# Copyright 2022 The Authors
# 
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# 
#     https://www.apache.org/licenses/LICENSE-2.0
# 
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import numpy as np
import torch
from torch import nn
from collections import OrderedDict


# AlexNet Back-End architecture
# Based on Torchvision implementation in
# https://github.com/pytorch/vision/blob/master/torchvision/models/alexnet.py
class AlexNetBackEnd(nn.Module):
    def __init__(self, num_classes=1000):
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(64, 192, kernel_size=5, stride=2, padding=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
            nn.Conv2d(192, 384, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(384, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
        )
        self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
        self.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(256 * 7 * 7, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, num_classes),
        )

    def forward(self, x):
        x = self.features(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x


# ResNet Back-End architecture
# Based on Torchvision implementation in
# https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    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):
    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
    __constants__ = ['downsample']

    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) # 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 ResNetBackEnd(nn.Module):
    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):
        super(ResNetBackEnd, self).__init__()
        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 "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        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)
                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_impl(self, x, features_only=False):
        # See note [TorchScript super()]

        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 not features_only:
            x = self.fc(x)

        return x

    def forward(self, x, **kwargs):
        return self._forward_impl(x, **kwargs)


# CORnet-S Back-End architecture
# Based on CORnet code in
# https://github.com/dicarlolab/CORnet
class Flatten(nn.Module):
    def forward(self, x):
        return x.view(x.size(0), -1)


class Identity(nn.Module):
    def forward(self, x):
        return x


class CORblock_S(nn.Module):

    scale = 4  # scale of the bottleneck convolution channels

    def __init__(self, in_channels, out_channels, times=1):
        super().__init__()

        self.times = times

        self.conv_input = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
        self.skip = nn.Conv2d(out_channels, out_channels,
                              kernel_size=1, stride=2, bias=False)
        self.norm_skip = nn.BatchNorm2d(out_channels)

        self.conv1 = nn.Conv2d(out_channels, out_channels * self.scale,
                               kernel_size=1, bias=False)
        self.nonlin1 = nn.ReLU(inplace=True) #

        self.conv2 = nn.Conv2d(out_channels * self.scale, out_channels * self.scale,
                               kernel_size=3, stride=2, padding=1, bias=False)
        self.nonlin2 = nn.ReLU(inplace=True) #

        self.conv3 = nn.Conv2d(out_channels * self.scale, out_channels,
                               kernel_size=1, bias=False)
        self.nonlin3 = nn.ReLU(inplace=True) #

        self.output = Identity()  # for an easy access to this block's output

        # need BatchNorm for each time step for training to work well
        for t in range(self.times):
            setattr(self, f'norm1_{t}', nn.BatchNorm2d(out_channels * self.scale))
            setattr(self, f'norm2_{t}', nn.BatchNorm2d(out_channels * self.scale))
            setattr(self, f'norm3_{t}', nn.BatchNorm2d(out_channels))

    def forward(self, inp):
        x = self.conv_input(inp)

        for t in range(self.times):
            if t == 0:
                skip = self.norm_skip(self.skip(x))
                self.conv2.stride = (2, 2)
            else:
                skip = x
                self.conv2.stride = (1, 1)

            x = self.conv1(x)
            x = getattr(self, f'norm1_{t}')(x)
            x = self.nonlin1(x)

            x = self.conv2(x)
            x = getattr(self, f'norm2_{t}')(x)
            x = self.nonlin2(x)

            x = self.conv3(x)
            x = getattr(self, f'norm3_{t}')(x)

            x += skip
            x = self.nonlin3(x)
            output = self.output(x)

        return output


class CORnetSBackEnd(nn.Module):
    def __init__(self, num_classes=1000):
        super(CORnetSBackEnd, self).__init__()

        self.V2 = CORblock_S(64, 128, times=2)
        self.V4 = CORblock_S(128, 256, times=4)
        self.IT = CORblock_S(256, 512, times=2)
        self.decoder = nn.Sequential(OrderedDict([
            ('avgpool', nn.AdaptiveAvgPool2d(1)),
            ('flatten', Flatten()),
            ('linear', nn.Linear(512, num_classes)),
            ('output', Identity())
        ]))

        # weight initialization
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, np.sqrt(2. / n))
            # nn.Linear is missing here because I originally forgot
            # to add it during the training of this network
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def forward(self, x):
        x = self.V2(x)
        x = self.V4(x)
        x = self.IT(x)
        x = self.decoder(x)
        return x
