# 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

class Shared(torch.nn.Module):

    def __init__(self,args):
        super(Shared, self).__init__()


        self.taskcla=args.taskcla
        self.latent_dim = args.latent_dim
        ncha,size,_ = args.inputsize

        self.pretrained = False

        if args.experiment == 'cifar100':
            hiddens = [64, 128, 256]

        elif args.experiment == 'miniimagenet':
            hiddens = [1024, 512, 256]

        else:
            raise NotImplementedError

        # Small resnet
        resnet = resnet18_small(self.latent_dim, shared=True)
        self.features = torch.nn.Sequential(*list(resnet.children())[:-2])

        if args.experiment == 'miniimagenet':
            # num_ftrs = 4608
            num_ftrs = 2304  # without average pool (-2)

        elif args.experiment == 'cifar100':
            # num_ftrs = 25088  # without average pool
            num_ftrs = 256
        else:
            raise NotImplementedError

        self.relu=torch.nn.ReLU()
        self.drop1=torch.nn.Dropout(0.2)
        self.drop2=torch.nn.Dropout(0.5)
        self.fc1=torch.nn.Linear(num_ftrs,hiddens[0])
        self.fc2=torch.nn.Linear(hiddens[0],hiddens[1])
        self.fc3=torch.nn.Linear(hiddens[1],hiddens[1])
        self.fc4=torch.nn.Linear(hiddens[1], self.latent_dim)

    def forward(self, x):
        x = x.view_as(x)
        x = self.features(x)
        x = torch.flatten(x, 1)
        x = self.drop2(self.relu(self.fc1(x)))
        x = self.drop2(self.relu(self.fc2(x)))
        x = self.drop2(self.relu(self.fc3(x)))
        x = self.drop2(self.relu(self.fc4(x)))
        return x





class Net(torch.nn.Module):

    def __init__(self, args):
        super(Net, self).__init__()
        ncha,size,_=args.inputsize
        self.image_size = ncha * size * size

        self.taskcla = args.taskcla
        self.latent_dim = args.latent_dim
        self.ntasks = args.ntasks
        self.samples = args.samples
        self.image_size = ncha * size * size
        self.use_memory = args.use_memory

        self.hidden1 = args.head_units
        self.hidden2 = args.head_units

        self.shared = Shared(args)
        self.private = resnet18_small(self.latent_dim, shared=False)

        self.head = torch.nn.Sequential(
                    torch.nn.Linear(2*self.latent_dim, self.hidden1),
                    torch.nn.ReLU(inplace=True),
                    torch.nn.Dropout(),
                    torch.nn.Linear(self.hidden1, self.hidden2),
                    torch.nn.ReLU(inplace=True),
                    torch.nn.Linear(self.hidden2, self.taskcla[0][1])
                )


    def forward(self, x_s, x_p, tt=None):

        x_s = x_s.view_as(x_s)
        x_p = x_p.view_as(x_p)

        # x_s = self.shared(x_s)
        # x_p = self.private(x_p)

        # x = torch.cat([x_p, x_s], dim=1)

        # if self.args.experiment == 'multidatasets':
        #     # if no memory is used this is faster:
        #     y=[]
        #     for i,_ in self.taskcla:
        #         y.append(self.head[i](x))
        #     return y[task_id]
        # else:
        #     return torch.stack([self.head[tt[i]].forward(x[i]) for i in range(x.size(0))])


        output = {}
        output['shared'] = self.shared(x_s)
        output['private'] = self.private(x_p)
        concat_features = torch.cat([output['private'], output['shared']], dim=1)
        if torch.is_tensor(tt):
            output['out'] = torch.stack([self.head[tt[i]].forward(concat_features[i]) for i in range(
                concat_features.size(0))])
        else:
            output['out'] = self.head(concat_features)
        return output

        # output['shared'] = self.shared(x_s)
        # output['private'] = self.private(x_p)
        #
        # concat_features = torch.cat([output['private'], output['shared']], dim=1)
        #
        # if torch.is_tensor(tt):
        #
        #     output['out'] = torch.stack([self.head[tt[i]].forward(concat_features[i]) for i in range(concat_features.size(0))])
        # else:
        #     if self.use_memory == 'no':
        #         output['out'] = self.head.forward(concat_features)
        #
        #     elif self.use_memory == 'yes':
        #         y = []
        #         for i, _ in self.taskcla:
        #             y.append(self.head[i](concat_features))
        #         output['out'] = y[task_id]
        #
        #     return output


    # def get_encoded_ftrs(self, x_s, x_p, task_id=None):
    #     return self.shared(x_s), self.private(x_p)


    def print_model_size(self):

        count_P = sum(p.numel() for p in self.private.parameters() if p.requires_grad)
        count_S = sum(p.numel() for p in self.shared.parameters() if p.requires_grad)
        count_H = sum(p.numel() for p in self.head.parameters() if p.requires_grad)

        print("Size of the network for one task including (S+P+p)")
        print('Num parameters in S       = %s ' % (self.pretty_print(count_S)))
        print('Num parameters in P       = %s ' % (self.pretty_print(count_P)))
        print('Num parameters in p       = %s ' % (self.pretty_print(count_H)))
        print('Num parameters in P+p    = %s ' % self.pretty_print(count_P + count_H))
        print('-------------------------->   Architecture size in total for all tasks: %s parameters (%sB)' % (
        self.pretty_print(count_S + self.ntasks*count_P + self.ntasks*count_H),
        self.pretty_print(4 * (count_S + self.ntasks*count_P + self.ntasks*count_H))))

        classes_per_task = self.taskcla[0][1]
        print("-------------------------->   Memory size: %s samples per task (%sB)" % (self.samples*classes_per_task,
                                                                                        self.pretty_print(
                                                                                            self.ntasks * 4 * self.samples * classes_per_task* self.image_size)))
        print("------------------------------------------------------------------------------")
        print("                               TOTAL:  %sB" % self.pretty_print(
            4 * (count_S + self.ntasks *count_P + self.ntasks *count_H) + self.ntasks * 4 * self.samples * classes_per_task * self.image_size))

    def pretty_print(self, num):
        magnitude = 0
        while abs(num) >= 1000:
            magnitude += 1
            num /= 1000.0
        return '%.1f%s' % (num, ['', 'K', 'M', 'G', 'T', 'P'][magnitude])




class _CustomDataParallel(torch.nn.DataParallel):
    def __init__(self, model):
        super(_CustomDataParallel, self).__init__(model)

    def __getattr__(self, name):
        try:
            return super(_CustomDataParallel, self).__getattr__(name)
        except AttributeError:
            return getattr(self.module, name)




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

    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):
    # 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 = 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):
        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, shared, block, layers, num_classes, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 64
        self.dilation = 1

        # small resnet
        if shared:
            hiddens = [32, 64, 128, 256]
        else:
            hiddens = [16, 32, 32, 64]

    # original resnet
        # hiddens = [64, 128, 256, 512]

        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.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, hiddens[0], layers[0])
        self.layer2 = self._make_layer(block, hiddens[1], layers[1], stride=2,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, hiddens[2], layers[2], stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, hiddens[3], layers[3], stride=2,
                                       dilate=replace_stride_with_dilation[2])
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(hiddens[3] * 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):
        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)
        x = self.relu(x)
        return x

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




def resnet18_small(latend_dim, shared):
    # r"""ResNet-18 model from
    # `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
    return ResNet(shared, BasicBlock, [2, 2, 2, 2], num_classes=latend_dim)

