import torch
import torch.nn as nn
import torchvision.models as models

from models.Base_Model import Base_Model

import random

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(Base_Model):
    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(Base_Model):
    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(Base_Model):

    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, mask_center=[5, 16, 27], aug_weight=1.0,
                 groups=1, width_per_group=64, first_conv_size=7, num_sample=3, mask_aug=False, samp_aug=False,
                 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
        self.num_sample = num_sample
        self.mask_aug = mask_aug
        self.samp_aug = samp_aug
        self.aug_weight = aug_weight
        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=first_conv_size, 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)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

        self.mask_center = mask_center

    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 create_mask(self, w, h, center_x, center_y, alpha=10.0):
        widths = torch.arange(w).view(1, -1).repeat(h,1)
        heights = torch.arange(h).view(-1, 1).repeat(1,w)
        mask = ((widths - center_x)**2 + (heights - center_y)**2).float().sqrt()
        mask = (mask.max() - mask + alpha) ** 0.3
        mask = mask / mask.max()
        mask = (mask + mask.clone().uniform_(0, 1)) > 0.9
        mask.float()
        return mask.unsqueeze(0)


    def _forward_impl_all_sample(self, x, loop=10, bpda=False):
        b, c, w, h = x.shape
        samples = []
        NUM_LOOP = loop
        NUM_INNER_SAMPLE = self.num_sample
        NUM_TOTAL_SAMPLE = NUM_LOOP * NUM_INNER_SAMPLE
        
        # generate all samples
        for i in range(NUM_TOTAL_SAMPLE):
            if self.mask_aug:
                if i % NUM_INNER_SAMPLE == 0:
                    center_x = self.mask_center[random.randint(0,2)]
                    center_y = self.mask_center[random.randint(0,2)]
                # attention
                mask = self.create_mask(w, h, center_x, center_y, alpha=10.0).to(x.device)

            # sample weight augmentation
            if self.samp_aug:
                weight = 0.75 + float(i) / NUM_TOTAL_SAMPLE / 2
            else:
                weight = self.aug_weight

            # neural spike
            sample = self.relu(x + x.detach().clone().uniform_(-1,1) * weight)
            #sample = self.relu(x + x.detach().clone().normal_(0,1) * weight)
            sample = sample / (sample + 1e-5)
            #sample = torch.clamp(x + torch.randn_like(x) * 0.1, min=0, max=1)
            if self.mask_aug:
                sample = sample * mask
            samples.append(sample)

        # run network
        outputs = []
        features = []
        for i in range(NUM_LOOP):
            if bpda:
                assert self.is_attack()
                inputs = x
            else:
                inputs = sum(samples[NUM_INNER_SAMPLE * i : NUM_INNER_SAMPLE * (i+1)]) / NUM_INNER_SAMPLE
            # Normalized input
            inputs = inputs - inputs.mean(-1, keepdim=True).mean(-2, keepdim=True)

            inputs = self.conv1(inputs)
            inputs = self.bn1(inputs)
            inputs = self.relu(inputs)
            inputs = self.maxpool(inputs)

            inputs = self.layer1(inputs)
            inputs = self.layer2(inputs)
            inputs = self.layer3(inputs)
            inputs = self.layer4(inputs)
            inputs = self.avgpool(inputs)
            feats = torch.flatten(inputs, 1)
            preds = self.fc(feats)
            features.append(feats)
            outputs.append(preds)

        final_pred = sum(outputs) / NUM_LOOP
        return final_pred, features, outputs

    def forward(self, x, loop=1, bpda=False):
        if x.shape[1] == 1:
            x = x.repeat(1, 3, 1, 1)
        if self.half_sample:
            raise ValueError('Half sample is not implemented!')
        else:
            return self._forward_impl_all_sample(x, loop=loop, bpda=bpda)

    def set_half_sample(self, half_sample=False):
        self.half_sample = half_sample

def resnet18(**kwargs):
    return ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)


def resnet34(**kwargs):
    return ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)


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


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


def resnet152(**kwargs):
    return ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)


def create_model(m_type='resnet18', num_classes=1000, first_conv_size=7, num_sample=3, mask_aug=False, samp_aug=False, aug_weight=1.0, mask_center=[5, 16, 27]):
    # create various resnet models
    if m_type == 'resnet18':
        model = resnet18(num_classes=num_classes, first_conv_size=first_conv_size, num_sample=num_sample, 
                         mask_aug=mask_aug, samp_aug=samp_aug, aug_weight=aug_weight, mask_center=mask_center)

    return model