from typing import Type, Any, Callable, Union, List, Optional

import torch
import torch.nn as nn
from torch import Tensor
import bregmanet.utils.activation as act
import torch.nn.functional as F

from torch.hub import load_state_dict_from_url
from torch._C import _log_api_usage_once


"""
    ResNet models are from "Deep Residual Learning for Image Recognition" 
        <https://arxiv.org/pdf/1512.03385.pdf>
    ResNeXt models are from "Aggregated Residual Transformation for Deep Neural Networks"
        <https://arxiv.org/pdf/1611.05431.pdf>
    WideResNet models are from "Wide Residual Networks"
        <https://arxiv.org/pdf/1605.07146.pdf>
"""

__all__ = [
    "ResNet",
    "bresnet18",
    "bresnet34",
    "bresnet50",
    "bresnet101",
    "bresnet152",
    "bresnext50_32x4d",
    "bresnext101_32x8d",
    "wide_bresnet50_2",
    "wide_bresnet101_2",
]


model_urls = {
    "resnet18": "https://download.pytorch.org/models/resnet18-f37072fd.pth",
    "resnet34": "https://download.pytorch.org/models/resnet34-b627a593.pth",
    "resnet50": "https://download.pytorch.org/models/resnet50-0676ba61.pth",
    "resnet101": "https://download.pytorch.org/models/resnet101-63fe2227.pth",
    "resnet152": "https://download.pytorch.org/models/resnet152-394f9c45.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: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d:
    """3x3 convolution with padding"""
    return nn.Conv2d(
        in_planes,
        out_planes,
        kernel_size=3,
        stride=stride,
        padding=dilation,
        groups=groups,
        bias=False,
        dilation=dilation,
    )


def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    expansion: int = 1

    def __init__(
        self,
        inplanes: int,
        planes: int,
        stride: int = 1,
        downsample: Optional[nn.Module] = None,
        groups: int = 1,
        base_width: int = 64,
        dilation: int = 1,
        norm_layer: Optional[Callable[..., nn.Module]] = None,
    ) -> None:
        super().__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: Tensor) -> 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 LambdaLayer(nn.Module):
    def __init__(self, lambd):
        super(LambdaLayer, self).__init__()
        self.lambd = lambd

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


class BregmanBasicBlock(nn.Module):
    expansion: int = 1

    def __init__(
            self,
            inplanes: int,
            planes: int,
            stride: int = 1,
            downsample: Optional[nn.Module] = None,
            groups: int = 1,
            base_width: int = 64,
            dilation: int = 1,
            norm_layer: Optional[Callable[..., nn.Module]] = None,
            activation='softplus',
            option='E',
    ) -> None:
        super().__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.activation, self.inverse, self.range = act.get(activation, version='bregman')
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride
        self.shortcut = nn.Sequential()
        if stride != 1 or inplanes != planes:
            if option == 'A':
                """
                For CIFAR10 ResNet paper uses option A.
                """
                self.shortcut = LambdaLayer(lambda x:
                                            F.pad(x[:, :, ::2, ::2], (0, 0, 0, 0, planes // 4, planes // 4), "constant",
                                                  0))
            elif option == 'B':
                self.shortcut = nn.Sequential(
                    nn.Conv2d(inplanes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
                    nn.BatchNorm2d(self.expansion * planes)
                )
            elif option == 'C':
                self.shortcut = nn.Sequential(
                    LambdaLayer(lambda x:
                                F.pad(x[:, :, ::2, ::2], (0, 0, 0, 0, planes // 4, planes // 4), "constant", 0)),
                    LambdaLayer(lambda x: self.activation(x)),
                    LambdaLayer(lambda x: self.inverse(x))
                )
            elif option == 'D':
                self.shortcut = nn.Sequential(
                    LambdaLayer(lambda x:
                                F.pad(x[:, :, ::2, ::2], (0, 0, 0, 0, planes // 4, planes // 4), "constant", 0))
                    # LambdaLayer(lambda x: torch.clamp(x, min=self.range[0] + 1e-5, max=self.range[1] - 1e-5)),
                )
            elif option == 'E':
                " The padding value is such that, after the inverse activation, it is 0 "
                val = float(self.activation(torch.zeros(1)))
                self.shortcut = LambdaLayer(lambda x: F.pad(x[:, :, ::2, ::2], (0, 0, 0, 0, planes // 4, planes // 4),
                                                            "constant", val))

    def forward(self, x: Tensor) -> Tensor:

        out = self.activation(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))

