - comment it for non-temporal models
        #x = torch.tensor(x[2]) # - comment it for non-temporal models
        #print(type(x))
        x0 = self.conv1(x)
        x0 = self.bn1(x0)
        x0 = self.relu(x0)
        x0 = self.maxpool(x0)

        if self.return_all_feature_maps:
            x1 = self.layer1(x0)
            x2 = self.layer2(x1)
            x3 = self.layer3(x2)
            x4 = self.layer4(x3)

            return [x0, x1, x2, x3, x4]
            #return [x0, x1, x2, x3]
        else:
            x0 = self.layer1(x0)
            x0 = self.layer2(x0)
            x0 = self.layer3(x0)
            x0 = self.layer4(x0)

            x0 = self.avgpool(x0)
            x0 = torch.flatten(x0, 1)

            return x0


def _resnet(arch, block, layers, pretrained, progress, **kwargs): # pylint: disable=unused-argument # TODO: remove unused args
    """
    ResNet model

    Args:
        arch (str): architecture name
        block (nn.Module): block type
        layers (list): list of layers
        pretrained (bool): pre-trained
        progress (bool): progress
    """
    model = ResNet(block, layers, **kwargs)
    # if pretrained:
    #     state_dict = load_state_dict_from_url(MODEL_URLS[arch], progress=progress)
    #     model.load_state_dict(state_dict)
    # Remove the last fc layer, since we only need the encoder part of resnet.
    model.fc = nn.Identity()
    return model


def resnet18(pretrained: bool = False, progress: bool = True, **kwargs) -> ResNet:
    """ResNet-18 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`

    Args:
        pretrained: If True, returns a model pre-trained on ImageNet
        progress: If True, displays a progress bar of the download to stderr

    Returns:
        ResNet: ResNet-18 model
    """
    
    # original architecture
    #return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress, **kwargs)
    
    # modified architecture
    
    return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress, **kwargs) # THE LAYERS SHOULD BE [2,2,2,2] FOR RESNET18
    
# downsized resnet18
def resnet9(pretrained: bool = False, progress: bool = True, **kwargs) -> ResNet:
    """
    ResNet-9 model

    first argument in _resnet() : architecture name from the URLs
    since URL for resnet9 is not available, therefore resnet18 is used with modifications
    
    Args:
        pretrained: If True, returns a model pre-trained on ImageNet
        progress: If True, displays a progress bar of the download to stderr
        
    Returns:
        ResNet: ResNet-9 model
    """
    # to print this architecture, print the model from the evaluator/evaluate file
    return _resnet('resnet18', BasicBlock, [1, 1, 1, 1], pretrained, progress, **kwargs)


def resnet34(pretrained=False, progress=True, **kwargs) -> ResNet:
    """ResNet-34 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`

    Args:
        pretrained: If True, returns a model pre-trained on ImageNet
        progress: If True, displays a progress bar of the download to stderr

    Returns:
        ResNet: ResNet-34 model
    """
    return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress, **kwargs)


def resnet50(pretrained: bool = False, progress: bool = True, **kwargs) -> ResNet:
    """ResNet-50 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`

    Args:
        pretrained: If True, returns a model pre-trained on ImageNet
        progress: If True, displays a progress bar of the download to stderr

    Returns:
        ResNet: ResNet-50 model
    """
    return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs)


def resnet101(pretrained: bool = False, progress: bool = True, **kwargs) -> ResNet:
    """ResNet-101 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`

    Args:
        pretrained: If True, returns a model pre-trained on ImageNet
        progress: If True, displays a progress bar of the download to stderr
    
    Returns:
        ResNet: ResNet-101 model
    """
    return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs)


def resnet152(pretrained: bool = False, progress: bool = True, **kwargs) -> ResNet:
    """ResNet-152 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`

    Args:
        pretrained: If True, returns a model pre-trained on ImageNet
        progress: If True, displays a progress bar of the download to stderr
    
    Returns:
        ResNet: ResNet-152 model
    """
    return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress, **kwargs)


def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs) -> ResNet:
    """ResNeXt-50 32x4d model from
    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`

    Args:
        pretrained: If True, returns a model pre-trained on ImageNet
        progress: If True, displays a progress bar of the download to stderr
    
    Returns:
        ResNet: ResNeXt-50 32x4d model
    """
    kwargs['groups'] = 32
    kwargs['width_per_group'] = 4
    return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs)


def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs) -> ResNet:
    """ResNeXt-101 32x8d model from
    `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`

    Args:
        pretrained: If True, returns a model pre-trained on ImageNet
        progress: If True, displays a progress bar of the download to stderr
    
    Returns:
        ResNet: ResNeXt-101 32x8d model
    """
    kwargs['groups'] = 32
    kwargs['width_per_group'] = 8
    return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs)


def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs) -> ResNet:
    """Wide ResNet-50-2 model from
    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
    The model is the same as ResNet except for the bottleneck number of channels
    which is twice larger in every block. The number of channels in outer 1x1
    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
    channels, and in Wide ResNet-50-2 has 2048-1024-2048.

    Args:
        pretrained: If True, returns a model pre-trained on ImageNet
        progress: If True, displays a progress bar of the download to stderr
    
    Returns:
        ResNet: Wide ResNet-50-2 model
    """
    kwargs['width_per_group'] = 64 * 2
    return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs)


def wide_resnet101_2(pretrained: bool = False, progress: bool = True, **kwargs) -> ResNet:
    """Wide ResNet-101-2 model from
    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_
    The model is the same as ResNet except for the bottleneck number of channels
    which is twice larger in every block. The number of channels in outer 1x1
    convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
    channels, and in Wide ResNet-50-2 has 2048-1024-2048.

    Args:
        pretrained: If True, returns a model pre-trained on ImageNet
        progress: If True, displays a progress bar of the download to stderr
    
    Returns:
        ResNet: Wide ResNet-101-2 model
    """
    kwargs['width_per_group'] = 64 * 2
    return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs)
