# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#

import torch
import torch.nn as nn

__all__ = [
    'resnet50',
    'resnet50w2',
    'resnet50w4',
    'resnet101',
    'resnet101w2',
    'resnet151',
    'resnet151w2',
    'resnet200',
    'resnet200w2'
]


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """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, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    expansion = 1
    __constants__ = ["downsample"]

    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):
        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):
    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 / 64.0)) * 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):
        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):
    def __init__(
        self,
        block,
        layers,
        zero_init_residual=False,
        groups=1,
        widen=1,
        width_per_group=64,
        replace_stride_with_dilation=None,
        norm_layer=None,
        use_maxpool=True
    ):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer
        self.use_maxpool = use_maxpool

        self.inplanes = width_per_group * widen
        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 "
                "or a 3-element tuple, got {}".format(replace_stride_with_dilation)
            )
        self.groups = groups
        self.base_width = width_per_group

        num_out_filters = width_per_group * widen
        self.conv1 = nn.Conv2d(
            3, num_out_filters, kernel_size=7, stride=2, padding=3, bias=False
        )
        self.bn1 = norm_layer(num_out_filters)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, num_out_filters, layers[0])
        num_out_filters *= 2
        self.layer2 = self._make_layer(
            block, num_out_filters, layers[1], stride=2, dilate=replace_stride_with_dilation[0]
        )
        num_out_filters *= 2
        self.layer3 = self._make_layer(
            block, num_out_filters, layers[2], stride=2, dilate=replace_stride_with_dilation[1]
        )
        num_out_filters *= 2
        self.layer4 = self._make_layer(
            block, num_out_filters, layers[3], stride=2, dilate=replace_stride_with_dilation[2]
        )
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))

        self.fc = None
        self.pred = None

        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)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        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_backbone(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        if self.use_maxpool:
            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)
        if self.fc is not None:
            x = self.fc(x)
        return x

    def _forward_head(self, x):
        if self.pred is not None:
            x = self.pred(x)
        return x

    def forward(self, inputs, return_before_head=False):
        if not isinstance(inputs, list):
            inputs = [inputs]
        idx_crops = torch.cumsum(torch.unique_consecutive(
            torch.tensor([inp.shape[-1] for inp in inputs]),
            return_counts=True,
        )[1], 0)
        start_idx = 0
        for end_idx in idx_crops:
            _h = self._forward_backbone(torch.cat(inputs[start_idx:end_idx]))
            _z = self._forward_head(_h)
            if start_idx == 0:
                h, z = _h, _z
            else:
                h, z = torch.cat((h, _h)), torch.cat((z, _z))
            start_idx = end_idx
        if return_before_head:
            return h, z
        return z


def resnet50(**kwargs):
    return ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)


def resnet50w2(**kwargs):
    return ResNet(Bottleneck, [3, 4, 6, 3], widen=2, **kwargs)


def resnet50w4(**kwargs):
    return ResNet(Bottleneck, [3, 4, 6, 3], widen=4, **kwargs)


def resnet101(**kwargs):
    return ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)


def resnet101w2(**kwargs):
    return ResNet(Bottleneck, [3, 4, 23, 3], widen=2, **kwargs)


def resnet151(**kwargs):
    return ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)


def resnet151w2(**kwargs):
    return ResNet(Bottleneck, [3, 8, 36, 3], widen=2, **kwargs)


def resnet200(**kwargs):
    return ResNet(Bottleneck, [3, 24, 36, 3], **kwargs)


def resnet200w2(**kwargs):
    return ResNet(Bottleneck, [3, 24, 36, 3], widen=2, **kwargs)