        # if self.downsample is not None:
        #    x = self.downsample(x)

        shortcut = torch.clamp(self.shortcut(x), min=self.range[0]+1e-5, max=self.range[1]-1e-5)
        out += self.inverse(shortcut)
        out = self.activation(out)

        return out


class Bottleneck(nn.Module):
    # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
    # while original implementation places the stride at the first 1x1 convolution(self.conv1)
    # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
    # This variant is also known as ResNet V1.5 and improves accuracy according to
    # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.

    expansion: int = 4

    def __init__(
        self,
        inplanes: int,
        planes: int,
        stride: int = 1,
        downsample: Optional[nn.Module] = None,
        groups: int = 1,
        base_width: int = 64,
        dilation: int = 1,
        norm_layer: Optional[Callable[..., nn.Module]] = None,
    ) -> None:
        super().__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.0)) * 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: Tensor) -> 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 BregmanBottleneck(nn.Module):
    # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
    # while original implementation places the stride at the first 1x1 convolution(self.conv1)
    # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
    # This variant is also known as ResNet V1.5 and improves accuracy according to
    # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.

    expansion: int = 4

    def __init__(
        self,
        inplanes: int,
        planes: int,
        stride: int = 1,
        downsample: Optional[nn.Module] = None,
        groups: int = 1,
        base_width: int = 64,
        dilation: int = 1,
        norm_layer: Optional[Callable[..., nn.Module]] = None,
        activation='softplus',
    ) -> None:
        super().__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.0)) * 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.activation, self.inverse, self.range = act.get(activation, version='bregman')
        self.downsample = downsample
        self.stride = stride

    def forward(self, x: Tensor) -> Tensor:
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.activation(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.activation(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += self.inverse(identity)
        out = self.activation(out)

        return out


class ResNet(nn.Module):
    def __init__(
        self,
        block: Type[Union[BasicBlock, Bottleneck]],
        layers: List[int],
        num_classes: int = 1000,
        zero_init_residual: bool = False,
        groups: int = 1,
        width_per_group: int = 64,
        replace_stride_with_dilation: Optional[List[bool]] = None,
        norm_layer: Optional[Callable[..., nn.Module]] = None,
    ) -> None:
        super().__init__()
        _log_api_usage_once(self)
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        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
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=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])
        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)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
            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)  # type: ignore[arg-type]
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)  # type: ignore[arg-type]

    def _make_layer(
        self,
        block: Type[Union[BasicBlock, Bottleneck]],
        planes: int,
        blocks: int,
        stride: int = 1,
        dilate: bool = False,
    ) -> nn.Sequential:
        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_impl(self, x: Tensor) -> Tensor:
        # See note [TorchScript super()]
        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)

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)

        return x

    def forward(self, x: Tensor) -> Tensor:
        return self._forward_impl(x)


class BregmanResNet(nn.Module):
    def __init__(
        self,
        block: Type[Union[BregmanBasicBlock, BregmanBottleneck]],
        layers: List[int],
        num_classes: int = 1000,
        zero_init_residual: bool = False,
        groups: int = 1,
        width_per_group: int = 64,
        replace_stride_with_dilation: Optional[List[bool]] = None,
        norm_layer: Optional[Callable[..., nn.Module]] = None,
        activation='softplus',
    ) -> None:
        super().__init__()
        #_log_api_usage_once(self)
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        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
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = norm_layer(self.inplanes)
        self.activation, self.inverse, self.range = act.get(activation, version='bregman')
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=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])
        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)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
            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)  # type: ignore[arg-type]
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)  # type: ignore[arg-type]

    def _make_layer(
        self,
        block: Type[Union[BregmanBasicBlock, BregmanBottleneck]],
        planes: int,
        blocks: int,
        stride: int = 1,
        dilate: bool = False,
    ) -> nn.Sequential:
        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_impl(self, x: Tensor) -> Tensor:
        # See note [TorchScript super()]
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.activation(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)

        return x

    def forward(self, x: Tensor) -> Tensor:
        return self._forward_impl(x)


def _resnet(
    arch: str,
    block: Type[Union[BasicBlock, Bottleneck]],
    layers: List[int],
    pretrained: bool,
    progress: bool,
    num_classes: 1000,
    **kwargs: Any,
) -> ResNet:
    model = ResNet(block, layers, num_classes=num_classes, **kwargs)
    if pretrained:
        state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
        model.load_state_dict(state_dict)
    return model


def _bresnet(
    block: Type[Union[BregmanBasicBlock, BregmanBottleneck]],  # Maybe have Bottleneck ?
    layers: List[int],
    num_classes: 1000,
    activation='softplus',
    **kwargs: Any,
) -> BregmanResNet:
    model = BregmanResNet(block, layers, activation=activation, num_classes=num_classes, **kwargs)
    return model


def bresnet18(activation='softplus', version='bregman', **kwargs: Any):
    r"""Bregman ResNet-18 model
    """
    if version.lower() == 'standard':
        return _resnet("resnet18", BasicBlock, [2, 2, 2, 2], **kwargs)
    else:
        return _bresnet(BregmanBasicBlock, [2, 2, 2, 2], activation=activation, **kwargs)


def bresnet34(activation='softplus', version='bregman', **kwargs: Any):
    r"""Bregman ResNet-34 model
    """
    if version.lower() == 'standard':
        return _resnet("resnet34", BasicBlock, [3, 4, 6, 3], **kwargs)
    else:
        return _bresnet(BregmanBasicBlock, [3, 4, 6, 3], activation=activation, **kwargs)


def bresnet50(activation='softplus', version='bregman', **kwargs: Any):
    r"""Bregman ResNet-50 model
    """
    if version.lower() == 'standard':
        return _resnet("resnet50", Bottleneck, [3, 4, 6, 3], **kwargs)
    else:
        return _bresnet(BregmanBottleneck, [3, 4, 6, 3], activation=activation, **kwargs)


def bresnet101(activation='softplus', version='bregman', **kwargs: Any):
    r"""Bregman ResNet-101 model
    """
    if version.lower() == 'standard':
        return _resnet("resnet101", Bottleneck, [3, 4, 23, 3], **kwargs)
    else:
        return _bresnet(BregmanBottleneck, [3, 4, 23, 3], activation=activation, **kwargs)


def bresnet152(activation='softplus', version='bregman', **kwargs: Any):
    r"""Bregman ResNet-152 model
    """
    if version.lower() == 'standard':
        return _resnet("resnet151", Bottleneck, [3, 8, 36, 3], **kwargs)
    else:
        return _bresnet(BregmanBottleneck, [3, 8, 36, 3], activation=activation, **kwargs)


def bresnext50_32x4d(activation='softplus', version='bregman', **kwargs: Any):
    r"""Bregman ResNeXt-50 32x4d model
    """
    kwargs["groups"] = 32
    kwargs["width_per_group"] = 4
    if version.lower() == 'standard':
        return _resnet("resnext50_32x4d", Bottleneck, [3, 4, 6, 3], **kwargs)
    else:
        return _bresnet(BregmanBottleneck, [3, 4, 6, 3], activation=activation, **kwargs)


def bresnext101_32x8d(activation='softplus', version='bregman', **kwargs: Any):
    r"""Bregman ResNeXt-101 32x8d model
    """
    kwargs["groups"] = 32
    kwargs["width_per_group"] = 8
    if version.lower() == 'standard':
        return _resnet("resnext101_32x8d", Bottleneck, [3, 4, 23, 3], **kwargs)
    else:
        return _bresnet(BregmanBottleneck, [3, 4, 23, 3], activation=activation, **kwargs)


def wide_bresnet50_2(activation='softplus', version='bregman', **kwargs: Any):
    r"""Wide Bregman ResNet-50-2 model
    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.

    Wide ResNet-50-2 model from
    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
    """
    kwargs["width_per_group"] = 64 * 2
    if version.lower() == 'standard':
        return _resnet("wide_resnet50_2", Bottleneck, [3, 4, 6, 3], **kwargs)
    else:
        return _bresnet(BregmanBottleneck, [3, 4, 6, 3], activation=activation, **kwargs)


def wide_bresnet101_2(activation='softplus', version='bregman', **kwargs: Any):
    r"""Wide Bregman ResNet-101-2 model
    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.

    Wide ResNet-101-2 model from
    `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
    """
    kwargs["width_per_group"] = 64 * 2
    if version.lower() == 'standard':
        return _resnet("wide_resnet101_2", Bottleneck, [3, 4, 23, 3], **kwargs)
    else:
        return _bresnet(BregmanBottleneck, [3, 4, 23, 3], activation=activation, **kwargs)
